From eb96bb533d7e41c2bb97a935df80ff999da1a7f3 Mon Sep 17 00:00:00 2001 From: Mani9550 <93199718+Mani9550@users.noreply.github.com> Date: Wed, 27 Oct 2021 15:41:34 +0530 Subject: [PATCH 01/11] Added all the 4 tasks --- Classification_Task3_203174002.ipynb | 1 + KNN_Task4 _203174002.ipynb | 1 + Linear_Regression_Task2_203174002.ipynb | 1 + 3 files changed, 3 insertions(+) create mode 100644 Classification_Task3_203174002.ipynb create mode 100644 KNN_Task4 _203174002.ipynb create mode 100644 Linear_Regression_Task2_203174002.ipynb diff --git a/Classification_Task3_203174002.ipynb b/Classification_Task3_203174002.ipynb new file mode 100644 index 0000000..1d8640a --- /dev/null +++ b/Classification_Task3_203174002.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Classification_Task3_203174002.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.8"}},"cells":[{"cell_type":"markdown","metadata":{"id":"7C5rAxwPGDQf"},"source":["# Importing useful libraries"]},{"cell_type":"code","metadata":{"id":"8qvrslgsF4Mn"},"source":["import numpy as np\n","import pandas as pd \n","from pandas import Series, DataFrame\n","\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","%matplotlib inline"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"6aYOLI2BHF6m"},"source":["## Loading the dataset.\n","The dataset can be found [here](https://github.com/shreedharmalpani/Intro-To-ML-Hello-FOSS/blob/main/iris.csv)"]},{"cell_type":"code","metadata":{"id":"_hccks2pF4Mq"},"source":["df = pd.read_csv(\"iris.csv\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"um0L09IOF4Ms","colab":{"base_uri":"https://localhost:8080/","height":205},"executionInfo":{"status":"ok","timestamp":1635010648975,"user_tz":-330,"elapsed":513,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"5adfbd69-3916-4a7d-9b21-496cfc4ab498"},"source":["df.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
05.13.51.40.2setosa
14.93.01.40.2setosa
24.73.21.30.2setosa
34.63.11.50.2setosa
45.03.61.40.2setosa
\n","
"],"text/plain":[" sepal_length sepal_width petal_length petal_width species\n","0 5.1 3.5 1.4 0.2 setosa\n","1 4.9 3.0 1.4 0.2 setosa\n","2 4.7 3.2 1.3 0.2 setosa\n","3 4.6 3.1 1.5 0.2 setosa\n","4 5.0 3.6 1.4 0.2 setosa"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"cbx8gP4zF4Mt","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635010651537,"user_tz":-330,"elapsed":11,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"6d8d789e-43c0-499a-9f8d-7f501f9b58bc"},"source":["df.info() "],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","RangeIndex: 150 entries, 0 to 149\n","Data columns (total 5 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 sepal_length 150 non-null float64\n"," 1 sepal_width 150 non-null float64\n"," 2 petal_length 150 non-null float64\n"," 3 petal_width 150 non-null float64\n"," 4 species 150 non-null object \n","dtypes: float64(4), object(1)\n","memory usage: 6.0+ KB\n"]}]},{"cell_type":"markdown","metadata":{"id":"vjH1pAqoJna2"},"source":["# Data Cleaning & Data Visualization"]},{"cell_type":"markdown","metadata":{"id":"PNfSyZF1F4Mu"},"source":["### 1) Remove unneeded columns\n","### 2) Check for duplicate rows \n","### 2) Check for rows with missing values\n"]},{"cell_type":"code","metadata":{"collapsed":true,"id":"HKFsVhubF4Mx"},"source":["df.isna().sum()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Rm7rlDcPR0aI"},"source":["df.isnull()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"L1cnZS5XF4M2"},"source":["### EDA (Exploratory Data Analysis) with Iris"]},{"cell_type":"code","metadata":{"id":"DjsnhAraF4M7","colab":{"base_uri":"https://localhost:8080/","height":458},"executionInfo":{"status":"ok","timestamp":1635011702579,"user_tz":-330,"elapsed":523,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"a7bf481b-9840-450b-b8f2-49a114bba973"},"source":["##Displaying a scatter plot to show the distribution of Sepal Length vs width the dataset\n","\n","fig = df[df.species == 'Iris-setosa'].plot(kind='scatter', x='petal_length', y='petal_width', color='orange', label='Setosa')\n","df[df.species == 'Iris-versicolor'].plot(kind='scatter', x='petal_length', y='petal_width', color='blue', label='Versicolor', ax=fig)\n","df[df.species == 'Iris-virginica'].plot(kind='scatter', x='petal_length', y='petal_width', color='green', label='Virginica', ax=fig)\n","\n","fig.set_xlabel('Petal Length')\n","fig.set_ylabel('Petal Width')\n","fig.set_title('Petal Length Vs Width')\n","\n","fig=plt.gcf()\n","fig.set_size_inches(10, 7)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"O_eYByFvF4M5","colab":{"base_uri":"https://localhost:8080/","height":235},"executionInfo":{"status":"error","timestamp":1635011142444,"user_tz":-330,"elapsed":515,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"fa78777e-0d87-449b-d9b7-a3995609ba6a"},"source":["#Display a scatter plot to show the distribution of Sepal Length vs width the dataset (Like previous Petal lenght vs width scatter plot)\n","\n","\n","#code\n","\n","fig.set_xlabel('Sepal Length')\n","fig.set_ylabel('Sepal Width')\n","fig.set_title('Sepal Length Vs Width')\n","\n","\n","fig=plt.gcf()\n","fig.set_size_inches(10, 7)\n","plt.show()\n","#example plot"],"execution_count":null,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m#code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Sepal Length'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Sepal Width'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Sepal Length Vs Width'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mAttributeError\u001b[0m: 'Figure' object has no attribute 'set_xlabel'"]}]},{"cell_type":"code","metadata":{"id":"EIh_yKQAF4M6","outputId":"91706df0-4dd1-4a8b-92e7-41e04e6e602d"},"source":["#plot the FacetGrid plot using the seaborn library\n","\n","#sns.FacetGrid(...)\\\n","# .map(...)\\\n","# .add_legend()\n"],"execution_count":null,"outputs":[{"data":{"text/plain":[""]},"execution_count":12,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAboAAAFgCAYAAADNUrzMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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GcSIb/xk4DrgW+CbQAywHPmCt/cGC7d6Ek5Ayg9OBXQLEgX/ESVYBeDdwBE5c2G3GmNOA/+N+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\nW2vT1tp9OFelCSAPlLpP+JC1dsJ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Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"2f79fbfa-369f-48a0-8027-ae3f747a9adf"},"source":["#Plot the distritbution of the features using histgram\n","fig = plt.gcf()\n","fig.set_size_inches(12,6)\n","plt.show()"],"execution_count":48,"outputs":[{"output_type":"display_data","data":{"text/plain":["
"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"xb-AFaG3PU0D"},"source":["## Importing alll the necessary packages to use the various classification algorithms\n"]},{"cell_type":"code","metadata":{"id":"cJVjbgAjF4M_"},"source":["from sklearn.linear_model import LogisticRegression # for Logistic Regression Algorithm\n","from sklearn import svm # for suport vector machine algorithm\n","from sklearn import metrics # for checking the model accuracy\n","from sklearn.tree import DecisionTreeClassifier # for using DTA"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"8LizCSuWF4NA"},"source":["df.shape"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"WW5Hp1fFF4NC"},"source":["Now, when we train any algorithm, the number of features and their correlation plays an important role. If there are features and many of the features are highly correlated, then training an algorithm with all the featues will reduce the accuracy. Thus features selection should be done carefully. This dataset has less featues but still we will see the correlation.\n"]},{"cell_type":"code","metadata":{"id":"YABeXMklF4ND","colab":{"base_uri":"https://localhost:8080/","height":270},"executionInfo":{"status":"ok","timestamp":1635011174157,"user_tz":-330,"elapsed":819,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"a9537d5f-a224-42a1-b4ae-f10891512872"},"source":["plt.figure(figsize=(8,4))\n","sns.heatmap(df.corr(), annot=True, cmap='cubehelix_r') # draws heatmap with input as correlation matrix calculated by df.corr() \n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"gsd6QaoaF4NE"},"source":["Observation--->\n","The Sepal Width and Length are not correlated The Petal Width and Length are highly correlated\n","We will use all the features for training the algorithm and check the accuracy.\n","\n","Then we will use 1 Petal Feature and 1 Sepal Feature to check the accuracy of the algorithm as we are using only 2 features that are not correlated. Thus we can have a variance in the dataset which may help in better accuracy. We will check it later.\n","\n","Steps To Be followed When Applying an Algorithm\n","\n","Split the dataset into training and testing dataset. The testing dataset is generally smaller than training one as it will help in training the model better.\n","\n","Select any algorithm based on the problem (classification or regression) whatever you feel may be good.\n","Then pass the training dataset to the algorithm to train it. We use the .fit() method\n","Then pass the testing data to the trained algorithm to predict the outcome. We use the .predict() method.\n","We then check the accuracy by passing the predicted outcome and the actual output to the model."]},{"cell_type":"markdown","metadata":{"id":"QAD_cNirF4NF"},"source":["# Splitting The Data into Training And Testing Dataset"]},{"cell_type":"code","metadata":{"id":"ZqSRd9GzF4NF","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635011177764,"user_tz":-330,"elapsed":6,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"58101a90-1ffd-45b9-8dbe-3ef9e5f914a2"},"source":["from sklearn.model_selection import train_test_split\n","train, test = train_test_split(df, test_size=0.3) # our main data split into train and test\n","# the attribute test_size=0.3 splits the data into 70% and 30% ratio. train=70% and test=30%\n","print(train.shape)\n","print(test.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(105, 5)\n","(45, 5)\n"]}]},{"cell_type":"code","metadata":{"id":"yO2J2FpjF4NG"},"source":["train_X = train[['sepal_length','sepal_width','petal_length','petal_width']] # taking the training data features\n","train_y = train.species # output of the training data\n","\n","test_X = test[['sepal_length','sepal_width','petal_length','petal_width']] # taking test data feature\n","test_y = test.species # output value of the test data"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"yR9D2qgQF4NG","colab":{"base_uri":"https://localhost:8080/","height":205},"executionInfo":{"status":"ok","timestamp":1635011258352,"user_tz":-330,"elapsed":749,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"f3bb7968-3684-416f-9a42-ab5fcbfff8f6"},"source":["train_X.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal_lengthsepal_widthpetal_lengthpetal_width
244.83.41.90.2
895.52.54.01.3
134.33.01.10.1
645.62.93.61.3
255.03.01.60.2
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"],"text/plain":[" sepal_length sepal_width petal_length petal_width\n","24 4.8 3.4 1.9 0.2\n","89 5.5 2.5 4.0 1.3\n","13 4.3 3.0 1.1 0.1\n","64 5.6 2.9 3.6 1.3\n","25 5.0 3.0 1.6 0.2"]},"metadata":{},"execution_count":36}]},{"cell_type":"code","metadata":{"id":"KcGbNGkcF4NH","colab":{"base_uri":"https://localhost:8080/","height":205},"executionInfo":{"status":"ok","timestamp":1635011262248,"user_tz":-330,"elapsed":623,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"dcc6f9ba-8240-4f3f-8d74-ff21e5ac54e3"},"source":["test_X.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal_lengthsepal_widthpetal_lengthpetal_width
34.63.11.50.2
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465.13.81.60.2
535.52.34.01.3
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"],"text/plain":[" sepal_length sepal_width petal_length petal_width\n","3 4.6 3.1 1.5 0.2\n","45 4.8 3.0 1.4 0.3\n","140 6.7 3.1 5.6 2.4\n","46 5.1 3.8 1.6 0.2\n","53 5.5 2.3 4.0 1.3"]},"metadata":{},"execution_count":37}]},{"cell_type":"code","metadata":{"id":"5sFmts-IF4NI","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635011269110,"user_tz":-330,"elapsed":486,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"5892e853-6e9a-4f5e-e684-8c34c4822f55"},"source":["train_y.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["24 setosa\n","89 versicolor\n","13 setosa\n","64 versicolor\n","25 setosa\n","Name: species, dtype: object"]},"metadata":{},"execution_count":38}]},{"cell_type":"markdown","metadata":{"id":"S_w4Me2bF4NL"},"source":["## Logistic Regression "]},{"cell_type":"code","metadata":{"id":"gOQ5JrqrF4NL","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635011271869,"user_tz":-330,"elapsed":7,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"73cfaedd-f9da-45c3-9bc3-32cfd91d6915"},"source":["model = LogisticRegression()\n","model.fit(train_X, train_y)\n","prediction = model.predict(test_X)\n","print('The accuracy of Logistic Regression is: ', metrics.accuracy_score(prediction, test_y))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["The accuracy of Logistic Regression is: 0.9777777777777777\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n"," https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n"," https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n"," extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"]}]},{"cell_type":"markdown","metadata":{"id":"e1NNX-EGF4NJ"},"source":["## Support Vector Machine SVM"]},{"cell_type":"code","metadata":{"id":"zSJmVzqnF4NK","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635011282010,"user_tz":-330,"elapsed":517,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"37f4bbd2-9e91-4fd6-f53c-3aea85881b5b"},"source":["clf = svm.SVC(kernel='linear')\n","clf.fit(train_X, train_y)\n","\n","#Predict the response for test dataset\n","prediction = clf.predict(test_X)\n","\n","print('The accuracy of Support Vector Machine is: ', metrics.accuracy_score(prediction, test_y))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["The accuracy of Support Vector Machine is: 1.0\n"]}]},{"cell_type":"markdown","metadata":{"id":"GWfemKzPF4NN"},"source":["## Decision Tree"]},{"cell_type":"code","metadata":{"id":"iRXy3EZIF4NN","outputId":"f470e075-fc92-4f3f-c343-7a8257e2c5d2"},"source":["#implementing using Decision Tree\n","#code\n","\n","print('The accuracy of Decision Tree is: ', metrics.accuracy_score(prediction, test_y))"],"execution_count":null,"outputs":[{"name":"stdout","output_type":"stream","text":["('The accuracy of Decision Tree is: ', 0.93333333333333335)\n"]}]},{"cell_type":"markdown","metadata":{"id":"uB2Co6f_F4NQ"},"source":["### We used all the features of iris in above models. Now we will use Petals and Sepals Seperately"]},{"cell_type":"markdown","metadata":{"id":"1_v6cAZMF4NQ"},"source":["### Creating Petals And Sepals Training Data"]},{"cell_type":"code","metadata":{"collapsed":true,"id":"e1Q-1b9YF4NQ"},"source":["petal = df[['PetalLengthCm','PetalWidthCm','Species']]\n","sepal = df[['SepalLengthCm','SepalWidthCm','Species']]"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Lv-nab5oF4NQ"},"source":["### For Iris Petal"]},{"cell_type":"code","metadata":{"collapsed":true,"id":"DuOqLUWZF4NQ"},"source":["train_p,test_p = train_test_split(petal, test_size=0.3, random_state=0) #petals\n","train_x_p = train_p[['PetalWidthCm','PetalLengthCm']] # taking the training data's Petal features\n","train_y_p = train_p.Species # output of the training data\n","\n","test_x_p = test_p[['PetalWidthCm','PetalLengthCm']] # taking the test data's Petal features\n","test_y_p = test_p.Species # output of the test data"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"bgNB8kaNF4NU"},"source":["### For Iris Sepal"]},{"cell_type":"code","metadata":{"id":"6hVj5MW3F4NU"},"source":["#Similarly define the split for sepals\n","#define the training and test data's Sepal features followed by the output of the training and test data\n","\n","#use naming- train_s,test_s ; train_x_s, train_y_s; test_x_s, test_y_s\n","\n","#code"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"y08e1O6aU9mx"},"source":["Implementing the algorithms just like we did on the complete dataset but separately on sepals and petals and calculating accuracy"]},{"cell_type":"markdown","metadata":{"id":"TeMWnQr6F4NV"},"source":["## SVM Algorithm"]},{"cell_type":"code","metadata":{"id":"jhlutJ78F4NV"},"source":["#code\n","print('The accuracy of the SVM using Petals is:',metrics.accuracy_score(prediction,test_y_p))\n","\n","#code\n","print('The accuracy of the SVM using Sepals is:',metrics.accuracy_score(prediction,test_y_s))"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Mli7zcq_F4NV"},"source":["## Logistic Regression"]},{"cell_type":"code","metadata":{"id":"2DqK_dFCF4NV"},"source":["#code\n","print('The accuracy of the Logistic Regression using Petals is:',metrics.accuracy_score(prediction,test_y_p))\n","\n","#code \n","print('The accuracy of the Logistic Regression using Sepals is:',metrics.accuracy_score(prediction,test_y_s))"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"aM-7Zx95F4NW"},"source":["## Decision Tree"]},{"cell_type":"code","metadata":{"id":"S8tXp-gMF4NW"},"source":["#code\n","print('The accuracy of the Decision Tree using Petals is:',metrics.accuracy_score(prediction,test_y_p))\n","\n","#code\n","print('The accuracy of the Decision Tree using Sepals is:',metrics.accuracy_score(prediction,test_y_s))"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"6ec0NUyJF4NW"},"source":["\n","\n","\n","### Question:\n","Does Using Petals over Sepals for training the data give a much better accuracy? Why?\n"]}]} \ No newline at end of file diff --git a/KNN_Task4 _203174002.ipynb b/KNN_Task4 _203174002.ipynb new file mode 100644 index 0000000..5dc9eee --- /dev/null +++ b/KNN_Task4 _203174002.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"KNN_Task4 _203174002.ipynb","provenance":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.8"}},"cells":[{"cell_type":"markdown","metadata":{"id":"cPP7BfqFSgyH"},"source":["# K-Nearest Neighbors Algorithm\n"]},{"cell_type":"markdown","metadata":{"id":"Zd0p7ZUpSgyL"},"source":["![example 1.png](data:image/png;base64,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)In this Jupyter Notebook we will focus on $KNN-Algorithm$. KNN is a data classification algorithm that attempts to determine what group a data point is in by looking at the data points around it.\n","\n","An algorithm, looking at one point on a grid, trying to determine if a point is in group A or B, looks at the states of the points that are near it. The range is arbitrarily determined, but the point is to take a sample of the data. If the majority of the points are in group A, then it is likely that the data point in question will be A rather than B, and vice versa.\n","
\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"TyGHDf4NSgyM"},"source":["# Imports"]},{"cell_type":"code","metadata":{"id":"iIEvA0xjSgyN","executionInfo":{"status":"ok","timestamp":1635250818719,"user_tz":-330,"elapsed":418,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["import numpy as np\n","from tqdm import tqdm_notebook"],"execution_count":5,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"rc8ruF56SgyO"},"source":["# How it works?\n","\n","We have some labeled data set $X-train$, and a new set $X$ that we want to classify based on previous classifications\n","\n"]},{"cell_type":"markdown","metadata":{"id":"rGbvEXbvSgyO"},"source":["## Seps"]},{"cell_type":"markdown","metadata":{"id":"B-nf9G4ZSgyP"},"source":["### 1. Calculate distance to all neighbours\n","### 2. Sort neightbours (based on closest distance)\n","### 3. Count possibilities of each class for k nearest neighbours \n","### 4. The class with highest possibilty is Your prediction"]},{"cell_type":"markdown","metadata":{"id":"LuWwKdFrSgyP"},"source":["# 1. Calculate distance to all neighbours\n","\n","Depending on the problem You should use different type of count distance method.\n","
\n","For example we can use Euclidean distance. Euclidean distance is the \"ordinary\" straight-line distance between two points in D-Dimensional space\n","\n","#### Definiton\n","$d(p, q) = d(q, p) = \\sqrt{(q_1 - p_1)^2 + (q_2 - p_2)^2 + \\dots + (q_D - p_D)^2} = \\sum_{d=1}^{D} (p_d - q_d)^2$\n","\n","#### Example\n","Distance in $R^2$\n","\n","\n","\n","$p = (4,6)$\n","
\n","$q = (1,2)$\n","
\n","$d(p, q) = \\sqrt{(1-4)^2 + (2-6)^2} =\\sqrt{9 + 16} = \\sqrt{25} = 5 $\n","\n"]},{"cell_type":"markdown","metadata":{"id":"vlvNZqiJSgyQ"},"source":["## Code"]},{"cell_type":"code","metadata":{"id":"EvmQi6nsSgyR","executionInfo":{"status":"ok","timestamp":1635250723579,"user_tz":-330,"elapsed":1651,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["def get_euclidean_distance(A_matrix, B_matrix):\n"," \n"," C = [ [ 0 for i in range(np.size(B_matrix, 0)) ] for j in range(np.size(A_matrix, 0)) ]\n"," \n"," for i in range (0, np.size(A_matrix, 0)):\n"," row1 = A_matrix[i,:]\n"," for j in range (0, np.size(B_matrix, 0)):\n"," row2 = B_matrix[j,:]\n"," \n"," C[i][j] = np.sum(np.square(row1 - row2))\n"," \n"," ## Use the distance formula for the matrices using numpy functions\n"," ## C is the sum of the squares of the distances\n","\n"," return np.sqrt(C)\n"],"execution_count":1,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"GABzTa_0SgyS"},"source":["## Example Usage"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"W6b8yBSoSgyS","executionInfo":{"status":"ok","timestamp":1635250824471,"user_tz":-330,"elapsed":411,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"966f10e6-5429-4f7e-e70f-50df76e1b2ca"},"source":["X = np.array([[1,2,3] , [-4,5,-6]])\n","\n","X_train = np.array([[0,0,0], [1,2,3], [4,5,6], [-4, 4, -6]])\n","\n","print(\"X: {} Exaples in {} Dimensional space\".format(*X.shape))\n","print(\"X_train: {} Exaples in {} Dimensional space\".format(*X_train.shape))\n","\n","\n","print()\n","\n","print(\"X:\")\n","print(X)\n","\n","print()\n","\n","print(\"X_train\")\n","print(X_train)\n"],"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["X: 2 Exaples in 3 Dimensional space\n","X_train: 4 Exaples in 3 Dimensional space\n","\n","X:\n","[[ 1 2 3]\n"," [-4 5 -6]]\n","\n","X_train\n","[[ 0 0 0]\n"," [ 1 2 3]\n"," [ 4 5 6]\n"," [-4 4 -6]]\n"]}]},{"cell_type":"code","metadata":{"id":"kB8IZcDpSgyT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635250828586,"user_tz":-330,"elapsed":423,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"e48c7a0f-c233-44a7-baa1-66354fc59437"},"source":["## Initialize the distance matrix using the get_euclidean_matrix\n","\n","C = get_euclidean_distance(X, X_train)\n","\n","## Euclidean distance b/w row i of X and row j of X_train is available as C[i][j]\n","\n","\n","## Print Distance between first example from X and first form X_train\n","print(f\"Distance between first example from X and first form X_train {C[0,0]}\")"],"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["Distance between first example from X and first form X_train 3.7416573867739413\n"]}]},{"cell_type":"markdown","metadata":{"id":"vbaJfBihSgyT"},"source":["# 2. Sort neightbours\n","\n","In order to find best fitting class for our observations we need to find to which classes belong observation neightbours and then to sort classes based on the closest distance\n"]},{"cell_type":"markdown","metadata":{"id":"b1VLHUj2SgyU"},"source":["## Code"]},{"cell_type":"code","metadata":{"id":"na0G1o_ASgyU"},"source":["def get_sorted_train_labels(distance_matrix, y):\n"," \"\"\"\n"," Function sorts y labels, based on probabilities from distances matrix\n"," Args:\n"," distance_matrix (numpy.ndarray): Distance Matrix, between points from X and X_train, size: N1:N2\n"," y (numpy.ndarray): vector of classes of X points, size: N1\n","\n"," Returns:\n"," numpy.ndarray: labels matrix sorted according to distances to nearest neightours, size N1:N2 \n","\n"," \"\"\"\n"," \n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"U0I8eltDSgyV"},"source":["# 3. Count possibilities of each class for k nearest neighbours \n","\n","In order to find best class for our observation $x$ we need to calculate the probability of belonging to each class. In our case it is quite easy. We need just to count how many from k-nearest-neighbours of observation $x$ belong to each class and then devide it by k \n","

\n","$p(y=class \\space| x) = \\frac{\\sum_{1}^{k}(1 \\space if \\space N_i = class, \\space else \\space 0) }{k}$ Where $N_i$ is $i$ nearest neightbour\n","\n"]},{"cell_type":"markdown","metadata":{"id":"j0ZtOC38SgyV"},"source":["## Code"]},{"cell_type":"code","metadata":{"id":"y2aaG2GdSgyV"},"source":["def get_p_y_x_using_knn(y, k):\n"," \"\"\"\n"," The function determines the probability distribution p (y | x)\n"," for each of the labels for objects from the X\n"," using the KNN classification learned on the X_train\n","\n"," Args:\n"," y (numpy.ndarray): Sorted matrix of N2 nearest neighbours labels, size N1:N2\n"," k (int): number of nearest neighbours for KNN algorithm\n","\n"," Returns: numpy.ndarray: Matrix of probabilities for N1 points (from set X) of belonging to each class,\n"," size N1:C (where C is number of classes)\n"," \"\"\"\n","\n"," ## Write your code here\n","\n"," return probabilities_matrix\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ThEbAnXISgyW"},"source":["# 4. The class with highest possibilty is Your prediction"]},{"cell_type":"markdown","metadata":{"id":"_i7NTtN4SgyW"},"source":["At the end we combine all previous steps to get prediction"]},{"cell_type":"markdown","metadata":{"id":"OzK6rY8mSgyW"},"source":["## Code"]},{"cell_type":"code","metadata":{"id":"DaYqr_i6SgyW","executionInfo":{"status":"ok","timestamp":1635250858682,"user_tz":-330,"elapsed":471,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["def predict(X, X_train, y_train, k, distance_function):\n"," \"\"\"\n"," Function returns predictions for new set X based on labels of points from X_train\n"," Args:\n"," X (numpy.ndarray): set of observations (points) that we want to label\n"," X_train (numpy.ndarray): set of lalabeld bservations (points)\n"," y_train (numpy.ndarray): labels for X_train\n"," k (int): number of nearest neighbours for KNN algorithm\n","\n"," Returns:\n"," (numpy.ndarray): label predictions for points from set X\n"," \"\"\"\n"," ## Write your code here\n","\n"," return prediction"],"execution_count":9,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"i9kzyASWSgyX"},"source":["# Accuracy"]},{"cell_type":"markdown","metadata":{"id":"v8bNPTPZSgyX"},"source":["To find how good our knn model works we should count accuracy"]},{"cell_type":"markdown","metadata":{"id":"dgFCnJ14SgyX"},"source":["## Code"]},{"cell_type":"code","metadata":{"id":"2ySpyThlSgyX"},"source":["def count_accuracy(prediction, y_true):\n"," \"\"\"\n"," Returns:\n"," float: Predictions accuracy\n","\n"," \"\"\"\n"," N1 = prediction.shape[0]\n"," \n"," ## Use np.sum to count the number of elements where predicted value == actual value and assign the count to the variable accuracy\n","\n"," return accuracy"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"b5g7YFY2SgyX"},"source":["## Example usage"]},{"cell_type":"code","metadata":{"id":"uLqCqmJNSgyY","colab":{"base_uri":"https://localhost:8080/","height":235},"executionInfo":{"status":"error","timestamp":1635250842268,"user_tz":-330,"elapsed":449,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"dcf624ee-b959-4577-b370-464112163929"},"source":["y_true = np.array([[0, 2]])\n","\n","predicton = predict(X, X_train, y_train, 3, get_euclidean_distance)\n","\n","\n","print(\"True classes:{}, accuracy {}%\".format(y_true, count_accuracy(predicton, y_true) * 100))"],"execution_count":8,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mpredicton\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_euclidean_distance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'predict' is not defined"]}]},{"cell_type":"markdown","metadata":{"id":"--WUpIcxSgyY"},"source":["# Find best k"]},{"cell_type":"markdown","metadata":{"id":"itkcD0DlSgyY"},"source":["Best k parameter is that one for which we have highest accuracy"]},{"cell_type":"markdown","metadata":{"id":"7GYEUBnnSgyY"},"source":["## Code"]},{"cell_type":"code","metadata":{"id":"Q6OhNBOoSgyY","executionInfo":{"status":"ok","timestamp":1635250862606,"user_tz":-330,"elapsed":413,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["def select_knn_model(X_validation, y_validation, X_train, y_train, k_values, distance_function):\n"," \"\"\"\n"," Function returns k parameter that best fit Xval points\n"," Args:\n"," Xval (numpy.ndarray): set of Validation Data, size N1:D\n"," Xtrain (numpy.ndarray): set of Training Data, size N2:D\n"," yval (numpy.ndarray): set of labels for Validation data, size N1:1\n"," ytrain (numpy.ndarray): set of labels for Training Data, size N2:1\n"," k_values (list): list of int values of k parameter that should be checked\n","\n"," Returns:\n"," int: k paprameter that best fit validation set\n"," \"\"\"\n","\n"," accuracies = []\n","\n"," for k in tqdm_notebook(k_values):\n"," prediction = predict(X_validation, X_train, y_train, k, distance_function)\n","\n"," accuracy = count_accuracy(prediction, y_validation)\n"," accuracies.append(accuracy)\n","\n"," best_k = k_values[accuracies.index(max(accuracies))]\n","\n"," return best_k, accuracies\n"],"execution_count":10,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"nGtIjD0WSgyY"},"source":["# Real World Example - Iris Dataset"]},{"cell_type":"markdown","metadata":{"id":"-o6MHMtKSgyZ"},"source":["\n","\n","\n","\n","This is perhaps the best known database to be found in the pattern recognition literature. The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other. \n","\n","Each example contains 4 attributes\n","1. sepal length in cm \n","2. sepal width in cm \n","3. petal length in cm \n","4. petal width in cm \n","\n","Predicted attribute: class of iris plant. \n","\n","\n","\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SY8oOngQSgyZ","executionInfo":{"status":"ok","timestamp":1635250867474,"user_tz":-330,"elapsed":414,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"270c2090-4cc4-43c8-dd20-5ed2670e0067"},"source":["from sklearn import datasets\n","import matplotlib.pyplot as plt\n","\n","iris = datasets.load_iris()\n","\n","iris_X = iris.data\n","iris_y = iris.target\n","\n","print(\"Iris: {} examples in {} dimensional space\".format(*iris_X.shape))\n","print(\"First example in dataset :\\n Speal lenght: {}cm \\n Speal width: {}cm \\n Petal length: {}cm \\n Petal width: {}cm\".format(*iris_X[0]))\n","\n","print(\"Avalible classes\", np.unique(iris_y))"],"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["Iris: 150 examples in 4 dimensional space\n","First example in dataset :\n"," Speal lenght: 5.1cm \n"," Speal width: 3.5cm \n"," Petal length: 1.4cm \n"," Petal width: 0.2cm\n","Avalible classes [0 1 2]\n"]}]},{"cell_type":"markdown","metadata":{"id":"-IlKSX7hSgyZ"},"source":["## Prepare Data\n","\n","In our data set we have 150 examples (50 examples of each class), we have to divide it into 3 datasets.\n","1. Training data set, 90 examples. It will be used to find k - nearest neightbours\n","2. Validation data set, 30 examples. It will be used to find best k parameter, the one for which accuracy is highest\n","3. Test data set, 30 examples. It will be used to check how good our model performs\n","\n","Data has to be shuffled (mixed in random order), because originally it is stored 50 examples of class 0, 50 of 1 and 50 of 2.\n"]},{"cell_type":"code","metadata":{"id":"RA1Q7kCPSgyZ","executionInfo":{"status":"ok","timestamp":1635250871691,"user_tz":-330,"elapsed":418,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["from sklearn.utils import shuffle\n","\n","iris_X, iris_y = shuffle(iris_X, iris_y, random_state=134)\n","\n","\n","test_size = 30\n","validation_size = 30\n","training_size = 90\n","\n","## Initialize X_test\n","## Initialize X_validation \n","## Initialize X_train \n","\n","## Initialize y_test\n","## Initialize y_validation\n","## Initialize y_train"],"execution_count":12,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"r9xJVLzrSgyZ"},"source":["## Find best k parameter"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":235},"id":"hbvZBVNBSgya","executionInfo":{"status":"error","timestamp":1635250875803,"user_tz":-330,"elapsed":430,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"f62b15f9-7fca-4789-bac2-2db5cbbcd8c0"},"source":["k_values = [i for i in range(3,50)]\n","\n","best_k, accuracies = select_knn_model(X_validation, y_validation, X_train, y_train, k_values, distance_function=get_euclidean_distance)\n","\n","## Plot accuracy vs k values graph"],"execution_count":13,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mk_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mbest_k\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mselect_knn_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_validation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_validation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistance_function\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mget_euclidean_distance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m## Plot accuracy vs k values graph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'X_validation' is not defined"]}]},{"cell_type":"markdown","metadata":{"id":"BjQBDWJMSgya"},"source":["## Count accuracy for training set"]},{"cell_type":"code","metadata":{"id":"_f-J5sSESgya","colab":{"base_uri":"https://localhost:8080/","height":201},"executionInfo":{"status":"error","timestamp":1635250882340,"user_tz":-330,"elapsed":434,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"d0dfb811-a65e-472c-bdc7-fad839ccc488"},"source":["prediction = predict(X_test, X_train, y_train, best_k, get_euclidean_distance)\n","\n","## Calculate Best accuracy using the best k value\n"],"execution_count":14,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbest_k\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_euclidean_distance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m## Calculate Best accuracy using the best k value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'X_test' is not defined"]}]},{"cell_type":"markdown","metadata":{"id":"72O5eXbCSgyc"},"source":["# Sources\n","\n","https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm - first visualisation image\n","\n","https://en.wikipedia.org/wiki/Euclidean_distance - euclidean distance visualisation\n","\n","https://rajritvikblog.wordpress.com/2017/06/29/iris-dataset-analysis-python/ - first iris image\n","\n","https://rpubs.com/wjholst/322258 - second iris image\n","\n"]}]} \ No newline at end of file diff --git a/Linear_Regression_Task2_203174002.ipynb b/Linear_Regression_Task2_203174002.ipynb new file mode 100644 index 0000000..c7b98ce --- /dev/null +++ b/Linear_Regression_Task2_203174002.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":5,"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.8"},"colab":{"name":"Linear_Regression_Task2_203174002.ipynb","provenance":[],"collapsed_sections":[]}},"cells":[{"cell_type":"markdown","metadata":{"id":"89223f98"},"source":["\n","\n","```\n","Import libraries\n","```\n","\n","### Importing useful libraries \n"],"id":"89223f98"},{"cell_type":"code","metadata":{"id":"26f77ebe","executionInfo":{"status":"ok","timestamp":1635012283386,"user_tz":-330,"elapsed":1123,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["# This Python 3 environment comes with many helpful analytics libraries installed\n","# For example, here's several helpful packages to load in\n","import numpy as np # linear algebra\n","import matplotlib.pyplot as plt # data visualization\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","import seaborn as sns"],"id":"26f77ebe","execution_count":1,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"31c8220d"},"source":["### Loading the dataset \n","#### For implementation we will be using house prediction dataset . The dataset can be found [here](https://github.com/vrinda01go/Hellofoss/blob/main/Room_price_data.csv)"],"id":"31c8220d"},{"cell_type":"code","metadata":{"id":"1c5d873a","executionInfo":{"status":"ok","timestamp":1635012317184,"user_tz":-330,"elapsed":552,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["df = pd.read_csv(\"Hostel_Linear-Dataset.csv\") #import text file \n"],"id":"1c5d873a","execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"id":"1ca9aba0","colab":{"base_uri":"https://localhost:8080/","height":205},"executionInfo":{"status":"ok","timestamp":1635012321185,"user_tz":-330,"elapsed":524,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"50a85b1a-cfee-4f7b-9ac1-5134a16822e9"},"source":["df.head()"],"id":"1ca9aba0","execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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PriceHostel No.OccupancyRoom SizeFloor
02540.0316868
12900.0329665
2NaN317888
32362.0329242
4NaN3210985
\n","
"],"text/plain":[" Price Hostel No. Occupancy Room Size Floor\n","0 2540.0 3 1 686 8\n","1 2900.0 3 2 966 5\n","2 NaN 3 1 788 8\n","3 2362.0 3 2 924 2\n","4 NaN 3 2 1098 5"]},"metadata":{},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"af08f245"},"source":["# Visualizing and Cleaning the data\n","\n","We will now be removing the nan values and identical values from the dataset\n","\n","For seeing if there are nan values in the dataset we will use the isna() function and then to remove them we will use the dropna() function. We will need to set additional parameters like rows and columns in the dropna function depending on the number of nan values present for each column\n","\n","Using the sum() function with isna() function we can get to know the number of missing values in each column"],"id":"af08f245"},{"cell_type":"code","metadata":{"id":"2fd4babb","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635012323753,"user_tz":-330,"elapsed":428,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"dd94b5ef-188f-4c3a-aec4-fe91cdc6a86d"},"source":["df.isna().sum()"],"id":"2fd4babb","execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Price 1531\n","Hostel No. 0\n","Occupancy 0\n","Room Size 0\n","Floor 0\n","dtype: int64"]},"metadata":{},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"83ef03c3"},"source":["After this we will proceed to remove the nan values \n","\n","Since there are not many nan values in the column 'Price' as compared to the number of rows we will remove the rows which have nan values. \n","\n","Reseting the index after removing the nan values and dropping the old index will also be important"],"id":"83ef03c3"},{"cell_type":"code","metadata":{"id":"b65e4503","executionInfo":{"status":"ok","timestamp":1635012326744,"user_tz":-330,"elapsed":459,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["df = df.dropna(subset = ['Price'],how= 'any')\n","df = df.reset_index(drop = True)\n","## df.isna().sum()"],"id":"b65e4503","execution_count":6,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"40784889"},"source":["Now we can use the drop_duplicate function to remove the duplicate values\n","\n","This function has a parameter calle 'keep' where we specifiy to drop and which value to keep\n","\n","For this excercise we will keep the first values and drop the rest of the duplicates"],"id":"40784889"},{"cell_type":"code","metadata":{"id":"75fa3dc8","executionInfo":{"status":"ok","timestamp":1635012329999,"user_tz":-330,"elapsed":425,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["df = df.drop_duplicates(keep = 'first')\n","df = df.reset_index(drop = True)\n","## df.duplicated().sum()"],"id":"75fa3dc8","execution_count":7,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"a007a33f"},"source":["For visualizing the data we will first start with looking at the distribution of different columns to see if there are enough number for each category in every column and dropping them if the data is biased for one category more than the other"],"id":"a007a33f"},{"cell_type":"code","metadata":{"id":"b325df62","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1635012366549,"user_tz":-330,"elapsed":1239,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"cf878a76-66d3-4e68-f009-819d724a4eae"},"source":["columns = df.columns\n","for column in columns:\n"," if(column== 'Price' or column=='Room Size'): \n"," continue\n"," fig = plt.figure(figsize=(5,5))\n"," ax = fig.gca()\n"," counts = df[column].value_counts()\n"," counts.plot.bar(ax = ax, color='blue')\n"," ax.set_title('No of rooms '+ column)\n"," ax.set_xlabel(column)\n"," ax.set_ylabel(\"No of rooms\")\n"," plt.show()"],"id":"b325df62","execution_count":9,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
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\n","text/plain":["
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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"9811a731"},"source":["We can clearly notice that for the Occupancy column the (occupancy) = 4 has a really low set of data points as compared to others. Hence we can proceed in dropping those rows where the occupancy is 4"],"id":"9811a731"},{"cell_type":"code","metadata":{"id":"825783c0","executionInfo":{"status":"ok","timestamp":1635012377084,"user_tz":-330,"elapsed":615,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["df = df[df['Occupancy'] != 4]\n","df = df.reset_index(drop= True)"],"id":"825783c0","execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"id":"30c64310","colab":{"base_uri":"https://localhost:8080/","height":205},"executionInfo":{"status":"ok","timestamp":1635012378828,"user_tz":-330,"elapsed":9,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"bbb5d4dc-f7e5-4b8e-a248-6ecbd09568da"},"source":["df.head()"],"id":"30c64310","execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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PriceHostel No.OccupancyRoom SizeFloor
02540.0316868
12900.0329665
22362.0329242
31432.0217063
41702.02210383
\n","
"],"text/plain":[" Price Hostel No. Occupancy Room Size Floor\n","0 2540.0 3 1 686 8\n","1 2900.0 3 2 966 5\n","2 2362.0 3 2 924 2\n","3 1432.0 2 1 706 3\n","4 1702.0 2 2 1038 3"]},"metadata":{},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"f333875b"},"source":["We will now write the columns between categorical and numerical\n","\n","categorical = Hostel No, occupancy, floor\n","\n","Numerical = price, occupancy, roomsize, floor, hostel No.\n","\n","Remember that we can treat Hostel Number and occupancy as numerical or categorical. For this notebook we will treat them as categorical for data visualization and numerical for the regression"],"id":"f333875b"},{"cell_type":"markdown","metadata":{"id":"0f34ca6a"},"source":["We will also plot the scatter plots and the correlation map to analyse the relation ships between different numerical columns"],"id":"0f34ca6a"},{"cell_type":"code","metadata":{"scrolled":false,"id":"f4a3ab6e","executionInfo":{"status":"ok","timestamp":1635012383025,"user_tz":-330,"elapsed":517,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["categorical = ['Hostel No.', 'Occupancy', 'Floor']\n","numerical = [ 'Price', 'Room Size']"],"id":"f4a3ab6e","execution_count":12,"outputs":[]},{"cell_type":"code","metadata":{"id":"df2b588a","colab":{"base_uri":"https://localhost:8080/","height":791},"executionInfo":{"status":"ok","timestamp":1635012386787,"user_tz":-330,"elapsed":1202,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"c61015b6-8eb0-4d49-cf99-2e50e4e7bd51"},"source":["for column1 in numerical:\n"," for column2 in numerical:\n"," if(column1 != column2):\n"," fig = plt.figure(figsize=(6,6))\n"," ax = fig.gca()\n"," df.plot.scatter(x=column1,y=column2,ax = ax)\n"," ax.set_title('Scatter plot of '+ column1 + ' vs ' + column2)\n"," ax.set_xlabel(column1)\n"," ax.set_ylabel(column2)\n"," plt.show()"],"id":"df2b588a","execution_count":13,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
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sbcrza5Ekr1daS5/eJXOSBSlD1EzIWKMeRbYmrP4dOBX7t+/Aj7jW36PcXgJGC4iBwOfBp4yxmw1xmwDngJOctcNNca8ZIwxwD2+YykNwsZtHUSt7Fcsalls3NZRpxYpilJtetomcqAx5m/u3+8AB7p/jwE2+Lbb6C4rtHxjwPJARORCEVkhIivef//97l2BUjJjRzTRlU5nLetKpxk7oqlOLVIUpdrUzbDuziBMD53rDmPMDGPMjP33378nTqkAIwfHueGMqQyIWgyJRxgQtbjhjKmMHByvd9MURakSkR4+37sicrAx5m+uSuo9d/kmYJxvu7Husk3AcTnLn3GXjw3YXmkw5kwfw6yJo9i4rYOxI5pUgChKH6OnZyKLAM/D6kvAQt/y81wvraOBHa7a60ngUyIywjWofwp40l23U0SOdr2yzvMdS2kwRg6OM23ccBUgitIHqdlMREQewJlFjBKRjTheVj8E5ovI+cBbwNnu5o8DpwCtwB7gKwDGmK0iMg/4P3e764wxnrH+/+F4gDUBv3P/KYqiKD2IOKaJ/sOMGTPMihUr6t0MRVGUXoWIvGKMmZG7XCPWFUVRlIpRIaIoiqJUjAoRRVEUpWJUiCiKoigVo0JEURRFqRgVIoqiKErFqBBRFEVRKkaFiKIoilIxKkQURVGUilEhoiiKolSMChFFURSlYlSIKIqiKBWjQkRRFEWpGBUiiqIoSsWoEFEURVEqRoWIoiiKUjEqRBRFUZSKUSGiKA1OW3uClRu209aeqHdTFCWPmtVYVxSl+yxs2cQVD68iall0pdPccMZU5kwfU+9mKUoGnYkoSoPS1p7giodXsbcrza5Ekr1daS5/eJXOSJSGQoWIojQoG7d1ELWyP9GoZbFxW0edWqQo+agQUZQGZeyIJrrS6axlXek0Y0c01alFipKPChFFaVBGDo5zwxlTGRC1GBKPMCBqccMZUxk5OF7vpilKBjWsK0oDM2f6GGZNHMXGbR2MHdGkAkRpOFSIKL2CtvZEv+1IRw6O97trVnoPKkSUhkfdXBWlcVGbiNLQqJurojQ2KkSUhkbdXBWlsVEhojQ06uaqKI2NChGloVE3V0VpbNSw3o/pLR5P6uaqKI2LCpF+SjkeT40gbNTNVVEaExUi/RC/x9NeHHvD5Q+vYtbEUXkdtbrXKopSCLWJ9ENK9XhS91pFUYqhQqQfUqrHk7rXKopSDBUi/ZBSPZ7UvVZRlGKoTaSfUorHkydsLs+xiaiBW1EUDxUi/ZhSPJ7mTB9D88FDadmwnenjhjPxwCE91DpFUXoDKkSUgqh3lqIohVCbiBKKemcpilIMFSJKKOqdpShKMVSIKKGod5aiKMWoixARkW+LyBoRWS0iD4jIABE5VESWi0iriPxGRGLutnH3d6u7/hDfca50l78uIp+ux7X0ZTT5oaIoxehxw7qIjAG+CTQbYzpEZD7wOeAU4EfGmAdF5GfA+cBP3f9vM8ZMFJHPAdcDnxWRZne/ycBo4GkRmWSMSfX0NfVlNPmhoiiFqJc6KwI0iUgEGAj8DTgBWOCu/xXwGffv093fuOtPFBFxlz9ojEkYY/4KtAJH9VD7+xUjB8eZNm64ChBFUfLocSFijNkE3AS8jSM8dgCvANuNMUl3s42A50c6Btjg7pt0tx/pXx6wj6IoitID9LgQEZEROLOIQ3HUUIOAk2p8zgtFZIWIrHj//fdreSpFUZR+RT3UWZ8A/mqMed8Y0wX8DzALGO6qtwDGApvcvzcB4wDc9cOANv/ygH2yMMbcYYyZYYyZsf/++1f7ehRFUfot9RAibwNHi8hA17ZxIrAWWAqc6W7zJWCh+/ci9zfu+iXGGOMu/5zrvXUocDjwcg9dg1ID2toTrNywXYMZFaUX0ePeWcaY5SKyAHgVSAJ/BO4AHgMeFJF/c5fd5e5yF/BrEWkFtuJ4ZGGMWeN6dq11j3ORemb1XjS9iqL0TsQZ1PcfZsyYYVasWFHvZig+2toTzLp+CXu79gU2DohaPH/FCeoRpigNgoi8YoyZkbtcI9YVoGdUSWHn0PQqitJ70Sy+fZC29kRZwYE9oUoqdA5Nr6IovRedifQxFrZsYtb1S/jCncuZdf0SFrUEOqxl6IlMvcXOoelVFKX3ojORPoS/s96LM7K//OFVzJo4KrRD9lRJ3vawT5UUtk+5M51SzqHpVRSld6JCpA9RiUAoV5VUieqr1HOUUmmxL1KuUFaURkLVWX2ISmwL5aiSKlV9qboqnHLVj4rSaOhMpA/hddaX58wUSqmjXooqqZKZTrnn6E9Uon6s9vn70vPoa9fTW1Ah0seotLMuRZXUXS+q/qquCqM7Qrm79LXgzr52Pb0JVWf1QWqVur0Waqn+nOqkJ1ybg+5vtTzyGuXZ9YSHoRKOzkSUsqimWqq/jx4rVT+WStj9rcYMqJGeXT1ndIoKEaUCqqGWqrc9oFGola2o0P3t7gyo0Z6dBqvWF1VnKXVBU53soxbqx0L3t7tqyUZ7dur9V190JqLUBR09lkalHkfF7m93ZkCN+OzU+69+6Eykj9AoRs5S0dFjcboTQ1LK/a10BtSoz65WDiVKYTQVfB+gkYyc5aK+/cFUKz1+Le+vPrv+RVgqeFVn9XIazchZKv4OaNq44fVuTsNRLY+jWsbmlHtsFTp9ExUivZze6N7Ym2dOPUUj2h26gz7zvovaRHo5va2z0cCw0mhUu0Ml6DPv2+hMpBfjqQfmntrMvMfW1iRgrZL21Cr/Vn/D73E0KGazuzNFW3ui190nfeZ9GxUiFVJv/W6uemDu7GamjB7WMO0JU1f0tplTvRk5OM6y1i29WhWkz7xvo+qsCqh3+u4g9cC8xWsDBUhP1U4vVV3Rl9Q0PUFfUAXpM+/b6EykTBrBG6pU9UAps4NqzKjKVVdoYFjp9BVVkD7zvosKkTJphI+6FPVAKcKuWh4zlRbD0o6kOH1JFaTPvG+i6qwyaYSPuhT1QLH8RtVUk/SkuqK3ReZ3F1UFKY2OzkTKpNbpu0ulmHqgmLCr9oyqJ9QV/TXWQFVBSiOjQqQCGuWjLqQeKCbsgoRMZyrNjo6uit1Ia6muaARbVD1RVZDSqKgQqZDe8FEXEna5QmZvMkUqneai+15tyFF+I9iiFEXJR4VIH6eQsPOEzJrNO7jgnhUkUrArkQQab5TfCLYoRVHyUcN6P6CQMXrk4DjDmmLEbDtreaMViFIDs6I0JjoT6eOUYozuLaP8RrFFKYqyD52J9GFKdePtTaP8Rio81N/cjRUliJJmIiIyCfgpcKAxZoqITAXmGGP+raatU7pFOcboao7y651XrCfor+7GipJLqeqsnwOXAf8NYIxZJSL3AypEGphy1VTV8DjrD51rf3c3VhQ/paqzBhpjXs5Zlqx2Y5Tq0tNqqr6QLLAUimUDUPJR1V/fpdSZyBYR+QBgAETkTOBvNWuVUjZhKqSeNEb3l1iO3uKI0Cj0h9lpf6ZUIXIRcAdwhIhsAv4KfKFmrVLKothH2lOBkf2lc22U1De9AVX99X1KEiLGmPXAJ0RkEGAZY3bVtllKqTTSR9qfOld1Ny6N/jI77c+U6p31A+AGY8x29/cI4BJjzL/WsnFKOJ76akdHV0N9pP2pc+0NqW/qTX+ZnfZnSlVnnWyM+Z73wxizTUROAVSI1AG/+qozlSbVYB+pdq6KR3+anfZXShUitojEjTEJABFpAvQtqANB6quoLcQjELNt/UiVhqOS2Wl/iDXqK5QqRO4D/iAiv3R/fwX4VW2apBQiSMc8IGJz+7l/x7CmmH50dUY7v2DKmZ2qN1fvolTD+vUisgo40V00zxjzZO2apYQRpmOePHpYWZ2WdnbVRzu/7tNIjiJKaZScgNEY8zvgdzVsi1ICleiYcwWGdnbVRzu/6lANby4dIPUsBYWIiCwzxhwjIrtwAw29VYAxxgyt5KQiMhy4E5jiHverwOvAb4BDgDeBs10DvgA/Bk4B9gBfNsa86h7nS+wz7v+bMaZfqNiCdMxhH06uwJg7u5l5i9dWpbPTj3Uf6spaHbrrzaUDpJ6noBAxxhzj/n9Ilc/7Y+AJY8yZIhIDBgLfA/5gjPmhiHwX+C5wBXAycLj7byZOIsiZIrIfcDUwA0cQvSIii4wx26rc1obEr2MO+3CCRsfXPrqWqCVZxyq1s/MLjWWtW/Rj9aGurNWhO95cOhusD0XVWSJiA2uMMUdU44QiMgw4FvgygDGmE+gUkdOB49zNfgU8gyNETgfuMcYY4CURGS4iB7vbPmWM2eoe9yngJOCBarSzt1DowwkcHdtCV7L8zi7brThF2kBXyujH6qKurNWj0lgjnQ3Wh6JCxBiTEpHXRWS8MebtKpzzUOB94JciMg14BfgWTpp5Lx/XO8CB7t9jgA2+/Te6y8KW5yEiFwIXAowfP74Kl9A4FPpwgkbHqbTh6tMmM++xtWXZVHIFVS76sVY/0LI/qwsriTXS2WB9KNWwPgJYIyIvA7u9hcaYORWe8yPAN4wxy0XkxziqqwzGGCMiJnDvCjDG3IGT+4sZM2ZU7biNQKEPJ2x0PGf6GE6aclDJHVSQoMpFP1aHagVaqm6/fHQ2WB9KFSJzq3jOjcBGY8xy9/cCHCHyrogcbIz5m6uues9dvwkY59t/rLtsE/vUX97yZ6rYzl5BsQ8nbHRcTmcXJKgiFtiWRczWj7Xa1FO3X43ZTz1nUP0p7U6jUMw7awDwdWAi8CfgLmNMt+qIGGPeEZENIvJBY8zrOLEna91/XwJ+6P5/obvLIuBiEXkQx7C+wxU0TwI/cPN4AXwKuLI7beutFPtw/AKjkg88TFDpx1ob6qXbr8bspxFmUJp2p2cpNhP5FdAFPIfjJdWMY7/oLt8A7nM9s9bjRMBbwHwROR94Czjb3fZxHPfeVhwX368AGGO2isg84P/c7a7zjOz9kVI+nO584IVmNN2lP+v+g6iHbr8asx/1juqfFBMizcaYDwOIyF1AbnXDijDGtOC45uZyYsC2BqeeSdBxfgH8ohpt6utU4wMvRVCVIhDUVbgw9dDtB81+LIQ1m3dy7KT9Kz6GOlz0fYoJkS7vD2NM0on7U3ojPfGBlzLTqYarcH+YufS0bj9o9rOnK8UF96zgxjNLE+zqHdU/KVZjfZqI7HT/7QKmen+LyM6eaKBSHWr9gZdSXz13m0TS0JXKdpYrVqt8YcsmZl2/hC/cuZxZ1y9hUcumqrS/ERk5OM60ccN7RFB6s594JHugmEjmP8dixxgQtRgSjzAgaqnDRT+gWMS63VMNUUqj0lF4rVQk5RTHKsVVuDMVLthU515b5kwfw/CBUb5+76vs6UxllpczY1XvqP5HyQkYlfrTXc+Xan/g5RbHCnMVFpHMjCSZSvN865bA61Kde+2ZPHoYaZM9Oyx3xqreUf2LYuospUEoRV1UCtVSkeSrptKICPGIhKoygtQd154+hXR6X6eVMvCd+S2B1xWmkhsUs1m5YXvZ90LJR1VSSrnoTKSXUO4ovNbG56D2xCIWPz33IwWLY3mzoTWbdwKGnR1JcswiJNMEegUFqeTOnjGW2bctU++uKqIqKaUcVIj0EsoxjPdEwFdQe3YnUmzY2sGxRx8AhAsyv1vv3mSKYPKz07S1J5gwchCLLz6G3Z0pBsVsZt+2TG0kNUBVUkqpqDqrgWhrT4SqZUpVM1RL7VWsXSMHx5k7uzlv23mPraWtPRHqRZXbvlzvLHAyDU8ePSzrvP7jzb5tGW+17WZ3Z4qolf0KF/PuUhSluuhMpEEoZfZQipph47YOIhXWCym3XVNGD2NQzGZ3jifPms07y0pPH7cFA0Rsi1TacOOZU7NmK57RPpkm63iLLz5G4xL6Mf0hXqg3oEKkASjHdbWYmmH1ph20J7JVRJV2rMXaNXZEE6kATx4wZaWnF0t4zFVRee2cdf2Soqnnd3emGjJrq3ZutacRcnQpDipEGoBqua62tSeY99javOVzZzdX1JkVa1dY7Mnk0cPKTk8/YlCM3Z0doefNxTvetHHDG8oIrJ1b7dF4ocZChUgDUK1o8qDOd1DMZsroYTVrV5iKrZz09MtatzDr+iVZdeBzzxu1BUsgZtt5x2sUI7B2bj2Dxgs1FipEGoBqRZMHVjI0pmIbQantCurES01PH9Txzlu8lrmnNudVX2ykGUcQ2rn1DJqjq7FQIdIgdNc339PDz53dzLzFpZe+rWW7SnsSc9gAACAASURBVJkhhHW8U8YM4/krTqhJ6vlaoZ1bz9AfKhj2JruaCpEq0t0HX6laJlcPP/fUZqaMGVa1F7DcdpVzH4qV9230D8hPLTq33tSZ9CR9OSCyt9nVxJg+VXK8KDNmzDArVqyo+nHr9eDb2hMZTyaPAVGL5684AaDHP7JK7sOilk2BdeB7K9Xq+HtbZ6J0n0Lfc70FpYi8YozJqwOlM5EqUE+Dapg66L7lb/Nfz7SW1QF1t/Or9D70tVGl396zcsP2itWT9TbS6yyo5+mNdjUVIlWgng8+SB3UmUpx+9JWEsnCHVB3KwzmdjLduQ+9TXVVjO7OIoLupS3C0tfe4/gjDqj5varlLEiFUzi90a6mQqQK1PPBB+nhLzpuInc8u55EMrwz726FwaBOZtbEUb3uA6gF1ZhFBOYm60xxzaNr+NeFq2uq2qrlLEhVdIXpjU4DmjurCtQ7ffac6WN4/ooTuPdrM3n+ihM4Z+b4gp15dysMhuXnAjSNOPtmEX7Kzenlf6cGxfbVhmtPpKqSD60Q1Wh/ELXI69YXyf2eG13I6kykStRbr5+rDgoazQCs3LCdHR2dRSPCO7pSWZ2Xn0L5ufz3wcur1daeqKsg6Wn1SbVmpt69XPrae1zz6JqsdDa1VJfWambdaOUMGpnepN5VIVJFGunBF4oK99RXhUimDaf+5DluPHNa3kioWH6ukYPjoTaW1nd30bJhO9PHDWfigUOqes1B1EN9Uk2VxMjBcY4/4gD+deHqrOW1VBPWSqXSnXIG1XZbV6qHuvjWgHqNoMLOG+Q2GLHAtqwsu0kQue6FQccC+Pd/nMK5MyeEbjMgajFn2mjmr9iYWXbex8Zz3ekfrso1hm1bT3fJar0Hbe0J7l/+NrctfSMr7UuthWEt3uNS3LnD3rHBcZtk2mTt01PfWl87TyWoi28PUa2Rb7kvU6HzBqkRmqIRLvnUJG544nX2dIUVhnI8gvzqhkL5ubw2B6nLLCFLgADc8+LbnHf0ISXPSMq9t6WoT2r50VZjZuq/ZhAuPPYwzpk5vtvHLeW6azGzzp0hA3lu0GEJOL3Zr2fkr8SjsBJ6ajbbW50OVIhUkWp5tZT7MpWSsj1IjXDMxFH8kNcKtqUrla1uCMvPtXrTDj57x4uh6rLOgOJTAC0btpckRCq5t8XUJ43+0QZd8+3PtHLOzPHdOm69r9sTTmHtCHpufpyaNTt6JI6mp+J1GiEuqFLUO6uKVMOrpZAHS1jlw2LnDfMem3jgkMDqhH4+83ejs17ioGN5yRL93l7GGOKRfdtc/ukPBh5/+rjhJd2XSu5tIa+53uApVOia29oTPLvufZ5d915om4Pel0a57kLtCPNM83AEjPRIVctaearV6zy1QGciVSRoBJVIhns5BRGmgrnzufXc9fybxGzJ0wt3J2V7UHVCP4tW/o0rTvpQliDxjrVm8068WuhB6rLbz/0Iw5qivmDEPdzz4tuZbc772PiSVVmVegyFXXdviAwOu+bVm3Zwxk+fxzNnRW3h5rOyHSDCRvnVvu5K1YHF2uF/bsvXt3HTU+uI2kLKffcnjx7aIzFJPRUD1huDDD10JlJF/COouO24wFqWMPu2ZZka48UIepn2dCb56f+upzOZDowTKDVOZeTgONPGDc9aPihm05UqpjrYmTeiXda6hQt/vYKL7vsjF9yzgo6uZNZ+Xek0k0cPzTrfdad/mKe/fSw3nTmVp799bFlG9e7E4gRddyN8tGEzS2+dl5U5a9Y3u5nrFq/B7w/RlTJctmDf+9DWnuDyBcGj/Gpet7/u/azrl5T8jkNp93/k4Dhvtu3mlqfXEbWErqTjpTVn+pgei83qa+epBeqdVQNa393FKT9ZRqfvSy/HK8jvwdKZSpFMG3L7+UFxm/u/djTTfOqgQt5ZQcu90SrA3q40MVvybBeeF1fMzo5MD/Ke8e9zy9nTa6Jnr6YhvJ6JHwvZJRa2bOLyBSuxxSJl0lw1e3LGvXXjtg4+f8dLec4QTVGLeadP4fgjDuC+5W9zy1PrstYPiUe492szmTZueFWuuxqeb8XaUco5+prXlHpnKYCTniJuW1lCpByVgX8qv6Ojk3+599U8dVNXKr/YVJA3TXZ6kzQXHz8xY5j1dNIeBvjeKUdwy1PrsgzkiWQ64wp8+cOruOOLMwoGK9qWxayJo4peZ1t7IqMSmzx6WMmzimp9XPUKEHVmCitJJPPTzABcMr/FnWk4z/zqRatZ/r1PZNqXMvn3vaMrzTWPruH7j6wmGTCz7PQ5SFTjuquhFgtrRyEvv9xz9FRsVl87TzVRIVIDqqEy8F6mtvYEqYDZ4tWnFa+bHuTxcfNT67ht6RtcfPzheR9oV8qQ6EpnikFt2LqHyx9eRVdqnwCzRVj//i46U+FuwTG7eGeysGWTr7MM1usXo9CordQRXT0+2vuWv00iGZxmZkdHF7mhO8k0rNm8k2Mn7c/IwXFuPHMa3/HdO4/cAFA/Fx8/Mc9BojvXXS21WG47gnK6dfccSm1Rm0gNqKZ+M9dTJWYL//6ZKZw0+aBQfbpHkMcHQCJpuG3pGySS+Z3OT5a0sm13J2+27eaSh1rYkzMD2t2Z4qbfrysY8V7sQ/dG4oX0+sUopI/vjq6+1rS1J7h9aWve8s5Uyr1nYTfWZPafMHIQT3zrWO756lFcc9qHGBwv7LgRj0i33YJzqYUOPyinW66XX2+xE/QndCZSI6qpKimUwqSQTruQv33MtpkzbTT3vfx21vLOVJqTb30OY0zeSNej0Ig3agtzZxeeJW3c1oEtFp66xsO2JHQG459ZAKE+9UHrLluwMtDfvlL9c3f01hu3dRCz8zMFXHz84YwcHGfy6GFEbclKiBm1hcmjhwXaUU6bNoYfPvF66PkiFtx45rSS21nOte3z0tsBCJNHDy3pHGGEBcXmevl1t91KdVEhUkOqqSrxq7dKDUryRouXLViV12l1dCX5yqxDWPDqxrx1uRl9PYIM77lELWHe4rUMiUdCVVNjRzQF6vVT6Xw7D+QboS86bmKortz7278ukTTcv/xtvnHi4aHHLNW43N1AvSDBHo9YmZnCyMFxbj5rGpctWIVtOS6tN57pJM8Meu7PX3FCJs+VbQm7E7mCuTT7VKXXVs2o8TAV2eTRQ4uqRhs5aLSvo+qsXka5QUlzpo/hsW8ck5d1V0QYMSjGjWdOJRYp7TUoJkAA9nSliwaweXp9/2mjtnDjmfmqiqCgtNuWvkFnKlgfP3ZEU6C95ralrVkusJUE3FUjUC9IDZR73XOmj+GF757AAxcczQvfPSErvsOPP3Py81ecwLWnTc6LSfLsU8Wo5NqqHbhYiYqsUYIn+zM6E+llVGLQ3N2ZoilqsyuxL5ZjQMTOdEDNBw/llFufyxISEcsJavQTt4U04TMVP8U8dXIDFsO8s4JUHBHb4vRpo3n41U1Zrsfe/hcffzg357i4+o39lXoWVStQrxRVZ+4stthzHzm4e9l+K7m2WgRslqsG7g1Bo30dnYnUgEJBZN2lktFasQ5o4oFDuOmsaVnHvPb0ycRzZihiCQ98bSYxO3tWE0QpndfIwXGOnbQ/x04KL/caWOEvkWJhy2bAcOGxh+UV7tlvUKxgeyr1LKqWR1Il+vtSnnt3jN2DYnaeo0Wxa6tVwGZQcGhPt0EpHQ02rDJOoFi2PrsRgu5KTcHtP2bYPt5ykzYkUoYBUYtU2vGkaYpGqp692DtfkM6/klT1pd6PSu9jIcrR3wfdi1Lcmr00NqUaoj27BjhBp3FbEEtKurZ6Bmw2Uhv6A2HBhipEqkhbe4Kj/+MPeZ41L115Yk2m1uUKknK3b313F8tatzBqcIyPfSDbcB/UYW3b3VlSwamwzq5QB9v67i4WrdzMnc+tZ49PQPgjscFJK/6FO5dnqe4GxWzuvyA7ur+S++Ht4/dGKqav9x+/rT3B3/9wSZYjQ1gE9upNO7hu8dqSByPlCKfcANRUOp3liReLWDz+jWNKzmvWHc8o/7uweUcHfi8vvzdeseOrd1bt0Yj1HmDN5h159oKulGHN5h0cO+mAqp6rEo+UcrzFrnrkT9zzUnayRH+uq0JBYoXak5tqxRv1fueTk7jpydfpTOVHcXsj5YglWQIE8lUXYanq/dv4O5xcwVKISjtqb9s32/bkecL59ffePrZIXoaCSx4KdlP2rqdUj72gbXOJ21ZoQs4gKvVCzH0XPASI2MKAiM3eZKqkGW5vjPTuK6gQqSphtoLiNoRyCOs0mg8eWlSNUey4G7d10JVMZQkQKFxAqlAnBtkjytxUK4mUgZThB4/n1zXxkj/m7gPO7CJlTKhdIKy0a6XuoEHXeNmCVQwfGMubkYRtawLcmvd0Jhk7oilrnyAKDUbKMS6HFXzKOlcP2BQKXa/Bud6u1L7ZpDez7C01NvoTKkSqyOTRQ4lYZKkGIhbdDsLKJagjMGnDKT9ZRtzuvq5+b0AkO4QXkArrxO5b/jb/9UxrwfiOQjgzCpNfSTFuc+1pkzn+iAMyaiK/KqNQTqZKC/8EXWMimebrv36FNNmp+YO2tS3BpC3IuXYRCd0nn+DBSDnG5aBto7ZgCVmldyG/4mA1WbN5B5aUP7hSz6vGo27eWSJii8gfRWSx+/tQEVkuIq0i8hsRibnL4+7vVnf9Ib5jXOkuf11EPl2fK9nHyMFxbjl7OvGIMDBqE48It5w9veovfGDdkpShM1mZr3yur32YC29YAamg9nSm0vzkD+uKxneEEbPFrRsxLF89lTYZARKW4iTIw6c7hX/Cov/3dOWn5g9UqaUNyYCZSDxqZYRdoWp+hQYj5XhlBW1781nTeOG7J3Lv12by/BUnYKCmaWMWtmzigntW5KXUKQX1vGo86uni+y3gz77f1wM/MsZMBLYB57vLzwe2uct/5G6HiDQDnwMmAycB/yUipVd/qhFOoNiJ/OyLH+Hn53205Ghhj1Lcg3M7gpgtDIhmP0pLxDUAFz9mYOea48ZbqIBUbnuitpBMpcnVVMRsm4uPn0g8UngEGotYPP7NjxetG1FuoFl33EH97RgYUGSsWCXJG8+cyjWnTcnbz4vSD7qHFo59ImrBt06cVLB9XsChJwjmTB8T+tyDtvWELlCz4L229gSLV27m0vkteQkow4jaQsRCc2c1MHVRZ4nIWOBU4N+B74gzpz8BOMfd5FfANcBPgdPdvwEWALe5258OPGiMSQB/FZFW4CjgxR66jFAqTQVRjr7er7IZFLOZfduyrPV7OlNccM8KPjtjHPNf2VjwmINiNomcGYLtxoS82bYn421VqF7JhJGDWHzxMWze0cEF96ygK6CP6EqnOWfmeKaNG8aF97zC3oDkXLGIxU1nTs0SWNWqTljMXlIMf66oC+5ZkdURdqZS7OjozJR3DQ2aE7j20bVZVfq8df59PO8sY6ArDbcteYPbn2kt+E74jcvF3qUwQ/TGbR152Q3KVSEFvScLWzZx6UMrA2e5A6M2N5w5lTWbd3DXsjeJRpxByMXHH55JB+MPSlUai3rZRP4TuBzweoqRwHZjjGdJ2wh4b/wYYAOAMSYpIjvc7ccAL/mO6d+nblSqdy9lv7D6G14FvOseXZvl/ZNImoyBPOiYbe0J7lv+NrcteYO0G50ej1iIwNzZzUQjdpbaKKhT8i9PJJPM+sAoJECAeOopL8lg2Dj0/vOPYsahI/OWB3V6xWYWQZ1ZdxNjOgGSB3DjmdMywqijK0nawEX3/THr3gS1+dyZEzhp8kGh5/d+n/3fL2Y/S9cB4bIFKxk+MFqw/opX2TCRLN/2s3rTjrwEm8XuqZ+g92TWxFFcvmBVqJo0jeFjHxjJ7Gmj+drHDwsUQJobq3HpcSEiIrOB94wxr4jIcT10zguBCwHGj69uSuxcapVSI6j+xuePGsf8FftmGV+ddQh3v/AmHSEePv5zLWvdkimM5CeRTDNn2sHMW7w2c9y5pzYz77G1gd5guYJv6boteeezBe7/2syMcBg5OM7Fx0/MS00C8OL6rXlCJKzjKjSzKNTxVMMd1J+2xZmVpDPeRMU67GLnd+qNBD/DRNLw9XtfJW1MaGcatH8p72Dru7u49tE1ecu9rMy593Tuqc2Ziou56kX/e3LHF4/EtoJVmP7BBeTfm1p5IvZ1ejJuph4zkVnAHBE5BRgADAV+DAwXkYg7GxkLeNa8TcA4YKOIRIBhQJtvuYd/nyyMMXcAd4ATbFj1K/I3ogYpNcLqb9zzYvYs4+fPrQ9N3+6xtytNVzLFFQ+vCtVLL1r5t6zjXvvomrwkjbYlLFq5OU/1EUTKwLl3vZwVMHfOzPH8ZMkbeUkdb1v6BufMHJ958e976S2uXbyWmO3k8srtOINmFt3xwiqHkYPjDGuK5qV2L1X9ExaRfvvSNwru5xmkL1uwCkuEoU2RzMwkbH9/ZcOgcy9s2cRlC1blPY+BUYu4bdH67q68e/r9R1YzOG5nnsuEkYMCB0LgqO5yidrC49/8eMGgxko9Eftz8GFPz9x6XIgYY64ErgRwZyKXGmPOFZGHgDOBB4EvAQvdXRa5v1901y8xxhgRWQTcLyK3AKOBw4GXe/JagqhU7z5ycJy5pzZz7aNriNpWVgzEyg3bA+tv5BIkQGxxOnGPmC08+8YW7DLcK6O2lde57E6k8qLHC5FIprM68mWtWwI7lphtZzrg+156i+8/4iQU7HQVnd+Z38KgmM2oIQMyHUTu6DXQxVaCa5V0N9p6R0dnXtbg3EFDmI0g6EN36o3YJJLJrGPaFuQ6tiWSaS5+4I+A4731rRMnMW3csMD9/ZUNg2YU8x5bm1XO2WOPW3a30y0QlYun+rpswUpuPmt64L2YPHooN545lUt8NpGIBTefNa1oVHyYJyKYTHtzBwj9Wf3VUwMoP40UJ3IF8KCI/BvwR+Aud/ldwK9dw/lWHI8sjDFrRGQ+sBZIAhcZY8r3GawBlejdF7ZsYt5ja4lFnA776tOaMy9+WP2NYgyMWiQNpHydQ2fKcOdzf2FPkOU7hJRx2jPPTcPh5a8qVYB4+D2YLl+wkiAVudcBt767i6sXrc5bn0zD+fe8gi0QjVh5KhUISdrYmWL15h1ZEerd6Wz8+6aN0yk2RSN0plJcdNzEgueYNXFU6Ice5up71pFjePjVzaF2hWTaKX0cs8HkxJP4KxsGdTJBM00/hYqQeSSShssfXpV1L/wDKC9b9LLW9xk1eAAf+8DIkr4Lb1B2SYhRHrJnfvXoRBuJemQ1rmsWX2PMM8aY2e7f640xRxljJhpjznK9rjDG7HV/T3TXr/ft/+/GmA8YYz5ojPldva4jiKA4hTD8L357IkVnMs28xWszbpUjB+fX38gl5rpC+knj1GIfELUY5Cuh6hcgxRLyxmzhouMmctLkg0JrVpSKp1K587n1gaq0WMRi7uxm7nxuPSf9+LmCqrmUcVRz339kNefe+VJejMjcU5vz9vHf0+7UoQiKq7Etiy8cPR4Q7nh2PbOuX8J9L70VeI41m3fmzQT9H3pQ2x/8v00kS0jB35kCYwwxW4hHLGK2ZFU2XLN5J1aOkInaFp0lutwWYk9nKnMvbj/3I1nZle976S1O+ckybnxyHd95aCVPrHmn5OPOmjiKQlpT/8yvO7FAfYF6ZDXWVPANQCkv/pzpY1j+vU9w2+enEw3owy/91Ae55ezpefEU586ckOn8B0bzH7dlCQv++WiuOe1DNEVz2yD4O8XnW7dw/BEHkApJ2hmz4QszxzMgJA4klU5z/RN/5qf/uz5vXdSCSz85iaseWc1P/3d9Xi2TQrQn8gP+powZlld73H9Pu9PZOOV9s68xYgl3Pf8mCV/A57WPrgl0l33xL1vyclP5P/Rx+wV/8KXeEWOcGWcimaYzZVjx1lbAF+TXlX3ulDGcf8whJR4dBsZsvvyxCaHrY7bFsKZoll3r+4+spjOZZnenM0j6/m9Xc99Lb5V0Pk/FF9SO3NiR/p4avpzA02qhQqQBKPXFHzk4zuxpYwKD1m56ah3NBw/NCyLz9ps+bjidQTaIiEU0YnPatDF5nVRX2tCZyh5FA9xwxtTAmiLxSIRPTT4IQuwtyTTMXxEc/XzGR8Zy45OvBaq4SsUvBMaOaMoTRNWoKQKOG2yeEEilyZXRlsDenA67M5XiF8+/mXfM73xiku9D716utdx7eM+Lb7Pir22uM0X2NccjjnfU1z5+WF4QaMQir6YMQNoYTvjQAXlC2iPXJTjI4wvg6kWraX13V+Z3WHBkcElh4Wdf+EheLZl6dKKNRlAwaS1RIdIDFIsYL/fFDxpldybTnHLrczzfuiWjRvPOe99LbzH7tmVYATOIzmQ6Y0+Ye2ozURuaIk6UdG4UvL8c6+Pf/HieHt0xqhrmznZUaE1BU6YQHlyxMS/CvRBBarjcSn+F7qm3Ph5xItDjkdI6m7b2BPMeW5u3fOah+7G7M/sC9iZNRiUXd7MKXHz84cTs/M/upt+/zqKWTe47kmvV2IfFvujts2eMzcssEMYjLZvzZl4An//o+ExMyyWf/CBRWzIj/FvOns4L3z2BSz45iXhEGBKPEI846s3Rw/KFNJB3Hzdu6yAacL3gDCpO+ckyFrVsCk1fA2EZAKaFFjOrpBOtZSG5Wh47jHLU6d2lkQzrfZJSjbelGuMdj6CuQCNjZ8pkfOgfX/0Oty99g4hVOK2353GzsGUT1zy6hq4UdJHGFjAhdczBqYb4mWkHM/+VfR97V8pkAu7mTD2Y37ZsLn6DKuToQ0fyDx/cn1ueXrfPy2h2c1bqkWL31Hj/NZL5VcxbKyxR4nOtbQXba4DHLj6GEYNi3P5Ma976zpThkodWYglELAsrwBsLHGeC28/9CJNHD2VZ6xb+59WNBc/rce/ytwM98u5+8S0OP2gIr/9tZyYwtSuV4uwZYzLv6TdOdCLH71v+NrcvbeWOZ9dz+zOtnD1jbCZOqTOVykSY+++b4xQSPr3sTKa5bMEqwJBI5pcBqDRItJxYoFp6c/UHTzEtSlVDgqrs5RYhKgf/C9nRlQSC66AbyHPJDWNgzOaGMz7MJQ/lqzoilhMP4s/u6jeUeu63teQTRxzAs2+8H3g9MVu4+rTJTBkzjNWbdjDvsbUlf6xBz8a5XiurbntQ/EFQ5cRiDIzZPOAWxlrUsolLF6wKdKktxqC4zf1fO5qxI5oqakcQUVsCByVPf/vYjAtu2Lu8+OJjigb9eZUH06k0nQHNHRizSadNVhqc3GJjxajUVbva32hPHbsehBWlUnVWDanUeBs0/c31CEqmwRKTp85IpEzJAgQcj5pvz28haCxhi8U1p03mkk8dzk1nTsskk2xrT3Dt4nyVTrWJ2cL1Z07le6d8KHB9Z8ow77G1DIrZmYj6Uj2tgp5NMk2WYTzoGH7VSlAixjC8RIvgeBvdcta0klVRfrpSznGC2u8RtYWLjjus5GOGpWRv2bA983fYu+xUIyyMp1566F9mseCfj85TgyaS6bw8auUYwwupwopRS2+uah+7HmqxUlB1VgWUOuqpxHhbKAgtVx0Rj0S48NjDMuqRSkelju03f99EKs2Vv90324hYcMvZ05kwchAxWzIBgLUilTY8sfodjimQCTlqWbRs2F62b3yx1OuFjlEoEWMYl3xyUl7qkHQZHmge5886pGj7jTEMa4oFrotY+bPXMG3E1t2dmb+Dzrc36ST5DJqp+sn9Xm46c2rGSWNvVzow6HTuqc0l2afWbN6RSd9TSVxILb25qnnsRlaL6UykTMoZ9ZRrMPcS5wWNqIM8gjpTaQ4dNZCrZzeTKtIhVoNk2olMHhSzA42qFjCgUDBLmaSMk1pj+ZtbOe9jwTnPutJppo8bXvbHmvts4hHJmxkUOoY/EWOhQD2PWMTi2XXvZz3fUieMTRHBFkeI3/vS2xl3a6/9g3KcLJJpuP6J1/JiK0758IF5zhIAZx05jjnTDspbfsvT62hrT2SEgOcw4dwvC2McO0ahmVvQ9zJn+hgWX3xMqBAdFLcZt9/ArFF37ijcO+7X7301T4iXM9qvpTdXtY5dLKap3jMUtYmUQTEdZ6FU6aXMXG79wxvckpOU0EuTfemClXkzjX3m4J5jYNTmgQuP5q223Vz+8Cpsy9Gnf2b6aBa2bEYgTzVhC0Ty8ksJXSWOxGMRixe/ewLbdndy1cI1vLB+nxHbq/3u6d0rKXvrPZvnW7dUdIzWd3dxyq3PFVQjRiwhZlt5MRrFiEcsrjz5CP7jd69l3T/vvQNY+tp7fO+3fyqqxpwz7SAWrcwP8hsUs+hKGdKGrMHBgKjF/ztuYlZ1Si9DwI6OLi6679VM2VrIt2MU+l42buvgrJ+9ENjmXNuU34CfmxA0iErsDrXMtdXdY6/csJ0v3Lk88F6/2ba7x2YoYTYRVWeVQaGUAoVqiJTiKRKWOG9PV4rvzG8JdPmsh/hPGWd0Pm3c8IxKZ2dHkkseWhmaeTZlwMqZKUhQvvgQorZkPsJXN2zLWjd/xUa+deKkLBUTSMklif3PptI08RMPHMJNZ03LUtHkkkwbkul8ARK1hXTahM5KROCw/QcHht5s3NbBtHHDnRigEqY1QQIEyHNN9tjblea2JevoTO1LxjnvsbU8f8UJJalqCn0vy9e3BbY5agEiJJLpzPuUm2g0LE3LwKidKVVcbmddjczOtTp22L0eFLMbIsWLCpEyqOXDDEu8B6V7WpXLwJhNZypdUjoNj6tmT86oCjzBaSGhAsTj7BnjefiP2cWxVry1NdNBFMIzShfLC1RJMTBvlDgoZme8jEr1CPLjF0Abtu7mUldtFcbAmJ1J5z58YJR//vUreSn8Y27MxehhA/KO5WVjBicv2ICoVRVPLT9OVUErK6mid7/HjmjiouMmctvSVmwrv8AWFP5ebvr963nni9nC9045gpt//0YmrX5wu/ITgsYjFj/74pFMHj20V3o+FSIsZQM9BwAAIABJREFUqevuzlSP58kKQoVIGfgfpqfGmTu7uSoPs5ihd0DUMcRGIxZdSccYGdT3ex90KaSN4drTJnPd4jUlGYdtEa5auJqmmE1XypBKp4umnveYMHJgRo3hjfIN8ODLbyNYdKXSiMCAqM3erhQiQjxqZXVO23Z35lVg3NuVZMPWPRUJcs9Y6RwnTdwWxJKS3IPD6puMHBwvajj1oq396dtz737MFh7/xjFMPHAIKzdsJ26Lm712H+fc9TJXz25m3H5Nocbx7mCJ5CX+7EqnWb1pB2f/94tuehUnUt8KSG5VsPOzrbyMv4hwzMT9+eET+QLGTyKZ4trTp2TVvLnhjKlMHj00K06oLxFW8qARUryoECmTOdPH8M6Ovdzw5GvEbIt5i9cy99Tmbj/MLAElEhggeOmnPshNv3+dqG1hSGPSjh7bT6kCJGoLc09t5tyjJ3DSlIO487n1gTmtso7tdlSlZHXN5eanXueMI8dm6cuveHgVnX7PMLdTuvDYwzjjI2MzMwNw7EW3L30jr2piVxoufuCPmay+WdcYIMg9j56dHV15Rbm86oFBwscTHF48ii1CVyrN1adN5qQp2ZUKczvP9kQyS0h89qPjOHbSAZnfIwfHmTFhBMt8AYszD90vE6MxdkQTYklePpPOpJOAckDUIpl2bE+WEBr5n1sWIJejDx1By8YdWR0zkCUE5p7a7Aal7jtQVxpIB983f/Euf3nbIMeMq09rZuKBQ7jhjKlcFlAwzUNEOGnyQVkVIpe1bmHW9UtCC2bVm2rYXHLVYpWWnag2algvk6AguwFRJ/vsvMXZncu5R4cnqQsj01lt3pFdXdA9vl9lEY9Y3HzWVLa0d3LT718vu3OP2sLNZ03bF0C4/C2uXbQWweSNeqvBJZ+cxDdOPBwINhb6iUec7LO79ibzOq1SyTWwFqrz7SfXQOzNWMKEuy0wMBbJU6G1vruLZa1b+MHjf85Sv8Qjws/Pm5GZibS+u4tP/OjZvOP6g/0WuW0vptqcPeVAfv/n9ypSgXrv09CmWJZayN8Brtm8g/N+8X+B+w+M2vzsi0dy7KT9s5bnuqeePWMs9y9/OzOLtS24bs6UrO/l2XXv8fV7X80U4PLjPR+nPTvZ2dHFJQ+15Akdf8GserrD1to9t6cKcKlhvQqEBdnZIowbMZCv/P0h3LXsr8QiFvMeW8uQAZGyXxZvtDFt3PCskVZgoSUL1r3bzrGHj6qok+1KGS5bsG/06NX/9pd8rSa3LW3N1LXY0dFFZ1BeD5dE0vDt37SUlZAxaoFYVlbFO39HWKjOt5/cBIKemiyMlCEjDLPTzjj2gtwOPbfEbVjkesuG7Rkh4tXjOOUnywpGui9e/S5RW4rOOoJIJNNcvuBPGeN0sGNIeIDkni4nbsRfwTKovkeuHSxiCSdNyXYxnjx6GOmQAW4ilWb5+jbOePK1gupUb1BVz3oiPVHfpJZOAaWgQqQMHON3fpDd3q4UX/vV/2U6C0/X292XJfflyFWZ7elMc+uSVm5d0sr0cUNp2bCz7HPYlmSlRx87ooljJ+3PVac1c/XCNWWlZC9GIplm7iOrWfL6e0Qti1Q6TcQKrsgI5XeCBnjg/KOIRuy8UdnGbR0F63x3pkyWTSQrgWBArqxCnHzrsxTz5PVG2Jc+tJILPn5o4DbTcwz8Ew8ckgnUEwMdITeuK2WI2RYxCd8mtF1d+e+u3/nASw4Z9mhyK1iWdP8Mee+gX1UDjs0q864Yww9+91rJ12TSpiz7ZDVH9vUoEtXTqBApg6D04uC4YQa7K1bvZfF/VAJ5njwtG3aWFXvhkUobVm/awWfveJGIO2r+zPTRLFq5mZgd3sFXyuOrHTdT76OKR4QLPn4ody17s+DMxI8tgOQ7ECTT8Lmfv8QtZ0/P87AaO6IppByvcOeXPsroYQMCc0CVEtnup9CMJUhgdqYMtz+Tb4s672PjGTEoxsoN27MMqRNGDmLxxcewecdevvLLl0MFbTQidHXj4eW6rnvXFrOKu5ZbQua9HzuiKd+AnkMiZXhpfRs/enpd5h28+rRmzp05IWNM7kqmOOfO5UB5aX284wcVUiunbHGl9If6JhqxXgZeR+6PbBbCb2IpL0tbe4Jn173Hs+veD4049SJSZ00cxdxTm0M727NmjC1YAS6XiAVXnbYvcMurqjh/xUb2dqXLKqHrxwK+MHNcSbmhYrZNUyyCiMlL7x40cxDgus9M4eXvncgP/3FK3j5eVH1u3rGN2zq46rTmrDZFLLjprGkcO2l/Jh44JKM29O+blSurjNT2gZjg+hx+LvvUJJ7+9rEcOWG/rEjvqx75E7OuX8I5P3+Jk299jtf+tpMffXZ6aLXLVNpw9WmTHRVfgfM1RW0u+9SkvFoinakc13VXOIaElGSxpzPNfcudglPLWrfkOX/kErXg5t+vy3oHc4tWvbMzQSQkV1gxYnZ2Juu29gS3/uEN/v6H2ZH03a126UWN+//uTtR6vSPRS0VnImWSW6rTEPxhlVKfItfQ6+Wm8o98FrZs4vIFqzKuu6l0OjBFOMCJRxzAw69uKtmWYVsWW9s786rvdQdLnM7/4Vc3lWR/2JtMcfvSNwI9cdJpRy3jF5oGuHrhajBO0Fmgm7PsmwHmjiyvOW2yWzlQsozHhUagfg+jQqP/YjTF9uU6M4bA53Tg0AGMGBTLtyO8lG1H+MHvXuMTH9qfBy84mtWbd/Bm2x4eeHlDVgbi+Ss2FK3RYjB87qjxjNtvYFYd81Q6ze9Wv1O2Ks9j/oqNnH3kWC5fUNyRwWlj/jmuWrSa6xavAUO3HD1EyAzmnO9pn+eX9wwuf3gVd3zxyIpUT7nZtUWEAZHsfGLlBrE2cq6sXFSIlMnGbR1ELIsE+VN0J6dUOrCugh/HzXRnnqHXG0X7ddGleBOBI4C27ekiagshDk95JJJpfrKklWSJaqTibRDSxtCVMiUb+tNpQzxmk0jm308DgTnBkmm4ZtHq0A4ykXTUUo4xPTs5nxdxneu+W8z4OXJwnMmjh2JZQqrCDq0rneacmeM5ecpBPLnmb9z4+/wMBdPHDS/ZDvP0n9/n6T+/T8y2EIGr3JgRELqSqSyXYY9ce8acaaPZuK2D5oOHZg2Okmm4bekbdKfK4nWL15YUfxRGKl26y3oYUVu48cypme/Jqe4YrHoGKVv1FPTugMkES/rfo3JsMo0QiV4qKkTKZOyIJroCOt2BMZtr50zm+COCq615eCOMsChv/yh6zeYdJXfGIsLW3Z1lu/mWaocohUqM8AOiNp0FOpqwy7fdEVoQXuDbfcvfLpicr5DnW9B2azbvcFy4K0g445WhfWL1O1z76BqitoUlZKl6zvvYeCYeOCQwiKwQ3jO8atEaIpajItzTFTySyG35/BUbebRlE11pk6c+jNk2Hz98VMaOVS6rNpXv6FEKTVGbzmSq4IwwYsEFxx7G1445rCQnia50msmjh5Ydd1FM4FdiFw3K2N3IxngVImUycnCcq0+bnBcr0plMFRUgpbmL+kc+pY8CUynDDU+W7rHSk9iWYIvBEisvOePerlRgLZNCRG0hbcLv4YCIzZrNO7h9aVAFwVTGkcAfmBY0AvVvtzeZIhUQ3FkKMdvi5rOmsWbzjkxAp2dsdlJ9fIhjJo7KMqTfcMZUvvVgS1niylF3Epg6pxAdrqDNHQR0plL84bX3yjpWT5BKm4LvTMwWHv/mxzPu0R5hThJ+1XO5qqdijheVGNGDMnY3sjFeDesVcNKUg/KMxhKQIS/XMLZm806sAoIhYsGNZ07LvLgbtu4J3M6WfKNzmnzPn+pZOgpTzIDurBYsS9x8TE7AmBfLUMqYO2YLTVEns+s//t2YAlXIPVfo/NTuAF895tC8AlbzHlubSXM+KG4Ti1h855OTsrbzstwGXl+Rtnem0nz7N38MzAgQtS3+bvwI1vxtZ5Yh/Z0de+uSYBMctWyhevBBCD33vnWm0oHvTMRyAkxvOmtangCB4PT/l3xyEi98N7sOuxenVcqoP/eYEcv5HipJ/e442bzPdQGxaKXUV6kXOhOpgI3bOhzDmS9J3ICInTXdzIvSPXIsv1mxMU+FFY8IN581naFN0bwo4XmPBVcPTBmIYIha4SkuoPIsv17cRKl8uvlAfr/23dB9cuNn4hGL//inD/Ot37RQyjTEEjjnqPHc//LbRCxh/or8uuK2wICYTTJluOi4ibz2t53Bqj3jxA1kLUobpowextxTmzOqppuefD0wH5S/Td5hSlEghttv0qzdvINrHl3rVPhzu8cbAxIU9gR+tSwQWA/ej205WYgbIe9FMg2Xf3pSVqBj7oyinJlGqfEiuccECu5XyLU4SM0dj1hMGTOs4LW3vruLlg3bmT5ueKAArSUqRCogaAqbSKYyvuiBUbo53jWwL7XH7Gmj89YV07UmDTXLBV/uYRf/6Z1M3qZS1D0isPS1d8tIFAl3v+i4e4YJqpSBjs4UGPjZ//4lMD0JwJ3Prc/r0BMpQ1cyxbzH1tKZMvviGgoI0mrFYCbTJqt6pIdTsra0kwyIOgWixo8YyBvv7+5We/xq2bb2RCZTb2ia/27ciK/8/QR++cJbxTcsg+ufeI0zjhzb7dIM5XpHBeW1KvW4syaOKqjmTiTTgXEuHlc98qes/sWrsdNTqDqrAvxT2LirMrEsYfZty1jUsqlg/WuPgTGbn583I/TFLDfIrVoI8A+Hh5ejDSNlSu9Y93al+e0fN5d9jmKkXdVYmAABp1vOVdEMiFq82bYn75nFbAlV58QqqI9eDqW7acPVs5u55rTJrN/SPQECzsRw2+7OTOXAO55dXzXvPT8CHH/EgZnvp1qkDLz4l7aK4z2geCXBSgk67qULVvHiX9oK9hdxOzhnGzgzkNwB6j0vvk3ru7u61dZyUCFSIXOmj+Herx6VmSd4AVmXP7yKQTG7qABIm30ZTf14dpRtuzu56LiJxCOSsR/0BAZ4+rX3a36enhePDsk0eQWejCGwxG5nynDppydxySedYDzPXvK9k48ItIEVo6mKpYM9Umm48rerufK3q0MnTuWcNWXglFuf45L5LWWX8S0HS4Tl67fUpFbOlva9eZ1y1LJYs3lnYEBgLmGldXOXhx0jbHnQ4LIzmebb81voCPGmA8AX55JLy4btZS2vBarOqpCFLZu4LCCGI2o50bFzZzdz7aNridpOkGBuic8gg1tQfQtEuPDYw/jYYftx5n+/1GPX11eJ28LFx0/k1iVvZJ5dZzLNktfeY+6pzXled7c8tY7nrziBc2aOz0o7Xk5N+3jE4pJPTqIrlQqMDak15QrsWhVB85MyweleukvUDq5JsqczyQX3rCBmO552xhiaovmZl8FxLAgqAuZXKYWpu8KWt7Un2NHRGZgCpitliNpC1DaBOdcuPn5f5utcm0tufjWPsOW1QIVIBeyrhZH/sXmuofMeW+vkskruSwv/rRMnZVXR89Ii+I/pf3mdKF3DbUtbmTbuyJpUr6sm9aj5XgiLgA5UhJOnHOQG0jkY3AjwI/ZncNzOMsiLgUdXbua0aaOZNm54JhtwqWmpbIHPfXQctzy9rrFuThUo1wGjlnjpZK46rTkziLt64erMc0oZSPlK7kJ25mV/IN/uzlReETC/SiksGLD54KGBy3ftTbr9gUXaBNd1GRCx+Y9/msJ3HlqVlaU5HrHYb1AsUyslKCLeG6B6ePFGPYUKkQooZPSeM210xjXUY95jazlpykEFS7gWOmYimebFv+RHHzcajdCdDIw693XO1DE8seadPF3yxcdPZHdnyg3mym7x06+9T679siOZ/v/bO/MwKcpzb99PVS8DAww4KDhsLiMSZgKoowMhEgXjLprLJQo5aNTjd3IkqwpGD65H456Y6IlHjScaNVExEXCLuCQoURTiQBhUmIDKokZHRNae7qr3+6Oqml6qerp7elbe+7rmmu7q6urqt6vqqfdZfg/XLFjFDc++w+1njuX95h0FSeRbCh58vbTB445AcBIlctmIfA1IvgkXxeL0ZzmM9Z/vTPbg2dGSvxsus5Bv6IBeyQZsHgl7t4hjUHFqw/ovsts1iHDt06toScm8i4YMDNtOS/CI2zYTDhyYVGn2rg8/OWZk1vtTK+J/8ngDpmFQHjGIWYpZxx3MRZMOLGT42oyOiRSBo0zqfyH509sbsrSoDBEaN23xDaxdNnc5i1b/q9U4ygOL1zHVJ4tLs5srThjFfxxVjSEGf16VbUCiIWFa/fCcv1/13n18l3u9V+56eXXJ97sroihcij+Is+raR/PJFCcp4tYzxlJTVZFW11PIvrdYNus/354mgpoZ87IUnPSrV5nfsDFQmdcvrha37KwkjIhp8IMpI7NEGYGkSvPDF9Yz56TR3PbCezn7xyRs5yZze4tNwlLcsXB1qzGfUqNnInmSmds98+hqbl+YfUEJiZF1h7ajxWnWM/Pog7LuVFIbFE0duy+PL93o+/khw+CphtJnNPUUykxh1L59ueh3y4gl7DT9sPKIiaVUWhzq4qOqufPl7BqIVR9vy/k5IcNsVdpcsxsTOPGrQ3jsrY0lnalGTIP7z61L1lYt95kF+FEeNWlJ2MmYyK6ERdyymfn7BsApWPzhlJFZdWDgnKuznlzB4tmTfeVRvNa+me2EM+u9PA21zDhbZntfL+W8EMKGwSNLPuR//tLUYeKN2ojkgaf8aYqBpWxuPWMs0+qH86uX12T9yC2WzXWn1XKdWzzmEUuoQEE7r0HRU29vSsZLMolbNpGQkdUQqzPpSjEQ5VbFZ15IyqMm156Srmk2r2Ej//vq2qK6/1k55FY02VjAMys2teoaK5RwSKjoFU5zQbWWEXnehBFM/so+yazIxk1buPDBpWmutoQNv3xpNUZAyq3n+goqWvRb3rcsFKjHlSoMmRpLuXZBIxGfbD5TnHKCspBzM2Mr0pJ7Wiw7qYrdUeKN2p3VCs3bYlzyuNO/eUfcSrZtBbj6lJqs9RUwbEBvbj9zDOHM0VWOTz6oN0XIEGI+bpaIKVx9Sk3BAoejB5cXtH4hCHDSVwe3ul6pMcW5GKR6CDy5mJqqflmpkrtaLMa5EhaerMSsuc4JW+hF7duHD+Wqk7N/c01uHlu6oeRpwjtiVlq2VGWfKHNOHp3zPU8s28BFv1vG4qbPqOwTpaJXhJBPHVDchmNG7ePb+yVVw8pPHsVR6N7Clp3x5LKp44awePZkHr6wnsWzJ2fNCho3fYmVcd4rpZzi2RRMA/78o0m88dMpPHxhPX+7fAq3nzk2zS028+hqImb6tSVVTLQ90DORVmjc9GVWJo6lnMrnCQdWZq1vKzj/t29iSLbURcxSnFA7mGn1w337mO+I20RDBglUsgte2BBEoG9ZiFtOH8OPH1+ed5Xwqo/bXnwWhMKpVG8rhc4GLOWINopA1JWev+z4UYyoLGfz9pasAK7jy36Nbx/uZLAEqSfnw2NvreenJ4yiPGKwPZ/uTJp2QwHf/Pki7jx7d/+d2qoKeocNdgRkMHozfO/OPKjbJcCL7/6LRy44gt+/+SHzGjYRDWe7RDPJ1R8oqEreKRVYkXWt8PsKAgwoj6Rty09y5a4M4dH2Fm/URqRV/A+y+19bS01VP9/Xgq5RYTdNsHpQlEkj9+ZWNxPDlN3pg94FzttG3C3DnvXkCh4+/4g291foahRzh/oHN50x4f42Nz77rpOVZdm+WUCxhM1Dr2fLzniETUf/qbV9iSUUNz+/Oqs1saZzUDg96vv3jlBVUcbzKz8KNCCpeHfmQwf04vyJ+/kKYyrb5uz7liRn/ypucd1ptYGxBS/1O7M/kLd/qbp4Hk2fbOWSx5fn7WHoFQ7RuOlLKnqF01xoqUZlXsPGtBqmsCkFiUAWgzYirVBTVUHIkKwfOnPKmA/xjF7PE6sHcu+/HcbaT7dz2wvv5ewFEjYMnm/8pODPDKKYeEBXJp+LRya9Iya2e3c5et9+HH/nolbrP3b6VYP1ML75lX1QSnWIckFbabEUF/z2LedmK09SZf6DVLWdiebubVoKrlvQyPE1g30vyBs27/Rt59xiKf7jd8uwUWkB7nkNG7nk8Ya8640AtsV2F0z6Bcy92ErqNg1xrjPtiTYirVDZJ8q1p9ZwZYZInpNHXpj0RSSlYCm1srXFslutgG6xLAb3K+XdRFcKi3c80ZBwz3cOpaaqInlR+OGUkb4Zd9GQUbQLrDuy8J3S9BAJCYgheTdWK5ZCDAjA/zvygKxarkzChmRtN7VhXGa2Zi7X2I54uhsNKNiAgHO2xlIKJjMD5n71KxEzXV28PdCB9TyYXj+CG06rJWJKstfCLaePoV+vwmyw7RYspQZ3t8YSxBI2IkI0JPQOUOucefRBjBxcuirUzGKqnoaXmx81hbKwwVl1Q4mEnH4hXm3BpJGO3LmXT79XeSRrO45U/xjfIGt3w0tGiIaMDtFiSyi4Z/qhdLWh83rc+2EawnlfG+HbBsBrGDevYSNfu+llzrnvDb5208vMb3DS8mceXZ3zu3puNL84ayqTDqr0zcwK2p5HUP1Kezez0jORPJk+fgT1+++VptnfvC1G2Mz/TstScMIvXyVsZN/ZloVM7p5+CIAbcE+RXAgZTKsfzvNFtindEznzsGF8d+J+bG+xWLK2mdteeA9TIBa3mXXcqCydo6DZ4FUn1ziSGRl+ZqVUwXeSXYGjRw7kldWfddjnXfjQsizByyBChjOu7e1mTdiQCJj5W7biiaXrsWwb05Dk7MLLAATSgucAP3qsgbDpNEwzDYNvjKxk0ZrPsq4L3gU9qNmcx5J1m3n0giM45743cvYL2pWw0gyEpy5eSHvfUqCNSJ4ECavdfuZYLpvriCa25vJQOHGRuJ8Im20nXSu3njE260AAAptU9TQKcbSlnuipzP37en5y7EiWrPuYG59Lbxt843PvgsDtL6xOawSVSXnUZNhevbjod8uy/MxXTa1NSmzEEhaJIlvndiSWokMNCDi/Y+pdf8Q0OHfCCO57bV3aeobAf59aw1XzG7E6OVjnZd5FQ3Dntw9Jaxi3aPW/soyDrdLdTC/5xJKiIWHOSaPZsHknS9//POfnR0yDcMjkBwHuVY+Ezzh52VqNm74E/JXCS402InkQJLg2sXogU8cNYeuuBNcsyG4slA+pwV3P1+pJH2xvsZI+13wrcrsjAoRNpxo8YducMmZfnsy330iAXyLkSn9f69NqFOCmZ99tdSRjcQu/AsaIaVJbVcHi2ZN5ZMmH3JHjRNekYxpw8OC+XHHiKG59/j1EnIuhaQg3PPtulzLEIdOgX68wFb3CKUuL8wOec/hwrn9mVVomZhAtljNjmVY/PGdDMIVTgjBp5N5py1P1+Vosm5lHVzOtfni7zUi0EckDv4CVcpvfTDiwkuufWeUr4Wy6jaeDevqETbjl9K8y4UAnOOY32xnrSjqXoknVYcMrWPbhlqzlvmq3HYjCuZtL2IoWSzF/ef7yLiFDfO9cHWkS5SjN+lT55/N9RYSqirKcfuY9RUurVOyM21zxxxXYgFK7Z5G2pbJkRjqbXa5cUWo21MTqgckarkJ49M31gXptkC5SmbBsnm/8mOn1I7j1jDFcOndFoH7WlztbkhLxQdXvty9czS9fXsM1U2uYXj+isB3Pgw4PeYnIMBF5RURWiUijiPzQXb6XiCwUkTXu/wHuchGRX4pIk4isEJFDU7Z1rrv+GhE5t732eeiAXlmV0LGEzczfv80PHn3bVYTNxlLOfUs05FSUhgzHn+51czMNg0ueWMGjS5xOZH7d1Jo+2cpyt8HMWYcNbdP3WL7hS9/lXWFuk7BJnmSFZOsGJQhc+PUDqKrolVO8LpPMX7Es5EjQ3HK6E1jvHTGJhgzmnDSaxk1fsmD5Jk9vpUdyfM0+5GgzXzQttvN7d0Ryx2HD/d05ppCs8r7ixFG+nSoNwylMTT0fAaYdMTxtvSOrcwfCTXHO+yCiISMtIG8puPJPK3nkjQ+YWD2QO84c4xuwF+CSJ1Yw/f43mHDTyzyy5IPArqpxSyW3WWpEdXCWjojsC+yrlPq7iPQFlgGnAecBnyulbhKRy4EBSqnZInIi8H3gRKAeuFMpVS8iewFLgTqcm9llwGFKqc25Pr+urk4tXbq0oH1u3hbjiBtfKqrQrzxi8uvvHJYsENq8vYUTf/Va1sUtbIBppvcLKQsb2LYiGqCT05kYOCdZoVIspWbKqL2zfNAC/PdptckY0q64XZQceVnYYPHsybzW9FlSO21X3HL8/KXZ/S6Naew2pN0VAzhi/wG8sS79shANGdw3Y7eA4/yGjcx6cgUGELNsTqgZzF9Wf5pWu9U3GuLu6Ydw0e+WpZ2n0ZDBIxccwbT7l2Rp6UVMOG3cEB5fli2sWh41sWzFxUdV87+L/plVJ2aK41LzGmklLNXqcXfFCaO448XVgenLEVN4/adTinJticgypVRd5vIOn4kopT5SSv3dfbwVeAcYApwKPOiu9iCOYcFd/pByeAPo7xqi44CFSqnPXcOxEDi+Pfa5cdOWoivF45ZNTVW/pMaO0/DGX6/Hr5tai6XcNGDVZQwIOLOXrlA9v2h1dhDTNOC6p1clWxaDI8cfLeBoj5jCxUdVs3l7C7OfXJHUTrPZMwwIOG7Y7mxAwDlOMw0IOC2SUwUcp44bwpyTRtNiKSzbkfTJvKg7bk3JutOPJWz+9PYmrj6lJqlj5XWzfPYHk5i/4qOsz7/ixFE8euH4ZNdMP7Veyw3Yb40liFuKSMjgZ9+qzTmrufWF9/ju1/YLTOE2Jbj9b7F0akxERPYDDgGWAIOUUt5ofwwMch8PAdanvG2Duyxoud/nXARcBDB8+HC/VVrb01bX6B0xabHsrIyJq0+pSbP6rcU2QobQK2wSc+WqCy2k6ki6wp75TaRFJMsNk7AV+XrcI6agUNy7aC2/eqUJ6eE1NXsime1um7dSe/IWAAAbfUlEQVTFuO7pVb4z615hpyPhLaePoaaqn29s45E3PyQaMrjqlNHUVlXkTIjpHTHYq3ckTbrk0m+OzMoizCRkCmHT8JWp94hbigdeWxeYJh23VcnrRjqtDEhE+gBPAj9SSqU565XjYyvZmauUulcpVaeUqtt7771bf0MGNVX9Wi2YspXiWvdOpDxqEgkZ3PCtWqaPTw9kebncmY2rPAxRfGf8cKBrG5CuQsJniOKWapO+VYvl9LreGkvQkrDT2qRqeg7PpdRdBcmWgOcGdY6Byj5Rpozax3e9WMLmugWNbNnZklzmd9O4o8XmqnkrmXizU6g4r2Ejty9cTbiVANT2mMV7H28llsg9O8x1vF58VHXJs7Q6ZSYiImEcA/KIUuqP7uJPRGRfpdRHrrvK017YCAxLeftQd9lG4KiM5X9pj/2t7BPl2qm1XPlUcBrvWXVDmT5+BMfXDs7qMZDJ1l2JwFhC2DT5zeL3u0zvao0TG4nHbbqTY6ezM+66A3e90pRMfc0lW+Kl2F42dzmGCAtXBRf9pjaZ82rJ5pw8mmsXrEJStuVpvV02d3lBsc77XlsXeAPaGobA5K8Man3FQrdb8i22gjh9J38DvKOUuiPlpfmAl2F1LjAvZfkMN0trPLDFdXv9GThWRAa4mVzHusvahdohFfSJBosuPvbWepq3xXx7DKTSvC3GtQsaA7ezK25l9yHRdCoJy8boCJ2QEmEC59QX47bds5CU+IBT5DsmZ7whllD8+PGGVrMHd7RYyWyuR974gOufXkVI/IuRlSo81llsMoshtIsESmfMRCYC/wb8Q0Qa3GVXADcBj4vIBcAHwFnua8/iZGY1ATuA7wIopT4XkeuBt9z1rlNK5S4FbQNDB/TK+eN54mxA1kwkVaxtw+adhAwJnGmIoGchXYTeEZOEKy/flZIaWsMCfv9msPS9xmFX3OadTVuS5+rUcUMYvW8//vT2en7913W+2XyFHAeGCFfPX5mzpqQj8xYy+8aXig43Ikqp1wiOVE/xWV8BFwds6wHggdLtXTBeLOOyuSt87ygSKfLSqcWCCtIKCOecPDqnMeoVDvGd8cN9exxoOg7BSc18YtmH3VIjS4fT8uPyP61kzvxGbj9zLIri1HUBeoUdtYVUI7OjnS1EkMsybDi/f6a9Kwu1j6Jvh9eJdDbF1Imk0rwtxqNLPuTOl1anHWxe7+PUg8hRflVpYoplYYOfHBOcieHlr7/70ZfctnA1YVNoSdh55Yh3R4K0r7oCYaOwwkdN96Wt4o/eeX3bC+8RMoUd7dD50qt18irmI2awR8MPU+DNK48p2ogE1Ylo2ZMimDRybyYcsBfn3P9GUu7EUmTJb5iGEM+4rVG2ov6ASs6qc9q1pmKIoxZ78SN/J27bXH3yaGqHVFAeMTnuF4t6VBMpD8tWSSXkiKuOO3VsFcP26s0vXmpqfQMFUoi4oyE6PL2n0Nai2cP3G8Ctf36XSMhIu2ksBSEDZh0/ilGD+7Fp8w7mzG8EVMFu7x7jzurOpGpb7UpYrV6NLFv59lmPJyzmNWRXsNrK+dsac3LAr39mFYtnT6Zx05ddzoCUsqWVN3uzlWNIn17xUbuk1ZaFBEOMZJOg1giSC9fsuURNA8u2s1LLX13TDEC8xDMQwTknGj78gttfWO3cmBZ5bkTDRru4s3QeUJ6kCpt5FaStuTpO/upgoqF0618WNni/eQeSRwGjaQivvPsvvtwZb8uulxQBzpswglA7ZCs5Aoy589zbgmUrLJX+owVlSxpCYN2AZs/FVtkGpD1ROMH3Z1d+TCxhtynOkrBKX2gIeiaSN35Kvq3xzMqPfae244b1b7UdLjjFRf/11EpsZWOKdIluhAr47eulF3HrCOI2HLx3b977ZHtyWaYXwxTHgMRtnSWnyaayPMLHW1taX7ELMvPo0hcagp6J5E0xUuypWlgRg2Rb3epBfblmam3W+oY4zWvKU+pRdiVsWiynIl7fF7edVAPih7WnqCtqiqK7GpBoSJjWTrVD2ojkiZfiG9ACvVVsEZ6e+XWmjnPkvaaPH8ENrphaNCSETbj8+FHcN6OO//zGgVnvV5B3m1FN24hrI6LpYZw6rko3peoKTB03hP69w1zw4NKCg1tR08hSRD2+ZjAbPt/B/a+tQxBufO7dnBWzEVPY1ZEO2T2c8ohJwlbUDR/A4rXNnb07mk6ku+fpPblsIxcdeSDVg/qWfNt6JlIgNVUVRQVcYwkrGdRq3hbjpufeof7GF/n1X9cSt3an6zk92LMNhSnay9LRjB1aASgaNnzR2bui6WS6swEBx0173C8WMd8nK7StaCNSIJ5bK9qarG8GXo72vIaNHHHDi9zz17UFVcZaCo6rGVzQZ2r8yfeX+9vaz4klVLfvqaHRgHMNuWzucpq3xUq6XW1EimDquCHcN6OOSAEzkrKQSeOmLcyau7zomo9n/vFR0TEZzW66+12lRlMsgpS8KZU2IkVSU9UPCRg9vwu91xXNDHpTHlh2aQXbot1ImVaj6Uy8BJjufsq0JOye05Squ1PZJ8pVp9RkLTeAX08/jEu+OZJoyGmV6aX21lT1yyp260xyFfWN3revTinWaFwm7L8X982o486zDyGkT4w0tABjEXjS7lt2xvnew8t8feYzJgznh1NGZsnCP7LkA656amXSpWWKIyCi69o0mq5NxNV46+6n6rhh/Xjq4iMLfp8WYCwRnn6WKUKLZWMHCLc99PqHzBi/H2OH9U977/VPr6IsbLKzxUIMiBjCTp22q9F0eXqKgkHD+i9Zuq6Zuv0rS7I97c4qgFT9rO0tFnHLmUEEzW4b1n8R+F4bJ8axszs2q9BoNN2aRWs+K9m2tBEpgA2bd7rup3SCgm3jUmYhnvaWpuPQrmuNxp9JBw0s2bb0Va0Ahg7oRdzKnjlEIyYn1abXcMyYMDytOrQY7S1N2yi180EbJU1P4LDhFSVzZYGOiRREZZ8oV59Sw5VPrUxbbtmK606r5cffHEnD+i8YN6x/lryAV6Q4y+1HsjOeQEQoC5m0WBYJW+FjnzRdCEOyW45qNN2NmiEVJd2eNiIFMn38CBC4dsEqwqbT2vWW08cks68OGtSXAeUR3/dOHTeEidUDkxlbQPLx/a+ubde+6r0jJifUDuLJv29qt8/o6WgDoukJeEk/pdLR0kakCKbXj3DEE1PSd1O7HsZtm1tOH5NU7E2lsk80TU3Te3zhkQdw76K1WReqs+qGMn/5puR2xw6tYMm6zQXvs60U3/tGtdM1UGeDaTR7NA3rvyiZEdExkSKp7BNl7LD+VPaJZnU93BW3mfXkipwaNc3bYixf/0Vynco+UX7+7XFETCftN2TADafVcssZY1k8ezIPX1jP0zO/zvINW/LeR1NIK3asHtSXbx8+rM3fPZfSsClO75S2ko80WaH6ZdmfIYRNSRaPhXQnQw3tG/v6xkGli0W0hdSkn7aijUgJ8Mu8ChtGoEbNvIaNTLz5Zb5z/xIm3vxyUlnT6RliEAoZmIZB3zJnougZrO0tlm92WBAh0+Du6YeyePZkpo4bQvO2GI8v3VDcl3TxCq78iJrCnWcfgmG2/bA68av75jRW4PRXOW/CiJzrhAzhrLqhvkYpYTuKyd7ELBFQ86PZcygLGdSNGNCmbeQ6bP+6pvNbCpiGBLrci0EbkRLgl3kVt/01aoJmLU2fbGX2kyuSfZRjiezZTFB2WBAR06CiVzjpMitJmrEIkYCzJGYptuyKc8vpYygLG3lpcxkCv5lxWNY25y//iG8dMoRoyEgaE+9/1JTk7Or7Uw7K6mMfNuFn36rlofMPZ8kVU7jljLE8/8NJnPzVQYQMoXfEDOyt7tE7YuQ1G9L0LHYlbN76oHB3scf3jjqA3pGuHSXoHTZLKsLYtb9tNyEz88qLifh1EvPr1R42DBrWf+G7fMPmncntBGWHBZFpyFpLMw4bTrzn92+tJxZQBHnpN0dyx4urA6PM1z+9isWzJ7N49mQ2bN7J+s93MOvJFezIkIaJmgaI4tYzxjKwbxmRkEGLlb7OU29v5NkfHMn2FovyiJn2P1VK5tYzxmaNfWo8al7DRi55vCEpva/iVlZv9UxaEjbXTq3l/ebt3PfquqzXj68ZxLT6EdRU9ePSJxp45b3SFW9puifRkHD6IUN54LXs46UrsSult1Ep0EakRGRmXgW1ogyatYwb1j+v2UxmdlhLwvZ1L0VDRpYh84zdpU8s95dwEOGgwX0D2/CWR0zqD6hMGkxDJMs4eIbPixcNHdALO0OfLRoyuG9GHTVV/ZIxJb/9CbvdIMe24r/NNfbN22LMmrs8rXdLPllWCRuuf2YVd59ziK8RufDr+1O3fyXN22Isbup8F4Wm84mYzg3OzKMP4vaFqzt7dwIJkmoqFj1hLyGpwfZc63junsygt99yv21Nrx/B65dP5tELx/Obcw+nTzRde7532OS+GXW+2WFTxw3h2R8cScTHVxO3FNc/vYo5J43OchEBWEoxdEAvpo4bwuLZk7nnO4dmrZdp+Py+761njGHSyL0zZlijAz8vH4LG3lEZKO4wDxsGjR9tzQq0HlldmSzW2rB5J+ESxIA0nUfUlXlvLQbXGt6xf0JtaZvHlTrQX6bdWd2foDvnfGczsDtVuHlbLCsgbKOoqeoX+N7qQX257YwxXDp3BS0ZbquwYVA7pIK/XT6FR5d8yF2vNBExs110lX2iTBq5j68rKXO/8/le0+tHgIJrFzQSNg0spQKNaCEMHdCraPn9Fsvi7lea0irfI6bwi7MPydh+9p2dIaS5zKIhg5aE3S4KsGHDmTnptIDCiJjCoxfWsyNuA4r1n+/kuqdX+bpyI6bkFGAMGTDnpNFs2LyT51d+VJL9KwsZ3HbmGHbFba6e31hUh81Tx+3LvIb0/Snk5iwftBR8D2B+w8acMYEgmj7Zyom/fDXt5CgLGyyePTl58fZk73MZtXzWyZdSbstjfsNGfpISE0klahpYys56LRoSZh59EPcuWsvWWCK5vG80xMMX1qe52LzxN0WIWzaXHnswd7y4ml3x3RstCxvMOWk0V83b3QYgZMCxNYN46Z1/ETKMoi4SpsCffzSJVR99yWVzl2OKQSzhbKe9iiNNwymyLQQvcWKfPhE2bNmdLLJfZS/eby5tp718CJtwzSm19CkLpdV3ffdr+/Hg3z5gR3z3b1EeMfnPow7k1heCXVSmONmQIUNK1k7ZOxcBJt78ctrxlEqvsIllKwSV1iOoPGLy6L+PZ+XGLVk3Z/lcHzIJkoLXRqSHUOzFt1gD1N1o3hbj9X828+PHG9JiSN7F/fpnVhE2DFosm5lHVzOtfjiQffJmGtnU7aeOf9C4Nm+L0bjpS0BRU1WRnE1u2LyTlZu2cP3TqzANYXssvwtRqlFL3YfN21s47s5X87rYt3aX3VYiJtx/7uHJ77t0XTOL1nzGpIMGsnHLrrSkh1yETYhbjoEvpmC2LGwQT9iO3FDYcFS47fQbCKf2SKVtvyxscPnxB3PNgncK/sxiCZvC7WeOTZ6L3vEEsCtuO5mPIpw/cT8mHFhJVUUvTr7rtcBjtRQ3Z9qIuPRUI9IW2uPuv6uS6+LuNwZtMbJNn2wN1FILonlbjAXLN3HTc++yK48ra5BRA/jVS2taDfD2DhvELLsg3baoKSgRRCnf7phhUzDECTTnGrPmbbGcd9jetuKWSs5kLj32YPYqj3DNgka2tWJonXidMPPoak6oHcymLbv494eWBmYegmOUL5p0AHf/pSntNx+9bz+O+fminJ+Xi/KImew/lI+9vuucQzh5bFXaMu8YLY+YPLvyY+5+ZU3aGAPtekOom1JpAsmUYunJBMVngsagkDhVKvnK4GTyWtNn/Oy5d3Ne6DyiIckZN5pWP5y7XlmT8649YSvKQqavC6Y8apLwuVsXQ3hm5teTKdfPrfyYuzIuaPmMmV+6eyoRU/D6CHozpTteXM3TM7/eamHoj6ZUc/SoQWmfv73FImIaOcc2bttMqx/OtPrhWfs/Y8JwHnr9w5yfm0p51HEzzTl5NLVVFckZYqYLOZOwKUw4MLuyPTUO+j9/aSKWUMQSjqt11pMr0lLrO/KGUBsRzR5HoUaz0PVTC0q9C+SsJ1cwsXpgzu1478vHgPSOmNzznUOZNHKfnPudmfhwVt1QHl+6Ifncc+VlcsUJo6g/oJKhA3qxuOmzrDvc1JnV9wf19b3otjZmQXVL5RETSykuPqqaexetJW7tjkmF3dhRrjTz8ojJ0aMGZaWG+32e36wpaP+vO/WrzBi/H681fcaNz76TZghCBpiGkUxCmXPSaGqHVGRdzCv7RLntzOzf5LG3NiRjTbeekTuhJKjWLDW1viPRRkSjKTG5TvJCLw69IyZzTvoKVy9YlZZJZysnptIafjOpH04Zmfa8b1nISQwwHNfR1aeMdrLlcmwjk2Jms35Fuql37QB3/6Up7T1eGu3YYf2ZWD2Qxk1bXBfV7gt6UPZRUFFwITPN6kF9qR7Ul73KI0VvJ5/fJBeFKGR0BDomotGUGD9ff67YRT7v85sNlNLf3ZlxsVyfnU9MqtC4Vam+a2eOWWckxOjAuos2IpqOoNiTPNf79qQEiFQ6Os28u9DR31kbERdtRDQdRbEn+Z54QdR0fXR2lkbTwRSb9bYnZctpuj9a9Eej0Wg0RaONiEaj0WiKRhsRjUaj0RSNNiIajUajKZpub0RE5HgReU9EmkTk8s7eH41Go9mT6NZGRERM4G7gBGA0cI6IZHc30mg0Gk270K2NCHAE0KSUWquUagH+AJzayfuk0Wg0ewzd3YgMAdanPN/gLtNoNBpNB9DdjUheiMhFIrJURJZ++umnnb07Go1G02Po7kZkIzAs5flQd1kaSql7lVJ1Sqm6vffeu8N2TqPRaHo63Vo7S0RCwGpgCo7xeAuYppRqzPGeT4EPOmYP242BwGedvRNdBD0W6ejxSEePRzrFjsdnAEqp4zNf6NbaWUqphIjMBP4MmMADuQyI+55uPxURkaV+Qmh7Inos0tHjkY4ej3TaYzy6tREBUEo9Czzb2fuh0Wg0eyLdPSai0Wg0mk5EG5Huyb2dvQNdCD0W6ejxSEePRzolH49uHVjXaDQaTeeiZyIajUajKRptRLogItJfROaKyLsi8o6ITBCRvURkoYiscf8PcNcVEfmlK0C5QkQO7ez9LzUi8mMRaRSRlSLyexEpE5H9RWSJ+70fE5GIu27Ufd7kvr5f5+592xGRB0TkXyKyMmVZwceDiJzrrr9GRM7tjO/SVgLG4lb3XFkhIn8Skf4pr/3UHYv3ROS4lOU9QrjVbzxSXrtERJSIDHSft8+xoZTSf13sD3gQuNB9HAH6A7cAl7vLLgdudh+fCDwHCDAeWNLZ+1/isRgCrAN6uc8fB85z/5/tLrsH+J77+D+Be9zHZwOPdfZ3KMEYTAIOBVamLCvoeAD2Ata6/we4jwd09ncr0VgcC4TcxzenjMVoYDkQBfYH/olTCmC6jw9wz6/lwOjO/m6lGg93+TCc0ocPgIHteWzomUgXQ0QqcA6M3wAopVqUUl/gCEs+6K72IHCa+/hU4CHl8AbQX0T27eDdbm9CQC+3uLQ38BEwGZjrvp45Ht44zQWmiIh04L6WHKXUIuDzjMWFHg/HAQuVUp8rpTYDC4GswrGujt9YKKVeUEol3Kdv4ChXgDMWf1BKxZRS64AmHNHWHiPcGnBsAPwcmAWkBr3b5djQRqTrsT/wKfB/IvK2iNwvIuXAIKXUR+46HwOD3Mc9WoRSKbURuA34EMd4bAGWAV+kXDhSv3NyPNzXtwCVHbnPHUShx0OPPk5SOB/nbhv20LEQkVOBjUqp5Rkvtct4aCPS9QjhTE9/rZQ6BNiO465Iopw56B6RVuf6+k/FMa5VQDnd8A66PdmTjodciMiVQAJ4pLP3pbMQkd7AFcBVHfWZ2oh0PTYAG5RSS9znc3GMyieem8r9/y/39bxEKLsxxwDrlFKfKqXiwB+BiThTcU9xIfU7J8fDfb0CaO7YXe4QCj0eevRxIiLnAScD012jCnvmWByIc8O1XETex/lufxeRwbTTeGgj0sVQSn0MrBeRg91FU4BVwHzAy5o4F5jnPp4PzHAzL8YDW1LcHD2BD4HxItLbjW144/EKcIa7TuZ4eON0BvByykWlJ1Ho8fBn4FgRGeDO7o51l3V7ROR4HP//VKXUjpSX5gNnuxl7+wMHAW/iCLUe5Gb4RXASMOZ39H63B0qpfyil9lFK7aeU2g/npvRQ97rSPsdGZ2cX6D/fjItxwFJgBfAUTsZEJfASsAZ4EdjLXVdwWgT/E/gHUNfZ+98O43Et8C6wEvgdTrbNATgXhCbgCSDqrlvmPm9yXz+gs/e/BN//9zjxoLh7UbigmOMBJ17Q5P59t7O/VwnHognHp9/g/t2Tsv6V7li8B5yQsvxEHAXwfwJXdvb3KuV4ZLz+Pruzs9rl2NAV6xqNRqMpGu3O0mg0Gk3RaCOi0Wg0mqLRRkSj0Wg0RaONiEaj0WiKRhsRjUaj0RSNNiIaTR6IiCUiDa6S8IJUpdgO+OzzReQfrvLqSlfWAhG5TkSO6aj90Gj80Cm+Gk0eiMg2pVQf9/GDwGql1A0d8LlDgb/iFIxtEZE+wN7KERTUaDodPRPRaArndVyBOhEZJyJvpPSyGNDK8r+IyM9FZKk4vWIOF5E/un0c/tvns/YBtgLbAJRS2zwDIiK/FZEzRKTOnSU1uDMW5b5+oIg8LyLLRORVERnV/kOj2dPQRkSjKQARMXGkVzyZjIeA2UqpMThVwFe3shygRSlVh9MHZR5wMVALnCcimYrDy4FPgHUi8n8ickrmPimlliqlximlxgHP46geg9NP+/tKqcOAS4H/acNX12h8CbW+ikajweln0oAzA3kHWOj2fumvlPqru86DwBNBy1O25RmgfwCNytU6E5G1OEJ4ScFIpZTlakMdjmO8fi4ihymlrsncQRH5No5Y57Gu2+tr7v54q0TbMgAajR96JqLR5MdO905/BI4G0cVt2FbM/W+nPPaeZ93YKYc3lVI/wxELPD1zHRGpBa7B6fZo4ZzbX3gzFPfvK23YZ43GF21ENJoCUI5K7A+AS3B6vWwWkSPdl/8N+KtSaovf8mI+T0SqUnth44hzfpCxTn8cIb4ZSqlP3f38EscFdqa7jojI2GL2QaPJhXZnaTQFopR6W0RWAOfgyLDf4zYDWgt8110taHmhhIHbRKQK2IXT9fI/MtY5FWeGdJ/nunJnTdOBX4vIf7nb+QNOjEWjKRk6xVej0Wg0RaPdWRqNRqMpGm1ENBqNRlM02ohoNBqNpmi0EdFoNBpN0WgjotFoNJqi0UZEo9FoNEWjjYhGo9FoikYbEY1Go9EUzf8H0/5KmpXDKoMAAAAASUVORK5CYII=\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"625b89a8"},"source":["We can notice that there are no linear relation present between the numerical columns. Hence no need to drop anything"],"id":"625b89a8"},{"cell_type":"markdown","metadata":{"id":"8a7f95fa"},"source":["Now we will plot box plots of categorical and numerical columns to get more information about the number of outliers and the distrubtion."],"id":"8a7f95fa"},{"cell_type":"code","metadata":{"id":"e73f0769","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1635012395663,"user_tz":-330,"elapsed":1890,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"ed580d37-65dc-4b57-867d-df02ac8fdeea"},"source":["for c in categorical:\n"," for n in numerical:\n"," sns.set_style(\"whitegrid\")\n"," sns.boxplot(x= c, y= n, data=df)\n"," plt.xlabel(c)\n"," plt.ylabel(n)\n"," plt.show()\n"," "],"id":"e73f0769","execution_count":14,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAZEAAAEGCAYAAACkQqisAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3df1iVdZ7/8ecBlDRFBOUwro6TpZtf19BJ1wjKpA6geBJSZq9pdhbZqy2nRmKdfpnJmDq126WNuW4tLHulM13TtUqho5hAmCKl1ZhmGTXLGCPOyGFQAc08yPF8/2A9G/5AOJ1zbu5zXo/r8vL63Jxzn/fNzTmvc//4fD4Wt9vtRkRExAthRhcgIiLmpRARERGvKURERMRrChEREfGaQkRERLwWYXQBgXbw4EEiIyONLkNExFScTieTJk26bHnIhUhkZCTjx483ugwREVOpra294nKdzhIREa8pRERExGsKERER8ZpCREREvKYQEekDmpubWbhwISdOnDC6FJFe8VuILF68mMTERGbPnu1Z9q//+q+kp6djt9t55JFHaGtr8/yssLAQm81GWloae/bs8Syvrq4mLS0Nm81GUVGRZ3lDQwPZ2dnYbDby8/Npb2/316aI+N2GDRs4dOgQGzZsMLoUkV7xW4jcd999FBcXd1mWlJTEtm3b2Lp1K9/73vcoLCwEoK6ujrKyMsrKyiguLubZZ5/F5XLhcrlYvnw5xcXFlJWVsW3bNurq6gBYtWoV8+fPp7KykqioKEpKSvy1KSJ+1dzczFtvvYXb7eatt97S0YiYit9CZOrUqQwZMqTLsuTkZCIiOrumTJo0icbGRgCqqqrIyMigf//+jBo1itGjR3Po0CEOHTrE6NGjGTVqFP379ycjI4Oqqircbjf79u0jLS0NgKysLKqqqvy1KSJ+tWHDBi7OyHDhwgUdjYipGHZN5I033uDOO+8EwOFwEB8f7/mZ1WrF4XBcdfmpU6eIioryBFJ8fDwOhyOwGyDiI5WVlZw/fx6A8+fPU1FRYXBFIj1nSI/1V155hfDwcO69996Av7bT6bxqz0sRI0yZMoV3330Xl8tFeHg4U6dO1d+omEbAQ+TNN99k165drF+/HovFAnQeYVw8tQWdRyZWqxXgisuHDh1KW1sbHR0dRERE0NjY6Hn8tWjYE+lr8vPz2bdvHy6Xi4iICPLz84mNjTW6LJEu+sSwJ9XV1RQXF/PKK68wYMAAz/KUlBTKyspob2+noaGB+vp6brnlFiZOnEh9fT0NDQ20t7dTVlZGSkoKFouFadOmUV5eDkBpaSkpKSmB3BQRnxk2bBgzZ87EYrEwc+ZMBYiYit+ORBYtWsQHH3zAqVOnuPPOO1m4cCFFRUW0t7eTm5sLQEJCAsuXL2fs2LHMnDmTWbNmER4eTkFBAeHh4QAUFBTwwAMP4HK5mDt3LmPHjgXg8ccf55//+Z9Zs2YN48ePJzs721+bIuJ3OTk51NfXk5OTY3QpIr1icV+8LSRE1NbW6nSWiEgvXe2zUz3WRUTEawqRIKKhM0Qk0BQiQURDZ4hIoClEgoSGzhARIyhEgoSGzhARIyhEgoSGzhARIyhEgoTNZqNfv34A9OvXj9TUVIMrEpFQoBAJEjk5OZ5hZMLCwtRpTUQCQiESJIYNG8aMGTMAmDFjhobOEAmgUL69XiEiIvIthfLt9QqRINHc3Mw777wDwDvvvBOS34hEjBDqt9crRIKEbvEVMUaov/cUIkFCt/iKGCPU33sKkSChW3xFjBHq7z2FSJDQLb4ixgj1955CJEhodjwRY4T6ey/gc6yL/2h2PBFjhPJ7TzMbiojINWlmQxER8TmFiIiIeE0hIiIiXlOIiIiI1xQiIiLiNYWIiIh4TSEiIvItaT4RERHxmuYT8YPFixeTmJjI7NmzPctaWlrIzc0lNTWV3NxcWltbAXC73axcuRKbzYbdbufw4cOe55SWlpKamkpqaiqlpaWe5Z9++il2ux2bzcbKlSsJsT6TItJHaD4RP7nvvvsoLi7usqyoqIjExEQqKipITEykqKgIgOrqaurr66moqGDFihUsW7YM6AyddevWsXHjRjZt2sS6des8wbNs2TJWrFhBRUUF9fX1VFdX+2tTRESuSvOJ+MnUqVMZMmRIl2VVVVVkZmYCkJmZydtvv91lucViYdKkSbS1tdHU1ERNTQ1JSUlER0czZMgQkpKS2LNnD01NTZw5c4ZJkyZhsVjIzMykqqrKX5siInJVoT6fSEAHYDxx4gRxcXEADB8+3HPY53A4iI+P9zwuPj4eh8Nx2XKr1XrF5Rcf3xNOp5Pa2lpfbI6ICFOmTOHdd9/F5XIRHh7O1KlTQ+ozxrBRfC0Wi2cM/kCKjIzUAIwi4jP5+fns27cPl8tFREQE+fn5QTkc/NWCMaB3Z8XGxtLU1ARAU1MTMTExQOcRRmNjo+dxjY2NWK3Wy5Y7HI4rLr/4eBGRQAv1+UQCGiIpKSls3rwZgM2bN3P33Xd3We52uzl48CCDBw8mLi6O5ORkampqaG1tpbW1lZqaGpKTk4mLi2PQoEEcPHgQt9vdZV0iIoGWk5PDLbfcovlEfGnRokV88MEHnDp1itjYWBYuXMg999xDfn4+x48fZ8SIEaxZs4bo6GjcbjfLly9nz549DBgwgOeee46JEycCUFJSQmFhIQALFixg7ty5AHzyyScsXryYc+fOceedd7J06dIenR7TfCIiIr13tc9OTUolIiLXpEmpRETE5xQiIiLiNYWIiIh4TSEiIiJeU4iIiIjXFCIiIuI1hYiIiHhNISLSB4TyzHhibgoRkT4glGfGE3NTiIgYLNRnxhNzU4iIGCzUZ8YTc1OIiBgs1GfGCwahfE1LISJiMJvNRr9+/QDo168fqampBlckvRXK17QUIiIGy8nJ8UxjEBYWFpJzUphZqF/TUoiIGCzUZ8Yzu1C/pqUQEekDQnlmPLML9WtaChERkW/BZrMREREBQERERMhd01KIiPQBoXxh1uxycnK4cOEC0Hk6K9SOJhUiIgYL9QuzYm4KERGDhfqFWbPbsGEDYWGdH6VhYWEht/8UIiIGC/ULs2ZXWVlJR0cHAB0dHSG3/xQiIgZTZ0Nzs9lsnn4+Fosl5PafQkTEYOpsaG52u91zOtLtdnPvvfcaXFFgKUREDKbOhua2devWLkciv/3tbw2uKLAUIiJ9gDobmldlZWWXIxFdEwmA9evXk5GRwezZs1m0aBFOp5OGhgays7Ox2Wzk5+fT3t4OQHt7O/n5+dhsNrKzszl27JhnPYWFhdhsNtLS0tizZ48RmyLiE8OGDePf/u3fdBRiQqF+TSvgIeJwOPjVr37FG2+8wbZt23C5XJSVlbFq1Srmz59PZWUlUVFRlJSUALBp0yaioqKorKxk/vz5rFq1CoC6ujrKysooKyujuLiYZ599FpfLFejNEZEQF+rXtAw5EnG5XJw7d46Ojg7OnTvH8OHD2bdvH2lpaQBkZWVRVVUFwM6dO8nKygIgLS2NvXv34na7qaqqIiMjg/79+zNq1ChGjx7NoUOHjNgcEQlhoX5NKyLQL2i1WvnHf/xHZsyYQWRkJElJSUyYMIGoqCjP+DPx8fE4HA6g88jlO9/5TmexEREMHjyYU6dO4XA4SEhI6LLei8/pjtPppLa21g9bJiKhauLEiZSXlzNx4sSQ+3wJeIi0trZSVVVFVVUVgwcP5tFHHw3o9YzIyEjGjx8fsNcLpObmZp599lmWLVsWct+GzE77zty2b9+O0+nkk08+wWazGV2OX1wtHAN+Ouu9995j5MiRxMTEeC5CffTRR7S1tXl6fTY2NmK1WoHOI4zjx48Dnb1BT58+zdChQ7FarTQ2NnrW63A4PM8JVRrEz7wKCwv5+OOPKSwsNLoU6aVQH/ss4CEyYsQIPv74Y77++mvcbjd79+7lpptuYtq0aZSXlwNQWlpKSkoKACkpKZSWlgJQXl7ObbfdhsViISUlhbKyMtrb22loaKC+vp5bbrkl0JvTZ4T6H7KZNTc3U1lZCUBFRYX2ncmE+thnAQ+RhIQE0tLSyMrKwm63c+HCBf7u7/6Oxx9/nFdffRWbzUZLSwvZ2dkAzJs3j5aWFmw2G6+++iqPPfYYAGPHjmXmzJnMmjWLBx54gIKCAsLDwwO9OX1GqP8hm1lhYWGXocR1NGIuoT72mcV98ZMnRNTW1gblNZH09HTOnj3raQ8cOJAdO3YYWJH0VEpKiudULnTeQLJz504DK5LeWL16NVu2bPG0MzMzWbRokYEV+cfVPjvVYz1IhHqHJzO79HtciH2vMz273d6lrbGzxJRCvcOTmd1zzz1d2sF6d0+weu2117q0f/3rXxtUiTEUIkEi1Ds8mdlDDz3UbVv6tt27d3dp79q1y5hCDKIQCSIaxM+cjhw50qVdX19vTCHilVA/HakQCSIaxM+cli1b1qW9dOlSYwoR8YJCRMRgZ86c6bYtfdtdd93VpT1jxgxjCjGIQkTEYIMGDeq2LX3b7Nmzu7R1d5aIBNSlp7NWrFhhTCHilZdeeqlL+8UXXzSoEmMoRIJIc3MzCxcu1LAZJvO3f/u3Xdq33nqrQZWINxoaGrptBzuFSBDRAIzm9MEHH3Rp79+/36BKRHpPIRIkNACjeenuLHMLCwvrth3sQmtrg5gGYDQv3Z1lbpeOMBBqQw4pRIJEqI8kamaXjj4dyqNRm1GojzigEAkSGoDRvEK9x3MwuDhu3cX/Q4lCJEhoAEbzuvSDJxQ/iMxsw4YNnusgYWFhIXcquUch8uWXX5KTk+PpVPP555/z8ssv+7Uw6R0NwGheI0aM6LYtfVtlZSUulwsAl8sVcqeSexQiS5cu5Wc/+xkREREA3HzzzWzfvt2vhUnv2e12Bg4cGHI9Zs2uubm527b0bXfccUeX9p133mlQJcboUYh8/fXXl81frot/fc/WrVs5e/Ysv/3tb40uRXrh0utXaWlpBlUi0ns9CpGhQ4dy9OhRz7naHTt2MHz4cL8WJr2jfiLm9c3rVxaLRdezTGbPnj1d2tXV1QZVYowehcjPf/5zCgoKOHLkCHfccQcbNmy4rIOUGEv9RMxNF9PNS6ezemDUqFGsX7+evXv38tZbb/H6668zcuRIf9cmvaB+IuZVWFjo+QLgdrspLCw0uCKRnutRiLz44ou0tbUxcOBABg0aRGtrK7/85S/9XZv0gvqJmFdVVVWX9ttvv21QJeINnc7qgerqaqKiojztIUOGhNwvqq9TPxHzUmdDc9PprB5wuVy0t7d72ufOnevSFuOpn4h5hfqHkJhbj0LEbreTk5PDpk2b2LRpE7m5uWRmZvq7Nukl9RMRCTydzuqBBx98kAULFnDkyBGOHDnCww8/zD/90z95/aJtbW3k5eWRnp7OzJkzOXDgAC0tLeTm5pKamkpubi6tra1A56H9ypUrsdls2O12Dh8+7FlPaWkpqamppKamUlpa6nU9wUL9RMwp1D+EzE6j+PbQ9OnTefLJJ3nyyScvO/zurV/84hfccccd7Nixgy1btnDjjTdSVFREYmIiFRUVJCYmUlRUBHS+oerr66moqGDFihWeW4tbWlpYt24dGzduZNOmTaxbt84TPKFI/UTM6+KQGVdrS99mt9u7tEPtTEC3IfLDH/4QgMmTJ/P973/f8+9i2xunT5/mww8/ZN68eQD079+fqKgoqqqqPKfIMjMzPXeoXFxusViYNGkSbW1tNDU1UVNTQ1JSEtHR0QwZMoSkpKTLvtGFEvUTETHGpk2burQ3btxoUCXGiOjuh6+//joABw4c8NkLHjt2jJiYGBYvXsznn3/OhAkTWLJkCSdOnCAuLg6A4cOHe75JOxwO4uPjPc+Pj4/H4XBcttxqteJwOK75+k6nk9raWp9tT19RXl7epZ/Ijh07mDlzpsFVSU9YLJYud2RZLJag/BsNVpWVlV3aFRUVZGVlGVRN4HUbItB5aJ2RkcGOHTt88oIdHR189tlnLF26lISEBFauXOk5dXWRxWLxWw/eyMhIxo8f75d1GyktLY3t27dz/vx5+vXrR3p6elBuZzCaPn06u3bt8rTvuusu7TsTudIt2sG4/672xeaa10TCw8O54YYb+POf/+yTQuLj44mPjychIQGA9PR0PvvsM2JjY2lqagKgqamJmJgYoPMIo7Gx0fP8xsZGrFbrZcsdDgdWq9UnNZqRxl8yr4tTLFwUaufUzU5zrPdAW1sbGRkZ5OTksGDBAs8/bwwfPpz4+HiOHDkCwN69e7nxxhtJSUlh8+bNAGzevJm7774bwLPc7XZz8OBBBg8eTFxcHMnJydTU1NDa2kprays1NTUkJyd7VVMwGDZsGMOGDQMgNjZW/URMZPXq1V3aL7zwgkGViDcunoa/KNS+zF7zdBbAo48+6tMXXbp0KY899hjnz59n1KhRPP/881y4cIH8/HxKSkoYMWIEa9asAToP9Xfv3o3NZmPAgAE899xzAERHR/Pwww97LtA/8sgjREdH+7ROM2lubub48eMAHD9+nBMnTihITOLifrtaW/q2b54RgdDbfxZ3N2MsOJ1OXn/9dY4ePcq4ceOYN2+eZ2Iqs6qtrQ3K85UFBQVdzqvPmDGDZ5991riCpMeu1ENdfUXMY/r06ZfdGLF7924DK/KPq312dns668knn+TTTz9l3LhxVFdX8y//8i9+K1C+nW8GCMA777xjTCHSa9ddd12X9oABAwyqRLwR6tdEuj2s+MMf/sDWrVsBmDdvHtnZ2QEpSiSUXDoOndPpNKgS8cY999xDeXm5p31pD/Zg121kfvPUldlPYwW7gQMHdtuWvuvChQvdtqVvu/TL9Q9+8AODKjFGt8nw+eefe3qmu91unE4n3//+93G73VgsFj766KOAFCnX9vXXX3fbFhH/uFKP9aefftqgagKv2xBRr1nz0JwUIsa4dBKxysrKkAqR0LoCFMTCw8O7bYuIf4T6AJoKkSBxzz33dGmH2sU9ETGGQiRIhPrFPTNLTEzs0r799tsNqkS8EepnARQiQeK1117r0v71r39tUCXSW1FRUd22pW8L9emNFSJB4tIespd2PpS+69Le6cHY2zmYXdqvJ9T6+ShEgoTuzjKvS8c405hn5rJ3794u7ffee8+gSoyhEBEx2KXTLPhq2gWRQFCIBIlbb721S3vKlCkGVSK9pR7rYmYKkSDxxRdfdNsWEfEHhUiQOHPmTJf26dOnDapEREKJQkRERLymEBEREa8pRERExGsKERER8ZpCREREvKYQERERrylERETEawoRERHxmkJERES8phARERGvKURERMRrEUa9sMvlYu7cuVitVgoLC2loaGDRokW0tLQwYcIEXnjhBfr37097eztPPPEEhw8fJjo6ml/+8peMHDkSgMLCQkpKSggLC+OZZ565bIYxEV/bsWMH27dv9/vr5OXl+Wxds2bNIj093WfrE/kmw0LkV7/6FTfeeKNn4MBVq1Yxf/58MjIyKCgooKSkhPvvv59NmzYRFRVFZWUlZWVlrFq1ijVr1lBXV0dZWRllZWU4HA5yc3MpLy/v8/MbB+pDCPRBZBYRERF0dHR42v369TOwmuCl955/GBIijY2N7Nq1iwULFrB+/Xrcbjf79u1j9erVAGRlZbFu3Truv/9+du7cyU9/+lMA0tLSWL58OW63m6qqKjIyMujfvz+jRo1i9OjRHDp0iMmTJxuxSRIi0tPTff6Gbm5u5r777vO0N27cqNkNxTQMCZHnnnuOxx9/nK+++gqAU6dOERUVRUREZznx8fE4HA4AHA4H3/nOdzqLjYhg8ODBnDp1CofDQUJCgmedVqvV85zuOJ1Oamtrfb1JPTZ69Gh+8pOf+Hy9RUVF7N+/39O+9dZbefDBB336Gkb+3oJdWFgYFy5cIDY2lqamJpqamowuKej467334YcfUlxc7Gk/+OCDl00S92315fdewEPknXfeISYmhr/5m7/h/fffD/TLExkZyfjx4wP+uv62ZMmSLt9mn3nmGX2bNZHx48dTX19PcXGx9pvJjB8/3hMi4eHh/P3f/73BFfnH1YIs4CHy0UcfsXPnTqqrq3E6nZw5c4Zf/OIXtLW10dHRQUREBI2NjVitVqDzCOP48ePEx8fT0dHB6dOnGTp0KFarlcbGRs96HQ6H5zmhaNiwYQwZMoTW1lZmzJihDyKT6devH2PHjtV+M6nvfve7HD16lIKCAqNLCbiA3+L7s5/9jOrqanbu3MmLL77IbbfdxurVq5k2bRrl5eUAlJaWkpKSAkBKSgqlpaUAlJeXc9ttt2GxWEhJSaGsrIz29nYaGhqor6/nlltuCfTm9CkjR47k+uuv9+lFPRG5tpiYGCZNmsSMGTOMLiXg+kw/kccff5xXX30Vm81GS0sL2dnZAMybN4+WlhZsNhuvvvoqjz32GABjx45l5syZzJo1iwceeICCgoI+f2eWv+nbrIgEmmG3+AJMmzaNadOmATBq1ChKSkoue0xkZCRr16694vN/8pOf+OVCmYiI9EyfORIRERHzUYiIiIjXFCIiIuI1hYiIiHhNISIiIl5TiIiIiNcUIiIi4jWFiIiIeE0hIiIiXlOIiIiI1xQiIiLiNYWIiIh4TSEiIiJeU4iIiIjXFCIiIuI1hYiIiHhNISIiIl5TiIiIiNcUIiIi4jWFiIiIeC3C6AJEfG3t2rXU1dUZXUav/M///A8AeXl5BlfSOzfddJPpahbfUohchT6IAsMfH0J1dXX8/tOP+O4gl0/X609RbgsA5+o/NLiSnjt6Jtwv69V7LzB89d5TiFxFXV0dBz75jAsDY4wupccsrs7duf8PjQZX0jNhZ0/6bd3fHeTimSln/LZ+gZW/G+SX9dbV1XHg8AGI9svq/eN/Lwwc+NMBY+voqRbfrUoh0o0LA2M49/9mG11G0Lrus21GlyB9VTRcuOuC0VUErbBdvrscrgvrIiLitYCHyPHjx/nxj3/MrFmzyMjIYMOGDQC0tLSQm5tLamoqubm5tLa2AuB2u1m5ciU2mw273c7hw4c96yotLSU1NZXU1FRKS0sDvSkiIiEv4CESHh7OU089xfbt2/nv//5vfvOb31BXV0dRURGJiYlUVFSQmJhIUVERANXV1dTX11NRUcGKFStYtmwZ0Bk669atY+PGjWzatIl169Z5gkdERAIj4CESFxfHhAkTABg0aBBjxozB4XBQVVVFZmYmAJmZmbz99tsAnuUWi4VJkybR1tZGU1MTNTU1JCUlER0dzZAhQ0hKSmLPnj2B3hwRkZBm6IX1Y8eOUVtbS0JCAidOnCAuLg6A4cOHc+LECQAcDgfx8fGe58THx+NwOC5bbrVacTgc13xNp9NJbW3tNR939uzZ3m6OeOHs2bM92h+9Xacu9gWGv/af+J+v9p1hIfLVV1+Rl5fH008/zaBBXW8VtFgsWCwWv7xuZGQk48ePv+bjBg4cCLT5pQb5PwMHDuzR/ujtOs/5dI1yNf7af5zy6SrlCnq7764WOIZ8YTt//jx5eXnY7XZSU1MBiI2NpampCYCmpiZiYjr7Z1itVhob/6/fQ2NjI1ar9bLlDocDq9UawK0QEZGAh4jb7WbJkiWMGTOG3Nxcz/KUlBQ2b94MwObNm7n77ru7LHe73Rw8eJDBgwcTFxdHcnIyNTU1tLa20traSk1NDcnJyYHeHBGRkBbw01n79+9ny5YtjBs3jjlz5gCwaNEiHnzwQfLz8ykpKWHEiBGsWbMGgOnTp7N7925sNhsDBgzgueeeAyA6OpqHH36YefPmAfDII48QHW2mLq4iIuYX8BCZMmUKX3zxxRV/drHPyDdZLBZ+/vOfX/Hx8+bN84SIr508eZKwsyfUq9qPws6e4OTJ/j5f78mTJ/nL6XC/Dcshnf54OpzhJ30/dM3Jkyehxbe9quUSLXBygG/2nfaSiIh4TWNnXUVMTAxfnmrX2Fl+dN1n2zw3UPhSTEwMA9v+oAEY/Wzl7wZxnZ/23x+//qPGzvKjsF1hPnvv6UhERES8phARERGvKURERMRrChEREfGaLqx3I+zsSVPd4ms5/zUA7n4DDK6kZzpnNoy/5uO8cfSMuW7xbW3vHOZnSH+3wZX03NEz4Yzz18rNdovvxXF2rjO0ip5rAf7KN6tSiFzFTTfdZHQJvXZxnuexN/rng9n34v3yezbjvmv4331n/d5YgyvpuXH453dtxv3nee/9lUn231/57vdscbvd5vnq4wO1tbU+HzCur8jLywNg7dq1BlcivaV9Z26hsP+u9tlpouNFERHpaxQiIiLiNYWIiIh4TSEiIiJeU4iIiIjXFCIiIuI1hYiIiHhNISIiIl5TiIiIiNc07ImIhIQdO3awfft2v6z74rAnF3uu+9KsWbNIT0/3+Xp9RSESYPpDNjd/7T/tO3OLjY01ugTDKESCSCj/IZud9p3/paenK0z9QAMwiojINWkARhER8TmFiIiIeE0hIiIiXjN9iFRXV5OWlobNZqOoqMjockREQoqpQ8TlcrF8+XKKi4spKytj27Zt1NXVGV2WiEjIMHWIHDp0iNGjRzNq1Cj69+9PRkYGVVVVRpclIhIyTN1PxOFwEB8f72lbrVYOHTrU7XOcTie1tbX+Lk1EJCSYOkS8ERkZqX4iIiK9dLUv36YOEavVSmNjo6ftcDiwWq3dPkdHIiIived0Oq+43NQhMnHiROrr62loaMBqtVJWVsbq1au7fc6kSZMCVJ2ISPAzdYhERERQUFDAAw88gMvlYu7cuYwdO9boskREQkbIjZ0lIiK+Y+pbfEVExFgKERER8ZpCREREvKYQERERr5n67izptHjxYnbt2kVsbCzbtm0zuhzppePHj/PEE09w4sQJLBYLP/jBD8jJyTG6LOkBp9PJj370I9rb23G5XKSlpflliuO+THdnBYEPP/yQgQMH8uSTTypETKipqYm//OUvTJgwgTNnzjB37lz+/d//nZtuusno0uQa3G43Z8+e5frrr+f8+fPcf//9LFmyJKT6o+l0VhCYOnUqQ4YMMboM8VJcXBwTJkwAYNCgQYwZMwaHw2FwVdITFouF66+/HoCOjg46OjqwWCwGVxVYChGRPuTYsWPU1qr5A24AAAQISURBVNaSkJBgdCnSQy6Xizlz5nD77bdz++23h9y+U4iI9BFfffUVeXl5PP300wwaNMjocqSHwsPD2bJlC7t37+bQoUP8/ve/N7qkgFKIiPQB58+fJy8vD7vdTmpqqtHliBeioqKYNm0ae/bsMbqUgFKIiBjM7XazZMkSxowZQ25urtHlSC+cPHmStrY2AM6dO8d7773HmDFjDK4qsHR3VhBYtGgRH3zwAadOnSI2NpaFCxeSnZ1tdFnSQ7/73e/40Y9+xLhx4wgL6/xet2jRIqZPn25wZXItn3/+OU899RQulwu32016ejo//elPjS4roBQiIiLiNZ3OEhERrylERETEawoRERHxmkJERES8phARERGvKUREemDy5Mld2m+++SbLly/v9Xpqa2vZvXv3NR/3/vvv89BDD11x+V//9V+zc+dOz7KHHnqI999/v9e1iPiCQkQkgHoaIt2Jj4/nP/7jP3xUkci3oxAR+ZaOHTvGP/zDP2C328nJyeHPf/4zAG+99RazZ8/m3nvv9cw5sXbtWrZv386cOXPYvn07Z8+eZfHixcybN4/MzEzefvvta77ezTffzODBg3n33Xcv+9nevXvJzMzEbrezePFi2tvbfb69It+kSalEeuDcuXPMmTPH025tbSUlJQWAlStXkpWVRVZWFiUlJaxcuZKXX36Zl19+mf/6r//CarXS1tZG//79ycvL49NPP6WgoACAF198kdtuu43nn3+etrY2srOzuf32269Zz4IFC3jppZdISkryLHM6nTz11FOsX7+eG264gSeeeILf/OY3zJ8/37e/DJFv0JGISA9cd911bNmyxfPvm7PXHThwgNmzZwMwZ84c9u/fD3ReR3nqqafYuHEjLpfriuutqanhP//zP5kzZw4//vGPcTqdHD9+/Jr1TJ06FegcMuWiL7/8kpEjR3LDDTcAkJWV1eXnIv6gIxERP1m+fDkff/wxu3btYu7cubzxxhtXfNzatWsvG7Svubn5mutfsGABr7zyChERehuLcXQkIvItTZ48mbKyMgC2bt3KlClTADh69CgJCQk8+uijDB06lMbGRq6//nq++uorz3OTk5N57bXXuDiE3Weffdbj101OTqatrY0vvvgCgBtuuIE//elP/PGPfwRgy5YtniMWEX9RiIh8S0uXLuXNN9/EbrezZcsWlixZAsALL7yA3W5n9uzZTJ48mZtvvplp06ZRV1fnubD+8MMP09HRwb333ktGRgYvvfRSr157wYIFntNfkZGRPP/88zz66KPY7XYsFgs//OEPAViyZAmffPKJbzdcBI3iKyIi34KORERExGsKERER8ZpCREREvKYQERERrylERETEawoRERHxmkJERES89v8B5rP4Fh01M74AAAAASUVORK5CYII=\n","text/plain":["
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\n","text/plain":["
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\n","text/plain":["
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\n","text/plain":["
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\n","text/plain":["
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\n","text/plain":["
"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"a74d69fd"},"source":["Now that we have analysed our data we can proceed to normalixing our data and regression"],"id":"a74d69fd"},{"cell_type":"markdown","metadata":{"id":"77d8c6ba"},"source":["### Importing useful libraries \n"],"id":"77d8c6ba"},{"cell_type":"code","metadata":{"id":"fffac537","executionInfo":{"status":"ok","timestamp":1635012398317,"user_tz":-330,"elapsed":2,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["# This Python 3 environment comes with many helpful analytics libraries installed\n","# For example, here's several helpful packages to load in\n","import numpy as np # linear algebra\n","import matplotlib.pyplot as plt # data visualization\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)"],"id":"fffac537","execution_count":15,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"04cd7e4e"},"source":["### Loading the dataset \n","#### For implementation we will be using house prediction dataset . The dataset can be found [here](https://github.com/vrinda01go/Hellofoss/blob/main/Insti_data.csv)"],"id":"04cd7e4e"},{"cell_type":"code","metadata":{"id":"4ac8e74b","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635012403815,"user_tz":-330,"elapsed":517,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"19c614d4-a1b3-46ba-80e7-9500e5fde8c1"},"source":["data = np.array(df, dtype=float)\n","data"],"id":"4ac8e74b","execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[2.540e+03, 3.000e+00, 1.000e+00, 6.860e+02, 8.000e+00],\n"," [2.900e+03, 3.000e+00, 2.000e+00, 9.660e+02, 5.000e+00],\n"," [2.362e+03, 3.000e+00, 2.000e+00, 9.240e+02, 2.000e+00],\n"," ...,\n"," [1.020e+03, 3.000e+00, 2.000e+00, 1.006e+03, 3.000e+00],\n"," [2.400e+03, 2.000e+00, 2.000e+00, 9.380e+02, 1.000e+00],\n"," [9.500e+02, 3.000e+00, 2.000e+00, 1.053e+03, 2.000e+00]])"]},"metadata":{},"execution_count":16}]},{"cell_type":"markdown","metadata":{"id":"627a5a3a"},"source":["#### Since our dataset has four features i.e Hostel No. , Occupancy, Room Size and Floor ,our hypothesis function becomes\n","### hθ(x) = θ0 + θ1x1 + θ2x2 +θ3x3 + θ4x4\n","#### where x1 ,x2,x3 and x4 are the two features (i.e. size of house and number of rooms)"],"id":"627a5a3a"},{"cell_type":"markdown","metadata":{"id":"58fdf0fe"},"source":["### So Your task is to define hypothesis function having 4 features and a corresponding cost function "],"id":"58fdf0fe"},{"cell_type":"code","metadata":{"id":"34dc5643"},"source":[" # define and complete hypothesis function "],"id":"34dc5643","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"6fae141b"},"source":["# define and complete cost function"],"id":"6fae141b","execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"178eb11f"},"source":["### Gradient Descent \n","#### So we have our hypothesis function and we have a way of measuring how well it fits into the data. Now we need to estimate the parameters in the hypothesis function. That's where gradient descent comes in.\n","### Your next task is to define gradient descent function having some specific value of learning rate and number of epochs.\n","#### Note that learning rate should be neither very high nor very low .Why?\n","#### Check out exact reason [here](https://towardsdatascience.com/understanding-learning-rates-and-how-it-improves-performance-in-deep-learning-d0d4059c1c10)\n","\n"],"id":"178eb11f"},{"cell_type":"code","metadata":{"id":"167b5bdc"},"source":["#define and complete Gradient Descent function "],"id":"167b5bdc","execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"724e407a"},"source":["### Now we want to visualize how our cost function varies with number of epochs .So your next task is to plot graph of updated costs vs number of epochs "],"id":"724e407a"},{"cell_type":"markdown","metadata":{"id":"e82f6ebf"},"source":["#### After plotting above graph you will notice that your cost function decreases with epochs.\n","#### Perfect! This is all what we wanted to seek by doing linear regression. \n","\n","#### Now it's time to test our model on some test data. \n","\n","#### For this you will define a test function that will take as input Hostel No. , Occupancy, Room Size , Floor and the final theta vector that was returned by our linear regression model and will give us the price of the house. Compute it for any set of features given and final value of theta as given by gradient descent function"],"id":"e82f6ebf"},{"cell_type":"code","metadata":{"id":"c522bca3"},"source":["# define and complete test function that will take required inputs .This function should return price of Room "],"id":"c522bca3","execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"947f35b1"},"source":["#### Now since we have defined all required functions , we can call functions one by one and get our final results .\n","#### Your final task is to use all functions defined above and predict the price of room for some input combinations to check how well your model works."],"id":"947f35b1"},{"cell_type":"markdown","metadata":{"id":"06e54ef5"},"source":["#### You can try playing with different values of alpha and epochs and see which combination gives most accurate results but do lookout for overfitting \n"],"id":"06e54ef5"},{"cell_type":"code","metadata":{"id":"3fc631fd"},"source":[""],"id":"3fc631fd","execution_count":null,"outputs":[]}]} \ No newline at end of file From ba19cd4344225e58169b9a23d9b627a6b66539fb Mon Sep 17 00:00:00 2001 From: Mani9550 <93199718+Mani9550@users.noreply.github.com> Date: Wed, 27 Oct 2021 15:44:45 +0530 Subject: [PATCH 02/11] Added quiz file and renamed it --- MCQ's.md => MCQ's_203174002.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) rename MCQ's.md => MCQ's_203174002.md (85%) diff --git a/MCQ's.md b/MCQ's_203174002.md similarity index 85% rename from MCQ's.md rename to MCQ's_203174002.md index b84bcd4..f450220 100644 --- a/MCQ's.md +++ b/MCQ's_203174002.md @@ -8,37 +8,37 @@ Change the markdown file for submission of the quiz - Suppose you are working on weather prediction and use a learning algorithm to predict tomorrow's temperature . What kind of problem would that be? - [ ] Classification - - [ ] Regression + - [X] Regression - Suppose that you have trained a logistic regression classifier, and it outputs on a new example x a prediction hθ​ (x) = 0.4. This means (check all that apply): - - [ ] Our estimate for P(y = 0| x,θ) = 0.6 - - [ ] Our estimate for P(y = 1| x,θ) = 0.4 + - [X] Our estimate for P(y = 0| x,θ) = 0.6 + - [X] Our estimate for P(y = 1| x,θ) = 0.4 - [ ] Our estimate for P(y = 1| x,θ) = 0.6 - [ ] Our estimate for P(y = 0| x,θ) = 0.4 - Which of the following are reasons for using feature scaling? - - [ ] It speeds up gradient descent by making it require fewer iterations to get to a good solution. + - [X] It speeds up gradient descent by making it require fewer iterations to get to a good solution. - [ ] It speeds up solving for θ using the normal equation. - [ ] It prevents the matrix XTX (used in the normal equation) from being non-invertable (singular/degenerate). - [ ] It is necessary to prevent gradient descent from getting stuck in local optima. - Which of the following statements are true? Check all that apply. - - [ ] The cost function J(θ) for logistic regression trained with m≥1 examples is always greater than or equal to zero. - - [ ] The sigmoid function g(z)=1/1+e^−z is never greater than one (>1). + - [X] The cost function J(θ) for logistic regression trained with m≥1 examples is always greater than or equal to zero. + - [X] The sigmoid function g(z)=1/1+e^−z is never greater than one (>1). - [ ] For logistic regression, sometimes gradient descent will converge to a local minimum (and fail to find the global minimum). - [ ] Linear regression always works well for classification if you classify by using a threshold on the prediction made by linear regression. - KNN algorithm does more computation on test time rather than train time. - - [ ] True + - [X] True - [ ] False - Which of the following distance metric can not be used in KNN? - [ ] Manhattan - [ ] Minkowski - [ ] Euclidean - - [ ] All of them can be used + - [X] All of them can be used - Which of the following machine learning algorithm can be used for imputing missing values of both categorical and continuous variables? - - [ ] KNN + - [X] KNN - [ ] Logistic Regression - [ ] Linear Regression - Suppose, you have given the following data where x and y are the 2 input variables and Class is the dependent variable. You want to predict the class of new data point x=1 and y=1 using eucledian distance in 3-NN. In which class this data point belong to? ![image](https://user-images.githubusercontent.com/73156496/138315461-b7127e2a-aa76-48bc-9934-53ed4e6d4166.png) - - [ ] + class + - [X] + class - [ ] - class - [ ] Can't Say From 9f89066fe2a152788653888836f692afdc4f6188 Mon Sep 17 00:00:00 2001 From: Mani9550 <93199718+Mani9550@users.noreply.github.com> Date: Fri, 29 Oct 2021 00:11:04 +0530 Subject: [PATCH 03/11] Delete Linear_Regression_Task2_203174002.ipynb --- Linear_Regression_Task2_203174002.ipynb | 1 - 1 file changed, 1 deletion(-) delete mode 100644 Linear_Regression_Task2_203174002.ipynb diff --git a/Linear_Regression_Task2_203174002.ipynb b/Linear_Regression_Task2_203174002.ipynb deleted file mode 100644 index c7b98ce..0000000 --- a/Linear_Regression_Task2_203174002.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":5,"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.8"},"colab":{"name":"Linear_Regression_Task2_203174002.ipynb","provenance":[],"collapsed_sections":[]}},"cells":[{"cell_type":"markdown","metadata":{"id":"89223f98"},"source":["\n","\n","```\n","Import libraries\n","```\n","\n","### Importing useful libraries \n"],"id":"89223f98"},{"cell_type":"code","metadata":{"id":"26f77ebe","executionInfo":{"status":"ok","timestamp":1635012283386,"user_tz":-330,"elapsed":1123,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["# This Python 3 environment comes with many helpful analytics libraries installed\n","# For example, here's several helpful packages to load in\n","import numpy as np # linear algebra\n","import matplotlib.pyplot as plt # data visualization\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","import seaborn as sns"],"id":"26f77ebe","execution_count":1,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"31c8220d"},"source":["### Loading the dataset \n","#### For implementation we will be using house prediction dataset . The dataset can be found [here](https://github.com/vrinda01go/Hellofoss/blob/main/Room_price_data.csv)"],"id":"31c8220d"},{"cell_type":"code","metadata":{"id":"1c5d873a","executionInfo":{"status":"ok","timestamp":1635012317184,"user_tz":-330,"elapsed":552,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["df = pd.read_csv(\"Hostel_Linear-Dataset.csv\") #import text file \n"],"id":"1c5d873a","execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"id":"1ca9aba0","colab":{"base_uri":"https://localhost:8080/","height":205},"executionInfo":{"status":"ok","timestamp":1635012321185,"user_tz":-330,"elapsed":524,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"50a85b1a-cfee-4f7b-9ac1-5134a16822e9"},"source":["df.head()"],"id":"1ca9aba0","execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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PriceHostel No.OccupancyRoom SizeFloor
02540.0316868
12900.0329665
2NaN317888
32362.0329242
4NaN3210985
\n","
"],"text/plain":[" Price Hostel No. Occupancy Room Size Floor\n","0 2540.0 3 1 686 8\n","1 2900.0 3 2 966 5\n","2 NaN 3 1 788 8\n","3 2362.0 3 2 924 2\n","4 NaN 3 2 1098 5"]},"metadata":{},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"af08f245"},"source":["# Visualizing and Cleaning the data\n","\n","We will now be removing the nan values and identical values from the dataset\n","\n","For seeing if there are nan values in the dataset we will use the isna() function and then to remove them we will use the dropna() function. We will need to set additional parameters like rows and columns in the dropna function depending on the number of nan values present for each column\n","\n","Using the sum() function with isna() function we can get to know the number of missing values in each column"],"id":"af08f245"},{"cell_type":"code","metadata":{"id":"2fd4babb","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635012323753,"user_tz":-330,"elapsed":428,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"dd94b5ef-188f-4c3a-aec4-fe91cdc6a86d"},"source":["df.isna().sum()"],"id":"2fd4babb","execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Price 1531\n","Hostel No. 0\n","Occupancy 0\n","Room Size 0\n","Floor 0\n","dtype: int64"]},"metadata":{},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"83ef03c3"},"source":["After this we will proceed to remove the nan values \n","\n","Since there are not many nan values in the column 'Price' as compared to the number of rows we will remove the rows which have nan values. \n","\n","Reseting the index after removing the nan values and dropping the old index will also be important"],"id":"83ef03c3"},{"cell_type":"code","metadata":{"id":"b65e4503","executionInfo":{"status":"ok","timestamp":1635012326744,"user_tz":-330,"elapsed":459,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["df = df.dropna(subset = ['Price'],how= 'any')\n","df = df.reset_index(drop = True)\n","## df.isna().sum()"],"id":"b65e4503","execution_count":6,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"40784889"},"source":["Now we can use the drop_duplicate function to remove the duplicate values\n","\n","This function has a parameter calle 'keep' where we specifiy to drop and which value to keep\n","\n","For this excercise we will keep the first values and drop the rest of the duplicates"],"id":"40784889"},{"cell_type":"code","metadata":{"id":"75fa3dc8","executionInfo":{"status":"ok","timestamp":1635012329999,"user_tz":-330,"elapsed":425,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["df = df.drop_duplicates(keep = 'first')\n","df = df.reset_index(drop = True)\n","## df.duplicated().sum()"],"id":"75fa3dc8","execution_count":7,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"a007a33f"},"source":["For visualizing the data we will first start with looking at the distribution of different columns to see if there are enough number for each category in every column and dropping them if the data is biased for one category more than the other"],"id":"a007a33f"},{"cell_type":"code","metadata":{"id":"b325df62","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1635012366549,"user_tz":-330,"elapsed":1239,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"cf878a76-66d3-4e68-f009-819d724a4eae"},"source":["columns = df.columns\n","for column in columns:\n"," if(column== 'Price' or column=='Room Size'): \n"," continue\n"," fig = plt.figure(figsize=(5,5))\n"," ax = fig.gca()\n"," counts = df[column].value_counts()\n"," counts.plot.bar(ax = ax, color='blue')\n"," ax.set_title('No of rooms '+ column)\n"," ax.set_xlabel(column)\n"," ax.set_ylabel(\"No of rooms\")\n"," plt.show()"],"id":"b325df62","execution_count":9,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
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\n","text/plain":["
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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"9811a731"},"source":["We can clearly notice that for the Occupancy column the (occupancy) = 4 has a really low set of data points as compared to others. Hence we can proceed in dropping those rows where the occupancy is 4"],"id":"9811a731"},{"cell_type":"code","metadata":{"id":"825783c0","executionInfo":{"status":"ok","timestamp":1635012377084,"user_tz":-330,"elapsed":615,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["df = df[df['Occupancy'] != 4]\n","df = df.reset_index(drop= True)"],"id":"825783c0","execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"id":"30c64310","colab":{"base_uri":"https://localhost:8080/","height":205},"executionInfo":{"status":"ok","timestamp":1635012378828,"user_tz":-330,"elapsed":9,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"bbb5d4dc-f7e5-4b8e-a248-6ecbd09568da"},"source":["df.head()"],"id":"30c64310","execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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PriceHostel No.OccupancyRoom SizeFloor
02540.0316868
12900.0329665
22362.0329242
31432.0217063
41702.02210383
\n","
"],"text/plain":[" Price Hostel No. Occupancy Room Size Floor\n","0 2540.0 3 1 686 8\n","1 2900.0 3 2 966 5\n","2 2362.0 3 2 924 2\n","3 1432.0 2 1 706 3\n","4 1702.0 2 2 1038 3"]},"metadata":{},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"f333875b"},"source":["We will now write the columns between categorical and numerical\n","\n","categorical = Hostel No, occupancy, floor\n","\n","Numerical = price, occupancy, roomsize, floor, hostel No.\n","\n","Remember that we can treat Hostel Number and occupancy as numerical or categorical. For this notebook we will treat them as categorical for data visualization and numerical for the regression"],"id":"f333875b"},{"cell_type":"markdown","metadata":{"id":"0f34ca6a"},"source":["We will also plot the scatter plots and the correlation map to analyse the relation ships between different numerical columns"],"id":"0f34ca6a"},{"cell_type":"code","metadata":{"scrolled":false,"id":"f4a3ab6e","executionInfo":{"status":"ok","timestamp":1635012383025,"user_tz":-330,"elapsed":517,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["categorical = ['Hostel No.', 'Occupancy', 'Floor']\n","numerical = [ 'Price', 'Room Size']"],"id":"f4a3ab6e","execution_count":12,"outputs":[]},{"cell_type":"code","metadata":{"id":"df2b588a","colab":{"base_uri":"https://localhost:8080/","height":791},"executionInfo":{"status":"ok","timestamp":1635012386787,"user_tz":-330,"elapsed":1202,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"c61015b6-8eb0-4d49-cf99-2e50e4e7bd51"},"source":["for column1 in numerical:\n"," for column2 in numerical:\n"," if(column1 != column2):\n"," fig = plt.figure(figsize=(6,6))\n"," ax = fig.gca()\n"," df.plot.scatter(x=column1,y=column2,ax = ax)\n"," ax.set_title('Scatter plot of '+ column1 + ' vs ' + column2)\n"," ax.set_xlabel(column1)\n"," ax.set_ylabel(column2)\n"," plt.show()"],"id":"df2b588a","execution_count":13,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"625b89a8"},"source":["We can notice that there are no linear relation present between the numerical columns. Hence no need to drop anything"],"id":"625b89a8"},{"cell_type":"markdown","metadata":{"id":"8a7f95fa"},"source":["Now we will plot box plots of categorical and numerical columns to get more information about the number of outliers and the distrubtion."],"id":"8a7f95fa"},{"cell_type":"code","metadata":{"id":"e73f0769","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1635012395663,"user_tz":-330,"elapsed":1890,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"ed580d37-65dc-4b57-867d-df02ac8fdeea"},"source":["for c in categorical:\n"," for n in numerical:\n"," sns.set_style(\"whitegrid\")\n"," sns.boxplot(x= c, y= n, data=df)\n"," plt.xlabel(c)\n"," plt.ylabel(n)\n"," plt.show()\n"," "],"id":"e73f0769","execution_count":14,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
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\n","text/plain":["
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\n","text/plain":["
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\n","text/plain":["
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\n","text/plain":["
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\n","text/plain":["
"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"a74d69fd"},"source":["Now that we have analysed our data we can proceed to normalixing our data and regression"],"id":"a74d69fd"},{"cell_type":"markdown","metadata":{"id":"77d8c6ba"},"source":["### Importing useful libraries \n"],"id":"77d8c6ba"},{"cell_type":"code","metadata":{"id":"fffac537","executionInfo":{"status":"ok","timestamp":1635012398317,"user_tz":-330,"elapsed":2,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["# This Python 3 environment comes with many helpful analytics libraries installed\n","# For example, here's several helpful packages to load in\n","import numpy as np # linear algebra\n","import matplotlib.pyplot as plt # data visualization\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)"],"id":"fffac537","execution_count":15,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"04cd7e4e"},"source":["### Loading the dataset \n","#### For implementation we will be using house prediction dataset . The dataset can be found [here](https://github.com/vrinda01go/Hellofoss/blob/main/Insti_data.csv)"],"id":"04cd7e4e"},{"cell_type":"code","metadata":{"id":"4ac8e74b","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635012403815,"user_tz":-330,"elapsed":517,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"19c614d4-a1b3-46ba-80e7-9500e5fde8c1"},"source":["data = np.array(df, dtype=float)\n","data"],"id":"4ac8e74b","execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[2.540e+03, 3.000e+00, 1.000e+00, 6.860e+02, 8.000e+00],\n"," [2.900e+03, 3.000e+00, 2.000e+00, 9.660e+02, 5.000e+00],\n"," [2.362e+03, 3.000e+00, 2.000e+00, 9.240e+02, 2.000e+00],\n"," ...,\n"," [1.020e+03, 3.000e+00, 2.000e+00, 1.006e+03, 3.000e+00],\n"," [2.400e+03, 2.000e+00, 2.000e+00, 9.380e+02, 1.000e+00],\n"," [9.500e+02, 3.000e+00, 2.000e+00, 1.053e+03, 2.000e+00]])"]},"metadata":{},"execution_count":16}]},{"cell_type":"markdown","metadata":{"id":"627a5a3a"},"source":["#### Since our dataset has four features i.e Hostel No. , Occupancy, Room Size and Floor ,our hypothesis function becomes\n","### hθ(x) = θ0 + θ1x1 + θ2x2 +θ3x3 + θ4x4\n","#### where x1 ,x2,x3 and x4 are the two features (i.e. size of house and number of rooms)"],"id":"627a5a3a"},{"cell_type":"markdown","metadata":{"id":"58fdf0fe"},"source":["### So Your task is to define hypothesis function having 4 features and a corresponding cost function "],"id":"58fdf0fe"},{"cell_type":"code","metadata":{"id":"34dc5643"},"source":[" # define and complete hypothesis function "],"id":"34dc5643","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"6fae141b"},"source":["# define and complete cost function"],"id":"6fae141b","execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"178eb11f"},"source":["### Gradient Descent \n","#### So we have our hypothesis function and we have a way of measuring how well it fits into the data. Now we need to estimate the parameters in the hypothesis function. That's where gradient descent comes in.\n","### Your next task is to define gradient descent function having some specific value of learning rate and number of epochs.\n","#### Note that learning rate should be neither very high nor very low .Why?\n","#### Check out exact reason [here](https://towardsdatascience.com/understanding-learning-rates-and-how-it-improves-performance-in-deep-learning-d0d4059c1c10)\n","\n"],"id":"178eb11f"},{"cell_type":"code","metadata":{"id":"167b5bdc"},"source":["#define and complete Gradient Descent function "],"id":"167b5bdc","execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"724e407a"},"source":["### Now we want to visualize how our cost function varies with number of epochs .So your next task is to plot graph of updated costs vs number of epochs "],"id":"724e407a"},{"cell_type":"markdown","metadata":{"id":"e82f6ebf"},"source":["#### After plotting above graph you will notice that your cost function decreases with epochs.\n","#### Perfect! This is all what we wanted to seek by doing linear regression. \n","\n","#### Now it's time to test our model on some test data. \n","\n","#### For this you will define a test function that will take as input Hostel No. , Occupancy, Room Size , Floor and the final theta vector that was returned by our linear regression model and will give us the price of the house. Compute it for any set of features given and final value of theta as given by gradient descent function"],"id":"e82f6ebf"},{"cell_type":"code","metadata":{"id":"c522bca3"},"source":["# define and complete test function that will take required inputs .This function should return price of Room "],"id":"c522bca3","execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"947f35b1"},"source":["#### Now since we have defined all required functions , we can call functions one by one and get our final results .\n","#### Your final task is to use all functions defined above and predict the price of room for some input combinations to check how well your model works."],"id":"947f35b1"},{"cell_type":"markdown","metadata":{"id":"06e54ef5"},"source":["#### You can try playing with different values of alpha and epochs and see which combination gives most accurate results but do lookout for overfitting \n"],"id":"06e54ef5"},{"cell_type":"code","metadata":{"id":"3fc631fd"},"source":[""],"id":"3fc631fd","execution_count":null,"outputs":[]}]} \ No newline at end of file From 8e8e0350effd10948ca7deef9795019ad65abf3c Mon Sep 17 00:00:00 2001 From: Mani9550 <93199718+Mani9550@users.noreply.github.com> Date: Fri, 29 Oct 2021 00:11:21 +0530 Subject: [PATCH 04/11] Delete KNN_Task4 _203174002.ipynb --- KNN_Task4 _203174002.ipynb | 1 - 1 file changed, 1 deletion(-) delete mode 100644 KNN_Task4 _203174002.ipynb diff --git a/KNN_Task4 _203174002.ipynb b/KNN_Task4 _203174002.ipynb deleted file mode 100644 index 5dc9eee..0000000 --- a/KNN_Task4 _203174002.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"KNN_Task4 _203174002.ipynb","provenance":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.8"}},"cells":[{"cell_type":"markdown","metadata":{"id":"cPP7BfqFSgyH"},"source":["# K-Nearest Neighbors Algorithm\n"]},{"cell_type":"markdown","metadata":{"id":"Zd0p7ZUpSgyL"},"source":["![example 1.png](data:image/png;base64,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)In this Jupyter Notebook we will focus on $KNN-Algorithm$. KNN is a data classification algorithm that attempts to determine what group a data point is in by looking at the data points around it.\n","\n","An algorithm, looking at one point on a grid, trying to determine if a point is in group A or B, looks at the states of the points that are near it. The range is arbitrarily determined, but the point is to take a sample of the data. If the majority of the points are in group A, then it is likely that the data point in question will be A rather than B, and vice versa.\n","
\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"TyGHDf4NSgyM"},"source":["# Imports"]},{"cell_type":"code","metadata":{"id":"iIEvA0xjSgyN","executionInfo":{"status":"ok","timestamp":1635250818719,"user_tz":-330,"elapsed":418,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["import numpy as np\n","from tqdm import tqdm_notebook"],"execution_count":5,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"rc8ruF56SgyO"},"source":["# How it works?\n","\n","We have some labeled data set $X-train$, and a new set $X$ that we want to classify based on previous classifications\n","\n"]},{"cell_type":"markdown","metadata":{"id":"rGbvEXbvSgyO"},"source":["## Seps"]},{"cell_type":"markdown","metadata":{"id":"B-nf9G4ZSgyP"},"source":["### 1. Calculate distance to all neighbours\n","### 2. Sort neightbours (based on closest distance)\n","### 3. Count possibilities of each class for k nearest neighbours \n","### 4. The class with highest possibilty is Your prediction"]},{"cell_type":"markdown","metadata":{"id":"LuWwKdFrSgyP"},"source":["# 1. Calculate distance to all neighbours\n","\n","Depending on the problem You should use different type of count distance method.\n","
\n","For example we can use Euclidean distance. Euclidean distance is the \"ordinary\" straight-line distance between two points in D-Dimensional space\n","\n","#### Definiton\n","$d(p, q) = d(q, p) = \\sqrt{(q_1 - p_1)^2 + (q_2 - p_2)^2 + \\dots + (q_D - p_D)^2} = \\sum_{d=1}^{D} (p_d - q_d)^2$\n","\n","#### Example\n","Distance in $R^2$\n","\n","\n","\n","$p = (4,6)$\n","
\n","$q = (1,2)$\n","
\n","$d(p, q) = \\sqrt{(1-4)^2 + (2-6)^2} =\\sqrt{9 + 16} = \\sqrt{25} = 5 $\n","\n"]},{"cell_type":"markdown","metadata":{"id":"vlvNZqiJSgyQ"},"source":["## Code"]},{"cell_type":"code","metadata":{"id":"EvmQi6nsSgyR","executionInfo":{"status":"ok","timestamp":1635250723579,"user_tz":-330,"elapsed":1651,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["def get_euclidean_distance(A_matrix, B_matrix):\n"," \n"," C = [ [ 0 for i in range(np.size(B_matrix, 0)) ] for j in range(np.size(A_matrix, 0)) ]\n"," \n"," for i in range (0, np.size(A_matrix, 0)):\n"," row1 = A_matrix[i,:]\n"," for j in range (0, np.size(B_matrix, 0)):\n"," row2 = B_matrix[j,:]\n"," \n"," C[i][j] = np.sum(np.square(row1 - row2))\n"," \n"," ## Use the distance formula for the matrices using numpy functions\n"," ## C is the sum of the squares of the distances\n","\n"," return np.sqrt(C)\n"],"execution_count":1,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"GABzTa_0SgyS"},"source":["## Example Usage"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"W6b8yBSoSgyS","executionInfo":{"status":"ok","timestamp":1635250824471,"user_tz":-330,"elapsed":411,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"966f10e6-5429-4f7e-e70f-50df76e1b2ca"},"source":["X = np.array([[1,2,3] , [-4,5,-6]])\n","\n","X_train = np.array([[0,0,0], [1,2,3], [4,5,6], [-4, 4, -6]])\n","\n","print(\"X: {} Exaples in {} Dimensional space\".format(*X.shape))\n","print(\"X_train: {} Exaples in {} Dimensional space\".format(*X_train.shape))\n","\n","\n","print()\n","\n","print(\"X:\")\n","print(X)\n","\n","print()\n","\n","print(\"X_train\")\n","print(X_train)\n"],"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["X: 2 Exaples in 3 Dimensional space\n","X_train: 4 Exaples in 3 Dimensional space\n","\n","X:\n","[[ 1 2 3]\n"," [-4 5 -6]]\n","\n","X_train\n","[[ 0 0 0]\n"," [ 1 2 3]\n"," [ 4 5 6]\n"," [-4 4 -6]]\n"]}]},{"cell_type":"code","metadata":{"id":"kB8IZcDpSgyT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635250828586,"user_tz":-330,"elapsed":423,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"e48c7a0f-c233-44a7-baa1-66354fc59437"},"source":["## Initialize the distance matrix using the get_euclidean_matrix\n","\n","C = get_euclidean_distance(X, X_train)\n","\n","## Euclidean distance b/w row i of X and row j of X_train is available as C[i][j]\n","\n","\n","## Print Distance between first example from X and first form X_train\n","print(f\"Distance between first example from X and first form X_train {C[0,0]}\")"],"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["Distance between first example from X and first form X_train 3.7416573867739413\n"]}]},{"cell_type":"markdown","metadata":{"id":"vbaJfBihSgyT"},"source":["# 2. Sort neightbours\n","\n","In order to find best fitting class for our observations we need to find to which classes belong observation neightbours and then to sort classes based on the closest distance\n"]},{"cell_type":"markdown","metadata":{"id":"b1VLHUj2SgyU"},"source":["## Code"]},{"cell_type":"code","metadata":{"id":"na0G1o_ASgyU"},"source":["def get_sorted_train_labels(distance_matrix, y):\n"," \"\"\"\n"," Function sorts y labels, based on probabilities from distances matrix\n"," Args:\n"," distance_matrix (numpy.ndarray): Distance Matrix, between points from X and X_train, size: N1:N2\n"," y (numpy.ndarray): vector of classes of X points, size: N1\n","\n"," Returns:\n"," numpy.ndarray: labels matrix sorted according to distances to nearest neightours, size N1:N2 \n","\n"," \"\"\"\n"," \n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"U0I8eltDSgyV"},"source":["# 3. Count possibilities of each class for k nearest neighbours \n","\n","In order to find best class for our observation $x$ we need to calculate the probability of belonging to each class. In our case it is quite easy. We need just to count how many from k-nearest-neighbours of observation $x$ belong to each class and then devide it by k \n","

\n","$p(y=class \\space| x) = \\frac{\\sum_{1}^{k}(1 \\space if \\space N_i = class, \\space else \\space 0) }{k}$ Where $N_i$ is $i$ nearest neightbour\n","\n"]},{"cell_type":"markdown","metadata":{"id":"j0ZtOC38SgyV"},"source":["## Code"]},{"cell_type":"code","metadata":{"id":"y2aaG2GdSgyV"},"source":["def get_p_y_x_using_knn(y, k):\n"," \"\"\"\n"," The function determines the probability distribution p (y | x)\n"," for each of the labels for objects from the X\n"," using the KNN classification learned on the X_train\n","\n"," Args:\n"," y (numpy.ndarray): Sorted matrix of N2 nearest neighbours labels, size N1:N2\n"," k (int): number of nearest neighbours for KNN algorithm\n","\n"," Returns: numpy.ndarray: Matrix of probabilities for N1 points (from set X) of belonging to each class,\n"," size N1:C (where C is number of classes)\n"," \"\"\"\n","\n"," ## Write your code here\n","\n"," return probabilities_matrix\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ThEbAnXISgyW"},"source":["# 4. The class with highest possibilty is Your prediction"]},{"cell_type":"markdown","metadata":{"id":"_i7NTtN4SgyW"},"source":["At the end we combine all previous steps to get prediction"]},{"cell_type":"markdown","metadata":{"id":"OzK6rY8mSgyW"},"source":["## Code"]},{"cell_type":"code","metadata":{"id":"DaYqr_i6SgyW","executionInfo":{"status":"ok","timestamp":1635250858682,"user_tz":-330,"elapsed":471,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["def predict(X, X_train, y_train, k, distance_function):\n"," \"\"\"\n"," Function returns predictions for new set X based on labels of points from X_train\n"," Args:\n"," X (numpy.ndarray): set of observations (points) that we want to label\n"," X_train (numpy.ndarray): set of lalabeld bservations (points)\n"," y_train (numpy.ndarray): labels for X_train\n"," k (int): number of nearest neighbours for KNN algorithm\n","\n"," Returns:\n"," (numpy.ndarray): label predictions for points from set X\n"," \"\"\"\n"," ## Write your code here\n","\n"," return prediction"],"execution_count":9,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"i9kzyASWSgyX"},"source":["# Accuracy"]},{"cell_type":"markdown","metadata":{"id":"v8bNPTPZSgyX"},"source":["To find how good our knn model works we should count accuracy"]},{"cell_type":"markdown","metadata":{"id":"dgFCnJ14SgyX"},"source":["## Code"]},{"cell_type":"code","metadata":{"id":"2ySpyThlSgyX"},"source":["def count_accuracy(prediction, y_true):\n"," \"\"\"\n"," Returns:\n"," float: Predictions accuracy\n","\n"," \"\"\"\n"," N1 = prediction.shape[0]\n"," \n"," ## Use np.sum to count the number of elements where predicted value == actual value and assign the count to the variable accuracy\n","\n"," return accuracy"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"b5g7YFY2SgyX"},"source":["## Example usage"]},{"cell_type":"code","metadata":{"id":"uLqCqmJNSgyY","colab":{"base_uri":"https://localhost:8080/","height":235},"executionInfo":{"status":"error","timestamp":1635250842268,"user_tz":-330,"elapsed":449,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"dcf624ee-b959-4577-b370-464112163929"},"source":["y_true = np.array([[0, 2]])\n","\n","predicton = predict(X, X_train, y_train, 3, get_euclidean_distance)\n","\n","\n","print(\"True classes:{}, accuracy {}%\".format(y_true, count_accuracy(predicton, y_true) * 100))"],"execution_count":8,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mpredicton\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_euclidean_distance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'predict' is not defined"]}]},{"cell_type":"markdown","metadata":{"id":"--WUpIcxSgyY"},"source":["# Find best k"]},{"cell_type":"markdown","metadata":{"id":"itkcD0DlSgyY"},"source":["Best k parameter is that one for which we have highest accuracy"]},{"cell_type":"markdown","metadata":{"id":"7GYEUBnnSgyY"},"source":["## Code"]},{"cell_type":"code","metadata":{"id":"Q6OhNBOoSgyY","executionInfo":{"status":"ok","timestamp":1635250862606,"user_tz":-330,"elapsed":413,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["def select_knn_model(X_validation, y_validation, X_train, y_train, k_values, distance_function):\n"," \"\"\"\n"," Function returns k parameter that best fit Xval points\n"," Args:\n"," Xval (numpy.ndarray): set of Validation Data, size N1:D\n"," Xtrain (numpy.ndarray): set of Training Data, size N2:D\n"," yval (numpy.ndarray): set of labels for Validation data, size N1:1\n"," ytrain (numpy.ndarray): set of labels for Training Data, size N2:1\n"," k_values (list): list of int values of k parameter that should be checked\n","\n"," Returns:\n"," int: k paprameter that best fit validation set\n"," \"\"\"\n","\n"," accuracies = []\n","\n"," for k in tqdm_notebook(k_values):\n"," prediction = predict(X_validation, X_train, y_train, k, distance_function)\n","\n"," accuracy = count_accuracy(prediction, y_validation)\n"," accuracies.append(accuracy)\n","\n"," best_k = k_values[accuracies.index(max(accuracies))]\n","\n"," return best_k, accuracies\n"],"execution_count":10,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"nGtIjD0WSgyY"},"source":["# Real World Example - Iris Dataset"]},{"cell_type":"markdown","metadata":{"id":"-o6MHMtKSgyZ"},"source":["\n","\n","\n","\n","This is perhaps the best known database to be found in the pattern recognition literature. The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other. \n","\n","Each example contains 4 attributes\n","1. sepal length in cm \n","2. sepal width in cm \n","3. petal length in cm \n","4. petal width in cm \n","\n","Predicted attribute: class of iris plant. \n","\n","\n","\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SY8oOngQSgyZ","executionInfo":{"status":"ok","timestamp":1635250867474,"user_tz":-330,"elapsed":414,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"270c2090-4cc4-43c8-dd20-5ed2670e0067"},"source":["from sklearn import datasets\n","import matplotlib.pyplot as plt\n","\n","iris = datasets.load_iris()\n","\n","iris_X = iris.data\n","iris_y = iris.target\n","\n","print(\"Iris: {} examples in {} dimensional space\".format(*iris_X.shape))\n","print(\"First example in dataset :\\n Speal lenght: {}cm \\n Speal width: {}cm \\n Petal length: {}cm \\n Petal width: {}cm\".format(*iris_X[0]))\n","\n","print(\"Avalible classes\", np.unique(iris_y))"],"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["Iris: 150 examples in 4 dimensional space\n","First example in dataset :\n"," Speal lenght: 5.1cm \n"," Speal width: 3.5cm \n"," Petal length: 1.4cm \n"," Petal width: 0.2cm\n","Avalible classes [0 1 2]\n"]}]},{"cell_type":"markdown","metadata":{"id":"-IlKSX7hSgyZ"},"source":["## Prepare Data\n","\n","In our data set we have 150 examples (50 examples of each class), we have to divide it into 3 datasets.\n","1. Training data set, 90 examples. It will be used to find k - nearest neightbours\n","2. Validation data set, 30 examples. It will be used to find best k parameter, the one for which accuracy is highest\n","3. Test data set, 30 examples. It will be used to check how good our model performs\n","\n","Data has to be shuffled (mixed in random order), because originally it is stored 50 examples of class 0, 50 of 1 and 50 of 2.\n"]},{"cell_type":"code","metadata":{"id":"RA1Q7kCPSgyZ","executionInfo":{"status":"ok","timestamp":1635250871691,"user_tz":-330,"elapsed":418,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}}},"source":["from sklearn.utils import shuffle\n","\n","iris_X, iris_y = shuffle(iris_X, iris_y, random_state=134)\n","\n","\n","test_size = 30\n","validation_size = 30\n","training_size = 90\n","\n","## Initialize X_test\n","## Initialize X_validation \n","## Initialize X_train \n","\n","## Initialize y_test\n","## Initialize y_validation\n","## Initialize y_train"],"execution_count":12,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"r9xJVLzrSgyZ"},"source":["## Find best k parameter"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":235},"id":"hbvZBVNBSgya","executionInfo":{"status":"error","timestamp":1635250875803,"user_tz":-330,"elapsed":430,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"f62b15f9-7fca-4789-bac2-2db5cbbcd8c0"},"source":["k_values = [i for i in range(3,50)]\n","\n","best_k, accuracies = select_knn_model(X_validation, y_validation, X_train, y_train, k_values, distance_function=get_euclidean_distance)\n","\n","## Plot accuracy vs k values graph"],"execution_count":13,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mk_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mbest_k\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mselect_knn_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_validation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_validation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistance_function\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mget_euclidean_distance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m## Plot accuracy vs k values graph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'X_validation' is not defined"]}]},{"cell_type":"markdown","metadata":{"id":"BjQBDWJMSgya"},"source":["## Count accuracy for training set"]},{"cell_type":"code","metadata":{"id":"_f-J5sSESgya","colab":{"base_uri":"https://localhost:8080/","height":201},"executionInfo":{"status":"error","timestamp":1635250882340,"user_tz":-330,"elapsed":434,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"d0dfb811-a65e-472c-bdc7-fad839ccc488"},"source":["prediction = predict(X_test, X_train, y_train, best_k, get_euclidean_distance)\n","\n","## Calculate Best accuracy using the best k value\n"],"execution_count":14,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbest_k\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_euclidean_distance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m## Calculate Best accuracy using the best k value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'X_test' is not defined"]}]},{"cell_type":"markdown","metadata":{"id":"72O5eXbCSgyc"},"source":["# Sources\n","\n","https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm - first visualisation image\n","\n","https://en.wikipedia.org/wiki/Euclidean_distance - euclidean distance visualisation\n","\n","https://rajritvikblog.wordpress.com/2017/06/29/iris-dataset-analysis-python/ - first iris image\n","\n","https://rpubs.com/wjholst/322258 - second iris image\n","\n"]}]} \ No newline at end of file From c2045dbcb1f52241082186a4de77d69394026e77 Mon Sep 17 00:00:00 2001 From: Mani9550 <93199718+Mani9550@users.noreply.github.com> Date: Fri, 29 Oct 2021 00:11:34 +0530 Subject: [PATCH 05/11] Delete Classification_Task3_203174002.ipynb --- Classification_Task3_203174002.ipynb | 1 - 1 file changed, 1 deletion(-) delete mode 100644 Classification_Task3_203174002.ipynb diff --git a/Classification_Task3_203174002.ipynb b/Classification_Task3_203174002.ipynb deleted file mode 100644 index 1d8640a..0000000 --- a/Classification_Task3_203174002.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Classification_Task3_203174002.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.8"}},"cells":[{"cell_type":"markdown","metadata":{"id":"7C5rAxwPGDQf"},"source":["# Importing useful libraries"]},{"cell_type":"code","metadata":{"id":"8qvrslgsF4Mn"},"source":["import numpy as np\n","import pandas as pd \n","from pandas import Series, DataFrame\n","\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","%matplotlib inline"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"6aYOLI2BHF6m"},"source":["## Loading the dataset.\n","The dataset can be found [here](https://github.com/shreedharmalpani/Intro-To-ML-Hello-FOSS/blob/main/iris.csv)"]},{"cell_type":"code","metadata":{"id":"_hccks2pF4Mq"},"source":["df = pd.read_csv(\"iris.csv\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"um0L09IOF4Ms","colab":{"base_uri":"https://localhost:8080/","height":205},"executionInfo":{"status":"ok","timestamp":1635010648975,"user_tz":-330,"elapsed":513,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"5adfbd69-3916-4a7d-9b21-496cfc4ab498"},"source":["df.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
05.13.51.40.2setosa
14.93.01.40.2setosa
24.73.21.30.2setosa
34.63.11.50.2setosa
45.03.61.40.2setosa
\n","
"],"text/plain":[" sepal_length sepal_width petal_length petal_width species\n","0 5.1 3.5 1.4 0.2 setosa\n","1 4.9 3.0 1.4 0.2 setosa\n","2 4.7 3.2 1.3 0.2 setosa\n","3 4.6 3.1 1.5 0.2 setosa\n","4 5.0 3.6 1.4 0.2 setosa"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"cbx8gP4zF4Mt","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635010651537,"user_tz":-330,"elapsed":11,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"6d8d789e-43c0-499a-9f8d-7f501f9b58bc"},"source":["df.info() "],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","RangeIndex: 150 entries, 0 to 149\n","Data columns (total 5 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 sepal_length 150 non-null float64\n"," 1 sepal_width 150 non-null float64\n"," 2 petal_length 150 non-null float64\n"," 3 petal_width 150 non-null float64\n"," 4 species 150 non-null object \n","dtypes: float64(4), object(1)\n","memory usage: 6.0+ KB\n"]}]},{"cell_type":"markdown","metadata":{"id":"vjH1pAqoJna2"},"source":["# Data Cleaning & Data Visualization"]},{"cell_type":"markdown","metadata":{"id":"PNfSyZF1F4Mu"},"source":["### 1) Remove unneeded columns\n","### 2) Check for duplicate rows \n","### 2) Check for rows with missing values\n"]},{"cell_type":"code","metadata":{"collapsed":true,"id":"HKFsVhubF4Mx"},"source":["df.isna().sum()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Rm7rlDcPR0aI"},"source":["df.isnull()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"L1cnZS5XF4M2"},"source":["### EDA (Exploratory Data Analysis) with Iris"]},{"cell_type":"code","metadata":{"id":"DjsnhAraF4M7","colab":{"base_uri":"https://localhost:8080/","height":458},"executionInfo":{"status":"ok","timestamp":1635011702579,"user_tz":-330,"elapsed":523,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"a7bf481b-9840-450b-b8f2-49a114bba973"},"source":["##Displaying a scatter plot to show the distribution of Sepal Length vs width the dataset\n","\n","fig = df[df.species == 'Iris-setosa'].plot(kind='scatter', x='petal_length', y='petal_width', color='orange', label='Setosa')\n","df[df.species == 'Iris-versicolor'].plot(kind='scatter', x='petal_length', y='petal_width', color='blue', label='Versicolor', ax=fig)\n","df[df.species == 'Iris-virginica'].plot(kind='scatter', x='petal_length', y='petal_width', color='green', label='Virginica', ax=fig)\n","\n","fig.set_xlabel('Petal Length')\n","fig.set_ylabel('Petal Width')\n","fig.set_title('Petal Length Vs Width')\n","\n","fig=plt.gcf()\n","fig.set_size_inches(10, 7)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"O_eYByFvF4M5","colab":{"base_uri":"https://localhost:8080/","height":235},"executionInfo":{"status":"error","timestamp":1635011142444,"user_tz":-330,"elapsed":515,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"fa78777e-0d87-449b-d9b7-a3995609ba6a"},"source":["#Display a scatter plot to show the distribution of Sepal Length vs width the dataset (Like previous Petal lenght vs width scatter plot)\n","\n","\n","#code\n","\n","fig.set_xlabel('Sepal Length')\n","fig.set_ylabel('Sepal Width')\n","fig.set_title('Sepal Length Vs Width')\n","\n","\n","fig=plt.gcf()\n","fig.set_size_inches(10, 7)\n","plt.show()\n","#example plot"],"execution_count":null,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m#code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Sepal Length'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Sepal Width'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Sepal Length Vs Width'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mAttributeError\u001b[0m: 'Figure' object has no attribute 'set_xlabel'"]}]},{"cell_type":"code","metadata":{"id":"EIh_yKQAF4M6","outputId":"91706df0-4dd1-4a8b-92e7-41e04e6e602d"},"source":["#plot the FacetGrid plot using the seaborn library\n","\n","#sns.FacetGrid(...)\\\n","# .map(...)\\\n","# .add_legend()\n"],"execution_count":null,"outputs":[{"data":{"text/plain":[""]},"execution_count":12,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAboAAAFgCAYAAADNUrzMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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GcSIb/xk4DrgW+CbQAywHPmCt/cGC7d6Ek5Ayg9OBXQLEgX/ESVYBeDdwBE5c2G3GmNOA/+N+\nbRb4ibX2KmPMqTjh0TPAJPDHOIsF3IKTr3kY8Dlr7U3VfFY/pSajfBc3pNO9FH49cJUx5mU4SzQc\ncnefg4oPy2RzXHXTz0hNHRitiC2LcMMVr2JZV2fQH0uqEGQkVjo9xb03fYiXPDtCfH8euyLEb49O\ncPYVHyUaLf6H0Mz0FNs/chXRoQlCszDeAen+lWz+8A10dusPqCaxDme40svhbvviyJ2l+2dr7beN\nMW9zXx+LcyvqHJwle7yilf4n8Elr7b+44SArgatxUrFuMsYcB3zZWnuaGzV2OU7g80XA7+N0dP/q\nho6cjtOxfgbn4qnPPf7t1tpvGWMOA34MBNbRlRWSaIzpBo50i+vGWaqn7vfMmsEd9+/kvkd2Mew+\nMzc8kea+R3Zxx/07q9p2YScHkJrKctVNPwvmg0jNDN15O2P33es8WzY7S3Z4mLH77mXoztur3ve9\nN32ITU/uo2d/nhDQsz/Ppif3ce9NH/LdbvtHrmL54AThWedP5/AsLB+cYPtHgl0RXZZkD849OS8v\nuO21YA96Ye12nADnfwY+D4SMMacZYx5w/50L/BVwpjHmxzgdVx44AbjMvUq8mcXB/i8F/staO2Ot\nnQX+EydR629wrtp+iHM1N4OTk/lGY8w/AR/EWSggMKUiwD5jjPk5sAvng+8HrrbWnmit/Z9BFtaM\ngooPe/Sp3y7q5OakprIMj08tvVipiyAjsSZSI7zkWe85Xy95doSJlHfb5Pgw0aEJz7bo0AST44sf\n9pb6c4cl/61I8101nH2ZL3zhLuMTt9aeC7wV+Adr7YPW2jPcf/cA7wCutdaejvO30h8BTwGfdlcu\nuAj4p4L9h9z2U9wVEjpwHknbgRP8/BV3dYPt7r7fC/zMWvsnwJ3uMQJT6h7dIPAu4FFr7aERqOij\nnCiuYit8+26bmvE9rn1+jN8/QcNNzaicSKyuNWsq2veePTuJ7897tsX259mzZycrj1scFj72zNOE\nioy3hGad9uUDul/XJN7n/u/5HJh1eVfB+0F4GrjGGHMRTgf1YY+v+TnwXWNMEmdV8rnbWP9ojHkH\nzlDmte7X/hRnZYTX4QxRPuTu90GcjvyVwC3GmP04neI7cCbd/IMx5hJgDMgaY6LW2kDyDEt1dJ04\n47jnFCy8N89a+5EgimpWgcWHxTp9OztzRG9lBUvggozEWrduI3ZFiB6Pzi61IoRZ5x0W0HvMcYx3\nOMOVC+U7nHZpDu4jBO95w3vv+gA1fI7OWvsb4PcWvPeVgpd/XGL77wDf8WhaNHnGWvtBnOFHgL91\n/xV6eGEtwLPAy/xqqKVy7tF1+Pw7pAQVH3bSS19CbJn33xyxZRHNvmxiQUZirYwl+O3R3jMsf3t0\noujsy+UIXWLcAAAgAElEQVQ9q0j3r/RsS/ev1OzLJvSdT50/+Z1Pnf9rPSwejFKzLq/zet8dfz06\nkIqaXFDxYX/0mqOLzrqU5hZkJNbZV3x0ftZlbH+eVMGsSz+bP3zDQbMu8wWzLkUONWVFgBlj3oUz\nc2ZFwdvPWmvrFrTYbMkojXyOrlVTI+qt3jWV8xxdpTUF+RydvnflaeWFVw91pe7RzXkv8HLgeuD9\nOGvRnR1QTS0h2hkuOvGkmm1X9SzTxJMWFYpGK554UsrKWMJz4kkpy3tWaeKJHPLKeo4OGLTWPgs8\nDpzg3tRcPDtFRESkyZR7RbffGPNanI7ujcaY/8Z5ul1ERKp00R1XzK9e8M2Lb9KElBor94ru/+BE\nt/wHTs6ZBf4hqKJqKT2TY3B00vNhbr+2ICUnM/zqNyMkJzNLrik9k2PPvv11rTmTyzA0OUwmt7je\nUlKZFHZkJ6lMqqb7nZ5Ksvc5y/SU9z2TfDrN1J69ng9sl9o2KJnhYQYf+DEZj0cRSp0Lv5rz6TSZ\nwcGqHk73Umq/QR231WqqxkV3XBG56I4rPo3zILUFtl90xxWfvuiOK8q9CPFkjDnKGPNfC947x30G\nrq6MMX9tjFnSuLub0PLSWtVQ1sm01m43xrwPJ7zzOuBCa633k6wuY0wYJybG4MSFXW6tfbKg/Q04\nDypmgVuttTdX9hG8+eVKAhXnVVYjk80WXZg1HAr51nTQ50mmScSDrzmXz/Gtnffw+NB2RtNj9EV7\nObHfWZwzHPKfgJPJZrhx6+fZk9pLnjwhQqyLreXKLe8kHApXvN/sTIaHb3EWOV2RyrK7YJHTSGeX\nb+5kLp/z3TYouakpnr36feRTBzr7UCzG0R//JES7fM+F3+cNh8KBZGyWyu4MMtuzlWqqkXZYvaDU\ncT/RiOMWKnfW5dnAV3FSrMM4idMX+a1iYIx5I3CetfYyY8wZwHustee7bZ3Ar4DfxYkVewj4Q2vt\nb4vtb6mzLr9x3w7ue2TXovfPOnkDQNG2S886kG9a65lf19z6c14YXHxlc/iaGOaIXt+a/D5PYc21\ndOeOu3lg14OL3j9jw2lcuOm8g95beK7+5uefYXdq8aod62OHcVzvMWXvd6GHbvoo/Y8uzrkdOulY\nTr3iQwze/nXG7rt3UXvvWWfz9OgzvtsGZedfvuugTm5OKBZj2ztf53su/D7vcX3HFP2say55c9n1\nLfze+Z3DNZe8uWR7LbRCTUW+puxZl+5w5Xaczm2hZ4GXVTqMObceHTDNElcvKPj9/HJr7X5jzJVA\nDvgX4EvAMmAKJ+EkjPNg+TDwPZwUlbfiJKD8t7X23caYr7i1/Bj4Mk5uchdO6tYj7nvHuPv6W2vt\nHW6e5uXAXpyosZU4F2YftNbeb4x5EideLFPO4gLlXgp8GvgDa+3J1toB4EJKJE1ba//NPRG4H2ys\noPl4YKe1dtRam8GJinlNmbWU5JcrudUOVZxXWY3kZIbdQ4t/2QHsHkrx6FPeffy2HftITmbqXnMm\nl+Hxoe2ebU/s2+473JjKpNiT2uvZ9mJqD48NPVHRfqenknTb5zzbuu1zTI4PF82dTG59lO6nflN0\n26CGMTPDw56dHEA+leLpZ7d5tj2xbzsTqRHfzzux1XuJyGoyNktld2aTycCyPVupphopZ/WCWvhn\na+1ZOJ0VHFi94A04qxQcNLJnrZ0B/hW4wH3rUpyIrxuBv3ezLm8E5q7U1gKvs9b+P+DPgHdZa18F\n/MoYU7jvy4HfuG2XAKcAfwEMWWt/HzgL+JgxZnXBNh8E7rXWvgan3/lH9znuGPDRclfQKXccOG2t\nfWzuhbX2Efdgvqy1WWPMV3ECQQsjZ1YC4wWvkzh/XRTV17ecSKS8IYg9+/YzkiyWSVn8h340OU24\nq5P+1QceF+zvj5d1zFJefHqIfJFr0vxs8bzL0eQ0yUze5/MsrrkW9qaGGE2PebaNTo8RjuXpjx18\nbubO1d7fvkge75HtWWYZTY97thXb75wXfv0CK1Le4dfLU1lyw7uK5k7mRkdZUWT0YnkqCzNj9B9x\nmGd7NQa3e/8CnhN/YR8cs/hxktHpMVKp3UU/74pUljzFMzZXhrMs61/t2e5l7ns3tWe/b3bnstSw\nb/tSj9uqNdXI3OoFR3m0Bbp6gTFmbvWCTuDv3bXkPuZ+ySdx1oq7yRjzlLOJHXbDoN9vjLkKJxlr\n7pfWs+4FCzgd3ZXGmKOBn3FwgpYB/t2t4WngM8aYzwH3ue8ljTG/xOmI5xwPfN1t322MmcBZWmjR\n5/JTbkf3sDHmFpx7blmc3vg3xpjXuAX8pNiG1tq3uifmYWPM71hr9wMTOIv4zYlz8BXfIqOj5V/B\n52ZyJOLFMimjdHRQNK8yl5mZH56o5dBlvCtEqAPPzi7UAT0rvPMu++LdxLtCPp/n4JprJZcL0Rft\nZSQ9uviY3b3kUiGGCq6CCs9VLLuSECHPzq6DDnqjKz07O6/9HqSzl/2xCHGPX/6TsQjhVRuK5k6G\n+/pIpseJ71989TsZi0BnbyAPKM+sPdK3PXn4apzR+4P1dfcSi61nT5HPuz8WoadrJXmPX/CRvgQT\nuQipMj9P4fcun4v4ZndOxVb5ti/luK1ck9/XlOubF980edEdV/wbB9+jm3NXDWdfFl29wBizDvip\ntfZonOejC7+uAydcem707ingRmvtT92JIqd77P9/4czHmDbGfB9niZ85c7er7jLGHIPTsf4UeDXw\nbWNMHGcpoGcXbPNqYJsxZj3ObP+5b7TvPJFC5Q5dHo/Ty34C55L1ZJwx3+s4kGB9EGPMnxpjrnZf\nTrpFzRX2K+A4Y0zCGNOFM2xZs4XX/HIlt5j+ivMqqxFf3sX6/phn2/r+GCe99CVFa4ov76p7zV3h\nLk7s3+zZdsLqzXSFi0/eiHXFWBdb69l2WGwdL+8/oaL9di+LM228O45pcyTLe1YVzZ2MbzmJ6Zce\nVXTb7mU1/Ut9XteqVYRi3t/3UCzGcUcPeLadsHozK2MJ38+7cstJnm3VZGyWyu6MxOOBZXu2Uk01\n9D6cBUmfxbmIeNZ9HfTqBWcYY36Cs0SO1+oF4KwmPgD8yH19Jc6qBz/GGcp83GObJ4D/NMbcj3Nv\n8OGCti8CxxRs/7c49/xWGWMeBB4ArrPWDhZs8zc46+L9BGclhHdYa72HOXyUNRmlEsaYFTg3Gdfi\nXB5/AidCLGat/VLBrMsQzqzLz/ntb6mTUQ7MUvTOpCzWVjiDsdaTUcqbdeldk9/nCXrW5RP7tjMy\nPUaiu5cTVnvPjlx4rsqZdVnOfhcqnIW4PJVlsuisy8W5k4WzLr22DUo5sy6LnQu/z3tg1uXiz7qU\nmYYLv3d+5/DgGY7VHbfVayryNRVFgOk5umCVO+vySJwx26NwLiO/AVzmLgVRF5VmXfrlSpbKqwwq\nby85mWHXYIoNa2LElx/8C7ZUTemZHOGuTnKZmcCuPhfK5DKMp5P0RONFr7iKnatUJsXu1F7Wx9YS\n6zr4yqac/RYzPZVkbPBFetcc5nk1lk+nWRnOMpGLLPqLvtS2QckMD9O59zlm1h5J16qDY7lKnQu/\nmsvJ2PRT7HtXar/VHrddalrwNcq6bELldnT/gXOZeQOwBXg78KfuTJi6aLZQ50ZqxrpUU3lUU3la\ntSZ1dM2p3DGv1XPPWVhrZ92Hu70XvBIREWki5XZ0U8aYDTgJJ7hTUZv24ZRyNSoC7FDiF21VTQRY\nOcfdmxqq6Lh+7X4RU9V8niDPRSO0WhSXtLdyHy94D/Bd4FhjzC9wZlxeGFhVAfOLBwsyAuxQ4hcf\nBlQcARbkcf22Dc1SNGIq31H556kmZq0ZtXAUl7Sxkh2dMeYPgV/iPP/w18BrgXsA71iGFnDH/TsP\nitMankjPvw4qTutQ862d9xwUbTWSHj3odbG2UhFgQR7Xb9vTtyYPipjKDg/Pv/7xlnjFn8fvmNWe\ni0YYuvP2ouepVlFc7eih8y+Yn3V56l3/qlmXNeZ7+eJmnF0DdOM8S/fXODMul+E8T9dy/OLBgowA\nO5T4xYc9PvQkjw096dlWKgKsuuNu57HB4sdNZVJFt/3l3idIFosW27aV7XsqizSrJmatGZWK6tIw\n5mIPnX9B5KHzLzho9YKHzr/g0w+df0HTrV6wlFUISh3LGPM2Y0zd/pIrdTL/FHiVtXbSGPMJ4G5r\n7S3uE/O/DL682htPpRnxSBgBJ05rPJWueOVwcYynk0Xjw0aKvA8wMj3GeDpJ//LKVsT2O+5oeoxZ\nvCfujkyPsTu1t+i2mdERcsWixUZGmBkD4ouH5Up9Ht/zVOW5aITs+LhvFFd2fDywFdhbWMusXrCU\nVQhKHctdvLtuSnV0s9baucvo1wKfB2fmpTGtucB4TyxKYmXxOK2eWFOnKLSEnmi8aHxYItrLLHj+\ngk9099ITrfy5Nr/j9kV7mZ2dZTTjfdz1sbVFt+3qSxBOQM4rWiyRoLM3AbnFkWalPo/vearyXDRC\npKfHN4or0uMbZ3vIcYcr31ik+fyHzr/gA9UOY7qrANRq9YKX46xCsBa4DGdE8BqczvldwAiQAe5w\nd/VS4AvucV/ASdf6ubX2CmPMtTgrE3wRZ23TV+KsaHANznyQL3Ig2Ppua+0HqzkPpWZeZI0xve6M\nywHgB+6JOBInrqbl+MWDBRkBdijxiw87sf9lvLz/ZZ5tpSLAqjvuZl6+pvhxY12xotv+ztoTiBeL\nFhvYwuZ1lUWaVROz1oxKRXU1eRRXI7Ta6gWFRq21pwGPAVcBpwKvw0m/WmgT8Oc4ndn/MMYU5gO+\nEefxtVfiXEydjPPZ/8ta+3p3m8uX+oEXKnVF9wngF+7X3WKt3WOMuQgnf+y6ag/eKHMxYMXiwaR6\nc7McvaKt5vi11eK4o9Nj9C3huH41h9w8da+IqTd1VP55yjlPraT/QmfVFK/zJIu02uoFXvvcCPxy\nbuTPGPNTj+PvtNYm3fY9OHM+5hjcnGNr7SjwIWPMSuB3jTGvxVkAoOq/kEomoxhjDsPpcR93X/8P\nYNJa+0C1B1+KIJJRGhUBVq1mrKtYTX7RVtVEgJWSyWUIx/LkUqElH9ev3S9iqpqotCDPRSlB/DwF\nFUvWSEEko7gTUbxWL/jMqXf9a8X36BYsvHq5tfYpY8zbcIYTvw6cbq397ILVCxbu44c4w5E3uYud\nfoUDQ5cvtdb+tbt23IM4I35pnCV35q7+5oYub7fW/p67z//CWf3mbThDl3uAC621f2KM6cEZUr0H\nWG+tvcoYsxGnU41YayvuA0rO7LHWvoizsvjc6+9VerBmE+0Ma+JJwLrCXUUnVPi11eS4sbjnkj+l\njuvXHopGi06oqObzBHkuGsHvPMlB5lYpOB9nyO4F4C6CX73gGnd0LoT/6gUf4cDqBYtYa/cZY24A\n/hOnU1yGs05dZ5m13A2c5a5eEMEZKXwe+IYx5lU4nefTwGHA7jL3uUhgqxfUmrIuD2jGulRTeVRT\neVq1pkqzLlv1OTp3BfGrrLXXu7Pxf4IzuaXoGqWNUNWzGiIiUj23c/t1o+tYKmtt1hizwhizFWfG\n5cM4V3dNRR2dBMrvXo3fEj6ltq1GNcf1a8smk6R37SK6YQOR+OJHA+byN3O5xfcNq9HI+3uVCup7\nG+QyPeLNWvt+4P2NrsOPOjoJhF/m4cxsruiirF2RrsDyEv0Wgy11XCiedTmby/H8xz9GZvcuyOch\nFKJr/QaOuPqDhLq6AsuzbMWczKC+t8rYFD/ha6+9ttE1lGVyMnNtvY+5YkWUycnmi2FqxroW1jT0\nzX9m7L57yU9NAZCfmmL6mWfIT0/x2ekfszv14nxSySyzJDNJnhx+ilev/z3fbVe87MSKa7rhkX+o\n+LiTv3yyaNu+f/s2mReeh7n73bOz5CbGST3+GL1nvJZ/ffq7PLDrQaZy0wBM5ab5zcTzTGXTbF5V\nefBCrfZbz5+ncr+3S62pVj8zfsqpacWKaMs+dtXOFNUvNeeXeTixbStDY96PB+1J7WUiNRJIXmIq\nk2JPam9Fx01u3Upqa5Gatj5KZtcLnm2Z3buYHB8OJM+yFXMyg8rCVMamlKKOTmrOL/MwNzLCsqkZ\nz7Y8efbs2VkyL7ESu93hykqOmxsdITtarKbRA1dyi3acZ+yZp0vmWVainJzMZlNOFmYz7Vfahzo6\nqbm5zEMv4USCqWXej9iECLFu3cai21aTl7g+tpZQkR/3UscN9yWI9BWrqQ86iswoD4XoPeY4+qK9\nns3V5FnO5WTWer9B8vu5qOZ7G9R+pX2oo5Oa88s8XDmwhf5e7wi/dbG1rIwlAslLjHXFWBdb69lW\n6rjxLVuIbSlS05aT6NrgHVfYtX4Dy3tWBZJn2Yo5mUFlYSpjU0rRZBQfzTjpA5qzroU1LT9+M/np\nKbLjE+TT00QSq1h56qn0X3gJv7fuZJ4cfor9mf3MMkuIEIfF1nHllncSDoV9t+1YwgrwC2s65SVb\nKj7uis0vK9rWc+qrST3+GLlU0hnGDIXo2nA4R1z9QTrCYV7at5GpbJpkJkk6mybR3ccpa092MjQ7\nKv9bs3C/09k0qyrcbz1/nsr93i61plr9zPjRZJTWpWQUH82YzgDNWVexmhr5HF2xmhr5HF2x/M1q\nVPscXSN+nkp9byutKcjn6IJMRpFg6Tk6CZRf5mGsK4ZJFF8xIqi8xGqO69cWiceJHH980f365W9W\noxVzMoP63ipjU7zoHp2IiLQ1dXSHiEwuw9DkcN2fr6rmuOMje7E/v5fxEe/n30odd29qqOafN59O\nkxkc9Hw2y69NRBpHQ5dtrlExUdUcd3oyxWPXXsnKkWlCOGtEPZXo5uXX3kj38sX31Gp1XD+VxoMp\nfkqk8dTRtblv7byHB3Y9OP96JD06//rCTec15XEfu/ZK+kam51+Hgb6RaR679kpO+X9fCOy4fobu\nvJ2x++6df50dHj7odbG2NZe8ueJjikhtaOiyjTUqJqqa446P7GVlQSdXaOXotO8wZlCf1z9iaivJ\nrY8WaVP8lEgzUEfXxhoVE1XNcffufKLoD2Vo1mkP4rh+fCOmRkbIKX5KpKmpo2tjjYqJqua4azee\nUCSREvIdTnsQx/XjGzGVSBBW/JRIU1NH18YaFRNVzXF7EmuZSHR7tk30ddOT8I7xqva4fvwjprYQ\n33JSkTbFT4k0A01GaXNv2ngu4NyjGpkeI9HdywmrN8+/34zHffm1NzqzLkenCc06V3ITfc6sy6Uc\nd3R6jL4afd652ZWpbdvIjo4Q6UsQGxiYf79Um4g0jiLAfDRj1BZUVle1MVGV1lTNccdH9rJ35xOs\n3XiC75Wcl6DitiqNB5vTjD9Tqqk8igBrXbqiO0Q0KiaqmuP2JNbS88qldXAHHTeAuK1K48FEpHF0\nj05ERNqaOjoREWlr6uikapXmSpbKwQwqn1OZlO1L31vxont0UrFKcyVLbdeIvEplUrY2fW/Fjzo6\nqViluZKltmtEXqUyKVubvrfiR0OXUpFKcyVLbZfKpBqQV6lMylam762Uoo5OKlJprmSp7Xan9tY/\nr1KZlC1N31spRR2dVKTSXMlS262Pra1/XqUyKVuavrdSijo6qUiluZKltot1xRqQV6lMylam762U\noskoUrFKcyVL5WAGlc9ZTl6ltCZ9b8WPsi59NGPeHjRfXZXmSpbKwaw2n7PYeSonkzIozfa9g/aq\nKcjvrbIuW5eu6KRqleZKlsrBDCqfU5mU7UvfW/Gie3QiItLW1NE1mWoijIKKzCrnuMUiwPxqasW4\npumpJHufs0zXeFWEUlrxXIk0Cw1dNolqIoyCiswqxe+4QNG20CwtF9eUncnw8C030G2fY0Uqy+5Y\nhGlzJKe8/SoincGs1A6KthKphcA6OmNMJ3ArcBQQBT5mrb27oP09wNuBIfetv7DW2qDqaXbVRBgF\nFZlVit9xgaJtp29Ntlxc08O33ED/o7+efx1PZYk/+mse5gZOveJDgR1X0VYi1Qty6PJPgGFr7auB\nc4DPLmg/CXiLtfYM998h28lVE2FUaRRXtfyO+/jQdh4bfNKz7Zd7nyDZYnFN01NJuu1znm3d9rnA\nhjEVbSVSG0EOXd4J/Iv73x1AdkH7ScDVxpi1wD3W2o/77ayvbzmRSP2Havr7K0viWIqpPft9I4xW\nhrMs61/tWdfe1FDRyKzR6THCsTz9sdp/Bt/jpseYxftpkMzoCLklftZq1OL798KvX2BFauGPr2N5\nKgszY/QfcVjNa6rk56JS9fg5XyrVJLUSWEdnrU0BGGPiOB3eBxd8ye3A54AJ4NvGmD+01n632P5G\nRyeDKrWoej1flM9FiCQSZIeHF7VF+hJM5CKkCuoorCuXC9EX7WUkPbpo277uXnKp0JKn/ZfD97jR\nXmZnZxnNLO4Iu/oShBOQK/OzVqNm37/OXvbHIsQ9OrvJWAQ6e8s+zlJqWurPRaXa6Tm6IJX5HF2d\nqpGlCHTWpTHmcOBHwNestd8oeL8D+Iy1dp+1NgPcAwwEWUszqybCqNIormr5HffE/s28fM3LPNt+\nZ+0JxFssrql7WZxpc6Rn27Q5ku5lwfxyU7SVSG0EORnlJcAPgHdZa3+4oHkl8KQx5nhgP3AmzsSV\nQ1Y1EUZBRWYt5bjFIsC8agod67S1UlzTKW+/iodxZl0uT2WZLJh1GSRFW4lUL7AIMGPM3wEXA08V\nvH0zsMJa+yVjzJ8C7wbSwA+ttdf47e9QiQArJ8KoWF3VRmZVyi8CzK+moKO4gvj+TU8lGRt8kd41\nh1V0Jdeq0Vb11qo1KQKsOSnr0kcz/p8NmrMu1VQe1VSeVq1JHV1zUjKKiIi0NXV0IiLS1tTRNZlG\n5VVWI5VJ8cRvLalMqtGliIgsoqzLJtGovMpqZLIZbtz6efak9pInT4gQ62JruXLLO+mK1G8yjIiI\nH13RNYm53MiR9CizzM5nQ35r5z2NLq2oG7d+nt2pF8mTByBPnt2pF7lx6+cbXJmIyAHq6JpAo/Iq\nq5HKpNiT2uvZtie1V8OYItI01NE1gfF0smhu5Mj0GOPp5ppmDbDbHa704lzZeXeCIiL1po6uCfRE\n4/RFez3bEt299ESbLz9vfWwtoSI/PiFCrI+trXNFIiLe1NE1gUblVVYj1hVjXZHObF1sLbGuWJ0r\nEhHxpo6uSbxp47mcseE0VnX30UEHq7r7OGPDaYHnVVbjyi3vZH3ssPkrO+dK7jCu3PLOBlcmInKA\nHi9oEuFQmAs3ncf5x57TkLzKSnRFunj/K/+SVCZFKjJBLLtSV3Ii0nTU0TWZrnAX/ctXNbqMJYl1\nxTi6f13TZROKiICGLkVEpM2poysiPZNjz779pGdyjS7lIJlchr2poaZ6tq4ZawpKK0a0iRzqNHS5\nQC6f5477d7JtxxAjyTSJeJSBTf1cfOZGwqHG/V3QjBFhzVhTUA6lzyrSbtTRLXDH/Tu575Fd86+H\nJ9Lzry89a1OjypqPCJszFxEGcOGm81RTwA6lzyrSbjR0WSA9k2PbjiHPtm079jVsGLMZI8Kasaag\nHEqfVaQdqaMrMJ5KMzKR9mwbTU4znvJuC1ozRoQ1Y01BOZQ+q0g7UkdXoCcWJbEy6tnWF++mJ+bd\nFrRmjAhrxpqCcih9VpF2pI6uQLQzzMCmfs+2gU2riXY2ZtJBM0aENWNNQTmUPqtIO9JklAUuPnMj\n4NyTG01O0xfvZmDT6vn3G2UuCuyJfdsZnR6jr7uXE1ZvbmhEWDPWFJTCzzoyPUaijT+rSLvpmJ2d\nbXQNZRkaSta10PRMjnBXJ7nMTMOu5LxkchnCsTy5VKhpriSasSaA/v54zdNaMrlMVRFtQdRULdVU\nnnJq6u+Pd9SpHFkCDV0WEe0Ms271iqbq5MAZRlsb62+qDqUZawrKXETbofBZRdqFOjoREWlr6uhE\nRKStqaOTtjQ9leSFX/+S6aml3+dRnqVIe9GsS2kr2ZkMD99yA932OVaksuyPRZg2R3LK268i0ul/\nX015liLtSVd00lYevuUG+h/9NfFUlhAQT2Xpf/TXPHzLDSW3ncuzHEmPMsvsfJ7lt3beE3zhIhIY\ndXTSNqanknTb5zzbuu1zvsOYyrMUaV/q6KRtjA2+yIpU1rNteSrL2OCLRbdVnqVI+1JHJ22jd81h\n7I9533aejEXoXXNY0W2VZynSvtTRSdvoXhZn2hzp2TZtjqR7WfHOSnmWIu1LHZ20lVPefhVDJx1L\nMhYhByRjEYZOOpZT3n5VyW3ftPFczthwGqu6++igg1XdfZyx4TTlWYq0OD1eIG0l0tnFqVd8yJl4\nMjMGnb2+V3KFwqEwF246j/OPPaeqPEsRaS7q6KQtdS+L03/EYRUFA8/lWYpIe9DQpYiItDV1dCIi\n0tbU0YmISFtTRyciIm1NHZ2IiLQ1dXQiItLW1NGJiEhbU0cnIiJtTR2diIi0NXV0IiLS1tTRiYhI\nW1NHJyIibU0dnYiItDV1dC0mk8uwNzVEJpdpdCkiIi0hsGV6jDGdwK3AUUAU+Ji19u6C9jcAHway\nwK3W2puDqqUd5PI5vrXzHh4f2s5oeoy+aC8n9m/mTRvPJRwKN7o8EZGmFeQV3Z8Aw9baVwPnAJ+d\na3A7wU8DrwNOB95hjHlJgLW0vG/tvIcHdj3ISHqUWWYZSY/ywK4H+dbOexpdmohIUwuyo7sT+JD7\n3x04V25zjgd2WmtHrbUZ4EHgNQHW0tIyuQyPD233bHti33YNY4qI+Ahs6NJamwIwxsSBfwE+WNC8\nEhgveJ0Eevz219e3nEik/kN0/f3xuh9zob2pIUbTY55to9NjhGN5+mONr7MZztVCqqk8qqk8zViT\nlBZYRwdgjDkc+DbweWvtNwqaJoDCn5g44P2b3DU6Oln7Akvo748zNJSs+3EXyuVC9EV7GUmPLmrr\n6+4llwoxNNXYOpvlXBVSTeVRTeUppyZ1hM0psKFL957bD4CrrLW3Lmj+FXCcMSZhjOnCGbb8WVC1\ntIu0vYkAAAhCSURBVLqucBcn9m/2bDth9Wa6wl11rkhEpHUEeUX3fqAP+JAxZu5e3c3ACmvtl4wx\nfwV8H6ezvdVauzvAWlremzaeCzj35Eanx+jr7uWE1Zvn3xcREW8ds7Ozja6hLENDyboX2ozDJ5lc\nhnAsTy4VaqoruWY8V6qpPKqpPGUOXXbUqRxZAj0w3mK6wl2sjfU3VScnItLM1NGJiEhbU0cnIiJt\nTR2diIi0NXV0IiLS1tTRiYhIW1NHJyIibU0dnYiItDV1dCIi0tbU0YmISFtTRyciIm1NHZ2IiLS1\nlgl1FhERqYSu6EREpK2poxMRkbamjk5ERNqaOjoREWlr6uhERKStqaMTEZG2po5ORETaWqTRBTQT\nY8wa4FHgbGvtUwXvvwd4OzDkvvUX1lpbh3q2AhPuy2ettX9W0PYG4MNAFrjVWntz0PWUUVOjztPV\nwHlAF/B5a+0/FrQ16jz51VT382SMeRvwNvdlN/AKYK21dsxtr/t5KqOmRpynTuCrwFFADvhfC34X\nNOTnSaqjjs7l/oB/EZjyaD4JeIu19tE61tMNdFhrz/Bo6wQ+DfwusB94yBhzt7X2t42qydWI83QG\n8PvAqcBy4MqCtkadp6I1uep+nqy1XwG+4tb3OZxf0nMdSkPOk19NrrqfJ+B/ABFr7e8bY84Grgcu\ncGtsyHmS6mno8oAbgS8AL3q0nQRcbYx50P1LvR5eDiw3xvzAGHO/Meb3CtqOB3Zaa0ettRngQeA1\nDa4JGnOeXg88AXwb+A7w3YK2Rp0nv5qgMecJAGPMycBma+2XCt5u1Hnyqwkac552ABFjTAhYCcwU\ntDX0PEnl1NExP4QyZK39fpEvuR24HDgTOM0Y84d1KGsSp/N9vXvsrxtj5q7AVwLjBV+bBHoaXBM0\n5jytBk4GLiyoqcNta9R58qsJGnOe5rwfuG7Be406T3O8aoLGnKcUzrDlU8DNwN8XtDX6PEmF1NE5\nLgPONsY8gHOf4DZjzFoA9xfUZ6y1+9y/4u4BBupQ0w7gn6y1s9baHcAwsM5tmwDiBV8bB8YIXtGa\nGniehoHvW2sz7v2baaDfbWvUeSpaUwPPE8aYXsBYa3+0oKlR56loTQ08T+/B+d5twhnB+Ko7ZA8N\nPE9SHd2jA6y188MPbmd3ubV2r/vWSuBJY8zxOOPyZwK31qGsy4ATgHcaYw5z69jjtv0KOM4Yk8D5\nC/Q1OFdajaypUefpQeD/GmP+FqfTXYHT0UDjzpNfTY06T+B8/h96vN+o8+RXU6PO0ygHhitHgE4g\n7L5u5HmSKuiKrghjzKXGmHdYa8dxhlZ+BPwnsN1a+706lPCPQK8x5kHgDpxO5iK3phngr4DvAz/D\nuYm/u8E1NeQ8WWu/C2wDfo5zP+x/Axc38jyVqKlRP08ABnhm/sWBn/FG/Tz51dSo8/RpYIsx5j+B\n+90azm+C8yRV0DI9IiLS1nRFJyIibU0dnYiItDV1dCIi0tbU0YmISFtTRyciIm1Nz9FJ4Iwxfwxc\njfPzFgJus9Z+sob7vxbAWnutMWbWWttRYpNqjvUG4Dhr7d8WHtfj69YBn8R5yDkLvAC821r7zMKv\nFZFg6YpOAmWMWQ98CnidtfblwKuAS4wx5zW2soqdhPMwc1HGmBXAj4GfAC9zP/c/A/e6wcAiUke6\nopOgrcZJl1gODFtrU8aYtwLTxpjfxXlAdzmwD2cZlmfddJpfAafgLN/yl9baHxhjXgb8AxAD1gCf\nstb+/aIjejDGnAN8xK3lWZzlV4aNMb8BvoaT37kCNy3fPdZXcP4/8p/AH+Asu3O5u7/n3F2/0hjz\nU2A98GX36u4S4MXCkGJr7deNMWkgaox5M3Cuu80G4DPAETjpH8PAH1hrp8v5XCJSmq7oJFDW2seA\nu4BnjDE/N8bcgBOp9DxwC3CptXYLzlVf4dpeUff9S3HyBrtw1ib7mLX2d4HX4iyhUpIxph/4BPB6\na+0ATrLFDQVfMmytfSXO6hXvd9/7KvBha+0rcJI7ItbaX7pf8wVr7Zfdr3uJW8tJwPuMMXGc4cqH\nPc7Fv1hrU+7LVwLnAK92P/u/W2tPdNteX87nEpHyqKOTwFlrr8BJhL8JOBL4L+CvgWOBu40xv8Dp\neI4p2Oxmd9tf4ORpngi8F+h2l2y5HufKrhyn4Fwx/cg91ruA4wra/8P93yeBhJtleFRB5JRfxuK/\nW2vT1tp9OFelCSAPlLpP+JC1dsJ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Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"2f79fbfa-369f-48a0-8027-ae3f747a9adf"},"source":["#Plot the distritbution of the features using histgram\n","fig = plt.gcf()\n","fig.set_size_inches(12,6)\n","plt.show()"],"execution_count":48,"outputs":[{"output_type":"display_data","data":{"text/plain":["
"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"xb-AFaG3PU0D"},"source":["## Importing alll the necessary packages to use the various classification algorithms\n"]},{"cell_type":"code","metadata":{"id":"cJVjbgAjF4M_"},"source":["from sklearn.linear_model import LogisticRegression # for Logistic Regression Algorithm\n","from sklearn import svm # for suport vector machine algorithm\n","from sklearn import metrics # for checking the model accuracy\n","from sklearn.tree import DecisionTreeClassifier # for using DTA"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"8LizCSuWF4NA"},"source":["df.shape"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"WW5Hp1fFF4NC"},"source":["Now, when we train any algorithm, the number of features and their correlation plays an important role. If there are features and many of the features are highly correlated, then training an algorithm with all the featues will reduce the accuracy. Thus features selection should be done carefully. This dataset has less featues but still we will see the correlation.\n"]},{"cell_type":"code","metadata":{"id":"YABeXMklF4ND","colab":{"base_uri":"https://localhost:8080/","height":270},"executionInfo":{"status":"ok","timestamp":1635011174157,"user_tz":-330,"elapsed":819,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"a9537d5f-a224-42a1-b4ae-f10891512872"},"source":["plt.figure(figsize=(8,4))\n","sns.heatmap(df.corr(), annot=True, cmap='cubehelix_r') # draws heatmap with input as correlation matrix calculated by df.corr() \n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"gsd6QaoaF4NE"},"source":["Observation--->\n","The Sepal Width and Length are not correlated The Petal Width and Length are highly correlated\n","We will use all the features for training the algorithm and check the accuracy.\n","\n","Then we will use 1 Petal Feature and 1 Sepal Feature to check the accuracy of the algorithm as we are using only 2 features that are not correlated. Thus we can have a variance in the dataset which may help in better accuracy. We will check it later.\n","\n","Steps To Be followed When Applying an Algorithm\n","\n","Split the dataset into training and testing dataset. The testing dataset is generally smaller than training one as it will help in training the model better.\n","\n","Select any algorithm based on the problem (classification or regression) whatever you feel may be good.\n","Then pass the training dataset to the algorithm to train it. We use the .fit() method\n","Then pass the testing data to the trained algorithm to predict the outcome. We use the .predict() method.\n","We then check the accuracy by passing the predicted outcome and the actual output to the model."]},{"cell_type":"markdown","metadata":{"id":"QAD_cNirF4NF"},"source":["# Splitting The Data into Training And Testing Dataset"]},{"cell_type":"code","metadata":{"id":"ZqSRd9GzF4NF","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635011177764,"user_tz":-330,"elapsed":6,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"58101a90-1ffd-45b9-8dbe-3ef9e5f914a2"},"source":["from sklearn.model_selection import train_test_split\n","train, test = train_test_split(df, test_size=0.3) # our main data split into train and test\n","# the attribute test_size=0.3 splits the data into 70% and 30% ratio. train=70% and test=30%\n","print(train.shape)\n","print(test.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(105, 5)\n","(45, 5)\n"]}]},{"cell_type":"code","metadata":{"id":"yO2J2FpjF4NG"},"source":["train_X = train[['sepal_length','sepal_width','petal_length','petal_width']] # taking the training data features\n","train_y = train.species # output of the training data\n","\n","test_X = test[['sepal_length','sepal_width','petal_length','petal_width']] # taking test data feature\n","test_y = test.species # output value of the test data"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"yR9D2qgQF4NG","colab":{"base_uri":"https://localhost:8080/","height":205},"executionInfo":{"status":"ok","timestamp":1635011258352,"user_tz":-330,"elapsed":749,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"f3bb7968-3684-416f-9a42-ab5fcbfff8f6"},"source":["train_X.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal_lengthsepal_widthpetal_lengthpetal_width
244.83.41.90.2
895.52.54.01.3
134.33.01.10.1
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"],"text/plain":[" sepal_length sepal_width petal_length petal_width\n","3 4.6 3.1 1.5 0.2\n","45 4.8 3.0 1.4 0.3\n","140 6.7 3.1 5.6 2.4\n","46 5.1 3.8 1.6 0.2\n","53 5.5 2.3 4.0 1.3"]},"metadata":{},"execution_count":37}]},{"cell_type":"code","metadata":{"id":"5sFmts-IF4NI","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635011269110,"user_tz":-330,"elapsed":486,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"5892e853-6e9a-4f5e-e684-8c34c4822f55"},"source":["train_y.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["24 setosa\n","89 versicolor\n","13 setosa\n","64 versicolor\n","25 setosa\n","Name: species, dtype: object"]},"metadata":{},"execution_count":38}]},{"cell_type":"markdown","metadata":{"id":"S_w4Me2bF4NL"},"source":["## Logistic Regression "]},{"cell_type":"code","metadata":{"id":"gOQ5JrqrF4NL","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635011271869,"user_tz":-330,"elapsed":7,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"73cfaedd-f9da-45c3-9bc3-32cfd91d6915"},"source":["model = LogisticRegression()\n","model.fit(train_X, train_y)\n","prediction = model.predict(test_X)\n","print('The accuracy of Logistic Regression is: ', metrics.accuracy_score(prediction, test_y))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["The accuracy of Logistic Regression is: 0.9777777777777777\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n"," https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n"," https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n"," extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"]}]},{"cell_type":"markdown","metadata":{"id":"e1NNX-EGF4NJ"},"source":["## Support Vector Machine SVM"]},{"cell_type":"code","metadata":{"id":"zSJmVzqnF4NK","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635011282010,"user_tz":-330,"elapsed":517,"user":{"displayName":"Desu Venkata Manikanta","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10818416874497828882"}},"outputId":"37f4bbd2-9e91-4fd6-f53c-3aea85881b5b"},"source":["clf = svm.SVC(kernel='linear')\n","clf.fit(train_X, train_y)\n","\n","#Predict the response for test dataset\n","prediction = clf.predict(test_X)\n","\n","print('The accuracy of Support Vector Machine is: ', metrics.accuracy_score(prediction, test_y))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["The accuracy of Support Vector Machine is: 1.0\n"]}]},{"cell_type":"markdown","metadata":{"id":"GWfemKzPF4NN"},"source":["## Decision Tree"]},{"cell_type":"code","metadata":{"id":"iRXy3EZIF4NN","outputId":"f470e075-fc92-4f3f-c343-7a8257e2c5d2"},"source":["#implementing using Decision Tree\n","#code\n","\n","print('The accuracy of Decision Tree is: ', metrics.accuracy_score(prediction, test_y))"],"execution_count":null,"outputs":[{"name":"stdout","output_type":"stream","text":["('The accuracy of Decision Tree is: ', 0.93333333333333335)\n"]}]},{"cell_type":"markdown","metadata":{"id":"uB2Co6f_F4NQ"},"source":["### We used all the features of iris in above models. Now we will use Petals and Sepals Seperately"]},{"cell_type":"markdown","metadata":{"id":"1_v6cAZMF4NQ"},"source":["### Creating Petals And Sepals Training Data"]},{"cell_type":"code","metadata":{"collapsed":true,"id":"e1Q-1b9YF4NQ"},"source":["petal = df[['PetalLengthCm','PetalWidthCm','Species']]\n","sepal = df[['SepalLengthCm','SepalWidthCm','Species']]"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Lv-nab5oF4NQ"},"source":["### For Iris Petal"]},{"cell_type":"code","metadata":{"collapsed":true,"id":"DuOqLUWZF4NQ"},"source":["train_p,test_p = train_test_split(petal, test_size=0.3, random_state=0) #petals\n","train_x_p = train_p[['PetalWidthCm','PetalLengthCm']] # taking the training data's Petal features\n","train_y_p = train_p.Species # output of the training data\n","\n","test_x_p = test_p[['PetalWidthCm','PetalLengthCm']] # taking the test data's Petal features\n","test_y_p = test_p.Species # output of the test data"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"bgNB8kaNF4NU"},"source":["### For Iris Sepal"]},{"cell_type":"code","metadata":{"id":"6hVj5MW3F4NU"},"source":["#Similarly define the split for sepals\n","#define the training and test data's Sepal features followed by the output of the training and test data\n","\n","#use naming- train_s,test_s ; train_x_s, train_y_s; test_x_s, test_y_s\n","\n","#code"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"y08e1O6aU9mx"},"source":["Implementing the algorithms just like we did on the complete dataset but separately on sepals and petals and calculating accuracy"]},{"cell_type":"markdown","metadata":{"id":"TeMWnQr6F4NV"},"source":["## SVM Algorithm"]},{"cell_type":"code","metadata":{"id":"jhlutJ78F4NV"},"source":["#code\n","print('The accuracy of the SVM using Petals is:',metrics.accuracy_score(prediction,test_y_p))\n","\n","#code\n","print('The accuracy of the SVM using Sepals is:',metrics.accuracy_score(prediction,test_y_s))"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Mli7zcq_F4NV"},"source":["## Logistic Regression"]},{"cell_type":"code","metadata":{"id":"2DqK_dFCF4NV"},"source":["#code\n","print('The accuracy of the Logistic Regression using Petals is:',metrics.accuracy_score(prediction,test_y_p))\n","\n","#code \n","print('The accuracy of the Logistic Regression using Sepals is:',metrics.accuracy_score(prediction,test_y_s))"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"aM-7Zx95F4NW"},"source":["## Decision Tree"]},{"cell_type":"code","metadata":{"id":"S8tXp-gMF4NW"},"source":["#code\n","print('The accuracy of the Decision Tree using Petals is:',metrics.accuracy_score(prediction,test_y_p))\n","\n","#code\n","print('The accuracy of the Decision Tree using Sepals is:',metrics.accuracy_score(prediction,test_y_s))"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"6ec0NUyJF4NW"},"source":["\n","\n","\n","### Question:\n","Does Using Petals over Sepals for training the data give a much better accuracy? Why?\n"]}]} \ No newline at end of file From b24c88681db685fefcfdf6c616da1badad8c0494 Mon Sep 17 00:00:00 2001 From: Mani9550 <93199718+Mani9550@users.noreply.github.com> Date: Fri, 29 Oct 2021 00:19:55 +0530 Subject: [PATCH 06/11] Updated Task-4 code as per your comments Added One pull request for each task. 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"_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + } + } + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "cPP7BfqFSgyH" + }, + "source": [ + "# K-Nearest Neighbors Algorithm\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zd0p7ZUpSgyL" + }, + "source": [ + "![example 1.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAANwAAADHCAYAAABySz3ZAAAABmJLR0QA/wD/AP+gvaeTAAAAB3RJTUUH2gwWARApxkGQoQAAF9dJREFUeJztnXmcFNW1x7/VPcMMyAwIgqgRZREQgoDgRgiCKy/ufoj6NKLG7bnGLTFq3J9L4hqXGPW5JXF5Ji68uC9oXOKuuKPiSnAX2SIqzNz3x+mWpu2ZqZ6u6nOr+nw/n/4w011T9Zuhbp17zz0LGIZhGMnHwf4OVtbWYRipx0E3B87BpdpaDCP1OPiVgyUOFpqVM4wYcdDVwbychVvi4HfamgwjtTg42sHi3IBzOSvXS1uXkXIc9NDWUG1y1u3LgsGWt3LnaGszUo6DlxyM0dZRTRwcVWTdzMoZ8eNgdO5Gu1dbS7XIWbcvSgy2vJU7V1ujkVIcPOigNfdkH6Gtpxo4OMLBojYGXN7K9dbWaaQMB2NyN5dz0OLgDm1NceOg0cHn7Qy2vJU7X1urkTIczMhZt8In+3BtXXHi4PAOrJtZOSN6HKxfYN3yrxYH/6etLS4cNIQYaPnXUgfXaGvWok5bQAo5H+he9F4GmOxgWACzFDTFzXhgEfBWiGObgXEOugTwbbyyjFTjYGwJ61Zo5aZrazSM1ODg4aK1W/FrkYOh2joNI/E4mSa1Zd0Krdxt2loNI/E4eKQD61bopRuirdfQIdAWkAYcjAOeQRwHroPDm4HnAxgbuzDDO8xLGQ3fArsBrSGOzWDeOcMwjMpxkGlvY7/Iwr04BYLbwH3Y8amDJnD/gFE/rVSkYaSIg4EpwLalPiwacEEvWdczsOPzOoABFYozjNTgIAucDvR0sGoAnxQfkynxcy1lXMPWIoaxnN1ZvkY/qdQBpQac4Q+rAFsBBwBnAIMLPlsVmAvMy73OKvrZ+3KfvwHcUvTZesBPkIDqrpGrrkFy1u1sxAvdBZjm5P9oBcxL6QdrABsA67LiwBkH7A+8C3wALCn47FNgJDCf0t7RrYGeQCPQreizNYE9gbWBQcD6wJzcZz2QRf87nf1lapTdWTGGth74DXBY4UFF+3Av7Q7ucr4ffNsWT8Co8Z3XaAC/BaYCTwMzgQvQnapvAFyHDNYngJOBVxT1eI8Tw/U+sHrRR4uBQYE8HAGzcNWiO7A54r0aDmxa8NkJwLEaotrgGURjL2AT4IuCz3oilvN+ZBprCHsATSXer0f+f3+Rf8PWcNXhLGAa8Bwy9ShkWfXlhGIecCfwUcF7PYDtgNeAu4FRCrq8ImfdzqT0gGsA9nHQJ/+GWbhoCYAfAzsCRxW8f1jpwxPH+8DPkCf35qxo/eqQvaJyvNxpYA/aX4J1AY4HjgSzcFGyI/LkPwN4kXT/bZcC9wD/KnhvIuLcOQ3oryGq2uSsW94z2RYNwH756JM03xTV5h1gZ8TCXUe4uMo0MQOYkPv6ccQDmnb2BFYKcVwdspYr6aW8nlALYtcdgndh1LByVaaADZFpwvGIVTNWpB6xgnmakEyK1JCzbkuBr1lxu6YUdcjfoHfRGi57D7SMC3fJAMguKFNn0lkVuBJYBwnheVNXjrcsLfr+ZGBj4ETgoerLiYWuwJZl/kx9HELSTCPwn9hUvDNsCzwPXK0txPCXHsAkbREpIqBGHCptYU/q0gTA3oi3cZKqknThkBC1PH2AI6ih7ama+UXL5JdI0PCWhKu16BkP1UHvMixJdhmM+KDj4yLHIdEsewH7IlPOVGM1TUrTCHxDx/VJPOXV/rDsfeDLjo91WQiaYZTmvbANEo0zHok/TC1m4YS1kbXFI7nvv9aTEhnzCdVbOwD9uMg7gbtI7AMuPLaGk9Ccx5EUGUOPwsHWFUlLSt0MrNYH3HFIJPemwI3KWozldAV2QsLHUtVpp9YH3P8APwJmawsxVmAesq6bgcw+GnTlREctDrguBV9/xvejIgw/cEhy7maIAysV1NqAOxi4VVuEURaFJRsDEr6uq5UBl0Eauuf3e4xksitwAwmeYtbKgBsKrAZMpkStQCMx/BVZ3z1AqC0P/6iVAfc64v7/SluIUREtwCHInl0is+jTvPHdHclTqrWU/1qguAZnYkirheuBVJYqWd89/SxrRSpsLQnxWopU6EoyiXakJJ1uSIjWKco6jOrQB6np2aejA4142B/xSBq1w8FIKlUiHSlpwKYYtccxwN+0RRhGLVGqGKtXZLUFRMQhyHTibW0hhirWPq0KbA/MwhbNxnJ6IdkGRsQMQ2pkDNUWYnhFX6QK9ERtIWmjN/ZHNUqzEfIw9iqxOOlruCVIgwnDKGYuUmaiEVvbV0zSHxSGkRiGIZ050xqWZqSYpAUvdwH+grQIqrXuNFmkuthQ5KEzEFnDdkc6uDQjMaQLgH8j5eYWIz3c3kP6IMxCple1mOV+MnARoUoHxkfSIjLyvcf2VtZRDXoira82R4ocrYV43t4E3kAK1M5n+cCaj5T3a0AGXvfcqycwABmkQ5GB+hkSb/pQ7vVdD+oUcxKwLtIbwgjJEORmSitDgFORYNwPgD8BP0cGSZSsgdx4VyID92XgHGD9iK/jE3VIZedttIUYuvQCDgL+iSTKnkD19xX7A4cDzwIvAb8CVq+yhmowFjhUW4ShwyDgcqRIzsVI/zQfGI6skd8HbgZG6coxqk0/4A6St95sixHAnxFHxlGEa1mrQT2wD+JouQN/HghGzFwN/FpbRAT0RXp/zwb+i+RUnsoAuyH5ZnciljnpZICp2iJ8ZCxygzZqC6mADDLA5iIPji7tH+4tGSS5dw7i8UvKA6MUAeKl3VlbiG+MA7bQFlEBoxCP43TErZ8GVgGuQrYmNlXWUgnjEA9tUh+ARhEHIK797bWFxMSPkBv2JJIbancDsLW2CKMymoHrkSmLV5HqMdAD8WTOQJxbSSOpD4rIGYtERySNoYhX7zSSFzZXCYciXtdNlHUYneRZkrd22wAJvarVSIYNgXeo2VqgyWUSkg2QJLZENoo30haizGDEmbK3so5yOQmYpi1CixuAXbRFlMFUZOtiuLYQT+gHzASO1BZSBhsh8aRpCa4oi5VIzvpnS+BVYE1tIZ7RE5mlHKQtpAwewjyWXrMR4igYrKzDV/og0SlJ2RaZBGylLcIozRBkD2q0thDP+QGyppukrMMbAni3ERZNCP8jLctgzMMxaNkWmUreDjMnQKaMcK6mx2DA1zFoKkUP4Cmknv2MKl0zyYwEbkcSad/TlaJPHXzVF9z9hEs9z0CmB/EsLo8BTpcvg0fBLSBcGYWV4au1kKiOanAVcCE22MLyMlIZ+0YkFMz36sh9gc+JqYRH3jkxn9CdR4I4LMnaSBJk/iaeR/ieZfNj0NMWhwDLgD9W8Zpp4B5ksJ2JPFh95nrgDODhOE7uS+Wr0UjqitMW0g7rI163A7SFJJQTkc3x7bSFdMAtxLgt5cuAux2p5eErdYhVmwYsVNaSVJYhfdbPRrYNfOUWYAdiirP0ZcD5zmHAA0gRGqPzzAEuQGJNfeUzZNM+lrSdpGwwa7IGUmrASgxEw1XAo8gyYqaylra4Oa4T+2DhLsTvSI1zkfXHV9pCUoJDsgsuoQZDqbQHXHekPuLHyjraYgKy3piuLSRlzES2C36mLaQdeiC5jZGiPeAmAY/hb+nt3/Dd3qARMWcCv0T/HmyLE4kh60F7DTcIKcHmI+OQUnH/1BbSJhcxiFZWAWAZb3MMnysrKoc5SL2XqcS4ZqqAB5BooouiPGn+6VJGGJWLsoLW74FrSlyjaxnniKui1/HIU9hPLmIQLczG8SSOJ8kykXNzgy9GHIyP8HRnI1WefVzLPYY4yiLVVoeE2jgkW7cjssi6K2YyH4KrA1pCHLwa0YcLDc+d98GIzxsdLcwmwxb8IqfxAhz1bEGMmh10Ax53MD6IJkl4NtKc5CdIzUufWAwch2wPfBPVSetgxMfIH9Ij1tNOedkPcV/7zbKqTyEPQ2IMzyM6S3cFsu/l24CDJNwDZTAVacfkG3VIbZLIPVSxcRGDuADHefH1AXDQ1cGXDpyDBRFOLTNIqlPs02Ef0PQQTcXPm3pL4EmSEsKVX8u1MpqjeTHGKx2KOJEAmhArFwWtwF+Rcuo+kppSerOQDAHfuBFZUySDC3D8ns3jvESRdXMxWLl1kYecb2SBT4hw0GlZuAakXW61ctjC0ghMBO7TFlIW8a/lDmK5dcvTBJwf0flfR/wIvpWDb0G2LyLzKWgNuG/ws+HfxsBzSGR7MsgyGMfcuE7voCvSJLK4rVYArOskGicKZgCbRXSuKHkVyVqPBM01nI/RJZPxeSugFC3Mpj7WpokH0nbkfDMSaxoFvg64O6huknNN8QhJ6/gZo3fSQaODz4vWbsWvBRFZuZ5IMV0jBn6Mf40uuiE93HyN7as6Dg53sKiDAeccPBPRJZ+m+v3Na4K7iG7uHxWbAPdqi/AFBw0hrFvUVu4yJCs8tWg9zddAmsn7xDpImJEh7It4kr8O8WpG+pZXyiz8tHC+BtiHZi5VicksizORqlyGHv8B3KQtogSfEFGLZa30nGlIcKhPDEXqyyeLW+lLhmlINax+wBcE3EvAn9ied5XVlcssYJi2iBLkyzZ+VOmJfEyL0OJlJMJkjraQ0NzOwTh+S0A9hU9gx1ICvqGVS9mJ4wi8Lj9YSBYp4hO2Jmm1uBMJsK54yWEDbjmfIrVVIkvFiJXpHEUrpxDQ1M5Ri4Gb2JH9qyUrAj5ANpoXaAuJgwBe7Q4tu5fxM9/CetdWcM0s4oJfVME54mABUscixwujIbth+B9vnQmjn45cVSmmMxR4AheqWvYCYCo78kDMqqLiVaRt1L+0hcRBHdAL3OWIt6kjAmTqcm0F1xwO/AG/UnNKJLsGU6D1rJCl3RuQ36k6Aw7OxYUuptqDgIuRAOEksBjatdqJprC3QMj/wFh6C2jTnZIWN4DQJRwy1WlScTn1tDKJoIzlQCur8XfWYLv4Yi4jZBH+ebAnIg0+Xqv0RBr7cM3AvxWu2x7NJGXNsAqrEoQqPVFIK60MiUVP9CzCPwu3GxH1AtQYcJ/g30aiIymJho0so1xnVwC0eBksXooMMbWK8gGNATcbqbrrEwvxbxpTmmf4lPK7DGWRnLPvcP421GjCP4daAxGlbFmgrrCQFTyUHnMKrbQynXAVzfK8xs58UfTeUw5GRKgsKnwccI1EtAyyASc4krQn2cCxhK+5Mp+AAwvfcNKnbQh+1t30ccDNIKLqBBoDrhdwtcJ1O2IJkt3sP9vwMY6f0/GNuYgsp7MDLxW9fz7ykNnM4Z0zpQn/wv6uQiKRKkZjwH0FbKNw3Y74EP9y9NpmJ24nYAekEcqKHtaAhcA8Ag5iuxXrjjgpI7EeYtG7AmdVR3Ao8iFqvlm4yNAIXv6a+MqTV8Is5Gk/W1tIaHbgIW5mLbqwBTAFWIuAj3HcT5a72K5ki63zWO4gygJbORgc+PF7DwLe1hYRJ/kBV4bHKpLeArOR/3Sfpg5vIJHqd3Xux1t1Mi924VtEc4e6c9bth6y4Xs1buZ/Goq88huBnTuJdRFQ6sQ6WfAV1z0AQYuM3yBDNNHRsBOeImjeBTZd/m5kF7gX4nnevBMHK0PpcXMIi5Hy+v6mcBaY4GBiE6y8RJ8OQmYZPZLHut7EwBnhYW0RcOBifK4VQqkTCMudH4udV+GFpC+kPvKItIo3UIwmG2j3zYsHBEw5a26lLssjBQGWZrwA/UNZQzDiiKR+hykhgC20RJbiHaPufeUEH1i3/WuqkzLsW/ZB1dKrR2vgeDOyldO32mIG0QU4bpdZuxdQB2zoYUAU9pZhEEktcJIQRRFfLMEo2BO7XFhElDibkLNjCnJVr7+Wc3k1/Bf520IkMrXCmOiRroA9+RYZnkRCedaDkHlbicOIMWodwAc8Z4OsApserqiSzkdqgnylcuz2OBi4lXIK216yDn/GLf6QGnrSesTF+FuFtgKp3ma05fkSnN7+NTnIJsKe2iBKMBh7VFpF2AmTztZ+2kBqhC/Ae32+H5QP7ABdHeULNLOd6ZCrhYx3I3pCrjGXEzbbIYLtZW0gJXgX+iWSSJJ4uSNiUjxvNawIvYfmC1eBexFlSE2jeUN8iT5AxihraYg5S8m6qtpCUMw554NpMokqcDRyjLaINBgPP4qcnNS3cCmyuLaINfoifJSgqYiB+Fyi9HlljGNEzEnhMW0Q7XAb8XFtErTEceBJby8XB35CkWV95D1g96pPajdQ+ryHriwO0haSMrZG12z3aQtpgXaRshW9NQyOhHj97guVpRjyWfbSFpIQGYCawlraQdmjEv+JKkdEP6ZTis7XdDbhGW0RKOBE4TltErfMgMFlbRAfcj5/VxpLEaOAFImrfa3SeAxGvkM/0RxIkfctITgpNyNR8nLaQDphAksoldpKVSUahlu2BR0hK4w+/uB44XFtECGbiZzBGzXI+fpYI95n9gdvwP4hgfeBFbRHGinQBHsfPdBIf2QwJ4QvTHlmbi4FDtUVUk7HAatoiQtAHSeHZXluI54wD3kLC5JJAE36mCcXGKcB/a4sIyQDEibKRthBPGYxEa9jfx2NWA+aSlC42Eg/4LrCBthDPWBuJ0vE1MNko4FpgP20RZbAJ8D52c+VZDymZvqO2kDKYCOysLUKLnviZlNoew5GqU7tqC1FmU8Tib9rRgZ7xILYeTxxrIk37jtQWosSuiGVbT1tImWyIeFF937IwStAbeAC4BX+b1kdNA+JOfwFZuyWNXwK7aIvwgT1IZomDDBKg+xb+hzFVyjrA80hYno9NNo0yGIPsdSVtPZdnMuIW/zWyWZ4mAmBfpPaLb+2ljAr4X+AQbREV0BdpdfQK6WkSMgqJtLkb/fZWlZCEyJeqMwA4QVtEBExCBt2fSUYkTSl6AhcijpEkTvULqUf2CUdqCzHiowtwLLKxfxHJSfPpBZyK6P4d0ps96RwB3KAtwqgOzcDxSJb7FcAgXTltsioywD4Ezsl9nwZ6I52E+msL8ZkA2AG/yzCUy0rInt07SDb53uhbjwYk4uJ25IFwKnKDpokAqTlpdMDfSWfqRAZZ412DWJO/IGFR1VrUr4SUq7ssd/2bkaiL+ipdv6ZI0u76D4Cn8LcBSBR0RQbbFCQ86nOkDfJDSDTEHMI1VmyPfkhF4YlIrtpAxOt4L7JhP7/C8xvtkKQBB1L7pA/JSeGplCHIft4kxKO2GhIo/WbutTD3WgAsQvo11CF5XT1z/3ZHNqiHIoPrC8RD9zAykF+m8kGcBC4D7gTu0BSRtAEXUBs3R1tkkXqOQ5HB2AMZUD0QZ0wjsBT4EliMDMIFyDpxVu7fpVVXrc9OwElIbt63yloMI9X0Q6biw7WFJJ1J1Fg6vNEpuiBtpI0KOQcJ/TIMowpkEQ9ereafGe3TTVtAGumDtD1K04a4UTnDECdRUmrjGEZisRKGhlFFbgIO0hZRCzQhYVE2d69tvPZcp2ntswj4GAlPSluGtRGef2sLqCUC4HKkU4tROxyB5O0ZCmTxu4WxES1nIHGhtpQwjJg5A3gMiSU1PCAADsNa3KaVyXjuJKk1AuAPwH3oZ1MbRk0QIPlzLyBbB0ZyaQb20RZhhGOCtgCjItZE2gGfoqzDMFLPWsAHwF7aQozOMQWZZma1hRihyJC8rjxGAc1IRMo9pK8MXFpIUxSUgThTjsUq8PrIaGAmsm4zUoY9Sf0hAI5Gug5N0ZUSD0ltBRUlrQVfj0bWdc8paal1GpFSfhsCnyprMarAJKRH9TlYtrBhVIUm4BKktLoRLxsD/8Ai/Q0s+jxO+iDJwm8C2yhrMTykP3AecqMYldMHqbZmicJGSboDpwEfISkhzbpyEkc3rEUUYC7xsCxGatOPRHobJK0ngxbNwK+AN0h+m2LDEyYjzTWM73MNkh41UFuIkR6mIV1p7kW6tNYqGaQ7jWHEThbYDvi1thAF1gROBt5G+s3Z/qWhxnXAiUgvt7SyE3A2MFhbiGGMRNKA3gCuVNZSCVlgE8RT+zSwuq4cw+iY4mI35wEnIOFkvm2yF8fY7otEhBwHjMG8tJ3G/nB6TEA8nOOBJcDOBZ8NQioIf1xFPVvlNIxCpocDkO0QI0JswPnJyYj3sxcSTL1+wWdDkayGz5Dy7q+zfGDUIVExIBZ1HjC34GcPAEYgJQu+AXYt+GwzZKr4MvAatdkLPHZswPlNFuiLRLjkGY9M8Xojg+pI4JXcZ6sDdyODpQW4Gin9nmdPZFDOAWYjeWeGYRjp5P8BB6S3Szw1L4kAAAAASUVORK5CYII=)In this Jupyter Notebook we will focus on $KNN-Algorithm$. KNN is a data classification algorithm that attempts to determine what group a data point is in by looking at the data points around it.\n", + "\n", + "An algorithm, looking at one point on a grid, trying to determine if a point is in group A or B, looks at the states of the points that are near it. The range is arbitrarily determined, but the point is to take a sample of the data. If the majority of the points are in group A, then it is likely that the data point in question will be A rather than B, and vice versa.\n", + "
\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TyGHDf4NSgyM" + }, + "source": [ + "# Imports" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "iIEvA0xjSgyN" + }, + "source": [ + "import numpy as np\n", + "from tqdm import tqdm_notebook\n", + "import matplotlib.pyplot as plt" + ], + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rc8ruF56SgyO" + }, + "source": [ + "# How it works?\n", + "\n", + "We have some labeled data set $X-train$, and a new set $X$ that we want to classify based on previous classifications\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rGbvEXbvSgyO" + }, + "source": [ + "## Seps" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "B-nf9G4ZSgyP" + }, + "source": [ + "### 1. Calculate distance to all neighbours\n", + "### 2. Sort neightbours (based on closest distance)\n", + "### 3. Count possibilities of each class for k nearest neighbours \n", + "### 4. The class with highest possibilty is Your prediction" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LuWwKdFrSgyP" + }, + "source": [ + "# 1. Calculate distance to all neighbours\n", + "\n", + "Depending on the problem You should use different type of count distance method.\n", + "
\n", + "For example we can use Euclidean distance. Euclidean distance is the \"ordinary\" straight-line distance between two points in D-Dimensional space\n", + "\n", + "#### Definiton\n", + "$d(p, q) = d(q, p) = \\sqrt{(q_1 - p_1)^2 + (q_2 - p_2)^2 + \\dots + (q_D - p_D)^2} = \\sum_{d=1}^{D} (p_d - q_d)^2$\n", + "\n", + "#### Example\n", + "Distance in $R^2$\n", + "\n", + "\n", + "\n", + "$p = (4,6)$\n", + "
\n", + "$q = (1,2)$\n", + "
\n", + "$d(p, q) = \\sqrt{(1-4)^2 + (2-6)^2} =\\sqrt{9 + 16} = \\sqrt{25} = 5 $\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vlvNZqiJSgyQ" + }, + "source": [ + "## Code" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EvmQi6nsSgyR" + }, + "source": [ + "def get_euclidean_distance(A_matrix, B_matrix):\n", + " \n", + " C=[]\n", + " for i in A_matrix:\n", + " temp=[]\n", + " for j in B_matrix:\n", + " temp.append(np.sum((i-j)**2))\n", + " C.append(temp)\n", + " \n", + " ## Use the distance formula for the matrices using numpy functions\n", + " ## C is the sum of the squares of the distances\n", + "\n", + " return np.sqrt(C)\n" + ], + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GABzTa_0SgyS" + }, + "source": [ + "## Example Usage" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "W6b8yBSoSgyS", + "outputId": "cdbad526-4c41-43ad-df70-ba8dee28c8be" + }, + "source": [ + "X = np.array([[1,2,3] , [-4,5,-6]])\n", + "\n", + "X_train = np.array([[0,0,0], [1,2,3], [4,5,6], [-4, 4, -6]])\n", + "\n", + "print(\"X: {} Exaples in {} Dimensional space\".format(*X.shape))\n", + "print(\"X_train: {} Exaples in {} Dimensional space\".format(*X_train.shape))\n", + "\n", + "\n", + "print()\n", + "\n", + "print(\"X:\")\n", + "print(X)\n", + "\n", + "print()\n", + "\n", + "print(\"X_train\")\n", + "print(X_train)\n" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "X: 2 Exaples in 3 Dimensional space\n", + "X_train: 4 Exaples in 3 Dimensional space\n", + "\n", + "X:\n", + "[[ 1 2 3]\n", + " [-4 5 -6]]\n", + "\n", + "X_train\n", + "[[ 0 0 0]\n", + " [ 1 2 3]\n", + " [ 4 5 6]\n", + " [-4 4 -6]]\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "kB8IZcDpSgyT", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3346c864-75da-451f-e6b6-7ced1b54988c" + }, + "source": [ + "## Initialize the distance matrix using the get_euclidean_matrix\n", + "\n", + "C = get_euclidean_distance(X, X_train)\n", + "\n", + "## Euclidean distance b/w row i of X and row j of X_train is available as C[i][j]\n", + "\n", + "\n", + "## Print Distance between first example from X and first form X_train\n", + "print(f\"Distance between first example from X and first form X_train {C[0,0]}\")" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Distance between first example from X and first form X_train 3.7416573867739413\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vbaJfBihSgyT" + }, + "source": [ + "# 2. Sort neightbours\n", + "\n", + "In order to find best fitting class for our observations we need to find to which classes belong observation neightbours and then to sort classes based on the closest distance\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b1VLHUj2SgyU" + }, + "source": [ + "## Code" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "na0G1o_ASgyU" + }, + "source": [ + "def get_sorted_train_labels(distance_matrix, y):\n", + " \"\"\"\n", + " Function sorts y labels, based on probabilities from distances matrix\n", + " Args:\n", + " distance_matrix (numpy.ndarray): Distance Matrix, between points from X and X_train, size: N1:N2\n", + " y (numpy.ndarray): vector of classes of X points, size: N1\n", + "\n", + " Returns:\n", + " numpy.ndarray: labels matrix sorted according to distances to nearest neightours, size N1:N2 \n", + "\n", + " \"\"\"\n", + "\n", + " labels=[[0 for i in range(distance_matrix.shape[1])] for j in range(distance_matrix.shape[0])]\n", + " for i in range(distance_matrix.shape[0]):\n", + " temp=[]\n", + " for j in range(distance_matrix.shape[1]):\n", + " temp.append([distance_matrix[i][j],y[j]])\n", + " temp.sort()\n", + " for j in range(distance_matrix.shape[1]):\n", + " labels[i][j]=temp[j][1]\n", + " return np.array(labels)\n", + "\n", + " \n" + ], + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "U0I8eltDSgyV" + }, + "source": [ + "# 3. Count possibilities of each class for k nearest neighbours \n", + "\n", + "In order to find best class for our observation $x$ we need to calculate the probability of belonging to each class. In our case it is quite easy. We need just to count how many from k-nearest-neighbours of observation $x$ belong to each class and then devide it by k \n", + "

\n", + "$p(y=class \\space| x) = \\frac{\\sum_{1}^{k}(1 \\space if \\space N_i = class, \\space else \\space 0) }{k}$ Where $N_i$ is $i$ nearest neightbour\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j0ZtOC38SgyV" + }, + "source": [ + "## Code" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "y2aaG2GdSgyV" + }, + "source": [ + "def get_p_y_x_using_knn(y, k):\n", + " \"\"\"\n", + " The function determines the probability distribution p (y | x)\n", + " for each of the labels for objects from the X\n", + " using the KNN classification learned on the X_train\n", + "\n", + " Args:\n", + " y (numpy.ndarray): Sorted matrix of N2 nearest neighbours labels, size N1:N2\n", + " k (int): number of nearest neighbours for KNN algorithm\n", + "\n", + " Returns: numpy.ndarray: Matrix of probabilities for N1 points (from set X) of belonging to each class,\n", + " size N1:C (where C is number of classes)\n", + " \"\"\"\n", + "\n", + " probabilities_matrix=[]\n", + " for i in y:\n", + " temp={}\n", + " for j in range(k):\n", + " if i[j] in temp.keys():\n", + " temp[i[j]]+=1/k\n", + " else:\n", + " temp[i[j]]=1/k\n", + " probabilities_matrix.append(temp)\n", + " return probabilities_matrix\n" + ], + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ThEbAnXISgyW" + }, + "source": [ + "# 4. The class with highest possibilty is Your prediction" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_i7NTtN4SgyW" + }, + "source": [ + "At the end we combine all previous steps to get prediction" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OzK6rY8mSgyW" + }, + "source": [ + "## Code" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "DaYqr_i6SgyW" + }, + "source": [ + "def predict(X, X_train, y_train, k, distance_function):\n", + " \"\"\"\n", + " Function returns predictions for new set X based on labels of points from X_train\n", + " Args:\n", + " X (numpy.ndarray): set of observations (points) that we want to label\n", + " X_train (numpy.ndarray): set of lalabeld bservations (points)\n", + " y_train (numpy.ndarray): labels for X_train\n", + " k (int): number of nearest neighbours for KNN algorithm\n", + "\n", + " Returns:\n", + " (numpy.ndarray): label predictions for points from set X\n", + " \"\"\"\n", + " distance_matrix=distance_function(X,X_train)\n", + " prob=get_p_y_x_using_knn(get_sorted_train_labels(distance_matrix, y_train), k)\n", + " \n", + " prediction=[]\n", + " for i in prob:\n", + " prediction.append(max(zip(i.values(), i.keys()))[1])\n", + " \n", + " return np.array(prediction)\n", + " return prediction" + ], + "execution_count": 14, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "i9kzyASWSgyX" + }, + "source": [ + "# Accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "v8bNPTPZSgyX" + }, + "source": [ + "To find how good our knn model works we should count accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dgFCnJ14SgyX" + }, + "source": [ + "## Code" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2ySpyThlSgyX" + }, + "source": [ + "def count_accuracy(prediction, y_true):\n", + " \"\"\"\n", + " Returns:\n", + " float: Predictions accuracy\n", + "\n", + " \"\"\"\n", + " N1 = prediction.shape[0]\n", + " \n", + " accuracy=np.sum(prediction==y_true)/len(prediction)\n", + "\n", + " return accuracy" + ], + "execution_count": 18, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b5g7YFY2SgyX" + }, + "source": [ + "## Example usage" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "uLqCqmJNSgyY", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ba253ddc-ec5c-4461-bd70-d191fa1e006c" + }, + "source": [ + "y_true = np.array([[0, 2]])\n", + "\n", + "predicton = predict(X, X_train, [2,2,0,2], 3, get_euclidean_distance)\n", + "\n", + "\n", + "print(\"True classes:{}, accuracy {}%\".format(y_true, count_accuracy(predicton, y_true) * 100))" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "True classes:[[0 2]], accuracy 50.0%\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "--WUpIcxSgyY" + }, + "source": [ + "# Find best k" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "itkcD0DlSgyY" + }, + "source": [ + "Best k parameter is that one for which we have highest accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7GYEUBnnSgyY" + }, + "source": [ + "## Code" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Q6OhNBOoSgyY" + }, + "source": [ + "def select_knn_model(X_validation, y_validation, X_train, y_train, k_values, distance_function):\n", + " \"\"\"\n", + " Function returns k parameter that best fit Xval points\n", + " Args:\n", + " Xval (numpy.ndarray): set of Validation Data, size N1:D\n", + " Xtrain (numpy.ndarray): set of Training Data, size N2:D\n", + " yval (numpy.ndarray): set of labels for Validation data, size N1:1\n", + " ytrain (numpy.ndarray): set of labels for Training Data, size N2:1\n", + " k_values (list): list of int values of k parameter that should be checked\n", + "\n", + " Returns:\n", + " int: k paprameter that best fit validation set\n", + " \"\"\"\n", + "\n", + " accuracies = []\n", + "\n", + " for k in tqdm_notebook(k_values):\n", + " prediction = predict(X_validation, X_train, y_train, k, distance_function)\n", + "\n", + " accuracy = count_accuracy(prediction, y_validation)\n", + " accuracies.append(accuracy)\n", + "\n", + " best_k = k_values[accuracies.index(max(accuracies))]\n", + "\n", + " return best_k, accuracies\n" + ], + "execution_count": 20, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nGtIjD0WSgyY" + }, + "source": [ + "# Real World Example - Iris Dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-o6MHMtKSgyZ" + }, + "source": [ + "\n", + "\n", + "\n", + "\n", + "This is perhaps the best known database to be found in the pattern recognition literature. The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other. \n", + "\n", + "Each example contains 4 attributes\n", + "1. sepal length in cm \n", + "2. sepal width in cm \n", + "3. petal length in cm \n", + "4. petal width in cm \n", + "\n", + "Predicted attribute: class of iris plant. \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SY8oOngQSgyZ", + "outputId": "0fd9baa5-e665-4de1-ab17-d986fe0ece4b" + }, + "source": [ + "from sklearn import datasets\n", + "import matplotlib.pyplot as plt\n", + "\n", + "iris = datasets.load_iris()\n", + "\n", + "iris_X = iris.data\n", + "iris_y = iris.target\n", + "\n", + "print(\"Iris: {} examples in {} dimensional space\".format(*iris_X.shape))\n", + "print(\"First example in dataset :\\n Speal lenght: {}cm \\n Speal width: {}cm \\n Petal length: {}cm \\n Petal width: {}cm\".format(*iris_X[0]))\n", + "\n", + "print(\"Avalible classes\", np.unique(iris_y))" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Iris: 150 examples in 4 dimensional space\n", + "First example in dataset :\n", + " Speal lenght: 5.1cm \n", + " Speal width: 3.5cm \n", + " Petal length: 1.4cm \n", + " Petal width: 0.2cm\n", + "Avalible classes [0 1 2]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-IlKSX7hSgyZ" + }, + "source": [ + "## Prepare Data\n", + "\n", + "In our data set we have 150 examples (50 examples of each class), we have to divide it into 3 datasets.\n", + "1. Training data set, 90 examples. It will be used to find k - nearest neightbours\n", + "2. Validation data set, 30 examples. It will be used to find best k parameter, the one for which accuracy is highest\n", + "3. Test data set, 30 examples. It will be used to check how good our model performs\n", + "\n", + "Data has to be shuffled (mixed in random order), because originally it is stored 50 examples of class 0, 50 of 1 and 50 of 2.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "RA1Q7kCPSgyZ" + }, + "source": [ + "from sklearn.utils import shuffle\n", + "\n", + "iris_X, iris_y = shuffle(iris_X, iris_y, random_state=134)\n", + "\n", + "\n", + "test_size = 30\n", + "validation_size = 30\n", + "training_size = 90\n", + "\n", + "## Initialize X_test\n", + "## Initialize X_validation \n", + "## Initialize X_train \n", + "X_test=iris_X[:test_size]\n", + "X_validation=iris_X[test_size:test_size+validation_size]\n", + "X_train=iris_X[test_size+validation_size:]\n", + "\n", + "y_test=iris_y[:test_size]\n", + "y_validation=iris_y[test_size:test_size+validation_size]\n", + "y_train=iris_y[test_size+validation_size:]\n", + "## Initialize y_test\n", + "## Initialize y_validation\n", + "## Initialize y_train" + ], + "execution_count": 22, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r9xJVLzrSgyZ" + }, + "source": [ + "## Find best k parameter" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 345, + "referenced_widgets": [ + "97fb6e979f4f4455ab53206969f946e9", + "9f89ed02ee3f40ceb2a5fd159f03c2ba", + "a975fbfaee414fd29b6319b69b5e0f8a", + "19a7e07e3413408a96e5ce3844185182", + "0ed469c169c14685a41571442dc7d6b5", + "2663fca766e340189787c5d257e1b5bc", + "3199e8deb1994dfda6cd9c03dc611655", + "06d256c3ed17466e9c67be388f9fa4d0", + "c362485c1cb14f859a5e34706ab94503", + "fc7918a519444d53ac61414b6d6e21c3", + "ec74538e65104e31bc1c22b6ec32dc2a" + ] + }, + "id": "hbvZBVNBSgya", + "outputId": "6318331d-bb76-42af-c5d8-ca9f25ae115f" + }, + "source": [ + "k_values = [i for i in range(3,50)]\n", + "\n", + "best_k, accuracies = select_knn_model(X_validation, y_validation, X_train, y_train, k_values, distance_function=get_euclidean_distance)\n", + "plt.plot(k_values,accuracies)\n", + "plt.xlabel(\"K\")\n", + "plt.ylabel(\"Accuracy\")\n", + "plt.grid(True)\n", + "plt.show()\n", + "## Plot accuracy vs k values graph" + ], + "execution_count": 23, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n", + "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "97fb6e979f4f4455ab53206969f946e9", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + " 0%| | 0/47 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BjQBDWJMSgya" + }, + "source": [ + "## Count accuracy for training set" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "_f-J5sSESgya", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "f22ab924-75bb-42a8-abe6-6802b16dd0bf" + }, + "source": [ + "prediction = predict(X_test, X_train, y_train, best_k, get_euclidean_distance)\n", + "\n", + "## Calculate Best accuracy using the best k value\n", + "print(\"Accuracy for best k=\",best_k,\":\", 100*count_accuracy(prediction,y_test),\"%\")\n" + ], + "execution_count": 24, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Accuracy for best k= 14 : 93.33333333333333 %\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "72O5eXbCSgyc" + }, + "source": [ + "# Sources\n", + "\n", + "https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm - first visualisation image\n", + "\n", + "https://en.wikipedia.org/wiki/Euclidean_distance - euclidean distance visualisation\n", + "\n", + "https://rajritvikblog.wordpress.com/2017/06/29/iris-dataset-analysis-python/ - first iris image\n", + "\n", + "https://rpubs.com/wjholst/322258 - second iris image\n", + "\n" + ] + } + ] +} \ No newline at end of file From 80d83a1fa1f3ad8696b2f2f21d76557c8dc4f115 Mon Sep 17 00:00:00 2001 From: Mani9550 <93199718+Mani9550@users.noreply.github.com> Date: Fri, 29 Oct 2021 00:21:36 +0530 Subject: [PATCH 07/11] Updated the code as per your comments Added one pull request for one task. please merge the PR --- Classification_Task3_203174002 (1).ipynb | 1329 ++++++++++++++++++++++ 1 file changed, 1329 insertions(+) create mode 100644 Classification_Task3_203174002 (1).ipynb diff --git a/Classification_Task3_203174002 (1).ipynb b/Classification_Task3_203174002 (1).ipynb new file mode 100644 index 0000000..926d289 --- /dev/null +++ b/Classification_Task3_203174002 (1).ipynb @@ -0,0 +1,1329 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Classification_Task3_203174002.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "7C5rAxwPGDQf" + }, + "source": [ + "# Importing useful libraries" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "8qvrslgsF4Mn" + }, + "source": [ + "import numpy as np\n", + "import pandas as pd \n", + "from pandas import Series, DataFrame\n", + "\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ], + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6aYOLI2BHF6m" + }, + "source": [ + "## Loading the dataset.\n", + "The dataset can be found [here](https://github.com/shreedharmalpani/Intro-To-ML-Hello-FOSS/blob/main/iris.csv)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "_hccks2pF4Mq" + }, + "source": [ + "df = pd.read_csv(\"iris.csv\")" + ], + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "um0L09IOF4Ms", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 205 + }, + "outputId": "b1cd9799-9cae-41f4-bcc3-ce0bb8679482" + }, + "source": [ + "df.head()" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "0 5.1 3.5 1.4 0.2 setosa\n", + "1 4.9 3.0 1.4 0.2 setosa\n", + "2 4.7 3.2 1.3 0.2 setosa\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "cbx8gP4zF4Mt", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "b449df47-a2ce-4924-e5d8-19476d34d618" + }, + "source": [ + "df.info() " + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "RangeIndex: 150 entries, 0 to 149\n", + "Data columns (total 5 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 sepal_length 150 non-null float64\n", + " 1 sepal_width 150 non-null float64\n", + " 2 petal_length 150 non-null float64\n", + " 3 petal_width 150 non-null float64\n", + " 4 species 150 non-null object \n", + "dtypes: float64(4), object(1)\n", + "memory usage: 6.0+ KB\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vjH1pAqoJna2" + }, + "source": [ + "# Data Cleaning & Data Visualization" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PNfSyZF1F4Mu" + }, + "source": [ + "### 1) Remove unneeded columns\n", + "### 2) Check for duplicate rows \n", + "### 2) Check for rows with missing values\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": true, + "id": "HKFsVhubF4Mx" + }, + "source": [ + "df.isna().sum()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Rm7rlDcPR0aI", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 422 + }, + "outputId": "09eb2b3d-e418-4c73-ff44-0cc68524710a" + }, + "source": [ + "df.isnull()" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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..................
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150 rows × 5 columns

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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "0 False False False False False\n", + "1 False False False False False\n", + "2 False False False False False\n", + "3 False False False False False\n", + "4 False False False False False\n", + ".. ... ... ... ... ...\n", + "145 False False False False False\n", + "146 False False False False False\n", + "147 False False False False False\n", + "148 False False False False False\n", + "149 False False False False False\n", + "\n", + "[150 rows x 5 columns]" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "L1cnZS5XF4M2" + }, + "source": [ + "### EDA (Exploratory Data Analysis) with Iris" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "DjsnhAraF4M7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 458 + }, + "outputId": "8b95cd96-2bff-4a55-919e-4eb5d4806892" + }, + "source": [ + "##Displaying a scatter plot to show the distribution of Sepal Length vs width the dataset\n", + "\n", + "fig = df[df.species == 'setosa'].plot(kind='scatter', x='petal_length', y='petal_width', color='orange', label='Setosa')\n", + "df[df.species == 'versicolor'].plot(kind='scatter', x='petal_length', y='petal_width', color='blue', label='Versicolor', ax=fig)\n", + "df[df.species == 'virginica'].plot(kind='scatter', x='petal_length', y='petal_width', color='green', label='Virginica', ax=fig)\n", + "\n", + "fig.set_xlabel('Petal Length')\n", + "fig.set_ylabel('Petal Width')\n", + "fig.set_title('Petal Length Vs Width')\n", + "\n", + "fig=plt.gcf()\n", + "fig.set_size_inches(10, 7)\n", + "plt.show()" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "O_eYByFvF4M5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 458 + }, + "outputId": "b623dd49-eb52-4d54-e6b9-abf1d3132e0b" + }, + "source": [ + "#Display a scatter plot to show the distribution of Sepal Length vs width the dataset (Like previous Petal lenght vs width scatter plot)\n", + "fig=sns.scatterplot(data=df,x=\"sepal_length\",y=\"sepal_width\",hue=\"species\")\n", + "\n", + "\n", + "#code\n", + "\n", + "fig.set_xlabel('Sepal Length')\n", + "fig.set_ylabel('Sepal Width')\n", + "fig.set_title('Sepal Length Vs Width')\n", + "\n", + "\n", + "fig=plt.gcf()\n", + "fig.set_size_inches(10, 7)\n", + "plt.show()\n", + "#example plot" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EIh_yKQAF4M6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 242 + }, + "outputId": "8bdd4cb6-6d9f-4bf7-d2f3-55e2bf5469b2" + }, + "source": [ + "#plot the FacetGrid plot using the seaborn library\n", + "\n", + "fg = sns.FacetGrid(df, col = \"species\")\n", + "fg.map(plt.scatter, \"sepal_length\", \"sepal_width\")\n", + "fg.add_legend()\n", + "\n" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 11 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "yadZQxoKF4M8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 390 + }, + "outputId": "168425dc-b925-409f-cb41-8499850aa047" + }, + "source": [ + "#Plot the distritbution of the features using histgram\n", + "plt.subplot(2, 2, 1)\n", + "sns.histplot(data=df,x=\"petal_length\")\n", + "plt.xlabel(\"\")\n", + "plt.ylabel(\"\")\n", + "plt.grid(True)\n", + "plt.title(\"Petal length\")\n", + "\n", + "plt.subplot(2, 2, 2)\n", + "sns.histplot(data=df,x=\"petal_width\")\n", + "plt.xlabel(\"\")\n", + "plt.ylabel(\"\")\n", + "plt.grid(True)\n", + "plt.title(\"Petal width\")\n", + "\n", + "plt.subplot(2, 2, 3)\n", + "sns.histplot(data=df,x=\"sepal_length\")\n", + "plt.xlabel(\"\")\n", + "plt.ylabel(\"\")\n", + "plt.grid(True)\n", + "plt.title(\"Sepal length\")\n", + "\n", + "plt.subplot(2, 2, 4)\n", + "sns.histplot(data=df,x=\"sepal_width\")\n", + "plt.xlabel(\"\")\n", + "plt.ylabel(\"\")\n", + "plt.grid(True)\n", + "plt.title(\"Sepal width\")\n", + "\n", + "#fig=sns.histplot(data=df,x=\"petal_length\")\n", + "fig=plt.gcf()\n", + "fig.set_size_inches(12,6)\n", + "plt.show()" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xb-AFaG3PU0D" + }, + "source": [ + "## Importing alll the necessary packages to use the various classification algorithms\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "cJVjbgAjF4M_" + }, + "source": [ + "from sklearn.linear_model import LogisticRegression # for Logistic Regression Algorithm\n", + "from sklearn import svm # for suport vector machine algorithm\n", + "from sklearn import metrics # for checking the model accuracy\n", + "from sklearn.tree import DecisionTreeClassifier # for using DTA" + ], + "execution_count": 21, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "8LizCSuWF4NA" + }, + "source": [ + "df.shape" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WW5Hp1fFF4NC" + }, + "source": [ + "Now, when we train any algorithm, the number of features and their correlation plays an important role. If there are features and many of the features are highly correlated, then training an algorithm with all the featues will reduce the accuracy. Thus features selection should be done carefully. This dataset has less featues but still we will see the correlation.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "YABeXMklF4ND", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 270 + }, + "outputId": "a9537d5f-a224-42a1-b4ae-f10891512872" + }, + "source": [ + "plt.figure(figsize=(8,4))\n", + "sns.heatmap(df.corr(), annot=True, cmap='cubehelix_r') # draws heatmap with input as correlation matrix calculated by df.corr() \n", + "plt.show()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gsd6QaoaF4NE" + }, + "source": [ + "Observation--->\n", + "The Sepal Width and Length are not correlated The Petal Width and Length are highly correlated\n", + "We will use all the features for training the algorithm and check the accuracy.\n", + "\n", + "Then we will use 1 Petal Feature and 1 Sepal Feature to check the accuracy of the algorithm as we are using only 2 features that are not correlated. Thus we can have a variance in the dataset which may help in better accuracy. We will check it later.\n", + "\n", + "Steps To Be followed When Applying an Algorithm\n", + "\n", + "Split the dataset into training and testing dataset. The testing dataset is generally smaller than training one as it will help in training the model better.\n", + "\n", + "Select any algorithm based on the problem (classification or regression) whatever you feel may be good.\n", + "Then pass the training dataset to the algorithm to train it. We use the .fit() method\n", + "Then pass the testing data to the trained algorithm to predict the outcome. We use the .predict() method.\n", + "We then check the accuracy by passing the predicted outcome and the actual output to the model." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QAD_cNirF4NF" + }, + "source": [ + "# Splitting The Data into Training And Testing Dataset" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ZqSRd9GzF4NF", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "257d6b8c-654b-49d6-f840-d476bc46ae86" + }, + "source": [ + "from sklearn.model_selection import train_test_split\n", + "train, test = train_test_split(df, test_size=0.3) # our main data split into train and test\n", + "# the attribute test_size=0.3 splits the data into 70% and 30% ratio. train=70% and test=30%\n", + "print(train.shape)\n", + "print(test.shape)" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(105, 5)\n", + "(45, 5)\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "yO2J2FpjF4NG" + }, + "source": [ + "train_X = train[['sepal_length','sepal_width','petal_length','petal_width']] # taking the training data features\n", + "train_y = train.species # output of the training data\n", + "\n", + "test_X = test[['sepal_length','sepal_width','petal_length','petal_width']] # taking test data feature\n", + "test_y = test.species # output value of the test data" + ], + "execution_count": 17, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "yR9D2qgQF4NG", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 205 + }, + "outputId": "0c62edeb-9629-49d0-c18e-bc232bb75be1" + }, + "source": [ + "train_X.head()" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width\n", + "3 4.6 3.1 1.5 0.2\n", + "45 4.8 3.0 1.4 0.3\n", + "140 6.7 3.1 5.6 2.4\n", + "46 5.1 3.8 1.6 0.2\n", + "53 5.5 2.3 4.0 1.3" + ] + }, + "metadata": {}, + "execution_count": 37 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5sFmts-IF4NI", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3a0b4cfb-4665-4f28-9de2-0f6540a403e7" + }, + "source": [ + "train_y.head()" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "11 setosa\n", + "27 setosa\n", + "143 virginica\n", + "12 setosa\n", + "3 setosa\n", + "Name: species, dtype: object" + ] + }, + "metadata": {}, + "execution_count": 19 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "S_w4Me2bF4NL" + }, + "source": [ + "## Logistic Regression " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gOQ5JrqrF4NL", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "f7c03398-1ca7-4a77-87de-3b3e3553b729" + }, + "source": [ + "model = LogisticRegression()\n", + "model.fit(train_X, train_y)\n", + "prediction = model.predict(test_X)\n", + "print('The accuracy of Logistic Regression is: ', metrics.accuracy_score(prediction, test_y))" + ], + "execution_count": 22, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "The accuracy of Logistic Regression is: 0.9777777777777777\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e1NNX-EGF4NJ" + }, + "source": [ + "## Support Vector Machine SVM" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zSJmVzqnF4NK", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c6618770-1391-4606-a015-64c020704bd8" + }, + "source": [ + "clf = svm.SVC(kernel='linear')\n", + "clf.fit(train_X, train_y)\n", + "\n", + "#Predict the response for test dataset\n", + "prediction = clf.predict(test_X)\n", + "\n", + "print('The accuracy of Support Vector Machine is: ', metrics.accuracy_score(prediction, test_y))" + ], + "execution_count": 23, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "The accuracy of Support Vector Machine is: 0.9777777777777777\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GWfemKzPF4NN" + }, + "source": [ + "## Decision Tree" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "iRXy3EZIF4NN", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c95f9bbc-8021-4b16-b52a-ebb1d641e8a7" + }, + "source": [ + "#implementing using Decision Tree\n", + "#code\n", + "\n", + "print('The accuracy of Decision Tree is: ', metrics.accuracy_score(prediction, test_y))" + ], + "execution_count": 24, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "The accuracy of Decision Tree is: 0.9777777777777777\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uB2Co6f_F4NQ" + }, + "source": [ + "### We used all the features of iris in above models. Now we will use Petals and Sepals Seperately" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1_v6cAZMF4NQ" + }, + "source": [ + "### Creating Petals And Sepals Training Data" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": true, + "id": "e1Q-1b9YF4NQ" + }, + "source": [ + "petal = df[['petal_length','petal_width','species']]\n", + "sepal = df[['sepal_length','sepal_width','species']]" + ], + "execution_count": 28, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Lv-nab5oF4NQ" + }, + "source": [ + "### For Iris Petal" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": true, + "id": "DuOqLUWZF4NQ" + }, + "source": [ + "train_p,test_p = train_test_split(petal, test_size=0.3, random_state=0) #petals\n", + "train_x_p = train_p[['petal_width','petal_length']] # taking the training data's Petal features\n", + "train_y_p = train_p.species # output of the training data\n", + "\n", + "test_x_p = test_p[['petal_width','petal_length']] # taking the test data's Petal features\n", + "test_y_p = test_p.species # output of the test data" + ], + "execution_count": 29, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bgNB8kaNF4NU" + }, + "source": [ + "### For Iris Sepal" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6hVj5MW3F4NU" + }, + "source": [ + "#Similarly define the split for sepals\n", + "#define the training and test data's Sepal features followed by the output of the training and test data\n", + "\n", + "#use naming- train_s,test_s ; train_x_s, train_y_s; test_x_s, test_y_s\n", + "train_s,test_s = train_test_split(sepal, test_size=0.3, random_state=0) #setals\n", + "train_x_s = train_s[['sepal_width','sepal_length']] # taking the training data's sepal features\n", + "train_y_s = train_s.species # output of the training data\n", + "\n", + "test_x_s = test_s[['sepal_width','sepal_length']] # taking the test data's sepal features\n", + "test_y_s = test_s.species # output of the test data\n", + "#code" + ], + "execution_count": 30, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "y08e1O6aU9mx" + }, + "source": [ + "Implementing the algorithms just like we did on the complete dataset but separately on sepals and petals and calculating accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TeMWnQr6F4NV" + }, + "source": [ + "## SVM Algorithm" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "jhlutJ78F4NV", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "58709533-b2cf-45b3-98cf-34556938a806" + }, + "source": [ + "clf = svm.SVC(kernel='linear')\n", + "clf.fit(train_x_p, train_y_p)\n", + "\n", + "#Predict the response for test dataset\n", + "prediction = clf.predict(test_x_p)\n", + "print('The accuracy of the SVM using Petals is:',metrics.accuracy_score(prediction,test_y_p))\n", + "\n", + "clf = svm.SVC(kernel='linear')\n", + "clf.fit(train_x_s, train_y_s)\n", + "\n", + "#Predict the response for test dataset\n", + "prediction = clf.predict(test_x_s)\n", + "#code\n", + "print('The accuracy of the SVM using Sepals is:',metrics.accuracy_score(prediction,test_y_s))" + ], + "execution_count": 34, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "The accuracy of the SVM using Petals is: 0.9777777777777777\n", + "The accuracy of the SVM using Sepals is: 0.8\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Mli7zcq_F4NV" + }, + "source": [ + "## Logistic Regression" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2DqK_dFCF4NV", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "fba2975d-3919-44a5-caa9-bcecdac3646c" + }, + "source": [ + "model = LogisticRegression()\n", + "model.fit(train_x_p, train_y_p)\n", + "prediction = model.predict(test_x_p)\n", + "\n", + "print('The accuracy of the Logistic Regression using Petals is:',metrics.accuracy_score(prediction,test_y_p))\n", + "\n", + "model = LogisticRegression()\n", + "model.fit(train_x_s, train_y_s)\n", + "prediction = model.predict(test_x_s)\n", + "\n", + "print('The accuracy of the Logistic Regression using Sepals is:',metrics.accuracy_score(prediction,test_y_s))" + ], + "execution_count": 38, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "The accuracy of the Logistic Regression using Petals is: 0.9777777777777777\n", + "The accuracy of the Logistic Regression using Sepals is: 0.8222222222222222\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aM-7Zx95F4NW" + }, + "source": [ + "## Decision Tree" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "S8tXp-gMF4NW", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "7c084045-f710-4c98-dfbe-de4048897fe7" + }, + "source": [ + "clf = DecisionTreeClassifier()\n", + "clf.fit(train_x_p, train_y_p)\n", + "\n", + "#Predict the response for test dataset\n", + "prediction = clf.predict(test_x_p)\n", + "print('The accuracy of Decision Tree Classifier using Petals is:',metrics.accuracy_score(prediction,test_y_p))\n", + "\n", + "clf = DecisionTreeClassifier()\n", + "clf.fit(train_x_s, train_y_s)\n", + "\n", + "#Predict the response for test dataset\n", + "prediction = clf.predict(test_x_s)\n", + "#code\n", + "print('The accuracy of Decision Tree Classifier using Sepals is:',metrics.accuracy_score(prediction,test_y_s))" + ], + "execution_count": 40, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "The accuracy of Decision Tree Classifier using Petals is: 0.9555555555555556\n", + "The accuracy of Decision Tree Classifier using Sepals is: 0.6444444444444445\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6ec0NUyJF4NW" + }, + "source": [ + "\n", + "\n", + "\n", + "### Question:\n", + "Does Using Petals over Sepals for training the data give a much better accuracy? Why?\n" + ] + } + ] +} \ No newline at end of file From 458d3861eb4b7220b22d59fe6659339ff209b376 Mon Sep 17 00:00:00 2001 From: Mani9550 <93199718+Mani9550@users.noreply.github.com> Date: Fri, 29 Oct 2021 00:22:24 +0530 Subject: [PATCH 08/11] Rename Classification_Task3_203174002 (1).ipynb to Classification_Task3_203174002.ipynb --- ..._203174002 (1).ipynb => Classification_Task3_203174002.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename Classification_Task3_203174002 (1).ipynb => Classification_Task3_203174002.ipynb (99%) diff --git a/Classification_Task3_203174002 (1).ipynb b/Classification_Task3_203174002.ipynb similarity index 99% rename from Classification_Task3_203174002 (1).ipynb rename to Classification_Task3_203174002.ipynb index 926d289..f709133 100644 --- a/Classification_Task3_203174002 (1).ipynb +++ b/Classification_Task3_203174002.ipynb @@ -1326,4 +1326,4 @@ ] } ] -} \ No newline at end of file +} From ac77d68e03b22efe66b06ba5e6518839f690c32e Mon Sep 17 00:00:00 2001 From: Mani9550 <93199718+Mani9550@users.noreply.github.com> Date: Fri, 29 Oct 2021 00:23:52 +0530 Subject: [PATCH 09/11] Updated the code as per your comments --- Linear_Regression_Task2_203174002 (1).ipynb | 1079 +++++++++++++++++++ 1 file changed, 1079 insertions(+) create mode 100644 Linear_Regression_Task2_203174002 (1).ipynb diff --git a/Linear_Regression_Task2_203174002 (1).ipynb b/Linear_Regression_Task2_203174002 (1).ipynb new file mode 100644 index 0000000..ed904a5 --- /dev/null +++ b/Linear_Regression_Task2_203174002 (1).ipynb @@ -0,0 +1,1079 @@ +{ + "nbformat": 4, + "nbformat_minor": 5, + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + }, + "colab": { + "name": "Linear_Regression_Task2_203174002.ipynb", + "provenance": [], + "collapsed_sections": [] + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "89223f98" + }, + "source": [ + "\n", + "\n", + "```\n", + "Import libraries\n", + "```\n", + "\n", + "### Importing useful libraries \n" + ], + "id": "89223f98" + }, + { + "cell_type": "code", + "metadata": { + "id": "26f77ebe" + }, + "source": [ + "# This Python 3 environment comes with many helpful analytics libraries installed\n", + "# For example, here's several helpful packages to load in\n", + "import numpy as np # linear algebra\n", + "import matplotlib.pyplot as plt # data visualization\n", + "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", + "import seaborn as sns" + ], + "id": "26f77ebe", + "execution_count": 30, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "31c8220d" + }, + "source": [ + "### Loading the dataset \n", + "#### For implementation we will be using house prediction dataset . The dataset can be found [here](https://github.com/vrinda01go/Hellofoss/blob/main/Room_price_data.csv)" + ], + "id": "31c8220d" + }, + { + "cell_type": "code", + "metadata": { + "id": "1c5d873a" + }, + "source": [ + "df = pd.read_csv(\"Hostel_Linear-Dataset.csv\") #import text file \n" + ], + "id": "1c5d873a", + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "1ca9aba0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 205 + }, + "outputId": "3b129ba4-1b3a-4288-9b62-fd3344787414" + }, + "source": [ + "df.head()" + ], + "id": "1ca9aba0", + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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PriceHostel No.OccupancyRoom SizeFloor
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" + ], + "text/plain": [ + " Price Hostel No. Occupancy Room Size Floor\n", + "0 2540.0 3 1 686 8\n", + "1 2900.0 3 2 966 5\n", + "2 NaN 3 1 788 8\n", + "3 2362.0 3 2 924 2\n", + "4 NaN 3 2 1098 5" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "af08f245" + }, + "source": [ + "# Visualizing and Cleaning the data\n", + "\n", + "We will now be removing the nan values and identical values from the dataset\n", + "\n", + "For seeing if there are nan values in the dataset we will use the isna() function and then to remove them we will use the dropna() function. We will need to set additional parameters like rows and columns in the dropna function depending on the number of nan values present for each column\n", + "\n", + "Using the sum() function with isna() function we can get to know the number of missing values in each column" + ], + "id": "af08f245" + }, + { + "cell_type": "code", + "metadata": { + "id": "2fd4babb", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "dd94b5ef-188f-4c3a-aec4-fe91cdc6a86d" + }, + "source": [ + "df.isna().sum()" + ], + "id": "2fd4babb", + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Price 1531\n", + "Hostel No. 0\n", + "Occupancy 0\n", + "Room Size 0\n", + "Floor 0\n", + "dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "83ef03c3" + }, + "source": [ + "After this we will proceed to remove the nan values \n", + "\n", + "Since there are not many nan values in the column 'Price' as compared to the number of rows we will remove the rows which have nan values. \n", + "\n", + "Reseting the index after removing the nan values and dropping the old index will also be important" + ], + "id": "83ef03c3" + }, + { + "cell_type": "code", + "metadata": { + "id": "b65e4503" + }, + "source": [ + "df = df.dropna(subset = ['Price'],how= 'any')\n", + "df = df.reset_index(drop = True)\n", + "## df.isna().sum()" + ], + "id": "b65e4503", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "40784889" + }, + "source": [ + "Now we can use the drop_duplicate function to remove the duplicate values\n", + "\n", + "This function has a parameter calle 'keep' where we specifiy to drop and which value to keep\n", + "\n", + "For this excercise we will keep the first values and drop the rest of the duplicates" + ], + "id": "40784889" + }, + { + "cell_type": "code", + "metadata": { + "id": "75fa3dc8" + }, + "source": [ + "df = df.drop_duplicates(keep = 'first')\n", + "df = df.reset_index(drop = True)\n", + "## df.duplicated().sum()" + ], + "id": "75fa3dc8", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a007a33f" + }, + "source": [ + "For visualizing the data we will first start with looking at the distribution of different columns to see if there are enough number for each category in every column and dropping them if the data is biased for one category more than the other" + ], + "id": "a007a33f" + }, + { + "cell_type": "code", + "metadata": { + "id": "b325df62", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "cf878a76-66d3-4e68-f009-819d724a4eae" + }, + "source": [ + "columns = df.columns\n", + "for column in columns:\n", + " if(column== 'Price' or column=='Room Size'): \n", + " continue\n", + " fig = plt.figure(figsize=(5,5))\n", + " ax = fig.gca()\n", + " counts = df[column].value_counts()\n", + " counts.plot.bar(ax = ax, color='blue')\n", + " ax.set_title('No of rooms '+ column)\n", + " ax.set_xlabel(column)\n", + " ax.set_ylabel(\"No of rooms\")\n", + " plt.show()" + ], + "id": "b325df62", + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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\n", 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" + ], + "text/plain": [ + " Price Hostel No. Occupancy Room Size Floor\n", + "0 2540.0 3 1 686 8\n", + "1 2900.0 3 2 966 5\n", + "2 2362.0 3 2 924 2\n", + "3 1432.0 2 1 706 3\n", + "4 1702.0 2 2 1038 3" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f333875b" + }, + "source": [ + "We will now write the columns between categorical and numerical\n", + "\n", + "categorical = Hostel No, occupancy, floor\n", + "\n", + "Numerical = price, occupancy, roomsize, floor, hostel No.\n", + "\n", + "Remember that we can treat Hostel Number and occupancy as numerical or categorical. For this notebook we will treat them as categorical for data visualization and numerical for the regression" + ], + "id": "f333875b" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0f34ca6a" + }, + "source": [ + "We will also plot the scatter plots and the correlation map to analyse the relation ships between different numerical columns" + ], + "id": "0f34ca6a" + }, + { + "cell_type": "code", + "metadata": { + "scrolled": false, + "id": "f4a3ab6e" + }, + "source": [ + "categorical = ['Hostel No.', 'Occupancy', 'Floor']\n", + "numerical = [ 'Price', 'Room Size']" + ], + "id": "f4a3ab6e", + "execution_count": 27, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "df2b588a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 791 + }, + "outputId": "0327277e-f4a5-4f0b-eae9-134ad15a1e86" + }, + "source": [ + "for column1 in numerical:\n", + " for column2 in numerical:\n", + " if(column1 != column2):\n", + " fig = plt.figure(figsize=(6,6))\n", + " ax = fig.gca()\n", + " df.plot.scatter(x=column1,y=column2,ax = ax)\n", + " ax.set_title('Scatter plot of '+ column1 + ' vs ' + column2)\n", + " ax.set_xlabel(column1)\n", + " ax.set_ylabel(column2)\n", + " plt.show()" + ], + "id": "df2b588a", + "execution_count": 28, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "625b89a8" + }, + "source": [ + "We can notice that there are no linear relation present between the numerical columns. Hence no need to drop anything" + ], + "id": "625b89a8" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8a7f95fa" + }, + "source": [ + "Now we will plot box plots of categorical and numerical columns to get more information about the number of outliers and the distrubtion." + ], + "id": "8a7f95fa" + }, + { + "cell_type": "code", + "metadata": { + "id": "e73f0769", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "1004ad8d-4668-4006-b743-bb73bd0dc8ca" + }, + "source": [ + "for c in categorical:\n", + " for n in numerical:\n", + " sns.set_style(\"whitegrid\")\n", + " sns.boxplot(x= c, y= n, data=df)\n", + " plt.xlabel(c)\n", + " plt.ylabel(n)\n", + " plt.show()\n", + " " + ], + "id": "e73f0769", + "execution_count": 31, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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4bJSCfoXIv/71L+Tk5GDBggUAgM8++wxvvvmmVwsjIvG0Wq3HZbnIyclxnuH7+/vLdniW+vp6ZxBbLBbZNvEFugJv48aNPgu6foXIc889h6efftp5xnDrrbdiz549Xi2MiMRTShPfqKgopKamQqVSITU1VbZn+MXFxc5RkTs7O2U7AoAU+hUi165dw4wZM1zWcT5zIvnqeVvIF610xPL17RcxlDICgBT6FSJjx47FxYsXnU3H9u3bx3H/iWQsIyPDZfmBBx6QqJK++fr2ixg9a5Nzrb7WrxB5/vnnsWrVKly4cAH33nsviouL8cILL3i5NCIS64MPPnDpL+CLyYmGs8uXL3tclhNfDxTZrxCJiYnB22+/jaNHj2Lv3r3Ytm0bJkyY4O3aiEik8vJyZ4c4QRBkO7MhoIzRcbuHPHG3LCfFxcWoqqry2XObfoXI+vXr0draijFjxiAkJAQtLS34wx/+4O3aiEgknU6HUaNGAejqdCbXprOAMkbHVcrkWfX19di7dy8EQcDevXt9Esz9+iQqKysRFhbmXA4PD5dtpyAi6npY3X07y8/PT7YPrZUyOq5Seqx/dx4Zh8Phk6uRfoWIw+GAzWZzLnd0dLgsE5G8REVFITExEQCQmJgo2wfBShkdt2cPdbn2WC8vL3c2Rbbb7T65jdmvEMnIyEBOTg5KSkpQUlKC3NxcZGZmit5pa2sr8vLykJKSgtTUVJw6dQrNzc3Izc2FXq9Hbm4uWlpaAHTdz12zZg10Oh0yMjLw6aefOrdTWloKvV4PvV6P0tJS0fUQkTSUMjquUkgxoGW/QmTJkiX41a9+hQsXLuDChQt4/PHH8ctf/lL0Tl9++WXce++92LdvH3bt2oUpU6agsLAQ8fHxKCsrQ3x8PAoLCwF03UqrqalBWVkZXnrpJWersObmZmzatAnbt29HSUkJNm3a5AweopGuvr4eFRUVAICKigrZ3iZSyui4Pa+Q5HrFJIV+Px2aP38+nnnmGTzzzDM3pN1AtLW14ZNPPkFWVhYAIDAwEGFhYTAajc6rm8zMTOcZSfd6lUqFmTNnorW1FXV1dThy5Ajmzp2LiIgIhIeHY+7cuTcM5kY0UhUXFzu/kOU8x/rNN9/scVkuel4hdT/HkRspRm8O8PTDn/zkJ9i2bRvuuOMO50M6oOtsQaVS4eTJkwPe4aVLl6BWq7FixQp89tlnuO222/Dss8+ioaEB0dHRAIBx48Y5z5wsFovLuD9arRYWi+WG9RqNpl9DO1itVlRXVw+4biIl2b9/Pzo7OwF0DdOxb98+pKamSlzVjb755psblpXy9ynHOkNDQ3Ht2jXnclhYmNfr9Bgi27ZtAwCcOnVqyHZot9tx7tw5PPfcc4iLi8OaNWuct666qVQql9AaSkFBQYiNjfXKtonkIjk5Gbt27XIup6SkyPL3PiUlxaXO1NRUWdZ5//33uwx1otPpZFlnz9uW9fX1Q1anuzDq83aWw+FASkrKkBQBdF1JaLVaxMXFAej6JTp37hwiIyNRV1cHAKirq4NarQbQdYXx3WGszWYzNBrNDestFots50wg8jWlDHvSs+mxXJsi92zSK9exyHqefHvrZPy7+gwRf39/fO9738PXX389JDscN24ctFotLly4AAA4evQopkyZgqSkJOzcuRMAsHPnTtx3330A4FwvCAJOnz6N0NBQREdHIyEhAUeOHEFLSwtaWlpw5MgRJCQkDEmNREpXUlLisrx9+3aJKvGssbHRZbmpqUmiSjzbtGmTy/If//hHiSrxrOd34GCeX/eXx9tZ3VpbW5Geno4ZM2YgODjYuf5Pf/qTqJ0+99xzWLZsGTo7OxETE4NXXnkF169fR35+Pnbs2IHx48fj9ddfB9D1QP/QoUPQ6XQIDg7G2rVrAQARERF4/PHHnQ/oly5dioiICFH1EA03vT0IXrlypUTVuLdmzRqX5dWrV2Pr1q0SVeNeTU2Nx2W5CAoK8rjsDSqhH23qjh8/3uv6O++8c8gL8rbq6mpZ3sskGkrz5893aS6rUqlkOc96b/0Y5Dgaxk9/+lNcunTJuTxhwgS88847ElbUO71ej46ODufy6NGjh6zDobvvTo9XIlarFdu2bcPFixcxffp0ZGVlOSemIiL58vPzg8PhcFmWo0mTJrmc1U+aNEmyWjyZOnWqS4hMmzZNwmrc6znPky/mffL4m/XMM8/g7NmzmD59OiorK/Hqq696vSAiGjyljPX0xBNPuCw/9dRTElXi2ccff+yyfOzYMYkq8ezKlSsel73BY4h8+eWXWLduHX784x9jw4YNOHHihNcLIqLBU8pYTz07CMvxlhuAG1p+siXotzyGyHdvXfE2FpGyfHdSKrnq2fNbrvOe9GydOlStVYcDjyHy2WefYdasWZg1axbuuOMOfP75587/nzVrlq9qJKIBKi4udt4P9/f3l+2wJ0qZ90RJk1L5msfLCzl26yeivvU2JHhBQYHEVd0oJycHe/fuBSDveU+6P0t3yyOZPJtsENGgKOUMPyoqCqmpqVCpVEhNTZXtvCchISEel0cyPuggGobkdIa/b98+7Nmzx+3Pv/nmGwQGBuKLL75AXl5er69JS0sb0uGXBqqgoACrV692Li9fvlyyWjzx9/d3adrtiya+DBEiherryzkwMBA2mw0hISF48cUXe32N1F/OQNfcQAEBAc4rJzk6c+aMy/KpU6ecM0fKSW8DRXobQ4RomHI4HPDz83OZMkEKKSkpHoOq++pjw4YNvippwL77xQx0BbgcnzE99thjKC8vx/Xr1+Hn5+eTpt0MESKFGg5fzkrRs4uDVF0e+rr6BIDg4GBcuXIF4eHhPrkC5YN1IqI+tLe3e1yWk+6rkPHjx/tkf7wSIaIRr68z/KCgIFitVpfl3hoBePsZU19Xn4Dvr0B5JUJE1IeJEyd6XB7JeCVCRCNef87wdTodrFYrJk2ahKKiIh9VJn+8EiEi6oeJEyfCz88Pq1atkroUWWGIEBH1w5gxYzBjxgxMnTpV6lJkhSFCRESiMUSIiEg0hggREYnGECEiItEYIkREJBpDhIiIRGOIEBGRaAwRIiISTbIQcTgcyMzMdI53X1tbi+zsbOh0OuTn58NmswEAbDYb8vPzodPpkJ2djUuXLjm3sXnzZuh0OiQnJ+Pw4cOSHAcR0UgmWYhs3boVU6ZMcS6vW7cOixcvRnl5OcLCwrBjxw4AQElJCcLCwlBeXo7Fixdj3bp1AACTyQSDwQCDwYCioiK8+OKLLtNCEhGR90kSImazGQcPHkRWVhYAQBAEHDt2DMnJyQCARYsWwWg0AgAOHDiARYsWAQCSk5Nx9OhRCIIAo9GI9PR0BAYGIiYmBhMnTkRVVZUUh0NENGJJMorv2rVrsXz5cly5cgUA0NTUhLCwMOdsYVqtFhaLBQBgsVhw8803dxUbEIDQ0FA0NTXBYrEgLi7OuU2NRuN8jydWqxXV1dVDfUhEsnP16lUAkP3vO+scWr6u0+chUlFRAbVajR/84Af4+OOPfb17BAUFITY21uf7JfK1MWPGAIDsf99Z59DyVp3uQsnnIXLy5EkcOHAAlZWVsFqtaG9vx8svv4zW1lbY7XYEBATAbDZDo9EA6LrCuHz5MrRaLex2O9ra2jB27FhoNBqYzWbndi0Wi/M9RETkGz5/JvL000+jsrISBw4cwPr163H33Xfjtddew1133YX9+/cDAEpLS5GUlAQASEpKQmlpKQBg//79uPvuu6FSqZCUlASDwQCbzYba2lrU1NRgxowZvj4cIqIRTTb9RJYvX44tW7ZAp9OhubkZ2dnZAICsrCw0NzdDp9Nhy5YtWLZsGQBg2rRpSE1NRVpaGh599FGsWrUK/v7+Uh4CEdGIoxIEQZC6CF+qrq6W/T1NoqGQl5cHANiwYYPX9rFhwwaYTKZBbeOLL74A0HViOBhTp051HrM3jKTPs7fP0t13J+dYJ5KhofwyGewXq6cvZ5PJhLNnziA0UPxXieC4DgD4d/WnorfRZrOLfq+cmEwmnD17FiEhIaK30X1dUFNTI+r97e3tA3o9Q4RIhkwmEz7932pEjIkWvQ0/RxAA4KsvG0Rvo/lqXZ+vCQ0MwJ2asaL3MRSOW5o8/lwuodyfq6WQkBDMmjVL9D4G6+TJkwN6PUOESKYixkQj8dYfS1pDxWf/V9L9DxWTyYTPz1YjJlQrehuhwmgAwNV/ew4sd2rbzH2/SIEYIkQ0IsSEavH0nbmS7f+141sk27c3yaZ1FhERKQ9DhIiIRGOIEBGRaAwRIiISjSFCRESiMUSIiEg0hggREYnGfiJEMtTY2Ijmq3WSd/ZrvlqH4EaV2583NjaizWbvs8e4t7XZ7GhsbJS0hqHQ2NiItra2AfcaH0ptbW0D+iwZIkQ07DU2NuKbNoukHf5q28wY1zj8xrtliBDJkFqtxrUmQRbDnqjVarc/V6vVaLNclsXYWZ7qVAq1Wo3W1lbJx84ayGfJECGiYU+tVmN0m0ryYU/GqKUNW2/gg3UiIhKNIUJERKLxdhaRTA22dVZH5xUAwOhRNw2qhlsQ6fE1g22dZf3PpFRB/uLPafszKVVtm3lQD9ZbrV2TNYUFiZswqrbNjP9G37ez2tvbB9U6y2azAQACAwNFvZ+TUhENA1OnTh30Nr74oquZ5i1T/kv0Nm5BpMdahqbOrsmeJg7B9LhiftZfX31RDwDQTowR9f7/xtg+6xjKz3PSpEmitzGQOjjHugf79u3Dnj17PL6muz21p9YMaWlpSElJ6X+RA9RXnf2pEWCd3ZRSZ198MSf4UGCdQ8tbdXKOdS9paOiaelTOzQuVUCPAOomUiCHiQUpKSp9nknI4O+mrTjnUCLBOouGIrbOIiEg0hggREYnGECEiItF8HiKXL1/Gww8/jLS0NKSnp6O4uBgA0NzcjNzcXOj1euTm5qKlpQUAIAgC1qxZA51Oh4yMDHz66afObZWWlkKv10Ov16O0tNTXh0JENOL5PET8/f3x29/+Fnv27ME//vEPvPPOOzCZTCgsLER8fDzKysoQHx+PwsJCAEBlZSVqampQVlaGl156CS+88AKArtDZtGkTtm/fjpKSEmzatMkZPERE5Bs+b50VHR2N6OhoAEBISAgmT54Mi8UCo9GIv/71rwCAzMxMPPzww1i+fDmMRiMyMzOhUqkwc+ZMtLa2oq6uDsePH8fcuXMREREBAJg7dy4OHz6MBQsW+PqQiCTRV3+W7k5n3a3JeuOLvixKqLM/fcJYZ+8kbeJ76dIlVFdXIy4uDg0NDc5wGTdunLMtvsVigVardb5Hq9XCYrHcsF6j0cBisfS5T6vViurq6iE7hqtXrwLAkG5zqCmhRoB1DtTXX3/trKU3ISFdw3N4es3XX3/t9eNQQp191QiwTnckC5ErV64gLy8PK1eudB50N5VKBZXK/WxqgxEUFNTvHuv9MWbMGAAY0m0ONSXUCLDOgYqNjcUjjzwiaQ39oYQ6lVAjIG2d7kJHktZZnZ2dyMvLQ0ZGBvR6PQAgMjISdXV1AIC6ujpnb2CNRgOz2ex8r9lshkajuWG9xWKBRqPx4VEQEZHPr0QEQcCzzz6LyZMnIzf32wlikpKSsHPnTixZsgQ7d+7Efffd51z/t7/9Denp6Thz5gxCQ0MRHR2NhIQErF+/3vkw/ciRIygoKBhQLRs2bIDJZBrU8fTn/mNfpk6dOqj3ExFJxechcuLECezatQvTp0/HwoULAQAFBQVYsmQJ8vPzsWPHDowfPx6vv/46AGD+/Pk4dOgQdDodgoODsXbtWgBAREQEHn/8cWRlZQEAli5d6nzI3l8mkwmn/vccro8RPwaSytH1EZ740tzHK3vnd7Wxz9cMNuyGIuiAvsNOCXXK5cQB4MkDDQ8+D5HZs2fj888/7/Vn3X1GvkulUuH555/v9fVZWVnOEBHr+hg1Or4vXYuu0ed29/kak8mE82dP4r9CHKL2ESZ0PV/qqPlE1PsB4GK7f5+vMZlMOPXpKWBgWf6t/9xcPfXVKZEbANDs+ccmkwmfnT4NreeXeRTcvavTp0VvQ9wpB5H8cABGhfivEAd+N3tgk8UMpTX/r58T8UQA1//nuneL8cDvYN+P+bQAfgHvNNdV2l4AAAkESURBVNzor/+DETUDAw1jHPaEiIhEY4gQEZFoI/p2VmNjI/yuNvTruYS3+F1tQGOj57mQGxsb8U2bf/9vKXnBv9v8Ma7RcyOAxsZGoLl/t5S8phloDHZfZ2NjIyyQ/nbSZQDX+/g8iZSAVyJERCTaiL4SUavV+FeTTfLWWX1Ns6pWqzGm9UvJH6yP7ked/772b8kfrHv6PNVqNfwuXpTFg/UITq9LwwCvRIiISDSGCBERiTaib2eRFwzmwXrHf/47enD7xy2eX2LG4B6sd99UHEwzBzPE98kkkpMRHyJ+VxsH1TpL1XkNACCMCu7jle73j370n77YLr51Vout6/5/eKD4L86L7f6Y3sdrpk6dKnr7wLfDiUy7ZZr4jdziuY7B1ggA3/ynzgnTxNcZMUS1EEltRIfIUPwRO7/4pogdSEPbZx2DrbP2PzVqJon/0pvejzoGOw5U9/s3bNgwqO30Zx9DsQ1v1kmkFCM6RJTyhaKEL2ciGpn4YJ2IiERjiBARkWgMESIiEo0hQkREojFEiIhINIYIERGJxhAhIiLRRnQ/kb7s27cPe/bs8fia7s6GnvpypKWlISUlZUhrI+/p69+9P//mAP/daWRgiAxSZGSk1CUoxnD5cua/OdG3GCIepKSkKOJMkl/OQ0sp/+5EcsAQGQH45UxE3sIQGQb45UxEUmHrLCIiEk3xIVJZWYnk5GTodDoUFhZKXQ4R0Yii6BBxOBxYvXo1ioqKYDAYsHv3bphMJqnLIiIaMRQdIlVVVZg4cSJiYmIQGBiI9PR0GI1GqcsiIhoxFP1g3WKxQKv9dkZBjUaDqqoqj++xWq2orq72dmlERCOCokNEjKCgIMTGxkpdBhGRorg7+Vb07SyNRgOz2exctlgs0Gg0ElZERDSyKDpEbr/9dtTU1KC2thY2mw0GgwFJSUlSl0VENGIo+nZWQEAAVq1ahUcffRQOhwMPPfQQpk2b5vE9fCZCRDRwVqu11/UqQRAEH9dCRETDhKJvZxERkbQYIkREJBpDhIiIRGOIEBGRaAwRIiISjSFCRESiKbqfiNRWrFiBgwcPIjIyErt375a6nF5dvnwZv/nNb9DQ0ACVSoUf/vCHyMnJkbqsG1itVvzsZz+DzWaDw+FAcnJyn9P5SqW7T5JGo8HmzZulLsetpKQk3HTTTfDz84O/vz/ee+89qUu6QWtrK373u9/h/PnzUKlUWLt2Le644w6py3Jx4cIF/PrXv3Yu19bWIi8vD4sXL5auKDfefvttlJSUQKVSYfr06XjllVcQFBTk3Z0KJNrx48eFs2fPCunp6VKX4pbFYhHOnj0rCIIgtLW1CXq9Xvjiiy8krupG169fF9rb2wVBEASbzSZkZWUJp06dkriq3v3lL38RCgoKhCVLlkhdikeJiYlCQ0OD1GV49Jvf/EbYvn27IAiCYLVahZaWFokr8sxutwv33HOPcOnSJalLuYHZbBYSExOFa9euCYIgCHl5ecK7777r9f3ydtYgzJkzB+Hh4VKX4VF0dDRuu+02AEBISAgmT54Mi8UicVU3UqlUuOmmmwAAdrsddrsdKpVK4qpuZDabcfDgQWRlZUldiuK1tbXhk08+cX6WgYGBCAsLk7gqz44ePYqYmBjccsstUpfSK4fDgY6ODtjtdnR0dCA6Otrr+2SIjCCXLl1CdXU14uLipC6lVw6HAwsXLsQ999yDe+65R5Z1rl27FsuXL4efnzL+dH7xi1/gwQcfxD/+8Q+pS7nBpUuXoFarsWLFCmRmZuLZZ5/F1atXpS7LI4PBgAULFkhdRq80Gg0eeeQRJCYmIiEhASEhIUhISPD6fpXxl0CDduXKFeTl5WHlypUICQmRupxe+fv7Y9euXTh06BCqqqpw/vx5qUtyUVFRAbVajR/84AdSl9Iv27ZtQ2lpKf785z/j73//Oz755BOpS3Jht9tx7tw5/OQnP8HOnTsRHBws6ymubTYbDhw4gJSUFKlL6VVLSwuMRiOMRiMOHz6Ma9euYdeuXV7fL0NkBOjs7EReXh4yMjKg1+ulLqdPYWFhuOuuu3D48GGpS3Fx8uRJHDhwAElJSSgoKMCxY8ewbNkyqctyq3tahMjISOh0uj4nbPM1rVYLrVbrvOJMSUnBuXPnJK7KvcrKStx2222IioqSupReffTRR5gwYQLUajVGjRoFvV6PU6dOeX2/DJFhThAEPPvss5g8eTJyc3OlLsetxsZGtLa2AgA6Ojrw0UcfYfLkyRJX5erpp59GZWUlDhw4gPXr1+Puu+/GunXrpC6rV1evXkV7e7vz/z/88MM+R7j2tXHjxkGr1eLChQsAup43TJkyReKq3DMYDEhPT5e6DLfGjx+PM2fO4Nq1axAEwWefJ5v4DkJBQQGOHz+OpqYmzJs3D08++SSys7OlLsvFiRMnsGvXLkyfPh0LFy4E0FX3/PnzJa7MVV1dHX7729/C4XBAEASkpKQgMTFR6rIUq6GhAUuXLgXQ9axpwYIFmDdvnsRV3ei5557DsmXL0NnZiZiYGLzyyitSl9Srq1ev4qOPPsLq1aulLsWtuLg4JCcnY9GiRQgICEBsbCx+9KMfeX2/HAqeiIhE4+0sIiISjSFCRESiMUSIiEg0hggREYnGECEiItHYxJfIi2JjYzF9+nTn8htvvIGvvvoKf/nLX2Q9AjBRfzFEiLxo9OjRNww98dVXXw3Jtu12OwIC+CdM0uJvIJGEmpubsXLlStTW1iI4OBirV6/Grbfe6nb9xo0bcfHiRdTW1mL8+PFYv3691IdAIxxDhMiLOjo6nCMFTJgwAW+88YbLzzdu3Ijvf//7ePPNN3H06FE888wz2LVrl9v1APDll1/inXfewejRo31+PEQ9MUSIvKi321nfdeLECWzcuBEAEB8fj+bmZrS3t7tdD3TNWMgAIblg6ywihQkODpa6BCInhgiRhGbPno33338fAPDxxx9j7NixCAkJcbueSG54O4tIQk888QRWrlyJjIwMBAcH49VXX/W4nkhuOIovERGJxttZREQkGkOEiIhEY4gQEZFoDBEiIhKNIUJERKIxRIiISDSGCBERifb/ATRmqQAz3cg0AAAAAElFTkSuQmCC\n", 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\n", 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" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a74d69fd" + }, + "source": [ + "Now that we have analysed our data we can proceed to normalixing our data and regression" + ], + "id": "a74d69fd" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "77d8c6ba" + }, + "source": [ + "### Importing useful libraries \n" + ], + "id": "77d8c6ba" + }, + { + "cell_type": "code", + "metadata": { + "id": "fffac537" + }, + "source": [ + "# This Python 3 environment comes with many helpful analytics libraries installed\n", + "# For example, here's several helpful packages to load in\n", + "import numpy as np # linear algebra\n", + "import matplotlib.pyplot as plt # data visualization\n", + "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)" + ], + "id": "fffac537", + "execution_count": 17, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "04cd7e4e" + }, + "source": [ + "### Loading the dataset \n", + "#### For implementation we will be using house prediction dataset . The dataset can be found [here](https://github.com/vrinda01go/Hellofoss/blob/main/Insti_data.csv)" + ], + "id": "04cd7e4e" + }, + { + "cell_type": "code", + "metadata": { + "id": "4ac8e74b", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "f8967616-b263-4127-bcc1-e4ce5d4799f4" + }, + "source": [ + "df['Room Size']=df['Room Size']/max(df['Room Size']) \n", + "\n", + "data = np.array(df, dtype=float)\n", + "data" + ], + "id": "4ac8e74b", + "execution_count": 21, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[2.54000000e+03, 3.00000000e+00, 1.00000000e+00, 3.92448513e-01,\n", + " 8.00000000e+00],\n", + " [2.90000000e+03, 3.00000000e+00, 2.00000000e+00, 5.52631579e-01,\n", + " 5.00000000e+00],\n", + " [ nan, 3.00000000e+00, 1.00000000e+00, 4.50800915e-01,\n", + " 8.00000000e+00],\n", + " ...,\n", + " [1.02000000e+03, 3.00000000e+00, 2.00000000e+00, 5.75514874e-01,\n", + " 3.00000000e+00],\n", + " [2.40000000e+03, 2.00000000e+00, 2.00000000e+00, 5.36613272e-01,\n", + " 1.00000000e+00],\n", + " [9.50000000e+02, 3.00000000e+00, 2.00000000e+00, 6.02402746e-01,\n", + " 2.00000000e+00]])" + ] + }, + "metadata": {}, + "execution_count": 21 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "627a5a3a" + }, + "source": [ + "#### Since our dataset has four features i.e Hostel No. , Occupancy, Room Size and Floor ,our hypothesis function becomes\n", + "### hθ(x) = θ0 + θ1x1 + θ2x2 +θ3x3 + θ4x4\n", + "#### where x1 ,x2,x3 and x4 are the two features (i.e. size of house and number of rooms)" + ], + "id": "627a5a3a" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "58fdf0fe" + }, + "source": [ + "### So Your task is to define hypothesis function having 4 features and a corresponding cost function " + ], + "id": "58fdf0fe" + }, + { + "cell_type": "code", + "metadata": { + "id": "34dc5643" + }, + "source": [ + "def hypotheses_fn(theta,X):\n", + " return X.dot(theta)" + ], + "id": "34dc5643", + "execution_count": 12, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "6fae141b" + }, + "source": [ + "def cost(theta,X,Y):\n", + " pred=hypotheses_fn(theta,X)\n", + " return (1/(2*len(Y))) * np.sum(np.square(pred-Y))" + ], + "id": "6fae141b", + "execution_count": 14, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "178eb11f" + }, + "source": [ + "### Gradient Descent \n", + "#### So we have our hypothesis function and we have a way of measuring how well it fits into the data. Now we need to estimate the parameters in the hypothesis function. That's where gradient descent comes in.\n", + "### Your next task is to define gradient descent function having some specific value of learning rate and number of epochs.\n", + "#### Note that learning rate should be neither very high nor very low .Why?\n", + "#### Check out exact reason [here](https://towardsdatascience.com/understanding-learning-rates-and-how-it-improves-performance-in-deep-learning-d0d4059c1c10)\n", + "\n" + ], + "id": "178eb11f" + }, + { + "cell_type": "code", + "metadata": { + "id": "167b5bdc" + }, + "source": [ + "\n", + "def gradient_descent(theta,X,Y,learning_rate,iterations):\n", + " len_y=len(Y)\n", + " costs=[]\n", + " for i in range(iterations):\n", + " pred=hypotheses_fn(theta,X)\n", + " theta-=(1/len_y)*learning_rate*(X.T.dot((pred-Y)))\n", + " costs.append(cost(theta,X,Y))\n", + " \n", + " plt.plot(costs)\n", + " plt.title(\"Costs changing with each epoch\")\n", + " plt.xlabel(\"Epoch\")\n", + " plt.ylabel(\"Cost\")\n", + " plt.show()\n", + " \n", + " return theta" + ], + "id": "167b5bdc", + "execution_count": 24, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "724e407a" + }, + "source": [ + "### Now we want to visualize how our cost function varies with number of epochs .So your next task is to plot graph of updated costs vs number of epochs " + ], + "id": "724e407a" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e82f6ebf" + }, + "source": [ + "#### After plotting above graph you will notice that your cost function decreases with epochs.\n", + "#### Perfect! This is all what we wanted to seek by doing linear regression. \n", + "\n", + "#### Now it's time to test our model on some test data. \n", + "\n", + "#### For this you will define a test function that will take as input Hostel No. , Occupancy, Room Size , Floor and the final theta vector that was returned by our linear regression model and will give us the price of the house. Compute it for any set of features given and final value of theta as given by gradient descent function" + ], + "id": "e82f6ebf" + }, + { + "cell_type": "code", + "metadata": { + "id": "c522bca3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 295 + }, + "outputId": "2d0d53e2-9fab-4e5e-f4d1-6cbfd84647e0" + }, + "source": [ + "X=data[:,1:]\n", + "X=np.c_[np.ones((len(X),1)),X]\n", + "Y=data[:,0]\n", + "theta=np.zeros(5)\n", + "theta_ideal=gradient_descent(theta,X,Y,0.001,200)" + ], + "id": "c522bca3", + "execution_count": 22, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "947f35b1" + }, + "source": [ + "#### Now since we have defined all required functions , we can call functions one by one and get our final results .\n", + "#### Your final task is to use all functions defined above and predict the price of room for some input combinations to check how well your model works." + ], + "id": "947f35b1" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "06e54ef5" + }, + "source": [ + "#### You can try playing with different values of alpha and epochs and see which combination gives most accurate results but do lookout for overfitting \n" + ], + "id": "06e54ef5" + }, + { + "cell_type": "code", + "metadata": { + "id": "3fc631fd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 422 + }, + "outputId": "7fe60643-2144-4929-ad41-d79bacb907f4" + }, + "source": [ + "def test(X,Y):\n", + " display(pd.DataFrame({\"Predicted values\":X.dot(theta_ideal),\"Actual values\":Y}))\n", + "\n", + "test(X,Y)" + ], + "id": "3fc631fd", + "execution_count": 23, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Predicted values Actual values\n", + "0 NaN 2540.0\n", + "1 NaN 2900.0\n", + "2 NaN NaN\n", + "3 NaN 2362.0\n", + "4 NaN NaN\n", + "... ... ...\n", + "6533 NaN 1624.0\n", + "6534 NaN 1470.0\n", + "6535 NaN 1020.0\n", + "6536 NaN 2400.0\n", + "6537 NaN 950.0\n", + "\n", + "[6538 rows x 2 columns]" + ] + }, + "metadata": {} + } + ] + } + ] +} \ No newline at end of file From fc8aa85838412fbdcfba72737b9b3541bfa145c4 Mon Sep 17 00:00:00 2001 From: Mani9550 <93199718+Mani9550@users.noreply.github.com> Date: Fri, 29 Oct 2021 00:25:00 +0530 Subject: [PATCH 10/11] Rename Linear_Regression_Task2_203174002 (1).ipynb to Linear_Regression_Task2_203174002.ipynb Created one pull request for each question. Please merge the PR --- ...3174002 (1).ipynb => Linear_Regression_Task2_203174002.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename Linear_Regression_Task2_203174002 (1).ipynb => Linear_Regression_Task2_203174002.ipynb (99%) diff --git a/Linear_Regression_Task2_203174002 (1).ipynb b/Linear_Regression_Task2_203174002.ipynb similarity index 99% rename from Linear_Regression_Task2_203174002 (1).ipynb rename to Linear_Regression_Task2_203174002.ipynb index ed904a5..c4373eb 100644 --- a/Linear_Regression_Task2_203174002 (1).ipynb +++ b/Linear_Regression_Task2_203174002.ipynb @@ -1076,4 +1076,4 @@ ] } ] -} \ No newline at end of file +} From 3d9bb16f2d7d870c658b08b57ad7d91e3ac45df2 Mon Sep 17 00:00:00 2001 From: Mani9550 <93199718+Mani9550@users.noreply.github.com> Date: Mon, 1 Nov 2021 09:50:46 +0530 Subject: [PATCH 11/11] Add files via upload Added separate PR for each task --- Linear_Regression_Task2_203174002 (1).ipynb | 1079 +++++++++++++++++++ 1 file changed, 1079 insertions(+) create mode 100644 Linear_Regression_Task2_203174002 (1).ipynb diff --git a/Linear_Regression_Task2_203174002 (1).ipynb b/Linear_Regression_Task2_203174002 (1).ipynb new file mode 100644 index 0000000..ed904a5 --- /dev/null +++ b/Linear_Regression_Task2_203174002 (1).ipynb @@ -0,0 +1,1079 @@ +{ + "nbformat": 4, + "nbformat_minor": 5, + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + }, + "colab": { + "name": "Linear_Regression_Task2_203174002.ipynb", + "provenance": [], + "collapsed_sections": [] + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "89223f98" + }, + "source": [ + "\n", + "\n", + "```\n", + "Import libraries\n", + "```\n", + "\n", + "### Importing useful libraries \n" + ], + "id": "89223f98" + }, + { + "cell_type": "code", + "metadata": { + "id": "26f77ebe" + }, + "source": [ + "# This Python 3 environment comes with many helpful analytics libraries installed\n", + "# For example, here's several helpful packages to load in\n", + "import numpy as np # linear algebra\n", + "import matplotlib.pyplot as plt # data visualization\n", + "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", + "import seaborn as sns" + ], + "id": "26f77ebe", + "execution_count": 30, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "31c8220d" + }, + "source": [ + "### Loading the dataset \n", + "#### For implementation we will be using house prediction dataset . The dataset can be found [here](https://github.com/vrinda01go/Hellofoss/blob/main/Room_price_data.csv)" + ], + "id": "31c8220d" + }, + { + "cell_type": "code", + "metadata": { + "id": "1c5d873a" + }, + "source": [ + "df = pd.read_csv(\"Hostel_Linear-Dataset.csv\") #import text file \n" + ], + "id": "1c5d873a", + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "1ca9aba0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 205 + }, + "outputId": "3b129ba4-1b3a-4288-9b62-fd3344787414" + }, + "source": [ + "df.head()" + ], + "id": "1ca9aba0", + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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PriceHostel No.OccupancyRoom SizeFloor
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" + ], + "text/plain": [ + " Price Hostel No. Occupancy Room Size Floor\n", + "0 2540.0 3 1 686 8\n", + "1 2900.0 3 2 966 5\n", + "2 NaN 3 1 788 8\n", + "3 2362.0 3 2 924 2\n", + "4 NaN 3 2 1098 5" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "af08f245" + }, + "source": [ + "# Visualizing and Cleaning the data\n", + "\n", + "We will now be removing the nan values and identical values from the dataset\n", + "\n", + "For seeing if there are nan values in the dataset we will use the isna() function and then to remove them we will use the dropna() function. We will need to set additional parameters like rows and columns in the dropna function depending on the number of nan values present for each column\n", + "\n", + "Using the sum() function with isna() function we can get to know the number of missing values in each column" + ], + "id": "af08f245" + }, + { + "cell_type": "code", + "metadata": { + "id": "2fd4babb", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "dd94b5ef-188f-4c3a-aec4-fe91cdc6a86d" + }, + "source": [ + "df.isna().sum()" + ], + "id": "2fd4babb", + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Price 1531\n", + "Hostel No. 0\n", + "Occupancy 0\n", + "Room Size 0\n", + "Floor 0\n", + "dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "83ef03c3" + }, + "source": [ + "After this we will proceed to remove the nan values \n", + "\n", + "Since there are not many nan values in the column 'Price' as compared to the number of rows we will remove the rows which have nan values. \n", + "\n", + "Reseting the index after removing the nan values and dropping the old index will also be important" + ], + "id": "83ef03c3" + }, + { + "cell_type": "code", + "metadata": { + "id": "b65e4503" + }, + "source": [ + "df = df.dropna(subset = ['Price'],how= 'any')\n", + "df = df.reset_index(drop = True)\n", + "## df.isna().sum()" + ], + "id": "b65e4503", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "40784889" + }, + "source": [ + "Now we can use the drop_duplicate function to remove the duplicate values\n", + "\n", + "This function has a parameter calle 'keep' where we specifiy to drop and which value to keep\n", + "\n", + "For this excercise we will keep the first values and drop the rest of the duplicates" + ], + "id": "40784889" + }, + { + "cell_type": "code", + "metadata": { + "id": "75fa3dc8" + }, + "source": [ + "df = df.drop_duplicates(keep = 'first')\n", + "df = df.reset_index(drop = True)\n", + "## df.duplicated().sum()" + ], + "id": "75fa3dc8", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a007a33f" + }, + "source": [ + "For visualizing the data we will first start with looking at the distribution of different columns to see if there are enough number for each category in every column and dropping them if the data is biased for one category more than the other" + ], + "id": "a007a33f" + }, + { + "cell_type": "code", + "metadata": { + "id": "b325df62", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "cf878a76-66d3-4e68-f009-819d724a4eae" + }, + "source": [ + "columns = df.columns\n", + "for column in columns:\n", + " if(column== 'Price' or column=='Room Size'): \n", + " continue\n", + " fig = plt.figure(figsize=(5,5))\n", + " ax = fig.gca()\n", + " counts = df[column].value_counts()\n", + " counts.plot.bar(ax = ax, color='blue')\n", + " ax.set_title('No of rooms '+ column)\n", + " ax.set_xlabel(column)\n", + " ax.set_ylabel(\"No of rooms\")\n", + " plt.show()" + ], + "id": "b325df62", + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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\n", 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" + ], + "text/plain": [ + " Price Hostel No. Occupancy Room Size Floor\n", + "0 2540.0 3 1 686 8\n", + "1 2900.0 3 2 966 5\n", + "2 2362.0 3 2 924 2\n", + "3 1432.0 2 1 706 3\n", + "4 1702.0 2 2 1038 3" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f333875b" + }, + "source": [ + "We will now write the columns between categorical and numerical\n", + "\n", + "categorical = Hostel No, occupancy, floor\n", + "\n", + "Numerical = price, occupancy, roomsize, floor, hostel No.\n", + "\n", + "Remember that we can treat Hostel Number and occupancy as numerical or categorical. For this notebook we will treat them as categorical for data visualization and numerical for the regression" + ], + "id": "f333875b" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0f34ca6a" + }, + "source": [ + "We will also plot the scatter plots and the correlation map to analyse the relation ships between different numerical columns" + ], + "id": "0f34ca6a" + }, + { + "cell_type": "code", + "metadata": { + "scrolled": false, + "id": "f4a3ab6e" + }, + "source": [ + "categorical = ['Hostel No.', 'Occupancy', 'Floor']\n", + "numerical = [ 'Price', 'Room Size']" + ], + "id": "f4a3ab6e", + "execution_count": 27, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "df2b588a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 791 + }, + "outputId": "0327277e-f4a5-4f0b-eae9-134ad15a1e86" + }, + "source": [ + "for column1 in numerical:\n", + " for column2 in numerical:\n", + " if(column1 != column2):\n", + " fig = plt.figure(figsize=(6,6))\n", + " ax = fig.gca()\n", + " df.plot.scatter(x=column1,y=column2,ax = ax)\n", + " ax.set_title('Scatter plot of '+ column1 + ' vs ' + column2)\n", + " ax.set_xlabel(column1)\n", + " ax.set_ylabel(column2)\n", + " plt.show()" + ], + "id": "df2b588a", + "execution_count": 28, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "625b89a8" + }, + "source": [ + "We can notice that there are no linear relation present between the numerical columns. Hence no need to drop anything" + ], + "id": "625b89a8" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8a7f95fa" + }, + "source": [ + "Now we will plot box plots of categorical and numerical columns to get more information about the number of outliers and the distrubtion." + ], + "id": "8a7f95fa" + }, + { + "cell_type": "code", + "metadata": { + "id": "e73f0769", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "1004ad8d-4668-4006-b743-bb73bd0dc8ca" + }, + "source": [ + "for c in categorical:\n", + " for n in numerical:\n", + " sns.set_style(\"whitegrid\")\n", + " sns.boxplot(x= c, y= n, data=df)\n", + " plt.xlabel(c)\n", + " plt.ylabel(n)\n", + " plt.show()\n", + " " + ], + "id": "e73f0769", + "execution_count": 31, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a74d69fd" + }, + "source": [ + "Now that we have analysed our data we can proceed to normalixing our data and regression" + ], + "id": "a74d69fd" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "77d8c6ba" + }, + "source": [ + "### Importing useful libraries \n" + ], + "id": "77d8c6ba" + }, + { + "cell_type": "code", + "metadata": { + "id": "fffac537" + }, + "source": [ + "# This Python 3 environment comes with many helpful analytics libraries installed\n", + "# For example, here's several helpful packages to load in\n", + "import numpy as np # linear algebra\n", + "import matplotlib.pyplot as plt # data visualization\n", + "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)" + ], + "id": "fffac537", + "execution_count": 17, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "04cd7e4e" + }, + "source": [ + "### Loading the dataset \n", + "#### For implementation we will be using house prediction dataset . The dataset can be found [here](https://github.com/vrinda01go/Hellofoss/blob/main/Insti_data.csv)" + ], + "id": "04cd7e4e" + }, + { + "cell_type": "code", + "metadata": { + "id": "4ac8e74b", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "f8967616-b263-4127-bcc1-e4ce5d4799f4" + }, + "source": [ + "df['Room Size']=df['Room Size']/max(df['Room Size']) \n", + "\n", + "data = np.array(df, dtype=float)\n", + "data" + ], + "id": "4ac8e74b", + "execution_count": 21, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[2.54000000e+03, 3.00000000e+00, 1.00000000e+00, 3.92448513e-01,\n", + " 8.00000000e+00],\n", + " [2.90000000e+03, 3.00000000e+00, 2.00000000e+00, 5.52631579e-01,\n", + " 5.00000000e+00],\n", + " [ nan, 3.00000000e+00, 1.00000000e+00, 4.50800915e-01,\n", + " 8.00000000e+00],\n", + " ...,\n", + " [1.02000000e+03, 3.00000000e+00, 2.00000000e+00, 5.75514874e-01,\n", + " 3.00000000e+00],\n", + " [2.40000000e+03, 2.00000000e+00, 2.00000000e+00, 5.36613272e-01,\n", + " 1.00000000e+00],\n", + " [9.50000000e+02, 3.00000000e+00, 2.00000000e+00, 6.02402746e-01,\n", + " 2.00000000e+00]])" + ] + }, + "metadata": {}, + "execution_count": 21 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "627a5a3a" + }, + "source": [ + "#### Since our dataset has four features i.e Hostel No. , Occupancy, Room Size and Floor ,our hypothesis function becomes\n", + "### hθ(x) = θ0 + θ1x1 + θ2x2 +θ3x3 + θ4x4\n", + "#### where x1 ,x2,x3 and x4 are the two features (i.e. size of house and number of rooms)" + ], + "id": "627a5a3a" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "58fdf0fe" + }, + "source": [ + "### So Your task is to define hypothesis function having 4 features and a corresponding cost function " + ], + "id": "58fdf0fe" + }, + { + "cell_type": "code", + "metadata": { + "id": "34dc5643" + }, + "source": [ + "def hypotheses_fn(theta,X):\n", + " return X.dot(theta)" + ], + "id": "34dc5643", + "execution_count": 12, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "6fae141b" + }, + "source": [ + "def cost(theta,X,Y):\n", + " pred=hypotheses_fn(theta,X)\n", + " return (1/(2*len(Y))) * np.sum(np.square(pred-Y))" + ], + "id": "6fae141b", + "execution_count": 14, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "178eb11f" + }, + "source": [ + "### Gradient Descent \n", + "#### So we have our hypothesis function and we have a way of measuring how well it fits into the data. Now we need to estimate the parameters in the hypothesis function. That's where gradient descent comes in.\n", + "### Your next task is to define gradient descent function having some specific value of learning rate and number of epochs.\n", + "#### Note that learning rate should be neither very high nor very low .Why?\n", + "#### Check out exact reason [here](https://towardsdatascience.com/understanding-learning-rates-and-how-it-improves-performance-in-deep-learning-d0d4059c1c10)\n", + "\n" + ], + "id": "178eb11f" + }, + { + "cell_type": "code", + "metadata": { + "id": "167b5bdc" + }, + "source": [ + "\n", + "def gradient_descent(theta,X,Y,learning_rate,iterations):\n", + " len_y=len(Y)\n", + " costs=[]\n", + " for i in range(iterations):\n", + " pred=hypotheses_fn(theta,X)\n", + " theta-=(1/len_y)*learning_rate*(X.T.dot((pred-Y)))\n", + " costs.append(cost(theta,X,Y))\n", + " \n", + " plt.plot(costs)\n", + " plt.title(\"Costs changing with each epoch\")\n", + " plt.xlabel(\"Epoch\")\n", + " plt.ylabel(\"Cost\")\n", + " plt.show()\n", + " \n", + " return theta" + ], + "id": "167b5bdc", + "execution_count": 24, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "724e407a" + }, + "source": [ + "### Now we want to visualize how our cost function varies with number of epochs .So your next task is to plot graph of updated costs vs number of epochs " + ], + "id": "724e407a" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e82f6ebf" + }, + "source": [ + "#### After plotting above graph you will notice that your cost function decreases with epochs.\n", + "#### Perfect! This is all what we wanted to seek by doing linear regression. \n", + "\n", + "#### Now it's time to test our model on some test data. \n", + "\n", + "#### For this you will define a test function that will take as input Hostel No. , Occupancy, Room Size , Floor and the final theta vector that was returned by our linear regression model and will give us the price of the house. Compute it for any set of features given and final value of theta as given by gradient descent function" + ], + "id": "e82f6ebf" + }, + { + "cell_type": "code", + "metadata": { + "id": "c522bca3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 295 + }, + "outputId": "2d0d53e2-9fab-4e5e-f4d1-6cbfd84647e0" + }, + "source": [ + "X=data[:,1:]\n", + "X=np.c_[np.ones((len(X),1)),X]\n", + "Y=data[:,0]\n", + "theta=np.zeros(5)\n", + "theta_ideal=gradient_descent(theta,X,Y,0.001,200)" + ], + "id": "c522bca3", + "execution_count": 22, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "947f35b1" + }, + "source": [ + "#### Now since we have defined all required functions , we can call functions one by one and get our final results .\n", + "#### Your final task is to use all functions defined above and predict the price of room for some input combinations to check how well your model works." + ], + "id": "947f35b1" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "06e54ef5" + }, + "source": [ + "#### You can try playing with different values of alpha and epochs and see which combination gives most accurate results but do lookout for overfitting \n" + ], + "id": "06e54ef5" + }, + { + "cell_type": "code", + "metadata": { + "id": "3fc631fd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 422 + }, + "outputId": "7fe60643-2144-4929-ad41-d79bacb907f4" + }, + "source": [ + "def test(X,Y):\n", + " display(pd.DataFrame({\"Predicted values\":X.dot(theta_ideal),\"Actual values\":Y}))\n", + "\n", + "test(X,Y)" + ], + "id": "3fc631fd", + "execution_count": 23, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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