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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "29c9b0a5-07bd-4243-b053-ba89c6397863",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[[-0.78182471 -1.71978888 -0.78182471 -0.24069153]\n",
+ " [ 0.80549928 -1.2508068 0.91372592 0.51689492]\n",
+ " [ 0.08398838 -0.8539758 1.31055692 1.0580281 ]\n",
+ " [-0.92612689 0.98587701 -0.56537144 0.94980147]]\n",
+ "\n",
+ " [[-1.07042907 1.27448137 1.20233028 -0.63752253]\n",
+ " [-0.70967362 -0.92612689 -1.28688234 -0.63752253]\n",
+ " [ 0.12006392 -0.89005134 -0.96220243 0.3365172 ]\n",
+ " [ 1.02195256 -1.03435352 -1.2508068 0.3365172 ]]\n",
+ "\n",
+ " [[ 0.73334819 0.6611971 -0.96220243 -0.31284262]\n",
+ " [ 1.20233028 -0.56537144 -1.53941116 -1.75586443]\n",
+ " [ 1.27448137 -1.2508068 0.19221501 1.0580281 ]\n",
+ " [ 1.52701019 0.04791283 -0.67359807 0.98587701]]\n",
+ "\n",
+ " [[ 1.77953901 -0.0603138 1.4548591 1.56308573]\n",
+ " [-0.67359807 -1.21473125 -0.20461598 0.69727265]\n",
+ " [-1.10650461 0.73334819 1.4548591 -0.24069153]\n",
+ " [-1.50333561 1.20233028 0.98587701 0.12006392]]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#question 1\n",
+ "import numpy as np\n",
+ "\n",
+ "a = np.random.randint(1,101,size=(4,4,4))\n",
+ "\n",
+ "mean = np.mean(a)\n",
+ "std = np.std(a)\n",
+ "\n",
+ "#the z score normalization array can be obtained as follows\n",
+ "z_score = (a-mean)/std\n",
+ "\n",
+ "print(z_score)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "3bf5b7d4-2b88-491f-85e4-f397601af270",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([-696., 0., 1080.])"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Q5. Using NumPy, calculate the dot product of two matrices and find the determinant of the resulting matrix.\n",
+ "import numpy as np\n",
+ "a = np.random.randint(7,size=(3,3,3))\n",
+ "b = np.random.randint(7,size=(3,3,3))\n",
+ "z = np.matmul(a,b)\n",
+ "np.linalg.det(z)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "5b1b0165-56b1-41c3-a57b-a84951e5f468",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[1, 2, 3],\n",
+ " [5, 6, 7]])"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Q8. Using NumPy, create a 2D array and perform a slicing operation to extract a specific sub-array.\n",
+ "a = np.array([[1,2,3,4,5],[5,6,7,8,9]])\n",
+ "a[:,0:3] # Slicing the 2D array to get the specific sub array"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "835d44fa-31d7-464e-84fa-137343d0d5ec",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " sepal_length sepal_width petal_length petal_width species\n",
+ "50 7.0 3.2 4.7 1.4 versicolor\n",
+ "51 6.4 3.2 4.5 1.5 versicolor\n",
+ "52 6.9 3.1 4.9 1.5 versicolor\n",
+ "53 5.5 2.3 4.0 1.3 versicolor\n",
+ "54 6.5 2.8 4.6 1.5 versicolor\n",
+ ".. ... ... ... ... ...\n",
+ "145 6.7 3.0 5.2 2.3 virginica\n",
+ "146 6.3 2.5 5.0 1.9 virginica\n",
+ "147 6.5 3.0 5.2 2.0 virginica\n",
+ "148 6.2 3.4 5.4 2.3 virginica\n",
+ "149 5.9 3.0 5.1 1.8 virginica\n",
+ "\n",
+ "[99 rows x 5 columns]\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "\n",
+ "iris = sns.load_dataset('iris')\n",
+ "\n",
+ "# Filter the rows where petal length is greater than 3.0\n",
+ "filtered_data = iris[iris['petal_length'] > 3.0]\n",
+ "\n",
+ "# Display the filtered data\n",
+ "print(filtered_data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "ad11c07b-d36a-4724-a1be-43e1e06b0ce3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " sepal_length | \n",
+ " sepal_width | \n",
+ " petal_length | \n",
+ " petal_width | \n",
+ " species | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 5.1 | \n",
+ " 3.5 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 4.9 | \n",
+ " 3.0 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 4.7 | \n",
+ " 3.2 | \n",
+ " 1.3 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 4.6 | \n",
+ " 3.1 | \n",
+ " 1.5 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 5.0 | \n",
+ " 3.6 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " 145 | \n",
+ " 6.7 | \n",
+ " 3.0 | \n",
+ " 5.2 | \n",
+ " 2.3 | \n",
+ " virginica | \n",
+ "
\n",
+ " \n",
+ " 146 | \n",
+ " 6.3 | \n",
+ " 2.5 | \n",
+ " 5.0 | \n",
+ " 1.9 | \n",
+ " virginica | \n",
+ "
\n",
+ " \n",
+ " 147 | \n",
+ " 6.5 | \n",
+ " 3.0 | \n",
+ " 5.2 | \n",
+ " 2.0 | \n",
+ " virginica | \n",
+ "
\n",
+ " \n",
+ " 148 | \n",
+ " 6.2 | \n",
+ " 3.4 | \n",
+ " 5.4 | \n",
+ " 2.3 | \n",
+ " virginica | \n",
+ "
\n",
+ " \n",
+ " 149 | \n",
+ " 5.9 | \n",
+ " 3.0 | \n",
+ " 5.1 | \n",
+ " 1.8 | \n",
+ " virginica | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
150 rows × 5 columns
\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\n",
+ ".. ... ... ... ... ...\n",
+ "145 6.7 3.0 5.2 2.3 virginica\n",
+ "146 6.3 2.5 5.0 1.9 virginica\n",
+ "147 6.5 3.0 5.2 2.0 virginica\n",
+ "148 6.2 3.4 5.4 2.3 virginica\n",
+ "149 5.9 3.0 5.1 1.8 virginica\n",
+ "\n",
+ "[150 rows x 5 columns]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "\n",
+ "data = sns.load_dataset('iris')\n",
+ "data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "297c7cb5-553b-4dc9-aaa8-08943cb231ac",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "filtered = data[data['petal_length']>3]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "0f09cf8c-d140-4358-b4cd-01dab600f404",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " sepal_length sepal_width petal_length petal_width species\n",
+ "50 7.0 3.2 4.7 1.4 versicolor\n",
+ "51 6.4 3.2 4.5 1.5 versicolor\n",
+ "52 6.9 3.1 4.9 1.5 versicolor\n",
+ "53 5.5 2.3 4.0 1.3 versicolor\n",
+ "54 6.5 2.8 4.6 1.5 versicolor\n",
+ ".. ... ... ... ... ...\n",
+ "145 6.7 3.0 5.2 2.3 virginica\n",
+ "146 6.3 2.5 5.0 1.9 virginica\n",
+ "147 6.5 3.0 5.2 2.0 virginica\n",
+ "148 6.2 3.4 5.4 2.3 virginica\n",
+ "149 5.9 3.0 5.1 1.8 virginica\n",
+ "\n",
+ "[99 rows x 5 columns]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(filtered)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "e435457a-b3b6-4c44-9ee5-76828b9a1e7d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Original DataFrame with NaN values:\n",
+ " A B C\n",
+ "0 0.374540 0.950714 0.731994\n",
+ "1 0.598658 0.156019 0.155995\n",
+ "2 0.058084 0.866176 0.601115\n",
+ "3 NaN 0.020584 0.969910\n",
+ "4 0.832443 0.212339 0.181825\n",
+ "5 0.183405 0.304242 0.524756\n",
+ "6 0.431945 NaN 0.611853\n",
+ "7 0.139494 0.292145 0.366362\n",
+ "8 0.456070 0.785176 NaN\n",
+ "9 0.514234 0.592415 0.046450\n",
+ "\n",
+ "DataFrame after replacing NaN values with the mean of the column:\n",
+ " A B C\n",
+ "0 0.374540 0.950714 0.731994\n",
+ "1 0.598658 0.156019 0.155995\n",
+ "2 0.058084 0.866176 0.601115\n",
+ "3 0.398764 0.020584 0.969910\n",
+ "4 0.832443 0.212339 0.181825\n",
+ "5 0.183405 0.304242 0.524756\n",
+ "6 0.431945 0.464423 0.611853\n",
+ "7 0.139494 0.292145 0.366362\n",
+ "8 0.456070 0.785176 0.465584\n",
+ "9 0.514234 0.592415 0.046450\n",
+ "\n",
+ "DataFrame sorted by columns 'A' (ascending) and 'B' (descending):\n",
+ " A B C\n",
+ "2 0.058084 0.866176 0.601115\n",
+ "7 0.139494 0.292145 0.366362\n",
+ "5 0.183405 0.304242 0.524756\n",
+ "0 0.374540 0.950714 0.731994\n",
+ "3 0.398764 0.020584 0.969910\n",
+ "6 0.431945 0.464423 0.611853\n",
+ "8 0.456070 0.785176 0.465584\n",
+ "9 0.514234 0.592415 0.046450\n",
+ "1 0.598658 0.156019 0.155995\n",
+ "4 0.832443 0.212339 0.181825\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Q4.Create a Pandas DataFrame with random values with at least 3 columns and perform the following operations: (i) Replace NaN values with the mean of the column. (ii) Sort the DataFrame based on any two columns.\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "np.random.seed(42)\n",
+ "data = np.random.rand(10, 3) \n",
+ "df = pd.DataFrame(data, columns=['A', 'B', 'C'])\n",
+ "\n",
+ "\n",
+ "df.iloc[3, 0] = np.nan\n",
+ "df.iloc[6, 1] = np.nan\n",
+ "df.iloc[8, 2] = np.nan\n",
+ "\n",
+ "\n",
+ "print(\"Original DataFrame with NaN values:\")\n",
+ "print(df)\n",
+ "df.fillna(df.mean(), inplace=True)\n",
+ "\n",
+ "print(\"\\nDataFrame after replacing NaN values with the mean of the column:\")\n",
+ "print(df)\n",
+ "\n",
+ "df_sorted = df.sort_values(by=['A', 'B'], ascending=[True, False])\n",
+ "\n",
+ "print(\"\\nDataFrame sorted by columns 'A' (ascending) and 'B' (descending):\")\n",
+ "print(df_sorted)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "fb305c20-6fce-41b2-9766-cdd039ae9010",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Category\n",
+ "A 11\n",
+ "B 15\n",
+ "C 9\n",
+ "Name: Value, dtype: int64\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Q6. With Pandas, group a DataFrame by a categorical column and calculate the sum of another numerical column for each group.\n",
+ "import pandas as pd\n",
+ "var = pd.DataFrame({\"Category\":['A','A','B','B','C'],\"Value\":[5,6,7,8,9]})\n",
+ "var\n",
+ "result = var.groupby('Category')['Value'].sum()\n",
+ "print(result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "04c18d21-9e85-4fa4-8afe-30204e901f7c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Category Value\n",
+ "0 A 5\n",
+ "1 B 6\n",
+ "2 C 7\n",
+ "3 D 8\n",
+ "4 E 9\n",
+ " Category ids\n",
+ "0 A 5\n",
+ "1 B 6\n",
+ "2 C 7\n",
+ "3 D 8\n",
+ "4 E 9\n"
+ ]
+ },
+ {
+ "data": {
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+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Q9. Using Pandas, perform a merge operation between two DataFrames based on a common column for dataset of your choice.\n",
+ "import pandas as pd\n",
+ "\n",
+ "var1 = pd.DataFrame({\"Category\":['A','B','C','D','E'],\"Value\":[5,6,7,8,9]})\n",
+ "var2 = pd.DataFrame({\"Category\":['A','B','C','D','E'],\"ids\":[5,6,7,8,9]})\n",
+ "print(var1)\n",
+ "print(var2)\n",
+ "pd.merge(var1,var2,on = \"Category\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "1d8cb0a3-9a75-4088-8ec8-ac0e109d6cdc",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Q3. Using Matplotlib, plot a line graph showing the trend of a numerical dataset. Customize the plot with title, axis labels, and a legend.\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "\n",
+ "x = np.arange(0,5,0.1)\n",
+ "y= np.sin(x)\n",
+ "\n",
+ "plt.plot(x, y, label='THE GRAPH', color='r', marker='o')\n",
+ "\n",
+ "plt.title('Numerical data') \n",
+ "plt.xlabel('X-values') \n",
+ "plt.ylabel('Y-value')\n",
+ "plt.legend() \n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "4d14aa53-dad3-473c-9c59-3e0360384c45",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Q7. Using Matplotlib, create a histogram to show the distribution of a numerical dataset. Customize the number of bins and add grid lines for better readability.\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import random \n",
+ "\n",
+ "x = np.random.randint(0,60,(50))\n",
+ "plt.hist(x,color='aqua',edgecolor='black',bins = [0,10,20,30,40,50,60],label=\"Histogram\")\n",
+ "plt.xlabel('RandomX')\n",
+ "plt.ylabel('RandomY')\n",
+ "plt.title('RandomHisto')\n",
+ "plt.grid()\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "4be0cc80-525d-4876-9016-1ede8e90b967",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Q10. Using Matplotlib, create a scatter plot comparing two numerical variables in the dataset of your choice. Customize the plot by changing the color and shape of the data points.\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "x =[1,2,3,4,5,6]\n",
+ "y=[5,6,2,6,8,9]\n",
+ "\n",
+ "plt.scatter(x, y, color='green', marker='o', label='Data Points')\n",
+ "\n",
+ "plt.title('Comparing Two Variables') \n",
+ "plt.xlabel('X-axis Label') \n",
+ "plt.ylabel('Y-axis Label') \n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "33bea7e1-4a78-42c0-84a3-6830d2e6e487",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.13.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/IITK/Assignments/Assignment 2/Assignment2_JaiGaikwad_230407/ENB2012_data.csv b/IITK/Assignments/Assignment 2/Assignment2_JaiGaikwad_230407/ENB2012_data.csv
new file mode 100644
index 00000000..37bc5cfb
--- /dev/null
+++ b/IITK/Assignments/Assignment 2/Assignment2_JaiGaikwad_230407/ENB2012_data.csv
@@ -0,0 +1,1297 @@
+X1,X2,X3,X4,X5,X6,X7,X8,Y1,Y2
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+0.90,563.50,318.50,122.50,7.00,5,0.00,0,19.68,29.60
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+0.69,735.00,294.00,220.50,3.50,2,0.00,0,6.85,11.74
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+0.66,759.50,318.50,220.50,3.50,2,0.00,0,7.18,12.40
+0.66,759.50,318.50,220.50,3.50,3,0.00,0,7.10,12.23
+0.66,759.50,318.50,220.50,3.50,4,0.00,0,7.10,12.40
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+0.62,808.50,367.50,220.50,3.50,5,0.00,0,8.50,12.04
+0.98,514.50,294.00,110.25,7.00,2,0.10,1,24.58,26.47
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+0.98,514.50,294.00,110.25,7.00,4,0.10,1,24.63,26.44
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diff --git a/IITK/Assignments/Assignment 2/JaiGaikwad_230407_Assignment2/ENB2012_data.xlsx b/IITK/Assignments/Assignment 2/JaiGaikwad_230407_Assignment2/ENB2012_data.xlsx
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "id": "43f3f026-7b38-43da-958d-fb94184f77f9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "c5016ffd-f0cc-4982-b206-5b67abe1b304",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv('ENB2012_data.csv')"
+ ]
+ },
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+ "execution_count": 46,
+ "id": "f1018a2c-c77f-440e-8c21-f97416d6ef32",
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+ },
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+ " Y1 | \n",
+ " Y2 | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 0.98 | \n",
+ " 514.5 | \n",
+ " 294.0 | \n",
+ " 110.25 | \n",
+ " 7.0 | \n",
+ " 2.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 15.55 | \n",
+ " 21.33 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 0.98 | \n",
+ " 514.5 | \n",
+ " 294.0 | \n",
+ " 110.25 | \n",
+ " 7.0 | \n",
+ " 3.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 15.55 | \n",
+ " 21.33 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 0.98 | \n",
+ " 514.5 | \n",
+ " 294.0 | \n",
+ " 110.25 | \n",
+ " 7.0 | \n",
+ " 4.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 15.55 | \n",
+ " 21.33 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 0.98 | \n",
+ " 514.5 | \n",
+ " 294.0 | \n",
+ " 110.25 | \n",
+ " 7.0 | \n",
+ " 5.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 15.55 | \n",
+ " 21.33 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 0.90 | \n",
+ " 563.5 | \n",
+ " 318.5 | \n",
+ " 122.50 | \n",
+ " 7.0 | \n",
+ " 2.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 20.84 | \n",
+ " 28.28 | \n",
+ "
\n",
+ " \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " 1291 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " 1292 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " 1293 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " 1294 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " 1295 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
1296 rows × 10 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " X1 X2 X3 X4 X5 X6 X7 X8 Y1 Y2\n",
+ "0 0.98 514.5 294.0 110.25 7.0 2.0 0.0 0.0 15.55 21.33\n",
+ "1 0.98 514.5 294.0 110.25 7.0 3.0 0.0 0.0 15.55 21.33\n",
+ "2 0.98 514.5 294.0 110.25 7.0 4.0 0.0 0.0 15.55 21.33\n",
+ "3 0.98 514.5 294.0 110.25 7.0 5.0 0.0 0.0 15.55 21.33\n",
+ "4 0.90 563.5 318.5 122.50 7.0 2.0 0.0 0.0 20.84 28.28\n",
+ "... ... ... ... ... ... ... ... ... ... ...\n",
+ "1291 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "1292 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "1293 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "1294 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "1295 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "\n",
+ "[1296 rows x 10 columns]"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "id": "74c3a5f3-d2ba-4933-8116-f8c8df3adae3",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "RangeIndex: 1296 entries, 0 to 1295\n",
+ "Data columns (total 10 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 X1 768 non-null float64\n",
+ " 1 X2 768 non-null float64\n",
+ " 2 X3 768 non-null float64\n",
+ " 3 X4 768 non-null float64\n",
+ " 4 X5 768 non-null float64\n",
+ " 5 X6 768 non-null float64\n",
+ " 6 X7 768 non-null float64\n",
+ " 7 X8 768 non-null float64\n",
+ " 8 Y1 768 non-null float64\n",
+ " 9 Y2 768 non-null float64\n",
+ "dtypes: float64(10)\n",
+ "memory usage: 101.4 KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "id": "12efad47-8c61-4bed-bcdb-25c06377954b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(1296, 10)"
+ ]
+ },
+ "execution_count": 48,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "id": "410894fd-b7ae-4e2d-9a78-a92732282e45",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "X1 528\n",
+ "X2 528\n",
+ "X3 528\n",
+ "X4 528\n",
+ "X5 528\n",
+ "X6 528\n",
+ "X7 528\n",
+ "X8 528\n",
+ "Y1 528\n",
+ "Y2 528\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 49,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.isnull().sum()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "95494fec-8899-479d-9b2d-ddb9c319ec3f",
+ "metadata": {},
+ "source": [
+ "**So we will be dropping the NaN values**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "id": "e507fbbd-1724-482e-9f4b-6209fe8d27c6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The number of null values now in every column is 0\n"
+ ]
+ }
+ ],
+ "source": [
+ "df = df.dropna()\n",
+ "print(f'The number of null values now in every column is {df.isnull().sum().sum()}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "96289a6d-188a-43d6-b8de-6293efb7ba69",
+ "metadata": {},
+ "source": [
+ "**Two target variables are heating load and the cooling load. We are supposed to predict the Heating Load**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "id": "48abff67-bee2-4438-b8dc-fb8f037f6b47",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.boxplot(x='Y2', data = df) #By continously changing the values of the x attribute inside the boxplot paranthesis we learned that there are no outliers which are needed to be handled"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "id": "d9c2a565-5a6d-4642-bef6-668d265dcf26",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " X1 X2 X3 X4 X5 \\\n",
+ "X1 1.000000e+00 -9.919015e-01 -2.037817e-01 -8.688234e-01 8.277473e-01 \n",
+ "X2 -9.919015e-01 1.000000e+00 1.955016e-01 8.807195e-01 -8.581477e-01 \n",
+ "X3 -2.037817e-01 1.955016e-01 1.000000e+00 -2.923165e-01 2.809757e-01 \n",
+ "X4 -8.688234e-01 8.807195e-01 -2.923165e-01 1.000000e+00 -9.725122e-01 \n",
+ "X5 8.277473e-01 -8.581477e-01 2.809757e-01 -9.725122e-01 1.000000e+00 \n",
+ "X6 4.678592e-17 -3.459372e-17 -2.429499e-17 -5.830058e-17 4.492205e-17 \n",
+ "X7 -2.960552e-15 3.636925e-15 -8.567455e-17 -1.759011e-15 1.489134e-17 \n",
+ "X8 -7.107006e-16 2.438409e-15 2.067384e-16 -1.078071e-15 -2.920613e-17 \n",
+ "Y1 6.222722e-01 -6.581202e-01 4.556712e-01 -8.618283e-01 8.894307e-01 \n",
+ "Y2 6.343391e-01 -6.729989e-01 4.271170e-01 -8.625466e-01 8.957852e-01 \n",
+ "\n",
+ " X6 X7 X8 Y1 Y2 \n",
+ "X1 4.678592e-17 -2.960552e-15 -7.107006e-16 0.622272 0.634339 \n",
+ "X2 -3.459372e-17 3.636925e-15 2.438409e-15 -0.658120 -0.672999 \n",
+ "X3 -2.429499e-17 -8.567455e-17 2.067384e-16 0.455671 0.427117 \n",
+ "X4 -5.830058e-17 -1.759011e-15 -1.078071e-15 -0.861828 -0.862547 \n",
+ "X5 4.492205e-17 1.489134e-17 -2.920613e-17 0.889431 0.895785 \n",
+ "X6 1.000000e+00 -9.406007e-16 -2.549352e-16 -0.002587 0.014290 \n",
+ "X7 -9.406007e-16 1.000000e+00 2.129642e-01 0.269841 0.207505 \n",
+ "X8 -2.549352e-16 2.129642e-01 1.000000e+00 0.087368 0.050525 \n",
+ "Y1 -2.586534e-03 2.698410e-01 8.736759e-02 1.000000 0.975862 \n",
+ "Y2 1.428960e-02 2.075050e-01 5.052512e-02 0.975862 1.000000 \n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 52,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import warnings\n",
+ "with warnings.catch_warnings():\n",
+ " warnings.simplefilter('ignore')\n",
+ " \n",
+ "correlation_matrix = df.corr()\n",
+ "print(correlation_matrix)\n",
+ "sns.heatmap(correlation_matrix, annot = True, cmap='coolwarm', fmt ='.2f')\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "47d60afd-3007-4d98-9f63-d3a02f3706fd",
+ "metadata": {},
+ "source": [
+ "**There is strong correlation between Y1,Y2 and X1,X2,X3,X4,X5**\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "id": "818d8703-abbb-4704-80e4-f61de980c869",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " X1 | \n",
+ " X2 | \n",
+ " X3 | \n",
+ " X4 | \n",
+ " X5 | \n",
+ " X6 | \n",
+ " X7 | \n",
+ " X8 | \n",
+ " Y1 | \n",
+ " Y2 | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 0.98 | \n",
+ " 514.5 | \n",
+ " 294.0 | \n",
+ " 110.25 | \n",
+ " 7.0 | \n",
+ " 2.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 15.55 | \n",
+ " 21.33 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 0.98 | \n",
+ " 514.5 | \n",
+ " 294.0 | \n",
+ " 110.25 | \n",
+ " 7.0 | \n",
+ " 3.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 15.55 | \n",
+ " 21.33 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 0.98 | \n",
+ " 514.5 | \n",
+ " 294.0 | \n",
+ " 110.25 | \n",
+ " 7.0 | \n",
+ " 4.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 15.55 | \n",
+ " 21.33 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 0.98 | \n",
+ " 514.5 | \n",
+ " 294.0 | \n",
+ " 110.25 | \n",
+ " 7.0 | \n",
+ " 5.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 15.55 | \n",
+ " 21.33 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 0.90 | \n",
+ " 563.5 | \n",
+ " 318.5 | \n",
+ " 122.50 | \n",
+ " 7.0 | \n",
+ " 2.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 20.84 | \n",
+ " 28.28 | \n",
+ "
\n",
+ " \n",
+ " 5 | \n",
+ " 0.90 | \n",
+ " 563.5 | \n",
+ " 318.5 | \n",
+ " 122.50 | \n",
+ " 7.0 | \n",
+ " 3.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 21.46 | \n",
+ " 25.38 | \n",
+ "
\n",
+ " \n",
+ " 6 | \n",
+ " 0.90 | \n",
+ " 563.5 | \n",
+ " 318.5 | \n",
+ " 122.50 | \n",
+ " 7.0 | \n",
+ " 4.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 20.71 | \n",
+ " 25.16 | \n",
+ "
\n",
+ " \n",
+ " 7 | \n",
+ " 0.90 | \n",
+ " 563.5 | \n",
+ " 318.5 | \n",
+ " 122.50 | \n",
+ " 7.0 | \n",
+ " 5.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 19.68 | \n",
+ " 29.60 | \n",
+ "
\n",
+ " \n",
+ " 8 | \n",
+ " 0.86 | \n",
+ " 588.0 | \n",
+ " 294.0 | \n",
+ " 147.00 | \n",
+ " 7.0 | \n",
+ " 2.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 19.50 | \n",
+ " 27.30 | \n",
+ "
\n",
+ " \n",
+ " 9 | \n",
+ " 0.86 | \n",
+ " 588.0 | \n",
+ " 294.0 | \n",
+ " 147.00 | \n",
+ " 7.0 | \n",
+ " 3.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 19.95 | \n",
+ " 21.97 | \n",
+ "
\n",
+ " \n",
+ " 10 | \n",
+ " 0.86 | \n",
+ " 588.0 | \n",
+ " 294.0 | \n",
+ " 147.00 | \n",
+ " 7.0 | \n",
+ " 4.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 19.34 | \n",
+ " 23.49 | \n",
+ "
\n",
+ " \n",
+ " 11 | \n",
+ " 0.86 | \n",
+ " 588.0 | \n",
+ " 294.0 | \n",
+ " 147.00 | \n",
+ " 7.0 | \n",
+ " 5.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 18.31 | \n",
+ " 27.87 | \n",
+ "
\n",
+ " \n",
+ " 12 | \n",
+ " 0.82 | \n",
+ " 612.5 | \n",
+ " 318.5 | \n",
+ " 147.00 | \n",
+ " 7.0 | \n",
+ " 2.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 17.05 | \n",
+ " 23.77 | \n",
+ "
\n",
+ " \n",
+ " 13 | \n",
+ " 0.82 | \n",
+ " 612.5 | \n",
+ " 318.5 | \n",
+ " 147.00 | \n",
+ " 7.0 | \n",
+ " 3.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 17.41 | \n",
+ " 21.46 | \n",
+ "
\n",
+ " \n",
+ " 14 | \n",
+ " 0.82 | \n",
+ " 612.5 | \n",
+ " 318.5 | \n",
+ " 147.00 | \n",
+ " 7.0 | \n",
+ " 4.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 16.95 | \n",
+ " 21.16 | \n",
+ "
\n",
+ " \n",
+ " 15 | \n",
+ " 0.82 | \n",
+ " 612.5 | \n",
+ " 318.5 | \n",
+ " 147.00 | \n",
+ " 7.0 | \n",
+ " 5.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 15.98 | \n",
+ " 24.93 | \n",
+ "
\n",
+ " \n",
+ " 16 | \n",
+ " 0.79 | \n",
+ " 637.0 | \n",
+ " 343.0 | \n",
+ " 147.00 | \n",
+ " 7.0 | \n",
+ " 2.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 28.52 | \n",
+ " 37.73 | \n",
+ "
\n",
+ " \n",
+ " 17 | \n",
+ " 0.79 | \n",
+ " 637.0 | \n",
+ " 343.0 | \n",
+ " 147.00 | \n",
+ " 7.0 | \n",
+ " 3.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 29.90 | \n",
+ " 31.27 | \n",
+ "
\n",
+ " \n",
+ " 18 | \n",
+ " 0.79 | \n",
+ " 637.0 | \n",
+ " 343.0 | \n",
+ " 147.00 | \n",
+ " 7.0 | \n",
+ " 4.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 29.63 | \n",
+ " 30.93 | \n",
+ "
\n",
+ " \n",
+ " 19 | \n",
+ " 0.79 | \n",
+ " 637.0 | \n",
+ " 343.0 | \n",
+ " 147.00 | \n",
+ " 7.0 | \n",
+ " 5.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 28.75 | \n",
+ " 39.44 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " X1 X2 X3 X4 X5 X6 X7 X8 Y1 Y2\n",
+ "0 0.98 514.5 294.0 110.25 7.0 2.0 0.0 0.0 15.55 21.33\n",
+ "1 0.98 514.5 294.0 110.25 7.0 3.0 0.0 0.0 15.55 21.33\n",
+ "2 0.98 514.5 294.0 110.25 7.0 4.0 0.0 0.0 15.55 21.33\n",
+ "3 0.98 514.5 294.0 110.25 7.0 5.0 0.0 0.0 15.55 21.33\n",
+ "4 0.90 563.5 318.5 122.50 7.0 2.0 0.0 0.0 20.84 28.28\n",
+ "5 0.90 563.5 318.5 122.50 7.0 3.0 0.0 0.0 21.46 25.38\n",
+ "6 0.90 563.5 318.5 122.50 7.0 4.0 0.0 0.0 20.71 25.16\n",
+ "7 0.90 563.5 318.5 122.50 7.0 5.0 0.0 0.0 19.68 29.60\n",
+ "8 0.86 588.0 294.0 147.00 7.0 2.0 0.0 0.0 19.50 27.30\n",
+ "9 0.86 588.0 294.0 147.00 7.0 3.0 0.0 0.0 19.95 21.97\n",
+ "10 0.86 588.0 294.0 147.00 7.0 4.0 0.0 0.0 19.34 23.49\n",
+ "11 0.86 588.0 294.0 147.00 7.0 5.0 0.0 0.0 18.31 27.87\n",
+ "12 0.82 612.5 318.5 147.00 7.0 2.0 0.0 0.0 17.05 23.77\n",
+ "13 0.82 612.5 318.5 147.00 7.0 3.0 0.0 0.0 17.41 21.46\n",
+ "14 0.82 612.5 318.5 147.00 7.0 4.0 0.0 0.0 16.95 21.16\n",
+ "15 0.82 612.5 318.5 147.00 7.0 5.0 0.0 0.0 15.98 24.93\n",
+ "16 0.79 637.0 343.0 147.00 7.0 2.0 0.0 0.0 28.52 37.73\n",
+ "17 0.79 637.0 343.0 147.00 7.0 3.0 0.0 0.0 29.90 31.27\n",
+ "18 0.79 637.0 343.0 147.00 7.0 4.0 0.0 0.0 29.63 30.93\n",
+ "19 0.79 637.0 343.0 147.00 7.0 5.0 0.0 0.0 28.75 39.44"
+ ]
+ },
+ "execution_count": 53,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head(20)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "id": "8ce464e3-d526-468e-a471-6b181abfa8fb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "independant_col = ['X1','X2','X3','X4','X5']\n",
+ "target_col=['Y1','Y2']\n",
+ "x = df[independant_col]\n",
+ "y =df[target_col]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "id": "cd5421f0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn import linear_model\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
+ "from sklearn.metrics import mean_squared_error, r2_score"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "id": "450409f1-68d1-49a6-b893-bd96786d93ef",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_train,x_test,y_train,y_test= train_test_split(x,y,test_size = 0.2,random_state = 42)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "id": "7ac73354-5427-4cf4-b067-eb622aadcad1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(614, 5)"
+ ]
+ },
+ "execution_count": 57,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x_train.shape\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "id": "154a9ff8-5dbf-44a8-94fa-df380c8a9c37",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(154, 5)"
+ ]
+ },
+ "execution_count": 58,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x_test.shape\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "id": "2e1f3eaf-5bb3-42a2-bf45-f348639e15f7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "3.987012987012987"
+ ]
+ },
+ "execution_count": 59,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "614/154"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dad45d2a-481b-4cc3-90de-ec9fd1e9e41d",
+ "metadata": {},
+ "source": [
+ "**Therefore the given data has been successfully splited into desired ratio**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "id": "ede4d806-3717-4a6a-81d7-df2a96a5d311",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lr_model = LinearRegression()\n",
+ "lr_model.fit(x_train, y_train)\n",
+ "Y_pred_lr = lr_model.predict(x_test)\n",
+ "\n",
+ "# Ridge Regression\n",
+ "ridge_model = Ridge(alpha=1.0)\n",
+ "ridge_model.fit(x_train, y_train)\n",
+ "Y_pred_ridge = ridge_model.predict(x_test)\n",
+ "\n",
+ "# Lasso Regression\n",
+ "lasso_model = Lasso(alpha=1.0)\n",
+ "lasso_model.fit(x_train, y_train)\n",
+ "Y_pred_lasso = lasso_model.predict(x_test)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "58fe5de8-1c93-4b54-895c-8790f6a8ad2a",
+ "metadata": {},
+ "source": [
+ "**The Scores due to the models used are caluclated below**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "id": "87c55bc3-6d84-46ff-82e2-e5d1c7cbb1b4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.8512285840905137"
+ ]
+ },
+ "execution_count": 61,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ridge_model.score(x_test,y_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "id": "e523bd3f-023b-4a4a-a187-8cfa30a9749b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.8516925183420243"
+ ]
+ },
+ "execution_count": 62,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "lr_model.score(x_test,y_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "id": "8aec8d40-7e90-4b24-858b-554f490b23f7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.7813668369997759"
+ ]
+ },
+ "execution_count": 63,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "lasso_model.score(x_test,y_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "id": "a4122725-6243-4f16-8b41-402b00ad56f0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "metrics = {}\n",
+ "def evaluate_model(y_true, y_pred, model_name):\n",
+ " r2 = r2_score(y_true, y_pred)\n",
+ " mse = mean_squared_error(y_true, y_pred)\n",
+ " rmse = np.sqrt(mse)\n",
+ " metrics[model_name] = {\"R2 Score\": r2, \"MSE\": mse, \"RMSE\": rmse}\n",
+ "\n",
+ "\n",
+ "evaluate_model(y_test, Y_pred_lr, \"Sklearn Linear Regression\")\n",
+ "evaluate_model(y_test, Y_pred_ridge, \"Ridge Regression\")\n",
+ "evaluate_model(y_test, Y_pred_lasso, \"Lasso Regression\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0a06c243-8dc5-4039-9aa4-6e7d83c3cb7f",
+ "metadata": {},
+ "source": [
+ "**Plot predicted**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "id": "e5bbf013-ab2d-41c3-a855-22813514afb5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "