30-Exploratory_data_analysis_cars.ipynb

{"cells":[{"cell_type":"markdown","id":"989c11af-38b2-4dfa-91e7-6f4c47684eaf","metadata":{},"outputs":[],"source":["\u003cp style=\"text-align:center\"\u003e\n","    \u003ca href=\"https://skills.network\" target=\"_blank\"\u003e\n","    \u003cimg src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"300\" alt=\"Skills Network Logo\"\u003e\n","    \u003c/a\u003e\n","\u003c/p\u003e\n","\n","\n","# Exploratory Data Analysis\n","\n","\n","Estimated time needed: **30** minutes\n","    \n","\n","## Objectives\n","\n","After completing this lab you will be able to:\n","\n","* Explore features or characteristics to predict price of car\n","* Analyze patterns and run descriptive statistical analysis\n","* Group data based on identified parameters and create pivot tables\n","* Identify the effect of independent attributes on price of cars\n"]},{"cell_type":"markdown","id":"43ea52b3-24cb-4c74-b695-017c8c07a3ac","metadata":{},"outputs":[],"source":["\u003ch2\u003eTable of Contents\u003c/h2\u003e\n","\n","\u003cdiv class=\"alert alert-block alert-info\" style=\"margin-top: 20px\"\u003e\n","\u003col\u003e\n","    \u003cli\u003e\u003ca href=\"#import_data\"\u003eImport Data from Module\u003c/a\u003e\u003c/li\u003e\n","    \u003cli\u003e\u003ca href=\"#pattern_visualization\"\u003eAnalyzing Individual Feature Patterns using Visualization\u003c/a\u003e\u003c/li\u003e\n","    \u003cli\u003e\u003ca href=\"#discriptive_statistics\"\u003eDescriptive Statistical Analysis\u003c/a\u003e\u003c/li\u003e\n","    \u003cli\u003e\u003ca href=\"#basic_grouping\"\u003eBasics of Grouping\u003c/a\u003e\u003c/li\u003e\n","    \u003cli\u003e\u003ca href=\"#correlation_causation\"\u003eCorrelation and Causation\u003c/a\u003e\u003c/li\u003e\n","\u003c/ol\u003e\n","\n","\u003c/div\u003e\n"," \n","\u003chr\u003e\n"]},{"cell_type":"markdown","id":"e9f2ae84-b3d8-4696-aac5-201aee93a4ff","metadata":{},"outputs":[],"source":["\u003ch3\u003eWhat are the main characteristics that have the most impact on the car price?\u003c/h3\u003e\n"]},{"cell_type":"markdown","id":"91954dcf-5fae-4532-9cee-f5dc2fd26638","metadata":{},"outputs":[],"source":["\u003ch2 id=\"import_data\"\u003e1. Import Data from Module 2\u003c/h2\u003e\n"]},{"cell_type":"markdown","id":"cc394b67-1756-4999-80d7-567c3135ee4c","metadata":{},"outputs":[],"source":["\u003ch4\u003eSetup\u003c/h4\u003e\n"]},{"cell_type":"markdown","id":"cf284640-0aed-4f72-b0d4-03d75973ff5b","metadata":{},"outputs":[],"source":["Import libraries: \n"]},{"cell_type":"code","id":"4ddd141b-7770-4e09-b5b4-5ce43d178dc2","metadata":{},"outputs":[],"source":["#install specific version of libraries used in lab\n#! mamba install pandas==1.3.3\n#! mamba install numpy=1.21.2\n#! mamba install scipy=1.7.1-y\n#!  mamba install seaborn=0.9.0-y"]},{"cell_type":"code","id":"853c58ab-4e06-4c21-b03b-ae91c2d222a5","metadata":{},"outputs":[],"source":["import pandas as pd\nimport numpy as np"]},{"cell_type":"markdown","id":"738ad24f-3bf4-4aa0-8263-44895f1b7c70","metadata":{},"outputs":[],"source":["Load the data and store it in dataframe `df`:\n"]},{"cell_type":"markdown","id":"c3df21ae-c7b7-4ce3-883d-ab35b02ef0a7","metadata":{},"outputs":[],"source":["This dataset was hosted on IBM Cloud object. Click \u003ca href=\"https://cocl.us/DA101EN_object_storage\"\u003eHERE\u003c/a\u003e for free storage.\n"]},{"cell_type":"code","id":"2ded06fc-b78e-4ab2-9534-976269942564","metadata":{},"outputs":[],"source":["path='https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Data%20files/automobileEDA.csv'\ndf = pd.read_csv(path)\ndf.head()"]},{"cell_type":"markdown","id":"52440616-9b24-4ed0-ad93-d7d9bffc5f57","metadata":{},"outputs":[],"source":["\u003ch2 id=\"pattern_visualization\"\u003e2. Analyzing Individual Feature Patterns Using Visualization\u003c/h2\u003e\n"]},{"cell_type":"markdown","id":"81a009cc-b56c-4220-9eea-22d35571fd9d","metadata":{},"outputs":[],"source":["To install Seaborn we use pip, the Python package manager.\n"]},{"cell_type":"markdown","id":"91a4d224-93d5-4a63-bda3-d0beaacc60b0","metadata":{},"outputs":[],"source":["Import visualization packages \"Matplotlib\" and \"Seaborn\". Don't forget about \"%matplotlib inline\" to plot in a Jupyter notebook.\n"]},{"cell_type":"code","id":"b5034b28-282d-48a8-963e-f99de5a4efe0","metadata":{},"outputs":[],"source":["import matplotlib.pyplot as plt\nimport seaborn as sns\n%matplotlib inline "]},{"cell_type":"markdown","id":"fd33df0a-a332-422e-a253-c3d9d3b31423","metadata":{},"outputs":[],"source":["\u003ch4\u003eHow to choose the right visualization method?\u003c/h4\u003e\n","\u003cp\u003eWhen visualizing individual variables, it is important to first understand what type of variable you are dealing with. This will help us find the right visualization method for that variable.\u003c/p\u003e\n"]},{"cell_type":"code","id":"bca2f808-48ac-4d56-9105-7051f1dcb663","metadata":{},"outputs":[],"source":["# list the data types for each column\nprint(df.dtypes)"]},{"cell_type":"markdown","id":"c8d7c216-2cb5-4bc3-b47c-e30844abd19d","metadata":{},"outputs":[],"source":["\u003cdiv class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\"\u003e\n","\u003ch3\u003eQuestion  #1:\u003c/h3\u003e\n","\n","\u003cb\u003eWhat is the data type of the column \"peak-rpm\"? \u003c/b\u003e\n","\u003c/div\u003e\n"]},{"cell_type":"code","id":"db3a66e1-9a1a-4a89-bd22-da3e8a704cd8","metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","id":"6e44aabb-9053-4d59-8d56-485a3a413aac","metadata":{},"outputs":[],"source":["\u003cdetails\u003e\u003csummary\u003eClick here for the solution\u003c/summary\u003e\n","\n","```python    \n","float64\n","```\n","\n","\u003c/details\u003e\n"]},{"cell_type":"markdown","id":"481820dc-a786-4330-b5c8-08bdb84cb034","metadata":{},"outputs":[],"source":["For example, we can calculate the correlation between variables  of type \"int64\" or \"float64\" using the method \"corr\":\n"]},{"cell_type":"code","id":"5d2ba248-0c23-484f-9356-91ef30125041","metadata":{},"outputs":[],"source":["df.corr()"]},{"cell_type":"markdown","id":"3472dbad-6993-4bd6-b19a-2a49aa50d401","metadata":{},"outputs":[],"source":["The diagonal elements are always one; we will study correlation more precisely Pearson correlation in-depth at the end of the notebook.\n"]},{"cell_type":"markdown","id":"c14d8794-8313-4daa-94db-0dfe7067c1c9","metadata":{},"outputs":[],"source":["\u003cdiv class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\"\u003e\n","\u003ch3\u003e Question  #2: \u003c/h3\u003e\n","\n","\u003cp\u003eFind the correlation between the following columns: bore, stroke, compression-ratio, and horsepower.\u003c/p\u003e\n","\u003cp\u003eHint: if you would like to select those columns, use the following syntax: df[['bore','stroke','compression-ratio','horsepower']]\u003c/p\u003e\n","\u003c/div\u003e\n"]},{"cell_type":"code","id":"8804f698-e4b8-486b-b0df-82174c707ac9","metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","id":"ca0b7a6d-c0f5-455e-8d20-c239b538a07d","metadata":{},"outputs":[],"source":["\u003cdetails\u003e\u003csummary\u003eClick here for the solution\u003c/summary\u003e\n","\n","```python\n","df[['bore', 'stroke', 'compression-ratio', 'horsepower']].corr()\n","```\n","\n","\u003c/details\u003e\n"]},{"cell_type":"markdown","id":"5b2f0fb1-7efc-4244-a575-41e6be689e99","metadata":{},"outputs":[],"source":["\u003ch2\u003eContinuous Numerical Variables:\u003c/h2\u003e \n","\n","\u003cp\u003eContinuous numerical variables are variables that may contain any value within some range. They can be of type \"int64\" or \"float64\". A great way to visualize these variables is by using scatterplots with fitted lines.\u003c/p\u003e\n","\n","\u003cp\u003eIn order to start understanding the (linear) relationship between an individual variable and the price, we can use \"regplot\" which plots the scatterplot plus the fitted regression line for the data. This will be useful later on for visualizing the fit of the simple linear regression model as well. \u003c/p\u003e\n"]},{"cell_type":"markdown","id":"66972d11-fb5f-4365-9e2c-69e75c2c0767","metadata":{},"outputs":[],"source":[" Let's see several examples of different linear relationships:\n"]},{"cell_type":"markdown","id":"e8d39d7f-4e35-45df-9da0-2f47e0117dcd","metadata":{},"outputs":[],"source":["\u003ch3\u003ePositive Linear Relationship\u003c/h4\u003e\n"]},{"cell_type":"markdown","id":"55defadf-65fb-4ff6-a3ff-06f6c1335867","metadata":{},"outputs":[],"source":["Let's find the scatterplot of \"engine-size\" and \"price\".\n"]},{"cell_type":"code","id":"a5abe931-f3e5-45ac-b5ab-4d48db465ceb","metadata":{},"outputs":[],"source":["# Engine size as potential predictor variable of price\nsns.regplot(x=\"engine-size\", y=\"price\", data=df)\nplt.ylim(0,)"]},{"cell_type":"markdown","id":"8b060b7c-e69d-40a2-92ad-f6728ce77c23","metadata":{},"outputs":[],"source":["\u003cp\u003eAs the engine-size goes up, the price goes up: this indicates a positive direct correlation between these two variables. Engine size seems like a pretty good predictor of price since the regression line is almost a perfect diagonal line.\u003c/p\u003e\n"]},{"cell_type":"markdown","id":"fdb2c2e6-0d51-4f0f-b11a-42f8f9ddc1fb","metadata":{},"outputs":[],"source":[" We can examine the correlation between 'engine-size' and 'price' and see that it's approximately 0.87.\n"]},{"cell_type":"code","id":"52756fcb-2f9f-4e39-8642-c351b380452c","metadata":{},"outputs":[],"source":["df[[\"engine-size\", \"price\"]].corr()"]},{"cell_type":"markdown","id":"2448a0a5-15c8-45a9-9482-45675642bd82","metadata":{},"outputs":[],"source":["Highway mpg is a potential predictor variable of price. Let's find the scatterplot of \"highway-mpg\" and \"price\".\n"]},{"cell_type":"code","id":"4a1f8fa1-5e70-4760-bbbb-603d20de74b2","metadata":{},"outputs":[],"source":["sns.regplot(x=\"highway-mpg\", y=\"price\", data=df)"]},{"cell_type":"markdown","id":"6348aecc-0bf3-4830-b7ae-764729830001","metadata":{},"outputs":[],"source":["\u003cp\u003eAs highway-mpg goes up, the price goes down: this indicates an inverse/negative relationship between these two variables. Highway mpg could potentially be a predictor of price.\u003c/p\u003e\n"]},{"cell_type":"markdown","id":"8fb6b4f6-8d38-43fe-8292-d931c1b82d86","metadata":{},"outputs":[],"source":["We can examine the correlation between 'highway-mpg' and 'price' and see it's approximately -0.704.\n"]},{"cell_type":"code","id":"ac6340fc-b57e-44d9-beb4-5539ce43715d","metadata":{},"outputs":[],"source":["df[['highway-mpg', 'price']].corr()"]},{"cell_type":"markdown","id":"0b72f597-1815-419c-a489-45b0e42170d3","metadata":{},"outputs":[],"source":["\u003ch3\u003eWeak Linear Relationship\u003c/h3\u003e\n"]},{"cell_type":"markdown","id":"bd15c77d-6db4-494a-8b78-e34c13bb5534","metadata":{},"outputs":[],"source":["Let's see if \"peak-rpm\" is a predictor variable of \"price\".\n"]},{"cell_type":"code","id":"b5db75e4-e832-4360-88ca-8de5da572c12","metadata":{},"outputs":[],"source":["sns.regplot(x=\"peak-rpm\", y=\"price\", data=df)"]},{"cell_type":"markdown","id":"5790eb83-46c7-45ca-b668-556859c78bd1","metadata":{},"outputs":[],"source":["\u003cp\u003ePeak rpm does not seem like a good predictor of the price at all since the regression line is close to horizontal. Also, the data points are very scattered and far from the fitted line, showing lots of variability. Therefore, it's not a reliable variable.\u003c/p\u003e\n"]},{"cell_type":"markdown","id":"b624aefe-143b-4271-81dc-a6fbdabfd8df","metadata":{},"outputs":[],"source":["We can examine the correlation between 'peak-rpm' and 'price' and see it's approximately -0.101616.\n"]},{"cell_type":"code","id":"6ecfbb3f-fb46-47e2-bfbf-64f5bf1ec85f","metadata":{},"outputs":[],"source":["df[['peak-rpm','price']].corr()"]},{"cell_type":"markdown","id":"d523a9da-57cc-4f05-89f0-3928cd7aee09","metadata":{},"outputs":[],"source":[" \u003cdiv class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\"\u003e\n","\u003ch1\u003e Question  3 a): \u003c/h1\u003e\n","\n","\u003cp\u003eFind the correlation  between x=\"stroke\" and y=\"price\".\u003c/p\u003e\n","\u003cp\u003eHint: if you would like to select those columns, use the following syntax: df[[\"stroke\",\"price\"]].  \u003c/p\u003e\n","\u003c/div\u003e\n"]},{"cell_type":"code","id":"0c43817a-9251-4156-9691-94383551e12d","metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute\n"]},{"cell_type":"markdown","id":"afb6dc43-40f2-4fc8-8c6e-3af43e226b04","metadata":{},"outputs":[],"source":["\u003cdetails\u003e\u003csummary\u003eClick here for the solution\u003c/summary\u003e\n","\n","```python\n","\n","#The correlation is 0.0823, the non-diagonal elements of the table.\n","\n","df[[\"stroke\",\"price\"]].corr()\n","\n","```\n","\n","\u003c/details\u003e\n"]},{"cell_type":"markdown","id":"475de5c4-135a-43a9-baab-cb5e291c5965","metadata":{},"outputs":[],"source":["\u003cdiv class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\"\u003e\n","\u003ch1\u003eQuestion  3 b):\u003c/h1\u003e\n","\n","\u003cp\u003eGiven the correlation results between \"price\" and \"stroke\", do you expect a linear relationship?\u003c/p\u003e \n","\u003cp\u003eVerify your results using the function \"regplot()\".\u003c/p\u003e\n","\u003c/div\u003e\n"]},{"cell_type":"code","id":"f7ba4994-a299-40a8-88b7-71f9b2d7c086","metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","id":"69bf9634-d320-44a9-a5b9-fed90909e2dc","metadata":{},"outputs":[],"source":["\u003cdetails\u003e\u003csummary\u003eClick here for the solution\u003c/summary\u003e\n","\n","```python\n","\n","#There is a weak correlation between the variable 'stroke' and 'price.' as such regression will not work well. We can see this using \"regplot\" to demonstrate this.\n","\n","#Code: \n","sns.regplot(x=\"stroke\", y=\"price\", data=df)\n","\n","```\n","\n","\u003c/details\u003e\n"]},{"cell_type":"markdown","id":"4b53ba93-a343-43f7-b27d-2ec84010e109","metadata":{},"outputs":[],"source":["\u003ch3\u003eCategorical Variables\u003c/h3\u003e\n","\n","\u003cp\u003eThese are variables that describe a 'characteristic' of a data unit, and are selected from a small group of categories. The categorical variables can have the type \"object\" or \"int64\". A good way to visualize categorical variables is by using boxplots.\u003c/p\u003e\n"]},{"cell_type":"markdown","id":"d72a2d9a-8bac-4223-9354-652d5690b042","metadata":{},"outputs":[],"source":["Let's look at the relationship between \"body-style\" and \"price\".\n"]},{"cell_type":"code","id":"e221d2eb-60ac-4db2-91b0-708e8e168991","metadata":{},"outputs":[],"source":["sns.boxplot(x=\"body-style\", y=\"price\", data=df)"]},{"cell_type":"markdown","id":"90d58d9d-f379-4085-a58d-37ab1e8fd0b5","metadata":{},"outputs":[],"source":["\u003cp\u003eWe see that the distributions of price between the different body-style categories have a significant overlap, so body-style would not be a good predictor of price. Let's examine engine \"engine-location\" and \"price\":\u003c/p\u003e\n"]},{"cell_type":"code","id":"9e343165-2193-419e-8d8d-429b4a7b18c5","metadata":{},"outputs":[],"source":["sns.boxplot(x=\"engine-location\", y=\"price\", data=df)"]},{"cell_type":"markdown","id":"b726f737-72f8-4544-ac11-d65cec0574d7","metadata":{},"outputs":[],"source":["\u003cp\u003eHere we see that the distribution of price between these two engine-location categories, front and rear, are distinct enough to take engine-location as a potential good predictor of price.\u003c/p\u003e\n"]},{"cell_type":"markdown","id":"9a46e693-8347-41d8-9fd4-7c8fd630c423","metadata":{},"outputs":[],"source":[" Let's examine \"drive-wheels\" and \"price\".\n"]},{"cell_type":"code","id":"927f5fcf-619f-42ee-b768-297f800e301a","metadata":{},"outputs":[],"source":["# drive-wheels\nsns.boxplot(x=\"drive-wheels\", y=\"price\", data=df)"]},{"cell_type":"markdown","id":"ccfffa75-e5ce-44f7-a3ab-6c443dc8370c","metadata":{},"outputs":[],"source":["\u003cp\u003eHere we see that the distribution of price between the different drive-wheels categories differs. As such, drive-wheels could potentially be a predictor of price.\u003c/p\u003e\n"]},{"cell_type":"markdown","id":"697b4329-cfe7-41a2-9905-ff6e9d1b5644","metadata":{},"outputs":[],"source":["\u003ch2 id=\"discriptive_statistics\"\u003e3. Descriptive Statistical Analysis\u003c/h2\u003e\n"]},{"cell_type":"markdown","id":"c9f2e2ea-80f3-4dcd-9c65-8ad518261eca","metadata":{},"outputs":[],"source":["\u003cp\u003eLet's first take a look at the variables by utilizing a description method.\u003c/p\u003e\n","\n","\u003cp\u003eThe \u003cb\u003edescribe\u003c/b\u003e function automatically computes basic statistics for all continuous variables. Any NaN values are automatically skipped in these statistics.\u003c/p\u003e\n","\n","This will show:\n","\u003cul\u003e\n","    \u003cli\u003ethe count of that variable\u003c/li\u003e\n","    \u003cli\u003ethe mean\u003c/li\u003e\n","    \u003cli\u003ethe standard deviation (std)\u003c/li\u003e \n","    \u003cli\u003ethe minimum value\u003c/li\u003e\n","    \u003cli\u003ethe IQR (Interquartile Range: 25%, 50% and 75%)\u003c/li\u003e\n","    \u003cli\u003ethe maximum value\u003c/li\u003e\n","\u003cul\u003e\n"]},{"cell_type":"markdown","id":"59ed03ed-f92b-4805-9dd6-555dea6c4beb","metadata":{},"outputs":[],"source":[" We can apply the method \"describe\" as follows:\n"]},{"cell_type":"code","id":"30c3f393-e7c6-4111-a000-0909d0f120d2","metadata":{},"outputs":[],"source":["df.describe()"]},{"cell_type":"markdown","id":"bb6e0b20-6c8d-4b2a-bc29-48365a3f4589","metadata":{},"outputs":[],"source":[" The default setting of \"describe\" skips variables of type object. We can apply the method \"describe\" on the variables of type 'object' as follows:\n"]},{"cell_type":"code","id":"397ea2bc-bec0-4856-a13b-b0ed4ab02400","metadata":{},"outputs":[],"source":["df.describe(include=['object'])"]},{"cell_type":"markdown","id":"29923536-8021-48dd-8877-ee647c188e0a","metadata":{},"outputs":[],"source":["\u003ch3\u003eValue Counts\u003c/h3\u003e\n"]},{"cell_type":"markdown","id":"13da1df1-fe0e-4071-862e-459773340084","metadata":{},"outputs":[],"source":["\u003cp\u003eValue counts is a good way of understanding how many units of each characteristic/variable we have. We can apply the \"value_counts\" method on the column \"drive-wheels\". Don’t forget the method \"value_counts\" only works on pandas series, not pandas dataframes. As a result, we only include one bracket \u003ccode\u003edf['drive-wheels']\u003c/code\u003e, not two brackets \u003ccode\u003edf[['drive-wheels']]\u003c/code\u003e.\u003c/p\u003e\n"]},{"cell_type":"code","id":"c4452c0e-fa38-4351-b04b-da2ec746fcce","metadata":{},"outputs":[],"source":["df['drive-wheels'].value_counts()"]},{"cell_type":"markdown","id":"cb83bec8-c467-4852-b526-c572cdf8f070","metadata":{},"outputs":[],"source":["We can convert the series to a dataframe as follows:\n"]},{"cell_type":"code","id":"cda6d59e-9dcc-4d53-8983-af11a7c55fce","metadata":{},"outputs":[],"source":["df['drive-wheels'].value_counts().to_frame()"]},{"cell_type":"markdown","id":"e3e18e28-3884-4738-b94c-9963570b9cdb","metadata":{},"outputs":[],"source":["Let's repeat the above steps but save the results to the dataframe \"drive_wheels_counts\" and rename the column  'drive-wheels' to 'value_counts'.\n"]},{"cell_type":"code","id":"ef41ccf4-ebb2-43f2-8fe7-c3e286ae352f","metadata":{},"outputs":[],"source":["drive_wheels_counts = df['drive-wheels'].value_counts().to_frame()\ndrive_wheels_counts.rename(columns={'drive-wheels': 'value_counts'}, inplace=True)\ndrive_wheels_counts"]},{"cell_type":"markdown","id":"e4871e51-1da5-4564-a941-88a05216689c","metadata":{},"outputs":[],"source":[" Now let's rename the index to 'drive-wheels':\n"]},{"cell_type":"code","id":"47e9511a-0361-42cf-9ebd-067dce237f37","metadata":{},"outputs":[],"source":["drive_wheels_counts.index.name = 'drive-wheels'\ndrive_wheels_counts"]},{"cell_type":"markdown","id":"8d5b684c-9d04-4643-87f2-07ec3193b5ff","metadata":{},"outputs":[],"source":["We can repeat the above process for the variable 'engine-location'.\n"]},{"cell_type":"code","id":"28feb576-e707-4a89-ab52-16677e12aa7e","metadata":{},"outputs":[],"source":["# engine-location as variable\nengine_loc_counts = df['engine-location'].value_counts().to_frame()\nengine_loc_counts.rename(columns={'engine-location': 'value_counts'}, inplace=True)\nengine_loc_counts.index.name = 'engine-location'\nengine_loc_counts.head(10)"]},{"cell_type":"markdown","id":"3d324071-0002-476b-9f49-1eccb9ff670f","metadata":{},"outputs":[],"source":["\u003cp\u003eAfter examining the value counts of the engine location, we see that engine location would not be a good predictor variable for the price. This is because we only have three cars with a rear engine and 198 with an engine in the front, so this result is skewed. Thus, we are not able to draw any conclusions about the engine location.\u003c/p\u003e\n"]},{"cell_type":"markdown","id":"a462b7c6-7381-4604-b520-8afef89b0603","metadata":{},"outputs":[],"source":["\u003ch2 id=\"basic_grouping\"\u003e4. Basics of Grouping\u003c/h2\u003e\n"]},{"cell_type":"markdown","id":"ab350712-3da1-4789-9a2a-5eeca0339254","metadata":{},"outputs":[],"source":["\u003cp\u003eThe \"groupby\" method groups data by different categories. The data is grouped based on one or several variables, and analysis is performed on the individual groups.\u003c/p\u003e\n","\n","\u003cp\u003eFor example, let's group by the variable \"drive-wheels\". We see that there are 3 different categories of drive wheels.\u003c/p\u003e\n"]},{"cell_type":"code","id":"225b37f7-30f0-4bb2-a9f3-d3e6764fdd40","metadata":{},"outputs":[],"source":["df['drive-wheels'].unique()"]},{"cell_type":"markdown","id":"876393e7-6424-40dc-ad18-762383261e04","metadata":{},"outputs":[],"source":["\u003cp\u003eIf we want to know, on average, which type of drive wheel is most valuable, we can group \"drive-wheels\" and then average them.\u003c/p\u003e\n","\n","\u003cp\u003eWe can select the columns 'drive-wheels', 'body-style' and 'price', then assign it to the variable \"df_group_one\".\u003c/p\u003e\n"]},{"cell_type":"code","id":"16883e27-4b56-4de4-8106-038905af290f","metadata":{},"outputs":[],"source":["df_group_one = df[['drive-wheels','body-style','price']]"]},{"cell_type":"markdown","id":"717e2129-64e6-4f24-8c3f-86940d21670b","metadata":{},"outputs":[],"source":["We can then calculate the average price for each of the different categories of data.\n"]},{"cell_type":"code","id":"22a8f983-75e5-40c7-85a3-697f757d4995","metadata":{},"outputs":[],"source":["# grouping results\ndf_group_one = df_group_one.groupby(['drive-wheels'],as_index=False).mean()\ndf_group_one"]},{"cell_type":"markdown","id":"406420f4-261f-4899-b7c7-3a5725c748a9","metadata":{},"outputs":[],"source":["\u003cp\u003eFrom our data, it seems rear-wheel drive vehicles are, on average, the most expensive, while 4-wheel and front-wheel are approximately the same in price.\u003c/p\u003e\n","\n","\u003cp\u003eYou can also group by multiple variables. For example, let's group by both 'drive-wheels' and 'body-style'. This groups the dataframe by the unique combination of 'drive-wheels' and 'body-style'. We can store the results in the variable 'grouped_test1'.\u003c/p\u003e\n"]},{"cell_type":"code","id":"092fc0eb-45ab-439a-8605-69b3ba75a0cf","metadata":{},"outputs":[],"source":["# grouping results\ndf_gptest = df[['drive-wheels','body-style','price']]\ngrouped_test1 = df_gptest.groupby(['drive-wheels','body-style'],as_index=False).mean()\ngrouped_test1"]},{"cell_type":"markdown","id":"774d5693-b1da-466f-8728-88d5b28fd533","metadata":{},"outputs":[],"source":["\u003cp\u003eThis grouped data is much easier to visualize when it is made into a pivot table. A pivot table is like an Excel spreadsheet, with one variable along the column and another along the row. We can convert the dataframe to a pivot table using the method \"pivot\" to create a pivot table from the groups.\u003c/p\u003e\n","\n","\u003cp\u003eIn this case, we will leave the drive-wheels variable as the rows of the table, and pivot body-style to become the columns of the table:\u003c/p\u003e\n"]},{"cell_type":"code","id":"07c87895-1b1d-4c84-a6d0-66cf3aed1561","metadata":{},"outputs":[],"source":["grouped_pivot = grouped_test1.pivot(index='drive-wheels',columns='body-style')\ngrouped_pivot"]},{"cell_type":"markdown","id":"f24ff9c5-5fe9-4324-ab5c-1cb98fcf5eed","metadata":{},"outputs":[],"source":["\u003cp\u003eOften, we won't have data for some of the pivot cells. We can fill these missing cells with the value 0, but any other value could potentially be used as well. It should be mentioned that missing data is quite a complex subject and is an entire course on its own.\u003c/p\u003e\n"]},{"cell_type":"code","id":"45135c3b-5e7a-4b78-8178-b70acc1a515a","metadata":{},"outputs":[],"source":["grouped_pivot = grouped_pivot.fillna(0) #fill missing values with 0\ngrouped_pivot"]},{"cell_type":"markdown","id":"aeb7d264-e9a5-4a21-be07-82425c63ef02","metadata":{},"outputs":[],"source":["\u003cdiv class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\"\u003e\n","\u003ch1\u003eQuestion 4:\u003c/h1\u003e\n","\n","\u003cp\u003eUse the \"groupby\" function to find the average \"price\" of each car based on \"body-style\".\u003c/p\u003e\n","\u003c/div\u003e\n"]},{"cell_type":"code","id":"89a72458-073a-4ee6-bcf8-9939a176f47c","metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","id":"3b0bb070-8179-4631-ab33-dee474d780e4","metadata":{},"outputs":[],"source":["\u003cdetails\u003e\u003csummary\u003eClick here for the solution\u003c/summary\u003e\n","\n","```python\n","# grouping results\n","df_gptest2 = df[['body-style','price']]\n","grouped_test_bodystyle = df_gptest2.groupby(['body-style'],as_index= False).mean()\n","grouped_test_bodystyle\n","\n","```\n","\n","\u003c/details\u003e\n"]},{"cell_type":"markdown","id":"4f5859d0-8ad5-4fdc-a81e-d70df61aacd7","metadata":{},"outputs":[],"source":["If you did not import \"pyplot\", let's do it again. \n"]},{"cell_type":"code","id":"e3825169-55d7-49f3-af18-717f716503c7","metadata":{},"outputs":[],"source":["import matplotlib.pyplot as plt\n%matplotlib inline "]},{"cell_type":"markdown","id":"83986a89-0641-44c0-b636-872157e2a9bf","metadata":{},"outputs":[],"source":["\u003ch4\u003eVariables: Drive Wheels and Body Style vs. Price\u003c/h4\u003e\n"]},{"cell_type":"markdown","id":"cf66c4fe-1a8c-46ec-941c-69c287231d1b","metadata":{},"outputs":[],"source":["Let's use a heat map to visualize the relationship between Body Style vs Price.\n"]},{"cell_type":"code","id":"360837d2-a1f5-4e96-850f-7f6325578cc2","metadata":{},"outputs":[],"source":["#use the grouped results\nplt.pcolor(grouped_pivot, cmap='RdBu')\nplt.colorbar()\nplt.show()"]},{"cell_type":"markdown","id":"f648c715-d378-4a72-a31c-02bad7470e2c","metadata":{},"outputs":[],"source":["\u003cp\u003eThe heatmap plots the target variable (price) proportional to colour with respect to the variables 'drive-wheel' and 'body-style' on the vertical and horizontal axis, respectively. This allows us to visualize how the price is related to 'drive-wheel' and 'body-style'.\u003c/p\u003e\n","\n","\u003cp\u003eThe default labels convey no useful information to us. Let's change that:\u003c/p\u003e\n"]},{"cell_type":"code","id":"e148bade-fd60-458c-be98-01e9e7f096fc","metadata":{},"outputs":[],"source":["fig, ax = plt.subplots()\nim = ax.pcolor(grouped_pivot, cmap='RdBu')\n\n#label names\nrow_labels = grouped_pivot.columns.levels[1]\ncol_labels = grouped_pivot.index\n\n#move ticks and labels to the center\nax.set_xticks(np.arange(grouped_pivot.shape[1]) + 0.5, minor=False)\nax.set_yticks(np.arange(grouped_pivot.shape[0]) + 0.5, minor=False)\n\n#insert labels\nax.set_xticklabels(row_labels, minor=False)\nax.set_yticklabels(col_labels, minor=False)\n\n#rotate label if too long\nplt.xticks(rotation=90)\n\nfig.colorbar(im)\nplt.show()"]},{"cell_type":"markdown","id":"6ae30012-3598-4953-a4dd-07b0ffab1eed","metadata":{},"outputs":[],"source":["\u003cp\u003eVisualization is very important in data science, and Python visualization packages provide great freedom. We will go more in-depth in a separate Python visualizations course.\u003c/p\u003e\n","\n","\u003cp\u003eThe main question we want to answer in this module is, \"What are the main characteristics which have the most impact on the car price?\".\u003c/p\u003e\n","\n","\u003cp\u003eTo get a better measure of the important characteristics, we look at the correlation of these variables with the car price. In other words: how is the car price dependent on this variable?\u003c/p\u003e\n"]},{"cell_type":"markdown","id":"eff19644-77da-4513-bb18-ffa7cff76005","metadata":{},"outputs":[],"source":["\u003ch2 id=\"correlation_causation\"\u003e5. Correlation and Causation\u003c/h2\u003e\n"]},{"cell_type":"markdown","id":"76543e10-09a0-495c-9d1f-b78f32eb81b0","metadata":{},"outputs":[],"source":["\u003cp\u003e\u003cb\u003eCorrelation\u003c/b\u003e: a measure of the extent of interdependence between variables.\u003c/p\u003e\n","\n","\u003cp\u003e\u003cb\u003eCausation\u003c/b\u003e: the relationship between cause and effect between two variables.\u003c/p\u003e\n","\n","\u003cp\u003eIt is important to know the difference between these two. Correlation does not imply causation. Determining correlation is much simpler  the determining causation as causation may require independent experimentation.\u003c/p\u003e\n"]},{"cell_type":"markdown","id":"328d8624-0688-46fb-ba30-b7a8f37ae3e0","metadata":{},"outputs":[],"source":["\u003cp\u003e\u003cb\u003ePearson Correlation\u003c/b\u003e\u003c/p\u003e\n","\u003cp\u003eThe Pearson Correlation measures the linear dependence between two variables X and Y.\u003c/p\u003e\n","\u003cp\u003eThe resulting coefficient is a value between -1 and 1 inclusive, where:\u003c/p\u003e\n","\u003cul\u003e\n","    \u003cli\u003e\u003cb\u003e1\u003c/b\u003e: Perfect positive linear correlation.\u003c/li\u003e\n","    \u003cli\u003e\u003cb\u003e0\u003c/b\u003e: No linear correlation, the two variables most likely do not affect each other.\u003c/li\u003e\n","    \u003cli\u003e\u003cb\u003e-1\u003c/b\u003e: Perfect negative linear correlation.\u003c/li\u003e\n","\u003c/ul\u003e\n"]},{"cell_type":"markdown","id":"f44a5ed8-1929-4dba-aba5-5b3606d62268","metadata":{},"outputs":[],"source":["\u003cp\u003ePearson Correlation is the default method of the function \"corr\". Like before, we can calculate the Pearson Correlation of the of the 'int64' or 'float64'  variables.\u003c/p\u003e\n"]},{"cell_type":"code","id":"54f32817-6220-4538-b3ba-d1477db3b3c8","metadata":{},"outputs":[],"source":["df.corr()"]},{"cell_type":"markdown","id":"6b5aa485-a144-4f35-aa22-ce2af8814c26","metadata":{},"outputs":[],"source":["Sometimes we would like to know the significant of the correlation estimate. \n"]},{"cell_type":"markdown","id":"c33ae02c-648f-4e3e-97d7-8e41d3fd4935","metadata":{},"outputs":[],"source":["\u003cb\u003eP-value\u003c/b\u003e\n","\u003cp\u003eWhat is this P-value? The P-value is the probability value that the correlation between these two variables is statistically significant. Normally, we choose a significance level of 0.05, which means that we are 95% confident that the correlation between the variables is significant.\u003c/p\u003e\n","\n","By convention, when the\n","\u003cul\u003e\n","    \u003cli\u003ep-value is $\u003c$ 0.001: we say there is strong evidence that the correlation is significant.\u003c/li\u003e\n","    \u003cli\u003ethe p-value is $\u003c$ 0.05: there is moderate evidence that the correlation is significant.\u003c/li\u003e\n","    \u003cli\u003ethe p-value is $\u003c$ 0.1: there is weak evidence that the correlation is significant.\u003c/li\u003e\n","    \u003cli\u003ethe p-value is $\u003e$ 0.1: there is no evidence that the correlation is significant.\u003c/li\u003e\n","\u003c/ul\u003e\n"]},{"cell_type":"markdown","id":"0c9c3c2d-97e1-484b-baf7-c18a3fd4e688","metadata":{},"outputs":[],"source":[" We can obtain this information using  \"stats\" module in the \"scipy\"  library.\n"]},{"cell_type":"code","id":"77b67c4d-9efc-414f-9e73-43c05ce1fcf2","metadata":{},"outputs":[],"source":["from scipy import stats"]},{"cell_type":"markdown","id":"5a5ed3bb-2d5c-45d1-9b7c-061145db37c9","metadata":{},"outputs":[],"source":["\u003ch3\u003eWheel-Base vs. Price\u003c/h3\u003e\n"]},{"cell_type":"markdown","id":"d666d3f8-686c-4bad-941c-a3614e31e882","metadata":{},"outputs":[],"source":["Let's calculate the  Pearson Correlation Coefficient and P-value of 'wheel-base' and 'price'. \n"]},{"cell_type":"code","id":"2c113b90-8a63-46fc-9750-c855137a8051","metadata":{},"outputs":[],"source":["pearson_coef, p_value = stats.pearsonr(df['wheel-base'], df['price'])\nprint(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value)  "]},{"cell_type":"markdown","id":"e873cf90-fa41-487a-b75c-b49df598d5d1","metadata":{},"outputs":[],"source":["\u003ch4\u003eConclusion:\u003c/h4\u003e\n","\u003cp\u003eSince the p-value is $\u003c$ 0.001, the correlation between wheel-base and price is statistically significant, although the linear relationship isn't extremely strong (~0.585).\u003c/p\u003e\n"]},{"cell_type":"markdown","id":"0b97a786-ad16-45c4-bcbb-ec1b61c02e0a","metadata":{},"outputs":[],"source":["\u003ch3\u003eHorsepower vs. Price\u003c/h3\u003e\n"]},{"cell_type":"markdown","id":"1f9eb814-6368-44b7-8eb9-5cd57b4fe50c","metadata":{},"outputs":[],"source":[" Let's calculate the  Pearson Correlation Coefficient and P-value of 'horsepower' and 'price'.\n"]},{"cell_type":"code","id":"95dd789d-9e24-4a45-b271-14fc88109288","metadata":{},"outputs":[],"source":["pearson_coef, p_value = stats.pearsonr(df['horsepower'], df['price'])\nprint(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value)  "]},{"cell_type":"markdown","id":"4a17ca3b-a8e6-4a41-8cca-d950ea66616c","metadata":{},"outputs":[],"source":["\u003ch4\u003eConclusion:\u003c/h4\u003e\n","\n","\u003cp\u003eSince the p-value is $\u003c$ 0.001, the correlation between horsepower and price is statistically significant, and the linear relationship is quite strong (~0.809, close to 1).\u003c/p\u003e\n"]},{"cell_type":"markdown","id":"562f7b53-7cc6-475b-8a19-6dc8554b8ae4","metadata":{},"outputs":[],"source":["\u003ch3\u003eLength vs. Price\u003c/h3\u003e\n","\n","Let's calculate the  Pearson Correlation Coefficient and P-value of 'length' and 'price'.\n"]},{"cell_type":"code","id":"22ba894a-e186-41a4-b359-0008aa15bfac","metadata":{},"outputs":[],"source":["pearson_coef, p_value = stats.pearsonr(df['length'], df['price'])\nprint(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value)  "]},{"cell_type":"markdown","id":"aaa998f3-0b1a-4ad5-b272-51b5566bbeed","metadata":{},"outputs":[],"source":["\u003ch4\u003eConclusion:\u003c/h4\u003e\n","\u003cp\u003eSince the p-value is $\u003c$ 0.001, the correlation between length and price is statistically significant, and the linear relationship is moderately strong (~0.691).\u003c/p\u003e\n"]},{"cell_type":"markdown","id":"2c465ce0-a57c-43e3-a75d-caedada44c61","metadata":{},"outputs":[],"source":["\u003ch3\u003eWidth vs. Price\u003c/h3\u003e\n"]},{"cell_type":"markdown","id":"e8bc2b42-158c-4d54-9290-83d02c92bdc5","metadata":{},"outputs":[],"source":[" Let's calculate the Pearson Correlation Coefficient and P-value of 'width' and 'price':\n"]},{"cell_type":"code","id":"8c6a02a3-baf1-4be4-91ef-663aec966ce1","metadata":{},"outputs":[],"source":["pearson_coef, p_value = stats.pearsonr(df['width'], df['price'])\nprint(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value ) "]},{"cell_type":"markdown","id":"a79b4c87-463b-46c6-b41a-3ee85f14fc91","metadata":{},"outputs":[],"source":["#### Conclusion:\n","\n","Since the p-value is \u003c 0.001, the correlation between width and price is statistically significant, and the linear relationship is quite strong (~0.751).\n"]},{"cell_type":"markdown","id":"c2be87da-77d5-4736-9da3-29c0807c0c00","metadata":{},"outputs":[],"source":["### Curb-Weight vs. Price\n"]},{"cell_type":"markdown","id":"22beff89-c126-404c-9643-3f89cb4b7221","metadata":{},"outputs":[],"source":[" Let's calculate the Pearson Correlation Coefficient and P-value of 'curb-weight' and 'price':\n"]},{"cell_type":"code","id":"f4c57d09-2991-4662-a35e-09e7e3f9fb07","metadata":{},"outputs":[],"source":["pearson_coef, p_value = stats.pearsonr(df['curb-weight'], df['price'])\nprint( \"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value)  "]},{"cell_type":"markdown","id":"c1ab6444-1a8f-4836-98c3-80a9fcffe560","metadata":{},"outputs":[],"source":["\u003ch4\u003eConclusion:\u003c/h4\u003e\n","\u003cp\u003eSince the p-value is $\u003c$ 0.001, the correlation between curb-weight and price is statistically significant, and the linear relationship is quite strong (~0.834).\u003c/p\u003e\n"]},{"cell_type":"markdown","id":"2220cbb6-438b-4507-b16b-e3a38a0b9ade","metadata":{},"outputs":[],"source":["\u003ch3\u003eEngine-Size vs. Price\u003c/h3\u003e\n","\n","Let's calculate the Pearson Correlation Coefficient and P-value of 'engine-size' and 'price':\n"]},{"cell_type":"code","id":"833e01d9-096a-4528-ba03-290c884b5d17","metadata":{},"outputs":[],"source":["pearson_coef, p_value = stats.pearsonr(df['engine-size'], df['price'])\nprint(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value) "]},{"cell_type":"markdown","id":"5e438cfe-d7db-42f6-b88c-0dd8acd1cb56","metadata":{},"outputs":[],"source":["\u003ch4\u003eConclusion:\u003c/h4\u003e\n","\n","\u003cp\u003eSince the p-value is $\u003c$ 0.001, the correlation between engine-size and price is statistically significant, and the linear relationship is very strong (~0.872).\u003c/p\u003e\n"]},{"cell_type":"markdown","id":"f809249d-41a1-45ef-b8eb-153954ad5b15","metadata":{},"outputs":[],"source":["\u003ch3\u003eBore vs. Price\u003c/h3\u003e\n"]},{"cell_type":"markdown","id":"53277aeb-a2a1-4a24-a257-432cb9fccda1","metadata":{},"outputs":[],"source":[" Let's calculate the  Pearson Correlation Coefficient and P-value of 'bore' and 'price':\n"]},{"cell_type":"code","id":"6b656411-4e42-4b16-b31b-f26ef98ef8f7","metadata":{},"outputs":[],"source":["pearson_coef, p_value = stats.pearsonr(df['bore'], df['price'])\nprint(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =  \", p_value ) "]},{"cell_type":"markdown","id":"c20ab6f6-d6b5-4e5e-93de-9c07615dbdc2","metadata":{},"outputs":[],"source":["\u003ch4\u003eConclusion:\u003c/h4\u003e\n","\u003cp\u003eSince the p-value is $\u003c$ 0.001, the correlation between bore and price is statistically significant, but the linear relationship is only moderate (~0.521).\u003c/p\u003e\n"]},{"cell_type":"markdown","id":"78d3d530-7af2-45ff-9dd4-78e59f25564e","metadata":{},"outputs":[],"source":[" We can relate the process for each 'city-mpg'  and 'highway-mpg':\n"]},{"cell_type":"markdown","id":"6a7b99e5-57a4-4597-830a-01f8d9ae1fa9","metadata":{},"outputs":[],"source":["\u003ch3\u003eCity-mpg vs. Price\u003c/h3\u003e\n"]},{"cell_type":"code","id":"b230167e-469b-4f81-8cdc-672f4e9ac56c","metadata":{},"outputs":[],"source":["pearson_coef, p_value = stats.pearsonr(df['city-mpg'], df['price'])\nprint(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value)  "]},{"cell_type":"markdown","id":"a9357cd8-43f8-463b-a469-9c3dfd0ea1af","metadata":{},"outputs":[],"source":["\u003ch4\u003eConclusion:\u003c/h4\u003e\n","\u003cp\u003eSince the p-value is $\u003c$ 0.001, the correlation between city-mpg and price is statistically significant, and the coefficient of about -0.687 shows that the relationship is negative and moderately strong.\u003c/p\u003e\n"]},{"cell_type":"markdown","id":"4540ded1-25db-43b3-b38b-f6f3b3cef8ce","metadata":{},"outputs":[],"source":["\u003ch3\u003eHighway-mpg vs. Price\u003c/h3\u003e\n"]},{"cell_type":"code","id":"b1364af0-a0c3-40ec-8a66-c704a24858a8","metadata":{},"outputs":[],"source":["pearson_coef, p_value = stats.pearsonr(df['highway-mpg'], df['price'])\nprint( \"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value ) "]},{"cell_type":"markdown","id":"a802961a-dad3-4e2f-b0f4-d97cb9dca0f3","metadata":{},"outputs":[],"source":["#### Conclusion:\n","Since the p-value is \u003c 0.001, the correlation between highway-mpg and price is statistically significant, and the coefficient of about -0.705 shows that the relationship is negative and moderately strong.\n"]},{"cell_type":"markdown","id":"2f9a77a0-d6ae-418c-940a-7fdfe97aeca8","metadata":{},"outputs":[],"source":["\u003ch3\u003eConclusion: Important Variables\u003c/h3\u003e\n"]},{"cell_type":"markdown","id":"6c93ce3e-6233-4d8e-bcfb-682b57f136db","metadata":{},"outputs":[],"source":["\u003cp\u003eWe now have a better idea of what our data looks like and which variables are important to take into account when predicting the car price. We have narrowed it down to the following variables:\u003c/p\u003e\n","\n","Continuous numerical variables:\n","\u003cul\u003e\n","    \u003cli\u003eLength\u003c/li\u003e\n","    \u003cli\u003eWidth\u003c/li\u003e\n","    \u003cli\u003eCurb-weight\u003c/li\u003e\n","    \u003cli\u003eEngine-size\u003c/li\u003e\n","    \u003cli\u003eHorsepower\u003c/li\u003e\n","    \u003cli\u003eCity-mpg\u003c/li\u003e\n","    \u003cli\u003eHighway-mpg\u003c/li\u003e\n","    \u003cli\u003eWheel-base\u003c/li\u003e\n","    \u003cli\u003eBore\u003c/li\u003e\n","\u003c/ul\u003e\n","    \n","Categorical variables:\n","\u003cul\u003e\n","    \u003cli\u003eDrive-wheels\u003c/li\u003e\n","\u003c/ul\u003e\n","\n","\u003cp\u003eAs we now move into building machine learning models to automate our analysis, feeding the model with variables that meaningfully affect our target variable will improve our model's prediction performance.\u003c/p\u003e\n"]},{"cell_type":"markdown","id":"0cf933ca-c085-46ee-b634-243a6bbda802","metadata":{},"outputs":[],"source":["### Thank you for completing this lab!\n","\n","\n","## Author\n","\n","\u003ca href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\"\u003eJoseph Santarcangelo\u003c/a\u003e\n","\n","\n","### Other Contributors\n","\n","\u003ca href=\"https://www.linkedin.com/in/mahdi-noorian-58219234/\" target=\"_blank\"\u003eMahdi Noorian PhD\u003c/a\u003e\n","\n","Bahare Talayian\n","\n","Eric Xiao\n","\n","Steven Dong\n","\n","Parizad\n","\n","Hima Vasudevan\n","\n","\u003ca href=\"https://www.linkedin.com/in/fiorellawever/\" target=\"_blank\"\u003eFiorella Wenver\u003c/a\u003e\n","\n","\u003ca href=\" https://www.linkedin.com/in/yi-leng-yao-84451275/ \" target=\"_blank\" \u003eYi Yao\u003c/a\u003e.\n","\n","\u003ca href=\"https://www.coursera.org/instructor/~129186572/\" target=\"_blank\" \u003eAbhishek Gagneja \u003c/a\u003e\n","\n","## Change Log\n","\n","\n","|  Date (YYYY-MM-DD) |  Version | Changed By  |  Change Description |\n","|---|---|---|---|\n","| 2023-09-28 | 2.2 | Abhishek Gagneja | Updated instructions |\n","| 2020-10-30  | 2.1  | Lakshmi |  changed URL of csv  |\n","| 2020-08-27  | 2.0  | Lavanya  |  Moved lab to course repo in GitLab  |\n","\n","\n","\n","\u003chr\u003e\n","\n","## \u003ch3 align=\"center\"\u003e © IBM Corporation 2023. All rights reserved. \u003ch3/\u003e\n"]}],"metadata":{"kernelspec":{"display_name":"Python","language":"python","name":"conda-env-python-py"},"language_info":{"name":""}},"nbformat":4,"nbformat_minor":4}