Machine Learning on Automobile CO$_2$ Emissions

Table of Content

1. Introduction
   

   • 1.1Fuel consumption testing & determination of CO2 emissions
     
   • 1.2Data Collection
     

2. Problem Statement
   

3. Selecting the important Python libraries
   

4. Data Loading and Description
   

   • 4.1Data Loading
     
   • 4.2Description of the data
     

5. Exploratory Data Analysis
   

   • 5.1Basic understanding of columns and dropping unwanted columns
     
   • 5.2Pandas Profiling
     
   • 5.3Removing Duplicate Entries
     
   • 5.4Understanding the distribution of each columns
     
     • 5.4.1MAKE
       
     • 5.4.2VEHICLE CLASS
       
     • 5.4.3ENGINE SIZE
       
     • 5.4.4CYLINDERS
       
     • 5.4.5TRANSMISSION
       
     • 5.4.6FUEL TYPE
       
     • 5.4.7FUEL CONSUMPTION CITY
       
     • 5.4.8FUEL CONSUMPTION HIGHWAY
       
     • 5.4.9FUEL CONSUMPTION COMBINATION
       
     • 5.4.10CO2 EMISSIONS
       
     • 5.4.11PAIR PLOT
       
     • 5.4.12HEAT MAP
       
     • 5.4.13Conclusion of EDA
       

6. Linear Regression
   

   • 6.1Data Preparation for ML
     
   • 6.1LINEAR REGRESSION ASSUMPTIONS
     
   • 6.3Train Test Split
     
   • 6.4Data Scaling
     
   • 6.5Linear Regression
     
     • 6.5.1Linear Regression Equation Development
       
     • 6.5.2Plotting Predicted vs Actual
       
     • 6.5.3Finding out RMSE and Adjusted R-squared for Train and Test Linear Regression Model
       
   • 6.6 Summary of Linear Regression
     

7. Decision Tree
   

   • 7.1Decision Tree Model 1
     
     • 7.1.1Model Development
       
     • 7.1.2Plotting the model
       
     • 7.1.3Finding out RMSE and Adjusted R-squared for Train and Test Linear Regression Model
       
   • 7.2Decision Tree Model 2
     
     • 7.2.1Model Development
       
     • 7.2.2Plotting the model
       
     • 7.2.3Finding out RMSE and Adjusted R-squared for Train and Test Linear Regression Model
       
   • 7.3Decision Tree GridSearchCV Model
     
     • 7.3.1Model Development
       
     • 7.3.2Plotting the model
       
     • 7.3.3Finding out RMSE and Adjusted R-squared for Train and Test Linear Regression Model
       
   • 7.4Summary of Decision Tree
     

8. Random Forest Regressor
   

   • 8.1Random Forest Model 1
     
     • 8.1.1Model Development
       
     • 8.1.2Plotting the model
       
     • 8.1.3Finding out RMSE and Adjusted R-squared for Train and Test Linear Regression Model
       
   • 8.2Random Forest Model 2
     
     • 8.2.1Model Development
       
     • 8.2.2Plotting the model
       
     • 8.2.3Finding out RMSE and Adjusted R-squared for Train and Test Linear Regression Model
       
   • 8.3Random Forest Model 3
     
     • 8.3.1Model Development
       
     • 8.3.2Plotting the model
       
     • 8.3.3Finding out RMSE and Adjusted R-squared for Train and Test Linear Regression Model
       
     • 8.3.4Summary of Random Forest
       

9. Conclusion
   

1. Introduction