MODEL'S CODE
Data Cleaning & Pre-Processing
Data Cleaning & Pre-Processing
1. Importing Necessary Libraries
2. Reading the Dataset
3. Separating Categorical and Numerical Data
4. Checking for Missing Values
5. Number of Unique Values in Each Column
6. Handling Rare Instances in 'Transmission'
7. Handling Rare Instances in 'Fuel Type'
8. Range of the Label (Price)
9. Label Encoding and One-Hot Encoding
10. Train Test Split
11. Feature Scaling
PCA
PCA
12. Principal Component Analysis (PCA)
13. Explained Variance
Simple Models
Simple Models
14. Importing Simple Models
15. Training and Performance Evaluation
16. Comparison Between Different Simple Models
Ensemble Models
Ensemble Models
17. Importing Ensemble Models
18. Training Ensemble Models
19. Comparison Between Different Ensemble Models
Testing the Models
Testing the Models
20. Creating a New Instance and Preprocessing
21. Prediction by Simple Models
22. Prediction by Ensemble Models
23. Saving the Models for Future Use
