added new files

[Sai-ganesh-0004]

Description

Implemented the Lasso Regression model for predicting car prices. The model includes preprocessing of the dataset, feature selection, and visualization of feature importance. Additionally, the code is structured into separate modules for better readability and maintainability.

Issue Resolved

This PR resolves #90 .

Changes Made

• Added data preprocessing functions to handle missing values and convert categorical features.
• Implemented Lasso Regression in predict.py for car price prediction.
• Created a function to visualize feature importance in model.py.
• Organized the code into a modular structure with separate files for data preprocessing, model training, evaluation, and visualization.

Additional Details

This implementation improves the model's performance by optimizing feature selection through regularization.

Checklist

• My code follows the current project structure
• I have thoroughly reviewed and updated the requirements.txt file to include any new packages
• The predict.py file includes a properly implemented model_details() function, or the notebook contains this function to print a detailed model report. The model will not be accepted without this function, as it is essential for generating the necessary model details.
• I have added relevant tests (if necessary).
• I have added comments in the code where needed.
• This PR is submitted under Hacktoberfest.
• This PR is submitted under GirlScript Summer of Code (GSSoC-Extd).

Screenshots of the output