Car Price Prediction using Machine Learning

This GitHub repository contains a comprehensive solution for predicting car prices based on various attributes. The code utilizes techniques in data preprocessing, feature engineering, and model evaluation. Below is an in-depth overview of the key components of this project:

Project Structure:

1. Introduction:

   • The project aims to predict the selling price of cars using machine learning algorithms.
   • Dataset: A detailed dataset including information on car name, manufacturing year, selling price, present price, kilometers driven, fuel type, seller type, transmission, and owner details.

2. Libraries Used:

   • pandas: Data manipulation and analysis.
   • numpy: Numerical computing.
   • seaborn and matplotlib: Data visualization.
   • sklearn: Implementation of machine learning algorithms.

3. Data Exploration and Preprocessing:

   • Imported the dataset and performed an initial exploration using df.head() and df.info() to understand the data structure.
   • Checked for null values and handled them appropriately.
   • Dropped irrelevant columns, such as 'Car_Name'.
   • Created a new feature, 'No_of_Years', representing the age of the car.
   • Converted categorical variables into numerical format using one-hot encoding.

4. Feature Importance:

   • Determined feature importance using the ExtraTreesRegressor algorithm to identify the most impactful features on selling prices.
   • Visualized the top 5 important features through a bar plot.

5. Model Training:

   • Split the data into training and testing sets using train_test_split.
   • Trained two models: Linear Regression and Random Forest Regression.

6. Model Evaluation:

   • Evaluated models using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) score.
   • Visualized predictions with density plots, scatter plots, and line plots.

7. Prediction:

   • Made predictions using both Linear Regression and Random Forest Regression models.
   • Provided a test data point to demonstrate how to predict the selling price of a car.

8. Advanced Features:

   • Explored advanced concepts such as feature scaling, cross-validation, and hyperparameter tuning for enhanced model performance.

9. Conclusion:

   • Encouraged users to clone the repository and explore the code for predicting car prices in their own datasets.
   • Highlighted the importance of understanding and adapting the code to specific requirements.

10. Notes for Users:

    • Users are advised to install necessary libraries and dependencies before running the code.
    • This README.md file serves as a detailed guide for understanding the code and its functionalities.

11. Future Improvements:

    • Provided suggestions for potential improvements, such as incorporating more advanced machine learning models or exploring ensemble techniques.

This GitHub repository serves as a robust solution for predicting car prices, integrating advanced techniques to ensure accurate and reliable results. Users can leverage this codebase for their own projects and further enhance it based on specific needs.