datasciencevishal/car_price_prediction

Car Price Prediction using Linear Regression, Ridge Regression and Lasso Regression

Car Price Prediction Using Regression Analysis

This project focuses on predicting car prices using various regression techniques including Linear Regression, Lasso Regression, and Ridge Regression. The dataset contains information about various car features such as fuel type, car body type, engine specifications, and more. Here's a breakdown of the project:

Objective

The main objective is to build regression models to predict car prices based on the provided features.

Sections Covered

1. Data Understanding and Exploration

   • Explored the dataset to understand its structure and distribution of features.
   • Examined the relationship between different features and the target variable (car price).

2. Data Cleaning

   • Checked for missing values and outliers in the dataset.
   • Handled categorical variables by encoding them appropriately.

3. Data Preparation

   • Scaled the numerical features to bring them within the same range.
   • Split the data into training and testing sets.

4. Model Building and Evaluation

   • Implemented Linear Regression to predict car prices.
   • Utilized Lasso Regression to perform feature selection and improve model performance.
   • Applied Ridge Regression to introduce regularization and prevent overfitting.
   • Evaluated the performance of each model using appropriate metrics such as Mean Squared Error (MSE) and R-squared.

Conclusion

This project demonstrates the application of regression techniques in predicting car prices. It highlights the importance of data preprocessing, feature selection, and model evaluation in building accurate predictive models for car price estimation.

For detailed implementation and code, refer to the Jupyter notebook provided in this repository.

Feel free to explore and contribute to further enhancements of the project!