Car Selling Price Prediction Project

Objective:

This project aims to predict the selling price of cars based on key features such as year of manufacture, kilometers driven, fuel type, seller type, transmission type, and ownership history using machine learning.

Overview:

Dataset:

The dataset comprises 4340 car listings with 8 columns including both numerical and categorical data.

Data Cleaning:

The dataset was thoroughly cleaned with no missing values.

Exploratory Data Analysis (EDA):

Analyzed feature distributions and correlations with selling price using visualizations.

Feature Engineering:

Temporal Data Handling:

Calculated car age from the 'year' feature to derive 'new_year'.

Scaling:

Applied logarithmic scaling to numerical features for normalization.

Model Training and Evaluation:

Linear Regression:

Trained a Linear Regression model and evaluated performance metrics.

Random Forest Regressor:

Utilized a Random Forest model for comparison and achieved higher prediction accuracy.

Conclusion:

This project demonstrates effective use of machine learning to predict car selling prices based on various attributes, providing insights for car buyers and sellers.