Car Price Prediction using Neural Networks

This repository contains Python code for predicting car prices using a neural network model. The dataset used for this prediction is cardekho_data.csv. The dataset contains information about various attributes of cars such as selling price, present price, kilometers driven, fuel type, seller type, transmission type, etc.

Requirements

• Python 3.x
• Libraries: pandas, numpy, seaborn, sklearn, matplotlib, tensorflow

Usage

1. Clone the repository:

git clone https://github.com/khush1709/Car-Price-Prediction-using-Neural-Networks.git

2. Install the required libraries:

pip install -r requirements.txt

3. Run the Python script:

python car_price_prediction.py

Description

• The Python script loads the dataset and preprocesses it by encoding categorical variables, handling outliers, and scaling the numerical features.
• A neural network model is built using TensorFlow's Keras API with multiple dense layers.
• The model is trained on the preprocessed data.
• Finally, the model is evaluated using mean squared error and R-squared score metrics.

Files

• cardekho_data.csv: Dataset containing car information.
• car_price_prediction.py: Python script for preprocessing, model building, training, and evaluation.
• README.md: This file containing information about the project and usage instructions.

Author

Khushal Gautam