ANN - Car Sales Price Prediction

Overview:

This project focuses on building a machine learning model using an Artificial Neural Network (ANN) to predict the price of cars based on multiple features. These features include year of manufacture, mileage, fuel type, transmission type, engine size, and more.

Technologies Used:

Python

TensorFlow and Keras for building the neural network

Pandas and NumPy for data manipulation

Matplotlib and Seaborn for data visualization

Steps Taken:

Collected and cleaned the dataset

Performed exploratory data analysis to understand patterns in the data

Applied encoding and normalization to prepare the data for modeling

Built and trained a neural network model using Keras

Evaluated the model using Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared score

Results:

The model achieved an accuracy of up to 98 percent in predicting car prices on the test dataset. This indicates strong performance and the ability to generalize to unseen data.

Applications:

This model can be used by car-selling platforms, dealerships, or individuals to estimate the fair market value of a vehicle. It can also be integrated into systems for automated price suggestions or comparison tools.