Car Price Prediction using Linear Regression and Streamlit

Introduction

This project focuses on predicting car prices using a Linear Regression model, with the additional feature of a user-friendly web interface built with Streamlit. The app allows users to input car details (such as car name, company, year, kilometers driven, and fuel type) and receive an estimated price prediction based on a trained model. The project also includes an animated GIF of a car for a visually interactive experience.

The machine learning model for predicting car prices is trained using a dataset loaded from a CSV file, and the model is serialized using pickle for easy deployment.

Project Features

• Interactive Web Interface: Built with Streamlit, the app allows users to input car details and get an estimated car price prediction.
• Car Animation: A car animation GIF is embedded on the left side of the web app for a more engaging user experience.
• Price Prediction: The app uses a pre-trained Linear Regression model to predict car prices based on user inputs.
• Data Preprocessing: Data is cleaned and processed before training the model.
• Model Deployment: The trained model is saved using pickle for deployment in the web app.

Code Structure

The code is divided into several sections, each with a specific purpose:

Importing Libraries and Loading Data

import pandas as pd
import numpy as np
import pickle
import streamlit as st 

Load the car dataset from a CSV file

car = pd.read_csv('Cleaned car.csv')

Running the Code

Make sure you have Python installed on your system.

1. Install the required libraries:

   pip install pandas numpy scikit-learn streamlit

2. Download the CSV file containing the car data and specify the correct file path in the code.

3. Run the Streamlit app:

   streamlit run app.py

The code will load the dataset, preprocess the data, train a Linear Regression model, and evaluate its performance. You can then use the trained model to make price predictions for new data.

Results

The app provides the predicted car price based on the input features. It also displays a car animation GIF for an enhanced user experience.

Future Enhancements

• Experiment with other machine learning models to improve prediction accuracy.
• Add more input features (e.g., car condition, location, etc.) for better predictions.
• Deploy the app on a cloud platform like Heroku or AWS to make it publicly accessible.