Car Price Prediction

Car Price Prediction

This project is a web application that predicts the selling price of a car based on various features like the car's present price, distance driven, fuel type, seller type, transmission type, number of owners, and the year it was purchased. The application is built using Streamlit and a trained machine learning model.

Features:

• Interactive Interface: Designed a user-friendly Streamlit application for predicting used cars price when provided the asked data.
• Detailed Feedback:Provides clear and actionable results of what would be the price of the car.

Usage: ]

1. Open your browser and go to https://car-price-prediction-nb.streamlit.app/.
2. Enter the required information about the car:
• Name of the Car
• Current Price of the Car (in Lakhs)
• Distance Driven (kms)
• Fuel Type (Petrol, Diesel, CNG)
• Dealer or Individual
• Transmission (Manual, Automatic)
• Number of Owners
• Year of Purchase
3. Click on the "Predict" button to get the predicted selling price of the car.

Technologies Used:

• Python
• Streamlit & Joblib:For building the interactive web application.
• Pandas: For data manipulation and preprocessing.
• Scikit-learn: For machine learning model training and evaluation.
• Matplotlib and Seaborn:For data visualization.

Machine Learning Models
This project utilizes several machine learning models and techniques for predicting if the email entered is spam or not:

Linear Regression:Applied to predict the price of the used cars and achieved an accuracy of 87.4%

Random Forest Classifier: Used to enhance regression accuracy and handle complex data features, improving overall model performance. Accuracy of 94% Achieved.