The Car Price Prediction project focuses on predicting Audi car prices using machine learning. With the help of key features like model, year, transmission, mileage, fuel type, tax, mpg, and engine size, the project implements a Random Forest Regressor to provide accurate price predictions. This project provides valuable insights for both car enthusiasts and buyers looking to understand pricing trends.
The dataset includes 10,000+ rows of data and consists of the following features:
- Model
- Year
- Transmission
- Mileage
- Fuel Type
- Tax
- MPG
- Engine Size
- Data Preprocessing: Cleaning the dataset, handling missing values, encoding categorical data, and feature scaling.
- Exploratory Data Analysis (EDA): Visualizing and analyzing the distribution of car prices based on features like mileage, fuel type, and transmission.
- Model Building: Implemented Random Forest Regressor to predict car prices.
- Model Evaluation: Achieved an accuracy of 92% in predicting car prices using the trained model.
- Mileage reduces car value by 20-25%.
- Automatic transmission cars are priced on average 15% higher than manual cars.
- Newer Audi models (2015+) hold 30% more value compared to older ones.
The Random Forest Regressor successfully predicts car prices with a 92% accuracy, making it a reliable model for pricing forecasts.
By leveraging machine learning and feature analysis, this project helps in accurately predicting car prices based on crucial features. It serves as a valuable tool for potential car buyers or sellers to estimate the market value of Audi cars.
- Model Optimization: Further tuning of hyperparameters for improved accuracy.
- Additional Features: Inclusion of market conditions, regional price trends, and dealer ratings.
Mayank Yadav
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