🚗 Car-Resale-Price-Predictor-RandomForest - Estimate Used Car Value Fast

🖥️ What This App Does

Car-Resale-Price-Predictor-RandomForest helps you check the resale price of a used car based on a few details, such as:

• Engine size
• Mileage
• Max power
• Fuel type
• Vehicle age
• Transmission type

It uses a Random Forest Regression model trained on more than 15,000 CarDekho listings. The model is built to give a clear price estimate from simple car details.

📦 What You Need

Before you run the app on Windows, make sure you have:

• A Windows 10 or Windows 11 PC
• Internet access for the first download
• Enough free space to save the app files
• A modern web browser if the app opens in one

If the app comes as a folder or file in the repository, you will need to download it from the link above and keep the files together in one place.

⬇️ Download the App

Use this link to visit the download page and get the files:

Go to the download page

After the page opens:

1. Look for the green Code button or any release file.
2. Download the repository as a ZIP file, or download the app file if one is listed.
3. Save it to your Desktop or Downloads folder.
4. If it is a ZIP file, right-click it and choose Extract All.

🪟 Run It on Windows

After you download and extract the files, follow these steps:

1. Open the folder you just extracted.
2. Look for the main app file.
3. If you see a file with .exe, double-click it.
4. If you see a Python file like app.py or main.py, open it from the project folder with the required Python setup.
5. Wait for the app window or browser page to open.
6. Enter the car details when asked.
7. Click the button to get the resale price estimate.

If Windows shows a security prompt, choose the option that lets the app run.

🧭 How to Use the App

Use the form one field at a time:

1. Enter the car brand and model if the app asks for them.
2. Type the year or age of the car.
3. Add the fuel type.
4. Add the transmission type.
5. Enter mileage.
6. Enter engine size.
7. Enter max power.
8. Submit the form to see the estimate.

For best results, use the details from the car’s registration card, service records, or listing page.

📋 Example Input

You may see fields like these:

• Fuel Type: Petrol
• Transmission: Manual
• Mileage: 20 kmpl
• Engine: 1197 cc
• Max Power: 82 bhp
• Year: 2018
• Kilometers Driven: 45,000

The app then gives a price estimate based on the trained model.

🔍 How the Model Works

The app uses Random Forest Regression. This model looks at many decision paths at the same time and combines the results into one estimate.

It was trained on cleaned CarDekho data with:

• Data cleaning
• Feature selection
• Exploratory data analysis
• Hyperparameter tuning
• Regression testing with scikit-learn

This helps the model learn how car features connect to resale value.

📁 Project Files

The repository may include files such as:

• app.py or main.py for the main app
• model.pkl or joblib file for the saved model
• requirements.txt for Python package setup
• README.md for project details
• notebooks for data cleaning and model work

Keep the files in the same folder if the app depends on them.

⚙️ Basic Setup on Windows

If the app needs Python, install it first:

1. Download Python from the official Python website.
2. During install, check Add Python to PATH.
3. Open Command Prompt.
4. Go to the project folder with the cd command.
5. Install the needed packages with the package list file, if included.
6. Start the app file.

A typical setup can include:

• Python
• pandas
• numpy
• scikit-learn
• joblib
• seaborn
• matplotlib

🧪 What Affects the Price Estimate

The model uses features that often change used car value:

• Brand and model
• Year
• Fuel type
• Transmission
• Engine size
• Mileage
• Max power
• Number of owners
• Kilometers driven

Cars with lower mileage, newer model years, and stronger engine power often get higher estimates, while older cars with high mileage often get lower ones.

🔐 Data Source

This project uses CarDekho listing data. The dataset contains more than 15,000 entries and reflects real used car market patterns.

That gives the model a wide base for price estimation across many car types and conditions.

📌 Common Use Cases

People may use this app to:

• Check a used car price before buying
• Compare a seller’s asking price with an estimate
• Review a listing before negotiation
• Get a quick market range for a vehicle

🛠️ If the App Does Not Open

Try these steps:

1. Check that all files are still in the same folder.
2. Make sure the ZIP file was fully extracted.
3. Run the app again from the extracted folder.
4. If the app uses Python, confirm Python is installed.
5. Make sure any model file is present in the project folder.
6. Close and reopen the app if it freezes on launch.

If the app opens in a browser, refresh the page and try again.

📥 Download Again

If you need the files again, use this link:

Download from GitHub

🧩 Useful Search Tags

anmol-patel, car-resale-price-prediction, cardekho, data-cleaning, data-science, exploratory-data-analysis, hyperparameter-tuning, joblib, machine-learning, python, random-forest, random-forest-regression, regression, scikit-learn, seaborn