🚗 Car Price Prediction

This project predicts car prices based on various features using machine learning techniques such as Linear Regression. It's a beginner-friendly project for Data Science learners.

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📌 Problem Statement

Estimate the price of a used car based on features like year, present price, number of owners, fuel type, seller type, and transmission.

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🧠 Algorithms Used

• Linear Regression
• Random Forest (optional advanced version)

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🛠️ Tech Stack

• Python
• Pandas
• NumPy
• Matplotlib / Seaborn
• Scikit-learn

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📊 Dataset

The dataset used is car_data.csv, which contains the following columns:

• Car_Name
• Year
• Selling_Price
• Present_Price
• Kms_Driven
• Fuel_Type
• Seller_Type
• Transmission
• Owner

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🔍 EDA (Exploratory Data Analysis)

• Removed car names for modeling.
• Converted categorical variables (Fuel Type, Seller Type, Transmission) to dummy variables.
• Created a new column: Car_Age = Current Year - Year.

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🧪 Model Building

python

from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split from sklearn.metrics import r2_score

model = LinearRegression() X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

model.fit(X_train, y_train) pred = model.predict(X_test) r2_score(y_test, pred)

✅ Results

R² Score: ~0.85

The model works well for price estimation but can be improved using ensemble methods.

📦 Installation

bash Copy Edit git clone (https://github.com/naveenk-DS/Car_Price_prediction) cd car-price-prediction pip install -r requirements.txt

💡 Future Improvements

Add more models (Random Forest, XGBoost).

Deploy using Flask or Streamlit.

Use a larger dataset for better accuracy.

🤝 Contributing

Feel free to fork this repo, improve the model or add a UI, and submit a pull request!

📧 Contact

Created by Naveen 📧 Email: naveends6k@gmail.com

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✅ Sample requirements.txt

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numpy pandas scikit-learn matplotlib seaborn