https://github.com/saikumar787/car_price_prediction_using_linear-regression

A machine learning project to predict the selling price of used cars using regression techniques. Includes data preprocessing, model training, evaluation, and testing on new data.
https://github.com/saikumar787/car_price_prediction_using_linear-regression

car-price-prediction-with-machine-learning data-analysis joblib jupiter-notebook linear-regression-models model-deployment python scikit-learn standardscaler

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A machine learning project to predict the selling price of used cars using regression techniques. Includes data preprocessing, model training, evaluation, and testing on new data.

• Host: GitHub
• URL: https://github.com/saikumar787/car_price_prediction_using_linear-regression
• Owner: saikumar787
• Created: 2025-06-17T15:01:43.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2025-06-17T15:16:57.000Z (12 months ago)
• Last Synced: 2025-06-17T16:25:08.806Z (12 months ago)
• Topics: car-price-prediction-with-machine-learning, data-analysis, joblib, jupiter-notebook, linear-regression-models, model-deployment, python, scikit-learn, standardscaler
• Language: Jupyter Notebook
• Homepage: https://saikumar787.github.io/Car_Price_Prediction_Using_Linear-Regression/
• Size: 239 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          

# 🚗 Car Price Prediction using Machine Learning

This project predicts the selling price of a used car based on features such as year, fuel type, kilometers driven, seller type, and more.

## 📂 Files Included

- `cars.csv` — The dataset containing used car listings with features like year, present price, kms driven, fuel type, seller type, transmission, etc.

- `Car_Price_Prediction.ipynb` — Jupyter Notebook for:

  - Exploratory Data Analysis (EDA)

  - Data preprocessing (handling categorical features using OneHotEncoding)

  - Model training using Linear Regression

  - Evaluation and visualization

- `model.pkl` — The trained machine learning model saved using `joblib`.

- `ss.pkl` — StandardScaler object used to scale input features before prediction.

- `testing_on_newdata.ipynb` — A separate notebook used to test the saved model on new data inputs.

## 🧠 Model Overview

- **Model Used**: Linear Regression

- **Target Variable**: Selling Price

- **Features Used**:

  - Year

  - Present Price

  - Kms Driven

  - Fuel Type (converted using one-hot encoding)

  - Seller Type

  - Transmission

## 📈 Example Prediction

```python

new_data = np.array([[2018, 8.5, 25000, 0, 1, 1]])  # Encoded input

scaled = scaler.transform(new_data)

price = model.predict(scaled)

print("Predicted Price:", price)

```

## ✅ Requirements

- pandas

- numpy

- matplotlib

- seaborn

- scikit-learn

- joblib

Install them using:

```bash

pip install -r requirements.txt

```

## 📌 How to Use

1. Clone the repo:

   ```bash

   git clone https://github.com/yourusername/Car-Price-Prediction.git

   ```

2. Open `Car_Price_Prediction.ipynb` to train the model or understand the process.

3. Use `testing_on_newdata.ipynb` to test the model on unseen data.

## 👨‍💻 Author

- Venkata Sai Kumar Routhu