House Price Prediction using Machine Learning

A machine learning project that predicts house prices using Linear Regression. The model is trained on a housing dataset and uses various property features to estimate house prices.

Features

• Predicts house prices using Linear Regression
• Uses a real-world housing dataset (Housing.csv)
• Performs data preprocessing using Pandas
• Converts categorical data into numerical values using One-Hot Encoding
• Splits data into training and testing sets
• Evaluates model performance using R² Score
• Displays predicted house prices

Technologies Used

• Python
• Pandas
• Scikit-learn
• Linear Regression
• Jupyter Notebook / Python IDE

Project Structure

House_Price_Prediction/
├── house_price_prediction.py
├── Housing.csv
├── images/
│   └── output.png
└── README.md

How to Run

1. Clone the repository

git clone git@github.com:vishakha-code27/house-price-prediction-python.git

2. Navigate to the project folder

cd house-price-prediction-python

3. Install required libraries

pip install pandas scikit-learn

4. Run the Python file

python house_price_prediction.py

Model Evaluation

The model performance is measured using the R² Score, which evaluates how well the model explains variations in house prices.

Output

The model displays:

• First 5 predicted house prices
• R² Score of the trained Linear Regression model

Project Screenshot

Learning Outcomes

This project helped me learn:

• Data preprocessing using Pandas
• Handling categorical data
• Linear Regression implementation
• Model training and testing
• Machine learning evaluation metrics
• Working with real-world datasets

Author

Vishakha Chavan

GitHub: https://github.com/vishakha-code27