A machine learning project to estimate housing prices based on features like location, area, and amenities.
- Predicts housing prices.
- Exploratory Data Analysis (EDA) on housing datasets.
- Visualizations to understand feature importance and correlations.
- Easy-to-use Jupyter Notebook for experimentation.
- Python
- Pandas, NumPy, Matplotlib, Seaborn
- Scikit-learn
- Jupyter Notebook
- Clone the repository:
git clone <your-repo-url>
2.Install dependencies: pip install -r requirements.txt
3.Open Housing_Price_Prediction.ipynb in Jupyter Notebook.
4.Run the notebook cells step by step to explore data and see predictions.
Housing_Price_Prediction.ipynb → Main notebook with analysis and prediction
.csv → Dataset
requirements.txt → Python dependencies