🏠 Housing Price Prediction

This is a beginner-friendly machine learning project that predicts housing prices using various features of a house (e.g., size, bedrooms etc). The project includes data cleaning, feature encoding, model training, and evaluation.

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📊 Dataset

The dataset contains housing features such as:

• Area
• Location on main road
• Number of bedrooms and bathrooms
• Furnishing status
• etc

✅ All categorical data is label-encoded for model training.

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🧪 Technologies Used

• Python 🐍
• Pandas & NumPy (for data manipulation)
• Matplotlib & Seaborn (for visualization)
• Scikit-learn (for ML modeling and evaluation)
• Jupyter Notebook (for development)

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🔍 Exploratory Data Analysis (EDA)

• Checked for missing values
• Visualized price distribution
• Generated a correlation heatmap to find the most relevant features

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🧠 Model

• Linear Regression was used to predict housing prices.
• Evaluated using:
  • Mean Absolute Error (MAE)
  • Mean Squared Error (MSE)
  • R² Score

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📈 Results

• Mean Absolute Error (MAE): 970043.40 Mean Squared Error (MSE): 1754318687330.66 R² Score: 0.65

🔍 Not optimized — this is a baseline model. Future improvements can include feature engineering, outlier handling, and advanced models like Random Forest or XGBoost.

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🚀 How to Run

1. Clone this repository
2. Open the notebook in Jupyter or VS Code
3. Run each cell to follow the full workflow

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🙌 Acknowledgements

• Based on learnings from Andrew Ng’s ML Course
• Dataset from kaggle: https://www.kaggle.com/datasets/harishkumardatalab/housing-price-prediction

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📬 Contact

Created by Piyush
📧 Drop a message or connect with me on LinkedIn

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