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read.me.txt
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# House Price Prediction using Linear Regression
## Overview
This project predicts house prices based on various features like crime rate, number of rooms, etc., using Linear Regression. The dataset used is the Boston Housing Dataset.
## Project Structure
- **data/**: Contains the dataset.
- **notebooks/**: Contains the Jupyter notebook with the analysis and model building.
- **models/**: Contains trained models (optional).
- **results/**: Contains plots and evaluation metrics (optional).
## Getting Started
1. Clone this repository: `git clone https://github.com/your-username/house-price-prediction.git`
2. Install the dependencies: `pip install -r requirements.txt`
3. Run the Jupyter notebook or Python script to train the model.
## Dataset
The dataset contains the following features:
- **CRIM**: Crime rate per capita
- **RM**: Average number of rooms per dwelling
- ... (Add more feature descriptions)
## Model
- A Linear Regression model is trained to predict house prices.
- Evaluation metrics: MAE, MSE, R²
## Results
- The model achieved an R² score of `0.65`.
## Future Work
- Explore advanced regression techniques like Ridge and Lasso.
- Apply cross-validation for more robust evaluation.