This project focuses on predicting housing prices in Boston suburbs using data from the UCI Machine Learning Repository. The dataset includes features related to housing, neighborhoods, and environmental factors, providing a comprehensive view of what influences housing prices.
The goal is to build an accurate Multiple Linear Regression (MLR) model to predict housing prices. Key steps include model development, hyperparameter tuning, and evaluation to ensure optimal performance.
- Develop a Multiple Linear Regression (MLR) model to predict housing prices.
- Tune the model for better accuracy and reliability.
- Evaluate model performance using key metrics.
- Python for data analysis and modeling.
- pandas, numpy for data preprocessing.
- scikit-learn for regression modeling and hyperparameter tuning.
- matplotlib, seaborn for visualizations.
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Data Preprocessing:
- Cleaned and explored the dataset to identify trends and correlations.
- Handled missing values and outliers.
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Model Development:
- Built a Multiple Linear Regression (MLR) model.
- Tuned the model to improve prediction accuracy.
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Evaluation:
- Used metrics like R² Score and Mean Squared Error (MSE) to assess model performance.
- Fine-tuned the model for enhanced reliability.