This project develops a machine learning model to predict housing prices using Linear Regression. The goal is to analyze housing data and understand how different property characteristics influence real estate prices.
The notebook demonstrates a complete data science workflow including exploratory data analysis, data preprocessing, feature analysis, model training, model evaluation, and interpretation of results.
Linear regression provides a transparent and interpretable baseline model that helps explain the relationship between housing features and property prices.
The dataset contains information about residential properties and includes variables commonly used in real estate price modeling.
| Feature | Description |
|---|---|
| Square Footage | Total living area of the property |
| Bedrooms | Number of bedrooms |
| Bathrooms | Number of bathrooms |
| Lot Size | Size of the property lot |
| Year Built | Year the property was constructed |
| Location Attributes | Neighborhood or geographic indicators |
| Price | Target variable representing housing value |
These variables are used to train a regression model capable of predicting housing prices.
Exploratory analysis is conducted to understand the dataset structure and identify patterns or anomalies. Key steps include summary statistics, distribution analysis, correlation exploration, outlier detection, and visualization of relationships between features and the target variable.
Visualizations are created using Matplotlib and Seaborn.
Before model training, the dataset is cleaned and prepared. This includes handling missing values, removing duplicate records, selecting relevant features, encoding categorical variables if present, and scaling numerical features when necessary.
These steps ensure the dataset is suitable for machine learning modeling.
A Linear Regression model is implemented using the Scikit-learn library.
The regression model estimates the relationship between housing features and price using the formula:
Price = β0 + β1X1 + β2X2 + ... + βnXn
Where:
- X represents input features
- β represents model coefficients
- β0 represents the intercept
This allows the model to estimate how much each feature contributes to the predicted housing price.
Model performance is evaluated using several regression metrics.
| Metric | Description |
|---|---|
| MAE | Mean Absolute Error – average magnitude of prediction error |
| MSE | Mean Squared Error – penalizes larger prediction errors |
| RMSE | Root Mean Squared Error – interpretable error metric |
| R² Score | Measures variance explained by the model |
These metrics help determine how accurately the model predicts housing prices.
The analysis highlights which housing characteristics most strongly influence property prices. Typical influential factors include property size, number of bedrooms and bathrooms, location attributes, property age, and lot size.
Understanding these relationships provides insights into housing market dynamics and property valuation.
- Python
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
Clone the repository:
git clone https://github.com/yourusername/housing-price-prediction.git
Navigate to the project directory:
cd housing-price-prediction
Install dependencies:
pip install -r requirements.txt
Launch Jupyter Notebook:
jupyter notebook
Then open:
Housing_Price_Prediction_Linear_Regression.ipynb
Possible enhancements include implementing Ridge Regression, Lasso Regression, Random Forest models, Gradient Boosting models, hyperparameter tuning, cross-validation, and additional feature engineering.
These improvements could further increase predictive accuracy and model robustness.
Daniel Miranda