This project implements a Housing Price Prediction model using Multiple Linear Regression. The model predicts the price of houses based on various factors such as average area income, house age, number of rooms, number of bedrooms, and area population.
This project demonstrates how to use Multiple Linear Regression to predict housing prices. It includes data preprocessing, model training, and evaluation.
The model uses multiple features to predict the price of a house, including:
- Avg. Area Income: The average income of the area where the house is located.
- Avg. Area House Age: The average age of the houses in the area.
- Avg. Area Number of Rooms: The average number of rooms in houses in the area.
- Avg. Area Number of Bedrooms: The average number of bedrooms in houses in the area.
- Area Population: The population of the area.
- Price: The price of the house (target variable).
The dataset used in this project contains the following columns:
| Column Name | Description |
|---|---|
| Avg. Area Income | The average income in the area |
| Avg. Area House Age | The average age of houses in the area |
| Avg. Area Number of Rooms | The average number of rooms in houses |
| Avg. Area Number of Bedrooms | The average number of bedrooms in houses |
| Area Population | The population of the area |
| Price | The price of the house (target variable) |
Clone the repository and install the necessary dependencies:
git clone https://github.com/yourusername/housing-price-prediction.git
cd housing-price-prediction
pip install -r requirements.txtpython housing_price_predictor.pyThe model's performance is evaluated using the following metrics:
Mean Squared Error (MSE) R² score: A measure of how well the model fits the data.