This repository contains a project aimed at predicting house prices using the California Housing Dataset. The dataset is based on housing data from the 1990 U.S. Census, focusing on predicting the median house value using features such as median income, total rooms, population, and more. The project involves two machine learning models: Linear Regression and Random Forest.
The dataset contains the following columns:
| Column Name | Description |
|---|---|
longitude |
Longitude of the district (geographical coordinate) |
latitude |
Latitude of the district (geographical coordinate) |
housing_median_age |
Median age of the houses in the district |
total_rooms |
Total number of rooms in all houses in the district |
total_bedrooms |
Total number of bedrooms in all houses in the district |
population |
Population of the district |
households |
Total number of households in the district |
median_income |
Median income of the households in the district |
median_house_value |
Median house value (target variable) |
ocean_proximity |
Proximity to the ocean (categorical variable) |
A simple regression model that assumes a linear relationship between the features and the target variable (median house value).
A more complex ensemble model that builds multiple decision trees and averages their predictions for better accuracy and handling non-linear relationships.
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Data Preprocessing:
- Handle missing values and outliers.
- Normalize/scale the data if required.
- Split the data into training and testing sets.
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Model Training:
- Train both Linear Regression and Random Forest models on the training set.
- Evaluate their performance using metrics such as Mean Squared Error (MSE) and R-squared (R²) on the test set.
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Model Comparison:
- Compare the performance of the two models to identify which one offers better accuracy.
- Clone the repository:
git clone https://github.com/AHSANATIQ98/Linear-Regression-Model-to-predict-House-Prices.git