House Price Prediction

Overview

This project aims to predict house prices based on various features using a linear regression model. The analysis is performed on a housing dataset, which includes details like the number of bedrooms, bathrooms, square footage, and the age of the house.

Table of Contents

• Installation
• Usage
• Data Preparation
• Modeling
• Results
• Visualizations
• Contributing
• License

Installation

To run this project, you'll need to have R and the following libraries installed:

• dplyr
• ggplot2
• ggcorrplot
• caTools

You can install the required libraries in R using:

install.packages(c("dplyr", "ggplot2", "ggcorrplot", "caTools"))

Usage

1. Clone the repository:

   git clone [https://github.com/yourusername/house-price-prediction.git]
   cd house-price-prediction

2. Load the dataset data.csv into your R environment.

3. Run the R script to perform data preparation, modeling, and visualization.

Data Preparation

The dataset is cleaned and preprocessed by:

• Removing unnecessary columns.
• Creating new features (e.g., age_house).
• Handling missing values and outliers.

Modeling

A linear regression model is fitted to predict house prices based on the selected features. The dataset is split into training and test sets to validate the model's performance.

Results

The model's performance can be evaluated using various metrics, including predictions of house prices. The coefficients of the model are also extracted for further analysis.

Visualizations

Several visualizations are included to explore the dataset and understand the relationships between features and house prices:

• Boxplots for identifying outliers.
• Correlation matrix heatmap.
• Scatter plots to visualize relationships.

Contributing

If you would like to contribute to this project, feel free to open issues or submit pull requests.

License

This project is licensed under the GNU General Public License. See the LICENSE file for details.