abderahmanvt7 / house-price-prediction

This project aims to predict house prices using a linear regression model based on various features such as the number of bedrooms, bathrooms, square footage, etc.

House Price Prediction Project

This project aims to predict house prices using a linear regression model based on various features such as the number of bedrooms, bathrooms, square footage, etc.

Table of Contents

• Overview
• Dataset
• Installation
• Usage
• Results
• Contributing
• License

Overview

Linear regression is a simple yet powerful machine learning technique used for predicting continuous values. In this project, we utilize linear regression to predict house prices based on a dataset containing various features of houses such as the number of bedrooms, bathrooms, square footage, etc.

Dataset

The dataset used for this project contains information about houses including their features and corresponding prices. It includes the following columns:

• id: Unique identifier for each house
• date: Date the house was sold
• price: Sale price of the house
• bedrooms: Number of bedrooms
• bathrooms: Number of bathrooms
• sqft_living: Square footage of the living area
• sqft_lot: Square footage of the lot
• floors: Number of floors
• waterfront: Whether the house has a waterfront view (binary: 0 or 1)
• view: Number of times the house has been viewed
• condition: Overall condition of the house
• grade: Overall grade given to the housing unit
• sqft_above: Square footage of house apart from the basement
• sqft_basement: Square footage of the basement
• yr_built: Year the house was built
• yr_renovated: Year the house was renovated
• zipcode: Zip code of the area
• lat: Latitude coordinate of the house
• long: Longitude coordinate of the house
• sqft_living15: Square footage of the living area of the nearest 15 neighbors
• sqft_lot15: Square footage of the lot of the nearest 15 neighbors

Installation

To run the code locally, you'll need Python 3.x and the following libraries:

• numpy
• pandas
• matplotlib
• scikit-learn

You can install these dependencies using pip:

pip install numpy pandas matplotlib scikit-learn

Usage

1. Clone the repository to your local machine:

git clone [email protected]:Abderahmanvt7/house-price-prediction.git

2. Navigate to the project directory:

cd house-price-prediction

3. Run the Jupyter notebook house_price_prediction.ipynb to train the linear regression model and make predictions.

4. Modify the code as needed or experiment with different models and parameters.