https://github.com/harshjuly12/house-price-prediction-using-linear-regression
This repository contains code for predicting house prices using the Linear Regression model. It includes data preprocessing, model training, evaluation, and insights into factors influencing house prices.
https://github.com/harshjuly12/house-price-prediction-using-linear-regression
linear-regression machine-learning prediction-model
Last synced: 18 days ago
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This repository contains code for predicting house prices using the Linear Regression model. It includes data preprocessing, model training, evaluation, and insights into factors influencing house prices.
- Host: GitHub
- URL: https://github.com/harshjuly12/house-price-prediction-using-linear-regression
- Owner: harshjuly12
- License: mit
- Created: 2023-11-27T18:35:04.000Z (almost 2 years ago)
- Default Branch: master
- Last Pushed: 2024-07-07T15:40:18.000Z (over 1 year ago)
- Last Synced: 2024-11-12T21:22:43.792Z (11 months ago)
- Topics: linear-regression, machine-learning, prediction-model
- Language: Jupyter Notebook
- Homepage:
- Size: 4.75 MB
- Stars: 6
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Predict House Prices Using Linear Regression
## Table of Contents
1. [Introduction](#introduction)
2. [Features](#features)
3. [Technologies Used](#technologies-used)
4. [Installation](#installation)
5. [Usage](#usage)
6. [Data](#data)
7. [Steps](#steps)
8. [Results](#results)
9. [License](#license)
10. [Acknowledgements](#acknowledgements)
## Introduction
This project aims to predict house prices using a linear regression model. The model is trained on a dataset of housing features and their corresponding prices. The goal is to provide accurate price predictions based on input features such as the number of bedrooms, square footage, location, and more.
## Features
- Data Preprocessing: Clean and preprocess the dataset for model training.
- Model Training: Implement a linear regression model to predict house prices.
- Evaluation: Assess the performance of the model using metrics such as Mean Absolute Error (MAE) and Mean Squared Error (MSE).
- Visualization: Visualize the relationship between different features and the house prices.
## Technologies Used
- Python 3.x
- Jupyter Notebook
- pandas
- numpy
- scikit-learn
- matplotlib
- seaborn
## Installation
To install and run the Predict House Prices project, follow these steps:
1. **Clone the repository:**
```sh
git clone https://github.com/yourusername/Predict-House-Prices.git
cd Predict-House-Prices
```
2. **Create a virtual environment:**
```sh
python -m venv venv
```
3. **Activate the virtual environment:**
- On Windows:
```sh
venv\Scripts\activate
```
- On macOS/Linux:
```sh
source venv/bin/activate
```
4. **Install the required packages:**
```sh
pip install -r requirements.txt
```
## Usage
1. Open the Jupyter Notebook file `Predict House Prices Using Linear Regression.ipynb`.
2. Follow the steps in the notebook to understand and execute the project.
## Data
The dataset used for this project is a CSV file containing various features of houses along with their prices. Make sure to place the dataset in the same directory as the notebook or provide the correct path to the dataset in the notebook.
## Steps
1. **Data Loading:** Load the dataset using pandas.
2. **Data Exploration:** Explore the dataset to understand its structure and features.
3. **Data Preprocessing:** Handle missing values, encode categorical variables, and scale numerical features.
4. **Model Training:** Split the data into training and testing sets, then train the linear regression model.
5. **Model Evaluation:** Evaluate the model's performance on the test set using appropriate metrics.
6. **Visualization:** Create visualizations to interpret the model's predictions and feature importance.
## Results
The final model is evaluated based on its accuracy in predicting house prices. The results, including evaluation metrics and visualizations, are presented in the notebook.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
## Acknowledgements
- [Kaggle](https://www.kaggle.com/) for providing the dataset.
- [scikit-learn](https://scikit-learn.org/) for the machine learning library.
## Contact
For any questions or suggestions, please contact:
- Harsh Singh: [harshjuly12@gmail.com](harshjuly12@gmail.com)
- GitHub: [harshjuly12](https://github.com/harshjuly12)