https://github.com/batthulavinay/house-price-prediction
This project aims to analyze and predict house prices based on various features such as location, size, and amenities. The dataset is processed and explored using Python, and machine learning models are applied to generate accurate price predictions.
https://github.com/batthulavinay/house-price-prediction
datacleaning datapreprocessing exploratory-data-analysis feature-engineering linear-regression modelevaluation performance-metrics random-forest xgboost
Last synced: 8 months ago
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This project aims to analyze and predict house prices based on various features such as location, size, and amenities. The dataset is processed and explored using Python, and machine learning models are applied to generate accurate price predictions.
- Host: GitHub
- URL: https://github.com/batthulavinay/house-price-prediction
- Owner: BatthulaVinay
- Created: 2025-01-21T17:38:09.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-19T16:09:25.000Z (over 1 year ago)
- Last Synced: 2025-04-13T21:56:26.981Z (about 1 year ago)
- Topics: datacleaning, datapreprocessing, exploratory-data-analysis, feature-engineering, linear-regression, modelevaluation, performance-metrics, random-forest, xgboost
- Language: Jupyter Notebook
- Homepage:
- Size: 1.07 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# House Price Prediction
## 📌 Project Overview
This project aims to analyze and predict house prices based on various features such as location, size, and amenities. The dataset is processed and explored using Python, and machine learning models are applied to generate accurate price predictions.
## 🚀 Installation
To run this project, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/yourusername/house-price-prediction.git
cd house-price-prediction
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Launch Jupyter Notebook:
```bash
jupyter notebook
```
Open `House_Price.ipynb` and run the cells.
## 📊 Dataset
The dataset used in this project contains house price information and related features. It is preprocessed for training machine learning models.
## ⚡ Features & Methods
- **Data Cleaning & Preprocessing**
- **Exploratory Data Analysis (EDA)**
- **Feature Engineering**
- **Machine Learning Models** (e.g., Linear Regression, Random Forest, XGBoost)
- **Model Evaluation & Performance Metrics**
## 📜 Usage
Run the notebook step by step to:
- Load and preprocess data
- Train different models
- Evaluate predictions
## 📂 Repository Structure
```
/house-price-prediction
│── House_Price.ipynb
│── README.md
└── data/
├── house_prices.csv
```
## 🤝 Contributing
Feel free to fork this repository, open issues, or submit pull requests to improve the project.
## 📌 License
This project is licensed under the MIT License.
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🚀 Happy Coding! 🎯