https://github.com/massimilianovisintainer/house-price-model-prediction

House price prediction model using XGBoost.
https://github.com/massimilianovisintainer/house-price-model-prediction

machine-learning models numpy pandas python sklearn xgboost xgboost-regression

Last synced: about 23 hours ago
JSON representation

House price prediction model using XGBoost.

• Host: GitHub
• URL: https://github.com/massimilianovisintainer/house-price-model-prediction
• Owner: MassimilianoVisintainer
• Created: 2024-07-24T18:54:50.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-07-24T18:56:56.000Z (2 months ago)
• Last Synced: 2024-09-23T06:32:44.303Z (5 days ago)
• Topics: machine-learning, models, numpy, pandas, python, sklearn, xgboost, xgboost-regression
• Language: Python
• Homepage:
• Size: 15.6 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
## House Price Prediction Model

### Overview

This repository contains a Jupyter Notebook (`House Price Prediction.ipynb`) utilizing XGBoost regression to predict house prices based on various features.

### Functionality

* Imports necessary libraries.

* Loads and analyzes the house price dataset.

* Explores data correlations.

* Splits data into training and testing sets.

* Trains an XGBoost regression model.

* Evaluates model performance on training and testing data.

* Visualizes actual vs. predicted prices.

### Requirements

* Python 3.x

* pandas

* numpy

* matplotlib

* seaborn

* scikit-learn

* XGBoost

### How to Use

1. Clone this repository.

2. Install required libraries: `pip install pandas numpy matplotlib seaborn scikit-learn xgboost`

3. Open `House Price Prediction.ipynb` in Jupyter Notebook.

4. Replace data path if necessary.

5. Run notebook cells to execute code and view results.