https://github.com/abrahamkoloboe27/housing-price-prediction-with-pycaret
https://github.com/abrahamkoloboe27/housing-price-prediction-with-pycaret
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/abrahamkoloboe27/housing-price-prediction-with-pycaret
- Owner: abrahamkoloboe27
- Created: 2023-08-22T12:17:07.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-08-22T12:20:23.000Z (almost 2 years ago)
- Last Synced: 2025-03-10T09:08:37.253Z (3 months ago)
- Language: Jupyter Notebook
- Size: 865 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Housing-Price-Prediction-with-Pycaret
## Author
Abraham KOLOBOE
* Email :
* Linkedin : [Abraham KOLOBOE](https://www.linkedin.com/in/abraham-koloboe-630683263)
This project aims to predict house prices using the "House Price Prediction" dataset. The PyCaret framework was employed to streamline the creation, training, and evaluation of prediction models.
## Project Description
The primary objective of this project is to develop a model capable of accurately predicting house prices based on various features. The "House Price Prediction" dataset contains information about different characteristics of houses along with their corresponding prices. This project explores how to leverage PyCaret to automate the modeling and evaluation process.
## Prerequisites
Make sure you have Python 3.x installed along with the following libraries:
* PyCaret
* Pandas
* Numpy
* Matplotlib
* Seaborn
Install the dependencies by running:
* pip install pycaret pandas numpy matplotlib seaborn
## About PyCaret
PyCaret is an open-source machine learning library that greatly simplifies the process of model creation, selection, training, and evaluation. It provides a user-friendly interface for performing complex tasks such as data preparation, feature selection, model comparison, and hyperparameter optimization.