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The house price was estimated based on the user's choices. Xgboost was used for training the model and Streamlit was used for deployment.

Python 100.00%
jupyter-notebook machine-learning-algorithms python streamlit streamlit-webapp xgboost machinelearningmodel

house-price-prediction's Introduction

House Price Prediction Project

Open in Streamlit

https://share.streamlit.io/enesbol/streamlitrepo/main/HousePrice.py

Description

This project is a part of K136. Kodluyoruz & Istanbul Metropolitan Municipality Data Science Bootcamp.The model is trying to estimate the house prices related to users choices.

Data

The dataset is available at Kaggle.

Project's Steps:

⚪️ Data was downloaded from kaggle and readed.

⚪️ Data is cleaned and get ready for model.

⚪️ Data was trained with XGBoost Regression Model.

⚪️ UI design with Streamlit

⚪️ Deploy with Streamlit Cloud

Contributions

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