https://github.com/djdurga/bengaluru-house-price-data
https://github.com/djdurga/bengaluru-house-price-data
Last synced: 12 months ago
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- Host: GitHub
- URL: https://github.com/djdurga/bengaluru-house-price-data
- Owner: Djdurga
- Created: 2024-05-03T06:13:47.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-09T09:00:42.000Z (about 2 years ago)
- Last Synced: 2025-03-27T18:31:39.648Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 392 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Bengaluru-House-Price-Data
This data science project series walks through the step-by-step process of how to build a real estate price prediction website. We will first build a model using sklearn and linear regression using the Banglore home prices dataset from kaggle.com. The second step would be to write a Python flask server that uses the saved model to serve http requests. The third component is the website built in HTML, CSS and JavaScript that allows the user to enter their home square ft area, bedrooms etc and it will call Python flask server to retrieve the predicted price. During model building, we will cover almost all data science concepts such as data load and cleaning, outlier detection and removal, feature engineering, dimensionality reduction, gridsearchcv for hyperparameter tunning, k fold cross validation etc. Technology and tools this project covers,
Python
Numpy and Pandas for data cleaning
Matplotlib for data visualization
Sklearn for model building
Jupyter notebook, visual studio code and pycharm as IDE
Python flask for http server
HTML/CSS/Javascript for UI