Housing Price Prediction in Python

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This data science project series leads you through the process of creating a real estate price prediction website step by step. First, we will create a model with sklearn and linear regression using the banglore home prices dataset from kaggle.com. The second step would be to create a Python Flask server that serves http requests using the stored model. The third component is a website developed in html, CSS, and javascript that allows users to enter information such as home square footage, bedrooms, and so on, and then calls a Python Flask server to receive the anticipated price. Almost all data science principles will be covered during model creation, including data load and cleaning, outlier identification and removal, feature engineering, dimensionality reduction, gridsearchcv for hyperparameter tuning, and k fold cross validation.

This project includes the following technologies and tools: 1) Python
2) Data cleansing with Numpy and Pandas
Matplotlib is a data visualization tool.
4) Sklearn for model construction
5) As an IDE, use Jupyter notebook, Visual Studio Code, or PyCharm.
6) Python flask for the http server 7) HTML/CSS/Javascript for the user interface

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