Boston House Pricing Prediction

Software and Tools Requirementds

1. Github Account
2. HerokuAccount
3. VSCodeIDE
4. GitCLI

Create a new environment:

Ctrl+Shift+P -> search for Python:Create Environment in VSCode -> conda

• Full Stack Web Application made using Krish Naik's youtube tutorial
• Created a Regression model using Python 3.10.0 in Google Colab and Jupyter Notebook
• Pickled the Regression model and the necessary Standarad Scaler
• Linked with Github under the repository "bostonhousepricing"
• Create a "requirement.txt" file which contains all the necessary libraries to be used
• Create a Flask python file which contains the backend functions of the Web App
• Create a HTML website which can be used to interact with the user
• Link the flask file and the html in the forms attribute
• Run the app.py file in the terminal and enjoy your Web App

Boston Housing Dataset Details

The boston dataset is uploaded from the sklearn library in the dataset attribute.

Data Set Characteristics:

:Number of Instances: 506 

:Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.

:Attribute Information (in order):
    - CRIM     per capita crime rate by town
    - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.
    - INDUS    proportion of non-retail business acres per town
    - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
    - NOX      nitric oxides concentration (parts per 10 million)
    - RM       average number of rooms per dwelling
    - AGE      proportion of owner-occupied units built prior to 1940
    - DIS      weighted distances to five Boston employment centres
    - RAD      index of accessibility to radial highways
    - TAX      full-value property-tax rate per $10,000
    - PTRATIO  pupil-teacher ratio by town
    - B        1000(Bk - 0.63)^2 where Bk is the proportion of black people by town
    - LSTAT    % lower status of the population