Boston House Pricing Prediction (https://vg-boston-cc1i.onrender.com)

Input & Output

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

Software and tools requirements

1. github
2. VS Code
3. Render
4. GitCLI

Create a new environment

conda create --name env_name python=3.7

Run for the requirements

pip install -r requirement.txt

Steps of the file are mention on Steps.md

Reference and guide materials

• https://www.kaggle.com/datasets/vikrishnan/boston-house-prices
• https://github.com/krishnaik06/
• https://www.kaggle.com/code/subbhashit/boston-house-pricing-99-accuracy