saransh-vashisht/House-prediction

Boston House Prediction Project

We build house price prediction project using the machine learning and deploying the model by using flask , github actions on Heroku

Model used

We used the Linear regression model to train our model

Software and Tools Requirement

• Github Account
• HerokuAccount
• VSCodeIDE
• GitCLI

Deployed site

https://housepriceprediction1223.herokuapp.com/

Dataset Characteristics

• Dataset used : https://www.kaggle.com/code/shreayan98c/boston-house-price-prediction

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 blacks by town
    - LSTAT    % lower status of the population

Authors

• @saransh-vashisht