This Project aims to predict the prices of house based on characteristics and features . By using machine learning algorithms and statistical modeling techniques we can predictthe prices of house. This project is made for a kaggle competition.

Pandas, Numpy, Matplotlib, scikit-learn libraries, joblib.

By entering the data like

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

MEDV - Median value of owner-occupied homes in $1000's

We can predict the price of house

You can find the Data set here

• @Akkaldevi Saivinayak