Boston Housing dataset

This dataset contains information collected by the U.S Census Service concerning housing in the area of Boston Mass. It was obtained from the StatLib archive, and has been used extensively throughout the literature to benchmark algorithms. However, these comparisons were primarily done outside of Delve and are thus somewhat suspect. The dataset is small in size with only 506 cases.

Columns of the dataset

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.
CS - 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

Usage of Streamlit

streamlit run stream.py

The result is as follows:

You can choose the model you want to predict the price of your house

Features

Deep Learning

• we have deep neural network with keras in tensorflow
  Accuracy of the model:

Machine Learning

• we trained the dataset with popular ML algorithms:

1. DecisionTreeRegressor
2. ExtraTreesRegressor
3. GradientBoostRegressor
4. AdaBoostRegressor
5. KNeighborsRegressor

Accuracies of this models on the dataset:

Installation

Install python and:

pip install -r requirements.txt