Boston-House-Prediction

The goal of this project is to create a Machine Learning model that is able to accurately estimate the price of the house given the features

Software And Tools Requirements

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

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

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

Algorithms Used

I have used several Algorithms To predict the price of house and able to achieve the following accuracy.

• Neural Network Accuracy: 85.76 %

• Random Forest Regressor Accuracy: 86.28 %

• Linear Regression Accuracy: 73.52 %

• Ridge Regression Accuracy: 72.94 %

• Lasso Regression Accuracy: 73.52 %

• ElasticNet Regression Accuracy: 73.52 %

The Maximum achievable accuracy is 86.28 % by using Random Forest Regressor Algorithm

Library Used

• pandas

• numpy

• matplotlib

• seaborn

• tensorflow

• sklearn