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

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