This repository contains famous machine learning models
1).Boston Housing price We will take the Housing dataset which contains information about different houses in Boston. This data was originally a part of UCI Machine Learning Repository and has been removed now. We can also access this data from the scikit-learn library. There are 506 samples and 13 feature variables in this dataset. The objective is to predict the value of prices of the house using the given features. data: contains the information for various houses target: prices of the house feature_names: names of the features DESCR: describes the dataset
To know more about the features use boston_dataset.DESCR The description of all the features is given below:
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 oxide 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 centers 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 [people of African American descent] by town LSTAT: Percentage of lower status of the population MEDV: Median value of owner-occupied homes in $1000s