ML | Boston Housing Kaggle Challenge with Linear Regression

Michael Zippo 18.07.2021

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Dataset description taken from

Let’s make a linear regression model predicting prices on housing

Libraries input and dataset.

# Importing libraries

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

# Data import

from sklearn.datasets import load_boston

boston = load_boston ()

Boston data entry and function_names form

boston.data.shape

       boston.feature_names

Converting nd array data to data frame and adding data names into data

data = pd.DataFrame (boston.data)

data.columns = boston.feature_names

data.head ( 10 )

Adding the "Price" column to the dataset

# Adding the "Price‚" column (target) to the data
boston.target.shape

data [ ’Price’ ] = boston.target

data.head ()

Boston dataset description

data.describe ()

Boston dataset information

data.info ()

Getting input and output data and further dividing the data into a dataset for training and testing.

       # Input data  
   x   =   boston.data  
  
  # Output  
   y   =   boston.target 
  
  
  # splitting data into training and test suites data.  
   from   sklearn.cross_validation   import   train_test_split 
   xtrain, xtest, ytrain, ytest   =   train_test_split (x, y, test_size   =   0.2  , 
     random_state   =   0  ) 
    
   print   (  "xtrain shape:"  , xtrain.shape)  
   print   (  "xtest shape :"  , xtest.shape) 
   print   ( " ytrain shape: " , ytrain.shape) 
   print   (  "ytest shape :"  , ytest.shape)