Data Description:

Here we applying Linear regression to predict the result, below the steps to complete whole task:

#import libraries

import numpy as np

import pandas as pd

from sklearn.model_selection import ShuffleSplit

from sklearn.linear_model import LinearRegression, Ridge

from sklearn.model_selection import train_test_split

from sklearn.metrics import r2_score

Assigning Index name because data has no index name:

column_names = ["CRIM","ZN","INDUS","CHAS","NOX","RM","AGE","DIS","RAD","TAX","PTRATIO","B","LSTAT","MEDV"]

Reading Data:

datafile = "housing.data"
dataFrame = pd.read_csv(datafile,header=None, delim_whitespace = True, names = column_names)

Selecting Target and features variable:

prices = dataFrame['MEDV']
features = dataFrame.drop('MEDV', axis = 1)

Finding mean of this target variable:

mean = dataFrame['MEDV'].mean()
mean

Finding median of this target variable:

median = np.median(dataFrame['MEDV'])
median

Split data:

# TODO: Shuffle and split the data into training and testing subsets
X_train, X_test, y_train, y_test = train_test_split(features, prices, test_size=0.2, random_state=10)

Implement a linear regression model with ridge regression that predicts median house prices from the other variables.

# initialize
from sklearn.linear_model import Ridge
from sklearn import metrics

Fit into model:

## training the model
ridgeReg = Ridge(alpha=0.05, normalize=True)
ridgeReg.fit(X_train,y_train)
pred_X = ridgeReg.predict(X_test)

Predicting Result:

pred_X

Finding Score:

ridgeReg.score(X_test,y_test)