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# Linear Regression Using Ridge Regression In Machine Learning | ML Project Help | Realcode4you

In this blog we will learn how to predict model using Linear Regression using Ridge Regression in machine learning by using "US Housing" data.

#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

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

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

#mean of MEDV column

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

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

### Split Main and target(price) 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

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

pred_X

### Output:

array([29.85464657, 31.58991004, 30.39384671, 23.8339747 , 19.20002968, 16.52364348, 35.47082139, 14.99344304, 24.85825037, 36.27645 , 21.24937724, 30.72977103, 27.03687178, 32.94870149, 33.52586804, 39.60795326, 24.54263511, 22.51146391, 25.18108709, 22.42702071, 32.17258486, 17.76590695, 25.28566841, 25.02621386, 32.75486912, 20.49126046, 19.74149725, 17.13948061, 38.19820331, 0.89647704, 32.28355326, 31.34462247, 26.36649416, 23.9634044 , 20.02479017, 19.73378455, 4.37382689, 33.82345754, 26.56580249, 27.30744146, 33.71708924, 28.99342938, 18.04451886, 31.16760619, 18.04608315, 28.36386332, 19.33119703, 21.30694095, 37.07740862, 16.79227521, 24.25671423, 19.10989473, 23.9144611 , 34.10410138, 26.49793341, 33.7790573 , 21.07749309, 19.72107809, 18.40396221, 24.54608723, 20.21132159, 23.5480848 , 39.77432893, 41.63314535, 29.86399045, 17.1161445 , 23.95275281, 3.5447882 , 30.47208809, 29.69934889, 18.41409878, 27.22029943, 19.36106254, 25.1757635 , 25.01064422, 10.25740163, 38.08187479, 8.21777277, 18.55666167, 30.60934051, 22.96269918, 22.42861937, 20.51809974, 28.14932011, 30.54180549, 27.8891282 , 26.33386253, 31.63993603, 22.56578847, -4.46560683, 21.97766633, 19.89380594, 24.96153737, 23.72440162, 19.1832791 , 19.19404134, 27.09906737, 22.63972762, 26.10936452, 23.21721482, 23.98122563, 19.77329451])

#print the score

ridgeReg.score(X_test,y_test)

#predicting X_train1 predict_train = ridgeReg.predict(X_train1)

#find the r2-score

from sklearn.metrics import r2_score def performance_metric(y_train1, predict_train): score = r2_score(y_train1, predict_train) # Return the score return score performance_metric(y_train1, predict_train)

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