This is a coding question. Now that you have worked out the gradient descent and the update rules. "Try to progrum a Ridge regression. Please complete the coding. Note that here the data set we use has just one explanatory variable and the Ridge regression we try to create here has just one variable (or feature).Now that you have finished the program. What are the observations and the corresponding predictions using Ridge? Now, make a plot to showease how well your model predicts against the observations. Use spatter plot for observations, line plot for your model predictions. Observations are in color red. and predictions are in color green. Add appropriate labels to the x axis and y axis and a title to the plot You may also nood to fine tune hyperparameters such as leurning rate and the number of'aterations. import numpy as np.import pandas as pdfrom sklearn.model selection import train_test_splitimport matplotlib.pyplot as pitRidge Regressionclass RidgeRegression ():definit__(self, learning_rate, iterations, 12_penality) :self.learning_rate learning_rateself.iterations iterationsself.12_penality 12_penalityFunction for model training.def fit( self, X, Y) 1#no_of_training examples, no_of_featuresself.m, self.n X.shapeweight initializationself. W np.zeros(self.n)self.b 0self.x = xself. Y Ygradient descent learningfor i in range ( self.iterations ) :self.update_weights ()return selfHelper function to update weights in gradient descentdef update_weights (self):#you need to figure this out.return selfHypothetical function h( x )def predict( self, X ) :return X.dot ( self.W) + self.b#Driver codedef main () 1Importing datasetdf pd.read_cav( "salary_data.csv")X df.iloc[:, :-1].valuesYdf.iloc[:, 1].values#Splitting dataset into train and test setX_train, x_test, Y train, Y_test= train_test_split( X, Y.,#Model trainingmodel Ridge Regression ( iterations 1000,model.fit( X_train, Y_train )test_size=1/3, random_state = 0)learning_rate= 0.01, 12 penality=1)Prediction on test setY pred model.predict( x_test )print("Predicted values ", np.round( Y pred [:3], 2 ) )print("Real values ", Y_test [:3] )Visualization on test setif_name__ "_main_" :main () A1 Years Experie Salary21.131.341.55262.2729日gonne 4 5 6 7 8 9 20122824 25 25 27 28 29 30 31 2 33 弘1011121315161718192326323.3.23.73.9444.14.54.95.15.35.966.87.17.98.28.799.59.610.310.539343462053773143525398915664260150544456444557189632185579456957570816111167938660298308881363939409173898273101302113812109431105582116969112635122391121872EF

Ridge Regression Algorithm:1. Import necessary libraries: numpy, pandas, sklearn, matplotlib.2.…

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Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

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This is a coding question. Now that you have worked out the gradient descent and the update rules. "Try to progrum a Ridge regression. Please complete the coding. Note that here the data set we use has just one explanatory variable and the Ridge regression we try to create here has just one variable (or feature). Now that you have finished the program. What are the observations and the corresponding predictions using Ridge? Now, make a plot to showease how well your model predicts against the observations. Use spatter plot for observations, line plot for your model predictions. Observations are in color red. and predictions are in color green. Add appropriate labels to the x axis and y axis and a title to the plot You may also nood to fine tune hyperparameters such as leurning rate and the number of'aterations.

import numpy as np.
import pandas as pd
from sklearn.model selection import train_test_split
import matplotlib.pyplot as pit
Ridge Regression
class RidgeRegression ():
definit__(self, learning_rate, iterations, 12_penality) :
self.learning_rate learning_rate
self.iterations iterations
self.12_penality 12_penality
Function for model training.
def fit( self, X, Y) 1
#no_of_training examples, no_of_features
self.m, self.n X.shape
weight initialization
self. W np.zeros(self.n)
self.b 0
self.x = x
self. Y Y
gradient descent learning
for i in range ( self.iterations ) :
self.update_weights ()
return self
Helper function to update weights in gradient descent
def update_weights (self):
#you need to figure this out.
return self
Hypothetical function h( x )
def predict( self, X ) :
return X.dot ( self.W) + self.b
#Driver code
def main () 1
Importing dataset
df pd.read_cav( "salary_data.csv")
X df.iloc[:, :-1].values
Ydf.iloc[:, 1].values
#Splitting dataset into train and test set
X_train, x_test, Y train, Y_test= train_test_split( X, Y.,
#Model training
model Ridge Regression ( iterations 1000,
model.fit( X_train, Y_train )
test_size=1/3, random_state = 0)
learning_rate= 0.01, 12 penality=1)
Prediction on test set
Y pred model.predict( x_test )
print("Predicted values ", np.round( Y pred [:3], 2 ) )
print("Real values ", Y_test [:3] )
Visualization on test set
if_name__ "_main_" :
main ()

A
1 Years Experie Salary
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1.1
3
1.3
4
1.5
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2
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2.2
7
29
日
gonne 4 5 6 7 8 9 20122824 25 25 27 28 29 30 31 2 33 弘
10
11
12
13
15
16
17
18
19
23
26
32
3.
3.2
3.7
3.9
4
4
4.1
4.5
4.9
5.1
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5.9
6
6.8
7.1
7.9
8.2
8.7
9
9.5
9.6
10.3
10.5
39343
46205
37731
43525
39891
56642
60150
54445
64445
57189
63218
55794
56957
57081
61111
67938
66029
83088
81363
93940
91738
98273
101302
113812
109431
105582
116969
112635
122391
121872
E
F

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