Department of Electronics& Computer Science
(PP Experiment No.6)
Semester S.E. Semester III– Electronics & Computer Science
Subject Python Programming Laboratory
Subject Professor Prof. Manoj Suryawanshi
In-charge Prof. Anuradha Joshi
Lab Teacher Prof. Manoj Suryawanshi
Laboratory L05
Student Name Atharva Joshi
Roll Number 24108A2010
Grade and Subject
Teacher’s Signature
Experiment Number 6
Python Application in Machine Learning
Experiment Title
Develop a machine learning model using Python to predict
Problem Statement
the salary of an individual based on their years of
experience. You are provided with a dataset that contains
information about employees, including their years of
experience and corresponding salaries. The goal is to use
this data to train a model that can accurately predict
salaries for new inputs based on experience. The model's
performance should be evaluated using appropriate metrics,
and the results should be analysed to ensure the reliability
of the predictions.
Resources / Python IDE
Apparatus Required
Objectives In this experiment students will understand the basics
(Skill Set / concepts of machine learning using Python
Knowledge Tested /
Imparted)
Dataset:
YearsExperien
ce Salary
1.1 39343
1.3 46205
1.5 37731
2 43525
1
Department of Electronics& Computer Science
(PP Experiment No.6)
2.2 39891
2.9 56642
3 60150
3.2 54445
3.2 64445
3.7 57189
3.9 63218
4 55794
4 56957
4.1 57081
4.5 61111
4.9 67938
5.1 66029
5.3 83088
5.9 81363
6 93940
6.8 91738
7.1 98273
7.9 101302
8.2 113812
8.7 109431
9 105582
9.5 116969
9.6 112635
10.3 122391
2
Department of Electronics& Computer Science
(PP Experiment No.6)
10.5 121872
Program:
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('Salary_Data.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2,
random_state = 1)
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
prediction = regressor.predict([[10]])
regressor.coef_
regressor.intercept_
#y=mx+C
Salary=9332*10+25609
print(Salary)
accuracy = Salary/prediction*100
print(accuracy)
Result:
3
Department of Electronics& Computer Science
(PP Experiment No.6)
