Shalini Joseph presented on "Python and Machine Learning". Python is an easy to learn, open source, high-level programming language developed in 1989. It is useful for a variety of applications including web development, data analysis, machine learning, and artificial intelligence. Machine learning uses algorithms to learn from data and make predictions. The presentation covered types of machine learning like linear regression, multiple linear regression, and logistic regression. As a project, linear and multiple linear regression were used to predict house prices based on features like size, location, and age. The internship provided hands-on experience with machine learning algorithms in Python.

1. IMPACT COLLEGE OF ENGINEERING AND APPLIED
SCIENCES
(affiliated to VTU , Belagavi and approved by AICTE)
COMPUTER SCIENCE AND ENGINEERING
Internship Presentation on
“Python and Machine Learning”
Under the Guidance of :
Mrs. GEETHA M
Assistant Professor
Department of Computer Science
and Engineering
Presented By :
Shalini Joseph
1IC21SCS04
3rd Sem , M.Tech.
Department of Computer Science
and Engineering
2022-2023

3. • Introduction
• Comparison of Python with C
• Features of Python
• Application of Python
• What is Machine Learning?
• Machine Learning Process
• Types Of Machine Learning
• Machine Learning application
• Project- House Price prediction Using Linear Regression and Multiple
linear Regression
• Internship Certificate
• References

4.  Python is a general purpose high level programming language.
 Python was developed by Guido Van Rossam in 1989 while working at
National Research Institute at Netherlands.
 Python was made available to public in 1991.
 It is recommended as first programming language for beginners.
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5. In C :
#include<stdio.h>
void main()
{
printf ("Hello world");
}
Output : Hello world
In Python :
Print(“Hello world”)
Output : Hello world
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6. • Simple
• Easy to learn
• Freeware
• Open Source
• High Level Programming language
• Platform Independent
• Portability
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7.  Developing Desktop Applications
 Developing web Applications
 Developing games
 Data Analysis Applications
 Machine Learning and IOT
 Developing Artificial Intelligence Applications.
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• Face Recognition
• Pattern Recognition
• Virtual Assistant
• Recommendation Engines
• Fraud Detection
• Speech Recognition

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PROJECT OBECTIVES
To Predict the House Price
Software Used: Anaconda, Jupyter Notebook, PyQT
Algorithm Used: Linear Regression Algorithm
Linear Regression Algorithm:
• Linear regression only models’ relationships between dependent and
independent variables that are linear.

13. 10 3/18/2023
• Read the csv file using pandas read csv () function
• Split data into training and test data
• Generate a Linear Regression model.
• Train or fit the data into the model
• Predicting the test set results.
• After we use pyqt for design the output page.
• Convert the ui file to py file and then copy & paste the py file to the jupyter
Notebook then run the code
• Predict the house price

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Linear Regression
• Linear Regression a relationship between dependent variable (Y) and
Independent variables (X).
• Linear Regression is used for solving Regression problems.

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Multiple Linear Regression
• Dealing with more than one predictor variable for finding out the value of
the response variable.

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• Gained knowledge in the domain of python with machine learning.
• Learnt different machine Learning algorithms. Such as:
Linear Regression
Multiple Linear Regression
Logistic Regression
• Done project on House price prediction using multiple Linear Regression
and Linear regression

19. 16 3/18/2023
[1].KARUNADU TECHNOLOGY PRIVATE LIMITED.
[2].https://www.geeksforgeeks.org/machine-learning-with-python.
[3].https://www.tutorialspoint.com/machine_learning_with_python
[4].https://www.mygreatlearning.com/academy/learn-for-
free/courses/python-for-machine-learning.

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THANK YOU