Maharashtra State Board of Technical Education,
Mumbai
MICRO PROJECT
On
Model for Experience and Salary Analysis
using regression
Submitted by
1. Jayesh Mangesh Kathale
2. Renuka Dattu Kathar
3. Megha Madhukar Katkar
4. Aniket Santosh Kawale
Artificial Intelligence and Machine Learning
Marathwada Institute of Technology (Polytechnic),
Chhatrapati Sambhajinagar.
Academic Year: - 2024-25
MAHARASHTRA STATE
BOARD OF TECHNICAL EDUCATION
CERTIFICATE
This is to certify that : -
Roll Enrollment Exam
Name of Student
No. No. Seat No.
13 Jayesh Mangesh Kathale 2200660093 381774
14 Renuka Dattu Katha 2200660094 381775
15 Megha Madhukar Katkar 2200660095 381776
16 Aniket Santosh Kawale 2200660096 381777
have successfully completed Micro-project the
for subject
as in the enclosed
Model for Experience and Salary Analysis using regression
‘Portfolio’ during his / her tenure of Completing the Diploma in Artificial
Intelligence and Machine Learning in the course AIML in Academic Year
2024-
25 from M.I.T. Polytechnic, Chhatrapati Sambhajinagar with Institute Code
0066.
Date:
S.S. shnedre Prof. R. D. Deshpande
Guide H.O.D.
Principal
Marathwada Institute of Technology, Polytechnic, Chhatrapati
Sambhajinagar
INDEX
Annexure-IV Micro Project Teacher Evaluation Sheet
Annexure-I A “Format for Micro-Project Proposal”
1.0 Aim/Benefits of Micro Project (minimum 30-50 words)
2.0 Course Outcomes Addressed
3.0 Proposed Methodology (procedure in brief that will be followed to do the micro- project
in about 100-200 words.)
4.0 Action Plan (Sequence and time required for major activity.)
5.0 Resources required (major resources such as row material, some machining facility, software
etc.)
Annexure-II A “Format for Micro-Project Report”
1.0 Rationale (Importance of the project, in about 30 to 50 words. This is a modified version of the
earlier one written after the work)
2.0 Aim/Benefits of Micro Project:
3.0 Course Outcomes Achieved (Add to the earlier list is more Cos are addressed)
4.0 Literature Review
5.0 Actual Methodology Followed (Write step wise work done, data collected and its analysis (if
any). The contribution of individual member may also be noted.)
6.0 Actual Resources Used (mention the actual resources used).
7.0 Outputs of the micro projects (Drawings of the prototype, drawing of survey, presentation of collected
data, findings etc.)
8.0 Skill Developed/Learning outcome of this micro project
9.0 Applications of this micro project
Annexure-IV
Micro Project
(Teacher Evaluation Sheet)
Name of Program: Artificial Intelligence and Machine Learning Semester: 5th Course
Title: Fundamental of AI and ML Algorithm Code:
22593
Title of Micro Project: Model for Experience and Salary Analysis
using regression
Course Outcomes Achieved:
a) Use different Classification and Regression Techniques
Process and Individual Total
Roll Name of Student Exam Product Presentation Marks
Enrollment No. Assessment
Seat No. / Viva (10)
No. (06) (04)
13 Jayesh Mangesh Kathale 2200660093 381774
14 Renuka Dattu Kathar 2200660094 381775
15 Megha Madhukar Katkar 2200660095 381776
16 Aniket Santosh Kawale 2200660096 381777
Comments / Suggestions about work:
Signature of Teacher :
Name and Designation : S.A.Shendre Annexure-I
the Teacher
Micro-Project Proposal
(Format for Micro-Project Proposal About 1-2 pages)
Title of Micro-Project: Model for Experience and
Salary Analysis Using regression
1.0 Aim / Benefits of Micro Project (minimum 30-50 words)
The salary prediction model offers practical insights for HR, aiding in salary decisions
based on experience. It introduces key machine learning concepts like linear regression,
data visualization, and model evaluation while enhancing data analysis skills such as
preprocessing and feature selection.
2.0 Course Outcomes Addressed
a) Use different Classification and Regression Techniques
1.Proposed Methodology (Procedure in brief that will be followed to do the micro- project in about
100-200 words.)
1. Data Collection: Gather a dataset with employee years of experience and salary.
2. Data Preprocessing: Clean the dataset, handle missing values, and perform exploratory data analysis
using scatter plots.
3. Model Building: Split the data into training and testing sets. Train a Linear Regression model using
the Scikit-learn library.
4. Model Evaluation: Assess the model's accuracy using metrics like Mean Absolute Error and R-squared
score.
5. Prediction: Test the model by predicting salaries for given experience values and visualize the results.
4.0 Action Plan (Sequence and time required for major activity.)
Sr. Details of Activity Planned Planned Name of
No Start Date Finish Date Responsible
Team Members
01 Data collection 04/09/2024 10/09/2024 Jayesh Kathale
02 Data Processing 11/09/2024 20/09/2024 Renuka Kathar
03 Train a model 22/09/2024 01/09/2024 Megha Katkar
04 Report Writing 04/09/2024 12/09/2024 Aniket Kawale
3.0 Resources required (major resources such as row material, some machining
facility, software etc.)
Sr. Name of Resource/Material Specification Qty. Remarks
No
01 Computer System (i5) Processor 1
02 Tool Jupiter Notebook _ 1
03 Search Engine Chatgpt , _ 1
Google
Names of Team Members with Roll No. : -
Roll No. Name of Student
13 Jayesh Mangesh Kathale
14 Renuka Dattu Katha
15 Megha Madhukar Katkar
16 Aniket Santosh Kawale
Annexure-II
Micro-Project Report
(Format for Micro-Project Report minimum 4 pages)
Title of Micro-Project: - Model for Experience and Salary Analysis
using regression
1.0 Rationale (Importance of the project, in about 30 to 50 words. This is a modified version of
the earlier one written after the work)
Predicting the Air Quality Index (AQI) using regression is important for several reasons. First, it helps
us understand how different environmental factors, like pollutants and weather conditions, affect air
quality using regression models, we can identify trends and make accurate predictions about future
AQI levels, Which is crucial for public health and safety.
2.0 Aim/Benefits of Micro Project:
The salary prediction model offers practical insights for HR, aiding in salary decisions based on
experience. It introduces key machine learning concepts like linear regression, data visualization, and
model evaluation while enhancing data analysis skills such as preprocessing and feature selection
3.0 Course Outcomes Achieved (Add to the earlier list is more Cos are addressed)
A.Use different Classification and Regression
1. Literature Review: -
This project demonstrates the practical application of linear regression in predicting
employee salaries based on years of experience. It highlights the importance of
using data-driven methods to make accurate salary forecasts, aiding businesses in
better decision-making and budgeting.
2.Actual Methodology Followed (Write step wise work done, data collected and its analysis (if
any). The contribution of individual member may also be noted.)
3.Data Collection: Gather a dataset with employee years of experience and salary.
4.Data Preprocessing: Clean the dataset, handle missing values, and perform exploratory data
analysis using scatter plots.
5.Model Building: Split the data into training and testing sets. Train a Linear Regression model
using the Scikit-learn library.
1. Model Evaluation: Assess the model's accuracy using metrics like Mean Absolute Error and
R-squared score.
6.0. Actual Resources Used (mention the actual resources used).
Sr. Name of Resource/Material Specification Qty. Remarks
No
1 Computer System (i5) Processor 1
2 Tool Jupiter Notebook _ 1
3 Search Engine Chatgpt , Google _ 1
1. Skill Developed/Learning outcome of this micro project
1. 1. Understanding Linear Regression: Gained knowledge of linear regression and its application in
predicting numerical values.
2. Data Analysis: Improved skills in data collection, cleaning, and exploratory analysis using
visualization.
3. Model Building: Learned how to implement and evaluate a predictive model using Python's Scikit-
learn library.
4. Practical Application: Enhanced ability to make data-driven decisions for real-world problems like
salary prediction.
9.0 Applications of this micro project: -
1. HR and Recruitment: Predicts salaries based on experience for better compensation planning.
2. Career Guidance: Helps individuals understand salary growth potential.
3. Business Decision Making: Assists in setting salary structures.
4. Data Analysis: Provides insights for data-driven employment decisions.
********
