The document outlines a mini project for the 2023-24 academic year focusing on developing a placement prediction and recommendation system using machine learning algorithms such as decision tree, logistic regression, and random forest. The project aims to predict the probability of student placements and recommend suitable job positions based on individual skills, utilizing a dataset of 1200 previous students' records. It highlights the significance of campus placements for college success and aims to streamline the training and placement department's efforts.

1. Department of CSE(AI&ML)
Academic year: 2023-24
Mini Project (21AIMP67) – Review 1 Presentation
GUIDE:
Guide Name
Designation
Department of ISE
DBIT, Bengaluru
PROJECT TEAM:
1. Name (USN)
2. Name (USN)
3. Name (USN)
4. Name (USN)
Project Title

2. Contents
• Index
• Introduction
• Problem Statement
• Objectives
• prototype design/model
• progress of the work done with coding
• code execution
• out put discussion
• Conclusion
• References

3. INTRODUCTION
• Placements are considered to be very important for each and every college. The basic success of the
college is measured by the campus placement of the students.
• Every student takes admission to the colleges by seeing the percentage of placements in the college.
Hence, in this regard the approach is about the predicting and recommending the placement necessary in
the colleges.
• The aim is to develop a placement prediction and recommendation system which predicts the probability
of student getting placed and also recommends the suitable job for particular skills of the student.
• Machine learning algorithms are used for prediction and recommendation .
• Decision tree, Logistic Regression, Random Forest are used to classify students into appropriate clusters
and the result would help them in improving their profile and accuracy of respected algorithms are noted
and with the comparison of various machine learning techniques.
• This would help both recruiters as well as students during placements and related activities.

4. PROBLEM STATEMENT
• Placements are considered to be very important for each and every college.
• Every student takes admission to the colleges by seeing the percentage of placements in the college.
• The aim is to develop a placement prediction and recommendation system which predicts the probability
of student getting placed and also recommends the suitable job for particular skills of the student.

5. • It is easy to predict the placement probability of the students.
• To minimize number of working hours for the staff of training and placement department.
• This model has used Placement data sets from online websites or college placement department. A
placement dataset is used to train the model and then another unplaced student's data set is used for
getting the result.
• When the data is considered, always a very large data set with a large no. of rows and columns will be
noted.
• The data set includes the academic and primary skills. The dataset consists of 1200 individual datasets
that are considered from the previous year students. The attributes are taken into consideration.
• Machine learning algorithms such as logistic regression, random forest and decision tree are used in
prediction of placement probability of the student.
• Content based filtering is used in recommendation system ,to recommend job position suitable for the
student based on their primary skills.
OBJECTIVES

6. PROJECT DESIGN/MODEL
There are sequences of important general steps involved
1. Model
2. Data input
3. Processing of data
4. Prototype out put

7. SOFTWARE & HARDWARE
REQUIREMENTS

8. TOOLS USED FOR DEVELOPMENT

9. OUTCOME

10. CONCLUSION & FUTURE SCOPE

11. References / Bibliography
[1] Senthil Kumar Thangavel , Divya Bharathi P, Abijith Sankar, International Conference on Advance“Data Mining Approach for
Predicting Student and Institution's Placement Percentage”, Professor. Ashok M Assistant Professor Apoorva A, 2016
International Conference on Computational Systems and Information Systems for Sustainable Solutions.
[2] “Student Placement Analyzer: A Recommendation System Using Machine Learning”,d Computing and Communication
Systems (ICACCS -2017), Jan. 06 - 07, 2017, Coimbatore, INDIA.
[3] "A Placement Prediction System Using K-Nearest Neighbors Classifier", Animesh Giri, M Vignesh V Bhagavath, Bysani
Pruthvi, Naini Dubey, Second International Conference on Cognitive Computing and Information Processing (CCIP), 2018.
[4] "Class Result Prediction using Machine Learning", Pushpa S K, Associate Professor, Manjunath T N, Professor and Head,
Mrunal T V, Amartya Singh, C Suhas, International Conference On Smart Technology for Smart Nation, 2020
[5]. Sheetal, M. B, Savita, Bakare. “Prediction of Campus Placement with Data Mining Algorithm-Fuzzy logic and Knearest
neighbor.” International Journal of Advanced Research in Computer and Communication Engineering 5.6 2019: 309-312.
[6.] Sumitha, R., E. S. Vinothkumar, and P. Scholar. "Prediction of Students Outcome Using Data Mining Techniques." Int. J. Sci.
Eng. Appl. Sci 2.6 (2016): 132-139,2022
[7]. Kavya g, pranitha y, sanjana a, sirisha dg, mamatha a.” smart system for student placement prediction “. international journal
of advance research,ideas and innovations in technology.2454-132x,2021.