ppt

1. CAMPUS PLACEMENTS
PREDICTION & ANALYSIS USING
MACHINE LEARNING
NAME::-MERUGU VENKATA REDDY
Roll No::-238X1F00B3

2. ABSTRACT
• Placement of students is a crucial objective for
educational institutions.
• Institutional reputation and admissions depend on
successful placements.
• The project aims to analyze past student data to predict
placement chances.
• Machine Learning algorithms such as Decision Tree and
Random Forest are used.
• The proposed model is compared with traditional
classification methods.
• Results indicate the proposed algorithm performs
significantly better.

3. EXISTING SYSTEM
• Logistic regression, ID3, J48, Decision Table, and other classification
methods were used.
• Accuracy varied: Logistic Regression (83.33%), ID3 (82.1%), C4.5 (80%).
• Naïve Bayes had an accuracy of 86.15%.
• Random Tree algorithm provided 73% accuracy for placement
prediction.
• WEKA tool was used for data analysis and classification.

4. DISADVANTAGES OF
EXISTING SYSTEM
• Attribute selection is not relevant to each other.
• Missing values were not handled properly.
• Data cleaning and preprocessing were not optimized.

5. PROPOSED SYSTEM
• Predicts placement probability of undergraduate students.
• Uses Decision Tree and Random Forest classification algorithms.
• Uses academic history (percentages, backlogs, credits) for
prediction.
• Algorithms applied to past student placement data.

6. ADVANTAGES OF
PROPOSED SYSTEM
• Irrelevant attributes are removed to improve accuracy.
• Uses Random Forest, an ensemble learning technique,
for better results.
• More accurate and efficient than previous models.
• Predicts placement outcomes with improved precision
and recall.

13. THANK YOU