I built the Engineering Placements Prediction using Machine Learning notebook on my GitHub in January 2026 to forecast whether engineering students get placed based on academic and personal background.
The dataset contains 2,966 student records with the following information.
Students with higher CGPA (8-9) have significantly better placement rates.
Removed duplicate records (1,829 duplicates found).
Applied label encoding for categorical variables (Gender, Stream).
Used MinMaxScaler for feature normalization.
Selected Decision Tree Classifier as it provided the best balance of training and testing accuracy.
Training Accuracy: 92.4%.
Testing Accuracy: 87.9%.
Other models evaluated included SVM (84% accuracy), Logistic Regression (76% accuracy), Random Forest (86% accuracy), and K‑Nearest Neighbors (83% accuracy).