Student-Placement-Prediction

Developed a Machine Learning classification project to predict whether a student will be placed or not based on academic, technical, and personal features.

• Built a Logistic Regression model for binary classification.

• Performed data preprocessing and exploratory data analysis.

• Applied Label Encoding and One-Hot Encoding to categorical features.

• Handled class imbalance using SMOTE.

• Evaluated model performance using Accuracy, Precision, Recall, F1-Score, and Confusion Matrix.

• Worked with separate training and testing datasets to evaluate the model on unseen data.

Technologies: Python, Pandas, NumPy, Scikit-learn, imbalanced-learn, Matplotlib, Seaborn.