Student Placement Prediction using Logistic Regression

Overview: This project predicts whether a student will be placed based on two features:

• CGPA
• IQ

The model is built using Logistic Regression and implemented in Python using Scikit-learn.

Dataset: The dataset contains 100 student records. Features

• CGPA
• IQ

Target: Placement

• 1 = Placed
• 0 = Not Placed

🛠 Technologies Used

• Python
• NumPy
• Pandas
• Matplotlib
• Scikit-learn
• Mlxtend

🚀 Machine Learning Workflow

1. Import libraries
2. Load dataset
3. Data preprocessing
4. Feature selection
5. Train-Test Split
6. Feature Scaling using StandardScaler
7. Train Logistic Regression model
8. Evaluate model
9. Visualize Decision Boundary

📊 Model Performance

Accuracy: 90%

📁 Project Structure

Student-Placement-Prediction/

├── placement.csv ├── Student_Placement_Prediction.ipynb ├── README.md ├── requirements.txt ├── model.pkl └── scaler.pkl

📌 Future Improvements

• Deploy using Streamlit
• Compare multiple ML algorithms
• Hyperparameter tuning
• Cross-validation

Author

Sakshi Gupta