📊 Placement Prediction using SVM

This project uses a Support Vector Machine (SVM) to predict student placement based on their CGPA and IQ score.

---

🔧 Technologies Used

• Python
• Pandas, Seaborn, Matplotlib
• scikit-learn
• mlxtend (for decision boundary plot)

---

📁 Dataset

• File: placement.csv
• Columns:
  • id (dropped)
  • cgpa (float)
  • iq (int)
  • placement (0 or 1)

---

📈 Model

• Classifier: SVC(kernel="linear")
• Feature scaling applied using StandardScaler
• Data split: 80% training / 20% testing

---

✅ Evaluation

Metric	Score
Training Accuracy	~91%
Testing Accuracy	~90%

Also includes:

• Confusion Matrix
• Classification Report

---

📉 Decision Boundary

---

🚀 How to Run

pip install pandas matplotlib seaborn scikit-learn mlxtend
python placement_svc.py