# MAP Misconception Detection – Classical Models

This repository contains classical machine learning components used for the **Kaggle MAP (Misconception Annotation Project)** competition. We developed two independent pipelines using **TF-IDF vectorization** and **Logistic Regression** for:

- **Task 1**: Category classification
- **Task 2**: Misconception label prediction

---

## 📁 Included Files

### 🔹 Task 1 (Category Classification)

| File | Description |
|------|-------------|
| `tfidf_task1.pkl` | TF-IDF vectorizer fitted on student answers for Task 1 |
| `logreg_task1.pkl` | Trained logistic regression model for predicting `category` labels |

---

### 🔹 Task 2 (Misconception Detection)

| File | Description |
|------|-------------|
| `tfidf_task2.pkl` | TF-IDF vectorizer fitted on student answers for Task 2 |
| `logreg_task2.pkl` | Trained logistic regression model for predicting misconception labels |

---

### 🔹 Label Encoders

| File | Description |
|------|-------------|
| `label_encoder_category.pkl` | LabelEncoder used to convert `category` labels into integers |
| `label_encoder_miscon.pkl` | LabelEncoder used to convert misconception labels into integers |

---

## 🧠 Usage

To use the models in your code:

```python
import joblib

# Load Task 1 components
tfidf1 = joblib.load("tfidf_task1.pkl")
clf1 = joblib.load("logreg_task1.pkl")

# Load Task 2 components
tfidf2 = joblib.load("tfidf_task2.pkl")
clf2 = joblib.load("logreg_task2.pkl")

# Load label encoders
le_category = joblib.load("label_encoder_category.pkl")
le_miscon = joblib.load("label_encoder_miscon.pkl")

🛠️ Requirements
These models were trained using:

- scikit-learn
- joblib

```bash
pip install scikit-learn joblib
```

📜 Licens:
MIT License. You are free to use, modify, and distribute this model with proper attribution.

✍️ Author:
Godspower Maurice (PythonCreate)
Created for the MAP Math Misconceptions @ Kaggle NLP competition — 2025.