initial commit for encoders and vectors

models/README.md

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+# 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.

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