🎙️ Interview Response Classifier

Builds a machine learning classification model to predict categories of interview responses using real-world conversational data.

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📌 Project Overview

This repository hosts a data-driven interview response classification project implemented in Python. It includes:

• A Jupyter notebook (Interview_Response_Classifier_F.ipynb) containing data exploration, feature engineering, model training, evaluation, and inference.
• CSV datasets: train_IA_-_train.csv, test_IA_-_test.csv, and data_with_pred.csv with predictions.
• Enables prediction of response categories (e.g., satisfactory, unsatisfactory) using popular supervised techniques like Logistic Regression, Random Forest, and SVM.

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📂 Repository Structure

├── Interview_Response_Classifier_F.ipynb # Interactive notebook with full ML pipeline ├── train_IA_-train.csv # Training dataset ├── test_IA-_test.csv # Test dataset ├── data_with_pred.csv # Test results + model predictions ├── .gitignore # Files to exclude from Git └── README.md # Project overview and instructions

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🛠️ Features & Workflow

• 📊 Data Loading & Exploration
  Insights into question-response patterns and class balance.

• 🧩 Feature Engineering
  Techniques like TF-IDF text encoding, response length, keyword frequency, sentiment scoring.

• 🤖 Modeling & Classification
  Implements models such as Logistic Regression, Random Forest, SVM for predicting response quality.

• 📈 Performance Evaluation
  Metrics include accuracy, precision, recall, F1‑score, confusion matrix, and ROC-AUC.

• 🔍 Result Analysis
  Predictions are saved in data_with_pred.csv for interpretation and further use.

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