https://github.com/nirmaldeepponnada/codeclauseinternshipproject2

Python, NLTK, Scikit-Learn, Pandas, NumPy, Pickle, SciPy, and JSON are used for text preprocessing, feature engineering, multi-label classification, and model persistence.
https://github.com/nirmaldeepponnada/codeclauseinternshipproject2

nltk numpy pandas pickle python scikit-learn scipy

Last synced: 14 days ago
JSON representation

Python, NLTK, Scikit-Learn, Pandas, NumPy, Pickle, SciPy, and JSON are used for text preprocessing, feature engineering, multi-label classification, and model persistence.

• Host: GitHub
• URL: https://github.com/nirmaldeepponnada/codeclauseinternshipproject2
• Owner: nirmaldeepponnada
• Created: 2024-11-08T09:42:18.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-08T09:52:11.000Z (2 months ago)
• Last Synced: 2024-11-08T10:35:40.733Z (2 months ago)
• Topics: nltk, numpy, pandas, pickle, python, scikit-learn, scipy
• Language: Jupyter Notebook
• Homepage:
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# CodeClauseInternshipProject2

### Project Description

1. **Objective**:

   - Predict the genres of movies based on their plot summaries and additional features (e.g., budget, runtime, vote average).

2. **Dataset**:

   - Combines `tmdb_5000_movies.csv` and `tmdb_5000_credits.csv`.

   - Key features used: `overview` (plot summary), `genres`, `budget`, `runtime`, `vote_average`.

3. **Technologies Used**:

   - Python for implementation.

   - NLTK for text preprocessing (lemmatization, stopword removal).

   - Scikit-Learn for feature extraction (TF-IDF), multi-label classification, and evaluation.

   - Pandas and NumPy for data handling.

   - Pickle for model persistence.

4. **Preprocessing**:

   - Text data (`overview`) cleaned using tokenization, lemmatization, and stopword removal.

   - JSON-formatted `genres` column converted to binary multi-label format.

   - Additional numerical features filled or normalized.

5. **Feature Engineering**:

   - Plot summaries converted to numerical features using TF-IDF vectorization.

   - Combined TF-IDF features with numerical features (budget, runtime, vote average).

6. **Model Training**:

   - Multi-label classification handled using `OneVsRestClassifier` with Logistic Regression.

   - Model trained to predict one or more genres for each movie.

7. **Evaluation**:

   - Metrics: Accuracy, precision, recall, and F1-score.

   - Evaluation done on a separate test set.

8. **Model Persistence**:

   - Trained model, TF-IDF vectorizer, and genre label binarizer saved for future predictions.

9. **Prediction**:

   - New movie summaries can be preprocessed and passed through the saved model to predict genres.

10. **Outcome**:

    - Demonstrates the use of NLP and machine learning techniques for multi-label text classification.