CineScope AI – Intelligent Streaming Content Analytics & Genre Prediction System
AI-powered streaming content analytics system for exploring titles and predicting genres from descriptions using machine learning.
Project overview
What this project is about.
CineScope AI is an intelligent streaming content analytics and genre prediction system built as a college project demonstration.
The system uses a historical Netflix titles dataset containing movies and TV shows. Users can explore the catalog, search for titles, apply filters, view detailed information, and analyze content using an interactive dashboard.
The project also includes an AI-based genre prediction feature. Users can enter a title description, and the machine learning model analyzes the description and predicts one or more normalized genres.
The prediction system uses Natural Language Processing (NLP) techniques. First, the description is cleaned using simple text preprocessing. Then, TF-IDF converts the text into numerical features that the machine learning model can understand. A One-vs-Rest Logistic Regression classifier then predicts the applicable genres.
The project uses 25 normalized genre labels created from the original dataset categories. Since a title can belong to multiple genres, the prediction task is treated as multi-label classification.
The application provides a complete interface for understanding the dataset, exploring content, viewing title details, making AI-based predictions, and learning how the machine learning pipeline works.
This project uses a historical dataset snapshot and is intended for educational and college demonstration purposes. It does not represent the current Netflix catalog and is not affiliated with Netflix.
Key features
- Interactive streaming content analytics dashboard
- Search and filter titles by name, type, genre, and release year
- Movie and TV show statistics with interactive charts
- Detailed title information with metadata and genre classification
- AI-based genre prediction from title descriptions
- Multi-label genre classification using machine learning
- TF-IDF based text feature extraction
- One-vs-Rest Logistic Regression classification
- 25 normalized genre categories
- Responsive interface for desktop, tablet, and mobile devices
- Light and dark mode support
- Methodology page explaining dataset preparation and ML pipeline
- Historical dataset analysis with missing-data handling
Technology stack
PythonDjangoSQLitePandasNumPyscikit-learnTF-IDFLogistic RegressionMultiLabelBinarizerJoblibHTML5CSS3JavaScriptChart.js
Usage and use case
1. Explore the streaming content catalog and view overall dataset statistics.
2. Search titles by name and filter them by type, genre, and release year.
3. Open individual title pages to view descriptions, metadata, and normalized genres.
4. Enter a title description into the AI Predictor to identify possible genres.
5. Analyze predicted genres generated using TF-IDF and Logistic Regression.
6. Review dataset preparation, genre normalization, model evaluation, and project methodology.
Requirements
Python 3.13+
Django 6.1.1
Pandas
NumPy
scikit-learn
Joblib
Chart.js
Netflix titles dataset
Pre-trained ML model artifacts
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