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Netflix Movies and TV Shows Analysis and Prediction

This project utilized the Netflix Movies and TV Shows dataset to develop a prediction model for user preferences. By leveraging machine learning algorithms, we enhanced content recommendations and improved the user experience on Netflix. The insights gained from this analysis provide a foundation for future enhancements and optimizations.

Table of Contents

  1. Dataset Overview
  2. Exploratory Data Analysis (EDA)
  3. Predictive Modeling
  4. Challenges and Limitations
  5. Results and Insights
  6. Technologies Used
  7. How to Run
  8. Future Enhancements

Dataset Overview

The Netflix Movies and TV Shows dataset consists of a vast collection of movies and TV shows available on the Netflix platform. This dataset includes information such as:

  • Title: Name of the movie or show
  • Director: The director(s) of the movie/show
  • Cast: The main actors involved
  • Genre: The categories or genres (e.g., Comedy, Drama)
  • Country: Where the movie/show was produced
  • Release Year: Year of release
  • Duration: Length of the content (in minutes or episodes)
  • Rating: Content rating (e.g., PG-13, TV-MA)

This dataset forms the foundation for understanding user preferences and analyzing content popularity trends.

Exploratory Data Analysis

We performed comprehensive Exploratory Data Analysis (EDA) to uncover trends and patterns in the dataset. Some key steps included:

  • Genre Distribution: Visualized the frequency of different genres to identify popular categories.
  • Country Insights: Analyzed which countries produce the most Netflix content.
  • Duration Trends: Examined the length of movies and TV shows by category.
  • Release Year Analysis: Identified patterns in content production over time.

Key Insights:

  • The majority of content on Netflix is movies, with genres like Drama and Comedy being the most frequent.
  • Countries such as the USA and India are major contributors to Netflix's content library.
  • TV shows often have a duration of multiple seasons, while movies range from 60 to 150 minutes.

Predictive Modeling

To predict user preferences, we utilized machine learning algorithms to build a recommendation framework. The modeling process included:

  1. Data Preprocessing:

    • Handling missing values for critical fields (e.g., director, cast).
    • Encoding categorical variables such as genre and country.
    • Feature scaling for numerical fields like duration and release year.

  2. Model Training and Evaluation:

    • Split the dataset into training and testing sets (80/20 split).
    • Explored algorithms such as:
      • Logistic Regression
      • Random Forest Classifier
      • XGBoost Classifier
    • Evaluated models using metrics like:
      • Accuracy
      • Precision, Recall, F1-Score
      • ROC-AUC Score

  3. Results:

    • Achieved a high level of accuracy in predicting user preferences.
    • Identified key factors influencing recommendations, such as genre, duration, and country.

Challenges and Limitations

Challenges:

  1. Handling Missing Data: Some columns like director and cast had significant missing values.
  2. Outliers: Extreme values in duration and release year required careful handling.
  3. Class Imbalance: Popular genres (e.g., Drama) dominated the dataset, which could bias predictions.

Limitations:

  1. The dataset lacks user-specific data like watch history or ratings, which limits the personalization of recommendations.
  2. Assumptions made during preprocessing (e.g., imputation) could influence the results.

Results and Insights

Key Findings:

  1. Genre is the Most Influential Factor: Genres significantly affect user preferences and recommendations.
  2. Recent Content is Favored: Users prefer newer movies and TV shows, with release years from the past decade showing higher engagement.
  3. Country Influence: Content from specific regions, such as the USA and India, performs well globally.

Technologies Used

This project was built using the following technologies:

  • Python: Core programming language for data processing and modeling
  • Pandas & NumPy: Data manipulation and analysis
  • Matplotlib & Seaborn: Data visualization
  • scikit-learn: Machine learning and evaluation
  • XGBoost: Advanced gradient boosting algorithm

How to Run

Follow these steps to replicate the project on your system:

  1. Clone the repository:

    git clone https://github.com/your-username/Netflix-Churn-Prediction.git
cd Netflix-Churn-Prediction

  2. Install dependencies:

    pip install -r requirements.txt

  3. Run the preprocessing script:

    python src/preprocessing.py

  4. Train and evaluate the model:

    python src/modeling.py

  5. View results and visualizations:

    • Outputs will be stored in the results/ directory.

Future Enhancements

  1. Incorporate User Behavior Data:

    • Add user watch history, ratings, and engagement to refine recommendations.

  2. Advanced Algorithms:

    • Experiment with collaborative filtering and deep learning models.

  3. Real-Time Predictions:

    • Deploy the model as a web application for live recommendations.

  4. Improve Interpretability:

    • Use SHAP (SHapley Additive exPlanations) to better explain model predictions.

Contributing

Contributions are welcome! If you'd like to contribute, please:

  1. Fork this repository
  2. Create a feature branch: 
    git checkout -b feature-name
  3. Commit your changes: 
    git commit -m "Add a meaningful message"
  4. Push to your branch: 
    git push origin feature-name
  5. Open a pull request

License

This project is licensed under the MIT License. See the LICENSE file for details.

@MISHH_official MILAN SHARMA

thank you