This project focuses on analyzing the Netflix dataset to gain insights into viewing patterns and build recommendation models. The dataset includes various features such as movie titles, genres, ratings, and user preferences. The goal is to explore the data, develop predictive models, and provide recommendations. This project using machine learning techniques, specifically Singular Value Decomposition (SVD), for collaborative filtering.

Python, Machine Learning, Data Visualization (AutoViz), SVD

The main objectives of this project are:

• Data Exploration: Understand the structure and distribution of the data, and identify key features that influence viewing habits.
• Data Cleaning: Handle missing values, outliers, and any inconsistencies in the dataset.
• Feature Engineering: Create new features that could improve the predictive power of the recommendation models.
• Model Building: Develop and train machine learning models to recommend movies.
• Model Evaluation: Assess the performance of the models using appropriate metrics and refine them for better accuracy.
• Visualization: Create visualizations to showcase the insights and results of the analysis.

The dataset used in this project is sourced from Netflix and includes information on various aspects of movie viewing, such as:

• Movie titles
• Genres
• User ratings
• Viewing history
• User preferences
• Other relevant features

• Python: For data analysis, model building, and visualization.
• Pandas: For data manipulation and cleaning.
• NumPy: For numerical operations.
• Scikit-learn: For building and evaluating machine learning models.
• Matplotlib/Seaborn/AutoViz: For creating visualizations.
• Jupyter Notebook: For interactive data analysis and visualization.

1. Clone the repository:

   git clone https://github.com/1vig/netflix-data-analysis.git
   cd netflix-data-analysis

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the Jupyter Notebooks to explore the data, build models, and visualize results:

   jupyter notebook

The project demonstrates the process of analyzing and making recommendations based on Netflix data using machine learning models. The results include:

• Data visualizations that highlight trends and insights from the dataset.
• Trained recommendation models with performance metrics.
• A detailed analysis of the factors affecting viewing preferences.

Contributions are welcome! If you have any suggestions or improvements, please create a pull request or open an issue.

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