Pull Request: Netflix Movie Dataset Analysis and Recommendation System
Overview
This pull request addresses the issue raised in #10 by providing a detailed analysis of the Netflix movie dataset, including data visualization, insights, and a movie recommendation system. The implementation meets the requirements outlined by the user, enhancing our project with comprehensive exploratory data analysis (EDA) and machine learning capabilities.
Key Features
Dataset Download:
Utilizes KaggleHub to download the Netflix Prize dataset, ensuring seamless access to the necessary data.
Exploratory Data Analysis (EDA):
Conducted a thorough EDA, including:
Visualization of rating distributions to understand user behavior.
Identification and handling of missing data.
Summary statistics for movies and customers, highlighting patterns in user ratings.
Data Visualization:
Created engaging visualizations using Matplotlib and Seaborn:
Bar charts and histograms to depict rating distributions and the frequency of movie reviews.
Deductions drawn from these visualizations offer insights into user engagement and movie popularity.
Dataset Trimming:
Cleaned the dataset by removing infrequently reviewed movies and customers to enhance the quality of subsequent analyses.
Pivot Table Creation:
Developed a pivot table for efficient rating analysis, allowing for straightforward correlation calculations.
Collaborative Filtering with SVD:
Implemented a Singular Value Decomposition (SVD) model for collaborative filtering to facilitate personalized movie recommendations.
Conducted cross-validation to evaluate model performance using RMSE and MAE metrics.
Movie Recommendation Function:
Developed a function that generates movie recommendations based on user input.
Leverages Pearson correlation to find similar movies, enhancing user experience.
Usage
The notebook provides a comprehensive workflow for analyzing the Netflix dataset and generating personalized recommendations.
Users can explore data trends, visualize results, and receive movie suggestions based on their preferences.
Visual Insights
Visualizations reveal:
A skewed rating distribution indicating that users generally favor movies.
Disparities in movie popularity, with a few movies receiving the majority of ratings.
Casual rating behavior from most users, with only a small number of highly active raters.
Conclusion
This pull request effectively addresses the analysis of the Netflix movie dataset as per issue #10. It combines data exploration, insightful visualizations, and a functional recommendation system, ultimately enriching the project's analytical capabilities. The work lays the foundation for further enhancements, such as user-based recommendations or incorporating additional features.
Please review the changes and provide feedback or suggestions for improvements. Thank you!
FAMFRIENDLYMONKE
commented
1 month ago
Pull Request: Netflix Movie Dataset Analysis and Recommendation System
Overview
This pull request addresses the issue raised in #10 by providing a detailed analysis of the Netflix movie dataset, including data visualization, insights, and a movie recommendation system. The implementation meets the requirements outlined by the user, enhancing our project with comprehensive exploratory data analysis (EDA) and machine learning capabilities.
Key Features
Dataset Download:
Exploratory Data Analysis (EDA):
Data Visualization:
Dataset Trimming:
Pivot Table Creation:
Collaborative Filtering with SVD:
Movie Recommendation Function:
Usage
Visual Insights
Conclusion
This pull request effectively addresses the analysis of the Netflix movie dataset as per issue #10. It combines data exploration, insightful visualizations, and a functional recommendation system, ultimately enriching the project's analytical capabilities. The work lays the foundation for further enhancements, such as user-based recommendations or incorporating additional features.
Please review the changes and provide feedback or suggestions for improvements. Thank you!