Title: Netflix Data Analysis using Python
Description: Welcome to the Netflix Data Analysis project repository, associated with MLR Institute of Technology! In this project, we delve into the fascinating realm of Netflix data analysis using Python, aiming to uncover valuable insights into viewership patterns, content preferences, and more.
Key Components: Data Gathering and Preprocessing: We gather Netflix data and preprocess it to ensure data quality and consistency for analysis. Exploratory Data Analysis (EDA): Through EDA, we uncover trends, viewership patterns, and correlations within the Netflix dataset. Key Metrics Definition: We define key metrics such as viewership trends, content ratings, genres, and more to gain a comprehensive understanding of the data. Time-Series Analysis: Time-series analysis is performed to identify temporal patterns in viewership behavior over time. Content Recommendation System: A basic content recommendation system is implemented to suggest shows or movies based on user preferences. Sentiment Analysis: Sentiment analysis on user reviews provides insights into the sentiment towards specific shows or movies. Geospatial Analysis: Geospatial analysis helps us understand regional preferences for Netflix content. Interactive Visualizations: Interactive visualizations are utilized to present our findings in an engaging and accessible manner. Insights and Recommendations: We conclude the analysis with insights and recommendations for Netflix or content creators to enhance their offerings based on our findings.
Tools and Libraries Used: Python: Core programming language for data analysis and manipulation. Pandas: Used for data manipulation and analysis. Matplotlib/Seaborn: Utilized for data visualization. Scikit-learn: Employed for implementing the content recommendation system. Jupyter Notebooks: Documentation of analysis steps and visualizations. Project Complexity: The complexity of the project may vary based on data availability and specific objectives, ranging from basic exploratory analysis to more advanced predictive modeling.