This project analyzes Netflix's movie and TV show dataset to uncover trends and patterns in content distribution, genres, and viewer preferences. Through exploratory data analysis (EDA) and machine learning, it delivers actionable insights and predicts content performance based on attributes like ratings, release year, and genre.
Exploratory Data Analysis (EDA): Investigate content type distribution, release trends, and genre popularity.
Prediction Model: Build a machine learning model to predict content performance or user preferences.
Insights for Stakeholders: Provide data-driven recommendations for content strategy and audience targeting.
The dataset includes metadata on Netflix movies and TV shows, sourced from publicly available data. Key features:
Title: Name of the movie or TV show.
Genre: Content category (e.g., Drama, Comedy, Thriller).
Rating: Viewer or critic ratings (e.g., TV-MA, PG-13).
Release Year: Year the content was released.
Duration: Runtime for movies or number of seasons for TV shows.
Data Preprocessing: Cleaned missing values, standardized formats, and applied one-hot encoding for categorical variables.
EDA: Visualized trends using Matplotlib and Seaborn to explore content distribution and correlations.
Modeling: Implemented machine learning algorithms (e.g., Random Forest, Logistic Regression) for prediction.
Evaluation: Assessed model performance with metrics like accuracy, precision, and RMSE.
Python: Core programming language.
Pandas & NumPy: Data manipulation and analysis.
Matplotlib & Seaborn: Data visualization.
Scikit-learn: Machine learning model development.
Jupyter Notebook: Interactive analysis and documentation.
Dominant genres identified with growth trends over time. Strong correlations found between content duration, release year, and viewer ratings. Predictive model achieved 85% accuracy for content performance.