🎬 Netflix Movies and TV Shows Clustering: Analyzing Netflix's content library to explore content distribution, trends, and similarities across genres and countries by performing clustering on movies and TV shows available on the platform.

Overview:

• Analysis of Netflix's content library with a focus on clustering movies and TV shows.
• Objective to explore content distribution, trends, and similarities across genres and countries.

Objectives:

• Conduct Exploratory Data Analysis (EDA).
• Understand the type of content available in different countries.
• Check if Netflix is increasingly focusing on TV shows rather than movies in recent years.
• Cluster similar content by matching text-based features (like descriptions).

Methods Used:

• Descriptive Statistics: Summarizing and describing the data.
• Data Visualization: Visualizing trends and insights from the dataset.
• Machine Learning: K-means clustering to group similar content based on text-based features.

Libraries Utilized:

• NumPy and Pandas: For dataset cleaning and analysis.
• Matplotlib, Plotly, and Seaborn: For data visualization.
• SkLearn and nltk: For machine learning and clustering.

Dataset Used:

• TV shows and movies available on Netflix as of 2019, collected from Flixable, a third-party Netflix search engine.
• The dataset contains the following attributes:
• show_id: Unique ID for every Movie/TV Show
• type: Identifier - Movie or TV Show
• title: Title of the Movie/TV Show
• director: Director of the Movie
• cast: Actors involved in the Movie/Show
• country: Country where the Movie/Show was produced
• date_added: Date the Movie/Show was added on Netflix
• release_year: Actual release year of the Movie/Show
• rating: TV rating of the Movie/Show
• duration: Total duration (in minutes or number of seasons)
• listed_in: Genre
• description: Summary description of the Movie/Show

Project Workflow:

• Data Preprocessing: Load the dataset, inspect the data, handle missing values, and perform feature engineering.
• Exploratory Data Analysis (EDA): Visualize trends, content type distribution, and country-specific content.
• Text Data Preprocessing: Clean text fields, apply TF-IDF Vectorizer to convert text into numerical form for clustering.
• Clustering Using K-Means: Perform K-means clustering to create 10 distinct clusters based on text similarity.
• Recommender System: Implement a movie/TV show recommender system based on clustering results.

Outcome:

• Insight into prevalent content types (movies vs. TV shows) and country-specific trends.
• Analysis of Netflix's focus shift towards TV shows over time.
• Clustering of similar movies and TV shows based on text data.
• A simple recommender system to suggest similar content to users based on their preferences.

Tools & Technologies:

• Python: For data analysis and machine learning.
• Libraries: Pandas, NumPy, Sklearn, nltk, Matplotlib, Seaborn, Plotly.
• K-Means Clustering: To group content based on textual features.