This project analyzes a dataset of Netflix shows using Python's data analysis libraries, including Pandas, Matplotlib, and Seaborn. The analysis covers various aspects such as genre distribution, show ratings, release year trends, and more.
Objectives:
Analyze Netflix content data to extract insights on show types, release trends, and genre distribution. Visualize patterns and trends using Python libraries to enhance interpretability and support data-driven decisions. Tools & Technologies:
Python: Core language used for data manipulation and analysis. Pandas: Managed and cleaned datasets, performed data transformations, and generated descriptive statistics. Matplotlib & Seaborn: Created visualizations to present data insights effectively, including bar charts, heatmaps, and timelines. Key Steps:
Data Collection: Imported and explored the Netflix dataset to understand its structure and identify key variables (e.g., title, genre, release year). Data Cleaning & Preparation: Cleaned the data by handling missing values and standardizing columns, ensuring accuracy for analysis. Exploratory Data Analysis (EDA): Content Trends: Analyzed trends over time, such as the volume of content added per year and distribution across genres. Genre Analysis: Visualized genre popularity and distribution, highlighting Netflix’s content focus areas. Type Analysis: Compared TV shows versus movies in terms of quantity and release trends. Visualization: Used Matplotlib and Seaborn to create clear, insightful visualizations. Notable visualizations included: Yearly content addition trends. Distribution of genres and show types. Popular release times and seasons.
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Displaying all the countries with most contribution to netflix content
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distribution of type of content on netflix
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Pie chart of content with percentage
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Netflix content releases over the years
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Number of content in Netflix
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Content distribution based on rating
