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Data Cleaning and Preparation:
- Check for missing or inconsistent values in the dataset. [Beginner]
- Convert appropriate columns to their correct data types. [Beginner]
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Exploratory Data Analysis (EDA):
- Count and visualize the distribution of different genres (listed_in). [Beginner, Matplotlib]
- Plot a pie chart to show the proportion of movies vs TV shows. [Beginner, Matplotlib]
- Create a histogram to display the distribution of release years. [Beginner, Matplotlib]
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Content Analysis:
- Create a bar plot to show the top 10 directors by the number of shows/movies. [Intermediate, Matplotlib]
- Visualize the distribution of countries by the number of shows/movies they produce. [Intermediate, Seaborn]
- Plot a word cloud for the most common cast members. [Intermediate, WordCloud, Matplotlib]
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Time Analysis:
- Plot a line chart to show the trend of content added over the years. [Intermediate, Matplotlib]
- Create a bar plot to display the number of shows/movies added per month. [Intermediate, Seaborn]
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Ratings Analysis:
- Create a count plot to visualize the distribution of ratings. [Intermediate, Seaborn]
- Plot a bar chart to show the highest-rated shows/movies. [Intermediate, Matplotlib]
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Duration Analysis:
- Create a histogram or box plot to display the distribution of content duration. [Advanced, Matplotlib, Seaborn]
- Explore the average duration of shows/movies using a violin plot. [Advanced, Seaborn]
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Description Analysis:
- Perform text analysis and create a word cloud for show/movie descriptions. [Advanced, WordCloud, Matplotlib]
- Analyze sentiment in descriptions and visualize using a stacked bar chart. [Advanced, Seaborn]
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Combination Analysis:
- Create a heatmap to visualize combinations of genres. [Advanced, Seaborn]
- Plot a grouped bar chart to display genre combinations and their frequency. [Advanced, Matplotlib]
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Recommendation System:
- Implement a basic content-based recommendation system based on genres. [Advanced]
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User Engagement Analysis (Hypothetical):
- Analyze user engagement data (e.g., views, likes) if available and visualize using appropriate plots. [Expert]
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Sentiment Analysis (Hypothetical):
- Perform sentiment analysis on user reviews or comments if available and visualize sentiment trends. [Expert]
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Interactive Visualizations (Hypothetical):
- Create interactive visualizations using Plotly or Bokeh to allow users to explore the dataset dynamically. [Expert, Plotly, Bokeh]
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Custom Advanced Plots (Hypothetical):
- Create custom advanced plots to showcase complex relationships and patterns in the data. [Expert, Matplotlib]
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Comparative Analysis (Hypothetical):
- Compare Netflix content statistics with other streaming platforms using appropriate visualizations. [Expert]