This project performs an in depth exploratory data analysis (EDA) on a Netflix Movie dataset containing more than 9,000 flims records. The Goal is to understand trends in genres, popularity, votes, and yearly release patterns through visual insights.
Netflix is known for its work in data science, AI & ML, particularly for building strong recommendations models and algorithms, that understand customer behaviour and patterns.
- Cleaned and preprocessed the Netflix dataset using Pandas
- Handled missing values and standardized categorical features
- Analyzed distribution of movies across genres
- Visualized popularity patterns and vote counts
- Identified most frequent genres on Netflix
- Determined highest-voted and lowest-voted movies with their genres
- Explored yearly movie production trends
- Created multiple visulaizations inclusing bar charts, count plots, and heatmaps
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Which genre appears most frequently on Netflix?
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Which genres have the highest votes?
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Which movies have the highest popularity and what are their genres?
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Which movies have the lowest popularity and what are their genres?
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Which year had the most movies filmed?
- Python
- Pandas
- Numpy
- Matplotlib
- Seaborn
- Jupyter Notebook
- Genre frequency bar graphs
- Popularity comparsion graphs
- Vote distributions plots
- Release-year bar charts
- Heatmaps for correlations
- Install dependencies: "pip install pandas numpy matplotlib seaborn"
- Run the notebook: jupyter notebook
-This analysis reveals Netflix's most popular genres, content trends over the years , and voting pattterns. The project demonstrates strong skills in data cleaning, visualization , and pattern extractions.