Netflix Data Analysis Project

Hey there!

This is a mini project where I explored a dataset of Netflix movies and TV shows. The goal was to practice data cleaning, analysis, and visualization while gaining insights into content trends on Netflix.

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What I Did

1. Data Cleaning & Preparation

   • Checked for missing values and filled them with "Not found" where needed (e.g., missing directors or ratings).
   • Created a new column title_len to store the length of each title for further analysis.

2. Exploration & Insights

   • Compared the number of Movies vs TV Shows, overall and per year.
   • Identified directors with the most content.
   • Found the longest titles in the dataset.
   • Explored content by country.

3. Statistics

   • Calculated mean, median, min, max, and percentile for title lengths using NumPy.

4. Visualizations

   • Trend of Movies vs TV Shows per year.
   • Ratings distribution (side by side for Movies and TV Shows).
   • Director analysis and longest titles.
   • Used tight_layout() and proper labeling for clarity.

5. Storytelling

   • Added Markdown explanations for every chart and table to make insights clear.
   • Focused on readability and presentation so that someone can understand the story behind the data.

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Libraries Used

• Python 3
• pandas
• NumPy
• Matplotlib

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Key Learnings

• Handling missing data and creating derived columns.
• Using pandas and NumPy for data exploration.
• Plotting clean, informative charts.
• Writing Markdown for storytelling and sharing insights.

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How to Run

1. Clone this repo:

git clone <[repo-link](https://github.com/Mukesh2006-dev/Netflix_dataset_analysis)>