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Machine Learning and Data Science/Basic/EDA on Netflix Data/Readme.md

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-Netflix is a popular entertainment service used by people around the world. This EDA will explore the Netflix dataset through visualizations and graphs using python libraries, matplotlib, and seaborn.
+# Netflix Exploratory Data Analysis
 
-Libraries used for this EDA are:
-1. Numpy
-2. Pandas
-3. Textblob
-4. Plotly
+Netflix is a popular entertainment service used by people around the world. In this exploratry data analysis(EDA), we delve into the Netflix dataset through visualizations and graphs using python libraries, Pandas, Matplotlib, Plotly, and TextBlob.
 
+## Libraries Used
+1. **Pandas**: For data manipulation and analysis.
+2. **Matplotlib**: For creating static visualizations.
+3. **Plotly**: For interactive and dynamic visualizations.
+4. **TextBlob**: For sentiment analysis on textual data.
 
-The outcomes of this EDA are:
-1. Top 5 Directors on Netflix
-2. Trend of content on Netlix over the years
-3. Sentiment Content on Netflix
 
-Studying this EDA one can learn the way to analyse data from the real world.
+## EDA Outcomes
+1. **Top 10 Countries with most releases on Netflix**: Analyzing the distribution of content releases across different countries.
+2. **Top 5 Directors on Netflix**: Exploring the top 5 Directors in the dataset based on the content.
+3. **Number of TV shows and Movies released over the years**: Examine the trend in TV show and Movie releases on Netflix through bar charts.
+4. **Sentiment Analysis on Netflix data**: Performed sentiment analysis based on their descriptions and the sentiments are categorized as 'Positive', 'Negative', and 'Neutral' based on polarity.
 
-![image](https://user-images.githubusercontent.com/90904360/157385054-a0436db8-5f23-4f7b-891c-cecd5ed11514.png)
+## In Summary
+Studying and exploring this EDA will provide insights into real-world data analytics. Feel free to explore the visualizations and gain insights from this EDA.