🍿🎞️📽️Netflix - Exploratory Data Analysis🎬📺🎦

🏷️Overview

Dive into the world of Netflix through my comprehensive Exploratory Data Analysis (EDA) project, meticulously crafted to unveil insightful perspectives on the streaming giant's offerings and user interactions.This repository contains an exploratory data analysis (EDA) project focusing on Netflix data. The goal of this project is to clean the data , unnest the comma seperated columns and treat the missing values , gain insights, visualize trends, and explore patterns within the Netflix dataset. I have done a thorough analysis of Netflix's content, user reviews, and other relevant information to uncover interesting findings.

🏷️Project Objective:

• Uncover patterns, visualize trends, and gain insights into Netflix's content and user reviews.

📚Dataset

Dataset: Explore the dataset located in the "Raw data" directory, providing a rich source for analysis.

🤔 Exploratory Data Analysis

📝 Project Report

• You can access the complete project python file here - Python
• You can access the complete project in pdf format here - Report

Key Highlights:

1. Data Cleaning and Preprocessing:

   • Rigorous cleaning process to ensure data integrity.
   • Addressed missing values and unnested comma-separated columns for a streamlined dataset.

2. Content Analysis:

   • In-depth exploration of Netflix's content diversity and categorization.
   • Unveiled hidden patterns in genres, release dates, and user ratings.

3. User Interactions:

   • Analyzed user reviews to gauge audience sentiments.
   • Explored viewing patterns and preferences through user interaction data.

4. Data Visualization:

   • Utilized advanced visualization tools, including Matplotlib and Seaborn, to create compelling charts and graphs.
   • Enhanced understanding through visual representation of trends and correlations.

5. Insights and Findings:

   • Unearthed fascinating insights into user behavior and preferences.
   • Identified potential areas for content improvement and user engagement.

Why This Project Matters:

• Provides a nuanced understanding of Netflix's content landscape.
• Offers actionable insights for content creators and strategists.
• Demonstrates proficiency in data cleaning, analysis, and visualization.

Explore the depths of my Netflix EDA project and witness the power of data in unraveling the intricacies of one of the world's leading streaming platforms. 🎬📊

In this EDA, we've covered a wide range of topics and questions, including but not limited to:

• Analysis of content types (Movies, TV shows)
• Trends in content releases over the years
• User rating distributions
• Genre preferences
• Popular actors and directors
• Correlations
• And more...

The analysis is performed using Python and popular data analysis libraries such as Numpy,Pandas, Matplotlib, and Seaborn. You can find the cleaned and segregated csv files in the "Cleaned Data" directory. You can find the code and detailed explanations in the Jupyter Notebook files in the "EDA Analysis" directory.

Give a ⭐ if u like this repo !!!

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