Netflix Data Analysis Project

Overview

This project performs an end-to-end analysis of Netflix content using Python, SQL, MySQL, and Power BI. The objective is to clean, transform, analyze, and visualize Netflix's catalog to uncover trends in content production, genres, ratings, countries, and audience preferences.

---

Project Objectives

• Clean and preprocess raw Netflix data.
• Store and query data using SQL and MySQL.
• Perform exploratory data analysis (EDA).
• Generate meaningful visualizations.
• Build an interactive Power BI dashboard.
• Extract business insights from Netflix content trends.

---

Dataset

The dataset contains information about Netflix movies and TV shows, including:

• Title
• Type (Movie / TV Show)
• Director
• Cast
• Country
• Release Year
• Rating
• Duration
• Genre
• Date Added
• Description

---

Tools & Technologies

Python

• Pandas
• NumPy
• Matplotlib
• Seaborn
• Jupyter Notebook

Database

• SQL
• MySQL

Visualization

• Power BI

---

Project Workflow

Step 1: Data Cleaning

Notebook: Netfilx_Cleaning_Step1.ipynb

Tasks performed:

• Missing value handling
• Date formatting
• Duration standardization
• Genre cleaning
• Country normalization
• Feature engineering

---

Step 2: Database Integration

Files:

• Netfilx_db_Step2A.sql
• NetFlix_conneting_mysql_Step2B.ipynb
• Netflix_Step_2C.sql

Tasks:

• Database creation
• Table design
• Data import
• SQL queries for analysis

---

Step 3: Exploratory Data Analysis

Notebook: EDA_Netflix_Step3.ipynb

Analysis includes:

• Movies vs TV Shows
• Content growth over time
• Top producing countries
• Most popular genres
• Monthly content additions
• Rating distribution
• Movie duration analysis
• Genre trends

---

Visualizations

Movies vs TV Shows

Content Growth Over Time

Top Countries

Top Genres

Monthly Content Heatmap

Rating Distribution

Movie Duration Analysis

Genre Trends

---

Power BI Dashboard

File: Netflix_analysis_dashboard.pbix

The dashboard provides:

• Content distribution
• Country-wise analysis
• Genre insights
• Rating breakdown
• Release trends
• Interactive filtering

---

Key Insights

• Movies significantly outnumber TV Shows.
• Netflix content experienced rapid growth after 2015.
• The United States contributes the largest amount of content.
• International dramas and comedies are among the most popular genres.
• TV-MA is one of the most common content ratings.
• Content additions show strong growth during recent years.

---

Repository Structure

Netflix-Data-Analysis/
│
├── NetFlix.csv
├── cleaned_netflix.csv
├── countries_exploded.csv
├── genres_exploded.csv
│
├── Netfilx_Cleaning_Step1.ipynb
├── NetFlix_conneting_mysql_Step2B.ipynb
├── EDA_Netflix_Step3.ipynb
│
├── Netfilx_db_Step2A.sql
├── Netflix_Step_2C.sql
│
├── Netflix_analysis_dashboard.pbix
│
├── chart1_movies_vs_tvshows.png
├── chart2_content_growth.png
├── chart2_top_countries.png
├── chart3_top_genres.png
├── chart4_monthly_heatmap.png
├── chart6_rating_dist.png
├── chart7_movie_duration.png
├── chart8_genre_trends.png
│
└── README.md

---

Author

Samriti

Data Analytics | Python | SQL | Power BI