🎬 Netflix Movies Data Analysis

An exploratory data analysis project that studies a Netflix movie dataset using Python, Pandas, NumPy, Matplotlib, and Seaborn.

The project focuses on data cleaning, exploratory data analysis, visualization, and extracting useful insights from movie-related information.

📌 Project Overview

Streaming platforms contain a large amount of entertainment data. Analyzing this data can help us understand:

The distribution of movies across genres
Popular and highly rated titles
Release-year trends
Runtime patterns
Language and country representation
Relationships between ratings, popularity, votes, and other movie attributes

This repository contains a Jupyter Notebook for the complete analysis, the dataset used in the project, and a PowerPoint presentation summarizing the work.

🎯 Project Objectives

The main objectives of this project are:

Import and understand the movie dataset
Identify missing values and duplicate records
Clean and prepare the data for analysis
Perform exploratory data analysis
Create meaningful charts and visualizations
Discover patterns and trends in the movie data
Present the findings in a clear and understandable format

✨ Key Features

Dataset inspection and summary
Missing-value analysis
Duplicate-record detection
Data-type correction
Statistical summary of numerical columns
Univariate and bivariate analysis
Genre-based analysis
Release-year trend analysis
Rating and popularity analysis
Runtime distribution analysis
Data visualization using charts and graphs
Project presentation included in PowerPoint format

🛠️ Tech Stack

Category	Technology
Programming Language	Python
Data Analysis	Pandas, NumPy
Data Visualization	Matplotlib, Seaborn
Development Environment	Jupyter Notebook
Dataset Format	CSV
Presentation	Microsoft PowerPoint
Version Control	Git and GitHub

📂 Repository Structure

-Project-Netflix-Data-Analysis-/
│
├── .DS_Store
├── Movies_Data_Analysis.ipynb
├── movie data analysis project ppt.pptx
├── mymoviedb.csv
└── README.md

File Description

File	Description
`Movies_Data_Analysis.ipynb`	Main notebook containing data cleaning, EDA, visualizations, and insights
`mymoviedb.csv`	Dataset used for the movie analysis
`movie data analysis project ppt.pptx`	Presentation summarizing the project and findings
`.DS_Store`	macOS-generated system file
`README.md`	Project documentation

🔍 Analysis Workflow

Raw Movie Dataset
        │
        ▼
Import Required Libraries
        │
        ▼
Load and Inspect Dataset
        │
        ▼
Handle Missing and Duplicate Values
        │
        ▼
Correct Data Types
        │
        ▼
Exploratory Data Analysis
        │
        ▼
Create Visualizations
        │
        ▼
Identify Trends and Insights
        │
        ▼
Prepare Final Presentation

📊 Exploratory Data Analysis

The notebook may include analyses such as:

1. Dataset Understanding

Number of rows and columns
Column names
Data types
Statistical summary
Null-value count
Duplicate-row count

2. Genre Analysis

Most common movie genres
Genre frequency distribution
Comparison of ratings across genres

3. Release-Year Analysis

Number of movies released each year
Growth or decline in movie production
Identification of years with the highest number of releases

4. Rating Analysis

Distribution of movie ratings
Highest-rated movies
Comparison between ratings and vote counts

5. Popularity Analysis

Most popular movies
Distribution of popularity scores
Relationship between popularity and ratings

6. Runtime Analysis

Runtime distribution
Average movie runtime
Identification of unusually short or long movies

7. Language or Country Analysis

When the required columns are available, the analysis can also explore:

Most common original languages
Countries producing the highest number of titles
Comparison of ratings across languages or countries

📈 Example Visualizations

The project can include charts such as:

Bar chart of the most common genres
Histogram of movie ratings
Line chart of releases by year
Bar chart of the most popular movies
Distribution plot of movie runtime
Scatter plot of rating versus popularity
Correlation heatmap for numerical features
Pie chart of language or genre distribution

💻 Installation and Setup

1. Clone the Repository

git clone https://github.com/vickycodeswith/-Project-Netflix-Data-Analysis-.git
cd -- -Project-Netflix-Data-Analysis-

If the cd command above does not work in your terminal, use:

cd ./-Project-Netflix-Data-Analysis-

2. Create a Virtual Environment

Windows

python -m venv venv
venv\Scripts\activate

macOS or Linux

python3 -m venv venv
source venv/bin/activate

3. Install the Required Libraries

pip install pandas numpy matplotlib seaborn jupyter

You can also create a requirements.txt file containing:

pandas
numpy
matplotlib
seaborn
jupyter

Then install all packages using:

pip install -r requirements.txt

4. Open the Jupyter Notebook

jupyter notebook

Open:

Movies_Data_Analysis.ipynb

Run the notebook cells from top to bottom.

🧪 Basic Python Example

import pandas as pd

df = pd.read_csv("mymoviedb.csv")

print(df.head())
print(df.shape)
print(df.info())
print(df.isnull().sum())
print(df.duplicated().sum())

🧹 Data Cleaning Example

# Remove duplicate rows
df = df.drop_duplicates()

# Remove unnecessary spaces from column names
df.columns = df.columns.str.strip()

# Display missing values
missing_values = df.isnull().sum()
print(missing_values)

The exact cleaning operations depend on the columns and data quality of mymoviedb.csv.

📊 Visualization Example

import matplotlib.pyplot as plt
import seaborn as sns

plt.figure(figsize=(10, 6))
sns.histplot(df["Vote_Average"], bins=20, kde=True)
plt.title("Distribution of Movie Ratings")
plt.xlabel("Rating")
plt.ylabel("Number of Movies")
plt.show()

Replace Vote_Average with the correct rating-column name from the dataset when necessary.

💡 Possible Business Insights

This type of analysis can help answer questions such as:

Which movie genres are most common?
Which genres receive the best average ratings?
Which years had the highest number of releases?
Are highly popular movies always highly rated?
What is the typical runtime of a movie?
Which titles have high ratings but low popularity?
Which language or country is most represented?

Then display them in this README:

![Genre Analysis](screenshots/genre-analysis.png)

![Ratings Distribution](screenshots/ratings-distribution.png)

![Release-Year Trend](screenshots/release-year-trend.png)

🚀 Future Improvements

Create an interactive dashboard using Streamlit
Build a Power BI or Tableau dashboard
Add filters for genre, year, language, and rating
Perform sentiment analysis on movie reviews
Add a movie recommendation system
Create an automated data-cleaning pipeline
Add more advanced statistical analysis
Deploy the analysis as a web application
Add a requirements.txt file
Remove .DS_Store from the repository
Add screenshots of important visualizations
Improve notebook documentation and code comments

🧹 Recommended Repository Cleanup

The .DS_Store file is generated automatically by macOS and is not needed in the repository.

Add the following entry to .gitignore:

.DS_Store

Then remove it from Git tracking:

git rm --cached .DS_Store
git commit -m "Remove unnecessary DS_Store file"
git push

🤝 Contributing

Contributions and suggestions are welcome.

Fork the repository
Create a new branch

git checkout -b feature/improvement

Commit your changes

git commit -m "Improve movie data analysis"

Push your branch

git push origin feature/improvement

Open a Pull Request

👨‍💻 Author

Nitesh Kumar Yadav

GitHub: @vickycodeswith
Repository: Netflix Movies Data Analysis

⭐ Support

If this project is useful, please give the repository a ⭐.

📄 License

This project is created for educational and portfolio purposes.

You may add an MIT License if you want other developers to reuse or contribute to the project.

Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
Movies_Data_Analysis.ipynb		Movies_Data_Analysis.ipynb
README.md		README.md
movie data analysis project ppt.pptx		movie data analysis project ppt.pptx
mymoviedb.csv		mymoviedb.csv

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