Engineeering & IT Projects and Resources: Netflix Movie Data Analysis

Netflix Movie Data Analysis

1. Introduction

Objective: Analyze Netflix's movie dataset to understand content distribution, ratings, and genres.
Purpose: Provide actionable insights for decision-making, such as content recommendations, genre popularity, and audience preferences.

2. Project Workflow

1. Problem Definition:
   - Explore and analyze Netflix data for patterns in content, genres, and ratings.
   - Answer specific questions:
     - What are the most common genres?
     - How do ratings vary across genres?
     - How has Netflix’s content evolved over time?
2. Data Collection:
   - Source: Use a publicly available Netflix dataset from platforms like Kaggle or other data repositories.
   - Dataset example: netflix_titles.csv containing attributes such as title, genre, rating, release_year, etc.
3. Data Preprocessing:
   - Handle missing data.
   - Standardize and format data for analysis.
   - Encode categorical data if needed.
4. Exploratory Data Analysis (EDA):
   - Statistical analysis and data visualization.
   - Identify trends, correlations, and anomalies.
5. Insights and Conclusions:
   - Summarize findings from the analysis.
   - Provide actionable recommendations.

3. Technical Requirements

- Programming Language: Python
- Libraries/Tools:
- Data Handling: Pandas, NumPy
- Visualization: Matplotlib, Seaborn, Plotly
- Interactive Analysis: Jupyter Notebook or Google Colab

4. Implementation Steps

Step 1: Setup Environment

Install required libraries:
```
pip install pandas numpy matplotlib seaborn plotly
```

Step 2: Load Dataset

Read the Netflix dataset:
```
import pandas as pd

df = pd.read_csv('netflix_titles.csv')
```

Step 3: Data Cleaning

Check for missing values:
```
print(df.isnull().sum())
```
Fill or drop missing values:
```
df.fillna('Unknown', inplace=True)
```

Step 4: Analyze Data

1. Genres Analysis:
   - Distribution of genres:
```
genre_count = df['listed_in'].str.split(',').explode().value_counts()
genre_count.plot(kind='bar', title='Genre Distribution')
```
2. Ratings Analysis:
   - Average rating per genre:
```
avg_rating = df.groupby('rating')['listed_in'].count()
avg_rating.plot(kind='bar', title='Ratings by Genre')
```
3. Temporal Analysis:
   - Content release trends over time:
```
df['release_year'].value_counts().sort_index().plot(kind='line', title='Content Over Years')
```
4. Visualize Insights:
   - Use Seaborn and Plotly for interactive visualizations.

Step 5: Generate Report

Compile insights into a structured document or dashboard.

5. Expected Outcomes

1. Clear understanding of popular genres and trends.
2. Insights into ratings distribution and audience preferences.
3. Recommendations for future content creation based on trends.

6. Additional Suggestions

- Advanced Techniques:
- Perform sentiment analysis on content descriptions.
- Apply clustering to group similar movies.
- Interactive Dashboards:
- Use Streamlit or Dash to create a live dashboard.

Pages