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.