Analytics
IMDb Web Scraping & Data Analysis
A comprehensive, end-to-end web scraping portfolio project that extracts movie data from IMDb's Top 250 list, performs data cleaning and analysis, and presents insights through an interactive visual dashboard.
Year :
2026
## 🎯 Project Objectives
This project demonstrates:
- **Web Scraping**: Ethical data extraction using BeautifulSoup4 & Requests
- **Data Engineering**: Complete ETL pipeline with cleaning and validation
- **Data Analysis**: Statistical analysis, correlation, and insights
- **Data Visualization**: Interactive Plotly dashboards & EDA notebooks
- **Software Engineering**: Professional, production-ready code
- **Portfolio Building**: GitHub-ready, well-documented project
### Business Questions Answered
- What are the quality distribution patterns of top-rated movies?
- Which genres dominate in terms of popularity and ratings?
- How have movie ratings evolved over decades?
- What's the relationship between vote count and rating?
- Which directors produce the highest-rated films?
## 🛠 Tech Stack
| Component | Technology |
|-----------|-----------|
| **Language** | Python 3.10+ |
| **Web Scraping** | BeautifulSoup4 + Requests |
| **Data Processing** | Pandas, NumPy |
| **Visualization** | Plotly (interactive), Seaborn + Matplotlib |
| **Storage** | CSV, SQLite (optional) |
| **Documentation** | Markdown |
| **Version Control** | Git + GitHub |
📂 Project Structure
imdb-webscraping/
│
├── data/
│ ├── raw_data.csv # Raw scraped data (unprocessed)
│ └── cleaned_data.csv # Cleaned & validated data
│
├── notebooks/
│ └── exploration.ipynb # Exploratory data analysis
│
├── scraper.py # Web scraping script (STEP 1)
├── cleaning.py # Data cleaning script (STEP 2)
├── dashboard.py # Visualization dashboard (STEP 3)
├── requirements.txt # Python dependencies
├── README.md # This file
└── .gitignore # Git ignore rules
**Output**: `dashboard.html` with interactive visualizations
**Features**:
- ✅ 8 professional visualizations
- ✅ Fully interactive Plotly charts (zoom, hover, export)
- ✅ Responsive design for all screen sizes
- ✅ Single HTML file (easy to share & embed)
- ✅ Summary statistics report
- ✅ Color-coded visualizations
- ✅ Mobile-friendly dashboard
**Visualizations Included**:
1. **Rating Distribution** - Histogram of all movie ratings (shows quality concentration)
2. **Top 10 Genres** - Bar chart of most common genres
3. **Average Rating by Genre** - Which genres have highest avg ratings?
4. **Votes vs Rating Correlation** - Scatter plot showing vote-rating relationship (log scale)
5. **Rating Trend Over Decades** - Line chart of avg rating evolution
6. **Top 10 Directors** - Directors with highest average ratings (min 2 films)
7. **Vote Distribution** - Box plot with outlier detection
8. **Movies by Certificate** - Pie chart of age ratings (PG, R, etc.)
## 📊 Data Fields Extracted
| Field | Description | Type | Source |
|-------|-------------|------|--------|
| rank | IMDb ranking (1-250) | Integer | Top 250 list |
| title | Movie title | String | List + Detail page |
| year | Release year | Integer | Detail page |
| rating | IMDb rating (1-10) | Float | Top 250 list |
| votes | Number of user votes | Integer | Detail page |
| runtime | Duration in minutes | Integer | Detail page |
| genre | Movie genre(s) | String | Detail page |
| director | Primary director name | String | Detail page |
| cast | Top 3 actors/actresses | String | Detail page |
| certificate | Age rating (PG, R, etc.) | String | Detail page |
| metascore | Metascore rating (0-100) | Integer | Detail page |
## 🔒 Legal & Ethical Considerations
### IMDb Scraping Policy
- ✅ IMDb `/robots.txt` allows scraping of public data
- ✅ We respect server resources with 2-second delays
- ✅ Using realistic User-Agent headers
- ✅ No authentication bypass or bot detection evasion
- ⚠️ For production use, consider using IMDb's API or official data sources
### Best Practices Implemented
1. **Rate Limiting**: 2-second delay between requests
2. **Respectful Headers**: Browser-like User-Agent
3. **Error Handling**: Graceful failures without disrupting server
4. **Logging**: Complete audit trail of operations
5. **Terms of Service**: Alignment with IMDb's ToS
## 📈 Expected Outputs
### After Step 1 Completion
- `data/raw_data.csv`: CSV file with ~250 movies and 11 fields
- Console logs showing scraping progress and statistics
- Zero errors (non-blocking warnings acceptable)
### After Step 2 Completion
- `data/cleaned_data.csv`: Validated dataset ready for analysis
- Cleaning report with statistics (rows removed, nulls filled, etc.)
### After Step 3 Completion
- `dashboard.html`: Interactive dashboard with 6+ visualizations
- Shareable, embed-able dashboard for portfolio
## 🔍 Code Quality Standards
All code adheres to:
- **PEP 8**: Python Enhancement Proposal style guide
- **Type Hints**: Function signatures with type annotations
- **Docstrings**: NumPy-style docstrings for all functions
- **DRY Principle**: No code duplication
- **Logging**: Comprehensive logging instead of print()
- **Error Handling**: Try-except blocks with specific exceptions
- **Constants**: No hardcoded values (all in config section)
More Projects
Analytics
IMDb Web Scraping & Data Analysis
A comprehensive, end-to-end web scraping portfolio project that extracts movie data from IMDb's Top 250 list, performs data cleaning and analysis, and presents insights through an interactive visual dashboard.
Year :
2026
## 🎯 Project Objectives
This project demonstrates:
- **Web Scraping**: Ethical data extraction using BeautifulSoup4 & Requests
- **Data Engineering**: Complete ETL pipeline with cleaning and validation
- **Data Analysis**: Statistical analysis, correlation, and insights
- **Data Visualization**: Interactive Plotly dashboards & EDA notebooks
- **Software Engineering**: Professional, production-ready code
- **Portfolio Building**: GitHub-ready, well-documented project
### Business Questions Answered
- What are the quality distribution patterns of top-rated movies?
- Which genres dominate in terms of popularity and ratings?
- How have movie ratings evolved over decades?
- What's the relationship between vote count and rating?
- Which directors produce the highest-rated films?
## 🛠 Tech Stack
| Component | Technology |
|-----------|-----------|
| **Language** | Python 3.10+ |
| **Web Scraping** | BeautifulSoup4 + Requests |
| **Data Processing** | Pandas, NumPy |
| **Visualization** | Plotly (interactive), Seaborn + Matplotlib |
| **Storage** | CSV, SQLite (optional) |
| **Documentation** | Markdown |
| **Version Control** | Git + GitHub |
📂 Project Structure
imdb-webscraping/
│
├── data/
│ ├── raw_data.csv # Raw scraped data (unprocessed)
│ └── cleaned_data.csv # Cleaned & validated data
│
├── notebooks/
│ └── exploration.ipynb # Exploratory data analysis
│
├── scraper.py # Web scraping script (STEP 1)
├── cleaning.py # Data cleaning script (STEP 2)
├── dashboard.py # Visualization dashboard (STEP 3)
├── requirements.txt # Python dependencies
├── README.md # This file
└── .gitignore # Git ignore rules
**Output**: `dashboard.html` with interactive visualizations
**Features**:
- ✅ 8 professional visualizations
- ✅ Fully interactive Plotly charts (zoom, hover, export)
- ✅ Responsive design for all screen sizes
- ✅ Single HTML file (easy to share & embed)
- ✅ Summary statistics report
- ✅ Color-coded visualizations
- ✅ Mobile-friendly dashboard
**Visualizations Included**:
1. **Rating Distribution** - Histogram of all movie ratings (shows quality concentration)
2. **Top 10 Genres** - Bar chart of most common genres
3. **Average Rating by Genre** - Which genres have highest avg ratings?
4. **Votes vs Rating Correlation** - Scatter plot showing vote-rating relationship (log scale)
5. **Rating Trend Over Decades** - Line chart of avg rating evolution
6. **Top 10 Directors** - Directors with highest average ratings (min 2 films)
7. **Vote Distribution** - Box plot with outlier detection
8. **Movies by Certificate** - Pie chart of age ratings (PG, R, etc.)
## 📊 Data Fields Extracted
| Field | Description | Type | Source |
|-------|-------------|------|--------|
| rank | IMDb ranking (1-250) | Integer | Top 250 list |
| title | Movie title | String | List + Detail page |
| year | Release year | Integer | Detail page |
| rating | IMDb rating (1-10) | Float | Top 250 list |
| votes | Number of user votes | Integer | Detail page |
| runtime | Duration in minutes | Integer | Detail page |
| genre | Movie genre(s) | String | Detail page |
| director | Primary director name | String | Detail page |
| cast | Top 3 actors/actresses | String | Detail page |
| certificate | Age rating (PG, R, etc.) | String | Detail page |
| metascore | Metascore rating (0-100) | Integer | Detail page |
## 🔒 Legal & Ethical Considerations
### IMDb Scraping Policy
- ✅ IMDb `/robots.txt` allows scraping of public data
- ✅ We respect server resources with 2-second delays
- ✅ Using realistic User-Agent headers
- ✅ No authentication bypass or bot detection evasion
- ⚠️ For production use, consider using IMDb's API or official data sources
### Best Practices Implemented
1. **Rate Limiting**: 2-second delay between requests
2. **Respectful Headers**: Browser-like User-Agent
3. **Error Handling**: Graceful failures without disrupting server
4. **Logging**: Complete audit trail of operations
5. **Terms of Service**: Alignment with IMDb's ToS
## 📈 Expected Outputs
### After Step 1 Completion
- `data/raw_data.csv`: CSV file with ~250 movies and 11 fields
- Console logs showing scraping progress and statistics
- Zero errors (non-blocking warnings acceptable)
### After Step 2 Completion
- `data/cleaned_data.csv`: Validated dataset ready for analysis
- Cleaning report with statistics (rows removed, nulls filled, etc.)
### After Step 3 Completion
- `dashboard.html`: Interactive dashboard with 6+ visualizations
- Shareable, embed-able dashboard for portfolio
## 🔍 Code Quality Standards
All code adheres to:
- **PEP 8**: Python Enhancement Proposal style guide
- **Type Hints**: Function signatures with type annotations
- **Docstrings**: NumPy-style docstrings for all functions
- **DRY Principle**: No code duplication
- **Logging**: Comprehensive logging instead of print()
- **Error Handling**: Try-except blocks with specific exceptions
- **Constants**: No hardcoded values (all in config section)
More Projects
Analytics
IMDb Web Scraping & Data Analysis
A comprehensive, end-to-end web scraping portfolio project that extracts movie data from IMDb's Top 250 list, performs data cleaning and analysis, and presents insights through an interactive visual dashboard.
Year :
2026
## 🎯 Project Objectives
This project demonstrates:
- **Web Scraping**: Ethical data extraction using BeautifulSoup4 & Requests
- **Data Engineering**: Complete ETL pipeline with cleaning and validation
- **Data Analysis**: Statistical analysis, correlation, and insights
- **Data Visualization**: Interactive Plotly dashboards & EDA notebooks
- **Software Engineering**: Professional, production-ready code
- **Portfolio Building**: GitHub-ready, well-documented project
### Business Questions Answered
- What are the quality distribution patterns of top-rated movies?
- Which genres dominate in terms of popularity and ratings?
- How have movie ratings evolved over decades?
- What's the relationship between vote count and rating?
- Which directors produce the highest-rated films?
## 🛠 Tech Stack
| Component | Technology |
|-----------|-----------|
| **Language** | Python 3.10+ |
| **Web Scraping** | BeautifulSoup4 + Requests |
| **Data Processing** | Pandas, NumPy |
| **Visualization** | Plotly (interactive), Seaborn + Matplotlib |
| **Storage** | CSV, SQLite (optional) |
| **Documentation** | Markdown |
| **Version Control** | Git + GitHub |
📂 Project Structure
imdb-webscraping/
│
├── data/
│ ├── raw_data.csv # Raw scraped data (unprocessed)
│ └── cleaned_data.csv # Cleaned & validated data
│
├── notebooks/
│ └── exploration.ipynb # Exploratory data analysis
│
├── scraper.py # Web scraping script (STEP 1)
├── cleaning.py # Data cleaning script (STEP 2)
├── dashboard.py # Visualization dashboard (STEP 3)
├── requirements.txt # Python dependencies
├── README.md # This file
└── .gitignore # Git ignore rules
**Output**: `dashboard.html` with interactive visualizations
**Features**:
- ✅ 8 professional visualizations
- ✅ Fully interactive Plotly charts (zoom, hover, export)
- ✅ Responsive design for all screen sizes
- ✅ Single HTML file (easy to share & embed)
- ✅ Summary statistics report
- ✅ Color-coded visualizations
- ✅ Mobile-friendly dashboard
**Visualizations Included**:
1. **Rating Distribution** - Histogram of all movie ratings (shows quality concentration)
2. **Top 10 Genres** - Bar chart of most common genres
3. **Average Rating by Genre** - Which genres have highest avg ratings?
4. **Votes vs Rating Correlation** - Scatter plot showing vote-rating relationship (log scale)
5. **Rating Trend Over Decades** - Line chart of avg rating evolution
6. **Top 10 Directors** - Directors with highest average ratings (min 2 films)
7. **Vote Distribution** - Box plot with outlier detection
8. **Movies by Certificate** - Pie chart of age ratings (PG, R, etc.)
## 📊 Data Fields Extracted
| Field | Description | Type | Source |
|-------|-------------|------|--------|
| rank | IMDb ranking (1-250) | Integer | Top 250 list |
| title | Movie title | String | List + Detail page |
| year | Release year | Integer | Detail page |
| rating | IMDb rating (1-10) | Float | Top 250 list |
| votes | Number of user votes | Integer | Detail page |
| runtime | Duration in minutes | Integer | Detail page |
| genre | Movie genre(s) | String | Detail page |
| director | Primary director name | String | Detail page |
| cast | Top 3 actors/actresses | String | Detail page |
| certificate | Age rating (PG, R, etc.) | String | Detail page |
| metascore | Metascore rating (0-100) | Integer | Detail page |
## 🔒 Legal & Ethical Considerations
### IMDb Scraping Policy
- ✅ IMDb `/robots.txt` allows scraping of public data
- ✅ We respect server resources with 2-second delays
- ✅ Using realistic User-Agent headers
- ✅ No authentication bypass or bot detection evasion
- ⚠️ For production use, consider using IMDb's API or official data sources
### Best Practices Implemented
1. **Rate Limiting**: 2-second delay between requests
2. **Respectful Headers**: Browser-like User-Agent
3. **Error Handling**: Graceful failures without disrupting server
4. **Logging**: Complete audit trail of operations
5. **Terms of Service**: Alignment with IMDb's ToS
## 📈 Expected Outputs
### After Step 1 Completion
- `data/raw_data.csv`: CSV file with ~250 movies and 11 fields
- Console logs showing scraping progress and statistics
- Zero errors (non-blocking warnings acceptable)
### After Step 2 Completion
- `data/cleaned_data.csv`: Validated dataset ready for analysis
- Cleaning report with statistics (rows removed, nulls filled, etc.)
### After Step 3 Completion
- `dashboard.html`: Interactive dashboard with 6+ visualizations
- Shareable, embed-able dashboard for portfolio
## 🔍 Code Quality Standards
All code adheres to:
- **PEP 8**: Python Enhancement Proposal style guide
- **Type Hints**: Function signatures with type annotations
- **Docstrings**: NumPy-style docstrings for all functions
- **DRY Principle**: No code duplication
- **Logging**: Comprehensive logging instead of print()
- **Error Handling**: Try-except blocks with specific exceptions
- **Constants**: No hardcoded values (all in config section)