IMDb Data Webscraping and Analysis Project
Geethasree Naguboina
IMDb Webscraping & Data Analysis Using Python
Project Overview
This project focused on extracting structured data from IMDb using Python, cleaning and organizing it, and generating meaningful insights through data analysis and visualizations. The goal was to transform raw HTML content into a well-structured dataset and uncover patterns through exploratory analysis.
My Role & Responsibilities
Built a complete web scraping pipeline using Python
Extracted movie information (titles, ratings, genres, year, etc.) from IMDb
Cleaned and structured the scraped data into a polished dataset
Performed exploratory data analysis to identify key insights
Created visualizations to present trends clearly and professionally
Process & Workflow
1. Imported Core Python Libraries
Used essential modules for scraping, data cleaning, and visualization:
numpy, pandas, matplotlib, seaborn, BeautifulSoup (bs4), requests, and time.2. Extracted IMDb HTML Content
Used BeautifulSoup to parse the HTML
Identified all relevant tags and structures containing movie information
3. Created Structured Lists for Data Storage
Separated list structures were created for:
Movie titles
Release year
IMDb ratings
Genre
Duration
Crew & cast
This ensured clean organization before building the DataFrame.
4. Wrote the Webscraping Code
Developed a robust scraping logic to:
Navigate page structure
Extract text content
Handle missing fields
Prevent request blocking using timed pauses
Code to store the Data
5. Built a DataFrame & Exported CSV
Compiled all lists into a pandas DataFrame
Performed column formatting
Exported the final structured dataset into a clean
.csv file6. Data Cleaning
Removed duplicates
Handled missing values
Split nested fields (genre, year)
Converted data types for analysis (numeric ratings, year)
7. Data Analysis
Conducted exploratory analysis to identify patterns such as:
Highest-rated movies
Popular genres
Rating distribution
Movie count by year
Correlations between variables
8. Data Visualization
Used Matplotlib and Seaborn to create clear, meaningful charts:
Bar charts
Histograms
Scatter plots
Genre distribution
Rating trends
These visualizations helped turn raw data into insights.
Tools Used
Python
BeautifulSoup (bs4)
Requests
Pandas
NumPy
Matplotlib
Seaborn
Deliverables
Complete Python scraping script
Cleaned & structured CSV dataset
Analysis report
A set of clear and professional data visualizations
Results & Impact
Successfully automated movie data extraction from IMDb
Delivered a clean dataset ready for further modeling or dashboard creation
Visual insights helped highlight trends across genres, ratings, and time
Demonstrated strong Python webscraping, data cleaning, and EDA skills
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Posted Dec 10, 2025
Developed a full Python pipeline to scrape IMDb, clean and structure movie data, and analyze trends using Pandas, BeautifulSoup, and advanced visualizations.
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