{"id":25508114,"url":"https://github.com/rafiqamar/imdb-movie-analysis","last_synced_at":"2026-04-13T17:33:37.319Z","repository":{"id":275420699,"uuid":"926031346","full_name":"RafiQamar/IMDb-Movie-Analysis","owner":"RafiQamar","description":"This project involves web scraping, data preprocessing, database storage and visualization of IMDb movie data from the last decade (2014-2024). The dataset includes details of 10,000 movies such as name, release year, genre, ratings, metascore and more. 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The dataset includes details of 10,000 movies such as name, release year, genre, ratings, actors, directors, metascore, and more. The project culminates in an interactive Power BI dashboard for in-depth insights and reporting.\n\n###\n\n\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"https://github.com/RafiQamar/IMDb-Movie-Analysis/blob/main/IMDb%20Dashboard%20Demo.gif?raw=true\" height=\"300\" alt=\"IMDB Dashboard gif\" /\u003e\n\u003c/div\u003e\n\n###\n\n## Technologies Used\n- **Web Scraping:** BeautifulSoup, Selenium, Requests, Time, Random\n- **Data Processing:** Pandas, NumPy\n- **Machine Learning:** Scikit-learn (RandomForestClassifier for genre prediction), TfidfVectorizer\n- **Database Management:** MySQL, SQLAlchemy, MySQL Connector\n- **Visualization:** Power BI\n\n## Project Steps\n### 1. Web Scraping\n- Used Selenium for automated navigation (clicking, entering data, filtering, and loading more results).\n- Extracted 10,000 movie links from IMDb using BeautifulSoup.\n- Iterated through each movie link to collect metadata such as genres, ratings, directors, actors, and metascore.\n- Implemented exception handling to manage missing values and errors.\n- Created a structured dataset and saved it as a CSV file.\n\n### 2. Data Preprocessing \u0026 Cleaning\n- Handled missing values and inconsistencies (e.g., correcting misplaced values in duration and rated columns).\n- Filled missing movie names based on actual movie titles.\n- Used RandomForestClassifier to predict missing genres using movie descriptions.\n- Finalized the cleaned dataset and stored it in a MySQL database.\n\n### 3. Database Management (MySQL)\n- Created an `imdb` database and imported the cleaned dataset.\n- Executed various SQL queries for insights, such as:\n  - Movies released in 2024\n  - Top 5 highest-rated movies\n  - Top 10 horror movies\n  - Movies directed by Christopher Nolan\n  - Highest-rated movie per genre\n  - Ranking movies by Metascore and ratings\n  - Most reviewed movies\n  - Longest-duration movie per genre\n  - Best-rated movie for each content rating\n\n### 4. Power BI Dashboard\nThe Power BI dashboard consists of four interactive pages:\n\n#### **Page 1: Individual Movie Insights**\n- Slicer for selecting a movie name.\n- Display of key metrics such as duration, ratings, number of ratings, release year, genre, description, user reviews, critic reviews, and metascore.\n- TreeMap showing total ratings by genre.\n\n#### **Page 2: Genre and Year-wise Trends**\n- Slicers for selecting genre and release year.\n- Bar chart showing total movies by genre.\n- Gauge chart displaying average Metascore.\n- Donut chart visualizing content rating breakdown.\n- Line chart illustrating the trend of total movie releases per year.\n- Key performance indicators (KPIs): total movies, average duration, average ratings, total number of ratings.\n\n#### **Page 3: Ratings \u0026 Metascore Analysis**\n- Slicers for genre and release year.\n- Measures displaying best-rated movie and best Metascore movie, with images.\n- Scatter plot showing the relationship between user ratings and Metascore.\n- Table ranking movies by Metascore.\n\n#### **Page 4: Actors, Directors \u0026 Duration Insights**\n- Slicers for genre and release year.\n- Measure displaying the longest movie by duration, with an image.\n- Bar chart showing top actors by the number of movies they appeared in.\n- Line chart comparing average user reviews vs. critic reviews by genre.\n- Column charts:\n  - Top 5 movies by ratings.\n  - Top 10 directors by the number of movies directed.\n  - Longest duration movies by genre.\n\n### 5. Interactive Features\n- Filters for genre and release year applied across all pages.\n- Buttons to clear all slicer filters.\n- Navigation buttons to move between different pages.\n\n## How to Run the Project\n### 1. Web Scraping \u0026 Data Processing\n- Open and run `Scrapping_IMDb_movies.ipynb` in Jupyter Notebook to extract movie data.\n- Run `Preprocessing-Modelling-Data Ingestion.ipynb` to clean, preprocess, and store data in MySQL.\n\n### 2. Power BI Dashboard\n- Open `IMDb Dashboard.pbix` in Power BI Desktop to explore interactive visualizations.\n\n## Future Enhancements\n- Expand the dataset beyond 10,000 movies for deeper analysis.\n- Improve genre prediction using advanced NLP techniques.\n- Automate data updates to keep the dashboard current.\n- Deploy the dashboard online for wider accessibility.\n\n## Author\nThis project was created by Rafi Qamar. For any inquiries or collaborations, feel free to reach out!\n\n---\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frafiqamar%2Fimdb-movie-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frafiqamar%2Fimdb-movie-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frafiqamar%2Fimdb-movie-analysis/lists"}