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I developed the IMDb Top 250 Movies Scraper using Python to automate the extraction of information about the top 250 movies listed on IMDb. This tool efficiently gathers data and stores it in a SQL Server database.

The primary objective of this project is to extract relevant movie and TV show data from IMDb. Organize the data in a structured format using pandas and CSV files. Visualize the insights through Matplotlib and Power BI.

The goal of this project was to extract, organize, movie and TV show data from the IMDb website and organize it into structured databases, clean and transform the data, and then visualize key insights using Power BI.

This project is focused on scraping data from IMDb for different movie categories, storing the information directly into an SQL database, and creating visualizations based on that data. The project is structured into six main folders, each containing Python scripts that handle specific tasks

In the data collection phase, data is scraped from ESPNcricinfo.com using APIs to gather the latest statistics and rankings. The data cleaning phase involves processing the scraped data to ensure it is in a usable format.

The project involves extracting the ranking tables from the webpage and saving the data into a CSV file. This can be useful for cricket analysts, fans, or anyone looking to automate the process of fetching ICC rankings.