In the Old days, no details and interactive charts could improve the spectator's experience. So, I have devised a plan to interactively check player performance and match details with ease of use. This project involves scraping, processing, analyzing, and visualizing cricket match data from Cricsheet. The data includes different match formats (ODI, T20, and Test) and is used to generate insights through SQL queries, Python EDA, and a Power BI dashboard.
Data Storage: Stored structured data in SQL tables for efficient querying.
SQL Analysis: Wrote 20+ SQL queries to analyze performance metrics, player stats, and match outcomes.
Exploratory Data Analysis (EDA): Used Matplotlib, Seaborn, and Plotly for data visualization.
Power BI Dashboard: Created interactive visualizations to showcase key insights.
Database: MySQL
Query Language: SQL
Data Analysis & EDA: Python (Pandas, Matplotlib, Seaborn, Plotly)
Data Visualization: Power BI
This folder has all the files that have been converted into .csv files by using the panda's library.
This folder contains all the general information that supports further analysis
This folder contains all the necessary information used for plotting and for further analysis.
This folder contains the preprocessed files that were present in the General Datasets folder that have been processed for the null values and dates.
This folder contains the preprocessed files that were present in the Innings Datasets folder that have been processed for the null values.
This folder contains the files that contribute to the Streamlit Applications (i.e.) Table View, Query View, and Visualizations.
This folder contains all the files that were used to extract, convert, and visualize the data from the JSON file.
This is an interactive dashboard that can be used to spectate and analyze the match data with much more ease than viewing it in a table format.
This project successfully transforms raw cricket match data from Cricsheet into meaningful insights through SQL queries, Python-based EDA, and Power BI visualizations.
By leveraging web scraping, data processing, and interactive dashboards, users can explore player performances, match outcomes, and team statistics in an intuitive way.
The combination of structured datasets, preprocessed data, and Streamlit applications makes cricket analytics more accessible and engaging.
This project not only enhances the viewing experience but also provides valuable insights for analysts, fans, and strategists.