https://github.com/mehraaaaa24/t20-data-analysis

https://github.com/mehraaaaa24/t20-data-analysis

csv-files data-analysis data-cleaning data-modelling data-preprocessing data-transformation data-visualization excel json pandas powerbi python

Last synced: 24 days ago
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• Host: GitHub
• URL: https://github.com/mehraaaaa24/t20-data-analysis
• Owner: mehraaaaa24
• Created: 2024-02-19T07:33:00.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2024-08-31T17:21:04.000Z (5 months ago)
• Last Synced: 2024-11-09T21:13:35.189Z (3 months ago)
• Topics: csv-files, data-analysis, data-cleaning, data-modelling, data-preprocessing, data-transformation, data-visualization, excel, json, pandas, powerbi, python
• Language: Jupyter Notebook
• Homepage:
• Size: 7.07 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
T20 World Cup 2022 Data Analysis Project

Welcome to the T20 World Cup 2022 Data Analysis project! 

This repository contains the code and resources used to analyze the T20 World Cup 2022 matches. Here's a breakdown of what this project entails:

1. Data Preprocessing with Python

The initial dataset was provided in JSON format, containing information about T20 World Cup 2022 matches. To analyze and visualize this data effectively, we utilized Python with libraries such as Pandas. The preprocessing steps involved:

Importing the JSON data into Python.

Utilizing Pandas to manipulate the data, treating it as tables.

Renaming columns to ensure clarity and consistency.

Conducting data cleaning tasks such as handling missing values, removing duplicates, and standardizing formats.

Performing transformations on the data to prepare it for analysis.

2. Data Transformation in Power BI

After preprocessing the data in Python, we converted it into CSV files. These CSV files were then imported into Power BI for further analysis. Within Power BI, we leveraged Power Query to perform additional data transformations. This included:

Splitting the data into different tables based on relevant attributes.

Applying filters and sorting operations to streamline the data.

Creating calculated columns and measures using DAX to derive meaningful insights.

Handling relationships between different tables to facilitate data modeling.

3. Data Modeling and Parameterization with DAX

In addition to data transformation, we utilized Data Analysis Expressions (DAX) within Power BI to enhance our analysis. This involved:

Building data models to establish relationships between different entities within the dataset.

Defining parameters to enable dynamic filtering and analysis.

Implementing complex calculations and business logic using DAX functions.

Fine-tuning the data model to optimize performance and usability.

4. Visualization in Power BI Dashboard

The ultimate goal of this project was to create informative and visually appealing dashboards to present the insights gleaned from the T20 World Cup 2022 data. We designed interactive visualizations within Power BI to:

Display key metrics and trends related to the T20 World Cup matches.

Provide users with the ability to explore the data dynamically.

Communicate actionable insights effectively through intuitive charts, graphs, and tables.

I hope this project provides valuable insights into the T20 World Cup 2022 matches and serves as a demonstration of data analysis techniques using Python and Power BI.