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https://github.com/ojas-arora/ipl-2022-analysis
https://github.com/ojas-arora/ipl-2022-analysis
Last synced: about 11 hours ago
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- Host: GitHub
- URL: https://github.com/ojas-arora/ipl-2022-analysis
- Owner: Ojas-Arora
- Created: 2024-07-13T07:42:32.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-07-13T08:15:11.000Z (4 months ago)
- Last Synced: 2024-07-14T08:57:17.670Z (4 months ago)
- Language: Jupyter Notebook
- Size: 1.46 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
IPL 2022 Analysis 🏏📊
Welcome to the IPL 2022 Analysis project! This repository contains an in-depth analysis of the Indian Premier League (IPL) 2022 season. The project aims to provide insights into various aspects of the tournament, including team performances, player statistics, match outcomes, and more.
1. Table of Contents
2. Introduction
3. Data Collection
4. Data Preprocessing
5. Exploratory Data Analysis (EDA)
6. Key Findings
7. Technologies Used
8. How to Use
9. Contributing
1. Introduction
The Indian Premier League (IPL) is one of the most popular and competitive T20 cricket leagues in the world. The 2022 season witnessed thrilling matches, outstanding performances, and memorable moments. This project aims to analyze the data from IPL 2022 to uncover patterns, trends, and insights that can be useful for fans, analysts, and enthusiasts.
2. Data Collection
The data for this project was collected from source. It includes detailed information about matches, players, teams, and individual performances throughout the IPL 2022 season.
3. Data Preprocessing
The raw data was cleaned and processed using various data preprocessing techniques to ensure accuracy and consistency. This involved handling missing values, correcting data types, and transforming variables for analysis.
4. Exploratory Data Analysis (EDA)
EDA was performed to explore the data and identify key patterns and trends. Various visualizations and statistical techniques were used to analyze:
5. Team performances
I. Player statistics (batting, bowling, fielding)
II. Match outcomes
III. Venue statistics
IV. Key moments and highlights
V. Key Findings
VI. Some of the key findings from the analysis include:
6. Top-performing teams and players
I. Impact of venues on match outcomes
II. Trends in batting and bowling performances
III. Key factors influencing match results
7. Technologies Used
I. Python: Data preprocessing and analysis
II. Pandas: Data manipulation and analysis
III. Matplotlib/Seaborn: Data visualization
IV. Jupyter Notebook: Interactive analysis and visualization
8. How to Use
I. Clone the repository:
git clone https://github.com/Ojas-Arora/IPL-2022-Analysis
II. Navigate to the project directory:
cd ipl-2022-analysis
III. Install the required dependencies:
pip install -r requirements.txt
IV. Run the Jupyter notebooks to explore the analysis:
jupyter notebook
9. Contributing
Contributions are welcome! If you have any suggestions, bug reports, or feature requests, please open an issue or submit a pull request. For major changes, please discuss them with us first.