j-riah/dav_project

DAV_Project_

This is DAV Course end Project divided into 2 parts of the semester.

Part-1 consists of Project title(1m), Abstract(2m), Objectives and outcomes(2m), Flowchart(3m), Dataset Preparation(2m).

Part-2 consists of use of python libraries, visualisation, presentation, Report.

Exploring Trends and Insights: IPL Data Analysis and Visualization (2008-2023)

Abstract:

The Indian Premier League (IPL) has risen as a premier cricket league globally since its inception in 2008. This study delves into an extensive dataset spanning 2008 to 2023, covering various facets such as seasons, venues, teams, players, match outcomes, and more. Through advanced data analysis and visualization techniques, this research aims to unearth valuable insights, trends, and patterns within IPL matches over the years. The dataset includes crucial information like match dates, venues, batting and bowling teams, runs scored, wides, toss outcomes, match results, and notable player performances. Leveraging statistical analysis, machine learning algorithms, and interactive visualizations, we aim to explore queries such as team dominance across seasons, the toss's impact on match results, player performance consistency, and the evolution of gameplay strategies. By thoroughly exploring and visualizing the IPL dataset, this study endeavors to offer cricket enthusiasts, analysts, and stakeholders a deeper comprehension of IPL dynamics and trends. This contributes to informed decision-making, strategic insights, and enriched fan experiences, thereby enhancing the overall IPL ecosystem.

Objectives of the Project:

• Introduce the use of NumPy for array manipulation and numerical operations on IPL dataset. • Utilize Pandas for data manipulation, including handling data frames, data aggregation, grouping, and time series analysis. • Implement data analysis techniques using Pandas. • Visualize the IPL dataset using Matplotlib for static, animated, and interactive visualizations, along with Seaborn for enhanced plotting capabilities.

Expected Outcomes:

• Proficiency in using NumPy for numerical operations on IPL dataset. • Mastery in data manipulation and analysis using Pandas, including preprocessing and statistical analysis. • Ability to visualize IPL dataset effectively using Matplotlib and Seaborn, enabling better understanding and interpretation of the data.

Dataset: https://www.kaggle.com/datasets/utkarshtomar736/ipl-mens-cricket-matches-data-2008-2023