cpanant/ipl_auction_data_

We are the engineering students and we are here with Data Analysis: IPL Auctioin Data 2022 We have consolidates the data for each of the player sold in the IPL 2022 auction and that of the reatained player from each franchise. build by using Microsoft Azure Services Technologies Machine Learning, + Microsoft Power BI, + Data Analysis

IPL_Auction_Data_2022

Data Analysis: IPL Auctioin Data We have consolidates the data for each of the player sold in the IPL 2022 auction and that of the reatained player from each franchise.

THE DATA CONTAINS INFORMATION LIKE- matches played, runs, wickets, average, strike rate, catches, runouts, stumps, etc.

BASIC ON THESE DATA POINTS- We will try to create the best 11 from these set of players from the current campaign. we can summarise our analysis in the following steps:

Extraction and loading the data.

Cleaning the data and removing the noise.

Analyze the data on different parameters.

Visualizing the important statistical findings.

Making the best team of the 11 - based on the ICC WORLD CUP winning squad formaton and last year's squad formation of the winning team in the IPL. i.e. how many batters. bowlers and allrounders should be included in the team.

Forming Our Best 11 for the Compaign based on the above analysis

We will consider the numbers of players from each category that the T20 WORLD CUP winning and the last year's IPL winning team played in their Final matches.

The Australia squad consist of 3 BAatters, 3 Allrounders, 4 Bowlers, with 1 Spin and 1 Wicket-Keepers.

The Chennai Super King squad consist of 4 BAatters, 3 Allrounders, 3 Bowlers, and 1 Wicket-Keepers.

For our fianl analysis we will consider the ratio of players in the best 11 as follows:

3 (Three) Batters

3 (Three) Allrounders

4 (Four) Bowlers with 2 Spin Options

1 (One) Wicket-Keeper

best playing 11 from our analysis

Player Name Team Nationality

0 KL Rahul Lucknow Indian

1 David Warner Delhi Overseas

2 Virat Kohli Bangalore Indian

3 Andre Russell Kolkata Overseas

4 Sunil Narine Kolkata Overseas

5 Hardik Pandya Gujarat Indian

6 MS Dhoni Chennai Indian

7 Yuzvendra Chahal Rajasthan Indian

8 Jasprit Bumrah Mumbai Indian

9 Nathan Coulter-Nile Rajasthan Overseas

10 Kagiso Rabada Punjab Overseas

Project Demo Link https://github.com/Cpanant/IPL_Auction_Data/blob/efd86b08a71fa83553d508aff3e7bc00bc569cbf/modules/IPLDAVD.ipynb

Project Demo Video Link https://youtu.be/wOGj03AyVYA

I have collected the information with the secondary source information of the IPL link https://github.com/Cpanant/IPL_Auction_Data_/blob/89125bc39ca0d2f25ebd90ce1311a723ffd2d279/IPLData.csv