IPL Insights - Exploratory Data Analysis | Data Science | Python | AI смотреть онлайн

#IPL Insights - Exploratory Data Analysis | #DataScience | Python | #AI #INSAID #EDA

1. Problem Statement
2. Data Loading and Description
2.1 IPL - The Prayer of Cricket Regligion Country
2.2 How has IPL Helped cricket in India?
3. Data Profiling
3.1 Data Set Reading and understanding basic structure & Info
3.2 Pre Profiling
4. Seeking Answers (World OF Ws)
4.1 How many matches being played in each season?
4.2 Which Team had won by maximum runs? (Overall & 2018)
4.3 Which Team had won by maximum wickets? (Overall & 2018)
4.4 Which Team had won by (closest margin) minimum runs?(Overall & 2018)
4.5 Which Team had won by minimum wickets? (Overall & 2018)
4.6 Which Season had most matches**
4.7 Which City is most Popular in IPL**
4.8 Which Stadium is getting Home Matches in IPL
4.9 Which Team is most successful in IPL**
4.10 How is Team Win Stats Standings YOY
4.11 Highest Win % Market Share in IPL
4.12 Top player of the match Winners
4.13 Top player of the match Winners - 2018 & 2017
4.14 Does Winning the Toss helps in Winning matches?**
4.15 Did Choosing to Bat first or Field First helped in Toss winning**
4.16 Match Winner is Toss Winner - Overall.
4.17 Which Team Chose What - On Winning the Toss
4.18 Analysing Impact of Toss Winning team Season By Season
4.19 Who is Most Trusted Umpire in IPL
4.20 Which Venue did Weather Affected Most**
4.21 Best Stadium to Chase and Win**
4.22 Best Team in Best Chasing Venue
4.23 Best Stadium to Bat first and Win**
4.24 Best team in Best Defending Venue
4.25 Which Team is Best in Defending*
4.26 Which Team is Best in Chasing**
4.27 Home Turf Advantage - Team wins in home city vs other cities¶

5. Conclusion
We have completed detailed analysis, but there is still scope further deep analysis. We have observed following relevent conclusions.




1. Problem Statement
In this we will analyse the IPL Data which was provided in Github link. The notebooks explores the basic use of Pandas and will cover the basic commands of Exploratory Data Analysis(EDA) which includes cleaning, munging, combining, reshaping, slicing, dicing, and transforming data for analysis purpose.

Exploratory Data Analysis
We will Understand the data by EDA and derive simple models with Pandas as baseline. EDA ia a critical and first step in analyzing the data and we do this for below reasons :
Finding patterns in Data
Determining relationships in Data
Checking of assumptions
Preliminary selection of appropriate models