EDA is one of the most important aspect for any Data Science project.
In order to get good model we need to understand our data,to do so EDA helps in acheving it.
EDA is an approach of Analysing the data and summarising the main features in your data.We offen uses statistical and visualization method.
An effective EDA performed on a dataset helps to identify relevant features that can be fed to our machine learning algorithms and generate a good model
Repository Overview
It contains the EDA performed python file and also the dataset of matches happend and track record of eack ball available between 2008 and 2020.
Brief Overview of the steps followed
1.Problem Statement
2.Importing Libraries
3.Loading Dataset
4.Data Cleaning & Data Preprocessing
5.Exploratory Data Analysis
1.Univarient
1.How many matches were played each season?
2.How many teams played each season?
3.How many matches played in each venue?
4.How many matches plaed in each city?
5.Top 10 player with most MoM
6.Do team bat first or bowl first after winning first?
7.Do team win by batting first or bowling first?
2.Bivarient/Multivarient:
1.Which team has dominated ipl so far?
2.Which team has more chance of winning match by winning toss?
3.Which team has high winning percentage?
4.Top 10 Batsman
5.Top 10 Bowlers
6.Top 10 Run Scores
7.Top 10 Bowlers who has bowled more extras
8.Percentagae of wicket falling based on each over
9.Percentage of runs scored based on each over