Learn practical skills, build real-world projects, and advance your career

Indian Premier League(IPL) Data Analysis

Indian Premier League Twenty_Twenty cricket format league in India founded by Board of control for Cricket India(BCCI),in the year 2008,Where individual team consists of players from "International Indian cricket" team,players from "Indian Under 19 cricket" team and also few foreign players are Represented as a single Team.cricket is one of the popular channel of entertainment for the people in India and is widely enjoyed by everyone.

The Theme of IPL is to betray the talent of Youngsters at this level and then they can be taken into the International Indian Team.IPL acts as Interface between the domestic and International Cricket.
For this project I have downloaded the dataset required to perform IPL Data Analysis from "Kaggle" a resource for gathering some of the datasets.We have two files in it one is the "deliveries.csv" and the other is "matches.csv" for perfomring the analysis.

The analysis part is done by using some libraries like numpy,pandas,seaborn,matplot etc which were taught in "zerotopandas" course.Once the explanation is done,It created interest in me to perform analysis on some dataset.The beauty of this course is such that it has a well structured plan that also included project in its course.

How to run the code

This is an executable Jupyter notebook hosted on Jovian.ml, a platform for sharing data science projects. You can run and experiment with the code in a couple of ways: using free online resources (recommended) or on your own computer.

Option 1: Running using free online resources (1-click, recommended)

The easiest way to start executing this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on mybinder.org, a free online service for running Jupyter notebooks. You can also select "Run on Colab" or "Run on Kaggle".

Option 2: Running on your computer locally
  1. Install Conda by following these instructions. Add Conda binaries to your system PATH, so you can use the conda command on your terminal.

  2. Create a Conda environment and install the required libraries by running these commands on the terminal:

conda create -n zerotopandas -y python=3.8 
conda activate zerotopandas
pip install jovian jupyter numpy pandas matplotlib seaborn opendatasets --upgrade
  1. Press the "Clone" button above to copy the command for downloading the notebook, and run it on the terminal. This will create a new directory and download the notebook. The command will look something like this:
jovian clone notebook-owner/notebook-id
  1. Enter the newly created directory using cd directory-name and start the Jupyter notebook.
jupyter notebook

You can now access Jupyter's web interface by clicking the link that shows up on the terminal or by visiting http://localhost:8888 on your browser. Click on the notebook file (it has a .ipynb extension) to open it.

Downloading the Dataset

The first step involves in collecting data from various sources. The data can be homogeneous or heterogeneous depending on the nature of the data.

For this project I have downloaded the dataset from the open source called "Kaggle",which has a bundle of datasets.
for downloading datasets we import a library called opendatasets and then we pass the url to the reference of the dataset and then execute a command od.download, after that the dataset is read into the dataframe using the pandas and thus the dataframe can be manipulated.

!pip install jovian opendatasets --upgrade --quiet

Let's begin by downloading the data, and listing the files within the dataset.