Given certain input parameters regarding the innings of an IPL T20 cricket match, predict the total runs scored by the batting team at the end of 6 overs. -- 2
Budget: ₹100 – ₹400 INR
Problem Statement:
Given certain input parameters regarding the innings of an IPL T20 cricket match, predict the total runs scored by the batting team at the end of 6 overs.
Programming Language to be used: only Python 3.9
Maximum size of zip file allowed: 5MB.
Maximum permitted execution time: 20secs.
We will be executing your code on a VM with the same docker image having a single threaded processor.
Data:
Training data: You will be provided two files for training data, they are as following:
IPL_Ball_by_Ball_2008_2022.csv: This file contains a ball by ball record of each IPL match since 2008 to 2022. The column names are self explanatory. This file is already present in the docker image.
IPL_Matches_Result_2008_2022.csv: This file contains results of each IPL match since 2008 to 2022. The column names are self explanatory. This file is already present in the docker image.
Sample test data: Each row represents a test sample.
test_file.csv: The column names are self explanatory.
Sample submission file: This file contains predicted runs by your model corresponding to each sample in test data.
sample_submission.csv: The column `id` contains the innings for which the prediction is made and the column `predicted_run` contains your prediction for the respective innings.
Note: The test file for an innings may have discrepancies that your model/code will have to take care of, e.g. new players making their debut in IPL 2023, change in team name, change in stadium name etc.
Setting up your training environment
Install docker desktop
You will be using docker image.
Run the following command to download the docker Image
> docker pull swarnimsoni/iitmbsiplcontest2023:latest
Above image contains all the necessary libraries and files to run your code.
Create a project directory with following files:
mymodelfile.py: Write your model's code in this file. Do not change the structure/signature of the "MyModel" class. You may create as many private variables and methods as you need. It has three important methods:
__init__(): You can use this method to define your model and include any preprocessing steps.
fit(): This method trains the parameters of the model. It receives training data as a pair of pandas dataframes, trains the model and returns reference to the MyModel object itself.
predict(): This method receives test data as pandas dataframe and returns the predictions in specified format.
The docker image uses Python 3.9.16. Please do not use packages other then the ones mentioned in the requirements.txt file.
How to run your model
Create a "project_dir" directory with following files:
test_file.csv: which will contain the test samples without the labels.
submission.csv: which contains the predictions by your model if there is no execution error.
mymodelfile.py: which contains your "MyModel" class.
logs.txt: which contains error messages in case the code throws any runtime exceptions, to help you debug your code.
Given certain input parameters regarding the innings of an IPL T20 cricket match, predict the total runs scored by the batting team at the end of 6 overs.
Programming Language to be used: only Python 3.9
Maximum size of zip file allowed: 5MB.
Maximum permitted execution time: 20secs.
We will be executing your code on a VM with the same docker image having a single threaded processor.
Data:
Training data: You will be provided two files for training data, they are as following:
IPL_Ball_by_Ball_2008_2022.csv: This file contains a ball by ball record of each IPL match since 2008 to 2022. The column names are self explanatory. This file is already present in the docker image.
IPL_Matches_Result_2008_2022.csv: This file contains results of each IPL match since 2008 to 2022. The column names are self explanatory. This file is already present in the docker image.
Sample test data: Each row represents a test sample.
test_file.csv: The column names are self explanatory.
Sample submission file: This file contains predicted runs by your model corresponding to each sample in test data.
sample_submission.csv: The column `id` contains the innings for which the prediction is made and the column `predicted_run` contains your prediction for the respective innings.
Note: The test file for an innings may have discrepancies that your model/code will have to take care of, e.g. new players making their debut in IPL 2023, change in team name, change in stadium name etc.
Setting up your training environment
Install docker desktop
You will be using docker image.
Run the following command to download the docker Image
> docker pull swarnimsoni/iitmbsiplcontest2023:latest
Above image contains all the necessary libraries and files to run your code.
Create a project directory with following files:
mymodelfile.py: Write your model's code in this file. Do not change the structure/signature of the "MyModel" class. You may create as many private variables and methods as you need. It has three important methods:
__init__(): You can use this method to define your model and include any preprocessing steps.
fit(): This method trains the parameters of the model. It receives training data as a pair of pandas dataframes, trains the model and returns reference to the MyModel object itself.
predict(): This method receives test data as pandas dataframe and returns the predictions in specified format.
The docker image uses Python 3.9.16. Please do not use packages other then the ones mentioned in the requirements.txt file.
How to run your model
Create a "project_dir" directory with following files:
test_file.csv: which will contain the test samples without the labels.
submission.csv: which contains the predictions by your model if there is no execution error.
mymodelfile.py: which contains your "MyModel" class.
logs.txt: which contains error messages in case the code throws any runtime exceptions, to help you debug your code.