Score prediction of IPL team with flask app
IPL has been a huge success in India and the best cricket league in the world. Why not build something which can help us use our skills of machine learning. So, we created a score prediction web app which can predict score of an IPL team by getting some details like runs scored in 5 overs, wickets down and give us a range of the score that the score of the team would be in this range. We used flask for web development and generated the main page.
Linear regression is a supervised learning machine learning algorithm. It performs the task to predict dependent variable (y) with respect to other independent variables (x …. Xn). It helps us find out a relationship between the dependent and independent variables.
The equation is Y = C+w1X + ….WnXn
Linear regression tries to find the best fit line where you get minimum error. To read more about Linear regression, check this link https://www.geeksforgeeks.org/ml-linear-regression/
Resume Parser: Resume parser is an NLP model which is used to get information from resumes such as details etc. then we have to train the NLP model according to dataset. Resume parsing helps recruiters to efficiently manage electronic resume documents sent electronically. Resume parsers are programs designed to scan the document, analyse it and extract information which are important to recruiters. They are extremely low-cost so that the data present in the resume can be searched, matched and can be displayed by recruiters.
How to Execute?
Make sure you have checked the add to path tick boxes while installing python, anaconda.
Refer to this link, if you are just starting and want to know how to install anaconda.
If you already have anaconda and want to check on how to create anaconda environment, refer to this article set up jupyter notebook. You can skip the article if you have knowledge of installing anaconda, setting up environment and installing requirements.txt
- Install necessary libraries from requirements.txt file provided.
- Go to the directory where your requirement.txt file is present.
cd <<directory of your file>>. E.g, If my file is in d drive, then
- cd d:
- cd d:\License-Plate-Recognition-main #CHANGE PATH AS PER YOUR PROJECT, THIS IS JUST AN EXAMPLE
If your project is in c drive, you can ignore step 1 and go with step 2.
Eg. cd C:\Users\Hi\License-Plate-Recognition-main #CHANGE PATH AS PER YOUR PROJECT, THIS IS JUST AN EXAMPLE
- Run command pip install -r requirements.txtor conda install requirements.txt (Requirements.txt is a text file consisting of all the necessary libraries required for executing this python file. If it gives any error while installing libraries, you might need to install them individually.)
All the necessary files will get downloaded. To run the code, open anaconda prompt. Go to virtual environment if created or operate from the base itself.
Type cd path-to-project-file and then “python app.py”. All the steps are shown in the image below.
When you run the main.py file, you get a link.
Copy and paste the link to get the results.
http://127.0.0.1:5000/ Note: Link can be different on your computer.
The dataset contains ball by ball data of IPL matches from 2008 – 2017. It has 15 rows like date of match, runs scored in that ball, result of the match. Total is our target column which we have to predict.
- Display page
- Enter values
Issues you may face while executing the code
- Go to the current working directory (path of your project) to run main.py
- Ensure you have all libraries installed.
Credit to https://www.analyticsvidhya.com/blog/2021/10/building-an-ipl-score-predictor-end-to-end-ml-project/ for the article
and https://github.com/DevloperHS/ipl-demo github repository for the code. You can view this page for more details.
All the required data has been provided over here. Please feel free to contact me for model weights and if you face any issues in execution score prediction.