IPL-2020

DRISHTI MINI PROJECT 2020

Problem Statement

IPL 2020 Winner Prediction:

Since the dawn of the IPL in 2008, it has attracted viewers all around the globe. High level of uncertainty and last moment nail biters has urged fans to watch the matches. In a cricket match, we might have seen the score line showing the probability of the team winning based on the current match situation. Its indeed clear that Data Analytics has been part of this sport for a long time. Sports analytics is nothing but the process of collecting past matches data and analysing them to extract the essential knowledge out of it, with a hope that it facilitates in effective decision making. Have you wondered what would be the predictions about the IPL 2020 if it were to happen in India? Who would have the highest chances of winning this season? We propose a project with an objective to predict the result of each IPL match by implementing different machine learning algorithms and models. Furthermore, the performance and accuracy of each model will be compared to decide the ultimate winner of IPL 2020!

Timeline

Schedule	Description
19/8 to 30/8	Learning Algorithms
30/8 to 2/9	Feature Selection
2/9 to 6/9	Data Collection
6/9 to 13/9	Model Training
13/9 to 18/9	Optimization or Debug

*Timeline is subject to change depending upon the progress.

RESOURCES

1. Machine Learning Full Course - Learn Machine Learning 10 Hours | Machine Learning Tutorial | Edureka

2. Predicting house prices with linear regression by Sara Gaspar

   • Data set

3. Feature Engineering

4. Multiple Linear Regression from Scratch in Numpy

5. R squared explanation video

6. Github usefuls:

   • Git and GitHub for Beginners - Crash Course

   • Learn how to write proper commit messages

   • How to create, merge, and compare branches,basics of branches

   • For Merging

   • For correcting mistakes in git

⚠️ NOTE: Use git push origin [name_of_your_new_branch] command to push your branch to origin / remote...and never use git push -f command as it will rewrite the repo

7. Real world implementation of Logistic Regression

8. Linear Regression With Multiple Variables | Features And Polynomial Regression

9. A Gentle Introduction to k-fold Cross-Validation

10. Lasso and Ridge Regression

11. K- Nearest Neighbors Model

12. Decision Tree Classifier

13. Random Forest Classifier

14. SVM model

15. XGB Model

ERRORS

1. Expected 2D ARRAY got 1D ARRAY

2. Jupyter notebook not running code. Stuck on In [*]

3. numpy.linalg.pinv() preferred over numpy.linalg.inv() for creating inverse of a matrix in linear regression

4. Regression Metrics And Common mistakes

5. How to convert Numpy array to Panda DataFrame

6. Plotting the relation between two columns using matplotlib or seaborn

7. Replacing column values in a pandas DataFrame

8. Pandas change value of a column based another column condition

Contributors ✨

Arjun Parmar 💻 📦 📖	Yatin Aditya Tekumalla 💻🎨📦	Anshoo Rajput 📖 🎨💻	Atul Dhamija 💻 🚧🎨	Banseedhar Gondaliya 💵 💻🎨	Gaurav Kumar 💻 🐛🎨	Harshit 💻🎨📦
Himanshu Pandey 📖💻🎨📦	Krithika Bala 📖💻🎨📦	Prashant Dodhiya 📖💻🎨📦	Sankirtana 💻 🚧 📦🎨	Sarvesh Khandelwal 💻🎨📦