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committedDec 9, 2020
updated readme
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‎README.md

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## Content
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* [Directory Tree](#directory-tree)
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* [Overview](#overview)
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* [What is xG](#what-is-xg)
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* [What is xG?](#what-is-xg)
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* [How xG is used?](#how-xg-is-used)
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* [xG Model](#xg-model)
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* [Simple Dataset](#simple-dataset)
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* [Steps to Run the script](#steps-to-run-the-script)
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* [Evaluating The Results](#evaluating-the-results)
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* [Basic Model](#basic-model)
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* [Intermediate Model](#intermediate-model)
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* [Advance Model](#advance-model)
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* [Steps to run the script](#steps-to-run-the-script)
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* [How to plot?](#how-to-plot)
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* [Reviewing the basic model](#reviewing-the-basic-model)
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* [Reviewing the intermediate model](#reviewing-the-intermediate-model)
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* [Reviewing the advance model](#reviewing-the-advance-model)
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## Directory Tree
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* Here, in this project I have tried to build an xG Model from scratch using machine learning algorithms.
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## What is Expected-Goals (xG)?
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## What is xG?
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* In layman's term, xG (or expected goals) is the **probability** that a shot will result in a goal based on the characteristics of that shot.
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* The machine learning algorithms implemented for making the models are: **Logistic Regression**, **Random Forest** and **xG Boost**(eXtreme Gradient Boosting).
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## Steps to Run the script
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## Steps to run the script
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* To execute all the scripts in one go I have created **run.sh** file, on executing it will create the required datasets, train and test the model and save the required model and corresponding categorical features.
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* And finally we will plot the aggregated xG values. Execute the cells in *aggregated xG values* by providing the right path.
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* One can run similarly plot for other models as well (i.e. for *intermediate* and *advance* model) but make sure to run or have the models for *intermediate* or *advance* before plotting for the same.
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## Reviewing the basic model
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* To review the basic model for all three machine learning algorithms used I made a table for top 25 players with the most number of goals for both train and test dataset.
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* Let's see how the table looks like for the train dataset([link](https://github.com/Slothfulwave612/xG-Model/blob/master/plots/basic_model/train_simple.png)):
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![train_basic_table](https://github.com/Slothfulwave612/xG-Model/blob/master/plots/basic_model/train_simple.png)
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* Table for test dataset([link](https://github.com/Slothfulwave612/xG-Model/blob/master/plots/basic_model/test_simple.png)) :
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![test_basic_table](https://github.com/Slothfulwave612/xG-Model/blob/master/plots/basic_model/test_simple.png)
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* To see all the plots visit [this](https://github.com/Slothfulwave612/xG-Model/tree/master/plots/basic_model).
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## Reviewing the intermediate model
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## Revie
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* Table for train dataset([link](https://github.com/Slothfulwave612/xG-Model/blob/master/plots/intermediate_model/train_simple.png)):
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![train_basic_table](https://github.com/Slothfulwave612/xG-Model/blob/master/plots/intermediate_model/train_simple.png)
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* Table for test dataset([link](https://github.com/Slothfulwave612/xG-Model/blob/master/plots/intermediate_model/test_simple.png)) :
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![test_basic_table](https://github.com/Slothfulwave612/xG-Model/blob/master/plots/intermediate_model/test_simple.png)
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* To see all the plots visit [this](https://github.com/Slothfulwave612/xG-Model/tree/master/plots/intermediate_model).
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## Reviewing the advance model
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* Table for train dataset([link](https://github.com/Slothfulwave612/xG-Model/blob/master/plots/advance_model/train_simple.png)):
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![train_basic_table](https://github.com/Slothfulwave612/xG-Model/blob/master/plots/advance_model/train_simple.png)
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* Table for test dataset([link](https://github.com/Slothfulwave612/xG-Model/blob/master/plots/advance_model/test_simple.png)) :
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![test_basic_table](https://github.com/Slothfulwave612/xG-Model/blob/master/plots/advance_model/test_simple.png)
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* To see all the plots visit [this](https://github.com/Slothfulwave612/xG-Model/tree/master/plots/advance_model1).

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