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In this REPO we use Machine Learning to Predict results of a game between team 1 and team 2, based on who's home and who's away, and on whether or not the game is friendly.

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Mburia/Machine-Learning-Comparison-Between-a-Polynomial-and-Logistic-Approach-to-Predicting-Game-Results

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Machine-Learning-Comparison-Between-a-Polynomial-and-Logistic-Approach-to-Predicting-Game-Results

In this REPO we use Machine Learning to Predict results of a game between team 1 and team 2, based on who's home and who's away, and on whether or not the game is friendly.

A. Specifying the Data Analytic Question: Predict results of a game between team 1 and team 2, based on who's home and who's away, and on whether or not the game is friendly (include rank in your training).

B. Defining the Metric for Success: i. Achieve OPTIMUM accuracy in modelling ii. Achieve an R.M.S.E. Score less than 10% of the mean of the actual data

C. Understanding the context: We have two possible approaches given the datasets we have: Input: Home team, Away team, Tournament type (World cup, Friendly, Other)

Approach 1: Polynomial approach:

What to train given: i. Rank of home team ii. Rank of away team iii. Tournament type

Model 1: Predict how many goals the home team scores Model 2: Predict how many goals the away team scores

Approach 2: Logistic approach: Feature Engineering: Figure out from the home team’s perspective if the game is a Win, Lose or Draw (W, L, D)

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In this REPO we use Machine Learning to Predict results of a game between team 1 and team 2, based on who's home and who's away, and on whether or not the game is friendly.

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