aviralgarg05 · aviralgarg05 · Jul 15, 2024
diff --git a/Poker Hand Prediction/Images/ANN.png b/Poker Hand Prediction/Images/ANN.png
diff --git a/Poker Hand Prediction/Images/Adaboost.png b/Poker Hand Prediction/Images/Adaboost.png
diff --git a/Poker Hand Prediction/Images/DNN.png b/Poker Hand Prediction/Images/DNN.png
diff --git a/Poker Hand Prediction/Images/Data Distribution.png b/Poker Hand Prediction/Images/Data Distribution.png
diff --git a/Poker Hand Prediction/Images/Multi Layer Perceptron.png b/Poker Hand Prediction/Images/Multi Layer Perceptron.png
diff --git a/Poker Hand Prediction/Images/Output Code Classifier.png b/Poker Hand Prediction/Images/Output Code Classifier.png
diff --git a/Poker Hand Prediction/Images/Random Forest.png b/Poker Hand Prediction/Images/Random Forest.png
diff --git a/Poker Hand Prediction/Images/Regression.png b/Poker Hand Prediction/Images/Regression.png
diff --git a/Poker Hand Prediction/Images/confMat.png b/Poker Hand Prediction/Images/confMat.png
diff --git a/Poker Hand Prediction/Images/svm.png b/Poker Hand Prediction/Images/svm.png
diff --git a/Poker Hand Prediction/Poker Hand Prediction/AdaBoost.ipynb b/Poker Hand Prediction/Poker Hand Prediction/AdaBoost.ipynb
diff --git a/Poker Hand Prediction/Poker Hand Prediction/Artificial Neural Network.ipynb b/Poker Hand Prediction/Poker Hand Prediction/Artificial Neural Network.ipynb
diff --git a/Poker Hand Prediction/Poker Hand Prediction/Deep Neural Network.ipynb b/Poker Hand Prediction/Poker Hand Prediction/Deep Neural Network.ipynb
diff --git a/Poker Hand Prediction/Poker Hand Prediction/Multi Layer Perceptron.ipynb b/Poker Hand Prediction/Poker Hand Prediction/Multi Layer Perceptron.ipynb
diff --git a/Poker Hand Prediction/Poker Hand Prediction/Output Code Classifier.ipynb b/Poker Hand Prediction/Poker Hand Prediction/Output Code Classifier.ipynb
diff --git a/Poker Hand Prediction/Poker Hand Prediction/Random Forest.ipynb b/Poker Hand Prediction/Poker Hand Prediction/Random Forest.ipynb
diff --git a/Poker Hand Prediction/Poker Hand Prediction/Regression.ipynb b/Poker Hand Prediction/Poker Hand Prediction/Regression.ipynb
diff --git a/Poker Hand Prediction/Poker Hand Prediction/SVM.ipynb b/Poker Hand Prediction/Poker Hand Prediction/SVM.ipynb
diff --git a/Poker Hand Prediction/README.md b/Poker Hand Prediction/README.md
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+# Pokerman
+
+TL;DR, predicting poker hands.
+
+## The Problem
+
+Given the sequence of 5 'community' cards, drawn from a standard deck of cards, what hand is most likely present with at least one of the players in the game.
+
+## Poker Hands
+
+Texas Hold Em is played by dealing each player 2 cards (face down), called the `hole` cards, and dealing 5 `community` cards (face up), on the table.
+
+The player makes a poker `hand` using any combination of the 3 cards dealt to them, and the 5 cards on the table. 
+
+The player with the strongest hands wins. Most commonly accepted ranking of hands, strongest to weekest :
+
+| Rank | Hand | Description |
+| :--: | :--: | :---------: |
+| 0 | Royal Flush | `A K Q J 10` all of the same suit |
+| 1 | Straight Flush | Any 5 cards of the same suit, in sequence |
+| 2 | Four of a Kind | 4 cards of the same rank, like, `4 4 4 4` |
+| 3 | Full House | A 3 of a kind, and a pair, of different ranks |
+| 4 | Flush | Any 5 cards of the same suit |
+| 5 | Straight | Any 5 cards in sequence |
+| 6 | Three of a Kind | Any 3 cards of the same rank |
+| 7 | Two Pair | Any 2 pairs of cards |
+| 8 | One Pair | Any 2 cards of the same rank |
+| 9 | High Card | Highest Ranked card in hand |
+
+
+#### Ranking of Cards
+
+A K Q J 10 9 8 7 6 5 4 3 2 1
+
+## Data
+
+This data was acquired from UCI's Machine Learning repository. The data comes already split into training and testing data.
+
+Find it [here](https://archive.ics.uci.edu/ml/datasets/Poker+Hand).
+
+
+##### NOTE: The hands' class labels are in the reverse order of their strength, i.e, 0 is the weakest hand.
+
+## Machine Learning 🖥 
+
+| Model | Accuracy |
+| :---: | :------: |
+| Linear Regression | 42% |
+| SVM | 58% | 
+| Adaboost | 49% | 
+| Output Code Classifier | 61% | 
+| Random Forest | 56% | 
+| Artificial Neural Network | 45% | 
+| Deep Neural Network | 87% |
+| Multi Layer Perceptron | 97% |
+
+## Conclusion
+
+The Multi-layer Perceptron is clearly the best model for the dataset in hand.
+