Merge pull request #710 from aviralgarg05/main

Poker Hand Prediction
abhisheks008 · Jul 21, 2024 · 0c5db61 · 0c5db61
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diff --git a/Poker Hand Prediction/Images/ANN.png b/Poker Hand Prediction/Images/ANN.png
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diff --git a/Poker Hand Prediction/Notebooks/AdaBoost.ipynb b/Poker Hand Prediction/Notebooks/AdaBoost.ipynb
diff --git a/Poker Hand Prediction/Notebooks/Artificial Neural Network.ipynb b/Poker Hand Prediction/Notebooks/Artificial Neural Network.ipynb
diff --git a/Poker Hand Prediction/Notebooks/Deep Neural Network.ipynb b/Poker Hand Prediction/Notebooks/Deep Neural Network.ipynb
diff --git a/Poker Hand Prediction/Notebooks/Multi Layer Perceptron.ipynb b/Poker Hand Prediction/Notebooks/Multi Layer Perceptron.ipynb
diff --git a/Poker Hand Prediction/Notebooks/Output Code Classifier.ipynb b/Poker Hand Prediction/Notebooks/Output Code Classifier.ipynb
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diff --git a/Poker Hand Prediction/Notebooks/Regression.ipynb b/Poker Hand Prediction/Notebooks/Regression.ipynb
diff --git a/Poker Hand Prediction/Notebooks/SVM.ipynb b/Poker Hand Prediction/Notebooks/SVM.ipynb
diff --git a/Poker Hand Prediction/README.md b/Poker Hand Prediction/README.md
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+## **Poker Hand Prediction**
+
+### 🎯 **Goal**
+
+To predict the most likely poker hand present with at least one of the players in the game, given the sequence of 5 'community' cards drawn from a standard deck.
+
+### 🧵 **Dataset**
+
+The dataset is acquired from UCI's Machine Learning repository. Find it [here](https://archive.ics.uci.edu/ml/datasets/Poker+Hand).
+
+### 🧾 **Description**
+
+Texas Hold 'Em is played by dealing each player 2 'hole' cards (face down) and 5 'community' cards (face up) on the table. The player makes a poker hand using any combination of the 2 cards dealt to them and the 5 cards on the table. The objective is to predict the rank of the poker hand that is most likely to be present among the players, given the 5 community cards.
+
+### 🧮 **What I had done!**
+
+1. **Data Acquisition**: Downloaded the dataset from UCI's Machine Learning repository.
+2. **Data Preprocessing**: Cleaned and prepared the data for analysis.
+3. **Model Selection**: Implemented various machine learning models to predict poker hands.
+4. **Model Training**: Trained the models on the training dataset.
+5. **Model Evaluation**: Evaluated the models on the testing dataset.
+6. **Performance Comparison**: Compared the accuracy of different models to determine the best one.
+
+### 🚀 **Models Implemented**
+
+1. **Linear Regression**: Basic model to establish a baseline.
+2. **Support Vector Machine (SVM)**: Chosen for its effectiveness in classification tasks.
+3. **Adaboost**: Implemented to improve model performance through boosting.
+4. **Output Code Classifier**: Used for multiclass classification.
+5. **Random Forest**: Chosen for its robustness and ensemble learning capabilities.
+6. **Artificial Neural Network (ANN)**: Implemented for its potential in capturing complex patterns.
+7. **Deep Neural Network (DNN)**: Used for its ability to learn from large datasets.
+8. **Multi-Layer Perceptron (MLP)**: Chosen for its superior performance in the dataset.
+
+### 📚 **Libraries Needed**
+
+- numpy
+- pandas
+- scikit-learn
+- tensorflow
+- matplotlib
+- seaborn
+
+### 📊 **Exploratory Data Analysis Results**
+
+![EDA Result 1](https://github.com/aviralgarg05/ML-Crate/blob/main/Poker%20Hand%20Prediction/Images/ANN.png)
+![EDA Result 2](https://github.com/aviralgarg05/ML-Crate/blob/main/Poker%20Hand%20Prediction/Images/DNN.png)
+
+### 📈 **Performance of the Models based on the Accuracy Scores**
+
+| 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 (MLP) is clearly the best model for the dataset in hand, achieving an accuracy of 97%. This indicates that MLP is highly effective in predicting the poker hands given the community cards.
+
+### ✒️ **Your Signature**
+
+**Aviral Garg**
+
+[LinkedIn](https://www.linkedin.com/in/aviral-garg-b7b053280/)