-
-
Notifications
You must be signed in to change notification settings - Fork 216
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #710 from aviralgarg05/main
Poker Hand Prediction
- Loading branch information
Showing
31 changed files
with
1,027,916 additions
and
0 deletions.
There are no files selected for viewing
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Large diffs are not rendered by default.
Oops, something went wrong.
393 changes: 393 additions & 0 deletions
393
Poker Hand Prediction/Notebooks/Artificial Neural Network.ipynb
Large diffs are not rendered by default.
Oops, something went wrong.
399 changes: 399 additions & 0 deletions
399
Poker Hand Prediction/Notebooks/Deep Neural Network.ipynb
Large diffs are not rendered by default.
Oops, something went wrong.
357 changes: 357 additions & 0 deletions
357
Poker Hand Prediction/Notebooks/Multi Layer Perceptron.ipynb
Large diffs are not rendered by default.
Oops, something went wrong.
259 changes: 259 additions & 0 deletions
259
Poker Hand Prediction/Notebooks/Output Code Classifier.ipynb
Large diffs are not rendered by default.
Oops, something went wrong.
Large diffs are not rendered by default.
Oops, something went wrong.
Large diffs are not rendered by default.
Oops, something went wrong.
Large diffs are not rendered by default.
Oops, something went wrong.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,70 @@ | ||
## **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/) |
Oops, something went wrong.