{"id":22342490,"url":"https://github.com/sayedgamal99/data-science","last_synced_at":"2026-05-07T13:37:08.727Z","repository":{"id":177453171,"uuid":"600228603","full_name":"sayedgamal99/Data-Science","owner":"sayedgamal99","description":"This is a repository for Data Science Projects. 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Data Science Projects\n\nThis repository contains a collection of machine learning and deep learning projects organized by categories.\n\n```\nData-Science/\n├── ML\n│   ├── Classification\n│   │   ├── AirLine Passenge Satisfaction\n│   │   │   ├── .ipynb_checkpoints\n│   │   │   │   └── Airline-Passenger-Satisfaction-checkpoint.ipynb\n│   │   │   ├── Airline-Passenger-Satisfaction.ipynb\n│   │   │   └── README.md\n│   │   ├── Bank Customer Churn Prediction\n│   │   │   ├── README.md\n│   │   │   ├── bank-customer-churn-prediction-0-891-auc-score.ipynb\n│   │   │   ├── image.png\n│   │   │   └── output.png\n│   │   ├── Titanic\n│   │   │   ├── .ipynb_checkpoints\n│   │   │   │   └── Titanic-checkpoint.ipynb\n│   │   │   └── Titanic.ipynb\n│   │   └── Titanic V2.0 Advanced Techniques\n│   │       ├── README.md\n│   │       ├── Titanic- Advanced Techniques-(Accuracy~80).ipynb\n│   │       ├── image-1.png\n│   │       └── image.png\n│   └── Regression\n│       └── Cost Predictions\n│           ├── .ipynb_checkpoints\n│           │   └── Cost Predictions -checkpoint.ipynb\n│           └── Cost Predictions .ipynb\n├── DL\n│   └── Image Classification\n│       └── Cats and Dogs\n│           └── Code.ipynb\n├── Educational\n│   └── Hands-On-Machine-Learning\n│       ├── CH10 Introduction to Artificial Neural Networks with Keras\n│       │   ├── ANN_functional1.png\n│       │   ├── ANN_functional2png.png\n│       │   ├── Exercises\n│       │   │   ├── pratical_mnist_project.ipynb\n│       │   │   └── thoery.md\n│       │   ├── Introduction to Artificial Neural Networks with Keras.ipynb\n│       │   └── model.png\n│       ├── CH11 Training Deep Neural Networks\n│       │   ├── Exercises\n│       │   │   ├── practical.ipynb\n│       │   │   └── theory.ipynb\n│       │   └── notebook.ipynb\n│       ├── CH12 Custom Models and Training with TensorFlow\n│       │   ├── Exercises\n│       │   │   ├── practical_1.ipynb\n│       │   │   ├── practical_2.ipynb\n│       │   │   └── theory.ipynb\n│       │   └── notebook.ipynb\n│       ├── CH13 Loading and Preprocessing Data with TensorFlow\n│       │   ├── Exercises\n│       │   │   ├── practical.ipynb\n│       │   │   ├── practical_2.ipynb\n│       │   │   └── theory.ipynb\n│       │   └── notebook.ipynb\n│       ├── CH14 Deep Computer Vision Using Convolutional Neural Networks\n│       │   ├── Digit-Recognizer-APP\n│       │   ├── Exercises\n│       │   │   ├── Images\n│       │   │   │   └── conv net.jpg\n│       │   │   ├── beans-leafs-diaster-classification.ipynb\n│       │   │   ├── practical_Q9.ipynb\n│       │   │   └── theory.md\n│       │   └── notebook.ipynb\n│       ├── CH15 Processing Sequences Using RNNs and CNNs\n│       │   ├── Exercises\n│       │   │   ├── Classification_of_Sequences_with_quickdraw_dataset.ipynb\n│       │   │   ├── lstm.png\n│       │   │   └── theory.md\n│       │   ├── datasets\n│       │   │   ├── ridership\n│       │   │   │   └── CTA_-_Ridership_-_Daily_Boarding_Totals.csv\n│       │   │   └── ridership.tgz\n│       │   └── notebook.ipynb\n│       ├── CH16 Natural Language Processing with RNNs and Attention\n│       │   ├── models\n│       │   │   └── shakespeare_model.keras\n│       │   └── notebook.ipynb\n│       ├── CH2 End-to-End Machine Learning Project\n│       │   ├── Exercises.ipynb\n│       │   ├── Housing_Project.ipynb\n│       │   └── images\n│       │       └── end_to_end_project\n│       │           └── district_cluster_plot.png\n│       ├── CH3 Classification\n│       │   ├── Exercises\n│       │   │   ├── .ipynb_checkpoints\n│       │   │   │   └── Titanic V2-checkpoint.ipynb\n│       │   │   ├── Reach97andAugmentation.ipynb\n│       │   │   ├── Titanic V2.ipynb\n│       │   │   └── titanic-classificat (2).ipynb\n│       │   └── mnist.ipynb\n│       ├── CH4 Training Models\n│       │   ├── Exercises.ipynb\n│       │   ├── GradienDescent.ipynb\n│       │   ├── LogisticRegression.ipynb\n│       │   ├── NormalEquation.ipynb\n│       │   ├── PolynomialRegression.ipynb\n│       │   └── Regularization.ipynb\n│       ├── CH5 Support Vector Machines\n│       │   ├── Exercises\n│       │   │   └── Exercises_notebook.ipynb\n│       │   └── notebook.ipynb\n│       ├── CH6 Decision Trees\n│       │   ├── Exercises\n│       │   │   └── Exercises_notebook.ipynb\n│       │   ├── notebook.ipynb\n│       │   ├── regression_tree.dot\n│       │   ├── tree.dot\n│       │   ├── tree3Regularized.dot\n│       │   ├── treePure.dot\n│       │   └── tree_withoutR.dot\n│       ├── CH7 Ensemble Learning and Random Forests\n│       │   ├── Exercises\n│       │   │   ├── Exercises_practical.ipynb\n│       │   │   └── Exercises_theory.ipynb\n│       │   ├── decision stumps.png\n│       │   └── notebook.ipynb\n│       ├── CH8 Dimensionality Reduction\n│       │   ├── Exercises\n│       │   │   ├── practical.ipynb\n│       │   │   ├── practical2.ipynb\n│       │   │   └── theory.md\n│       │   └── notebook.ipynb\n│       ├── CH9 Unsupervised Learning Techniques\n│       │   ├── Exercises\n│       │   │   ├── practical.ipynb\n│       │   │   └── theory.md\n│       │   ├── ladybug.png\n│       │   └── notebook.ipynb\n│       └── Ch1 Machine Learning Landscape\n│           └── Exercises.md\n├── .github\n│   └── workflows\n│       └── update_readme.yml\n├── .gitignore\n├── .gitmodules\n├── Analysis\n│   ├── Automobile\n│   │   └── Automobil EDA.ipynb\n│   └── Candy Hierarchy\n│       └── Candy Data Cleaning and Visualization.ipynb\n├── README.md\n├── requirements.txt\n└── update_readme.py\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsayedgamal99%2Fdata-science","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsayedgamal99%2Fdata-science","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsayedgamal99%2Fdata-science/lists"}