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https://github.com/sayedgamal99/data-science
This is a repository for Data Science Projects.
https://github.com/sayedgamal99/data-science
data-analysis data-science deep-learning machine-learning python regression supervised-learning
Last synced: about 2 months ago
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This is a repository for Data Science Projects.
- Host: GitHub
- URL: https://github.com/sayedgamal99/data-science
- Owner: sayedgamal99
- Created: 2023-02-10T21:54:55.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-19T13:47:50.000Z (8 months ago)
- Last Synced: 2024-05-19T14:47:12.300Z (8 months ago)
- Topics: data-analysis, data-science, deep-learning, machine-learning, python, regression, supervised-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 32.7 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Data Science Projects
This repository contains a collection of machine learning and deep learning projects organized by categories.
```
Data-Science/
├── ML
│ ├── Classification
│ │ ├── AirLine Passenge Satisfaction
│ │ │ ├── .ipynb_checkpoints
│ │ │ │ └── Airline-Passenger-Satisfaction-checkpoint.ipynb
│ │ │ ├── Airline-Passenger-Satisfaction.ipynb
│ │ │ └── README.md
│ │ ├── Bank Customer Churn Prediction
│ │ │ ├── README.md
│ │ │ ├── bank-customer-churn-prediction-0-891-auc-score.ipynb
│ │ │ ├── image.png
│ │ │ └── output.png
│ │ ├── Titanic
│ │ │ ├── .ipynb_checkpoints
│ │ │ │ └── Titanic-checkpoint.ipynb
│ │ │ └── Titanic.ipynb
│ │ └── Titanic V2.0 Advanced Techniques
│ │ ├── README.md
│ │ ├── Titanic- Advanced Techniques-(Accuracy~80).ipynb
│ │ ├── image-1.png
│ │ └── image.png
│ └── Regression
│ └── Cost Predictions
│ ├── .ipynb_checkpoints
│ │ └── Cost Predictions -checkpoint.ipynb
│ └── Cost Predictions .ipynb
├── DL
│ └── Image Classification
│ └── Cats and Dogs
│ └── Code.ipynb
├── Educational
│ └── Hands-On-Machine-Learning
│ ├── CH10 Introduction to Artificial Neural Networks with Keras
│ │ ├── ANN_functional1.png
│ │ ├── ANN_functional2png.png
│ │ ├── Exercises
│ │ │ ├── pratical_mnist_project.ipynb
│ │ │ └── thoery.md
│ │ ├── Introduction to Artificial Neural Networks with Keras.ipynb
│ │ └── model.png
│ ├── CH11 Training Deep Neural Networks
│ │ ├── Exercises
│ │ │ ├── practical.ipynb
│ │ │ └── theory.ipynb
│ │ └── notebook.ipynb
│ ├── CH12 Custom Models and Training with TensorFlow
│ │ ├── Exercises
│ │ │ ├── practical_1.ipynb
│ │ │ ├── practical_2.ipynb
│ │ │ └── theory.ipynb
│ │ └── notebook.ipynb
│ ├── CH13 Loading and Preprocessing Data with TensorFlow
│ │ ├── Exercises
│ │ │ ├── practical.ipynb
│ │ │ ├── practical_2.ipynb
│ │ │ └── theory.ipynb
│ │ └── notebook.ipynb
│ ├── CH14 Deep Computer Vision Using Convolutional Neural Networks
│ │ ├── Digit-Recognizer-APP
│ │ ├── Exercises
│ │ │ ├── Images
│ │ │ │ └── conv net.jpg
│ │ │ ├── beans-leafs-diaster-classification.ipynb
│ │ │ ├── practical_Q9.ipynb
│ │ │ └── theory.md
│ │ └── notebook.ipynb
│ ├── CH15 Processing Sequences Using RNNs and CNNs
│ │ ├── datasets
│ │ │ ├── ridership
│ │ │ │ └── CTA_-_Ridership_-_Daily_Boarding_Totals.csv
│ │ │ └── ridership.tgz
│ │ └── notebook.ipynb
│ ├── CH2 End-to-End Machine Learning Project
│ │ ├── Exercises.ipynb
│ │ ├── Housing_Project.ipynb
│ │ └── images
│ │ └── end_to_end_project
│ │ └── district_cluster_plot.png
│ ├── CH3 Classification
│ │ ├── Exercises
│ │ │ ├── .ipynb_checkpoints
│ │ │ │ └── Titanic V2-checkpoint.ipynb
│ │ │ ├── Reach97andAugmentation.ipynb
│ │ │ ├── Titanic V2.ipynb
│ │ │ └── titanic-classificat (2).ipynb
│ │ └── mnist.ipynb
│ ├── CH4 Training Models
│ │ ├── Exercises.ipynb
│ │ ├── GradienDescent.ipynb
│ │ ├── LogisticRegression.ipynb
│ │ ├── NormalEquation.ipynb
│ │ ├── PolynomialRegression.ipynb
│ │ └── Regularization.ipynb
│ ├── CH5 Support Vector Machines
│ │ ├── Exercises
│ │ │ └── Exercises_notebook.ipynb
│ │ └── notebook.ipynb
│ ├── CH6 Decision Trees
│ │ ├── Exercises
│ │ │ └── Exercises_notebook.ipynb
│ │ ├── notebook.ipynb
│ │ ├── regression_tree.dot
│ │ ├── tree.dot
│ │ ├── tree3Regularized.dot
│ │ ├── treePure.dot
│ │ └── tree_withoutR.dot
│ ├── CH7 Ensemble Learning and Random Forests
│ │ ├── Exercises
│ │ │ ├── Exercises_practical.ipynb
│ │ │ └── Exercises_theory.ipynb
│ │ ├── decision stumps.png
│ │ └── notebook.ipynb
│ ├── CH8 Dimensionality Reduction
│ │ ├── Exercises
│ │ │ ├── practical.ipynb
│ │ │ ├── practical2.ipynb
│ │ │ └── theory.md
│ │ └── notebook.ipynb
│ ├── CH9 Unsupervised Learning Techniques
│ │ ├── Exercises
│ │ │ ├── practical.ipynb
│ │ │ └── theory.md
│ │ ├── ladybug.png
│ │ └── notebook.ipynb
│ └── Ch1 Machine Learning Landscape
│ └── Exercises.md
├── .github
│ └── workflows
│ └── update_readme.yml
├── .gitignore
├── .gitmodules
├── Analysis
│ ├── Automobile
│ │ └── Automobil EDA.ipynb
│ └── Candy Hierarchy
│ └── Candy Data Cleaning and Visualization.ipynb
├── README.md
└── update_readme.py
```