https://github.com/sardhendu/data-science-projects

{PySpark, R, Python}: Several Data Science projects
https://github.com/sardhendu/data-science-projects

autoencoder bayesian-methods boosting-algorithms classification credit-card-fraud deep-neural-networks linear-regression logistic-regression machine-learning-algorithms pyspark python-3 random-forest regression svm-classifier tensorflow

Last synced: 19 days ago
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{PySpark, R, Python}: Several Data Science projects

• Host: GitHub
• URL: https://github.com/sardhendu/data-science-projects
• Owner: Sardhendu
• Created: 2017-03-12T04:03:58.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2018-05-14T03:37:20.000Z (almost 7 years ago)
• Last Synced: 2025-04-12T23:44:32.806Z (19 days ago)
• Topics: autoencoder, bayesian-methods, boosting-algorithms, classification, credit-card-fraud, deep-neural-networks, linear-regression, logistic-regression, machine-learning-algorithms, pyspark, python-3, random-forest, regression, svm-classifier, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 103 MB
• Stars: 15
• Watchers: 3
• Forks: 8
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Data-Science-Projects:

## Techniques:

	

**Feature Selection**:

   * PCA (Principal Component Analysis)

   * AIC (Akiake Information criterion)

   * BIC (Bayesian Information criterion)

   * LASSO (Least Absolute Shrinkage and Selection Operator)

1. [Credit Card Fraud Detection](https://github.com/Sardhendu/Data-Science-Projects/tree/master/CreditCardFraudDetection): {Python: Sckit-learn, Tensorflow, R} (Ongoing

 * Models:

    1. Random Forest

    2. Gradient Boosting

    3. XGBoost

    4. Deep Neural Nets

    5. Autoencoders

    6. Bayesian Methods  

2. [Diabetic-Readmission Analysis](https://github.com/Sardhendu/Data-Science-Projects/blob/master/Diabetic-Readmission/DiabeticReadmission-Spark.ipynb): {PySpark, R}

 * Classification:

    1. GLM {RIDGE/LASSO/ELNET}

    2. Random Forests

3. [Crime Prediction](https://github.com/Sardhendu/Data-Science-Projects/blob/master/Crime-Prediction/crimePrediction.ipynb): {Python: Sckit-learn}

 * Regression:

    1. Linear Regression

    2. Polynomial Regression

 * Classification:

    1. Decision Trees

    2. Gaussian Naive Bayes

    3. Support Vector Machines, Linear SVC, POLY, RBF

    4. Random Forests

4. [Credit default](https://github.com/Sardhendu/Data-Science-Projects/blob/master/Credit-Defaulters/CreditDefault.ipynb): {R}:

 * Classification:

    1. Logistic Regression (GLM): RIDGE/LASSO

    2. Naive Bayes

    3. Decision Trees

    4. Random Forests      

    

5. [Loan Default](https://github.com/Sardhendu/Data-Science-Projects/tree/master/Loan-Defaults): {R}

 

 * Classification:

    1.  GLM (Generalized Linear Model)

--> Data {source URL} : 

		1. http://archive.ics.uci.edu/ml/

		2. https://www.lendingclub.com/info/download-data.action