Initial Exploration

[jkuruzovich]

• [ ] Identify high quality notebooks in classification for business & eng.
• [ ] identify data to be used
• [ ] create initial directory.

Some sites to look at for initial analysis:

DATA8 http://data8.org https://github.com/data-8/materials-su18

Data100 http://www.ds100.org https://github.com/DS-100/fa17

[jkuruzovich]

Figure out easy way to get multiple datasets.
https://github.com/Kaggle/kaggle-api

[jkuruzovich]

https://github.com/amueller/scipy-2018-sklearn

[sunnyr3]

Types of classification algorithms in machine learning:

• [ ] Linear Classifiers: Logistic Regression, Naive Bayes Classifier
• [ ] Support Vector Machines
• [ ] Decision Trees
• [ ] Boosted Trees
• [ ] Random Forest
• [ ] Neural Networks
• [ ] Nearest Neighbor

[sunnyr3]

Sample analysis using logistic regression: https://github.com/SidSachdev/AdvertisingLogisticRegresssion

[sunnyr3]

An article that simply summarizes each classification algorithm: Types of classification algorithms in Machine Learning by Mandeep Sidana

[jkuruzovich]

https://github.com/amueller/scipy-2018-sklearn

[sunnyr3]

Example notebook for Random Forest: reproduce-stock-market-direction-random-forests

[JacksonFWong]

Notebook that explores classification using a census dataset

Performs:

• Feature Exploration (Uni and Bi-variate)
• Feature Imputation
• Feature Selection
• Feature Encoding
• Feature Ranking
• Machine Learning with sklearn and Tensorflow
• Random Search
• Accuracy, Precision, Recall, and f1 calculations
• ROC Curve

Link: https://github.com/dformoso/sklearn-classification.git

Also uploaded to notebooks folder