machinelearning

Collection of python files and jupyter notebooks for machine learning. After taking a few different online machine learning courses I decided to compile everything I learned in one location.

Regression

• Some environments will complain about the shape of y or y_pred with errors like:

Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

or

ValueError: Expected 2D array, got scalar array instead:
array=6.5.

I didn't run into this problem until running the code on a different laptop than it was originally written on. If this happens, reshape y with y = y.reshape(-1,1) and reshape the value you are trying to predict (in this case its 6.5) with [[6.5]]

whats included:

• polynomial regression
• linear regression
• multivariate regression
• support vector regression
• decision tree
• random forest
• Position_Salaries.csv

Classification

whats included:

• K-nearest-neighbors
• naive-bayes
• decision tree
• random forest (Jupyter Notebook)
  • visualize trees
  • visualize decision boundaries
  • cumulative accurcy profile
  • k fold cross validation
• support vector machine
• logistic regression (as a binary classifier)
• Social_Network_Ads.csv

Clustering:

jupyter notebooks from here on

• hierarchical clustering
  • dendrogram
• k-means clustering
  • elbow method
  • dendrogram
• Mall_Customers.csv

Association Rule Learning

whats included:

• apriori [WIP]
• apyori (library which implements apriori)
• Market_Basket_Optimisation.csv
• store_data.csv

Reinforcement Learning

whats included:

• reinforcement learning
  • UCB
  • thompson sampling
  • histogram
  • custom plot
  • selection trace plot
• Ads_CTR_Optimisation.csv

Natural Language Processing

Whats included:

• nlp bag of words
  • cumulative accuracy profile
  • grid search (random forest)
  • random search (random forest)
• Restaurant_Reviews.tsv

Dimensionality Reduction

Whats included:

• dim reduction PCA LDA
• kernel PCA
• Wine.csv

Model Boosting

Whats included:

• xgboost
• Churn_Modelling.csv

Fraud Detection

• this is NOT my original code, I can't remember where I found it from (I think kaggle).