This section contains commonly used basic machine learning techniques.

Where applicable, cross-validation and feature importance is included.

1. Classification
   • Random Forest
   • Decision Tree
   • Naive Bayes
   • ADABoost
   • Logistic Regression
     • sklearn
     • statsmodel
     • (includes ROC and AUC scores)
   • SVC (Polynomial, RBF, Sigmoid, Linear)
   • Gradient Boosting
   • XGBoost
   • Neural Network
   • Voting
   • LSTM
   • Evaluations
     • Confusion matrix
     • Classification report
     • Accuracy (cv training sets)
     • Accuracy (manual)
     • Accuracy (sklearn)
     • F1
2. Regression
   • Random Forest
   • Lasso Regression (L1)
   • Ridge Regression (L2)
   • Neural Network (keras)
   • LSTM
   • Evaluations
     • MSE
     • To be updated

This section contains commonly used oversample/undersample techniques.

1. Classification
   • SMOTE
   • ADASYN
2. Regression
   • SMOTER

This section contains commonly used pre-processing techniques including but limited to:

1. Describing datasets
   • value_counts
   • missing values
   • PDPBox
2. Manipulation
   • datetime values
   • data types
   • splitting & concat
   • merge / join
   • sorting
   • renaming / mapping
   • reordering categorical variables
   • dicretizing
     • using .at
     • using if/elif (user-defined breaks)
     • equal width bins
     • equal frequency bins
     • k-means (silhouette or ssd)
   • row/column conditional selection
   • dropping / appending
   • imputation
   • encoding
     • Onehot
     • mean/target
3. Plotting
   • all categorical
   • all numerical
     • distplot
     • binned
4. Train test split
5. Scaling
   • StandardScalar
   • MinMaxScaler
6. Others
   • pivot tables / crosstabs
   • binning
   • importing list of tiles
   • startswith, endswith, contains
   • np.where (adding new column based on conditions)
   • extract first # digits
   • duplicates