End-to-end ML Modeling process for classification problem
  1. Data reading and preprocessing: missing data, visualization, scaling
  2. Algorithm selection
  3. Model Evaluation: train-test split, k-fold cross validation, stratified cv, metrics (accuracy,recall,precision,f1-score,confusion matrix)
  4. Hyperparameter tuning: grid search

Algorithms
  1. Logistic Regression
  2. Naive Bayes (NB)
  3. Decision Tree
  4. Support Vector Machine (SVM)
  5. Random Forest
  6. Gradient Boosting Tree (GBT)
 
Examples
  1. kaggle titanic data (binary class)
  2. UCI wine data (multiclass)