Workflow in a team of data scientists 

Understanding of data science process data models 

Understanding of data science process data models 

Types of ML 

Supervised Machine Learning 

Data Science workflow by CRISP-DM 

Business understanding ● Define problem in terms of business - define business question to the future model Example: detect and prevent frauds intrusion ● Define data science problem Example: who are considered to be frauds, how to detect frauds ● Define what we need to solve the problem - what data to gather and analyze 

Data understanding ● Gather dataset 

Data Understanding ● EDA (Exploratory Data Analysis) Objectives: 1. Discover patterns 2. Spot anomalies 3. Frame hypothesis 4. Check assumptions 

Data Preparation (50-70% of project time) ● Data Preprocessing (handle missing values, wrong data types, etc.) ● Dataset Labeling one class problem 

Data Preparation ● Divide dataset into train/test (validate, folds) ● Feature Engineering 

Modeling ● Creating baseline model ● Choosing algorithms ● Feature selection 

Modeling ● Tune model hyperparameters - to achieve higher accuracy - to improve model performance sklearn.ensemble.GradientBoostingClassifier(loss=’deviance’, learning_rate=0.1, n_estimators=100, subsample=1.0, criterion=’friedman_mse’, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_decrease=0.0, min_impurity_split=None, init=None, random_state=None, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, presort=’auto’) 

Modeling ● Tune model hyperparameters - to avoid overfitting 

Evaluation - Model Performance Metrics (AUC, Gini, F1, Confusion matrix, etc.) - Business metrics (profits, approval rate, default rate, etc.) - Evaluate achievement of business Purposes Some models may not get to deployment stage after evaluation. 

De data about an application OneKarma Scoring service API trained model { model score fetch data 

trained model Response JSON Request JSON 

Terms for each stage Business understanding - 1 week Data understanding - 3 weeks Data preparation - 5 weeks Modeling - 2 weeks Evaluation - 1 week Deployment - 1 week Full model development process - ~ 13 weeks 

Data scientists Data engineers Data scientists Business side Data scientists Data engineers Data scientists Data scientists Data engineers Development team QA team Data scientists Business side 

Thank you for your attention.