README.md

Workshop on "Machine learning model developments for classification problems"

Workshop Day 1

1. Covered general introduction to build ML Model
2. Classification problem use case
3. HR analysitcs Data - Context and content of the data
4. Practical Demo using R

• Data Cleaning
• Data Pre-processing
• Missing Imputations
• Exploratory data analysis
• Feature Engineering
• Feature Selection
• Data scaling

Created a master data with all existing and derived features. We derived ~190 additional features after processing and feature engineering.

Workshop Day 2

1. Created Train and Valid data based off 80:20 ratio
2. Logistic Regression

• GLM model development
• Predict score on validataion data
• Evaluation of the model before optimising the probability cut off
• Evaluation of the model after optimising the probability cut off
• AUC, Confusion matric and ROC curve
• Gain table

3. Random Forest Model

• GLM model development
• Predict score on validataion data
• Evaluation of the model before optimising the probability cut off
• Evaluation of the model after optimising the probability cut off
• AUC, Confusion matric and ROC curve
• Gain table

4. XGBoost Model

• GLM model development
• Predict score on validataion data
• Evaluation of the model before optimising the probability cut off
• Evaluation of the model after optimising the probability cut off
• AUC, Confusion matric and ROC curve
• Gain table

5. Model comparision
6. Model Deployment

Reference:

0. Data Source:

• https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists

2. Logistic Regression

• https://www.kdnuggets.com/2018/02/logistic-regression-concise-technical-overview.html#%2EWoceyX3ClwM%2Elinkedin
• http://www.real-statistics.com/logistic-regression/receiver-operating-characteristic-roc-curve/
• https://medium.com/convoy-tech/down-the-auc-rabbit-hole-and-into-open-source-part-1-42c47e90e357

2. Random Forest

• https://www.hackerearth.com/practice/machine-learning/machine-learning-algorithms/tutorial-random-forest-parameter-tuning-r/tutorial/
• https://blog.clairvoyantsoft.com/entropy-information-gain-and-gini-index-the-crux-of-a-decision-tree-99d0cdc699f4
• https://towardsdatascience.com/an-implementation-and-explanation-of-the-random-forest-in-python-77bf308a9b76
• https://www.topcoder.com/thrive/articles/understanding-random-forest-and-hyper-parameter-tuning
• https://towardsdatascience.com/hyperparameter-tuning-the-random-forest-in-python-using-scikit-learn-28d2aa77dd74

3. XGBoost

• https://xgboost.readthedocs.io/en/latest/
• http://explained.ai/gradient-boosting/index.html
• https://eng.uber.com/productionizing-distributed-xgboost/
• https://machinelearningmastery.com/gentle-introduction-xgboost-applied-machine-learning/