Predictive Modeling

  Predictive Modeling

The concept of the model in analytics and how it is used?
Common terminology used in analytics & modeling process
Popular modeling algorithms
Different Phases of Predictive Modeling

    Data explration for modeling

Need for structured exploratory data
EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
Identify missing data
Identify outliers data
Visualize the data trends and patterns

     Linear regression: solving regression problems

Introduction – Applications
Assumptions of Linear Regression
Building a Linear Regression Model
Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis, etc)
Assess the overall effectiveness of the model
Interpretation of Results

     Logistic regression: Solving classifiction problems

Introduction – Applications
Building the Logistic Regression Model
Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve, etc)
Validation of Logistic Regression Models
Standard Business Outputs (ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)

    Supervised learning: naive bayes

The concept of Conditional Probability
Bayes Theorem and Its Applications
Naïve Bayes for classification
Applications of Naïve Bayes in Classifications

    Time series forecasting: Solving forecasting problems

Introduction – Applications
Basic Techniques – Averages, Smoothening, etc
Advanced Techniques – AR Models, ARIMA, etc
Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc

    Supervised Learning: Decision Trees

Introduction – Applications
Basic Techniques – Averages, Smoothening, etc
Advanced Techniques – AR Models, ARIMA, etc
Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc
Decision Trees – Introduction – Applications
Types of Decision Tree Algorithms
Construction of Decision Trees through Simplified Examples; Choosing the “Best” attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi-Square, Regression Trees
Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
Pruning a Decision Tree; Cost as a consideration
Decision Trees – Validation
Overfitting – Best Practices to avoid

   Unsupervised Learning: Segmentation

What is segmentation & Role of ML in Segmentation?
K-Means Clustering
Expectation Maximization
Principle Component Analysis (PCA)

   Supervised learning: support vector machines

Motivation for Support Vector Machine & Applications
Interpretation of Outputs and Fine tune the models with hyper parameters

   Supervised Learning: KNN

What are KNN & Applications?
KNN for missing treatment
KNN For solving regression problems
KNN for solving classification problems
Validating KNN model
Model fine tuning with hyperparameters