` 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