#### 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

#### Supervised Learning: Support Vector Machine

• Concept of Ensembling
• Random forest (Logic, Practical Applications)