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1. Preparation

What is supervised learning
Defining Business Goals for Predictive Analytics
Effective Data Preparation
Data partitioning and holdout samples
Choosing variables (features)
Handling missing data
Visualization and exploration

2. Classification and Prediction

Assessing classification models
Confusion matrix
Misclassification costs
Assessing prediction models
Common metrics
K-Nearest-Neighbors (KNN)
Measuring distance
Choosing K
Generating classifications and predictions

3. Bayesian Classifiers; CART

Full Bayes classifier
Naive Bayes classifier
Classification and Regression Trees (CART)
Growing the tree
Avoiding overfit - pruning
Using trees for classifications and predictions

4. Ensembles

Combine multiple algorithms
Improve results

Fees: 18,500 Rs/-
Duration: 2 Months