Course

Unit I

Introduction – Overview of the Data Mining Process – The Steps in Data Mining – Preliminary Steps – Predictive Power and Over fitting – Building a Predictive Model – Data Exploration and Dimension Reduction – Data Visualization – Dimension Reduction – Correlation Analysis – Reducing the Number of Categories in Categorical Variables – Converting a Categorical Variable to a Numerical Variable -Principal Components Analysis – Performance Evaluation – Evaluating Predictive Performance – Judging Classifier Performance.

Unit II

Prediction and Classification Methods – Multiple Linear Regression – Explanatory vs. Predictive Modeling – Estimating the Regression Equation and Prediction – The k-NN Classifier (Categorical Outcome) – The Naive Bayes Classifier – Classification and Regression Trees – Evaluating the Performance of a Classification Tree – Avoiding Overfitting – Logistic Regression – Neural Nets – Fitting a Network to Data – Discriminant Analysis – Classification Performance of Discriminant Analysis – Combining Methods: Ensembles and Uplift Modeling – Association Rules and Collaborative Filtering – Cluster Analysis – Measuring Distance – Hierarchical (Agglomerative) Clustering – The k-Means Algorithm.

Unit III

Forecasting Time Series – Descriptive vs. Predictive Modeling. Popular Forecasting Methods in Business – Regression-Based Forecasting – A Model with Trend – A Model with Seasonality – A Model with Trend and Seasonality – Autocorrelation and ARIMA Models – Smoothing Methods – Introduction – Moving Average – Simple Exponential Smoothing – Data Analytics – Social Network Analytics – Directed vs. Undirected Networks – Visualizing and Analyzing Networks – Using Network Metrics in Prediction and Classification -Text Mining – The Tabular Representation of Text: Term-Document Matrix and “Bag-of-Words” – Bag-of-Words vs. Meaning Extraction at Document Level – Preprocessing the Text – Implementing Data Mining Methods-Case Studies.

Course Objectives

• The course presents an applied approach to data mining concepts and methods using Python software for illustration.
• Students will learn how to implement a variety of popular data mining algorithms to tackle business problems and opportunities.
• It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining, and network analysis.

Course Outcomes

CO1: Apply data mining processes, visualize data spread, and employ various techniques of data reduction such as

PCA, build predictive models, and evaluate the models

CO2: Apply feature extraction techniques and design a solution for a classification problem employing Regression,

NB Classifier, and Decision trees and their variants

CO3: Apply ARIMA and other forecasting methods in business

CO4: Implement Data Analytics on social networks

CO5: Apply knowledge of text representation for extraction and display of embedded information

CO-PO Mapping

Evaluation Pattern: 70:30

*CAT – Can be Quizzes, Assignments, and Reports

*CAL – Can be Lab Assessments, Project, and Report

**End Semester can be theory examination/ lab-based examination/ project presentation

Textbook(s)

Shmueli G, Bruce PC, Yahav I, Patel NR, Lichtendahl Jr KC. “Data mining for business analytics: concepts, techniques, and applications”, R. John Wiley & Sons; 2017.

Reference(s)

VanderPlas J. “Python data science handbook: essential tools for working with data”. ” O’Reilly Media, Inc.”; 2016.

McKinney W. “Python for data analysis: Data wrangling with Pandas, NumPy, and IPython”. ” O’Reilly Media, Inc.”; 2012.