**Project 01 **– Market Basket Analysis

**Domain **– Inventory Management

**Problem Statement** – As a new manager in the company, you are assigned the task of increasing cross selling

**Topics** – Association Rule Mining, Data Extraction, Data Manipulation

**Highlights**

- Performing association rule mining
- Understanding where to implement Apriori Algorithm
- Setting association rules with respect to confidence

**Project 02** – Credit Card Fraud Detection

**Domain **– Banking

**Problem Statement** – Analysis of probability of being involved in a fraudulent operation

**Topics** – Algorithms, V17 Predictor, Data Visualization, R Language

**Highlights**

- Understanding working with the credit card dataset
- Performing data analysis on various labels in the data
- Making use of V17 as predictor and using V14 for analysis
- Plotting score performance with respect to variables

**Project 03 **– Data Cleaning using Census Dataset

**Domain **– Government

**Problem Statement **– Performing Data Cleansing operation on a raw dataset

**Topics **– Data Analysis, Data preprocessing, Cleaning Ops, Data Visualization, R Language

**Highlights**

- Understanding working with the census dataset
- Changing around various with respect to a label to perform analysis
- Creation of functions to eliminate values which are not required
- Verifying the completion of data cleansing operation

**Project 04 **– Loan Approval Prediction

**Domain** -Banking

**Problem Statement** – Prediction of approval rate of a loan by using multiple labels

**Topics **– Data Analysis, Data preprocessing, Cleaning Ops, Data Visualization, R Language

**Highlights**

- Performing Data Preprocessing
- Building a model and applying PCA
- Building a Naïve Bayes model on the training dataset
- Prediction of values after performing analysis

**Project 05 **– Book Recommendation System

**Domain** – E-Commerce

**Problem Statement **– Creating a model, which can recommend books, based on user interest

**Topics **– Data Cleaning, Data Visualization, User Based Collaborative Filtering

**Highlights**

- Finding the most popular books using various techniques
- Creating a Book Recommender model using User Based Collaborative Filtering

**Project 06** – Netflix Recommendation System

**Domain **– E-Commerce

**Problem Statement** Simulating the Netflix Recommendation System

**Topics** – Data Cleaning, Data Visualization, Distribution, Recommender Lab

**Highlights**

- Working with raw data
- Using the Recommender Lab library in R
- Making use of real data from Netflix

**Project 07** – Creating a Pokemon Game using Machine Learning

**Domain** – Gaming

**Problem Statement** – Creating a game engine for Pokemon using Machine Learning

**Topics** – Decision Tress, Regression, Data Cleaning, Data Visualization

**Highlights**

- Predicting which Pokemon will win based Attack vs Defense
- Finding whether a Pokemon is legendary using Decision Trees
- Understanding the dynamics of decision making in Machine Learning

**Case Study 01** – Introduction to R Programming

**Problem Statement **– Working with various operators in R

**Topics** – Arithmetic Operators, Relational Operators, Logical Operators

**Highlights**

- Working with Arithmetic Operators
- Working with Relational Operators
- Working with Logical Operators

**Case Study 02** – Solving Customer Churn using Data Exploration

**Problem Statement** – Understanding what to do to reduce customer churn using Data Exploration

**Topics** – Data Exploration

**Highlights**

- Extracting Individual columns
- Creating and applying filters to manipulate data
- Using loops for redundant operations

**Case Study 03** – Creating Data Structures in R

**Problem Statement** – Implementing various Data Structures in R for various scenarios

**Topics** – Vectors, list, Matrix, Array

**Highlights**

- Creating and Implementing Vectors
- Understanding Lists
- Using Arrays to store Matrices
- Creating and implementing Matrices

**Case Study 04** – Implementing SVD in R

**Problem Statement** – Understanding the use Single Value Decomposition in R by making use of the MovieLense Dataset

**Topics** – 5-fold cross validation, Real Rating Matrix

**Highlights**

- Creating a custom recommended movie set for each user
- Creating User Based Collaborative Filtering Model
- Creating RealRatingMatrix for Movie recommendation

**Case Study 05** – Time Series Analysis

**Problem Statement** – Performing TSA and understanding concepts of ARIMA for a given scenario

**Topics** – Time Series Analysis, R Language, Data Visualization, ARIMA model

**Highlights**

- Understand how to fit an ARIMA model
- Plotting PACF charts and finding optimal parameters
- Building the ARIMA model
- Prediction of values after performing analysis