Project 1 : Augmenting retail sales with Data Science
Industry : Retail
Problem Statement : How to deploy the various rules and algorithms of Data Science for analyzing stationary store purchase data.
Topics : In this project you will deploy the various tools of Data Science like association rule, Apriori algorithm in R, support, lift and confidence of association rule. You will analyze the purchase data of the stationary outlet for three days and understand the customer buying patterns across products.
- Association rules for transaction data
- Association mining with Apriori algorithm
- Generating rules and identifying patterns.
Project 2 : Analyzing pre-paid model of stock broking
Industry : Finance
Problem Statement : Finding out the deciding factor for people to opt for the pre-paid model of stock broking.
Topics : In this Data Science project you will learn about the various variables that are highly correlated in pre-paid brokerage model, analysis of various market opportunities, developing targeted promotion plans for various products sold under various categories. You will also do competitor analysis, the advantages and disadvantages of pre-paid model.
- Deploying the rules of statistical analysis
- Implementing data visualization
- Linear regression for predictive modeling.
Project 3 : Cold Start Problem in Data Science
Industry : Ecommerce
Problem Statement : how to build a recommender system without the historical data available
Topics : This project involves understanding of the cold start problem associated with the recommender systems. You will gain hands-on experience in information filtering, working on systems with zero historical data to refer to, as in the case of launching a new product. You will gain proficiency in working with personalized applications like movies, books, songs, news and such other recommendations. This project includes the various ways of working with algorithms and deploying other data science techniques.
- Algorithms for Recommender
- Ways of Recommendation
- Types of Recommendation -Collaborative Filtering Based Recommendation, Content-Based Recommendation
- Complete mastery in working with the Cold Start Problem.
Project 4 : Recommendation for Movie, Summary
Topics : This is real world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provide data-driven recommendations. This project involves understanding recommender systems, information filtering, predicting ‘rating’, learning about user ‘preference’ and so on. You will exclusively work on data related to user details, movie details and others. The main components of the project include the following:
Recommendation for movie
- Two Types of Predictions – Rating Prediction, Item Prediction
- Important Approaches: Memory Based and Model-Based
- Knowing User Based Methods in K-Nearest Neighbor
- Understanding Item Based Method
- Matrix Factorization
- Decomposition of Singular Value
- Data Science Project discussion
- Collaboration Filtering
- Business Variables Overview
Project 5 : Prediction on Pokemon dataset
Industry : Gaming
Problem Statement : For the purpose of this case study, you are a Pokemon trainer who is on his way to catch all the 800 Pokemons
Topics : This real-world project will give you a hands-on experience on the data science life cycle. You’ll understand the structure of the ‘Pokemon’ dataset & use machine learning algorithms to make some predictions. You will use the dplyr package to filter out specific Pokemons and use decision trees to find if the Pokemon is legendary or not.
- dplyr package to filter Pokemons
- Decision Tree algorithm
- Linear regression algorithm.
Project 6 : Book Recommender System
Industry : E-commerce
Problem Statement : Building a book recommender system for readers with similar interests
Topics : This real-world project will give you a hands-on experience in working with a book recommender system. Depending on what books are read by a particular user, you will be in a position to provide data-driven recommendations. You will understand the structure of the data and visualize it to find interesting patterns.
- Data analysis & visualization
- Recommender Lab
- User Based Collaborative Filtering Model.
Project 7: Census Income
Problem Statement: In this project, you will process the data and then develop an understanding of different features of the data by performing explanatory analysis and creating the visualizations. After having enough knowledge about the attributes, you will perform a predictive task of classification to predict whether an individual makes over 50K a year or less by using different Machine Learning Algorithms.
Topics: An end-to-end exhaustive project comprising topics in:
- Data Processing
- Data Manipulation
- Data Visualization
- Linear Regression
- Logistic Regression
- Decision Tree
- Random Forest
Project 8: Loan Prediction
Problem Statement: You are the Senior Data Scientist at a major private bank. Since the last 6 months, the number of customers who are not able to repay their loan has increased. Keeping this in mind, you have to look at your customer data and analyse which customers should be given the loan approval and which customers should be denied.
Topics: An exhaustive project on Customer_loan Dataset comprising topics in:
- Data Processing
- Model Building
Project 9: Capstone
Problem Statement: Predicting if the customer will churn or not.
Topics: An end-to-end capstone project comprising:
- Manipulating and envisioning the data for insights.
- Implementing the linear regression model to predict continuous values.
- Implementing classification models – decision tree, logistic regression, and random forest on “customer churn”.
An end-to-end capstone project covering all the modules. You’ll start off by manipulating and visualizing the data to get interesting insights. Then you’d have to implement the linear regression model to predict continuous values. Following which you’ll implement these classification models – logistic regression, decision tree & random forest on the “customer churn” data frame to find if the customer will churn or not.