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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Capstone Projects – Great Lakes Business Analytics Program

Kunal Jain
Kunal Jain Last Updated : 24 Sep, 2015
6 min read

Introduction

I have been associated with the Great Lakes Business Analytics Program as a visiting faculty for some time now. This is one of the ways, I interact with people wanting to make their career in analytics. The energy and the desire to make a career in business analytics in some of these students is just contagious!

As part of the curriculum, people undergoing this course are expected to complete a Capstone Project. This Capstone Project is like a climax at the end of a yearlong movie – it looks difficult at the start, but everything you do in the entire movie comes together at the end.

I had the privilege of sitting in the evaluation panel for a few projects. In today’s article, I plan to give an overview of what happened in these projects.

 

Why share these projects?

Well, there are a few reasons, why I wanted to share these projects in form of an article with a larger audience:

  1. I think these projects are good examples of small projects, people who want to pursue analytics can undertake on their own to create a portfolio of data science projects.
  2. We get a lot of queries about this program and I think this article can help address a lot of those queries directly or indirectly.

I have already informed Great Lakes about the article and I’ve published this with their consent.

 

Overview – How do these projects work?

As mentioned before, every person undergoing this course is expected to submit a Capstone Project. In order to do this project, a project group is formed with participants from diverse backgrounds. These groups, then work along with a mentor to complete these projects over a period of 4– 5 months. About 80% of these projects happen with mentors from industry. Mentors from various companies like Value Labs, Whiskkers Marketing Pvt. Ltd., BRIDGE i2i, Analytics Vidhya participate in helping students with these projects.

 

Importance of Capstone Project for students

Analytics is a very hands on subject. I personally believe that until and unless you complete a few projects end to end (i.e. from data collection to building a model to implementing a model), your knowledge and approach stays theoretical in nature.

The same view was reflected by students, when I talked to a few of them. Here is one of the student sharing his experience undergoing the capstone project:

During our course at Great Lakes, we learnt about various techniques like, ways of cleaning-up data, multiple algorithms for analysis and modeling, goodness of fit to check model fitness and had numerous sessions on business intelligence. But it is the Capstone Project that introduced us to the real world challenges of applying our learning, testing adequacy, usefulness and subjectivity of certain algorithms. We also got some real time insight into the processes and steps involved in a typical analytics project. Without Capstone Project our learning would have remained incomplete.  –  Sriram Alagarsamy, Great Lakes PGPBA Alumnus

 

The Projects

In the rest of this article, I’ll provide a few examples of the Capstone Projects, which happened in last batches of Great Lakes and the industry sponsors / mentors were happy to share the project details through this article.

Project 1: Credit Default Prediction

Students: Kanthimathi Gayatri Sukumar, S.R.Balaji, S.Ramnath, Rudragouda Patil, Mithur Niranjan

In this project, the students had built several models to predict credit default for 2 wheeler loans in villages in India. The variables available to the candidates included:

  1. Interest rate (Finance rate)
  2. Finance charges in Rupees
  3. Finance amount (Loaned amount) in Rupees
  4. Average EMI (Equated Monthly Instalment) in Rupees
  5. Income to Instalment ratio (noir)
  6. First information report (fire)
  7. Gross income of the customer (gih) in Rupees
  8. Gross income of spouse(gis) in Rupees
  9. Gross income of co-borrower(gic) in Rupees
  10. Average bank balance/EMI ratio (noisb)

The group had built several models including Logistic regression, Classification tree – CART, Classification tree (Ensemble) – Random forest, Neural networks and Discriminant analysis. Here is the comparison of various modeling techniques:

1

The group finally used Logistic regression because of high accuracy and ease of insight generation (rather than a black box approach to modeling)

 

Project 2: Statistical Analysis of Consumer Durables Retail Sales

Students: Soumya Tiwari, Remina Surendrababu, Arnab Majumdar, Vijayalekshmi L, Siju Joseph

The project team analyzed about 0.5 million transactions for a retail chain having 20 branches across Chennai. The team collected the data for a period of three years to find a reasonable practical solution to the following business problems faced by a consumer durable retail chain:

  1. Purchase Basket Analysis
  2. Analysis of loyalty to any particular brand across categories

This group used R and built a recommender system using Apriori algorithm.

 

Project 3: Web & Text Mining – Sentiment Analysis

Student: Anjana Agrawal

Anjana has been doing freelance IT consulting to clients across the globe. Her Capstone Project required her to enable a political organization to understand people’s concerns, views and sentiments on topics in near real time. She also has a publication, which came out of her work (commendable! I must say).

The project started by obtaining input text data from “Twitter” for one of the leading political organization and the tweets were scored for sentiment analysis. Output generated in terms of “Common Key Words”, “Association between common key words”, “Sentiment Score” was observed and analyzed for a period of approx. 3 weeks. This output was important for the organization considering the fact that this political organization was campaigning for the elections scheduled in near future and based on the “Citizen” sentiments, organization was able to refine their next course of action.

2

The project ended up being used by the party in real time and helped them in addressing the concerns of its people quickly.

 

Project 4: Credit card – Risk Analytics

Students: Amit Madan, Praveen Panwar

The objective of this project was to develop a model to calculate the probability of default (PD Model) for credit card holders of a bank. The model was supposed to be run after the customer has spent more than a year with the bank and predicts the probability of default over a period of next 3 months.

Amit & Praveen had more than 2 million data points with 20 variables (18 numeric and 2 character) to start with. They created more than 50 additional variables based on their internal brainstorming. After removing variables with high correlation and the insignificant variables, they used Logistic Regression to build the model.

The final model had a concordant ratio of 91.4% and a list of 230%. Here is the Lorenz curve for the same.

3

It was amazing to see the amount or learning the students had undergone through this project. Even if you join a bank as an analyst, it would take you a few years before you can get your hands dirty on a problem like this!

Amit reflected similar sentiment in my discussion with him later:

There are three important steps in the computational modelling of any physical process (1) Problem definition (2) mathematical model and (3) computer simulation. CAPSTONE provided for an opportunity to synthesize domain expertise, mathematical representation and computational skills to re-produce a modelling framework so as to be able to cull out the insights present in a data. It was by virtue of CAPSTONE, we could apply all of what we had learned at Great Lakes and it helped generate an urge to keep undertaking similar assignments for sustained mental stimulation.   –  Amit Madan, Great Lakes PGPBA, Alumnus

 

Project 5: Developing Least Cost Effective Intervention in Schools

Students: Balamuril S, RamKumar R, Senthil J, Sreeraman K, Sriram A

This group applied the power of analytics to impact government aided schools in 5 districts. They created clusters of schools, developed insights about their performance and translated them into quality improvement program for these schools.

They further created a model to rank these schools, simulate the effect of government interventions and then measure them as well. The project should not only impact the 5 districts, but can have a far larger impact by implementing these insights for schools across the nation.

 

Project 6: Lead Generation for Health Insurance Firms using Web and Social Media Data

Students: Harpreet Kaur, Aneet Sachdeva, Peeyush Tiwari

This group first collected open data and social media data, applied text mining and natural language processing (NLP) techniques to extract features out of this data. They then applied various clustering and bag of words techniques to pull out insights into insurance purchase behavior. Some of these groups were identified as high potential leads for Health Insurance products.

 

End Notes

As you can see, the projects varied from predicting default in rural regions to helping parties contest elections. It was heartening to see the output from this group of people, most of whom had very little knowledge about analytics a year ago.

I hope this article would provide learners in analytics with a few ideas to do their own Capstone Projects and would have provided a glimpse of the program to those interested. You can read more details about the program here.

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.

Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

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Responses From Readers

Clear

Akash
Akash

Hi Kunal, Can you also share what all tools were used for each project mentioned above? Thanks,

123

Rahul Bhosale
Rahul Bhosale

Hi, I would like to Participate in Capstone Project. pl confirm

123

ALIP BISWAS
ALIP BISWAS

Hi, I would like to do a project on business analyst. is there any opportunity to do that.

123

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8 Awesome Data Science Capstone Projects from Praxis Business School
8 Awesome Data Science Capstone Projects from Praxis Business School
Mystory: I became a Data Scientist after 8 years working as a Software Test Engineer
Mystory: I became a Data Scientist after 8 years working as a Software Test Engineer

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