Hello World!
I am Swapnil, a Software Engineer turned analytics professional. I have a graduate degree in Business Analytics and I enjoy solving complex business problems using data. I love photography and cannot sleep without watching at least one episode of The Office!
This is a repository of some of the projects I worked on.
Customer lifetime value modeling
CLTV modeling has been gaining popularity in Marketing Analytics these days. Some of the models were proposed as early as 1987, however new work continues to improve predictions. This project is aimed at comaparing various CLTV models and their prediction accuracy.
Following models are discussed-
MLE based models- These are the models in which parameter estimation happens based on maximum likelihood
- NBD
- PNBD
- MBG/NBD
- BG/CNBD-k
- MBG/CNBD-k
Markov-Chain-Monte-Carlo based models- These models use MCMC simulation for parameter estimation
- Pareto/NBD
- Pareto/GGG
ML Algorithm benchmarking
This blog is aimed at comparing top machine learning algorithm performance in solving a binary classification problem. The data used for this project pertains to bankruptcy. The objective is to predict if an organization will go bankrupt or not. False possitive predictions are penalized 15 times False negative prediction.
Following are the alorithms that wil be considered for comparison :
- Logistic Regression
- Lasso Regression -L1 Norm Regularization
- Classification Tree
- RandomForest
- Generalized Additive Model
- Neural Nets
- Linear discriminant analysis
- Boosting Algorithms
Unsupervised learning
Data Science and Analytics is nowadays widely used in retail industry. With the advent of bid data tools and higher computing power, sophisticated algorithms can crunch huge volumes of transactional data to extract meaningful insights. Companies such as Kroger invest heavily to transform more than a hundred-year-old retail industry through analytics.
This project is an attempt to apply unsupervised learning algorithms on the transactional data to formulate strategies to improve the sales of the products.
Following steps are discussed:
- Customer Segmentation
- Deciding optimum number of clustes
- Market Basket Analysis
Regression Diagnostics
The purpose of this project is to solve a simple problem using linear regression and understand various feature selection and model selection techniques along the way. In this project we predict the landing distance of the aircrafts based on different details. There have been many major mishaps during the landing of the aircraft, resulting in the loss of many lives.
Following major steps are discussed :
- Exploratory Data Analysis
- Model building
- Regression model diagnostics
ML Hackathon
This is a code for an online hackathon to predict likelihood of someone suffering from stroke. Various algorithms are used to get predictions. Since the time was limited, the documentation in this code is sloppy.
Exploratory Data Analysis
Exploratory data analysis of SF Bay area Fordgobike bike share data.
Following major steps are discussed :
- Data cleaning
- Exploratory Data Analysis
- Implementation of interactive graphs in R
- Embedding Tableau workbooks in R
- Geospatial Analysis
- Business Recommendations
iPhoneX vs Galaxy Note8
This is an old analysis done sometime after both Note8 and iPhoneX were launched. One limitation of Twitter free API is that you cannot extract historical tweets. So the sentiments may not be relevant today but what the heck, data is always good for understanding the process!
*iphoneX was launched on Sep 12, 2017 and it went on sale sometime on Nov’17. Galaxy Note8 was already out a few months before iPhoneX.
Sentiment Analysis of cryptocurrencies
There has been so much buzz about Crypto Currencies and their long-term validity these days. The world has been divided on the credibility of the Crypto Currency as an authentic investment. Some countries have even gone to the extent of banning the trade of Crypto Currencies. Let’s explore what Twitterati think about this phenomenon.
Based on the data available at Cryptocurrency Market Capitalizations, top three Crypto Currencies with highest market capitalization were selected for analysis.
Following three Crypto Currencies were selected-
- Bitcoin (BTC)
- Ripple (XRP)
- Ethereum (ETH)