Blogs

I often write about what I am learning, teaching, or working through: the mathematics behind machine learning algorithms, coding problems, ideas from statistics and probability, and the occasional note on something else entirely. This page gathers that writing, along with the software and data-science projects I have built. The writing is organised into five collections, each of which opens in a new tab.

Data Science & Machine Learning Machine learning algorithms in Python, from linear models to deep learning, alongside the data-science workflow: collection, cleaning, EDA, evaluation, and model optimization. Open the blog

Data Structures & Algorithms LeetCode-style problems worked through in Python: binary search, arrays, strings, sorting, graph search, and dynamic programming. Open the blog

Statistics & Probability Notes on basic statistics and probability: distributions, estimation, and the ideas that underpin inference and sampling. Open the blog

Mathematics Linear algebra notes and proofs, numerical linear algebra, gradient methods for optimization, and mathematical modeling in biology. Open the posts

Miscellaneous Everything else: building a personal website with Quarto, writing in Bengali, and other notes that do not fit a single subject. Open the posts

Portfolio

Software I have written and data-science projects I have worked on.

Software, packages & apps

Python Package: holmc

Streamlit Web App

Python Application Library: desgld packaging

Data science & machine learning projects

Auto Loan Decision Model

Predicting Product Success Using Customer Reviews and Sales Data

Insurance Cost Forecast by using Linear Regression

Disease diagnosis using classification and NLP

Lendingclub’s loan default prediction

Feature Selection: A linear regression approach to find the impact of the features of e-commerce sales data

Classification Probelm: Predict the chance of survival of a voager on Titanic based on the voager’s information