Blog
This is where you’ll find detailed technical analyses of projects, general documentation of my learnings as I delve deeper into the world of Data Science and Machine Learning; and my general ramblings. Enjoy!
2023
Popular Git Commands
This blog briefly goes through some essential git commands that may prove handy. Continue reading Popular Git Commands
Image Search Engine
The purpose of this project is to build an image search engine that would help customers search for products similar to what they are searching for. Continue reading Image Search Engine
2022
10 steps to using Virtual Environments with pip
Creating, Activating and De-activating virtual environments Continue reading 10 steps to using Virtual Environments with pip
Fruit Classification using CNN (Keras)
The goal for the project is to build a convolutional neural network (CNN) that would classify fruits for a robotics company, for use in sorting grocery items. We use Image Augmentation and Dropout to enhance the performance of the network. We also used Keras tuner to come up with the optimal setup for the highest accuracy. Continue reading Fruit Classification using CNN (Keras)
Building my Data Science Portfolio using Jekyll and Netlify
How I built my Data Science Portfolio (from start to ongoing…) Continue reading Building my Data Science Portfolio using Jekyll and Netlify
Quantifying Sales Uplift With Causal Impact Analysis
This blog goes into a detailed review of Causal Impact Analysis and how it is used to quanitfy sales uplift for a grocery chain. Continue reading Quantifying Sales Uplift With Causal Impact Analysis
Understanding Alcohol Product Relationships Using Association Rule Learning
In this project we use Association Rule Learning to analyse the transactional relationships & dependencies between products in the alcohol section of a grocery store. Continue reading Understanding Alcohol Product Relationships Using Association Rule Learning
Compressing Feature Space for Classification Using PCA
In this project, we use Principal Component Analysis (PCA) to compress 100 unlabelled, sparse features into a more manageable number for classifying buyers of Ed Sheeran’s latest album. Continue reading Compressing Feature Space for Classification Using PCA
The "You Are What You Eat" Customer Segmentation
In this project, we use k-means clustering to segment up our client’s customer base in order to increase business understanding, and to enhance the relevancy of targeted messaging & customer communications. Continue reading The "You Are What You Eat" Customer Segmentation
Enhancing Targeting Accuracy using ML
The goal for the project is to build a model that would accurately predict the customers that would sign up for our client’s delivery club services. We use different classification algorithms to perform this task and understand what the main drivers are. Continue reading Enhancing Targeting Accuracy using ML
Predicting Customer Loyalty Using ML
The goal for the project is to build a model that would accurately predict the customers that would sign up for our client’s delivery club services. We use different classification algorithms to perform this task and understand what the main drivers are. Continue reading Predicting Customer Loyalty Using ML
Assessing Campaign Performance Using Chi-Square Test For Independence
In this project, we apply Chi-Square Test For Independence (a Hypothesis Test) to assess the performance of two types of mailers that were sent out to promote a new service! Continue reading Assessing Campaign Performance Using Chi-Square Test For Independence
