Here are some of the impactful side projects I’ve worked on—pushing the boundaries of AI, automation, and data-driven decision-making:
Insights from Hotel Reviews: A Data-driven Approach to Customer Satisfaction
This project leverages data science to transform unstructured hotel reviews into structured, actionable insights. It aims to predict hotel ratings based on customer feedback. The analysis encompasses data wrangling, exploratory data analysis (EDA), and machine learning modeling to uncover and predict the layers of customer sentiment affecting the hospitality industry.
Projects
Single-cell RNA Sequencing Analysis
This project explores the detailed dynamics of cellular differentiation and maturation using single-cell RNA sequencing (scRNA-seq) techniques. It covers extensive data wrangling, exploratory data analysis, and predictive modeling to investigate the gene-protein interactions as bone marrow stem cells evolve into mature blood cells. The complete analysis pipeline is documented across multiple Jupyter notebooks, each focusing on a crucial aspect of the data science workflow.
Stock Price EDA and Analysis (Time Series)
An Exploratory Data Analysis (EDA) and Time Series Analysis have been conducted on stock prices. The data is fetched for four companies: Apple, Google, Microsoft, and Amazon from 2018-01-01 to 2023-07-31 using Yahoo Finance. By the end of this notebook, we hope to have extracted meaningful insights from our data and built a reliable time series model that can predict future stock prices.
