Projects
• Designed a PostgreSQL database with multiple tables by optimizing data integration and management.
• Developed data models and tested hypotheses with 3 scenarios, identifying 8 KPIs to assess customer storage needs.
• Crafted and executed 50+ complex SQL queries for data extraction and analysis, and built ETL processes for integrating and transforming financial and customer data.
• Compiled a PowerPoint report with insights, metrics, and forecasts, presenting actionable recommendations for strategic business planning and customer growth.
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Improved decision-making for stakeholders by leveraging data analytics and visualization techniques using Power BI to analyze over 1,200 coffee brand reviews and ratings, generating key performance indicators [KPIs] and metrics.
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Utilized data modeling for reporting and data integration using Power BI to combine datasets and calculate key
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Created dynamic visualization report, showcased geographical distribution trends, driving at least a 10% increase in data-driven decision-making efficiency.
This project involves a comprehensive analysis of layoff data, utilizing PostgreSQL and Python to extract, clean, and analyze data for insights into trends across companies, locations, industries, and more. Key tasks included data cleaning, removing duplicates, standardizing data, and handling null values to ensure data integrity. Advanced SQL queries were executed to uncover critical metrics, and the findings were compiled into detailed reports. The project aimed to provide actionable insights into the severity and distribution of layoffs, supporting strategic decision-making.
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Developed a comprehensive Web Application using Streamlit for predicting 5 diseases, integrating Supervised Machine Learning and Boosting Algorithms for accurate diagnostics.
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Implemented data preprocessing techniques such as PCA, min-max normalization, one-hot encoding, and split the data into train/test (75/25) to enhance model accuracy, utilizing disease-specific datasets.
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The application allows users to input medical data to predict disease outcomes, enhance early detection capabilities, and reduce healthcare costs by at least 10%.
In today’s healthcare landscape, the use of data and advanced technologies like machine learning has improved our understanding of diseases. However, lung cancer and pneumonia are still major health concerns worldwide. This project uses X-ray images and user input along with machine learning and deep learning techniques to predict these diseases early. It aims to make diagnosis faster and more accurate, potentially saving lives and improving patient care
In this project, I Encompassed comprehensive data preparation and exploratory analysis in MS Excel and created insightful visualizations in Power BI by Leveraging DAX functions. I conducted advanced data modeling to extract actionable insights on age demographics, spending patterns, and transaction behaviors. This analysis guided strategic recommendations to optimize credit card features, target specific customer segments, and enhance overall customer engagement for Mitron Bank's new credit card line.
This project focuses on forecasting daily average temperatures using the Autoregressive Integrated Moving Average (ARIMA) model. The dataset, sourced from "MaunaLoaDailyTemps.csv", contains daily temperature records indexed by date.
