Project 1. Absenteeism Analysis using Pandas
Absenteeism analysis project aimed to tackle the issue of absenteeism’s impact on organizational productivity. Using a CSV dataset with employee records featuring variables like distance to work, reason for absence, age, transportation expense, body mass index, and pets, conducted data preprocessing using pandas.
This involved loading and exploring the data, handling missing values, removing errors, and transforming and reorganizing the dataset.
The outcome was a clean, well-organized dataset poised for analysis, providing actionable insights to stakeholders for improving employee attendance and overall productivity.
Absenteeism analysis project aimed to tackle the issue of absenteeism’s impact on organizational productivity. Using a CSV dataset with employee records featuring variables like distance to work, reason for absence, age, transportation expense, body mass index, and pets, conducted data preprocessing using pandas.
This involved loading and exploring the data, handling missing values, removing errors, and transforming and reorganizing the dataset.
The outcome was a clean, well-organized dataset poised for analysis, providing actionable insights to stakeholders for improving employee attendance and overall productivity.
Project 2. Credit Risk Modeling Preprocessing with Numpy
In this project, I cleaned and preprocessed a loan dataset using NumPy to prepare it for credit risk modeling. The process involved handling missing values by replacing them with column means, standardizing currency fields, and transforming interest rates and payment amounts for consistency.
Additionally, I converted categorical string values to dummy variables to enable quantitative analysis. The data was sorted based on unique identifiers to ensure proper organization. The final cleaned dataset was exported to a CSV file, providing a structured and reliable foundation for data scientists to build accurate credit risk models, ultimately improving the assessment and management of credit risk.
Project 2. Credit Risk Modeling Preprocessing with Numpy
In this project, I cleaned and preprocessed a loan dataset using NumPy to prepare it for credit risk modeling. The process involved handling missing values by replacing them with column means, standardizing currency fields, and transforming interest rates and payment amounts for consistency.
Additionally, I converted categorical string values to dummy variables to enable quantitative analysis. The data was sorted based on unique identifiers to ensure proper organization. The final cleaned dataset was exported to a CSV file, providing a structured and reliable foundation for data scientists to build accurate credit risk models, ultimately improving the assessment and management of credit risk.
Project 3. Austin Real Estate Research Tool using Power BI
An interactive Power BI dashboard that analyzes the real estate market in Austin, Texas.This dashboard/ Research Tool offers comprehensive insights through various views, including pricing trends, property locations, school proximity, and housing features influenced by AI-driven analytics.
I utilized cutting-edge techniques such as robust data modeling, advanced DAX queries, calculated columns, interactive visuals, custom tooltips, meticulous data cleaning, and dashboard optimization to create an engaging and insightful tool for homebuyers, investors, and real estate enthusiasts. Dive in and explore the dynamic world of Austin’s real estate!
Project 3. Austin Real Estate Research Tool using Power BI
An interactive Power BI dashboard that analyzes the real estate market in Austin, Texas.This dashboard/ Research Tool offers comprehensive insights through various views, including pricing trends, property locations, school proximity, and housing features influenced by AI-driven analytics.
I utilized cutting-edge techniques such as robust data modeling, advanced DAX queries, calculated columns, interactive visuals, custom tooltips, meticulous data cleaning, and dashboard optimization to create an engaging and insightful tool for homebuyers, investors, and real estate enthusiasts. Dive in and explore the dynamic world of Austin’s real estate!
Project 3. Advance SQL Queries for Online Retail Data Insights
In this project, I executed over 30 advanced SQL queries, including DDL, DML, window functions, CTEs, joins, views, and triggers. This comprehensive approach enabled me to extract and analyze critical data for an online retail company, resulting in detailed insights into their operations.
By focusing on enhancing data retrieval efficiency and providing actionable business intelligence, this project significantly sharpened my SQL skills and reinforced my commitment to data-driven decision-making.
This project showcases my ability to handle complex datasets and deliver valuable insights, demonstrating my proficiency in SQL and my dedication to leveraging data for impactful business outcomes.
Project 5. HR Headcount, Retention & Turnover Tool using Power BI
I have created this HR Analytics dashboard to provide an interactive, data-driven approach to understanding workforce dynamics. Featuring headcount, retention, and turnover metrics, it allows HR professionals to effortlessly explore trends and key insights.
Through dynamic charts and graphs, users can visualize patterns, identify talent retention opportunities, and develop targeted strategies for improving employee engagement. This powerful tool simplifies HR analytics, enabling data-backed decisions to optimize workforce management and foster organizational growth.
Project 5. HR Headcount, Retention & Turnover Tool using Power BI
I have created this HR Analytics dashboard to provide an interactive, data-driven approach to understanding workforce dynamics. Featuring headcount, retention, and turnover metrics, it allows HR professionals to effortlessly explore trends and key insights.
Through dynamic charts and graphs, users can visualize patterns, identify talent retention opportunities, and develop targeted strategies for improving employee engagement. This powerful tool simplifies HR analytics, enabling data-backed decisions to optimize workforce management and foster organizational growth.