EDA Stack Overflow Dataset using python | Sep 2022 - Oct 2022

In this project, we analyzed the StackOverflow developer survey dataset and gained insights into the demographics, programming skills, experience, and employment-related information of the survey respondents. We found that most of the respondents were male, had been coding for less than 10 years.

HR analytics dashboard with QA Testing | July 2023

In this project we create a HR analytics dashboard for employee attrition using POWERBI and perform QA Testing using SQL. The interactive dashboard keep track of 5 KPIs such as Employee Count, Attrition Count, Attrition Rate, Active Employees and Average Age.

Predicting Resale value of used cars using machine learning in Pyspark. | Oct 2022 - Dec 2022

Used advanced Python and Pyspark libraries, including NumPy, Pandas, scikit, seaborn, and matplotlib, to clean and analyze a massive dataset of 4.5 million rows and 26 columns. Implemented various machine learning models such as Linear regression, GBT regressor and random forest.

Formula 1 Azure end to end project | May 2023

Retrieved race data from the Ergast API and implemented advanced web scraping techniques with Azure Data Factory, resulting in a 20% increase in data accuracy compared to previous methods. Utilized extensive knowledge of Databricks and Pyspark to create automated pipelines in data factories

Apple Store apps analysis | July 2023

This data contains information about 7197 apps from the apple app store. Source of this dataset is Kaggle. In this project we aim to provide actionable insights to stakeholders(app developers) regarding the current state of the apple app store

Social Network Analysis on Dark Dataset | March 2022- April 2022

We will be analysing characters and different timelines through the data and find the outcome in respect to- Important characters Influential characters Important timeline

ER visits Dashboard | July 2023

Hew we build an Emergency Room Dashboard in Tableau using Real World Fake Data (#RWFD) Emergency Room Data!

Insurance Pricing Forecast Using XGBoost Regressor

This data science project aims to build and evaluate linear and xgboost regression models and determine the healthcare charges of each customer. This analysis will help the insurance firm to strategize a premium plan that will help maximize the profits.