Running exciting analyses on interesting datasets is the dream of every data scientist. But first, some importing and cleaning must be done. In this series of four case studies, you'll revisit key concepts from our courses on importing and cleaning data in R.

Repository containing self built case studies and analysis leveraging most of the phases in the entire executable data science chain end to end, ranging from harvesting of resources to building cutting edge deep dive data models.

data science projects

Heart_dieases_Prediction-Classification_Project

It is a Problem Which I got During the ZS Data Science Challenge From Interview Bit Hiring Challenge Where I secured a 40th Rank out of 10,000 Students across India. It is a Dataset which requires Intensive Cleaning and Processing. Here I have Performed Classification Using Random Forest Classifier and Used Hyper Tuning of the Parameters to achi…

Data Science beginner with R project

Designed a Power BI dashboard to track employee data for the HR team, including working hours, attendance, performance, and leaves. The dashboard streamlined HR processes and increased efficiency. This Dashboard can save 3-4hrs of work for the HR daily.

Analysis of the performance of Tesla and Gamestop stocks with Python

This project performs Global Sales analytics using MS-Excel. It include data cleaning, filtering, conditional formatting, calculated columns, formulas, LOOKUP functions, pivot table and data visualization.

Data Analysis on the IMDb Dataset using Python & Power BI.

This is a repo that includes random mini projects I've done, including some automation work for prior employers and personal scripts I've made to pull in data on some of my favorite things including Suns basketball and Magic: The Gathering.

Scraping different products and thereafter analyzing different aspects respective to the products.

Joining, Cleaning, Querying, Performing ETL on Twitter Posts Dataset.

case study to implement and showcase my data analysis skills

Project split in 2 parts: Data cleaning & Exploratory data analysis (Use cleaned dataset built in part 1 to complete part 2)