Collection of various algorithms implemented in R.

A poor man's version of a pandas DataFrame for dart.

Collection of personal SQL projects and queries I've worked on, showcasing my skills and expertise in database management, data analysis, and data manipulation using SQL.

Analyzing the historical cryptocurrency trading dataset, to portrait its dynamic landscape and dig into features of crypt currencies to figure out if any patterns in their price movement.

High performance & light weight alternative to Pandas with ML focused tooling. (Work in progress x100!)

In this online program, I completed similar tasks that KPMG Graduates do in the company. I learned what it is like working at one of the world’s best data analytics team, and built skills required to excel as a analytics consultant.

Welcome to my GitHub project repository, where I have meticulously curated a collection of my data-related endeavors. Within these digital pages, you'll find a comprehensive overview of my projects, showcasing my skills and experiences in the realm of data analysis.

Bike rental prediction, blending predictive analytics and machine learning, optimizes inventory, pricing, and operations. It harnesses historical data, weather patterns, and time dynamics to enhance efficiency and elevate customer experiences.

This is a data analysis and time series prediction that was based on labor absent records in Thulhiriye MAS Factory.

Analyzing various apps found on the Google Play Store with the help of different python libraries. The dataset is chosen from Kaggle. It is the web scraped data of 10k Play Store apps for analyzing the Android market. It consists of in total of 10841 rows and 13 columns.

Datacamp Projects

This repository contain all frequency ask interview questions in data structure and algo.

Inaccurate informations can be detected using this approach.

This repository is demonstration of Pandas library of Python's super powers.

DataCamp Project Solutions

This Projects creates a model that predicts Google Play Store Apps Rating based on parameters like No. of Installs, reviews, size, category , genres etc. It compares several classification model like Xgboost(booster ensembler), Random Forest(bagger ensembler), Logistic regression, Support Vector Machine(SVC) and Bayesian Classifier.

This project involves working with comprehensive football dataset covering the Top 5 leagues in Europe from 2014-2020. I worked on Data Extraction,Data cleaning and manipulation,Data Modelling and Data Loading

This repository is for newcomers into the data science world. It Summarizes three key areas Data Exploration,Manipulation and Data Cleaning