📉 Create Diminutive Distribution Charts

Top 5th percentile solution to the Kaggle knowledge problem - Bike Sharing Demand

The project aims to perform various visualizations and provide various insights from the considered Indian automobile dataset by performing data analysis that utilizing machine learning algorithms in R programming language.

A comprehensive collection of R scripts focusing on Probability and Statistics. Includes foundational data manipulation, Exploratory Data Analysis (EDA), statistical visualization (histograms, boxplots), and implementation of various probability distributions like Binomial, Normal, and Poisson. Ideal for showcasing core statistical computing skills

This repository is my collection of various projects involving Data Visualization of various datasets using Python

This repository presents analyses that can be run using my code for exported Facebook Analytics Post data.

A simple boxplot Javascript library with various quantiles options (like R)

An app to select movies for streaming services

O objetivo desse repositório é mostrar o uso e a importância de histogramas e box plots no contexto de Data Science.

Python EDA and Visualization Using , Matplotlib, Seaborn,Plotly and Bokeh. Map visualization using Folium

Miscellaneous scripts...

R program to analyze and calculate statistical values given a dataset of home prices that include information about the number of baths, bedrooms, year, square-feet, taxes, and other features. Learn more:

Web scraping additional data to building a model to predict football coaches' salaries

🔴 Credit Risk Prediction 🔴 A machine-learning–based analysis designed to predict whether a loan applicant is likely to default. Using a refined Credit Risk Dataset, I cleaned, processed, and visualized key financial features such as income, loan amount, and credit history. Multiple classification models were trained with accuracy.

Stats and bootstrapping of gene categories

Dataset analysis as a final exercise to test the various concepts learned throughout the semester in the subject 'Statistics for DataSc', and its application using python

Exploratory Data Analysis using python.

Predicting Online News Popularity

Pokédash is your personal Pokéguide to understand your lil pocket monster

Conducting one-factor analysis of variance and its assumptions (normality and homogeneity of variance) with R if two groups of data show statistically different behavior. Visualizing data with boxplots to understand if the given state of the fly (fed/starved) affects the feeding and resting pattern.

Building Plotly plots in Python, displaying those plots via Flask.

Create barplots or boxplots with significant level annotations.

This repository contains my data analysis to determine the key influences that increases or decreases insurance rates.

Missing values imputation using KNN and visualization with Boxplots and Violin plots in R

It has been a few days since you sent your boxplot to the senior scientist at Pymaceuticals and today they finally got back to you with feedback. They said your inital For this, I will leverage the same drug regimen data from last class and utilize subplots to create an advanced visualization that is packed with insightful information!

These are the solutions to the supplementary exercises for STA258. I automated the questions with things like pdf readers and functions built to answer all the questions. Please enjoy the fruit of my work. I maintain all "intellectual" property. All questions belong to Alison Weir.

Exploring univariate statistics in R, summarising data and finding patterns in the data.

Rstudio script to make make some of the figures displayed in Devos et al.2024

Statistical study of various health related questions

Repository for multi-visit CF study plots - R code