To evaluate different implementations of an in-app currency feature with ABC Analysis

A machine learning model to accurately predict house prices based on various features such as quality, size, and location, utilizing Random Forest and XGBoost algorithms (Python)

Weather parameters forecast by applying ARIMA model in order to accurately predict the parameters.

A statistics package with a variety of bootstrap and other resampling tools. This repository is synced to the same-named repository owned by GNU-Octave. It exists to facilitate publication of the developmental version of the statistics-resampling toolbox at MathWorks FileExchange.

OFA Tool

A statistics package with a variety of bootstrap and other resampling tools

Sentiment Distribution Analysis of News Headlines using Natural Language Processing and ANOVA Techniques

Summary of Assignment Two from the first semester of the MSc in Data Analytics program. This repository contains the CA2 assignment guidelines from the college and my submission. To see all original commits and progress, please visit the original repository using the link below.

Analyzing Demand Drivers for Yulu's Micro-Mobility: A Hypothesis Testing Study

This project aims to predict taxi fare amounts in New York City using a dataset of historical taxi rides. We employ machine learning techniques to create models that can estimate the total fare amount based on various features of the trips.

IU Lessons

Mediante un Análisis de Regresión Lineal Multiple se identifican los factores que afectan el nivel de colesterol.

solving problems with data analysis, hypotheses and the most used statistical tests in ecology

Performed rigorous preprocessing, and data cleaning, and conducted exploratory data analysis to identify trends, patterns, and outliers, leading to valuable insights. Employed various statistical methods concepts to get insights about the data for prediction.

Analyze Data with Python | Hypothesis Testing with Scipy

ANOVA_diamonds_analysis

My Python learning experience 📚🖥📳📴💻🖱✏

This project aims to analyze a dataset of sales transactions from a UK-based e-commerce store specializing in gifts and homewares over one year. With 500,000 rows and 8 columns, including transaction numbers, dates, product details, prices, quantities, customer identifiers, and countries, the analysis focuses on understanding customer behavior

Hypothesis Testing

Explore Python implementations of predictive modeling techniques like F-test, t-test, ANOVA, linear square estimation, autocorrelation, and least squares in this practical-driven GitHub repository