Julia package containing utilities intended for Time Series analysis.

Forecast airline passenger demand using time series models like AR, ARMA, and LSTM to improve operations, optimize scheduling, enhance resource allocation, and streamline supply chain management through accurate demand predictions

Compute a one-parameter Box-Cox transformation of 1+x.

Demonstrativo Detalhado de uma análise de Regressão Linear, e Não Linear Múltipla em planos de saúde.

Compute the inverse of a one-parameter Box-Cox transformation.

Prediction of road casualties and evaluate the impact of transformations in Time Series Modeling and Forecasting with ARIMA using the R programming language

A MLR algorithm that analyzes diabetes data in African Americans to predict a diabetes diagnosis

Time series analysis and forecasting

The purpose of this project is to develop a model for the Sale Price of a home in Ames, Iowa based on the other variables in the data set

📔 This repository of Delhivery's logistical endeavors, emphasizing the utilization of data processing, feature extraction, and hypothesis testing methodologies, A meticulous comparison analysis of ACTUAL vs OSRM time-distance metrics, we unveil intricate patterns, providing invaluable insights and metrics for decision-making with precision.

Compute the inverse of a one-parameter Box-Cox transformation for 1+x.

Compute a one-parameter Box-Cox transformation.

Multivariate least squares regression model that predicts cancer mortality rates for US counties

This repository contains the descriptive statistics notebook.

📘This repository provides a detailed exploration of Walmart's BlackFridaySales data using the Central Limit Theorem (CLT) coupled with Confidence Interval Analysis. Leveraging statistical techniques, we delve into the nuances of customer behavior, purchase patterns during one of the busiest shopping events of the year.

In this project we have performed all types of feature transfromation on the titanic dataset and we have seen the usage of qqplot to check whether a feature is normal/gaussian distributed or not.

Power Transformer works best on linear model and The Power Transformer actually automates this decision making by introducing a parameter called lambda. It decides on a generalized power transform by finding the best value of lambda

Trying to solve heteroscedasticity using transformations, and differents models.

📗 This repository contains the EDA of loan defaulters, analyzing factors like loan type, ROI, and credit scores. It utilizes Random Forest and XGBoost to clean discrepancies, providing insights to enhance risk assessment and inform lending strategies, making it ideal for financial analysts to mitigate loan default risks.