This small project keeps monitoring the tempreature of a room/chamber, whenever the temperature rises the threshold , we get an alert, whenever temperature rises anomaly, we get an alert and also whenever temperature is predicted between a certain range (by a simple linear regression) crosses the minimum threshold, we get an alert (all alerts on…

it was a great delphi pjt(D5 -> XE7) using TChart.Series, normsdist, z-score for korean pediatrics.

⚖ Normalization, ( Min-Max, Z-Score, Decimal Scaling ) Method from data in Excel in Data Mining

Welcome to the Statistics repository! Here, you'll find files that cover various statistical topics. The repository is organized into different categories to help you navigate through the content!

This SAS-driven project analyzes 4 business scenarios: evaluating cosmetics sales training via hypothesis tests, modeling pub revenues for acquisitions using regression, forecasting age-based insurance claims with log transformations, and assessing loan delinquency by age groups through chi-square tests.

Auditors at a small community bank randomly sample 100 withdrawal transactions made during the week at an ATM machine located near the bank’s main branch. Over the past 2 years, the average withdrawal amount has been $50 with a standard deviation of $40. Since audit investigations are typically expensive, the auditors decide to not initiate furt…

A project focused on using Lasso and Ridge Regression models to predict an output

Analysis of Sample Healthcare data. Primarily Linear Regression and Data Manipulation.

All my statistics notes from ineuron.ai

In this project, we applied advanced machine learning techniques to predict athletic performance outcomes based on various physical metrics from a dataset of 10,000 evaluation experiences over the last 5 years.

Shows how the conversation in the New York Times (NYT) changed following the 2016 election of Donald Trump.

This project implements a K-Nearest Neighbors (KNN) model, standardizes and normalizes a dataset, evaluates different values of KK and distance metrics, and analyzes model performance using RMSE.

A Superrvised Learning model to predict customer's Level of Satisfaction In a Restaurant.