Basic Statistics-01 Solution for Questions on Basic statistics

An outlier is a data point that is noticeably different from the rest. They represent errors in measurement, bad data collection, or simply show variables not considered when collecting the data.An outlier is a data point that is noticeably different from the rest. They represent errors in measurement, bad data collection, or simply show variabl…

The Credit Card Fraud Detection project uses statistical techniques and machine learning for identifying fraudulent transactions. It includes data preprocessing, outlier detection using Boxplots and Z-scores, and a decision tree model. Evaluation goes beyond accuracy, considering precision, recall, F1-score, and ROC AUC.

Basic Statistic operations using Python

LMS growth chart algorithm

This is a paleolimnological analysis using tidypaleo in R. View the Github page to walk through each step of the analysis.

the Altman's z score is a Solvency Predictor, was once known as a 'good' financial predictor of a company's solvency, based on logical Common Sense & Accounting Ratios

RFM Analysis-Python

A collection of statistical algorithms.

My second project from Udacity's data analysis advanced track, provided by FWD scholarship.

Categories the problem in a Technical Support Data using K mean clustering

In January 2005, a company that monitors Internet traffic (WebSideStory) reported that its sampling revealed that the Mozilla Firefox browser launched in 2004 had grabbed a 4.6% share of the market. I. If the sample were based on 2,000 users, could Microsoft conclude that Mozilla has a less than 5% share of the market? II. WebSideStory claims that

The algorithm identifies clusters and gaps in integer datasets, calculates their Z-scores based on mean density and distance, and outputs the results as JSON.

An example of improving data quality and identifying anomalies within a real-life dataset for master data management or data engineering.

Various projects directly incorporating statistical concepts to generate insights critical to Data Analysis

This project aims to practice the steps of Crisp Data Mining ( CRISP-DM ). The repository includes 3 phases, data understanding, supervised learning, and unsupervised learning.

quartile , and modified Z-score cli to detect abnormal values