z-score
There are 82 repositories under z-score topic.
knn-tensorflowjs-spfx-extension
KNN (k-nearest neighbors machine learning algorithm) TensorFlow.js SharePoint Framework (SPFx) extension
AB-Test-Results
An analysis of A/B Test results to help an e-commerce site decide whether or not they should implement a new page design.
MovieClub
Analysis of Movie Ratings For Movie Club
Statistical-Machine-Learning-Assignments
All assignments of Statistical Machine Learning Course
Test_a_Perceptual_Phenomenon
Use descriptive statistics to describe qualities of a sample, set up a hypothesis test, make inferences from a sample, and draw conclusions based on the results.
AGD
Analysis of Growth Data
secure-auth-ai
An npm package for advanced sign-in security, including AI-based risk assessment, anomaly detection, real-time PostgreSQL database, automated user management and MFA. Compatible with React, Vue, and Angular
bitpin_project
A high-performance Django application with DRF for viewing and rating articles, designed to handle large datasets efficiently.
iris_classification
This repository contains a project using the Iris dataset for data visualization, outlier detection, normalization, and classification. The project includes models like GaussianNB, RandomForestClassifier, and DecisionTreeClassifier, with hyperparameter tuning and PCA for dimensionality reduction.
Assignment-2-Set4-Q3--Basic-Statistic-Level-2
Q3.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
Assignment-2-Set3-Q5--Basic-Statistic-Level-2
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
Hypothesis-Tests
Python package to perform statistical hypothesis tests.
develop_zscore_and_baseline_results
ROC, AUC, and Z-score functions for anomaly detection
Paleolimnological_Analysis_in_R
This is a paleolimnological analysis using tidypaleo in R. View the Github page to walk through each step of the analysis.
lyagushka
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.
DataAnalysis-and-KnowledgeDiscovery
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.
Outlier-Imbalanced-Fraud-Detection
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.
data_Stats
Probs practice
Feature-Transformation-Assignment-6.3-
Feature transformation is a technique in machine learning that is used to modify the original features of a dataset in order to improve the performance of machine learning algorithms.
AltMan_ZScore
Altman z-score for Single & Multiple Securities
Basic-Statictics-02
Solutions for the basic statictics problems
Basic-Statictics-lvl-1
Basic Statistics-01 Solution for Questions on Basic statistics
Ab-test-project
Comparing two different options for a webpage
RFM_Analysis_Customer_Segmentation_Python
RFM Analysis-Python
Trending_Topic_Spark_Streaming_Scala
This is an End to End solution to read data from streaming source (kafka), extract different topic from data in each time window, calculating Hot Topics using a modified Z-Score Algorithm and storing Final Trend Topics in Postgres SQL Database
K-mean-clustering
Categories the problem in a Technical Support Data using K mean clustering
statistics-library
A collection of statistical algorithms.
Outlier-Detection-and-Removal
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 variables not considered when collecting the data.
Detecting-Outliers-in-a-Dataset
An example of improving data quality and identifying anomalies within a real-life dataset for master data management or data engineering.
Analyze-A-B-Test-Results
My second project from Udacity's data analysis advanced track, provided by FWD scholarship.
Z-Score-Altman
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
Assignment-Set3-Q_5
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 its sample includes all the daily Internet users. If that’s the case, then can Microsoft conclude that Mozilla has a less than 5% share of the market?