48 skills found · Page 1 of 2
ambertests / Explore With PostmanVarious materials for Exploratory API Testing with Postman workshops
CoenMeintjes / Data Science Notebook TemplatesA comprehensive collection of Jupyter Notebook templates for data science tasks, developed to improve workflow efficiency and cover a wide range of topics, including exploratory data analysis, hypothesis testing, regression models, and machine learning models.
exploripy / ExploripyPre-Modelling Analysis of the data, by doing various exploratory data analysis and Statistical Test.
disojn / Time Series EDA And ForecastIn this section, I begin with the excel file of sales data, which I obtained from the Tableau Community Forum. As a recall, the data contains mostly categorical variables and components of the vectors from the description column. The index column is a timeseries format. The major objective of this section is to understand the general trends in the data, and gain some quick insights, and then predict and forcast the Sales of the category "Technology" of the given sales data.The statistical significance of these observations will be also tested in 'Exploratory Data Analysis'.
morvader / ExploratoryTestingChromeExtensionExploratory Testing Chrome Extension
NikhilaThota / CapstoneProject House Prices PredictionUnderstand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and suggested a best model to predict the final sale price of a house. Seaborn is used to plot graphs and scikit learn package is used for statistical analysis.
labri-progress / AIFEXAIFEX - Improve the quality, efficiency and diversity of your exploratory test sessions
DataResearchLabs / My Test Case ManagerA simple flexible test case manager for small projects and UAT. Good for exploratory testing. Requires Microsoft Excel 365.
abebual / Predicting ICU Patient Clinical Deterioration ReportFor this project, I used publicly available Electronic Health Records (EHRs) datasets. The MIT Media Lab for Computational Physiology has developed MIMIC-IIIv1.4 dataset based on 46,520 patients who stayed in critical care units of the Beth Israel Deaconess Medical Center of Boston between 2001 and 2012. MIMIC-IIIv1.4 dataset is freely available to researchers across the world. A formal request should be made directly to www.mimic.physionet.org, to gain access to the data. There is a required course on human research ‘Data or Specimens Only Research’ prior to data access request. I have secured one here -www.citiprogram.org/verify/?kb6607b78-5821-4de5-8cad-daf929f7fbbf-33486907. We built flexible and better performing model using the same 17 variables used in the SAPS II severity prediction model. The question ‘Can we improve the prediction performance of widely used severity scores using a more flexible model?’ is the central question of our project. I used the exact 17 variables used to develop the SAPS II severity prediction algorithm. These are 13 physiological variables, three underlying (chronic) disease variables and one admission variable. The physiological variables includes demographic (age), vital (Glasgow Comma Scale, systolic blood pressure, Oxygenation, Renal, White blood cells count, serum bicarbonate level, blood sodium level, blood potassium level, and blood bilirubin level). The three underlying disease variables includes Acquired Immunodeficiency Syndrome (AIDS), metastatic cancer, and hematologic malignancy. Finally, whether admission was scheduled surgical or unscheduled surgical was included in the model. The dataset has 26 relational tables including patient’s hospital admission, callout information when patient was ready for discharge, caregiver information, electronic charted events including vital signs and any additional information relevant to patient care, patient demographic data, list of services the patient was admitted or transferred under, ICU stay types, diagnoses types, laboratory measurments, microbiology tests and sensitivity, prescription data and billing information. Although I have full access to the MIMIC-IIIv1.4 datasets, I can not share any part of the data publicly. If you are interested to learn more about the data, there is a MIMIC III Demo dataset based on 100 patients https://mimic.physionet.org/gettingstarted/demo/. If you are interested to requesting access to the data - https://mimic.physionet.org/gettingstarted/access/. Linked repositories: Exploratory-Data-Analysis-Clinical-Deterioration, Data-Wrangling-MIMICIII-Database, Clinical-Deterioration-Prediction-Model--Inferential-Statistics, Clinical-Deterioration-Prediction-Model--Ensemble-Algorithms-, Clinical-Deterioration-Prediction-Model--Logistic-Regression, Clinical-Deterioration-Prediction-Model---KNN © 2020 GitHub, Inc.
shanejansen / TouchstoneTouchstone is a testing framework for your services that focuses on component, end-to-end, and exploratory testing.
eviltester / ObservatronA prototype exploratory testing observation tool for Chrome browser
AtlasmanYevgenii / Practicum100 By YandexCourse included such topics, as Data Preprocessing, Exploratory Data Analysis (EDA), Statistical Data Analysis (SDA), Data Collection and Storage (PostgreSQL), Business Analytics, Making Business Decisions Based on Data (Hypotheses testing), How to Tell a Story Using Data (Presentation and Data Visualization - Maplotlib, Seaborn, Plotly), Automation (Dash, Tableau), Forecasts and Predictions, 2 Integrated projects.
Aayushi-2808 / Cervical Cancer Detection Using ML# Cervical_cancer_detection_using_ML # Introduction According to World Health Organisation (WHO), when detected at an early stage, cervical cancer is one of the most curable cancers. Hence, the main motive behind this project is to detect the cancer in its early stages so that it can be treated and managed in the patients effectively. # Flow of project is as explained below: This project is divided into 5 parts: 1. Data Cleaning 2. Exploratory Data Analysis 3. Baseline model: Logistic Regression 4. Ensemble Models: Bagging with Decision Trees, Random forest and Boosting 5. Model Comparison and results # Refer below for References: Link to basic information regarding cervical cancer : https://www.cdc.gov/cancer/cervical/basic_info/index.htm The dataset for tackling the problem is supplied by the UCI repository for Machine Learning. Link to Dataset : https://archive.ics.uci.edu/ml/datasets/Cervical+cancer+%28Risk+Factors%29 The dataset contains a list of risk factors that lead up to the Biopsy examination. The generation of the predictor variable is taken care of in part 2 (Exploratory data analysis) of this report. We will try to predict the 'biopsy' variable from the dataset using Logistic Regression, Random Forest, Bagging with Decision Trees and Boosting with XGBoost Classifier. # Results: Based on our Base model and The Ensemble Models we used, we observed - 1. After the entire process of training, hyperparameter tuning and tackling class imbalance was complete , we obtained the results as depicted through the graphics. 2. We observe that Bagging and Random Forest gives the highest accuracy and precision of 97.09 and 80% resp. 3. Plotting the Confusion matrix showed us that Random Forest using upsampling and class weights gives us 2 false positives and 3 false negatives with auc of 0.87 # Why random forest is the best model?? 1. So as we see, while comparing all of our models,RF has maximum f1_score and accuracy along with Bagging i.e. 76.2 n 97.09% resp. 2. And it also produces the same amount of false negatives with a recall of 72.73% just like all the other models. 3. But we still consider RF better coz of its added advantage that, the decision trees are decorrelated as compared to bagging leading to lesser variance and greater ability to generalize. # Conclusion: On observing the feature importance of the best model i.e random forest, we can see that the most important features are Schiller, Hinselmann, HPV, Citology, etc. This also makes sense because Schiller and Hinselmann are actually the tests used to detect cervical cancer. # Problems Faced: A major problem encountered while training the model was that it had too little data to train. On collaborating with all the hospitals in India, we can have enough data points to train a model with a higher recall, thus making the model better. # Scope of Improvement As next steps I would want to do exactly that, to deploy the model and refine it. We may also modify the number of the predictor variables, as it may well turn out that there are other predictors which may not be present in our current dataset. This can only be found by practical implementation of our predictions.
Brett-Kennedy / DataConsistencyCheckerA python tool to examine datasets for consistency. Performs approximately 150 tests. For EDA (Exploratory Data Analysis) and interpretable outlier detection
3024k / QA RESOURCESThis repository is for storing the knowledge resources regarding SQA and other useful technologies, tools
keshcodes / Jalandhar Apple Analytics A Data Driven Market StudyThis repository contains a comprehensive analysis of the market prices of apples in Jalandhar, based on data sourced from data.gov.in. The analysis includes extensive Exploratory Data Analysis (EDA), various statistical tests, and analytical techniques, performed using Python libraries like pandas, numpy, matplotlib, seaborn, and scipy.
hanystudy / Snowy OwlOwl exploratory testing
Ryan2486 / Nlp Emotion AnalysisExploratory NLP project using the DistilBERT transformer model and the HuggingFace "emotion" dataset. Includes tokenizer and encoder testing, UMAP-based visualization of embeddings, logistic regression classification, and fine-tuning of a pre-trained model for emotion detection.
SaurabhSSB / Wage Gap Analysis UsaAn exploratory data analysis (EDA) project that investigates wage disparities between Black and White workers in the United States over time. This analysis uses statistical methods, visualizations, and hypothesis testing to uncover trends, measure wage gaps, and identify patterns across race and gender from historical data.
Maaikees / Exploratory TestingResources for your Exploratory Testing with the Team journey
