Tag: machinelearning
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Applications of ML/AI in HR – Predicting Employee Attrition
A critical arm of artificial intelligence (AI), machine learning (ML) is making strides in every area of HR. We discuss its role of ML/AI in HR and people-centric transformation. Results have a crucial influence on customer satisfaction and retention, especially when employees come into regular contact with customers. Knowing when employees are most likely to have…
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Towards Optimized ML Wildfire Prediction
Towards Optimized ML Wildfire Prediction: This Python case example stems from the initial research into ML/AI wildfire prediction using the dataset that was downloaded from the UCI Machine Learning Repository.
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Macroaxis AI Investment Opportunity
The research presented examines the portfolio optimization potential of Macroaxis’ new artificial intelligence (AI) system. This tool automates processes like asset allocation, portfolio diversification and rebalancing, and equity research, providing a list of suggested investments. The AI makes suggestions based on market conditions and investor risk tolerance, and has demonstrated higher average returns than the…
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ML/AI Wildfire Prediction
A wildfire, forest fire, bushfire, wildland fire or rural fire is an unplanned, uncontrolled and unpredictable fire in an area of combustible vegetation starting in rural and urban areas. Wildland fire is a widespread and critical element of the Earth’s system. Presently, global annual area burned is estimated to be approximately 420 Mha (Giglio et al. 2018), which is greater in area than the country of India. Wildland fires…
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AI-Guided Drug Recommendation
AI-Guided Drug Recommendation in Python using NLP text processing. Key steps: WordCount images, NLP Pre-Processing, NER via spacy, LDA topic modelling, and Word2Vec Vectorization for reviews using pretrained glove model. Input data: the Kaggle UCI ML Drug Review dataset. Applications in the pharmaceutical industry, including drug R&D, drug repurposing, improving pharmaceutical productivity, and clinical trials,…
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ANOVA-OLS Prediction of Surgical Volumes
Operating rooms (ORs) are some of the most valuable hospital assets, generating a large part of hospital revenue. Statistical models have been developed using datasets to predict daily surgical volumes weeks in advance. We focus on the VUMC dataset for evaluation of our statistical models. We use the ANOVA null-hypothesis test for the total number…
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AI-Driven Skin Cancer Diagnosis
Using TensorFlow library in Python, we can implement an image recognition skin cancer classifier that tries to distinguish between benign (nevus and seborrheic keratosis) and malignant (melanoma) skin diseases from only photographic 2D RGB images
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Bear Market Similarity Analysis using Nasdaq 100 Index Data
We calculate similarities between the current market conditions and the selected six historical bear market events using the Nasdaq 100 Index Data. Results suggest that Covid19 pandemic, 1987 black Monday, and 1990 recession are closest to the current bear market.
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Stock Forecasting with FBProphet
Prophet from Meta (Facebook) is a procedure for forecasting time series data such as stocks. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
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Brazilian E-Commerce Showcase
This is a hands-on end-to-end Brazilian e-commerce use-case with detailed Exploratory Data Analysis (EDA) steps and business action items. We apply the RFM Segmentation and Customer Analysis to the Brazilian E-Commerce Public Dataset by Olist. It focuses on the lifetime value of customers, and it’s the preferred customer segmentation methodology for eCommerce businesses that focus…
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K-means Cluster Cohort E-Commerce
K-means Clusters – Cohort Analysis applied to E-Commerce Understanding who your customers are and what they want is a fundamental part of any successful business. It can become increasingly challenging to create a one-size-fits-all customer profile. This is where the concept of cluster-based cohort analysis comes in.
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Cybersecurity Monthly Update
Cybersecurity Monthly Update Securing Your Digital Transformation Focus on DevSecOps Maintain velocity without compromising security GitLab has been a catalyst for change when it comes to the evolution of DevSecOps versus traditional application security testing. Weeam: Are you ransomware resistant?
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Customer Churn/Retention Rate ML/AI Strategies that Work!
Telco Customer Churn/Retention Rate ML/AI Strategies that Work! Machine learning could predict customers with high probability to churn! Cohort analysis is a way to understand customer churn (aka attrition). In doing so, we maximize the ratio max [ (Customer Retention Rate)/(Customer Churn Rate) ]
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The Application of ML/AI in Diabetes
The study uses machine learning (ML) to predict diabetes in patients. Classifying diabetics is complex, but ML can offer quick and accurate predictions. The study focuses on type 2 diabetes and uses the Pima Indians database for diagnostic measurements. Models were trained with Python and the Anaconda library. Feature engineering and exploratory data analysis revealed…
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Inflation-Resistant Stocks to Buy
Inflation-Resistant Stocks to Buy AAPL Example Python workflow Download 3 historical datasets – stock price and monthly/annual CPI Compute the monthly/annual stock performance (%) and CPI rate (%) Apply linear regression to the stock vs CPI performance cross-plot Check the slope or gradient of the linear trend – positive, negative or zero.
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AI-Powered Customer Churn Prediction
AI-Powered Customer Churn Prediction Churn is a good indicator of growth potential. Churn rates track lost customers, and growth rates track new customers—comparing and analyzing both of these metrics tells you exactly how much your business is growing over time. In this project, we explored the churn rate in-depth and examined an example implementation of…
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AI-Powered Stroke Prediction
Contents: Introduction Stroke, also known as brain attack, happens when blood flow to the brain is blocked, preventing it from getting oxygen and nutrients from it and causing the death of brain cells within minutes. According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally after ischemic heart disease,…
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ML/AI GHG Monitoring and Forecast
The graphs show monthly mean carbon dioxide measured at Mauna Loa Observatory, Hawaii. We extract some insights about the historical data and its statistical properties using the scikit-learn ML/AI Python library. We discovered that atmospheric CO2 concentration is expected to increase in future, highlighting the fact that emissions must be reduced, to ensure the prosperity…
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Diabetes Prediction using ML/AI in Python
The post focuses on developing a machine-learning model to predict diabetes using patient diagnostic data from the UCI Machine Learning Repository, featuring blood tests and obesity metrics. Implemented classifiers include a random forest, decision tree, XGBoost, and an SVM. The model is trained on this data, achieving highest accuracy with the random forest method (approx.…
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E-Commerce Cohort Analysis in Python
Cohort analysis (CA) is a beneficial tool in e-commerce marketing to monitor campaign health, particularly in customer retention rates. By grouping customers into cohorts, marketers can assess the effectiveness of their efforts, track customer behaviors, and identify the most valuable demographic segments. The article exemplifies how CA can be applied to customer transaction data using…
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