Category: Data-Driven Tech
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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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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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A Shamelessly Simple E-Restaurant Order
This simplest Python online restaurant order system gets your restaurant online fast. So you can keep cooking without sacrificing what matters.
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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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Webscraping in R – The IMDb Showcase
IMDB ETL Showcase in R Studio Read content Data editing Visuals & Insights
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Firsthand Data Visualization in R: Examples
The post discusses the power of R, a programming language and software environment used for data analysis and graphic representation. It provides examples on using various R packages like ggplot2 for data visualization and tidycensus for downloading geographic and demographic data. It also explains how to create different types of plots such as choropleth maps,…
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Short-Term Stock Market Price Prediction using Deep Learning Models
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ML/AI Regression for Stock Prediction – AAPL Use Case
1. Install Yahoo finance library 2. Call all dependencies that we will use for this exercise 3. Define the ticker you will use 4. Let’s look at the data table 5. Data Exploration Phase 6. Data Preparation, Pre-Processing & Manipulation 7. Apply Linear Regression 8. Perform ML QC Analysis 9. Final Output
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HealthTech ML/AI Use-Cases
About Supervised ML/AI Breast Cancer Diagnostics Python Use-Case Supervised ML/AI in Breast Cancer (BC) Classification Heart Failure Prediction using Supervised ML/AI Technique Diabetes Prediction using ML/AI in Python
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E-Commerce Data Science Use-Case
In response to the surge of online shopping, a Data Science (DS) based customer analytics platform focusing on customer profiling, sentiment analysis, and customer lifetime value prediction is being developed. This platform utilizes the Exploratory Data Analysis (EDA) pipeline employing the Python library, Pandas. The platform is capable of product recommendation, customer trend analysis, sales…
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E-Commerce ML/AI Classification
The article outlines the process of using TensorFlow to categorize clothing images. It involves pre-processing data, building an ML model, and making predictions. The application relies on Python and Jupyter Anaconda IDE, using the tf.keras high-level API. The dataset comprises 60,000 grayscale images in various fashion categories, with data divided into training and testing sets…
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Brand Architecture: Google vs. Amazon
This report builds and examines the brand architecture of two brands – GCP and AWS – in the Cloud Computing (CC) sector. The positioning of CC, while initially seen as a disruptive technology influence on both buyers and seller prospects, is now evolving into a trade-off between low-cost arbitrage and added value QoS. You will…
