Tag: Python

  • Effective 2D Image Compression with K-means Clustering

    Effective 2D Image Compression with K-means Clustering

    The post explores the application of the K-means clustering algorithm, a popular unsupervised Machine Learning algorithm, for image compression. By segmenting 2D images into different clusters, the algorithm effectively reduces storage space without compromising on image quality or resolution. It also demonstrates the application of this approach through a case study, where optimal results were…

  • Dealing with Imbalanced Data in HealthTech ML/AI – 1. Stroke Prediction

    Dealing with Imbalanced Data in HealthTech ML/AI – 1. Stroke Prediction

    This post discusses the prediction of stroke using machine learning (ML) models, focusing on the use of early warning systems and data balancing techniques to manage the highly imbalanced stroke data. It includes a detailed exploration of the torch artificial neural network training and performance evaluation, as well as the implementation and evaluation of various…

  • Working with FRED API in Python: U.S. Recession Forecast & Beyond

    Working with FRED API in Python: U.S. Recession Forecast & Beyond

    The FRED API, or Federal Reserve Economic Data, provides over 267,000 economic time series from 80 sources, offering a wealth of data to promote economic education and research. It encompasses U.S. economic and financial data, including interest rates, monetary indicators, exchange rates, and regional economic data. Additionally, we analyzed correlations, trained currency exchange prediction models,…

  • Dabl Auto EDA-ML

    Dabl Auto EDA-ML

    Dabl, short for Data Analysis Baseline Library, is a high-level data exploration library in Python that automates repetitive data wrangling tasks in the early stages of supervised machine learning model development. Developed by Andreas Mueller and the scikit-learn community, it facilitates data preprocessing, advanced integrated visualization, exploratory data analysis (EDA), and ML model development, demonstrated…

  • A Closer Look at the Azure Cloud Portfolio – 1. Essentials

    A Closer Look at the Azure Cloud Portfolio – 1. Essentials

    The article presents an extensive overview of Microsoft Azure services in comparison with Amazon Web Services (AWS) and Google Cloud Platform (GCP). It reveals that Azure’s cloud revenue for 2021 outperformed AWS and GCP combined, comprising nearly 80% of Fortune 500 companies as clients. The piece elaborates on Azure’s cloud concepts, Azure Synapse SQL Pool,…

  • Joint Analysis of Bitcoin, Gold and Crude Oil Prices

    Joint Analysis of Bitcoin, Gold and Crude Oil Prices

    The content discusses a comprehensive analysis on a joint time-series analysis of Bitcoin, Gold and Crude Oil prices from 2021 to 2023. It explores data processing, exploratory data analysis before running a range of statistical tests, ARIMA models fitting, and finally, using the Markowitz portfolio optimization method. It then presents a detailed analysis, including data…

  • Video Game Sales Data Exploration

    Video Game Sales Data Exploration

    The post explores the gaming industry’s size and state, highlighting a potential market value of $314bn by 2027. It emphasizes the industry’s three main subsectors: console, PC, and smartphone gaming. Moreover, the post conducts extensive data analysis on video game sales data, using Python to examine aspects such as genre profitability, platform sales prices, and…

  • Data Visualization in Python – 1. Stock Technical Indicators

    Data Visualization in Python – 1. Stock Technical Indicators

    Featured Photo by Monstera on Pexels. In this project, we will implement the following Technical Indicators in Python: Conventionally, we will look at the following three main groups of technical indicators: Input Stock Data Let’s set the working directory VIZ import osos.chdir(‘VIZ’)os. getcwd() and import the key libraries import datetime as dtimport pandas as pdimport…

  • Using AI/ANN AUC>90% for Early Diagnosis of Cardiovascular Disease (CVD)

    Using AI/ANN AUC>90% for Early Diagnosis of Cardiovascular Disease (CVD)

    The project utilizes AI-driven cardiovascular medicine with a focus on early diagnosis of heart disease using Artificial Neural Networks (ANN). Aiming to improve early detection of heart issues, the project processed a dataset of 303 patients using Python libraries and conducted extensive exploratory data analysis. A Sequential ANN model was subsequently built, revealing excellent performance…

  • Top 6 Reliability/Risk Engineering Learnings

    Top 6 Reliability/Risk Engineering Learnings

    The content provides a review of Eric Marsden’s e-learning Python courseware on risk engineering, loss prevention and safety management. It includes discussions of various topics such as the failure of light bulbs, electronic components, large computing facility maintenance, and oil field pumps. The content also delves into stock market risk analysis like Value at Risk…

  • Overview of AWS Tech Portfolio 2023

    Overview of AWS Tech Portfolio 2023

    This summary focuses on the extensive capabilities of Amazon Web Services (AWS) by 2023, highlighting its 27% year-on-year growth and a net sales increase to $127.1 billion. AWS emerges as the top cloud service provider, offering over 200 services including compute, storage, databases, networking, AI, and machine learning. It is constantly expanding operations, having opened…

  • Gold Price Linear Regression

    Gold Price Linear Regression

    This content focuses on predicting gold prices using machine learning algorithms in Python. With an 80% R2-score and a Sharpe ratio of 2.33, it suggests a potential 8% revenue from an investment starting in December 2022. The forecasted next-day price for SPDR Gold Trust Shares is $185.136, aligning with Barchart’s “100% BUY” signal.

  • 90% ACC Diabetes-2 ML Binary Classifier

    90% ACC Diabetes-2 ML Binary Classifier

    A study aims to develop an ML-driven e-diagnosis system for detecting and classifying Type 2 Diabetes as an IoMT application. By leveraging advanced supervised ML algorithms, the system can predict a person’s diabetes risk based on several factors, provide a preliminary diagnosis, and relay doctor’s guidance on diet, exercise, and blood glucose testing. The Pima…

  • Deep Reinforcement Learning (DRL) on $MO 8.07% DIV USA Stock Data 2022-23

    Deep Reinforcement Learning (DRL) on $MO 8.07% DIV USA Stock Data 2022-23

    This study applies the Deep Reinforcement Learning (DRL) algorithm to USA stocks with +4% DIV in 2022-23, focusing on Altria Group, Inc. The study addresses accurate stock price predictions and the challenges in traditional methods. Recent advances in DRL have shown improved accuracy in stock forecasting, making it suitable for turbulent markets and investment decision-making.

  • JPM Breakouts: Auto ARIMA, FFT, LSTM & Stock Indicators

    JPM Breakouts: Auto ARIMA, FFT, LSTM & Stock Indicators

    The post discusses predicting JPM stock prices for 2022-2023 using several predictive models like ARIMA, FFT, LSTM, and Technical Trading Indicators (TTIs) such as EMA, RSI, OBV, and MCAD. The ARIMA model used historical data, while the partial spectral decompositions of stock prices served as features for the FFT model. TTIs were calculated to validate…

  • Post-SVB Risk Aware Investing

    Post-SVB Risk Aware Investing

    The recent collapse of Silicon Valley Bank and its repercussions have prompted a reevaluation of risk-aware investing in the US financial sector. The crisis has exposed the vulnerability of banks invested in long-term fixed income assets, highlighting the importance of diversification and risk management. Market indicators suggest continued volatility and uncertainty, urging investors to exercise…

  • LSTM Price Predictions of 4 Tech Stocks

    LSTM Price Predictions of 4 Tech Stocks

    The given content explains the process of using Exploratory Data Analysis (EDA) and Long Short-Term Memory (LSTM) Sequential model for comparing the risk/return of four major tech stocks: Apple, Google, Microsoft, and Amazon, considering the tech scenario in 2023. The analysis involves examining stock price patterns, their correlations, risk-return assessment, and predicting stock prices using…

  • About Face Recognition ML Algorithms

    About Face Recognition ML Algorithms

    Facial Recognition (FR) involves mapping an individual’s facial features mathematically and storing the data as a faceprint. This case study outlines the process of Exploratory Data Analysis (EDA) and performance QC analysis for ML/AI workflows using public-domain datasets and real-time webcam GUI. The study includes the use of SVM for FR, dataset splitting, ML model…

  • Portfolio Optimization of 20 Dividend Growth Stocks

    Portfolio Optimization of 20 Dividend Growth Stocks

    The post discusses implementing a stochastic optimization algorithm to create a balanced portfolio of 20 dividend growth stocks for maximum return within defined risk tolerance. By analyzing daily stock and benchmark data, the algorithm optimizes the portfolio to outperform the benchmark index and achieve desired risk-reward outcomes. The results facilitate spreading investment capital across diverse…

  • Towards Max(ROI/Risk) Trading

    Towards Max(ROI/Risk) Trading

    This post compares 1-year ROI/Risk of selected stocks vs ETF using stock analyzer functions. It includes comparing prices, visualizing annual risk and return, and examining correlation matrix of stock returns. It provides insights for selecting CPB stock for trading based on low correlation with ^GSPC, high return (~20%), and low risk (~23%).