Category: Data-Driven Tech

  • AI-Guided Drug Recommendation

    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, among others.

  • Algorithmic Testing Stock Portfolios to Optimize the Risk/Reward Ratio

    Algorithmic Testing Stock Portfolios to Optimize the Risk/Reward Ratio

    Investors can optimize their stock portfolio by invoking backtesting within the realm of algorithmic trading. The goal is to optimize the specific portfolio by maximizing returns and the Sharpe ratio.

  • 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 of surgeries. The Ordinary Least Squares (OLS) linear regression formula is applied to our target variable Actual Surgery.

  • Drag Review Data Analytics

    UCI Drag Review Data Analytics An AI assisted medication recommender framework is truly vital with the goal that it can assist specialists and help patients to build their knowledge of drugs on specific health conditions. We Build a Drug Recommendation System that recommends the most effective drug for a certain condition based on available reviews of various drugs used to treat this condition.

  • Cloud Tech Trends June 2022

    Cloud Tech Trends June 2022

    Let’s discuss the Cloud Computing (CC) sector specializing on the on-demand availability of computer system resources, especially data storage (cloud storage) and computing power, without direct active management by the user. 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 […]

  • Algorithmic Trading using Monte Carlo Predictions and 62 AI-Assisted Trading Technical Indicators (TTI)

    Algorithmic Trading using Monte Carlo Predictions and 62 AI-Assisted Trading Technical Indicators (TTI): The goal of the here presented pilot study is to develop and test an end-to-end Python-3 script in Jupyter that implements algorithmic trading. Thanks to high-level automation and integration of multiple tasks, the script can simultaneously analyze hundreds of technical indicators, run simulations and forecasts, perform real-time technical analysis and generate trading signals and alerts at a speed and frequency that is impossible for a human trader.

  • AI-Driven Skin Cancer Diagnosis

    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

  • Simple E-Commerce Sales BI Analytics

    Simple E-Commerce Sales BI Analytics

    Good businesses learn from previous efforts and test future ideas using e-commerce analytics (ECA). ECA enable you to delve deep into historical BI data, and future forecasting so that you can make the optimized business decisions.  The key benefits of ECA are as follows: Let’s look at the warehouse optimization problem by analyzing Kaggle sales […]

  • Bear Market Similarity Analysis using Nasdaq 100 Index Data

    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.

  • Basic Stock Price Analysis in Python

    Basic Stock Price Analysis in Python

    Our basic stock price analysis in Python includes stock prices, stock volume, market capitalization, 50/200-day moving average, scattered X-plot matrix, and stock volatility or standard deviation.