Category: Data-Driven Tech
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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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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.
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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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Drug 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…
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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…
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Quant 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…
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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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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…
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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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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.
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Track All Markets with TradingView
Track All Markets with TradingView: getting started tips teaching a new dog old tricks making the most of your market analysis Global screener new features and more
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S&P 500 Algorithmic Trading with FBProphet
We use Facebook’s Prophet to forecast S&P 500 stock adjusted close price. We plot the results simulating an initial investment of $1,000.
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Predicting Trend Reversal in Algorithmic Trading using Stochastic Oscillator in Python
This is the example stochastic oscillator in Python for algorithmic trading $NVIDIA candlestick chart vs a stochastic oscillator chart over our trading period.
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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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