Tag: Python
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Prediction of NASA Turbofan Jet Engine RUL: OLS, SciKit-Learn & LSTM
We predict the Remaining Useful Life (RUL) of NASA turbofan jet engines by comparing the statsmodels OLS, ML SciKit-Learn regression vs LSTM Keras in Python. The input dataset is the Kaggle version of the public dataset for asset degradation modeling from NASA. It includes Run-to-Failure simulated data from turbo fan jet engines.
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Health Insurance Cross Sell Prediction with ML Model Tuning & Validation
The content discusses the use of AI and Machine Learning (ML) for insurance cross-selling. It covers topics such as data preparation, model training with different algorithms, parameter optimization, and model evaluation. The study showcases the ability of ML models (HGBM, XGBoost, Random Forest) to predict cross-sell customers in the insurance sector, providing potential for improved…
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Weather Forecasting & Flood De-Risking using Machine Learning, Markov Chain & Geospatial Plotly EDA
Foto door Pok Rie Scope: Business Value: Table of Contents U.S.A. Weather Forecast Australian Rainfall Prediction Kerala Flood Prediction Squares are categorical associations (uncertainty coefficient & correlation ratio) from 0 to 1. The uncertainty coefficient is asymmetrical, (i.e. ROW LABEL values indicate how much they PROVIDE INFORMATION to each LABEL at the TOP). • Circles are the symmetrical numerical…
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A Balanced Mix-and-Match Time Series Forecasting: ThymeBoost, Prophet, and AutoARIMA
The post evaluates the performance of popular Time Series Forecasting (TSF) methods, namely AutoARIMA, Facebook Prophet, and ThymeBoost on four real-world time series datasets: Air Passengers, U.S. Wholesale Price Index (WPI), BTC-USD price, and Peyton Manning. Each TSF model uses historical data to identify trends and make future predictions. Studies indicate that ThymeBoost, which combines…
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Hugging Face NLP, Streamlit, PyGWalker, TF & Gradio App
Table of Contents Streamlit/Dash/Jupyter PyGWalker EDA Demo PyGWalker and Dash — Creating a Data Visualization Dashboard In Less Than 20 Lines of Code PyGWalker Test PyGWalker Tutorial: A Tableau-Like Python Library for Interactive Data Exploration and Visualization PyGWalker: A Python Library for Visualizing Pandas Dataframes You’ll Never Walk Alone: Use Pygwalker to Visualize Data in…
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Low-Code AutoEDA of Dutch eHealth Data in Python
The article details the usage of Python’s Low-Code AutoEDA for examining Dutch Healthcare Authority’s eHealth data. Utilizing various Python libraries like D-Tale, SweetViz, etc., the study aims to understand the healthcare data’s key features to ready it for AI techniques. The motivations include the Dutch government’s support for digital healthcare applications, especially amidst the recent…
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Dividend-NG-BTC Diversify Big Tech
SEO Title: Can Dividends, Natural Gas and Crypto Diversify Big Techs? Ultimately, we need to answer the following fundamental question: Can Dividend Kings, NGUSD and BTC-USD Diversify Growth Tech assets? Dividends are very popular among investors, especially those who want a steady stream of income from their investments. Some companies choose to share their profits…
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Anomaly Detection using the Isolation Forest Algorithm
The post describes the application of Isolation Forest, an unsupervised anomaly detection algorithm, to identify abnormal patterns in financial and taxi ride data. The challenge is to accurately distinguish normal and abnormal data points for fraud detection, fault diagnosis, and outlier identification. Using real-world datasets of financial transactions and NYC taxi rides, the algorithm successfully…
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Returns-Volatility Domain K-Means Clustering and LSTM Anomaly Detection of S&P 500 Stocks
This study aims to implement and evaluate the K-means algorithm for ranking/clustering S&P 500 stocks based on average annualized return and volatility. The second goal is to detect anomalies in the best performing S&P 500 stocks using the Isolation Forest algorithm. Additionally, anomalies in the S&P 500 historical stock price time series data will be…
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NVIDIA Returns-Drawdowns MVA & RNN Mean Reversal Trading
The study presents a machine learning-focused analytical approach to optimize NVIDIA’s stock performance using moving average crossovers and aims at comparing the outcomes with simple RNN mean reversal trading strategies. The steps taken involve preparing the stock data, calculating moving averages and drawdowns, plotting heatmaps of returns and drawdowns, and predicting returns and cumulative returns…
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Oracle Monte Carlo Stock Simulations
Oracle Corporation’s significant developments in Generative AI have led to lucrative partnerships with Nvidia and Elon Musk’s xAI. Having secured contracts exceeding $4 billion for its Generation 2 Cloud designed for AI model training, Oracle’s earnings doubled in Q4 2023. Monte Carlo simulations align with Zacks Rank 3-Hold for ORCL, implying bullish potential with projected…
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NVIDIA Rolling Volatility: GARCH & XGBoost
This post examines the prediction of NVIDIA stock volatility using two models: the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and the Extreme Gradient Boosting (XGBoost). Both models are compared in terms of MSE and MAPE. The post discovers that the machine learning-based XGBoost model outperforms the GARCH model in NVDA volatility forecasting, showing the effectiveness of…
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Machine Learning-Based Crop Yield Prediction, Classification, and Recommendations
We have implemented a Machine Learning-Based decision support tool for crop yield prediction, including supporting decisions on what crops to grow and what to do during the growing season of the crops.
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IQR-Based Log Price Volatility Ranking of Top 19 Blue Chips
The focus is on risk assessment of top blue chips. We determine market regimes using standard deviation (STD) of log-domain stock prices.
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An Implemented Streamlit Crop Prediction App
Precision agriculture or smart farming: We implement the Streamlit crop prediction app. This is an ML-driven app that requires the trained model as input.
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Multiple-Criteria Technical Analysis of Blue Chips in Python
Blue chip stocks are the stocks of well-known, high-quality companies. We demonstrate that the proposed approach can help optimize the blue-chip portfolios comprehensively.
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Blue-Chip Stock Portfolios for Quant Traders
This post delves into optimizing blue-chip stock portfolios using Python fintech libraries for private DIY self-traders. It includes steps for examining trading signals, comparing stock returns, performing analyses, and implementing forecast models. The content covers AAPL trading signals, risk vs. ROI analysis, a 4-stock portfolio, Monte-Carlo predictions, SPY return/volatility, and SPY Prophet forecast. The examples…
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Time Series Forecasting of Hourly U.S.A. Energy Consumption – PJM East Electricity Grid
Table of Contents PJME Data Let’s set the working directory YOURPATH and import the following key libraries Let’s read the input csv file in our working directory Let’s plot the time series Data Preparation Output: (113926, 1, 9) (113926,) (31439, 1, 9) (31439,) LSTM TSF Let’s plot the LSTM train/test val_loss history Output: MSE: 1811223.125…
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Wind Energy ML Prediction & Turbine Power Control
This text presents a detailed project on modeling the power curve of a wind turbine, which is crucial in wind energy management and forecasting. By using machine learning techniques such as Random Forest and Gradient Boosting Regressors, and validating with real-world Scada data from a Turkish wind farm, the project shows it’s possible to create…
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Image Based Fast Forest Fire Detection with TensorFlow
A recent study showcases the use of artificial intelligence (AI) and deep learning (DL) for efficient wildfire prediction and management. Utilizing a fast DL approach based on the TensorFlow Convolution Neural Network (CNN) algorithm, researchers trained models to distinguish between fire and non-fire images using a public-domain dataset. The implemented system predicted fires accurately and…
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