Category: Data-Driven Tech
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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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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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Wind Farm AEP Optimization in SciPy – Horns Rev 1 Site
The post outlined the process of optimizing the Annual Energy Production (AEP) of the Horns Rev 1 wind farm using the SciPy algorithm. The results derived from SciPy were cross-validated using other optimizers such as Optuna and PyWake. The study iterates the importance of offshore wind farms in meeting Denmark’s goals of increased electricity generation.
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Automating Microsoft Excel Tasks & Reports with Python
The article provides a detailed guide on automating Excel tasks using Python and the Openpyxl library. It includes topics like Excel-to-Python data import, operations on Excel (like writing, creating new columns, new rows, and implementing formulas), working with worksheets, adding basic charts, and even formatting cells. However, advanced features like running macros and converting Excel…
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Robust Fake News Detection: NLP Algorithms for Deep Learning and Supervised ML in Python
The project aims at setting up a robust system for fake news detection using Python. The system adopts a hybrid framework, leveraging Natural Language Processing (NLP) techniques to classify text-based fake vs real news. Involving exploratory data analysis, multi-model training, testing, validation, and performance metrics comparison, it assesses different Deep Learning, Supervised Machine Learning, and…
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Supervised ML Room Occupancy IoT
The article presents a study on applying machine learning (ML) to IoT sensor data for workspace occupancy detection. Comparing 14 popular scikit-learn classifiers, the ML systems built use the gathered IoT sensor data to predict room occupancy with high certainty. The results suggest temperature and light are the significant factors affecting occupancy detection. The study…
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EUR/USD Forecast: Prophet vs JPM
JP Morgan (JPM) analysis predicts the EUR/USD exchange rate to hold at 1.08 in December 2023, while ING forecasts suggest rates of $1.00 throughout 2023 and $1.02 in Q1 2024, rising to $1.10 by Q4 2024. Using the FB Prophet model, predictions show a hold at 1.08 +/- 0.07 in December 2023, aligning with JPM’s…
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WA House Price Prediction: EDA-ML-HPO
A predictive model of house sale prices in King County, Washington, was developed using multiple supervised machine learning (ML) regression models, including LinearRegression, SGDRegressor, RandomForestRegressor, XGBRegressor, and AdaBoostRegressor. The best-performing model, XGBRegressor, explained 90.6% of the price variance, with a RMSE of $18472.7. These results, valuable to local realtors, indicate houses with a waterfront are…
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NLP & Stock Impact of ChatGPT-Related Tweets
This Python project extends a recent study on half a million tweets about OpenAI’s language model, ChatGPT. It uncovers public sentiment about this rapidly growing app and examines its impact on the future of AI-powered LLMs, including stock influences. The project uses data analysis techniques such as text processing, sentiment analysis, identification of key influencers,…
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ML Prediction of High/Low Video Game Hits with Data Resampling and Model Tuning
The post outlines a ML-based approach to forecast video game sales, using several techniques to enhance training, accuracy, and prediction. The Kaggle’s VGChartz dataset, containing sales data and other game-specific information, was used to build and refine the model. Several ML techniques including RandomForestClassifier and Logistic Regression yielded top predictors, with the critic’s score deemed…
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An Overview of Video Games in 2023: Trends, Technology, and Market Research
The gaming industry is rapidly growing, projected to reach a revenue of $365.6 billion in 2023. Major trends include Web3 gaming, AI integration, and a push for consolidation. Fashion brands collaborate for virtual sales, and advances in gaming technology, such as AR/VR and cloud-based gaming, promise an even more immersive experience for gamers.
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Customer Reviews NLP Spacy Analysis and ML/AI Demand Forecasting of the Steam PC Video Game Service
Steam, a leading digital distribution platform for PC gaming, has seen over 6000 new games released in 2022, averaging over 34 games each day. This post aims to conduct comprehensive customer reviews NLP sentiment analysis and ML/AI demand forecasting using public-domain datasets. It covers EDA, NLP Spacy analysis, ML/AI pipeline, model validation, word clouds, and…
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