Tag: data-driven technology
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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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A Comparison of Automated EDA Tools in Python: Pandas-Profiling vs SweetViz
Exploratory Data Analysis (EDA) is an important part of data science projects, designed to identify patterns, anomalies, and relationships. It can employ univariate, bivariate, and multivariate data analytics, and can be accelerated using automated EDA tools. The article discusses Python libraries such as Pandas-Profiling and SweetViz for automating EDA and demonstrates their application to improve…
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Improved Multiple-Model ML/DL Credit Card Fraud Detection: F1=88% & ROC=91%
In 2023, the global card industry is projected to suffer $36.13 billion in fraud losses. This has necessitated a priority focus on enhancing credit card fraud detection by banks and financial organizations. AI-based techniques are making fraud detection easier and more accurate, with models able to recognize unusual transactions and fraud. The post discusses a…
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Unsupervised ML, K-Means Clustering & Customer Segmentation
The goal of this post is to describe the most popular, easy-to-use, and effective data-driven techniques that help organizations streamline their operations, build effective relationships with with individual people (customers, service users, colleagues, or suppliers), increase sales, improve customer service, and increase profitability. Customer Segmentation (CS) is a core concept in this technology. This is the…
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Risk-Aware Strategies for DCA Investors
Dollar-Cost Averaging (DCA) is an investment approach that involves investing a fixed amount regularly, regardless of market price. It offers benefits such as risk reduction and market downturn resilience. It’s useful for beginners and can be combined with other strategies for a disciplined investment approach. References include Investopedia and Yahoo Finance.
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The $0 MarTech Stack for Small Business
The post is a comprehensive guide to marketing technology, or martech. It covers the development of customer data platforms for managing marketing operations, as well as ten categories of free martech SaaS tools useful for startups. The categories include marketing automation, social media, SEO, lead generation, graphic design, PR, email marketing, project management, conversion rate…
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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…
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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…
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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…
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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…
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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…
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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…
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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%).
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SARIMAX Crude Oil Prices Forecast – 2. Brent
This study focuses on validating the EIA energy forecast for the 2023 Brent crude oil spot price using SARIMAX time-series cross-validation. It includes prerequisites, data loading, ETS decomposition, ADF test, SARIMAX modeling, predictions, model evaluation, and summary. The predictions align with the EIA forecast, with discrepancies within predicted confidence intervals.
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Trending YouTube Video Data Science, NLP Predictions & Sentiment Analysis
YouTube (YT) is probably the most popular web platform that is easy to share non-textual content such as videos and animations. The goal of this post is to get data science insights into YouTube trending videos for many countries, to see what is common between these videos. Specifically, the following questions need to be answered: How many views,…
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SARIMAX Crude Oil Prices Forecast – 1. WTI
The content discusses a detailed forecast of Brent and WTI oil prices for 2023, using Python, SARIMAX and Time Series Analysis. The data indicates volatility in the oil market starting 2023, with prices set to decrease from 2022 levels. Experts also warn of a potential US recession in 2023, which could further impact the oil…
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Top E-Commerce Trends in 2023
Featured Photo by PhotoMIX Company on Pexels E-commerce has grown to become a giant pillar in the global economy. The current global e-commerce industry is measured as $5.7 trillion in 2022. The global ecommerce growth rate for 2023 is forecast at 10.4%, bringing global ecommerce sales worldwide to $6.3 trillion. This marks a 0.7 percentage point increase from 2022’s growth…
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SARIMAX Forecasting of Online Food Delivery Sales
This article provides a beginner-friendly guide to understanding and evaluating ARIMA-based time-series forecasting models such as SARIMA and SARIMAX. It focuses on an QC-optimized SARIMA(X) model to forecast the e-commerce sales of a food delivery company. The post covers essential concepts, data processing, model comparisons, and insights. It also includes a comparison between SARIMA and…
