Projects with this topic
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Aston University DG1AID lab repository with AI and data science notes, Python notebooks, NumPy, Pandas, search algorithms and machine learning practice.
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Данный проект - одно из моих тестовых заданий. Суть проекта: обработка информации с помощью ChatGpt и дальнейшая работа с результатом. Скрипт написан на python с использованием библиотек: pandas, request, os.
Кратко принцип работы:
1)Загружаем данные из CSV файла в Python с помощью Pandas.
2)Обрабатываем данные в форму, необходимую для отправки запроса в ChatGpt
3)Через OpenAI API и библиотеки requests отправляем запрос в ChatGpt и получаем ответ.
4)Обрабатываем ответ в Python и с помощью библиотеки Pandas сохраняем новый файл с обработанными данными в ту же директорию.
Мой контакт Telegram - @Jenyahard
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Materials on various sections in Computer Science (CS).
🎓 с с++ shell Bash makefile Git GitLab github SQL computer sci... PowerShell Mobile Devel... Android iOS devops game develop... Web Development JavaScript Java Python Docker HTML CSS TypeScript C++ Rust game Go C C# python3 nodejs golang Django Node.js MySQL Kotlin Windows PostgreSQL Flutter machine lear... js Ruby Qt Markdown R Swift cybersecurity Cyber Security bioinformatics deep learning big data NumPy pandas matplotlib scikit-learn scipy development softwareUpdated -
Practical tasks on Deep Learning (DL) and Neural Networks (NN).
🤖 Python machine lear... deep learning NumPy matplotlib pandas AI mathematics computer vision natural lang... speech proce... PyTorch scikit-learn artificial i... ML DL big data data analysis scipy keras TensorFlow seaborn plotly nltk opencv dask Deep Nerual ... programming openml google colab google colla... google drive computer sci... CSV API python3 jupyter jupyter note... Anaconda Bash shell LaTeX MarkdownUpdated -
Fundamental theory and practice in Data Science (DS).
🧮 data analysis AI ML DL machine lear... deep learning data science data-enginee... artificial i... data-science data preproc... Python C C++ NumPy pandas mathematics Algorithm algorithms Data Enginee... big data scipy scikit-learn xgboost lightgbm catboost TensorFlow keras PyTorch matplotlib seaborn plotly nltk opencv dask linear-algebra calculus probability statistics Discrete Mat... RUpdated -
Enterprise Oracle Database monitoring and analytics dashboard built with Python, Streamlit, Oracle SQL, Plotly, and automated alerting.
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General purposes Jupyter Notebooks (XalapaCode presentations and data, testing, prototypes).
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Explore and analyze data provided by Motivate, a bike share system provider for many major cities in the United States. This tool uncovers bike share usage patterns and compares system usage between Chicago, New York City, and Washington, DC. The challenge involves building a robust, interactive Command Line Interface (CLI) application capable of quickly filtering, crunching, and displaying descriptive statistics on large datasets.
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ETL pipeline project using Python, pandas and SQLite to import and structure CSV, Excel and HTML data files.
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A practical, linear-algebra-first introduction to data science.
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Simple ML Project using regression models and KMeans clustering to predict Y from A and B, classify results, and expose predictions through a FastAPI API.
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FastAPI-based microservice that predicts the risk level of a user session https://user-risk-detection-api.vercel.app
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Predicts the risk level of a user session using behavioral signals
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Analisi della transizione energetica nell'UE27 (2000–2030) con Python, Power BI e Machine Learning. Tre indici sintetici originali (ITE, ICP, RGI) calcolati su dati Our World in Data e proiettati al 2030 tramite regressione lineare.
Progetto IFTS Data Analysis & AI - SIAM1838
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Experimental implementations of mathematical functions focusing on HPC techniques
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Analysis of Kilter Board data, along with predictive models for V-grades based on holds and angle.
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Analysis of Tension Board 2 data, along with predictive models for V-grades based on holds and angle.
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Supervised learning pipeline for rare event operational failure prediction, integrating leakage resistant preprocessing, class weighted modeling, precision recall threshold calibration, ROC AUC benchmarking, and permutation based feature importance to analyze production stress drivers.
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