pandas

Materials on various sections in Computer Science (CS). 🎓

Practical tasks on Deep Learning (DL) and Neural Networks (NN). 🤖

Fundamental theory and practice in Data Science (DS). 🧮

Enterprise Oracle Database monitoring and analytics dashboard built with Python, Streamlit, Oracle SQL, Plotly, and automated alerting.

General purposes Jupyter Notebooks (XalapaCode presentations and data, testing, prototypes).

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.

Aston University DG1AID lab repository with AI and data science notes, Python notebooks, NumPy, Pandas, search algorithms and machine learning practice.

ETL pipeline project using Python, pandas and SQLite to import and structure CSV, Excel and HTML data files.

A practical, linear-algebra-first introduction to data science.

Bu programın amacı, İKYS Sisteminde bulunan verileri baz alarak, KBS Sisteminde hata olup olmadığını kontrol etmek amacıyla yazılmıştır.

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.

FastAPI-based microservice that predicts the risk level of a user session https://user-risk-detection-api.vercel.app

Predicts the risk level of a user session using behavioral signals

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

Experimental implementations of mathematical functions focusing on HPC techniques

Analysis of Kilter Board data, along with predictive models for V-grades based on holds and angle.

Analysis of Tension Board 2 data, along with predictive models for V-grades based on holds and angle.

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.

Projet d’analyse du comportement des utilisateurs Instagram à partir d’un large dataset synthétique (plus d’un million d’utilisateurs).

Le projet explore :

Analyse exploratoire des données (EDA) Prédictions de variables comportementales (stress, âge, clics publicitaires, revenu) Identification de profils utilisateurs avec clustering (KMeans) Interprétation des résultats statistiques en lien avec la vie réelle

Méthodologie : visualisation, corrélations, sélection de modèles, entraînement, évaluation et interprétation.

Technologies : Python, Pandas, NumPy, Matplotlib, Scikit-learn, Jupyter.

Projet réalisé dans le cadre de la formation Développeur en Intelligence Artificielle (Simplon).