S

statistics

Python mathplotlib graphs of web articles. Contains plots of basic article characteristics over time.

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).

An unsupervised learning program for analyzing molecular dynamics data

Project for the Master's course Statistics and Probability at the BUT FIT. Bayesian estimation and regression, statistical methods, prediction, approximation. Created using Jupyter notebook.

Python resourcer til brug i undervisning ved NBI.

Se også https://cholmcc.gitlab.io/nbi-python/

Personal notes/codes of my studies about Statistics.

My approach on solving mathemetical modeling labs (university course)

Book: Intro to Python for Computer Science and Data Science by Paul Deitel & Harvey Deitel

Notebook for discipline "Big Data Analyse"

This jupyter notebook goes through how to use python in you data analysis. Mainly analysis of experiments, such as peak analysis and curve fitting.

Materials in this project correspond to the "Machine Learning for Bioinformatics" course Dr. Jacob Machado gave at UNC Charlotte during the Fall semester of 2022. See more details in the README.md file.

Using the one-factor Vasicek model to simulate the credit risk distribution of a portfolio of risky loans

Project for "Metodi Matematici e Statistici" 's exam @University of Catania

A Jupyter notebook for analysis of corona cases

This is a jupyter notebook (in Swedish) where error propagation and confidence interval handling of data is explained. It includes some examples and assignments.

Questo lavoro consiste nella scrittura di vari algoritmi "from scratch" tra cui adaboost basato su stump per la predizione del tipo di terreno in forest cover type dataset.

Frequently Asked Questions (FAQ) about Science Climate coding using Python packages: Pandas, Xarray, Numpy and Matplotlib.

Climate Statistics with Python.