pandas

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

Predicting 30-Day Hospital Readmission in Diabetic Patients

Reads in a csv file (uploaded to Amazon S3 bucket) as a pandas dataframe. Performs basic data transformations and adds a column with flags based on ranges in temperature values in the dataframe. Uploads a copy of the modified data as a csv file in a different bucket. Emails list of recipients with a presigned URL to download the object.

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

Python SDK for Special Relativity Financial Modeling: Lorentz factors, spacetime intervals and geodesic signals on OHLCV data with pandas.

Categorizza automaticamente i movimenti del conto BPER e genera un consuntivo mensile con grafici, budget, abbonamenti ricorrenti e suggerimenti di risparmio. Include uno userscript per esportare i movimenti dall'home banking.

Análisis de datos de principio a fin sobre 741.894 versiones de clases Java: calidad de datos (Pandas), modelo estrella y KPIs en SQL, dashboard en Power BI y evaluación de un modelo predictivo con validación por repositorio.

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

JSON-like data manipulation and transofrmation to and from nested parent-child and flat label-value data items.

njson: efficient JSON-like data transformation tool. Provide high-performance JSON-like data transformation to and from nested parent-child and flat label-value data items, such as Pandas Series with MultiIndex index.

Jupyter notebook to pull data from the FPL website, specifically made for H2H Draft Leagues.

The main database of the French National Health Data System (SNDS) contains data from Health Insurance reimbursements, hospital treatment and medical causes of death. In order to characterise its use for health research and innovation, an interactive cartography has been produced to understand the framework of its use and to identify the stakeholders of the SNDS ecosystem. A bibliographic search via PubMed (available here), LiSSa, HAL was conducted to identify scientific articles published starting January 2007 on studies using SNDS data. The list of authors, their affiliations, keywords, the number of citations and much more were collected. A descriptive analysis was carried out in order to assess temporal and geographical trends in the use of SNDS main database. The graphs where generated with networkx, a python package used for the creation manipulation and study of complex networks. To generate the Author/Affiliations graphs we first create the adjacency matrix between the Authors/Affiliations and the article PMIDs. We then use the networkx.Graph class to create the needed undirected graphs, using the adjacency matrices as the data to intialize the graphs.

Data analysis with the pandas DataFrame library in Python.

Analysing biological datasets with the pandas DataFrame library in Python.

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.

Ce projet porte sur le développement d'un modèle de scoring bancaire destiné à prédire le risque associé à une demande de crédit à partir des données historiques de prêts du dataset Lending Club.

L'objectif est de construire une chaîne complète de Data Science, allant de l'analyse et de la préparation des données jusqu'à la modélisation prédictive et l'évaluation des performances.

Travaux réalisés :

Analyse exploratoire du dataset et étude des différentes variables disponibles. Nettoyage et préparation des données. Traitement des valeurs manquantes et des variables catégorielles. Sélection et transformation des variables pertinentes. Réalisation de Feature Engineering afin de construire des variables adaptées à la prédiction. Préparation des jeux de données pour l'entraînement et l'évaluation. Expérimentation de différents modèles de Machine Learning. Expérimentation d'un modèle de Deep Learning avec TensorFlow Évaluation des modèles à l'aide de métriques de classification. Analyse comparative des performances afin d'identifier l'approche la plus pertinente