Financial distress prediction from Kaggle
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Updated
Oct 13, 2023 - Jupyter Notebook
Financial distress prediction from Kaggle
XAI analytics to understand the working of SHAP values and applying it to the breast cancer dataset to get the explanation behind the predictions made.
This project was developed during the course Laboratory of Computational Physics
Experimenting with SHAP values to explain how a given Machine Learning model works.
This project aims to predict heart failure outcomes by applying statistical learning algorithms. The goal is to improve the prediction accuracy through the SuperLearner algorithm.
XAI analytics to understand the working of SHAP values
Modelo de boosting que a partir de los datos del usuario de una fintech predice si activaría la tarjeta de debito que ofrece la misma empresa, y en cuantos dias lo haría
Loan-Default-Prediction
The purpose of this work is the modeling of the wine preferences by physicochemical properties. Such model is useful to support the oenologist wine tasting evaluations, improve and speed-up the wine production. A Neural Network was trained using Tensorflow, which was later tuned in order to achieve high-accuracy quality predictions.
Repo for Manzano Analytics HTML website
Android malware detection using machine learning.
Prediction if patients with symptoms have COVID-19 based on clinical variables (blood related variables, urine related variables, age, etc)
🐍 Mental Maps Related to Contents in Data Science 🐍
erformed a predictive analysis on the customer's Bank Loan Application data to predict loan status. Using python, pandas, scipy, seaborn, AutoML libraries, and machine learning techniques. Used Machine Learning techniques to accurately predict the evaluation scheme if the particular loan will be 'Fully Paid' or 'Charged Off'. This means if Bank …
Github Repository for the paper "Different Algorithms (Might) Uncover Different Patterns: A Brain-Age Prediction Case Study" - BIBM 2023
🐝 Materials and homework assignments for HSE recommender systems course
ML-solution of the case of the District hackathon Leaders of Digital 2023. The task was to predict accidents (accidents, pipe ruptures, fires) based on the weather forecast for each of the urban districts. Gradient boosting (macro f1), cross-validation, shap values.
Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles
Análisis de modelos de Deep Learning mediante SHAP values. Desarrollo y programación de herramientas que permitan interpretar modelos de Deep Learning usando las SHAP values, generando una mejor explicación de los factores en los que se basa el modelo a la hora de tomar sus predicciones.
Predicción del precio de venta de las viviendas en venta y de las viviendas en alquiler de Barcelona.
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