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Streamlit component for the Yellowbrick visualization and model diagnostics library
Fancylit is a python module that contains pre-packaged Streamlit code to render fancy visualizations, run modeling tasks, and data exploration
Training neural networks to classify network traffic by L7 protocol.
For our final project, our group chose to use a dataset (from Kaggle) that contained medical transcriptions and the respective medical specialties (4998 datapoints). We chose to implement multiple supervised classification machine learning models - after heavily working on the corpora - to see if we were able to correctly classify the medical specialty based on the transcription text.
Evaluation of Machine Learning Models with Yellowbrick
Here I will share some of my data visualizations using a variety of datasets, technologies and tools.
FastRide: NYC Taxi Trip Duration Prediction employs machine learning techniques to accurately predict the duration of taxi trips in New York City, helping transportation businesses optimize operations and enhance customer satisfaction.
2023년 7월 논문게재(한국벤처창업연구) : COVID-19에 따른 글로벌 창업 트렌드 분석: Cruchbase를 중심으로(Analysis of Global Entrepreneurship Trends Due to COVID-19: Focusing on Crunchbase, 1저자
This repo consists of data visualization project done for wealth management dataset from Kaggle. I have used various Machine Learning classifiers to calculate accuracy and precision to determine which model works best for this dataset. The agenda of this project is to analyze the trend of customer churn from a wealth management company.
Análisis de datos utilizando datasets obtenidos de Kaggle, una plataforma de competencias y recursos de ciencia de datos
Build a clustering model to study driver behaviors and lifetime value.
Build a model to predict whether a loan is flagged as 'bad'.
Vous êtes consultant pour Olist, une solution de vente sur les marketplaces en ligne. Olist souhaite que vous fournissiez à ses équipes d'e-commerce une segmentation des clients qu’elles pourront utiliser au quotidien pour leurs campagnes de communication.
An analysis that predicts individual health insurance costs charged by health insurance companies based on age, sex, BMI, children, smoking, and region using predictive modeling and machine learning.
K-means++ clustering on fragrance accords :robot:
A Deep Dive of Craigslist US Used Car Sales Data Using ML and Visualizations Presented Within a Webpage
This repository contains some data science projects I have done for practical purposes.
Performing a clustering model for Bank Customer Dataset using K-Means clustering
Capstone Project for the Data Scientist Nanodegree by Udacity.
In this analysis, I will demonstrate how PCA and K-Means clustering can be applied to credit risk data. In this data set, we do not have a target variable, which leads us to build an unsupervised machine learning model.
Desafio de clusterização de clientes feito para o IFood e Tera. Utilizando as bibliotecas Plotly, Sklearn e Yellowbrick conseguimos fazer a clusterização em 3 dimensões de forma eficiente e visual utilizando as features construídas no feature engineering a partir de bases de clientes, pedidos e sessões do iFood.
Unsupervised Learning Model Evaluation
Yellowbrick is an useful machine learning visualization library for visualizing model performance. This Jupyter notebook gives an example for using yellowbrick to visualize model performance of a ternary classification task.
Evaluate Machine Learning Models with Yellowbrick
Perform Feature Analysis with Yellowbrick!
An assignment on basics of scikit-learn
An assignment on setting up python environment for machine learning
Yellowbrick wraps the scikit-learn and matplotlib to create publication-ready figures and interactive data explorations. It is a diagnostic visualization platform for machine learning that allows us to steer the model selection process by helping to evaluate the performance, stability, and predictive value of our models and further assist in diagnosing the problems in our workflow.