Topic: roc-auc-score Goto Github
Some thing interesting about roc-auc-score
Some thing interesting about roc-auc-score
roc-auc-score,credit card lead prediction
User: developedbysm
roc-auc-score,Beta Bank is losing customers monthly. Employees want to focus on client retention. As a Data Scientist, I created a model to predict the chance of a customer leaving, based on past behavior and contract terminations.
User: gunturwibawa
roc-auc-score,Exoplanet Hunting in Deep Space.
User: harmanveer-2546
Home Page: https://www.kaggle.com/datasets/keplersmachines/kepler-labelled-time-series-data
roc-auc-score,The goal is to eliminate manual work in identifying faulty wafers. Opening and handling suspected wafers disrupts the entire process. False negatives result in wasted time, manpower, and costs.
User: harmanveer-2546
roc-auc-score,The goal is to eliminate manual work in identifying faulty wafers. Opening and handling suspected wafers disrupts the entire process. False negatives result in wasted time, manpower, and costs.
User: harmanveer2546
roc-auc-score,It is a Hackathon problem statement solution, which is arranged by Analytics Vidhya.
User: justabhishek
roc-auc-score,Lead generation for credit card
User: lakshmipriya-s
roc-auc-score,Clustering validation with ROC Curves
User: pajaskowiak
roc-auc-score,Used libraries and functions as follows:
User: patilsukanya
roc-auc-score,Scrapped tweets using twitter API (for keyword βNetflixβ) on an AWS EC2 instance, ingested data into S3 via kinesis firehose. Used Spark ML on databricks to build a pipeline for sentiment classification model and Athena & QuickSight to build a dashboard
User: rochitasundar
roc-auc-score,Develop and train image classification models using advanced deep learning techniques to identify diseases specific to apples.
User: safaa-p
roc-auc-score,OilyGiant mining company finding the best place for 200 new well points, As an Data Scientist we're creating a model who can choose the best 200 point by profit and risk.
User: sultanazhari
roc-auc-score,Bank Beta Company focus on retain existing customers, our task is to create a model that predicts whether or not a customer will leave the bank soon.
User: sultanazhari
roc-auc-score,ROC, AUC, and Z-score functions for anomaly detection
User: tacotuesday
roc-auc-score,A Portuguese hotel group seeks to understand reasons for its excessive cancellation rates.
User: tpurcell0122github
roc-auc-score,Perform Dimensionality Reduction using AutoEncoder.
User: utkarsh-21st
roc-auc-score,Assignment-06-Logistic-Regression. Output variable -> y y -> Whether the client has subscribed a term deposit or not Binomial ("yes" or "no") Attribute information For bank dataset Input variables: # bank client data: 1 - age (numeric) 2 - job : type of job (categorical: "admin.","unknown","unemployed","management","housemaid","entrepreneur","student", "blue-collar","self-employed","retired","technician","services") 3 - marital : marital status (categorical: "married","divorced","single"; note: "divorced" means divorced or widowed) 4 - education (categorical: "unknown","secondary","primary","tertiary") 5 - default: has credit in default? (binary: "yes","no") 6 - balance: average yearly balance, in euros (numeric) 7 - housing: has housing loan? (binary: "yes","no") 8 - loan: has personal loan? (binary: "yes","no") # related with the last contact of the current campaign: 9 - contact: contact communication type (categorical: "unknown","telephone","cellular") 10 - day: last contact day of the month (numeric) 11 - month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec") 12 - duration: last contact duration, in seconds (numeric) # other attributes: 13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted) 15 - previous: number of contacts performed before this campaign and for this client (numeric) 16 - poutcome: outcome of the previous marketing campaign (categorical: "unknown","other","failure","success") Output variable (desired target): 17 - y - has the client subscribed a term deposit? (binary: "yes","no") 8. Missing Attribute Values: None
User: vaitybharati
roc-auc-score,Increased the ROC AUC score by 2.14% of predicting the churn of users in telecommunication company using hypertuning parameter and feature engineering.
User: waannuullll
Home Page: https://docs.google.com/presentation/d/1SILSy1kEJtrAjnwukdsZM6UHjriVGoXG/edit
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