saikrishnabudi/Random-Forests

Use Random Forest to prepare a model on fraud data. Treating those who have taxable income <= 30000 as "Risky" and others are "Good" and A cloth manufacturing company is interested to know about the segment or attributes causes high sale.

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DanniRodrJ/milling-machine_failure-prediction

Machine Learning aplicado al mantenimiento predictivo. Se realizaron 2 modelos: 1 por medio de clasificación binaria que predice si una máquina fresadora estará en riesgo de fallar o no, y el 2 modelo a través de clasificación multiclase que predecirá el modo de falla

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domingosdeeulariadumba/MarketingCampaignPrediction2

Continuing with telemarketing model to predict campaign subscriptions in a portuguese bank institution. For this project I have evaluated the performance of four resampling techniques and selected the best one to implement the logistic model.

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shanuhalli/Assignment-Random-Forest

Use Random Forest to prepare a model on fraud data. Treating those who have taxable income <= 30000 as "Risky" and others are "Good" and A cloth manufacturing company is interested to know about the segment or attributes causes high sale.

Language: Jupyter Notebook - Size: 2.82 MB - Last synced: about 1 year ago - Pushed: over 1 year ago - Stars: 0 - Forks: 0

shivtosh/Feature-engineering

This repository has the code for implementation of Principal Component Analysis, Upsampling (SMOTE), Downsampling (Random Undersampler) and combined via SMOTETomek.

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MoinDalvs/Assignment_SVM_Forest_Fire_Prediction

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MoinDalvs/Assignment_Decision_Tree_2

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SebaPansecchi/DataScience-Aprobacion-Tarjetas-de-Credito

Proyecto final del curso de DataScience en CoderHouse que intenta ayudar a una entidad bancaria a la hora de decidir si emite una tarjeta de crédito al solicitante

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Internship-BVoc/InsuranceClaimPrediction

Predicting whether Insurance claims will accepted or rejected for online Travel Agencies

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Chandradithya8/Handling_Imbalanced_Dataset

Imbalanced data sets are a special case for classification problem where the class distribution is not uniform among the classes. Typically, they are composed by two classes: The majority (negative) class and the minority (positive) class.

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