GitHub topics: robust-scaling
sharmaroshan/Predicting_Money_Spent_at_Resort
It is From Analytics Vidhya Hackathons, Sponsored by Club Mahindra. It is based on Regression Problem, Where Accuracy matters the most, It is measured by RMSE Score. Different Techniques such as Stacking, Ensembling, Boosting and Scientific Operations such box-cox Operations to reduce skewness of the data.
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EmamulHossen/Exploratory-Data-Analysis-EDA-
Exploratory Data Analysis (EDA) is one of the techniques used for extracting vital features and trends used by machine learning and deep learning models in Data Science
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loopiiu/Deep_Learning_Task2_Action_Recognition
Action recognition using LSTM
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shahriar-rahman/Netflix-Customer-Retention-using-GPR
Forecasting Netflix Customer Retention based on Gaussian Process Regression
Language: Python - Size: 72.4 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 5 - Forks: 1

y656/Weather-data-clustering
This repository contains clustering techniques applied to minute weather data. It contains K-Means, Heirarchical Agglomerative clustering. I have applied various feature scaling techniques and explored the best one for our dataset
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Rizwan-Hasan/Breast-Cancer-Diagnosis-Using-Probabilistic-Ensemble-Based-Machine-Learning-Algorithms Fork of skinan/Breast-Cancer-Diagnosis-Using-Probabilistic-Ensemble-Based-Machine-Learning-Algorithms
This project is a part of research on Breast Cancer Diagnosis with a Machine Learning algorithm using data-driven approaches. The final outcomes of the research were later published at an IEEE Conference and added to IEEE Xplore Digital Library.
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Davidelvis/BankClientTermDeposit_Prediction
Bank Institution Term Deposit Predictive Model
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skinan/Breast-Cancer-Diagnosis-Using-Probabilistic-Ensemble-Based-Machine-Learning-Algorithms
This project is a part of research on Breast Cancer Diagnosis with Machine Learning algorithm using data-driven approaches. The final outcomes of the research were later published at an IEEE Conference and added to IEEE Xplore Digital Library.
Language: Jupyter Notebook - Size: 1.54 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 5 - Forks: 3

nani757/Feature_Scaling
Feature_Scaling_Normalization_MinMaxScaling_MaxAbsScaling_RobustScaling
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bkraffa/desafioclustering
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.
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RafeyIqbalRahman/Data-Scaling-Techniques
This repository demonstrates the scaling of the data using Scikit-learn's StandardScaler, MinMaxScaler, and RobustScaler.
Language: Python - Size: 10.7 KB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 0
