This repository demonstrates the scaling of the data using Scikit-learn's StandardScaler, MinMaxScaler, and RobustScaler.
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Updated
Aug 4, 2020 - Python
This repository demonstrates the scaling of the data using Scikit-learn's StandardScaler, MinMaxScaler, and RobustScaler.
[Data Analysis] Project in 2022 - Selection of additional areas for elementry care facilities, Used 8 Clustering methods
[Modeling] Project in 2022 - Simple Model of important factors in the incidence of heart disease and prediction model
Action recognition using LSTM
Boston house price prediction using Linear Regression.
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.
Detecting ideal clusters from imdb's movie dataset to segment using unsupervised learning
Feature_Scaling_Normalization_MinMaxScaling_MaxAbsScaling_RobustScaling
Bank Institution Term Deposit Predictive Model
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
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
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.
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.
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.
Forecasting Netflix Customer Retention based on Gaussian Process Regression
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