Topic: minmaxscaling Goto Github
Some thing interesting about minmaxscaling
Some thing interesting about minmaxscaling
minmaxscaling,Aircraft Engine Run-to-Failure Simulation
User: aysenuryilmazz
minmaxscaling,Exploratory Data Analysis for HR dataset
User: aysenuryilmazz
minmaxscaling,Artificial Neural Network using Keras in python to identify customers who are likely to churn.
User: bhargavflash
minmaxscaling,This project is based on a classification algorithm i.e. Naive Bayes which is run on a mobile dataset consisting of 2000 rows and 15 columns. It is a multi-class problem where mobile phones are classified in accordance with their price range. There are four classes of price ranging from 0 to 3, 0 indicating cheaper mobiles phones and 3 representing expensive mobile phones. Univariate analysis is conducted to understand individual predictors and bivariate analysis is conducted to infer relationship between predictors with other predictors and target variable. Important features are identified by Random Forest.
User: bushra-ansari
minmaxscaling,Bank Customer Behaviour Prediction
User: datasciencevishal
minmaxscaling,Linear Regression+Decision Tree+Random Forest
User: datasciencevishal
minmaxscaling,Feature transformation is a technique in machine learning that changes the way features are represented in order to improve the performance of machine learning algorithms. This can be done by transforming the features to a different scale, removing outliers, or creating new features from existing
User: emamulhossen
Home Page: https://github.com/EmamulHossen/FeatureTransformation
minmaxscaling,Telecommunication Company Churn Project
User: erenonal
minmaxscaling,Cloud image generation with Python and OpneCV
User: harunatsuko
minmaxscaling,Predictive model that tells important factors(or features) affecting the demand for shared bikes
User: kshitij-raj
minmaxscaling,Created machine learning models capable of classifying candidate exoplanets from a raw dataset.
User: lorsmo
minmaxscaling,Stock price prediction is the process of forecasting future stock prices based on historical data and market indicators.
User: manishshee24
minmaxscaling,Data Set: House Prices: Advanced Regression Techniques Feature Engineering with 80+ Features
User: moindalvs
minmaxscaling,
User: ovinueza
minmaxscaling,In this project we will apply Recurrent Neural Network (LSTM) which is best suited for time-series and sequential problem, we will be creating a LSTM model, train it on data and make predictions to check its performance.
User: prankur16shukla
minmaxscaling,This repository demonstrates the scaling of the data using Scikit-learn's StandardScaler, MinMaxScaler, and RobustScaler.
User: rafeyiqbalrahman
minmaxscaling,Build a machine learning model to predict if a credit card application will get approved.
User: shreeratn
Home Page: https://github.com/shreeratn/Predicting-Credit-Card-Approval
minmaxscaling,Forecasting time series data using ARIMA models. Used covariance matrix to find dependencies between stocks.
User: shubhammandhare10
minmaxscaling,Wrangled real estate data from multiple sources and file formats, brought it into a single consistent form and analysed the results.
User: siddharth1989
minmaxscaling,
User: williamadams1
minmaxscaling,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
User: y656
minmaxscaling,Final Cybersecurity ML project of Marc Mestre and Yana Veitsman for Data Mining and Machine Learning course at University of Valencia, Spring 2021
User: yan-vei
minmaxscaling,Time Series Model
User: yyyukeqi
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