minmaxscaling
There are 24 repositories under minmaxscaling topic.
erenonal/XGBoost_churn_analysis
Telecommunication Company Churn Project
kshitij-raj/Bike-Share-Prediction
Predictive model that tells important factors(or features) affecting the demand for shared bikes
shubhammandhare10/Analyzing-Forecasting-Stock-Prices
Forecasting time series data using ARIMA models. Used covariance matrix to find dependencies between stocks.
yan-vei/cybersecurity
Final Cybersecurity ML project of Marc Mestre and Yana Veitsman for Data Mining and Machine Learning course at University of Valencia, Spring 2021
bhargavflash/Customer_Churn_Modelling
Artificial Neural Network using Keras in python to identify customers who are likely to churn.
EmamulHossen/FeatureTransformation-Assignment-
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
MoinDalvs/Learn_Feature_Engineering
Data Set: House Prices: Advanced Regression Techniques Feature Engineering with 80+ Features
shreeratn/Predicting-Credit-Card-Approval
Build a machine learning model to predict if a credit card application will get approved.
Siddharth1989/WranglingRealEstateData
Wrangled real estate data from multiple sources and file formats, brought it into a single consistent form and analysed the results.
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
Aysenuryilmazz/DamagePropagationModellingCMAPSS
Aircraft Engine Run-to-Failure Simulation
Aysenuryilmazz/HR_Analytics_EDA
Exploratory Data Analysis for HR dataset
bushra-ansari/Naive-Bayes-Classifier
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.
DataScienceVishal/Bank_Customer_Behaviour
Bank Customer Behaviour Prediction
Harunatsuko/cloud_generator
Cloud image generation with Python and OpneCV
DataScienceVishal/House-Price-Prediction
Linear Regression+Decision Tree+Random Forest
Lorsmo/Exoplanet-Exploration
Created machine learning models capable of classifying candidate exoplanets from a raw dataset.
ManishShee24/Stock-Price-Prediction
Stock price prediction is the process of forecasting future stock prices based on historical data and market indicators.
prankur16shukla/Power_Consumption_Prediction
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.
RafeyIqbalRahman/Data-Scaling-Techniques
This repository demonstrates the scaling of the data using Scikit-learn's StandardScaler, MinMaxScaler, and RobustScaler.
yyyukeqi/Predict-Sales-Revenue-
Time Series Model