How to standardise IRIS Data in Python?
DATA MUNGING DATA CLEANING PYTHON MACHINE LEARNING RECIPES PANDAS CHEATSHEET     ALL TAGS

How to standardise IRIS Data in Python?

How to standardise IRIS Data in Python?

This recipe helps you standardise IRIS Data in Python

0
In [2]:
## How to standarise IRIS Data in Python 
def Kickstarter_Example_41():
    print()
    print(format('How to standarise IRIS Data in Python', '*^82'))
    import warnings
    warnings.filterwarnings("ignore")

    from sklearn import datasets
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler

    iris = datasets.load_iris()
    X = iris.data
    y = iris.target
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,
                                                        random_state=42)
    sc = StandardScaler()
    sc.fit(X_train)
    X_train_std = sc.transform(X_train)
    X_test_std = sc.transform(X_test)

    print(); print(X_train[0:5])
    print(); print(X_train_std[0:5])
    print(); print(X_test[0:5])
    print(); print(X_test_std[0:5])
Kickstarter_Example_41()
**********************How to standarise IRIS Data in Python***********************

[[5.5 2.4 3.7 1. ]
 [6.3 2.8 5.1 1.5]
 [6.4 3.1 5.5 1.8]
 [6.6 3.  4.4 1.4]
 [7.2 3.6 6.1 2.5]]

[[-0.4134164  -1.46200287 -0.09951105 -0.32339776]
 [ 0.55122187 -0.50256349  0.71770262  0.35303182]
 [ 0.67180165  0.21701605  0.95119225  0.75888956]
 [ 0.91296121 -0.02284379  0.30909579  0.2177459 ]
 [ 1.63643991  1.41631528  1.30142668  1.70589097]]

[[6.1 2.8 4.7 1.2]
 [5.7 3.8 1.7 0.3]
 [7.7 2.6 6.9 2.3]
 [6.  2.9 4.5 1.5]
 [6.8 2.8 4.8 1.4]]

[[ 0.3100623  -0.50256349  0.484213   -0.05282593]
 [-0.17225683  1.89603497 -1.26695916 -1.27039917]
 [ 2.23933883 -0.98228318  1.76840592  1.43531914]
 [ 0.18948252 -0.26270364  0.36746819  0.35303182]
 [ 1.15412078 -0.50256349  0.54258541  0.2177459 ]]

Relevant Projects

Ecommerce product reviews - Pairwise ranking and sentiment analysis
This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.

Loan Eligibility Prediction using Gradient Boosting Classifier
This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.

Ensemble Machine Learning Project - All State Insurance Claims Severity Prediction
In this ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.

Time Series Forecasting with LSTM Neural Network Python
Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.

Predict Macro Economic Trends using Kaggle Financial Dataset
In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.

PySpark Tutorial - Learn to use Apache Spark with Python
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.

Human Activity Recognition Using Smartphones Data Set
In this deep learning project, you will build a classification system where to precisely identify human fitness activities.

Predict Credit Default | Give Me Some Credit Kaggle
In this data science project, you will predict borrowers chance of defaulting on credit loans by building a credit score prediction model.

Mercari Price Suggestion Challenge Data Science Project
Data Science Project in Python- Build a machine learning algorithm that automatically suggests the right product prices.

Learn to prepare data for your next machine learning project
Text data requires special preparation before you can start using it for any machine learning project.In this ML project, you will learn about applying Machine Learning models to create classifiers and learn how to make sense of textual data.