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Print(df.head(n=10)) # this is to print the first 10 rows of the data Splitting the data into train and test data set X_train, X_test, y_train, y_test = train_test_split(df.iloc,df.iloc,test_size=0.35) Importing the modules and data sets import matplotlib.pyplot as pltįrom sklearn.model_selection import train_test_split Naming the columns of the Iris dataset using a pandas data frame col_names = "Sepal_Length Sepal_Width Petal_Length Petal_Width".split(' ')ĭf = pd.DataFrame(iris_data.data,columns=col_names) Then fit the model and plot a scatter plot using matplotlib, and also find the model score.Train the model using LinearRegression from sklearn.linear_model.Then I’ll split the dataset into test and training datasets.I’ll import the iris data set from the sklearn.datasets.The measure is actually the percent of data assigned for each purpose. Test_size and train_size are by default set to 0.25 and 0.75 respectively if it is not explicitly mentioned. Mostly, parameters – x,y,test_size– are used and shuffle is by default True so that it picks up some random data from the source you have provided. The syntax: train_test_split(x,y,test_size,train_size,random_state,shuffle,stratify) Here, I have used sklearn’s very well known Iris data set to demonstrate the “ sklearn.model_ain_test_split” function. This utility function comes under the sklearn’s ‘ model_selection‘ function and facilitates in separating training data-set to train your machine learning model and another testing data set to check whether your prediction is close or not? Modules Required and Versions of them: import matplotlib.pyplot as pltįrom sklearn import datasets, linear_modelįrom sklearn.model_selection import train_test_split In this post, I will be explaining about scikit learn’s “ train_tets_split" function.