added my data ML test

other examples/eg1.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Feb 16 11:56:57 2022
+
+@author: tanu
+"""
+
+# source
+#https://machinelearningmastery.com/automate-machine-learning-workflows-pipelines-python-scikit-learn/
+
+###############################################
+# Pipeline 1: Data Preparation and Modeling
+###############################################
+
+# Create a pipeline that standardizes the data then creates a model
+from pandas import read_csv
+from sklearn.model_selection import KFold
+from sklearn.model_selection import cross_val_score
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import Pipeline
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+
+# load data
+url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv"
+names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
+dataframe = read_csv(url, names=names)
+array = dataframe.values
+X = array[:,0:8]
+Y = array[:,8]
+
+# create pipeline
+estimators = []
+estimators.append(('standardize', StandardScaler()))
+estimators.append(('lda', LinearDiscriminantAnalysis()))
+model = Pipeline(estimators)
+
+# evaluate pipeline
+seed = 7
+#kfold = KFold(n_splits=10, random_state=seed)
+kfold = KFold(n_splits=10, random_state=None)
+results = cross_val_score(model, X, Y, cv=kfold)
+print(results.mean())
+
+###############################################
+# Pipeline 2: Feature Extraction and Modeling
+###############################################
+
+# Create a pipeline that extracts features from the data then creates a model
+from sklearn.pipeline import FeatureUnion
+from sklearn.linear_model import LogisticRegression
+from sklearn.decomposition import PCA
+from sklearn.feature_selection import SelectKBest
+
+# load data
+url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv"
+names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
+dataframe = read_csv(url, names=names)
+array = dataframe.values
+X = array[:,0:8]
+Y = array[:,8]
+
+# create feature union
+features = []
+features.append(('pca', PCA(n_components=3)))
+features.append(('select_best', SelectKBest(k=6)))
+feature_union = FeatureUnion(features)
+
+# create pipeline
+estimators = []
+estimators.append(('feature_union', feature_union))
+estimators.append(('logistic', LogisticRegression()))
+model = Pipeline(estimators)
+
+# evaluate pipeline
+seed = 7
+kfold = KFold(n_splits=10, random_state=seed)
+results = cross_val_score(model, X, Y, cv=kfold)
+print(results.mean())
+
+#%%############################################################################
+from sklearn import datasets
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+from sklearn.tree import DecisionTreeClassifier
+# import some data within sklearn for iris classification
+iris = datasets.load_iris()
+X = iris.data
+y = iris.target
+ 
+# Splitting data into train and testing part
+# The 25 % of data is test size of the data
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)
+# importing pipes for making the Pipe flow
+from sklearn.pipeline import Pipeline
+# pipe flow is :
+# PCA(Dimension reduction to two) -> Scaling the data -> DecisionTreeClassification
+pipe = Pipeline([('pca', PCA(n_components = 2))
+                 , ('std', StandardScaler())
+                 , ('decision_tree', DecisionTreeClassifier())]
+                 , verbose = True)
+ 
+# fitting the data in the pipe
+pipe.fit(X_train, y_train)
+ 
+# scoring data
+from sklearn.metrics import accuracy_score
+print(accuracy_score(y_test, pipe.predict(X_test)))