added my data ML test

practice_d1.py

@@ -0,0 +1,69 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Feb 21 13:06:25 2022
+
+@author: tanu
+"""
+X_train
+scaler = preprocessing.MinMaxScaler()
+scaler.fit(X_train)
+
+x_train_scaled = scaler.transform(X_train)
+x_train_scaled
+
+
+foo = scaler.fit(X_train)
+
+x_train_scaled2  = foo.transform(X_train)
+x_train_scaled2
+
+(x_train_scaled == x_train_scaled2).all()
+
+
+toy = pd.DataFrame({
+'numeric': [1., 2., 3., 4., 5.],
+'category': ['a', 'a', 'b', 'c', 'b']
+})
+numeric_features = ['numeric']
+categorical_features = ['category']
+preprocessor = ColumnTransformer(transformers=[('num', StandardScaler(), numeric_features),
+('cat', OneHotEncoder(), categorical_features)
+])
+preprocessor.fit(toy)
+bar = preprocessor.transform(toy)
+bar
+#############
+toy2 = pd.DataFrame({
+'numeric': [1., 2., 3., 4., 5.],
+'numeric2': [1., 2., 3., 4., 6.],
+'category': ['a', 'a', 'b', 'c', 'b'],
+'category2': ['b', 'a', 'b', 'e', 'f']
+})
+numeric_features = ['numeric', 'numeric2']
+categorical_features = ['category', 'category2']
+preprocessor = ColumnTransformer(transformers=[
+('num', StandardScaler(), numeric_features),
+('cat', OneHotEncoder(), categorical_features)
+])
+
+preprocessor.fit(toy2)
+bar2 = preprocessor.transform(toy2)
+bar2
+
+####
+import pandas as pd
+from pandas import DataFrame
+import numpy as np
+
+from sklearn.decomposition import PCA
+from pandas import DataFrame
+pca = PCA(n_components = 2)
+pca.fit(toy2.iloc[:, 0:2])
+
+columns = ['pca_%i' % i for i in range(2)]
+df_pca = DataFrame(pca.transform(toy2.iloc[:, 0:2])
+                   , columns=columns
+                   , index=toy2.index)
+df_pca.head()
+