Python for Machine Learning | Preprocessing | fit, transform and fit_transform

Veröffentlicht am: 04 August 2018
auf dem Kanal: technologyCult

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Python for Machine Learning | Preprocessing | fit, transform and fit_transform
Table of content:
00:17 Import pandas library and the dataset
00:34 Get the Null value counts using dataframe df.isnull().sum()
00:46 Check the Null value by expanding dataframe df in the variable explorer
01:07 Import Imputer and create the object of Imputer()
01:15 Imputer object is created with different parameter like missing_value'NaN' and strategy='mean'
02:13 Fit the Imputer object created with Age and Salary column and execute it
02:36 imputer.statistics_
03:22 imputer.transform to be applied on Age and Salary column
03:58 Save imputer.transform to some variable
04:14 How to apply imputer's fit and transform in a single step
04:42 What is Label Encoding and How fit, transform and fit_transform works separately
04:52 Import LabelEncoder and create its object
05:15 Apply Label Encoding on the country column
05:37 Fit LabelEncoder object with country column
05:54 After fitting LabelEncoder object how to get the unique classes using encoder.classes_
06:01 Transform LabelEncoder object to get the numeric value assigned to each country
06:31 Use LabelEncoder's fit_transform on the country column
07:12 Apply fit, transform and fit_transform on standardscaler
07:32 Import numpy and create an array
07:40 import StandardScaler from sklearn.preprocessing
07:50 Create an object of StandardScaler
08:13 Scale the dataset using fit_transform
08:23 Formula to calculate StandardScaler
09:00 Get the mean using standardscaler object standardscaler.mean
09:34 Get the variance using standardscaler object standardscaler.variance_
09:52 How to calculate the standard deviation of each column
10:37 Apply fit on standardscaler object
11:16 Apply transform on standardscaler object

Topic to be covered -

How fit(), transform() and fit_transform() works ?
OR
Difference between fit(), transform() and fit_transform()

Code Starts Here
==============

import pandas as pd
from sklearn.preprocessing import Imputer, LabelEncoder

df = pd.read_csv('Datapreprocessing.csv')

imputer = Imputer(missing_values='NaN',strategy='mean',axis=0)

imputer.fit(df[['Age','Salary']])
X = imputer.transform(df[['Age','Salary']])

imputer.fit_transform(df[['Age','Salary']])

encode = LabelEncoder()

encode.fit(df['Country'])
encode.transform(df['Country'])

encode.fit_transform(df['Country'])

import numpy as np
from sklearn.preprocessing import StandardScaler

x1 = np.array([[1,2,3],
[4,5,6],
[7,8,9]])

standscaler = StandardScaler()

x_scaler = standscaler.fit_transform(x1)
print(x_scaler)

(Xi - Xmean) / (standard Deviation of that feature)

standscaler.fit(x1)
standscaler.transform(x1)

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