Скачать или смотреть Python for Machine Learning | Label Encoding | Preprocessing - P16

Python for Machine Learning | Label Encoding | Preprocessing - P16

Table of content
0:00 Introductions
00:08 What is Label Encoding
00:22 Why do we use Label Encoding
02:01 How to fill the categorical column with the modal values
02:21 How to use Dataframe.isnull().sum()
02:43 Extract features and labels
03:09 Import Imputer and OneHotEncoder
03:26 Create Dataframe with only features
03:34 Check Dataframe attribute with Dataframe.info()
03:54 Check the null values in the Dataframe df1 using df1.isnull().sum()
04:09 Label Encoding
04:14 Identify the columns on which we will apply Label Encoding
04:46 Define the object of Label Encoder
05:20 Apply Label Encoding on the respective categorical columns
05:41 Apply fit_transform on LabelEncoder()
06:14 How to create dataframe with the transformed features

Topic to be covered - Label Encoding

import pandas as pd
import numpy as np

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

'''Get the rows that contains NULL (NaN)'''
df.isnull().sum()

'''Fill the NaN values for Occupation, Emplyment Status and Employement Type'''

col = ['Occupation','Employment Status','Employement Type']
df[col] = df[col].fillna(df.mode().iloc[0])

features = df.iloc[:,:-1].values

labels = df.iloc[:,-1].values

from sklearn.preprocessing import Imputer, OneHotEncoder

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

'''2 step transformation
Fit and Tranform'''

imputer.fit(features[:,[1,6]])

features[:,[1,6]] = imputer.fit_transform(features[:,[1,6]])

'''------------------------------- L A B E L E N C O D I I N ------------------'''

from sklearn.preprocessing import LabelEncoder
encode = LabelEncoder()

features[:,0] = encode.fit_transform(features[:,0])
features[:,2] = encode.fit_transform(features[:,2])
features[:,3] = encode.fit_transform(features[:,3])
features[:,4] = encode.fit_transform(features[:,4])
features[:,5] = encode.fit_transform(features[:,5])


All Playlist of this youtube channel
====================================

1. Data Preprocessing in Machine Learning
   • Data Preprocessing in Machine Learning| Li...  

2. Confusion Matrix in Machine Learning, ML, AI
   • Confusion Matrix in Machine Learning, ML, AI  

3. Anaconda, Python Installation, Spyder, Jupyter Notebook, PyCharm, Graphviz
   • Anaconda | Python Installation | Spyder | ...  

4. Cross Validation, Sampling, train test split in Machine Learning
   • Cross Validation | Sampling | train test s...  

5. Drop and Delete Operations in Python Pandas
   • Drop and Delete Operations in Python Pandas  

6. Matrices and Vectors with python
   • Matrices and Vectors with python  

7. Detect Outliers in Machine Learning
   • Detect Outliers in Machine Learning  

8. TimeSeries preprocessing in Machine Learning
   • TimeSeries preprocessing in Machine Learning  

9. Handling Missing Values in Machine Learning
   • Handling Missing Values in Machine Learning  

10. Dummy Encoding Encoding in Machine Learning
   • Label Encoding, One hot Encoding, Dummy En...  

11. Data Visualisation with Python, Seaborn, Matplotlib
   • Data Visualisation with Python, Matplotlib...  

12. Feature Scaling in Machine Learning
   • Feature Scaling in Machine Learning  

13. Python 3 basics for Beginner
   • Python | Python 3 Basics | Python for Begi...  

14. Statistics with Python
   • Statistics with Python  

15. Sklearn Scikit Learn Machine Learning
   • Sklearn Scikit Learn Machine Learning  

16. Python Pandas Dataframe Operations
   • Python Pandas Dataframe Operations  

17. Linear Regression, Supervised Machine Learning
   • Linear Regression | Supervised Machine Lea...  

18 Interiew Questions on Machine Learning and Data Science
   • Interview Question for Machine Learning, D...  

19. Jupyter Notebook Operations
   • Jupyter and Spyder Notebook Operations in ...