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 Learnin...
2. Confusion Matrix in Machine Learning, ML, AI
• Confusion Matrix in Machine Learning,...
3. Anaconda, Python Installation, Spyder, Jupyter Notebook, PyCharm, Graphviz
• Anaconda | Python Installation | Spyd...
4. Cross Validation, Sampling, train test split in Machine Learning
• Cross Validation | Sampling | train t...
5. Drop and Delete Operations in Python Pandas
• Drop and Delete Operations in Python ...
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 L...
9. Handling Missing Values in Machine Learning
• Handling Missing Values in Machine Le...
10. Dummy Encoding Encoding in Machine Learning
• Label Encoding, One hot Encoding, Dum...
11. Data Visualisation with Python, Seaborn, Matplotlib
• Data Visualisation with Python, Matpl...
12. Feature Scaling in Machine Learning
• Feature Scaling in Machine Learning
13. Python 3 basics for Beginner
• Python | Python 3 Basics | Python for...
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 Machin...
18 Interiew Questions on Machine Learning and Data Science
• Interview Question for Machine Learni...
19. Jupyter Notebook Operations
• Jupyter and Spyder Notebook Operation...
On this page of the site you can watch the video online Python for Machine Learning | Label Encoding | Preprocessing - P16 with a duration of hours minute second in good quality, which was uploaded by the user technologyCult 01 January 2018, share the link with friends and acquaintances, this video has already been watched 16,384 times on youtube and it was liked by 134 viewers. Enjoy your viewing!
