Data Preprocessing in Machine Learning using Python - SimpleImputer, OneHotEncoder, train_test_split
GitHub: https://github.com/bibhutighimire/Dat...
Google Collab: https://colab.research.google.com/dri...
Data Preprocessing in Machine Learning using Python 2023
#SimpleImputer #OneHotEncoder #train_test_split #StandardScaler #LabelEncoder
Website: https://www.javatpoint.com/how-to-get...
//Import Library:
import pandas as pd
import numpy as np
//Import Dataset:
dataset = pd.read_csv('Data.csv')
print(dataset)
//Split dataset into X and y i.e. independent and dependent model
X = dataset.iloc[:,:-1].values
print(X)
y = dataset.iloc[:,-1].values
print(y)
//Handling missing data:
from sklearn.impute import SimpleImputer
si = SimpleImputer(missing_values= np.nan , strategy='mean')
si.fit(X[:,1:3])
X[:, 1:3] = si.transform(X[:,1:3])
print(X)
//Encoding categorical data:
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [0])] , remainder= 'passthrough')
X = ct.fit_transform(X)
print(X)
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y = le.fit_transform(y)
print(y)
//Splitting data into training and testing model:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)
print(X_train)
print(y_train)
print(y_test)
print(X_test)
//Feature Scaling:
from sklearn.preprocessing import StandardScaler
ss = StandardScaler()
X_train[:,3:5] = ss.fit_transform(X_train[:,3:5])
X_test[:,3:5] = ss.transform(X_test[:,3:5])
print(X_train)
print(X_test)
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