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# Logistic Regression

The Logistic Regression or Logistic Model also called the Logit Model is a classification algorithm that predicts a categorical feature based on a set of independent variables. Logistic Regression is one of the simplest classification algorithms that can be used to predict values for a categorical dependent variable.

For a deeper understanding of Logistic Regression, use the following resources:

• #### Logistic Regression

In this practice session, we will learn to code Logistic Regression. We will perform the following steps to build a simple classifier using the popular Iris dataset. You can find the dataset here.

Step 1. Data Preprocessing

• Importing the libraries.
• Importing dataset (Dataset Link https://archive.ics.uci.edu/ml/datasets/iris).
• Dealing with the categorical variable.
• Classifying dependent and independent variables.
• Splitting the data into a training set and test set.
• Feature scaling.

Step 2. Logistic Regression

• Create a Logistic classifier.
• Feed the training data to the classifier.
• Predicting the species for the test set.
• Using the confusion matrix to find accuracy.

Click on Start/Continue Hackathon to go to the Practice page.

## 4.7

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1. ### Good course

5

Simple and easy to understand

2. ### Finished

4

#Importing libraries
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings(“ignore”)

#Importing dataset

#Dealing with categorical variable
print(“\n————————-\nLabel Encoding The Categorical Variable – Species\n————————-“)

from sklearn.preprocessing import LabelEncoder
labelencoder = LabelEncoder()
dataset[‘Species’] = labelencoder.fit_transform(dataset[‘Species’])

print(“\n————————-\nDataset after Label Encoding Species:- Species\n————————-\n”, dataset.head())

“””Classifying dependent and independent variables
here SepalLengthCm, SepalWidthCm, PetalLengthCm and PetalWidthCm are independent variables where as Species is dependent”””
X = dataset.iloc[:,:-1].values
y = dataset.iloc[:,-1].values

#Splitting into training set and test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train,y_test = train_test_split(X,y,test_size = 0.25, random_state=0)

print(“\n————————-\nScaling or Normalizing the features \n————————-“)

#Feature scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

print(“\n————————-\nDataset after Scaling:\n————————-\n”, )

print(“\nX_train :\n”, X_train)
print(“————————-“)
print(“\nX_test :\n”, X_test)

######### Logistic Regression ################

#Create a Logistic classifier

from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state=0, C= 10.617591834830002, penalty = ‘l1’,n_jobs=-1)

#Feed the training data to the classifier
classifier.fit(X_train,y_train)

#Predicting the species for test set
y_pred = classifier.predict(X_test)

#Using confusion matrix to find the accuracy
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test,y_pred)

accuracy = cm.diagonal().sum()/cm.sum()

print(“\n—————————\n”)
print(“Accuracy of Predictions = “,accuracy )

3. ### Logistic Regression

5

The usage of logistic regression and standardizing the data using StandardSclaer()
Insight on confusion matrix , and how to find the accuracy using it.

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