In this tutorial, You’ll learn **Logistic Regression.** Here you’ll know what exactly is Logistic Regression and you’ll also see an Example with **Python**. Logistic Regression is an important topic of **Machine Learning** and I’ll try to make it as simple as possible.

In the early twentieth century, Logistic regression was mainly used in Biology after this, it was used in some social science applications. If you are curious, You may ask where we should use logistic regression? **So we use Logistic Regression when our independent variable is categorical.**

**Examples:**

- To predict whether a person will buy a car (1) or (0)
- To know whether the tumor is malignant (1) or (0)

Now let us consider a scenario where you have to classify whether a person will buy a car or not. In this case, if we use simple linear regression, we will need to specify a threshold on which classification can be done.

Let say the actual class is the person will buy the car, and predicted continuous value is 0.45 and the threshold we have considered is 0.5, then this data point will be considered as the person will not buy the car and this will lead to the wrong prediction.

So we conclude that we can not use linear regression for this type of classification problem. As we know linear regression is bounded, So here comes **logistic regression** where value strictly ranges from 0 to 1.

**Simple Logistic Regression:**

**Output: ** 0 or 1**Hypothesis: ** K = W * X + B**hΘ(x)** =** **sigmoid(K)

**Sigmoid Function:**

**Types of Logistic Regression:**

**Binary Logistic Regression**

Only two possible outcomes(Category).

Example: The person will buy a car or not.

**Multinomial Logistic Regression**

More than two Categories possible without ordering.

**Ordinal Logistic Regression**

More than two Categories possible with ordering.

Real-world Example with Python:

Now we’ll solve a real-world problem with Logistic Regression. We have a Data set having 5 columns namely: **User ID**, **Gender**, **Age**, **EstimatedSalary** and **Purchased**. Now we have to build a model that can predict whether on the given parameter a person will buy a car or not.

**Steps To Build the Model:**

1. Importing the libraries

Here we’ll import libraries which will be needed to build the model.

```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
```

2. Importing the Data set

We’ll import our Data set in a variable (i.e dataset) using pandas.

`dataset = pd.read_csv('Social_Network_Ads.csv')`

3. Splitting our Data set in Dependent and Independent variables.

In our Data set we’ll consider **Age** and **EstimatedSalary **as Independent variable and **Purchased** as Dependent Variable.

```
X = dataset.iloc[:, [2,3]].values
y = dataset.iloc[:, 4].values
```

Here **X** is Independent variable and **y **is Dependent variable.

3. Splitting the Data set into the Training Set and Test Set

Now we’ll split our Data set into Training Data and Test Data. Training data will be used to train our

Logistic model and Test data will be used to validate our model. We’ll use **Sklearn** to split our data. We’ll import **train_test_split** from **sklearn.model_selection**

```
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)
```

4. Feature Scaling

Now we’ll do feature scaling to scale our data between 0 and 1 to get better accuracy.

Here Scaling is important because there is a huge difference between **Age **and **EstimatedSalay.**

- Import
**StandardScaler**from**sklearn.preprocessing** - Then make an instance
**sc_X**of the object**StandardScaler** - Then fit and transform
**X_train**and transform**X_test**

```
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
```

5. Fitting Logistic Regression to the Training Set

Now we’ll build our classifier (Logistic).

- Import
**LogisticRegression**from**sklearn.linear_model** - Make an instance
**classifier**of the object**LogisticRegression**and give**random_state = 0**to get the same result every time. - Now use this classifier to fit
**X_train**and**y_train**

```
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state=0)
classifier.fit(X_train, y_train)
```

Cheers!! After executing the above command you’ll have a classifier that can predict whether a person will buy a car or not.

Now use the **classifier** to make the prediction for the Test Data set and find the accuracy using Confusion matrix.

6. Predicting the Test set results

`y_pred = classifier.predict(X_test)`

Now we’ll get **y_pred**

Now we can use **y_test** (Actual Result) and **y_pred** ( Predicted Result) to get the accuracy of our model.

7. Making the Confusion Matrix

Using Confusion matrix we can get accuracy of our model.

```
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
```

You’ll get a matrix **cm** .

**Use cm to calculate accuracy as shown below:**

**Accuracy **=** ( **cm[0][0] **+** cm[1][1]** ) /** **(** Total test data points **)**

Here we are getting accuracy of 89 % . Cheers!! we are getting a good accuracy.

Finally, we’ll Visualise our Training set result and Test set result. We’ll use matplotlib to plot our Data set.

**Visualizing the Training Set result**

```
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Logistic Regression (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
```

**Visualizing the Test Set result**

```
from matplotlib.colors import ListedColormap
X_set, y_set = X_test, y_test
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Logistic Regression (Test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
```

**Now You can build your own classifier for Logistic Regression.****Thanks!! Keep Coding !!**

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