# Logistic Regression from scratch in Python

Classification techniques are used to handle categorical variables. Logistic Regression is a linear classifier which returns probabilities(P(Y=1) or P(Y=0)) as a function of the dependent variable(X).The dependent variable is a binary variable that contains data in the form of either success(1) or failure(0).

Let’s say we want to predict for a person, knowing their age, whether he will take up the offer or not. The offer is ‘to purchase a Lenovo 800 mobile model’.How about instead we will state a probability or a likelihood of that person taking that offer.

- Your system must have a Spyder(Python 3.7) or any other latest version software installed.
- You need to have a dataset file, which is generally an ms-excel file, with a .csv extension.
- Set the folder as a working directory, in which your dataset is stored.
- You need to have a basic understanding of Python programming language.

### Step by step implementation:

Make sure that you check the prerequisites before proceeding. Also, your system should be efficient and lag-free.

### 1. Importing the libraries:

Firstly, let us import the necessary libraries.

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

### 2. Importing the dataset

The dataset is as shown below:

dataset = pd.read_csv('lenovo 800_customers.csv') X = dataset.iloc[:, [2, 3]].values y = dataset.iloc[:, 4].values

### 3. Deciding the training and the test set

from sklearn.model_selection import train_test_split X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size = 0.25, random_state = 0)

### 4. Feature Scaling

from sklearn.preprocessing import StandardScaler ss = StandardScaler() X_trainset = ss.fit_transform(X_trainset) X_testset = ss.transform(X_testset)

### 5. Fitting Logistic Regression to the Training set

from sklearn.linear_model import LogisticRegression classifier = LogisticRegression(random_state = 0) classifier.fit(X_trainset, y_trainset)

### 6. Predicting the Test set results

y_pred = classifier.predict(X_testset) from sklearn.metrics import confusion_matrix cm = confusion_matrix(y_testset, y_pred)

### 7. Plotting the Test set results

Finally, we can best understand the concept of *logistic regression *through the following plot:

from matplotlib.colors import ListedColormap X_set, y_set = X_testset, y_testset 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(('orange', 'blue'))) 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(('orange', 'blue'))(i), label = j) plt.title('Test set') plt.xlabel('Age') plt.ylabel('Salary') plt.legend() plt.show()

So, you can clearly spot incorrect predictions with the respective colors.

### Conclusion:

As we can clearly see from the plot, we get a straight line for linear models. We can use the model to test on similar datasets with more number of independent variables.

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