Logistic regression in Python using Excel data

In the following code, we generate random sample data with two features (Feature1 and Feature2) and a binary target variable (Target) based on a simple condition. We perform logistic regression, evaluate the model using accuracy, the confusion matrix, and a classification report, visualize the results for binary classification, and interpret the coefficients.

The following is a step-by-step code example:

1. Again, we start with importing the necessary libraries:

   # Import necessary libraries
   import pandas as pd
   import numpy as np
   import matplotlib.pyplot as plt
   from sklearn.model_selection import train_test_split
   from sklearn.linear_model import LogisticRegression
   from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

   For this example, we will use a different sample dataset:

   # Step 0: Generate sample data
   np.random.seed(0)
   n_samples = 100
   X = np.random.rand(n_samples, 2)  # Two features
   y = (X...