#Linear Regression

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.datasets import make_classification
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

X, y = make_classification(n_samples = 1000, n_features = 2, n_redundant =0, n_informative = 2,  n_classes = 2, random_state = 42)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)

linear_model = LinearRegression()
linear_model.fit(X_train, y_train)
y_pred_prob = linear_model.predict(X_test)
y_pred = (y_pred_prob >=0.5).astype(int)

cm = confusion_matrix(y_test, y_pred)
plt.figure(figsize = (6, 4))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False)