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add cm plot

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Pritimay Sarkar
2023-10-01 15:10:18 +05:30
parent 23bba3f5a1
commit 2bca96a0f6
1 changed files with 13 additions and 1 deletions
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scripts/logistic.py
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@@ -2,7 +2,7 @@ import pandas as pd
from sklearn.model_selection import train_test_split from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score, classification_report from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
import pickle import pickle
import statsmodels.api as sm import statsmodels.api as sm
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
@@ -38,6 +38,18 @@ accuracy = accuracy_score(y_test, y_pred)
classification_report_result = classification_report(y_test, y_pred) classification_report_result = classification_report(y_test, y_pred)
#sns.heatmap(pd.DataFrame(classification_report_result).iloc[:-1, :].T, annot=True) #sns.heatmap(pd.DataFrame(classification_report_result).iloc[:-1, :].T, annot=True)
# Calculate the confusion matrix
confusion = confusion_matrix(y_test, y_pred)
# Plot the confusion matrix using Seaborn
plt.figure(figsize=(8, 6))
sns.heatmap(confusion, annot=True, fmt='d', cmap='Blues', linewidths=0.5)
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.title('Confusion Matrix')
plt.show()
print(f"Accuracy: {accuracy}") print(f"Accuracy: {accuracy}")
print("Classification Report:") print("Classification Report:")
print(classification_report_result) print(classification_report_result)