Upload _1434.py

_1434.py

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+# -*- coding: utf-8 -*-
+""".1434
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1zCqF_BIYa91iouRTczXbC21smYapzDHu
+"""
+
+# Commented out IPython magic to ensure Python compatibility.
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+import warnings
+warnings.filterwarnings('ignore')
+# %matplotlib inline
+
+file_path = '/content/Fake Postings.csv'
+df = pd.read_csv(file_path)
+
+df.head()
+
+df.isnull().sum()
+
+sns.countplot(x='fraudulent', data=df)
+plt.title('Distribution of Fraudulent Job Postings')
+plt.show()
+
+sns.countplot(y='employment_type', data=df, order=df['employment_type'].value_counts().index)
+plt.title('Distribution Type Distribution')
+plt.show()
+
+plt.figure(figsize=(10, 8))
+sns.countplot(y='industry', data=df, order=df['industry'].value_counts().index[:10])
+
+df.fillna('Unknown', inplace=True)
+df['fraudulent'] = df['fraudulent'].astype(int)
+
+df['description_length'] = df['description'].apply(len)
+df['num_requirements'] = df['requirements'].apply(lambda x: len(x.split(',')))
+
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
+
+features = ['description_length', 'num_requirements']
+X = df[features]
+y = df['fraudulent']
+
+if len(y.unique()) < 2:
+       print("The target variable 'fraudulent' must have at least two classes. Exiting...")
+else:
+  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=-.2, random_state=42)
+
+  model = LogisticRegression()
+  model.fit(X_train, y_train)
+
+if len(y.unique()) >= 2:
+  y_pred = model.predict(X_test)
+
+  accuracy = accuracy_score(y_test, y_pred)
+  print(f'Accuracy: {accuracy:.2}')
+
+if len(y.unique()) >= 2:
+  conf_matrix = confusion_matrix(y_test, y_pred)
+  sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')
+  plt.title('Confusion Matrix')
+  plt.xlabel('Predicted')
+  plt.ylabel('Actual')
+  plt.show()
+
+if len(y.unique()) >= 2:
+  print(classification_report(y_test, y_pred))