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+ # -*- coding: utf-8 -*-
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+ """.1393
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+
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+ Automatically generated by Colab.
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+
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+ Original file is located at
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+ https://colab.research.google.com/drive/1-65IULC0-UxJ7kZBDYo3KQ2a6m5JzwVV
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+ """
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+
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+ # Commented out IPython magic to ensure Python compatibility.
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+ import pandas as pd
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+ import numpy as np
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+ import seaborn as sns
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+ import matplotlib.pyplot as plt
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+ import warnings
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+ warnings.filterwarnings('ignore')
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+ # %matplotlib inline
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+
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+ file_path = '/content/Fake Postings (2).csv'
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+ df = pd.read_csv(file_path)
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+
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+ df.head()
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+
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+ df.isnull().sum()
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+
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+ sns.countplot(x='fraudulent', data=df)
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+ plt.title('Distribution of Fraudulent Job Postings')
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+ plt.show()
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+
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+ sns.countplot(y='employment_type', data=df, order=df['employment_type'].value_counts().index)
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+ plt.title('Employment Type Distribution')
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+ plt.show()
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+
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+ plt.figure(figsize=(10, 8))
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+ sns.countplot(y='industry', data=df, order=df['industry'].value_counts().index[:10])
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+ plt.title('Top 10 Industries by Job Postings')
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+ plt.show()
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+
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+ df.fillna('Unknown', inplace=True)
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+ df['fraudulent'] = df['fraudulent'].astype(int)
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+
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+ df['description_length'] = df['requirements'].apply(lambda x: len(x.split(',')))
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+
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+ from sklearn.model_selection import train_test_split
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+ from sklearn.linear_model import LogisticRegression
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+ from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
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+
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+ # Select features and target
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+ features = ['description_length', 'num_requirements']
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+ X = df[features]
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+ y = df['fraudulent']
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+
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+ # Ensure there are at least two classes in the target variable
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+ if len(y.unique()) < 2:
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+ print("The target variable 'fraudulent' must have at least two classes. Exiting...")
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+ else:
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+ # Split the data
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+ X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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+
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+ # Train the model
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+ model = LogisticRegression()
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+ model.fit(X_train, y_train)