Upload _1393.py
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_1393.py
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# -*- coding: utf-8 -*-
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""".1393
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Automatically generated by Colab.
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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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# 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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file_path = '/content/Fake Postings (2).csv'
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df = pd.read_csv(file_path)
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df.head()
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df.isnull().sum()
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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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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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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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df.fillna('Unknown', inplace=True)
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df['fraudulent'] = df['fraudulent'].astype(int)
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df['description_length'] = df['requirements'].apply(lambda x: len(x.split(',')))
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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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# 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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# 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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# Train the model
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model = LogisticRegression()
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model.fit(X_train, y_train)
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