_1393.py

# -*- coding: utf-8 -*-
""".1393

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1-65IULC0-UxJ7kZBDYo3KQ2a6m5JzwVV
"""

# 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 (2).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('Employment Type Distribution')
plt.show()

plt.figure(figsize=(10, 8))
sns.countplot(y='industry', data=df, order=df['industry'].value_counts().index[:10])
plt.title('Top 10 Industries by Job Postings')
plt.show()

df.fillna('Unknown', inplace=True)
df['fraudulent'] = df['fraudulent'].astype(int)

df['description_length'] = 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

# Select features and target
features = ['description_length', 'num_requirements']
X = df[features]
y = df['fraudulent']

# Ensure there are at least two classes in the target variable
if len(y.unique()) < 2:
    print("The target variable 'fraudulent' must have at least two classes. Exiting...")
else:
    # Split the data
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    # Train the model
    model = LogisticRegression()
    model.fit(X_train, y_train)