ig_fake_account_detector.py

# -*- coding: utf-8 -*-
"""IG Fake Account Detector

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/11AvA8ysxhTbkhDXq-Sn2HnnKRgdGeT9U
"""

# Commented out IPython magic to ensure Python compatibility.
import numpy as np
import pandas as pd
import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt
from matplotlib import style
# %matplotlib inline
import warnings
warnings.filterwarnings('ignore')

df = pd.read_csv('/content/final-v1.csv')

df.head(5)

df.tail(5)

df.shape

df.columns

print(df)
print('dimensions:')
print(df.shape)
print('Information:')
df.info()

print(df.apply(lambda col: col.unique()))

df.nunique()

df.corr()

df.isnull().sum()

df.describe().T

df.drop(["has_guides"],axis=1,inplace=True)
df.drop(["edge_follow"],axis=1,inplace=True)
df.drop(["has_channel"],axis=1,inplace=True)
df.drop(["edge_followed_by"],axis=1,inplace=True)

df.head(5)

account = df.groupby("is_business_account")
account = account.size()
account

plt.pie(account.values , labels = ("Business Account", "Personal Account"), autopct='%1.1f%%',colors=['Lavender','lightgreen'], radius = 1, textprops = {"fontsize" : 16})
plt.title("Account Type", c="Blue")
plt.show()

account1 = df.groupby("is_private")
account1 = account1.size()
account1

plt.pie(account1.values, labels = ("Private", "Public"), autopct='%1.1f%%', colors=['pink', 'skyblue'], radius = 1.2, textprops = {"fontsize" : 16})
plt.title("Account Type", c="Blue")
plt.show()

fake_account_counts = df['is_fake'].value_counts()
labels = ['Yes', 'No']
colors = ['Skyblue', 'lightgreen']
explode = (0.1, 0)

plt.figure(figsize=(6, 4))
plt.pie(fake_account_counts, labels=labels, colors=colors, autopct='%1.1f%%', startangle=140, pctdistance=0.85, explode=explode)
plt.title('Fake Accounts Distribution', fontsize=16)


centre_circle = plt.Circle((0,0),0.70,fc='white')
fig = plt.gcf()
fig.gca().add_artist(centre_circle)
plt.axis('equal')
plt.show()

def barplot(column, horizontal):
  plt.figure(figsize=(4, 4))
  sns.countplot(x=column, data=df, palette='viridis')
  plt.xlabel(column)
  plt.ylabel("Fake")
  plt.title(f"Users have Business Account", fontweight='bold')
  plt.xticks(rotation=45)
  sns.despine()
  plt.tight_layout()
  plt.show()

barplot('is_business_account', True)

def barplot(column, horizontal):
  plt.figure(figsize=(4, 4))
  sns.countplot(x=column, data=df, palette='viridis')
  plt.xlabel(column)
  plt.ylabel("Fake")
  plt.title(f"Private Account", fontweight='bold')
  plt.xticks(rotation=45)
  sns.despine()
  plt.tight_layout()
  plt.show()

barplot('is_private', True)

def barplot(column, horizontal):
  plt.figure(figsize=(4, 4))
  sns.countplot(x=column, data=df, palette='viridis')
  plt.xlabel(column)
  plt.ylabel("Fake")
  plt.title(f"User name has number", fontweight='bold')
  plt.xticks(rotation=45)
  sns.despine()
  plt.tight_layout()
  plt.show()

barplot('username_has_number', True)

def barplot(column, horizontal):
  plt.figure(figsize=(4, 4))
  sns.countplot(x=column, data=df, palette='viridis')
  plt.xlabel(column)
  plt.ylabel("Fake")
  plt.title(f"User's full Name Has Number", fontweight='bold')
  plt.xticks(rotation=45)
  sns.despine()
  plt.tight_layout()
  plt.show()

barplot('full_name_has_number', True)

def barplot(column, horizontal):
  plt.figure(figsize=(4, 4))
  sns.countplot(x=column, data=df, palette='viridis')
  plt.xlabel(column)
  plt.ylabel("Fake")
  plt.title(f"Users are Joined Recently", fontweight='bold')
  plt.xticks(rotation=45)
  sns.despine()
  plt.tight_layout()
  plt.show()

barplot('is_joined_recently', True)

def barplot(column, horizontal):
  plt.figure(figsize=(6, 6))
  sns.countplot(x=column, data=df, palette='viridis')
  plt.xlabel(column)
  plt.ylabel("Fake")
  plt.title(f"User's full name length", fontweight='bold')
  plt.xticks(rotation=45)
  sns.despine()
  plt.tight_layout()
  plt.show()

barplot('username_length', True)