IGAudit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IG Audit\n",
    "Objective: Using Simple Statistical Tools and Machine Learning to Audit Instagram Accounts for Authenticity\n",
    "\n",
    "Motivation: During lockdown, businesses have started increasing the use of social media influencers to market their products while their physical outlets are temporary closed. However, it is sad that there are some that will try and game the system for their own good. But in a world where a single influencer's post is worth as much as an average 9-5 Joe's annual salary, influencer marketing fake followers and fake engagement is a price that brands shouldn't have to pay for.\n",
    "\n",
    "*Inspired by igaudit.io that was taken down by Facebook only recently.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold, learning_curve\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier\n",
    "\n",
    "from instagram_private_api import Client, ClientCompatPatch\n",
    "import getpass\n",
    "\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: Understanding and Splitting the Data\n",
    "Dataset source: https://www.kaggle.com/eswarchandt/is-your-insta-fake-or-genuine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"train.csv\")\n",
    "test = pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>profile pic</th>\n",
       "      <th>nums/length username</th>\n",
       "      <th>fullname words</th>\n",
       "      <th>nums/length fullname</th>\n",
       "      <th>name==username</th>\n",
       "      <th>description length</th>\n",
       "      <th>external URL</th>\n",
       "      <th>private</th>\n",
       "      <th>#posts</th>\n",
       "      <th>#followers</th>\n",
       "      <th>#follows</th>\n",
       "      <th>fake</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
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       "      <td>955</td>\n",
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       "      <th>1</th>\n",
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       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>286</td>\n",
       "      <td>2740</td>\n",
       "      <td>533</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>0.10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>159</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>679</td>\n",
       "      <td>414</td>\n",
       "      <td>651</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>151</td>\n",
       "      <td>126</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   profile pic  nums/length username  fullname words  nums/length fullname  \\\n",
       "0            1                  0.27               0                   0.0   \n",
       "1            1                  0.00               2                   0.0   \n",
       "2            1                  0.10               2                   0.0   \n",
       "3            1                  0.00               1                   0.0   \n",
       "4            1                  0.00               2                   0.0   \n",
       "\n",
       "   name==username  description length  external URL  private  #posts  \\\n",
       "0               0                  53             0        0      32   \n",
       "1               0                  44             0        0     286   \n",
       "2               0                   0             0        1      13   \n",
       "3               0                  82             0        0     679   \n",
       "4               0                   0             0        1       6   \n",
       "\n",
       "   #followers  #follows  fake  \n",
       "0        1000       955     0  \n",
       "1        2740       533     0  \n",
       "2         159        98     0  \n",
       "3         414       651     0  \n",
       "4         151       126     0  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The features in the training data are the following:\n",
    "- profile pic: does the user have a profile picture?\n",
    "- nums/length username: ratio of numerical to alphabetical characters in the username\n",
    "- fullname words: how many words are in the user's full name?\n",
    "- nums/length fullname: ratio of numerical to alphabetical characters in the full name\n",
    "- name==username: is the user's full name the same as the username?\n",
    "- description length: how many characters is in the user's Instagram bio?\n",
    "- external URL: does the user have an external URL linked to their profile?\n",
    "- private: is the user private?\n",
    "- #posts: number of posts\n",
    "- #followers: number of people following the user\n",
    "- #follows: number of people the user follows\n",
    "- fake: if the user is fake, fake=1, else fake=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>profile pic</th>\n",
       "      <th>nums/length username</th>\n",
       "      <th>fullname words</th>\n",
       "      <th>nums/length fullname</th>\n",
       "      <th>name==username</th>\n",
       "      <th>description length</th>\n",
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       "      <th>private</th>\n",
       "      <th>#posts</th>\n",
       "      <th>#followers</th>\n",
       "      <th>#follows</th>\n",
       "      <th>fake</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>576.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>5.760000e+02</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>576.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.701389</td>\n",
       "      <td>0.163837</td>\n",
       "      <td>1.460069</td>\n",
       "      <td>0.036094</td>\n",
       "      <td>0.034722</td>\n",
       "      <td>22.623264</td>\n",
       "      <td>0.116319</td>\n",
       "      <td>0.381944</td>\n",
       "      <td>107.489583</td>\n",
       "      <td>8.530724e+04</td>\n",
       "      <td>508.381944</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.458047</td>\n",
       "      <td>0.214096</td>\n",
       "      <td>1.052601</td>\n",
       "      <td>0.125121</td>\n",
       "      <td>0.183234</td>\n",
       "      <td>37.702987</td>\n",
       "      <td>0.320886</td>\n",
       "      <td>0.486285</td>\n",
       "      <td>402.034431</td>\n",
       "      <td>9.101485e+05</td>\n",
       "      <td>917.981239</td>\n",
       "      <td>0.500435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.900000e+01</td>\n",
       "      <td>57.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.505000e+02</td>\n",
       "      <td>229.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>81.500000</td>\n",
       "      <td>7.160000e+02</td>\n",
       "      <td>589.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7389.000000</td>\n",
       "      <td>1.533854e+07</td>\n",
       "      <td>7500.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       profile pic  nums/length username  fullname words  \\\n",
       "count   576.000000            576.000000      576.000000   \n",
       "mean      0.701389              0.163837        1.460069   \n",
       "std       0.458047              0.214096        1.052601   \n",
       "min       0.000000              0.000000        0.000000   \n",
       "25%       0.000000              0.000000        1.000000   \n",
       "50%       1.000000              0.000000        1.000000   \n",
       "75%       1.000000              0.310000        2.000000   \n",
       "max       1.000000              0.920000       12.000000   \n",
       "\n",
       "       nums/length fullname  name==username  description length  external URL  \\\n",
       "count            576.000000      576.000000          576.000000    576.000000   \n",
       "mean               0.036094        0.034722           22.623264      0.116319   \n",
       "std                0.125121        0.183234           37.702987      0.320886   \n",
       "min                0.000000        0.000000            0.000000      0.000000   \n",
       "25%                0.000000        0.000000            0.000000      0.000000   \n",
       "50%                0.000000        0.000000            0.000000      0.000000   \n",
       "75%                0.000000        0.000000           34.000000      0.000000   \n",
       "max                1.000000        1.000000          150.000000      1.000000   \n",
       "\n",
       "          private       #posts    #followers     #follows        fake  \n",
       "count  576.000000   576.000000  5.760000e+02   576.000000  576.000000  \n",
       "mean     0.381944   107.489583  8.530724e+04   508.381944    0.500000  \n",
       "std      0.486285   402.034431  9.101485e+05   917.981239    0.500435  \n",
       "min      0.000000     0.000000  0.000000e+00     0.000000    0.000000  \n",
       "25%      0.000000     0.000000  3.900000e+01    57.500000    0.000000  \n",
       "50%      0.000000     9.000000  1.505000e+02   229.500000    0.500000  \n",
       "75%      1.000000    81.500000  7.160000e+02   589.500000    1.000000  \n",
       "max      1.000000  7389.000000  1.533854e+07  7500.000000    1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 576 entries, 0 to 575\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   profile pic           576 non-null    int64  \n",
      " 1   nums/length username  576 non-null    float64\n",
      " 2   fullname words        576 non-null    int64  \n",
      " 3   nums/length fullname  576 non-null    float64\n",
      " 4   name==username        576 non-null    int64  \n",
      " 5   description length    576 non-null    int64  \n",
      " 6   external URL          576 non-null    int64  \n",
      " 7   private               576 non-null    int64  \n",
      " 8   #posts                576 non-null    int64  \n",
      " 9   #followers            576 non-null    int64  \n",
      " 10  #follows              576 non-null    int64  \n",
      " 11  fake                  576 non-null    int64  \n",
      "dtypes: float64(2), int64(10)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(576, 12)"
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>fullname words</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.33</td>\n",
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       "      <td>30</td>\n",
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       "      <td>488</td>\n",
       "      <td>604</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>319</td>\n",
       "      <td>328</td>\n",
       "      <td>668</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>143</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>14890</td>\n",
       "      <td>7369</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>225</td>\n",
       "      <td>356</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   profile pic  nums/length username  fullname words  nums/length fullname  \\\n",
       "0            1                  0.33               1                  0.33   \n",
       "1            1                  0.00               5                  0.00   \n",
       "2            1                  0.00               2                  0.00   \n",
       "3            1                  0.00               1                  0.00   \n",
       "4            1                  0.50               1                  0.00   \n",
       "\n",
       "   name==username  description length  external URL  private  #posts  \\\n",
       "0               1                  30             0        1      35   \n",
       "1               0                  64             0        1       3   \n",
       "2               0                  82             0        1     319   \n",
       "3               0                 143             0        1     273   \n",
       "4               0                  76             0        1       6   \n",
       "\n",
       "   #followers  #follows  fake  \n",
       "0         488       604     0  \n",
       "1          35         6     0  \n",
       "2         328       668     0  \n",
       "3       14890      7369     0  \n",
       "4         225       356     0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>profile pic</th>\n",
       "      <th>nums/length username</th>\n",
       "      <th>fullname words</th>\n",
       "      <th>nums/length fullname</th>\n",
       "      <th>name==username</th>\n",
       "      <th>description length</th>\n",
       "      <th>external URL</th>\n",
       "      <th>private</th>\n",
       "      <th>#posts</th>\n",
       "      <th>#followers</th>\n",
       "      <th>#follows</th>\n",
       "      <th>fake</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>120.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>1.200000e+02</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>120.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.758333</td>\n",
       "      <td>0.179917</td>\n",
       "      <td>1.550000</td>\n",
       "      <td>0.071333</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>27.200000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.308333</td>\n",
       "      <td>82.866667</td>\n",
       "      <td>4.959472e+04</td>\n",
       "      <td>779.266667</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.429888</td>\n",
       "      <td>0.241492</td>\n",
       "      <td>1.187116</td>\n",
       "      <td>0.209429</td>\n",
       "      <td>0.200664</td>\n",
       "      <td>42.588632</td>\n",
       "      <td>0.301258</td>\n",
       "      <td>0.463741</td>\n",
       "      <td>230.468136</td>\n",
       "      <td>3.816126e+05</td>\n",
       "      <td>1409.383558</td>\n",
       "      <td>0.502096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.725000e+01</td>\n",
       "      <td>119.250000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.165000e+02</td>\n",
       "      <td>354.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>58.250000</td>\n",
       "      <td>5.932500e+02</td>\n",
       "      <td>668.250000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.890000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1879.000000</td>\n",
       "      <td>4.021842e+06</td>\n",
       "      <td>7453.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       profile pic  nums/length username  fullname words  \\\n",
       "count   120.000000            120.000000      120.000000   \n",
       "mean      0.758333              0.179917        1.550000   \n",
       "std       0.429888              0.241492        1.187116   \n",
       "min       0.000000              0.000000        0.000000   \n",
       "25%       1.000000              0.000000        1.000000   \n",
       "50%       1.000000              0.000000        1.000000   \n",
       "75%       1.000000              0.330000        2.000000   \n",
       "max       1.000000              0.890000        9.000000   \n",
       "\n",
       "       nums/length fullname  name==username  description length  external URL  \\\n",
       "count            120.000000      120.000000          120.000000    120.000000   \n",
       "mean               0.071333        0.041667           27.200000      0.100000   \n",
       "std                0.209429        0.200664           42.588632      0.301258   \n",
       "min                0.000000        0.000000            0.000000      0.000000   \n",
       "25%                0.000000        0.000000            0.000000      0.000000   \n",
       "50%                0.000000        0.000000            0.000000      0.000000   \n",
       "75%                0.000000        0.000000           45.250000      0.000000   \n",
       "max                1.000000        1.000000          149.000000      1.000000   \n",
       "\n",
       "          private       #posts    #followers     #follows        fake  \n",
       "count  120.000000   120.000000  1.200000e+02   120.000000  120.000000  \n",
       "mean     0.308333    82.866667  4.959472e+04   779.266667    0.500000  \n",
       "std      0.463741   230.468136  3.816126e+05  1409.383558    0.502096  \n",
       "min      0.000000     0.000000  0.000000e+00     1.000000    0.000000  \n",
       "25%      0.000000     1.000000  6.725000e+01   119.250000    0.000000  \n",
       "50%      0.000000     8.000000  2.165000e+02   354.500000    0.500000  \n",
       "75%      1.000000    58.250000  5.932500e+02   668.250000    1.000000  \n",
       "max      1.000000  1879.000000  4.021842e+06  7453.000000    1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 120 entries, 0 to 119\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   profile pic           120 non-null    int64  \n",
      " 1   nums/length username  120 non-null    float64\n",
      " 2   fullname words        120 non-null    int64  \n",
      " 3   nums/length fullname  120 non-null    float64\n",
      " 4   name==username        120 non-null    int64  \n",
      " 5   description length    120 non-null    int64  \n",
      " 6   external URL          120 non-null    int64  \n",
      " 7   private               120 non-null    int64  \n",
      " 8   #posts                120 non-null    int64  \n",
      " 9   #followers            120 non-null    int64  \n",
      " 10  #follows              120 non-null    int64  \n",
      " 11  fake                  120 non-null    int64  \n",
      "dtypes: float64(2), int64(10)\n",
      "memory usage: 11.4 KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120, 12)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check for NULL values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "print(train.isna().values.any().sum())\n",
    "print(test.isna().values.any().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a correlation matrix for the features in the training data to check for significantly relevant features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a5f4590>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,10))  \n",
    "corr=train.corr()\n",
    "sns.heatmap(corr, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the training set into data and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>profile pic</th>\n",
       "      <th>nums/length username</th>\n",
       "      <th>fullname words</th>\n",
       "      <th>nums/length fullname</th>\n",
       "      <th>name==username</th>\n",
       "      <th>description length</th>\n",
       "      <th>external URL</th>\n",
       "      <th>private</th>\n",
       "      <th>#posts</th>\n",
       "      <th>#followers</th>\n",
       "      <th>#follows</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>1000</td>\n",
       "      <td>955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>286</td>\n",
       "      <td>2740</td>\n",
       "      <td>533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>159</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>679</td>\n",
       "      <td>414</td>\n",
       "      <td>651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>151</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   profile pic  nums/length username  fullname words  nums/length fullname  \\\n",
       "0            1                  0.27               0                   0.0   \n",
       "1            1                  0.00               2                   0.0   \n",
       "2            1                  0.10               2                   0.0   \n",
       "3            1                  0.00               1                   0.0   \n",
       "4            1                  0.00               2                   0.0   \n",
       "\n",
       "   name==username  description length  external URL  private  #posts  \\\n",
       "0               0                  53             0        0      32   \n",
       "1               0                  44             0        0     286   \n",
       "2               0                   0             0        1      13   \n",
       "3               0                  82             0        0     679   \n",
       "4               0                   0             0        1       6   \n",
       "\n",
       "   #followers  #follows  \n",
       "0        1000       955  \n",
       "1        2740       533  \n",
       "2         159        98  \n",
       "3         414       651  \n",
       "4         151       126  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Labels\n",
    "train_Y = train.fake\n",
    "train_Y = pd.DataFrame(train_Y)\n",
    "\n",
    "# Data\n",
    "train_X = train.drop(columns='fake')\n",
    "train_X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the test set into data and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>profile pic</th>\n",
       "      <th>nums/length username</th>\n",
       "      <th>fullname words</th>\n",
       "      <th>nums/length fullname</th>\n",
       "      <th>name==username</th>\n",
       "      <th>description length</th>\n",
       "      <th>external URL</th>\n",
       "      <th>private</th>\n",
       "      <th>#posts</th>\n",
       "      <th>#followers</th>\n",
       "      <th>#follows</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>488</td>\n",
       "      <td>604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>319</td>\n",
       "      <td>328</td>\n",
       "      <td>668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>143</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>14890</td>\n",
       "      <td>7369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>225</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   profile pic  nums/length username  fullname words  nums/length fullname  \\\n",
       "0            1                  0.33               1                  0.33   \n",
       "1            1                  0.00               5                  0.00   \n",
       "2            1                  0.00               2                  0.00   \n",
       "3            1                  0.00               1                  0.00   \n",
       "4            1                  0.50               1                  0.00   \n",
       "\n",
       "   name==username  description length  external URL  private  #posts  \\\n",
       "0               1                  30             0        1      35   \n",
       "1               0                  64             0        1       3   \n",
       "2               0                  82             0        1     319   \n",
       "3               0                 143             0        1     273   \n",
       "4               0                  76             0        1       6   \n",
       "\n",
       "   #followers  #follows  \n",
       "0         488       604  \n",
       "1          35         6  \n",
       "2         328       668  \n",
       "3       14890      7369  \n",
       "4         225       356  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Labels\n",
    "test_Y = test.fake\n",
    "test_Y = pd.DataFrame(test_Y)\n",
    "\n",
    "# Data\n",
    "test_X = test.drop(columns='fake')\n",
    "test_X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: Comparing Classification Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Baseline Classifier**\n",
    "<br>Classify everything as the majority class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline accuracy: 0.5\n"
     ]
    }
   ],
   "source": [
    "# Baseline classifier\n",
    "fakes = len([i for i in train.fake if i==1])\n",
    "auth = len([i for i in train.fake if i==0])\n",
    "fakes, auth\n",
    "\n",
    "# classify everything as fake\n",
    "pred = [1 for i in range(len(test_X))]\n",
    "pred = np.array(pred)\n",
    "print(\"Baseline accuracy: \" + str(accuracy_score(pred, test_Y)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Statistical Method**\n",
    "<br>Classify all users with a following to follower ratio above a certain threshold as 'fake'.\n",
    "<br> i.e. a user with 10 follower and 200 followings will be classified as fake if the threshold r=20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Accuracy for the statistical method: 0.7\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Statistical method\n",
    "def stat_predict(test_X, r):\n",
    "    pred = []\n",
    "    for row in range(len(test_X)):   \n",
    "        followers = test_X.loc[row]['#followers']\n",
    "        followings = test_X.loc[row]['#follows']\n",
    "        if followers == 0:\n",
    "            followers = 1\n",
    "        if followings == 0:\n",
    "            followings == 1\n",
    "\n",
    "        ratio = followings/followers\n",
    "\n",
    "        if ratio >= r:\n",
    "            pred.append(1)\n",
    "        else:\n",
    "            pred.append(0)\n",
    "    \n",
    "    return np.array(pred)\n",
    "accuracies = []\n",
    "for i in [x / 10.0 for x in range(5, 255, 5)]:\n",
    "    prediction = stat_predict(test_X, i)\n",
    "    accuracies.append(accuracy_score(prediction, test_Y))\n",
    "\n",
    "f, ax = plt.subplots(figsize=(20,10))\n",
    "plt.plot([x / 10.0 for x in range(5, 255, 5)], accuracies)\n",
    "plt.plot([2.5 for i in range(len(accuracies))], accuracies, color='red')\n",
    "plt.title(\"Accuracy for different thresholds\", size=30)\n",
    "plt.xlabel('Ratio', fontsize=20)\n",
    "plt.ylabel('Accuracy', fontsize=20)\n",
    "print(\"Maximum Accuracy for the statistical method: \" + str(max(accuracies)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/athiyadeviyani/miniconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:73: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/Users/athiyadeviyani/miniconda3/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    }
   ],
   "source": [
    "lm = LogisticRegression()\n",
    "\n",
    "# Train the model\n",
    "model1 = lm.fit(train_X, train_Y)\n",
    "\n",
    "# Make a prediction\n",
    "lm_predict = model1.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression accuracy: 0.9083333333333333\n"
     ]
    }
   ],
   "source": [
    "# Compute the accuracy of the model\n",
    "acc = accuracy_score(lm_predict, test_Y)\n",
    "print(\"Logistic Regression accuracy: \" + str(acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**KNN Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/athiyadeviyani/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  \n",
      "/Users/athiyadeviyani/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  \n",
      "/Users/athiyadeviyani/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  \n",
      "/Users/athiyadeviyani/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  \n",
      "/Users/athiyadeviyani/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  \n",
      "/Users/athiyadeviyani/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  \n",
      "/Users/athiyadeviyani/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  \n",
      "/Users/athiyadeviyani/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  \n",
      "/Users/athiyadeviyani/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The highest accuracy obtained using KNN is 0.8666666666666667 achieved by a value of n=7\n"
     ]
    },
    {
     "data": {
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hewAA4Ltcr+ChUQ8peAhXU5hfz7L9g7xz6rluc+hrzwbu76N6ln2qYImRXmvZuNDX0Q3MNhoc+jpM0rI6133RwGM3JCP09f26VzjnqszsYwX3bkpXsDQ6XAucc/UdRrhR/37eCh1+NlrBvcSuMbP67qtcwdfg2wyR1FLS/AYKmU9Vf+mzX33vgY2hr+3ruW5DA4XghwrOPau9nTMUPNzuw3rW/0jffF/st845t72B22xWcOh/hoKHbH6mhl9zAABQB4UUAAD4Vs65bDN7QdJFZnaJc+65JniYXfUsqzqA6xIbuL9tdReESp9CBWcS7dch9PV/viNf63qWbf2O29SVGvr6VQPX71/e7iDvtyHFDSyv0n+e2Ka9gvOPOukAB9g3YP/z+8Zr/x3LJUnOufry7t/O8Qdxf/u3S2qtZamSipxzFfU8bn3vi7r3Vfc2u83sGAXnfJ2n4KGQklRoZtMl/dY5V9lAPgAAIM6yBwAADswtCg4Hv+9bznq2/9Cphv7g1VhFy4HoUneBmSVI6qjgwOz99pddo51z9i2Xv9bzGK6eZd9m/2N1beD6bnXWC5f9j5f3Ha9BvbtO1bL/df3Ga/8dyw9VQ/e3//Wt/TrukpRmZt8oMBt4X+zX4DZ2zm1yzv1MwSJrhKSrFDy08dehCwAA+BYUUgAA4Ds559YoeDayfpKubGC1/TObetW9wswG6j/3WGlqJ9az7DgF97TJq7VsXujr8U2e6N+Pe1LdK0KlyP4M9Z3Rr8mEZiotlXSEmaUdxl2tUHCm0igza1PP9Y09g6y3mfWtZ/lJoa+1t3Oegp97T6hn/RMUfF8c0uvugpY65/4o6bTQ4gsO5b4AAIglFFIAAOBA3aXgYWC3qf5D2FYouJfJ+Wb29eFPZtZSwUHT4XS7mX09d8jMkiXdF/rxL7XW+4uCz+kOM6s76FtmFmdmJzVSplcUPAPcxNDhXrVdo2DZ955zrjHmRx2shxU8O9zTZvaNPdnMrH1oVlKDQofDPadg8firOrcfreAA+cYUL+l+M/v686yZ9VNwT6UqSTNrrft06Ot9oZlZ+9dPkfS70I9PHegDm9kRZlbfHlr7l5Ue6H0BABCrmCEFAAAOiHOuyMzulfRAA9dXmtmjkm6XlGdmLyv4WeM0BQeUb6nvdk1kuaSlodlXlQqe4W6ApDf077P3yTkXMLOLJL0saZ6Z/UvBvYWcgnt6jVNwzlTy4QZyzpWY2U8lPS/pIzN7XsHh5ZmSTldwXtEvDvdxDjHb02aWKelySflmtv9sfGkKFmUnKFjeXfYdd3WzpJMl3WhmRys4DL+bpAkKDv6+QP8+tPNwLZJ0tKQcM3tHwUNCJ4S+3uicy6/1/Gab2fmh65ea2SsKbuMLQs/vuXrO5PhtTpP0oJllS1olabuCA/bPV/D5PXi4Tw4AgGhHIQUAAA7GYwqWFn0buP4OBfcO+R9JP1ewZHlW0p365lnqmtIEBYuxyZK6K3hGtDsl/c459x9zgZxz/zKzUZJuUHA49fGSKhQs0N6X9GJjhXLOvWpmx0q6NfRYqQq+Rk9Kuts5F87Srm62aWb2loKl06kKFjtFChZTD+o/9zhq6D62mdl4SfdKOlvBwmilgu+ZvQoWQPXNajoUOyWdpWBB+hNJbRV8jz3knJtdz/oTFTw73k/17+JvuaTfS5pxkI/9T0m9FSzqzg899leS3pX0sHMu6yDvDwCAmGN1PpMBAAAAjc7M7lGwiDvTOfdP33kAAIBfFFIAAABoNGbWve6eXmY2UsHD9yok9XDOlXkJBwAAmg0O2QMAAEBjmm9mayQtUfAwvUGSvq/gyXR+QRkFAAAk9pACAABAIzKzOxScFdVXUhsFz2I4T8HZTh/6SwYAAJoTCikAAAAAAACEVZzvAAAAAAAAAIgtFFIAAAAAAAAIKwopAAAAAAAAhBWFFAAAAAAAAMKKQgoAAAAAAABh9f8BSNFT/iQWY3AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracies = []\n",
    "\n",
    "# Compare the accuracies of using the KNN classifier with different number of neighbors\n",
    "for i in range(1,10):\n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    model_2 = knn.fit(train_X,train_Y)\n",
    "    knn_predict = model_2.predict(test_X)\n",
    "    accuracy = accuracy_score(knn_predict,test_Y)\n",
    "    accuracies.append(accuracy)\n",
    "\n",
    "max_acc = (0, 0)\n",
    "for i in range(1, 10):\n",
    "    if accuracies[i-1] > max_acc[1]:\n",
    "        max_acc = (i, accuracies[i-1])\n",
    "\n",
    "max_acc\n",
    "\n",
    "f, ax = plt.subplots(figsize=(20,10))\n",
    "plt.plot([i for i in range(1,10)], accuracies)\n",
    "plt.plot([7 for i in range(len(accuracies))], accuracies, color='red')\n",
    "plt.title(\"Accuracy for different n-neighbors\", size=30)\n",
    "plt.xlabel('Number of neighbors', fontsize=20)\n",
    "plt.ylabel('Accuracy', fontsize=20)\n",
    "\n",
    "print(\"The highest accuracy obtained using KNN is \" + str(max_acc[1]) + \" achieved by a value of n=\" + str(max_acc[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Decision Tree Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "DT = DecisionTreeClassifier()\n",
    "\n",
    "# Train the model\n",
    "model3 = DT.fit(train_X, train_Y)\n",
    "\n",
    "# Make a prediction\n",
    "DT_predict = model3.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree accuracy: 0.9\n"
     ]
    }
   ],
   "source": [
    "# Compute the accuracy of the model\n",
    "acc = accuracy_score(DT_predict, test_Y)\n",
    "print(\"Decision Tree accuracy: \" + str(acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Random Forest Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/athiyadeviyani/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "rfc = RandomForestClassifier()\n",
    "\n",
    "# Train the model\n",
    "model_4 = rfc.fit(train_X, train_Y)\n",
    "\n",
    "# Make a prediction\n",
    "rfc_predict = model_4.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest accuracy: 0.925\n"
     ]
    }
   ],
   "source": [
    "# Compute the accuracy of the model\n",
    "acc = accuracy_score(rfc_predict, test_Y)\n",
    "print(\"Random Forest accuracy: \" + str(acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3: Obtaining Instagram Data\n",
    "We are going to use the hassle-free unofficial Instagram API. <br> To install: ```$ pip install git+https://git@github.com/ping/instagram_private_api.git@1.6.0```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Log in to your Instagram account (preferably not your personal one! I created one just for this project 😉)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "username: ins.tapolice\n",
      "password: ········\n"
     ]
    }
   ],
   "source": [
    "def login():\n",
    "    username = input(\"username: \")\n",
    "    password = getpass.getpass(\"password: \")\n",
    "    api = Client(username, password)\n",
    "    return api\n",
    "\n",
    "api = login()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the Instagram user ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ID(username):\n",
    "    return api.username_info(username)['user']['pk']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The user used for the experiment below is anonymised!\n",
    "# i.e. this cell was run and then changed to protect the user's anonymity\n",
    "userID = get_ID('<USERNAME HERE>') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The API needs some sort of rank to query followers, posts, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "rank = api.generate_uuid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the user's list follower usernames (this may take a while, depending on how many followers the user have)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_followers(userID, rank):\n",
    "    followers = []\n",
    "    next_max_id = True\n",
    "    \n",
    "    while next_max_id:\n",
    "        if next_max_id == True: next_max_id=''\n",
    "        f = api.user_followers(userID, rank, max_id=next_max_id)\n",
    "        followers.extend(f.get('users', []))\n",
    "        next_max_id = f.get('next_max_id', '')\n",
    "    \n",
    "    user_fer = [dic['username'] for dic in followers]\n",
    "    \n",
    "    return user_fer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "followers = get_followers(userID, rank)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can check the number of followers if you'd like to\n",
    "# len(followers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 4: Preparing the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect the data (and what other data can you obtain from it) and compare it with the train and test tables above. Find out what you need to do to obtain the features for a data point in order to make a prediction.\n",
    "\n",
    "Recall that the features for a data point are the following:\n",
    "- profile pic: does the user have a profile picture?\n",
    "- nums/length username: ratio of numerical to alphabetical characters in the username\n",
    "- fullname words: how many words are in the user's full name?\n",
    "- nums/length fullname: ratio of numerical to alphabetical characters in the full name\n",
    "- name==username: is the user's full name the same as the username?\n",
    "- description length: how many characters is in the user's Instagram bio?\n",
    "- external URL: does the user have an external URL linked to their profile?\n",
    "- private: is the user private?\n",
    "- #posts: number of posts\n",
    "- #followers: number of people following the user\n",
    "- #follows: number of people the user follows\n",
    "- fake: if the user is fake, fake=1, else fake=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This will print the first follower username on the list\n",
    "# print(followers[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['pk', 'username', 'full_name', 'is_private', 'profile_pic_url', 'profile_pic_id', 'is_verified', 'has_anonymous_profile_picture', 'media_count', 'geo_media_count', 'follower_count', 'following_count', 'following_tag_count', 'biography', 'biography_with_entities', 'external_url', 'external_lynx_url', 'total_igtv_videos', 'total_clips_count', 'total_ar_effects', 'usertags_count', 'is_favorite', 'is_favorite_for_stories', 'is_favorite_for_highlights', 'live_subscription_status', 'is_interest_account', 'has_chaining', 'hd_profile_pic_versions', 'hd_profile_pic_url_info', 'mutual_followers_count', 'has_highlight_reels', 'can_be_reported_as_fraud', 'is_eligible_for_smb_support_flow', 'smb_support_partner', 'smb_delivery_partner', 'smb_donation_partner', 'smb_support_delivery_partner', 'displayed_action_button_type', 'direct_messaging', 'fb_page_call_to_action_id', 'address_street', 'business_contact_method', 'category', 'city_id', 'city_name', 'contact_phone_number', 'is_call_to_action_enabled', 'latitude', 'longitude', 'public_email', 'public_phone_country_code', 'public_phone_number', 'zip', 'instagram_location_id', 'is_business', 'account_type', 'professional_conversion_suggested_account_type', 'can_hide_category', 'can_hide_public_contacts', 'should_show_category', 'should_show_public_contacts', 'personal_account_ads_page_name', 'personal_account_ads_page_id', 'include_direct_blacklist_status', 'is_potential_business', 'show_post_insights_entry_point', 'is_bestie', 'has_unseen_besties_media', 'show_account_transparency_details', 'show_leave_feedback', 'robi_feedback_source', 'auto_expand_chaining', 'highlight_reshare_disabled', 'is_memorialized', 'open_external_url_with_in_app_browser'])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This will get the information on a certain user\n",
    "info = api.user_info(get_ID(followers[0]))['user']\n",
    "\n",
    "# Check what information is available for one particular user\n",
    "info.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that we have pretty much all the features to make a user data point for prediction, but we need to filter and extract them, and perform some very minor calculations. The following function will do just that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(info):\n",
    "    \n",
    "    \"\"\"Extract the information from the returned JSON.\n",
    "    \n",
    "    This function will return the following array:\n",
    "        data = [profile pic,\n",
    "                nums/length username,\n",
    "                full name words,\n",
    "                nums/length full name,\n",
    "                name==username,\n",
    "                description length,\n",
    "                external URL,\n",
    "                private,\n",
    "                #posts,\n",
    "                #followers,\n",
    "                #followings]\n",
    "    \"\"\"\n",
    "    \n",
    "    data = []\n",
    "    \n",
    "    # Does the user have a profile photo?\n",
    "    profile_pic = not info['has_anonymous_profile_picture']\n",
    "    if profile_pic == True:\n",
    "        profile_pic = 1\n",
    "    else:\n",
    "        profile_pic = 0\n",
    "    data.append(profile_pic)\n",
    "    \n",
    "    # Ratio of number of numerical chars in username to its length\n",
    "    username = info['username']\n",
    "    uname_ratio = len([x for x in username if x.isdigit()]) / float(len(username))\n",
    "    data.append(uname_ratio)\n",
    "    \n",
    "    # Full name in word tokens\n",
    "    full_name = info['full_name']\n",
    "    fname_tokens = len(full_name.split(' '))\n",
    "    data.append(fname_tokens)\n",
    "    \n",
    "    # Ratio of number of numerical characters in full name to its length\n",
    "    if len(full_name) == 0:\n",
    "        fname_ratio = 0\n",
    "    else:\n",
    "        fname_ratio = len([x for x in full_name if x.isdigit()]) / float(len(full_name))\n",
    "    data.append(fname_ratio)\n",
    "    \n",
    "    # Is name == username?\n",
    "    name_eq_uname = (full_name == username)\n",
    "    if name_eq_uname == True:\n",
    "        name_eq_uname = 1\n",
    "    else:\n",
    "        name_eq_uname = 0\n",
    "    data.append(name_eq_uname)\n",
    "    \n",
    "    # Number of characters on user bio \n",
    "    bio_length = len(info['biography'])\n",
    "    data.append(bio_length)\n",
    "    \n",
    "    # Does the user have an external URL?\n",
    "    ext_url = info['external_url'] != ''\n",
    "    if ext_url == True:\n",
    "        ext_url = 1\n",
    "    else:\n",
    "        ext_url = 0\n",
    "    data.append(ext_url)\n",
    "    \n",
    "    # Is the user private or no?\n",
    "    private = info['is_private']\n",
    "    if private == True:\n",
    "        private = 1\n",
    "    else:\n",
    "        private = 0\n",
    "    data.append(private)\n",
    "    \n",
    "    # Number of posts\n",
    "    posts = info['media_count']\n",
    "    data.append(posts)\n",
    "    \n",
    "    # Number of followers\n",
    "    followers = info['follower_count']\n",
    "    data.append(followers)\n",
    "    \n",
    "    # Number of followings\n",
    "    followings = info['following_count']\n",
    "    data.append(followings)\n",
    "    \n",
    "  \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 0.0, 3, 0.0, 0, 118, 1, 0, 589, 22227, 510]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if the function returns as expected\n",
    "get_data(info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately the Instagram Private API has a very limited number of API calls per hour so we will not be able to analyse *all* of the user's followers. \n",
    "\n",
    "Fortunately, I took Statistics and learned that **random sampling** is useful to cull a smaller sample size from a larger population and use it to research and make generalizations about the larger group. \n",
    "\n",
    "This will allow us to make user authenticity approximations despite the API limitations and still have a data that is representative of the user's followers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get a random sample of 50 followers\n",
    "random_followers = random.sample(followers, 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get user information for each follower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_infos = []\n",
    "\n",
    "for follower in random_followers:\n",
    "    info = api.user_info(get_ID(follower))['user']\n",
    "    f_infos.append(info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract the relevant features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " [1, 0.0, 3, 0.08333333333333333, 0, 100, 0, 1, 56, 39, 190],\n",
       " [1, 0.13333333333333333, 1, 0.0, 0, 103, 0, 1, 109, 1053, 6221],\n",
       " [1, 0.0, 1, 0.0, 0, 0, 0, 0, 49, 412, 520],\n",
       " [1, 0.0, 1, 0, 0, 7, 0, 0, 110, 317, 334],\n",
       " [1, 0.0, 1, 0.0, 0, 31, 1, 0, 141, 2490, 1043],\n",
       " [1, 0.18181818181818182, 2, 0.0, 0, 35, 1, 0, 320, 2345, 861],\n",
       " [1, 0.0, 3, 0.0, 0, 115, 0, 1, 1336, 1018, 1208],\n",
       " [1, 0.0, 1, 0.0, 0, 0, 0, 1, 39, 37, 611],\n",
       " [1, 0.0, 1, 0.0, 0, 0, 0, 1, 0, 513, 633],\n",
       " [1, 0.0, 2, 0.0, 0, 46, 0, 0, 23, 83, 306],\n",
       " [1, 0.0, 1, 0.0, 0, 0, 0, 0, 30, 126, 372]]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_table = []\n",
    "\n",
    "for info in f_infos:\n",
    "    f_table.append(get_data(info))\n",
    "    \n",
    "f_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>profile pic</th>\n",
       "      <th>nums/length username</th>\n",
       "      <th>fullname words</th>\n",
       "      <th>nums/length fullname</th>\n",
       "      <th>name==username</th>\n",
       "      <th>description length</th>\n",
       "      <th>external URL</th>\n",
       "      <th>private</th>\n",
       "      <th>#posts</th>\n",
       "      <th>#followers</th>\n",
       "      <th>#follows</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>483</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.500000</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>481</td>\n",
       "      <td>592</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>773</td>\n",
       "      <td>3639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3639</td>\n",
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       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>19</td>\n",
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       "      <td>1046</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>10</td>\n",
       "      <td>202</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>52</td>\n",
       "      <td>240</td>\n",
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       "      <th>13</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>1864</td>\n",
       "      <td>692</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>275</td>\n",
       "      <td>2039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>98</td>\n",
       "      <td>847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2686</td>\n",
       "      <td>6570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>18</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>1628</td>\n",
       "      <td>1037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>1730</td>\n",
       "      <td>1298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>219</td>\n",
       "      <td>183</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>645</td>\n",
       "      <td>4452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>1258</td>\n",
       "      <td>952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>629</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1270</td>\n",
       "      <td>951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>299</td>\n",
       "      <td>1669</td>\n",
       "      <td>1133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>753</td>\n",
       "      <td>853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>213</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>133</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>28</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1395</td>\n",
       "      <td>794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>831</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>680</td>\n",
       "      <td>566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1729</td>\n",
       "      <td>6114</td>\n",
       "      <td>5758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>2104</td>\n",
       "      <td>7091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>728</td>\n",
       "      <td>4139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>83</td>\n",
       "      <td>458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>2035</td>\n",
       "      <td>1035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>11549</td>\n",
       "      <td>712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>39</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>1053</td>\n",
       "      <td>6221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>412</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>317</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>2490</td>\n",
       "      <td>1043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>320</td>\n",
       "      <td>2345</td>\n",
       "      <td>861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1336</td>\n",
       "      <td>1018</td>\n",
       "      <td>1208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>37</td>\n",
       "      <td>611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>513</td>\n",
       "      <td>633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>83</td>\n",
       "      <td>306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>126</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    profile pic  nums/length username  fullname words  nums/length fullname  \\\n",
       "0             1              0.000000               3              0.000000   \n",
       "1             1              0.000000               1              0.000000   \n",
       "2             1              0.000000               3              0.000000   \n",
       "3             1              0.500000               3              0.000000   \n",
       "4             1              0.000000               1              0.000000   \n",
       "5             1              0.000000               1              0.000000   \n",
       "6             1              0.000000               1              0.000000   \n",
       "7             1              0.000000               3              0.000000   \n",
       "8             1              0.000000               4              0.000000   \n",
       "9             1              0.000000               2              0.000000   \n",
       "10            1              0.363636               1              0.000000   \n",
       "11            1              0.000000               1              0.000000   \n",
       "12            1              0.000000               2              0.000000   \n",
       "13            1              0.000000               1              0.000000   \n",
       "14            1              0.000000               1              0.000000   \n",
       "15            1              0.062500               3              0.000000   \n",
       "16            1              0.000000               3              0.000000   \n",
       "17            1              0.000000               2              0.000000   \n",
       "18            1              0.000000               2              0.000000   \n",
       "19            1              0.000000               3              0.000000   \n",
       "20            1              0.000000               1              0.000000   \n",
       "21            0              0.181818               2              0.000000   \n",
       "22            1              0.000000               2              0.000000   \n",
       "23            1              0.000000               2              0.000000   \n",
       "24            1              0.000000               1              0.000000   \n",
       "25            1              0.235294               1              0.000000   \n",
       "26            1              0.000000               1              0.000000   \n",
       "27            1              0.000000               2              0.000000   \n",
       "28            1              0.200000               2              0.000000   \n",
       "29            1              0.000000               1              0.000000   \n",
       "30            1              0.000000               2              0.000000   \n",
       "31            1              0.000000               2              0.000000   \n",
       "32            1              0.000000               3              0.000000   \n",
       "33            1              0.000000               2              0.000000   \n",
       "34            1              0.000000               2              0.000000   \n",
       "35            1              0.000000               3              0.000000   \n",
       "36            1              0.000000               2              0.000000   \n",
       "37            1              0.000000               2              0.000000   \n",
       "38            1              0.000000               2              0.000000   \n",
       "39            1              0.000000               3              0.083333   \n",
       "40            1              0.133333               1              0.000000   \n",
       "41            1              0.000000               1              0.000000   \n",
       "42            1              0.000000               1              0.000000   \n",
       "43            1              0.000000               1              0.000000   \n",
       "44            1              0.181818               2              0.000000   \n",
       "45            1              0.000000               3              0.000000   \n",
       "46            1              0.000000               1              0.000000   \n",
       "47            1              0.000000               1              0.000000   \n",
       "48            1              0.000000               2              0.000000   \n",
       "49            1              0.000000               1              0.000000   \n",
       "\n",
       "    name==username  description length  external URL  private  #posts  \\\n",
       "0                0                  43             0        1     108   \n",
       "1                0                  45             0        0       1   \n",
       "2                0                  90             0        0     536   \n",
       "3                0                   0             0        0     157   \n",
       "4                0                 102             0        1      24   \n",
       "5                0                  59             0        1      19   \n",
       "6                0                   8             0        1       0   \n",
       "7                0                  90             1        0      27   \n",
       "8                0                 148             0        1     458   \n",
       "9                0                   0             0        1      35   \n",
       "10               0                  96             0        1      96   \n",
       "11               0                   0             0        1       2   \n",
       "12               0                 135             1        1     159   \n",
       "13               0                  20             0        0      87   \n",
       "14               0                   0             0        1      35   \n",
       "15               0                  98             0        0       9   \n",
       "16               0                  92             0        1      10   \n",
       "17               0                  69             0        1      16   \n",
       "18               0                  68             0        1      31   \n",
       "19               0                   6             0        0      27   \n",
       "20               0                   2             0        0      21   \n",
       "21               0                   0             0        1     219   \n",
       "22               0                  38             0        0      11   \n",
       "23               0                  30             1        0      42   \n",
       "24               0                   9             0        0       2   \n",
       "25               0                  62             0        1      12   \n",
       "26               0                  86             0        0     299   \n",
       "27               0                  14             0        0      11   \n",
       "28               0                   9             0        0       0   \n",
       "29               0                 133             0        1      11   \n",
       "30               0                   0             0        1       3   \n",
       "31               0                   0             0        0      71   \n",
       "32               0                  29             0        0      61   \n",
       "33               0                  64             0        0    1729   \n",
       "34               0                  17             0        0      73   \n",
       "35               0                  36             0        1      20   \n",
       "36               0                 106             0        1      23   \n",
       "37               0                  31             0        1      78   \n",
       "38               0                  35             0        1      12   \n",
       "39               0                 100             0        1      56   \n",
       "40               0                 103             0        1     109   \n",
       "41               0                   0             0        0      49   \n",
       "42               0                   7             0        0     110   \n",
       "43               0                  31             1        0     141   \n",
       "44               0                  35             1        0     320   \n",
       "45               0                 115             0        1    1336   \n",
       "46               0                   0             0        1      39   \n",
       "47               0                   0             0        1       0   \n",
       "48               0                  46             0        0      23   \n",
       "49               0                   0             0        0      30   \n",
       "\n",
       "    #followers  #follows  \n",
       "0          788       764  \n",
       "1          252       483  \n",
       "2         1818      7486  \n",
       "3          148       813  \n",
       "4          481       592  \n",
       "5          773      3639  \n",
       "6            3      3639  \n",
       "7           63        19  \n",
       "8          682       436  \n",
       "9         1054      1046  \n",
       "10          50        98  \n",
       "11          10       202  \n",
       "12          52       240  \n",
       "13        1864       692  \n",
       "14         275      2039  \n",
       "15          98       847  \n",
       "16          11        46  \n",
       "17        2686      6570  \n",
       "18          18        64  \n",
       "19        1628      1037  \n",
       "20        1730      1298  \n",
       "21         183       275  \n",
       "22         645      4452  \n",
       "23        1258       952  \n",
       "24         629       485  \n",
       "25        1270       951  \n",
       "26        1669      1133  \n",
       "27         753       853  \n",
       "28         213       700  \n",
       "29          28       169  \n",
       "30        1395       794  \n",
       "31         831      1024  \n",
       "32         680       566  \n",
       "33        6114      5758  \n",
       "34        2104      7091  \n",
       "35         728      4139  \n",
       "36          83       458  \n",
       "37        2035      1035  \n",
       "38       11549       712  \n",
       "39          39       190  \n",
       "40        1053      6221  \n",
       "41         412       520  \n",
       "42         317       334  \n",
       "43        2490      1043  \n",
       "44        2345       861  \n",
       "45        1018      1208  \n",
       "46          37       611  \n",
       "47         513       633  \n",
       "48          83       306  \n",
       "49         126       372  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = pd.DataFrame(f_table,\n",
    "                         columns = ['profile pic', \n",
    "                                    'nums/length username', \n",
    "                                    'fullname words',\n",
    "                                    'nums/length fullname',\n",
    "                                    'name==username',\n",
    "                                    'description length',\n",
    "                                    'external URL',\n",
    "                                    'private',\n",
    "                                    '#posts',\n",
    "                                    '#followers',\n",
    "                                    '#follows'])\n",
    "test_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 5: Make the prediction!\n",
    "In part 2, we have compared the different classifiers and found that the Random Forest Classifier had the highest accuracy at 92.5%. Therefore, we are going to use this classifier to make the prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/athiyadeviyani/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "rfc = RandomForestClassifier()\n",
    "\n",
    "# Train the model\n",
    "# We've done this in Part 2 but I'm redoing it here for coherence ☺️\n",
    "rfc_model = rfc.fit(train_X, train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_labels = rfc_model.predict(test_data)\n",
    "rfc_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the number of fake accounts in the random sample of 50 followers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_fakes = len([x for x in rfc_labels if x==1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the Instagram user's authenticity,\n",
    "    <br>where authenticity = (#followers - #fakes)*100 / #followers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User X's Instagram Followers is 82.0% authentic.\n"
     ]
    }
   ],
   "source": [
    "authenticity = (len(random_followers) - no_fakes) * 100 / len(random_followers)\n",
    "print(\"User X's Instagram Followers is \" + str(authenticity) + \"% authentic.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 6: Extension - Fake Likes\n",
    "The method above can also be extended to check fake likes within a post."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the user's posts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_user_posts(userID, min_posts_to_be_retrieved):\n",
    "    # Retrieve all posts from my profile\n",
    "    my_posts = []\n",
    "    has_more_posts = True\n",
    "    max_id = ''\n",
    "    \n",
    "    while has_more_posts:\n",
    "        feed = api.user_feed(userID, max_id=max_id)\n",
    "        if feed.get('more_available') is not True:\n",
    "            has_more_posts = False \n",
    "            \n",
    "        max_id = feed.get('next_max_id', '')\n",
    "        my_posts.extend(feed.get('items'))\n",
    "        \n",
    "        # time.sleep(2) to avoid flooding\n",
    "        \n",
    "        if len(my_posts) > min_posts_to_be_retrieved:\n",
    "            print('Total posts retrieved: ' + str(len(my_posts)))\n",
    "            return my_posts\n",
    "            \n",
    "        if has_more_posts:\n",
    "            print(str(len(my_posts)) + ' posts retrieved so far...')\n",
    "           \n",
    "    print('Total posts retrieved: ' + str(len(my_posts)))\n",
    "    \n",
    "    return my_posts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total posts retrieved: 18\n"
     ]
    }
   ],
   "source": [
    "posts = get_user_posts(userID, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pick one post to analyse (here I'm just going to pick by random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_post = random.sample(posts, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get post likers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['taken_at', 'pk', 'id', 'device_timestamp', 'media_type', 'code', 'client_cache_key', 'filter_type', 'carousel_media_count', 'carousel_media', 'can_see_insights_as_brand', 'location', 'lat', 'lng', 'user', 'can_viewer_reshare', 'caption_is_edited', 'comment_likes_enabled', 'comment_threading_enabled', 'has_more_comments', 'next_max_id', 'max_num_visible_preview_comments', 'preview_comments', 'can_view_more_preview_comments', 'comment_count', 'inline_composer_display_condition', 'inline_composer_imp_trigger_time', 'like_count', 'has_liked', 'top_likers', 'photo_of_you', 'usertags', 'caption', 'can_viewer_save', 'organic_tracking_token'])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_post[0].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "likers = api.media_likers(random_post[0]['id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get a list of usernames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "likers_usernames = [liker['username'] for liker in likers['users']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get a random sample of 50 users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_likers = random.sample(likers_usernames, 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retrieve the information for the 50 users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "l_infos = []\n",
    "\n",
    "for liker in random_likers:\n",
    "    info = api.user_info(get_ID(liker))['user']\n",
    "    l_infos.append(info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 0.0, 1, 0, 0, 30, 0, 0, 6, 21, 177],\n",
       " [1, 0.0, 1, 0.0, 0, 69, 0, 1, 131, 942, 1229],\n",
       " [1, 0.0, 2, 0.0, 0, 83, 0, 1, 609, 1558, 2925],\n",
       " [1, 0.0, 1, 0.0, 0, 39, 0, 0, 851, 2940, 1255],\n",
       " [1, 0.0, 1, 0.0, 0, 36, 1, 0, 106, 1626, 1050],\n",
       " [0, 0.0, 1, 0, 0, 0, 0, 1, 7, 371, 350],\n",
       " [1, 0.0, 2, 0.0, 0, 96, 1, 0, 405, 1656, 2843],\n",
       " [1, 0.0, 2, 0.0, 0, 5, 1, 0, 9, 1363, 854],\n",
       " [1, 0.0, 1, 0, 0, 1, 0, 1, 5, 433, 371],\n",
       " [1, 0.0, 6, 0.0, 0, 93, 1, 0, 73, 1356, 1081],\n",
       " [1, 0.0, 3, 0.0, 0, 80, 1, 1, 188, 966, 966],\n",
       " [1, 0.0, 3, 0.0, 0, 0, 0, 1, 156, 1401, 1249],\n",
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       " [1, 0.0, 1, 0.0, 0, 80, 0, 0, 99, 1413, 2479],\n",
       " [1, 0.0, 1, 0.0, 0, 23, 0, 1, 12, 1116, 1031],\n",
       " [1, 0.0, 1, 0.0, 0, 20, 0, 0, 87, 1864, 692],\n",
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       " [1, 0.0, 4, 0.0, 0, 17, 0, 1, 127, 546, 536],\n",
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       " [1, 0.0, 2, 0.0, 0, 8, 0, 0, 39, 1490, 1321],\n",
       " [1, 0.0, 2, 0.0, 0, 0, 0, 0, 10, 519, 547],\n",
       " [1, 0.0, 2, 0.0, 0, 0, 0, 0, 43, 933, 1101],\n",
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       " [1, 0.25, 3, 0.0, 0, 139, 1, 0, 104, 1738, 999],\n",
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       " [1, 0.0, 1, 0.0, 0, 20, 0, 1, 107, 749, 857],\n",
       " [1, 0.0, 4, 0.0, 0, 119, 1, 0, 655, 675, 1904],\n",
       " [1, 0.0, 1, 0.0, 0, 103, 1, 0, 48, 10075, 2379],\n",
       " [1, 0.0, 1, 0.0, 0, 0, 0, 0, 12, 534, 563],\n",
       " [1, 0.0, 1, 0, 0, 0, 0, 1, 58, 2220, 1418],\n",
       " [1, 0.0, 1, 0.0, 0, 11, 1, 1, 18, 775, 514],\n",
       " [1, 0.0, 3, 0.0, 0, 30, 0, 0, 10, 1070, 1364],\n",
       " [1, 0.0, 1, 0.0, 0, 18, 0, 0, 108, 1148, 832],\n",
       " [1, 0.0, 2, 0.0, 0, 133, 0, 1, 52, 394, 432],\n",
       " [1, 0.0, 1, 0, 0, 30, 1, 0, 48, 3441, 1293],\n",
       " [1, 0.0, 2, 0.0, 0, 40, 1, 0, 1434, 1642, 1684],\n",
       " [1, 0.0, 1, 0.0, 0, 64, 1, 0, 33, 17955, 781],\n",
       " [1, 0.0, 2, 0.0, 0, 91, 1, 1, 217, 1014, 1409],\n",
       " [1, 0.0, 1, 0, 0, 0, 0, 1, 1, 1347, 872],\n",
       " [1, 0.3076923076923077, 1, 0.0, 0, 0, 0, 0, 59, 161, 544],\n",
       " [1, 0.0, 3, 0.0, 0, 141, 1, 1, 274, 922, 913],\n",
       " [1, 0.0, 1, 0.0, 0, 69, 1, 0, 69, 904, 596],\n",
       " [1, 0.0, 1, 0.0, 0, 42, 0, 0, 598, 1877, 6379],\n",
       " [1, 0.0, 2, 0.0, 0, 4, 0, 1, 11, 660, 643],\n",
       " [1, 0.0, 2, 0.0, 0, 24, 0, 0, 6, 345, 358],\n",
       " [1, 0.0, 2, 0.0, 0, 29, 0, 0, 23, 293, 538],\n",
       " [1, 0.0, 1, 0.0, 0, 10, 1, 1, 3, 690, 549]]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l_table = []\n",
    "\n",
    "for info in l_infos:\n",
    "    l_table.append(get_data(info))\n",
    "\n",
    "l_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>profile pic</th>\n",
       "      <th>nums/length username</th>\n",
       "      <th>fullname words</th>\n",
       "      <th>nums/length fullname</th>\n",
       "      <th>name==username</th>\n",
       "      <th>description length</th>\n",
       "      <th>external URL</th>\n",
       "      <th>private</th>\n",
       "      <th>#posts</th>\n",
       "      <th>#followers</th>\n",
       "      <th>#follows</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>131</td>\n",
       "      <td>942</td>\n",
       "      <td>1229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>609</td>\n",
       "      <td>1558</td>\n",
       "      <td>2925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>851</td>\n",
       "      <td>2940</td>\n",
       "      <td>1255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>106</td>\n",
       "      <td>1626</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>371</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>405</td>\n",
       "      <td>1656</td>\n",
       "      <td>2843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1363</td>\n",
       "      <td>854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>433</td>\n",
       "      <td>371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>93</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>1356</td>\n",
       "      <td>1081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>188</td>\n",
       "      <td>966</td>\n",
       "      <td>966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>1401</td>\n",
       "      <td>1249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>6557</td>\n",
       "      <td>2423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>84</td>\n",
       "      <td>1552</td>\n",
       "      <td>661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>1413</td>\n",
       "      <td>2479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1116</td>\n",
       "      <td>1031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>1864</td>\n",
       "      <td>692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>1266</td>\n",
       "      <td>1107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>636</td>\n",
       "      <td>579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>127</td>\n",
       "      <td>546</td>\n",
       "      <td>536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>918</td>\n",
       "      <td>678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>1490</td>\n",
       "      <td>1321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>519</td>\n",
       "      <td>547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>933</td>\n",
       "      <td>1101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>613</td>\n",
       "      <td>612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>139</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>104</td>\n",
       "      <td>1738</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>2973</td>\n",
       "      <td>1339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>107</td>\n",
       "      <td>749</td>\n",
       "      <td>857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>655</td>\n",
       "      <td>675</td>\n",
       "      <td>1904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>103</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>10075</td>\n",
       "      <td>2379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>534</td>\n",
       "      <td>563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>2220</td>\n",
       "      <td>1418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>775</td>\n",
       "      <td>514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1070</td>\n",
       "      <td>1364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>1148</td>\n",
       "      <td>832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>133</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>394</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>3441</td>\n",
       "      <td>1293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1434</td>\n",
       "      <td>1642</td>\n",
       "      <td>1684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>17955</td>\n",
       "      <td>781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>217</td>\n",
       "      <td>1014</td>\n",
       "      <td>1409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1347</td>\n",
       "      <td>872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>161</td>\n",
       "      <td>544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>274</td>\n",
       "      <td>922</td>\n",
       "      <td>913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>904</td>\n",
       "      <td>596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>598</td>\n",
       "      <td>1877</td>\n",
       "      <td>6379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>660</td>\n",
       "      <td>643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>345</td>\n",
       "      <td>358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>293</td>\n",
       "      <td>538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>690</td>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    profile pic  nums/length username  fullname words  nums/length fullname  \\\n",
       "0             1              0.000000               1                   0.0   \n",
       "1             1              0.000000               1                   0.0   \n",
       "2             1              0.000000               2                   0.0   \n",
       "3             1              0.000000               1                   0.0   \n",
       "4             1              0.000000               1                   0.0   \n",
       "5             0              0.000000               1                   0.0   \n",
       "6             1              0.000000               2                   0.0   \n",
       "7             1              0.000000               2                   0.0   \n",
       "8             1              0.000000               1                   0.0   \n",
       "9             1              0.000000               6                   0.0   \n",
       "10            1              0.000000               3                   0.0   \n",
       "11            1              0.000000               3                   0.0   \n",
       "12            1              0.000000               2                   0.0   \n",
       "13            1              0.000000               1                   0.0   \n",
       "14            1              0.000000               1                   0.0   \n",
       "15            1              0.000000               1                   0.0   \n",
       "16            1              0.000000               1                   0.0   \n",
       "17            1              0.000000               3                   0.0   \n",
       "18            1              0.000000               2                   0.0   \n",
       "19            1              0.000000               4                   0.0   \n",
       "20            1              0.000000               1                   0.0   \n",
       "21            1              0.285714               1                   0.0   \n",
       "22            1              0.000000               2                   0.0   \n",
       "23            1              0.000000               2                   0.0   \n",
       "24            1              0.000000               2                   0.0   \n",
       "25            1              0.000000               2                   0.0   \n",
       "26            1              0.250000               3                   0.0   \n",
       "27            1              0.000000               3                   0.0   \n",
       "28            1              0.000000               1                   0.0   \n",
       "29            1              0.000000               4                   0.0   \n",
       "30            1              0.000000               1                   0.0   \n",
       "31            1              0.000000               1                   0.0   \n",
       "32            1              0.000000               1                   0.0   \n",
       "33            1              0.000000               1                   0.0   \n",
       "34            1              0.000000               3                   0.0   \n",
       "35            1              0.000000               1                   0.0   \n",
       "36            1              0.000000               2                   0.0   \n",
       "37            1              0.000000               1                   0.0   \n",
       "38            1              0.000000               2                   0.0   \n",
       "39            1              0.000000               1                   0.0   \n",
       "40            1              0.000000               2                   0.0   \n",
       "41            1              0.000000               1                   0.0   \n",
       "42            1              0.307692               1                   0.0   \n",
       "43            1              0.000000               3                   0.0   \n",
       "44            1              0.000000               1                   0.0   \n",
       "45            1              0.000000               1                   0.0   \n",
       "46            1              0.000000               2                   0.0   \n",
       "47            1              0.000000               2                   0.0   \n",
       "48            1              0.000000               2                   0.0   \n",
       "49            1              0.000000               1                   0.0   \n",
       "\n",
       "    name==username  description length  external URL  private  #posts  \\\n",
       "0                0                  30             0        0       6   \n",
       "1                0                  69             0        1     131   \n",
       "2                0                  83             0        1     609   \n",
       "3                0                  39             0        0     851   \n",
       "4                0                  36             1        0     106   \n",
       "5                0                   0             0        1       7   \n",
       "6                0                  96             1        0     405   \n",
       "7                0                   5             1        0       9   \n",
       "8                0                   1             0        1       5   \n",
       "9                0                  93             1        0      73   \n",
       "10               0                  80             1        1     188   \n",
       "11               0                   0             0        1     156   \n",
       "12               0                 118             1        0     115   \n",
       "13               0                  12             0        0      84   \n",
       "14               0                  80             0        0      99   \n",
       "15               0                  23             0        1      12   \n",
       "16               0                  20             0        0      87   \n",
       "17               0                  62             1        0      17   \n",
       "18               0                  20             0        1      15   \n",
       "19               0                  17             0        1     127   \n",
       "20               0                  18             0        0       5   \n",
       "21               0                   0             0        1       0   \n",
       "22               0                   8             0        0      39   \n",
       "23               0                   0             0        0      10   \n",
       "24               0                   0             0        0      43   \n",
       "25               0                  10             0        1      19   \n",
       "26               0                 139             1        0     104   \n",
       "27               0                  42             1        0      17   \n",
       "28               0                  20             0        1     107   \n",
       "29               0                 119             1        0     655   \n",
       "30               0                 103             1        0      48   \n",
       "31               0                   0             0        0      12   \n",
       "32               0                   0             0        1      58   \n",
       "33               0                  11             1        1      18   \n",
       "34               0                  30             0        0      10   \n",
       "35               0                  18             0        0     108   \n",
       "36               0                 133             0        1      52   \n",
       "37               0                  30             1        0      48   \n",
       "38               0                  40             1        0    1434   \n",
       "39               0                  64             1        0      33   \n",
       "40               0                  91             1        1     217   \n",
       "41               0                   0             0        1       1   \n",
       "42               0                   0             0        0      59   \n",
       "43               0                 141             1        1     274   \n",
       "44               0                  69             1        0      69   \n",
       "45               0                  42             0        0     598   \n",
       "46               0                   4             0        1      11   \n",
       "47               0                  24             0        0       6   \n",
       "48               0                  29             0        0      23   \n",
       "49               0                  10             1        1       3   \n",
       "\n",
       "    #followers  #follows  \n",
       "0           21       177  \n",
       "1          942      1229  \n",
       "2         1558      2925  \n",
       "3         2940      1255  \n",
       "4         1626      1050  \n",
       "5          371       350  \n",
       "6         1656      2843  \n",
       "7         1363       854  \n",
       "8          433       371  \n",
       "9         1356      1081  \n",
       "10         966       966  \n",
       "11        1401      1249  \n",
       "12        6557      2423  \n",
       "13        1552       661  \n",
       "14        1413      2479  \n",
       "15        1116      1031  \n",
       "16        1864       692  \n",
       "17        1266      1107  \n",
       "18         636       579  \n",
       "19         546       536  \n",
       "20         918       678  \n",
       "21          20        35  \n",
       "22        1490      1321  \n",
       "23         519       547  \n",
       "24         933      1101  \n",
       "25         613       612  \n",
       "26        1738       999  \n",
       "27        2973      1339  \n",
       "28         749       857  \n",
       "29         675      1904  \n",
       "30       10075      2379  \n",
       "31         534       563  \n",
       "32        2220      1418  \n",
       "33         775       514  \n",
       "34        1070      1364  \n",
       "35        1148       832  \n",
       "36         394       432  \n",
       "37        3441      1293  \n",
       "38        1642      1684  \n",
       "39       17955       781  \n",
       "40        1014      1409  \n",
       "41        1347       872  \n",
       "42         161       544  \n",
       "43         922       913  \n",
       "44         904       596  \n",
       "45        1877      6379  \n",
       "46         660       643  \n",
       "47         345       358  \n",
       "48         293       538  \n",
       "49         690       549  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate pandas dataframe \n",
    "l_test_data = pd.DataFrame(l_table,\n",
    "                         columns = ['profile pic', \n",
    "                                    'nums/length username', \n",
    "                                    'fullname words',\n",
    "                                    'nums/length fullname',\n",
    "                                    'name==username',\n",
    "                                    'description length',\n",
    "                                    'external URL',\n",
    "                                    'private',\n",
    "                                    '#posts',\n",
    "                                    '#followers',\n",
    "                                    '#follows'])\n",
    "l_test_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, make the prediction!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/athiyadeviyani/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier()\n",
    "rfc_model = rfc.fit(train_X, train_Y)\n",
    "rfc_labels_likes = rfc_model.predict(l_test_data)\n",
    "rfc_labels_likes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the fake accounts that liked the user's media"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_fake_likes = len([x for x in rfc_labels_likes if x==1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the media likes authenticity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The media with the ID:XXXXX has 92.0% authentic likes.\n"
     ]
    }
   ],
   "source": [
    "media_authenticity = (len(random_likers) - no_fake_likes) * 100 / len(random_likers)\n",
    "print(\"The media with the ID:XXXXX has \" + str(media_authenticity) + \"% authentic likes.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 7: Comparison With Another User\n",
    "I have specifically chosen user X because I trusted their social media 'game' and seemed to have a loyal and engaged following. Let's compare their metrics with a user Y, a user that has a noticable follower growth spike when examined on SocialBlade.\n",
    "\n",
    "I am going to skip the explanation here because it's just a repetition of the steps performed on user X."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "username: ins.tafakebusters\n",
      "password: ········\n"
     ]
    }
   ],
   "source": [
    "# Re-login because of API call limits \n",
    "api = login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "userID_y = get_ID('<USERNAME>')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "rank = api.generate_uuid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**USER Y FOLLOWERS ANALYSIS**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_followers = get_followers(userID_y, rank)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_random_followers = random.sample(y_followers, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_infos = []\n",
    "\n",
    "for follower in y_random_followers:\n",
    "    info = api.user_info(get_ID(follower))['user']\n",
    "    y_infos.append(info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " [1, 0.05263157894736842, 2, 0.0, 0, 1, 0, 0, 15, 55, 5313],\n",
       " [1, 0.18181818181818182, 2, 0.0, 0, 0, 0, 0, 16, 95, 1228],\n",
       " [1, 0.15384615384615385, 1, 0.0, 0, 0, 0, 0, 16, 56, 3665],\n",
       " [1, 0.0, 1, 0, 0, 0, 0, 0, 15, 5, 1568],\n",
       " [0, 0.16666666666666666, 2, 0.0, 0, 0, 0, 1, 3, 8, 28],\n",
       " [1, 0.4117647058823529, 2, 0.0, 0, 0, 0, 0, 1, 69, 196]]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_table = []\n",
    "\n",
    "for info in y_infos:\n",
    "    y_table.append(get_data(info))\n",
    "    \n",
    "y_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>profile pic</th>\n",
       "      <th>nums/length username</th>\n",
       "      <th>fullname words</th>\n",
       "      <th>nums/length fullname</th>\n",
       "      <th>name==username</th>\n",
       "      <th>description length</th>\n",
       "      <th>external URL</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>87</td>\n",
       "      <td>6741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>5862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1289</td>\n",
       "      <td>689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>636</td>\n",
       "      <td>1293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>3617</td>\n",
       "      <td>1346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>71</td>\n",
       "      <td>7495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>113</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>305</td>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>115</td>\n",
       "      <td>7506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>272</td>\n",
       "      <td>1446</td>\n",
       "      <td>2362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1150</td>\n",
       "      <td>732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>60</td>\n",
       "      <td>1631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>831</td>\n",
       "      <td>5424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>158</td>\n",
       "      <td>7063</td>\n",
       "      <td>1355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>2045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0</td>\n",
       "      <td>127</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>196</td>\n",
       "      <td>486</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>509</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5079</td>\n",
       "      <td>879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1778</td>\n",
       "      <td>1477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>29</td>\n",
       "      <td>543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>784</td>\n",
       "      <td>526</td>\n",
       "      <td>1235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9123</td>\n",
       "      <td>6144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>134</td>\n",
       "      <td>416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>506</td>\n",
       "      <td>804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>27</td>\n",
       "      <td>2557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>1151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>1212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1224</td>\n",
       "      <td>774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>2056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>27</td>\n",
       "      <td>395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>444</td>\n",
       "      <td>1116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>214</td>\n",
       "      <td>2377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>6047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>55</td>\n",
       "      <td>5313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>95</td>\n",
       "      <td>1228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>56</td>\n",
       "      <td>3665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>1568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    profile pic  nums/length username  fullname words  nums/length fullname  \\\n",
       "0             1              0.142857               1              0.000000   \n",
       "1             1              0.222222               1              0.000000   \n",
       "2             1              0.250000               2              0.000000   \n",
       "3             1              0.000000               4              0.000000   \n",
       "4             1              0.363636               2              0.000000   \n",
       "5             1              0.250000               2              0.000000   \n",
       "6             1              0.000000               1              0.000000   \n",
       "7             1              0.000000               1              0.000000   \n",
       "8             1              0.000000               1              0.000000   \n",
       "9             1              0.000000               1              0.000000   \n",
       "10            1              0.000000               4              0.000000   \n",
       "11            1              0.214286               2              0.000000   \n",
       "12            1              0.133333               2              0.000000   \n",
       "13            0              0.444444               2              0.000000   \n",
       "14            1              0.000000               3              0.000000   \n",
       "15            0              0.062500               2              0.000000   \n",
       "16            1              0.153846               2              0.000000   \n",
       "17            1              0.000000               2              0.000000   \n",
       "18            1              0.000000               1              0.000000   \n",
       "19            1              0.000000               1              0.000000   \n",
       "20            1              0.230769               1              0.000000   \n",
       "21            1              0.250000               1              0.000000   \n",
       "22            1              0.500000               1              0.000000   \n",
       "23            1              0.000000               3              0.000000   \n",
       "24            1              0.000000               1              0.000000   \n",
       "25            1              0.000000               4              0.055556   \n",
       "26            1              0.000000               1              0.000000   \n",
       "27            1              0.000000               2              0.000000   \n",
       "28            1              0.000000               1              0.000000   \n",
       "29            1              0.000000               2              0.000000   \n",
       "30            1              0.000000               3              0.000000   \n",
       "31            1              0.000000               2              0.000000   \n",
       "32            1              0.000000               2              0.000000   \n",
       "33            1              0.000000               2              0.000000   \n",
       "34            1              0.000000               2              0.000000   \n",
       "35            1              0.125000               2              0.000000   \n",
       "36            1              0.421053               2              0.000000   \n",
       "37            1              0.000000               1              0.000000   \n",
       "38            1              0.000000               5              0.000000   \n",
       "39            1              0.000000               1              0.000000   \n",
       "40            1              0.428571               1              0.000000   \n",
       "41            1              0.000000               2              0.000000   \n",
       "42            1              0.000000               1              0.000000   \n",
       "43            1              0.047619               2              0.000000   \n",
       "44            1              0.052632               2              0.000000   \n",
       "45            1              0.181818               2              0.000000   \n",
       "46            1              0.153846               1              0.000000   \n",
       "47            1              0.000000               1              0.000000   \n",
       "48            0              0.166667               2              0.000000   \n",
       "49            1              0.411765               2              0.000000   \n",
       "\n",
       "    name==username  description length  external URL  private  #posts  \\\n",
       "0                0                   0             0        0      16   \n",
       "1                0                   0             0        1      15   \n",
       "2                0                   0             0        0       5   \n",
       "3                0                  97             0        0       1   \n",
       "4                0                   0             0        0      16   \n",
       "5                0                  13             0        0      15   \n",
       "6                0                   0             0        1      21   \n",
       "7                0                  13             0        1      27   \n",
       "8                0                  29             0        1       0   \n",
       "9                0                 119             0        0      32   \n",
       "10               0                  20             0        0     144   \n",
       "11               0                   0             0        0      17   \n",
       "12               0                 113             0        1       3   \n",
       "13               0                   0             0        1       1   \n",
       "14               0                   0             0        0      17   \n",
       "15               0                   0             0        1     272   \n",
       "16               0                   0             0        0       6   \n",
       "17               0                   0             0        0      15   \n",
       "18               0                  13             0        0      15   \n",
       "19               0                   1             0        1       0   \n",
       "20               0                   0             0        0       0   \n",
       "21               0                  20             0        0       1   \n",
       "22               0                   0             0        0       1   \n",
       "23               0                 150             1        0     158   \n",
       "24               0                   0             0        1      15   \n",
       "25               0                 127             0        0     196   \n",
       "26               0                  76             0        1       7   \n",
       "27               0                  48             0        0       1   \n",
       "28               0                  19             0        1       9   \n",
       "29               0                   0             0        0      15   \n",
       "30               0                  77             0        1     784   \n",
       "31               0                  81             1        0       3   \n",
       "32               0                  33             0        0      15   \n",
       "33               0                  79             0        1      38   \n",
       "34               0                   0             0        0      20   \n",
       "35               0                   0             0        0      15   \n",
       "36               0                   0             0        0      18   \n",
       "37               0                   0             0        0      15   \n",
       "38               0                  25             0        0      12   \n",
       "39               0                   0             0        0      15   \n",
       "40               0                   0             0        0      18   \n",
       "41               0                   0             0        1      10   \n",
       "42               0                  43             0        0      57   \n",
       "43               0                   0             0        1      15   \n",
       "44               0                   1             0        0      15   \n",
       "45               0                   0             0        0      16   \n",
       "46               0                   0             0        0      16   \n",
       "47               0                   0             0        0      15   \n",
       "48               0                   0             0        1       3   \n",
       "49               0                   0             0        0       1   \n",
       "\n",
       "    #followers  #follows  \n",
       "0           32      1549  \n",
       "1          337      2058  \n",
       "2          310      6343  \n",
       "3        14107      7514  \n",
       "4            8      1050  \n",
       "5           87      6741  \n",
       "6           24      5862  \n",
       "7         1289       689  \n",
       "8           31       148  \n",
       "9          636      1293  \n",
       "10        3617      1346  \n",
       "11          71      7495  \n",
       "12         305       303  \n",
       "13          63       283  \n",
       "14         115      7506  \n",
       "15        1446      2362  \n",
       "16        1150       732  \n",
       "17          60      1631  \n",
       "18          11       221  \n",
       "19          21        23  \n",
       "20           4       173  \n",
       "21          29       457  \n",
       "22         831      5424  \n",
       "23        7063      1355  \n",
       "24          39      2045  \n",
       "25         486       198  \n",
       "26         509       372  \n",
       "27        5079       879  \n",
       "28        1778      1477  \n",
       "29          29       543  \n",
       "30         526      1235  \n",
       "31        9123      6144  \n",
       "32         134       416  \n",
       "33         506       804  \n",
       "34          27      2557  \n",
       "35           9      1151  \n",
       "36          12      1212  \n",
       "37          14       600  \n",
       "38        1224       774  \n",
       "39          23      2056  \n",
       "40          27       395  \n",
       "41         444      1116  \n",
       "42         214      2377  \n",
       "43          15      6047  \n",
       "44          55      5313  \n",
       "45          95      1228  \n",
       "46          56      3665  \n",
       "47           5      1568  \n",
       "48           8        28  \n",
       "49          69       196  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate pandas dataframe \n",
    "y_test_data = pd.DataFrame(y_table,\n",
    "                         columns = ['profile pic', \n",
    "                                    'nums/length username', \n",
    "                                    'fullname words',\n",
    "                                    'nums/length fullname',\n",
    "                                    'name==username',\n",
    "                                    'description length',\n",
    "                                    'external URL',\n",
    "                                    'private',\n",
    "                                    '#posts',\n",
    "                                    '#followers',\n",
    "                                    '#follows'])\n",
    "y_test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict (no retraining!)\n",
    "rfc_labels_y = rfc_model.predict(y_test_data)\n",
    "rfc_labels_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the number of fake accounts in the random sample of 50 followers\n",
    "no_fakes_y = len([x for x in rfc_labels_y if x==1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User Y's Instagram Followers is 38.0% authentic.\n"
     ]
    }
   ],
   "source": [
    "# Calculate the authenticity\n",
    "y_authenticity = (len(y_random_followers) - no_fakes_y) * 100 / len(y_random_followers)\n",
    "print(\"User Y's Instagram Followers is \" + str(y_authenticity) + \"% authentic.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahh, the joys of being right!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**USER Y LIKES ANALYSIS**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total posts retrieved: 18\n"
     ]
    }
   ],
   "source": [
    "y_posts = get_user_posts(userID_y, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_random_post = random.sample(y_posts, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_likers = api.media_likers(y_random_post[0]['id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_likers_usernames = [liker['username'] for liker in y_likers['users']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_random_likers = random.sample(y_likers_usernames, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_likers_infos = []\n",
    "\n",
    "for liker in y_random_likers:\n",
    "    info = api.user_info(get_ID(liker))['user']\n",
    "    y_likers_infos.append(info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 0.0, 2, 0.0, 0, 0, 0, 0, 2, 897, 830],\n",
       " [0, 0.0, 2, 0.0, 0, 0, 0, 1, 0, 129, 132],\n",
       " [1, 0.0, 2, 0.0, 0, 8, 0, 1, 72, 1157, 698],\n",
       " [1, 0.0, 1, 0, 0, 10, 0, 1, 6, 1410, 619],\n",
       " [1, 0.0, 1, 0.0, 0, 0, 0, 0, 0, 1916, 731],\n",
       " [1, 0.2222222222222222, 3, 0.0, 0, 72, 0, 1, 13, 950, 649],\n",
       " [1, 0.0, 1, 0.0, 0, 19, 0, 1, 17, 1543, 1289],\n",
       " [1, 0.2, 5, 0.0, 0, 11, 0, 0, 33, 1076, 606],\n",
       " [1, 0.0, 1, 0.0, 0, 104, 0, 1, 6, 202, 485],\n",
       " [1, 0.2, 1, 0.0, 0, 15, 0, 0, 7, 1262, 679],\n",
       " [1, 0.15384615384615385, 2, 0.0, 0, 0, 0, 0, 6, 1150, 732],\n",
       " [1, 0.0, 1, 0.0, 0, 17, 1, 0, 28, 2442, 629],\n",
       " [1, 0.0, 2, 0.0, 0, 61, 0, 0, 159, 556, 765],\n",
       " [1, 0.0, 2, 0.0, 0, 34, 0, 1, 10, 531, 526],\n",
       " [1, 0.0, 3, 0.0, 0, 127, 0, 0, 23, 1137, 909],\n",
       " [1, 0.0, 2, 0.0, 0, 66, 0, 1, 25, 583, 805],\n",
       " [1, 0.13333333333333333, 2, 0.0, 0, 67, 1, 0, 141, 4615, 1948],\n",
       " [1, 0.0, 2, 0.0, 0, 47, 0, 1, 387, 75, 162],\n",
       " [1, 0.0, 1, 0.0, 0, 142, 0, 1, 8144, 664, 1527],\n",
       " [1, 0.0, 3, 0.0, 0, 4, 0, 1, 1, 466, 325],\n",
       " [1, 0.058823529411764705, 1, 0.0, 0, 32, 0, 0, 14, 419, 414],\n",
       " [1, 0.0, 3, 0.0, 0, 75, 1, 0, 353, 1399, 764],\n",
       " [1, 0.0, 1, 0, 0, 0, 0, 0, 9, 611, 554],\n",
       " [1, 0.0, 1, 0.0, 0, 29, 0, 1, 3, 2064, 1077],\n",
       " [1, 0.0, 1, 0.0, 0, 26, 0, 1, 37, 628, 714],\n",
       " [1, 0.0, 2, 0.0, 0, 89, 1, 1, 243, 2316, 1030],\n",
       " [1, 0.0, 2, 0.0, 0, 140, 1, 0, 666, 4460, 492],\n",
       " [1, 0.0, 2, 0.0, 0, 20, 0, 0, 71, 4101, 878],\n",
       " [1, 0.0, 2, 0.0, 0, 5, 0, 0, 148, 424, 716],\n",
       " [1, 0.0, 1, 0, 0, 0, 0, 1, 2, 640, 730],\n",
       " [1, 0.0, 2, 0.0, 0, 64, 0, 1, 8, 1141, 891],\n",
       " [1, 0.0, 3, 0.0, 0, 29, 0, 1, 10, 1378, 986],\n",
       " [1, 0.0, 2, 0.0, 0, 14, 0, 1, 3, 994, 698],\n",
       " [1, 0.0, 1, 0.0, 0, 29, 0, 1, 43, 181, 169],\n",
       " [1, 0.0, 1, 0.0, 0, 58, 1, 0, 24, 1144, 1091],\n",
       " [1, 0.0, 2, 0.0, 0, 25, 0, 1, 36, 687, 574],\n",
       " [1, 0.0, 3, 0.0, 0, 8, 0, 1, 33, 1846, 996],\n",
       " [1, 0.5714285714285714, 2, 0.0, 0, 18, 0, 1, 202, 1180, 600],\n",
       " [1, 0.0, 2, 0.0, 0, 7, 0, 0, 45, 1206, 676],\n",
       " [1, 0.0, 2, 0.0, 0, 76, 0, 0, 12, 661, 3004],\n",
       " [1, 0.0, 1, 0.0, 0, 9, 0, 1, 5, 759, 706],\n",
       " [0, 0.0, 3, 0.0, 0, 61, 0, 1, 9, 439, 612],\n",
       " [1, 0.16666666666666666, 1, 0.0, 0, 0, 0, 1, 3, 911, 822],\n",
       " [1, 0.4, 2, 0.0, 0, 82, 0, 0, 99, 556, 733],\n",
       " [1, 0.0, 2, 0.0, 0, 80, 0, 1, 21, 478, 385],\n",
       " [1, 0.0, 1, 0, 0, 0, 0, 1, 0, 653, 312],\n",
       " [1, 0.0, 1, 0.0, 0, 13, 0, 1, 40, 713, 657],\n",
       " [1, 0.0, 2, 0.0, 0, 0, 0, 1, 4, 113, 311],\n",
       " [1, 0.0, 2, 0.0, 0, 33, 0, 0, 74, 3564, 1051],\n",
       " [1, 0.0, 1, 0.0, 0, 121, 0, 0, 958, 904, 479]]"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_likers_table = []\n",
    "\n",
    "for info in y_likers_infos:\n",
    "    y_likers_table.append(get_data(info))\n",
    "    \n",
    "y_likers_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>profile pic</th>\n",
       "      <th>nums/length username</th>\n",
       "      <th>fullname words</th>\n",
       "      <th>nums/length fullname</th>\n",
       "      <th>name==username</th>\n",
       "      <th>description length</th>\n",
       "      <th>external URL</th>\n",
       "      <th>private</th>\n",
       "      <th>#posts</th>\n",
       "      <th>#followers</th>\n",
       "      <th>#follows</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>897</td>\n",
       "      <td>830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>129</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>72</td>\n",
       "      <td>1157</td>\n",
       "      <td>698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1410</td>\n",
       "      <td>619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1916</td>\n",
       "      <td>731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>950</td>\n",
       "      <td>649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1543</td>\n",
       "      <td>1289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>1076</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>104</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>202</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1262</td>\n",
       "      <td>679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1150</td>\n",
       "      <td>732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>2442</td>\n",
       "      <td>629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>159</td>\n",
       "      <td>556</td>\n",
       "      <td>765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>531</td>\n",
       "      <td>526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>127</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>1137</td>\n",
       "      <td>909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>583</td>\n",
       "      <td>805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>4615</td>\n",
       "      <td>1948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>387</td>\n",
       "      <td>75</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8144</td>\n",
       "      <td>664</td>\n",
       "      <td>1527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>466</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>419</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>353</td>\n",
       "      <td>1399</td>\n",
       "      <td>764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>611</td>\n",
       "      <td>554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2064</td>\n",
       "      <td>1077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>628</td>\n",
       "      <td>714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>243</td>\n",
       "      <td>2316</td>\n",
       "      <td>1030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>666</td>\n",
       "      <td>4460</td>\n",
       "      <td>492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>4101</td>\n",
       "      <td>878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>424</td>\n",
       "      <td>716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>640</td>\n",
       "      <td>730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1141</td>\n",
       "      <td>891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1378</td>\n",
       "      <td>986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>994</td>\n",
       "      <td>698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>181</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>1144</td>\n",
       "      <td>1091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>687</td>\n",
       "      <td>574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>1846</td>\n",
       "      <td>996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>202</td>\n",
       "      <td>1180</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1206</td>\n",
       "      <td>676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>661</td>\n",
       "      <td>3004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>759</td>\n",
       "      <td>706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>439</td>\n",
       "      <td>612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>911</td>\n",
       "      <td>822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>556</td>\n",
       "      <td>733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>478</td>\n",
       "      <td>385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>653</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>713</td>\n",
       "      <td>657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>113</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>3564</td>\n",
       "      <td>1051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>121</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>958</td>\n",
       "      <td>904</td>\n",
       "      <td>479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    profile pic  nums/length username  fullname words  nums/length fullname  \\\n",
       "0             1              0.000000               2                   0.0   \n",
       "1             0              0.000000               2                   0.0   \n",
       "2             1              0.000000               2                   0.0   \n",
       "3             1              0.000000               1                   0.0   \n",
       "4             1              0.000000               1                   0.0   \n",
       "5             1              0.222222               3                   0.0   \n",
       "6             1              0.000000               1                   0.0   \n",
       "7             1              0.200000               5                   0.0   \n",
       "8             1              0.000000               1                   0.0   \n",
       "9             1              0.200000               1                   0.0   \n",
       "10            1              0.153846               2                   0.0   \n",
       "11            1              0.000000               1                   0.0   \n",
       "12            1              0.000000               2                   0.0   \n",
       "13            1              0.000000               2                   0.0   \n",
       "14            1              0.000000               3                   0.0   \n",
       "15            1              0.000000               2                   0.0   \n",
       "16            1              0.133333               2                   0.0   \n",
       "17            1              0.000000               2                   0.0   \n",
       "18            1              0.000000               1                   0.0   \n",
       "19            1              0.000000               3                   0.0   \n",
       "20            1              0.058824               1                   0.0   \n",
       "21            1              0.000000               3                   0.0   \n",
       "22            1              0.000000               1                   0.0   \n",
       "23            1              0.000000               1                   0.0   \n",
       "24            1              0.000000               1                   0.0   \n",
       "25            1              0.000000               2                   0.0   \n",
       "26            1              0.000000               2                   0.0   \n",
       "27            1              0.000000               2                   0.0   \n",
       "28            1              0.000000               2                   0.0   \n",
       "29            1              0.000000               1                   0.0   \n",
       "30            1              0.000000               2                   0.0   \n",
       "31            1              0.000000               3                   0.0   \n",
       "32            1              0.000000               2                   0.0   \n",
       "33            1              0.000000               1                   0.0   \n",
       "34            1              0.000000               1                   0.0   \n",
       "35            1              0.000000               2                   0.0   \n",
       "36            1              0.000000               3                   0.0   \n",
       "37            1              0.571429               2                   0.0   \n",
       "38            1              0.000000               2                   0.0   \n",
       "39            1              0.000000               2                   0.0   \n",
       "40            1              0.000000               1                   0.0   \n",
       "41            0              0.000000               3                   0.0   \n",
       "42            1              0.166667               1                   0.0   \n",
       "43            1              0.400000               2                   0.0   \n",
       "44            1              0.000000               2                   0.0   \n",
       "45            1              0.000000               1                   0.0   \n",
       "46            1              0.000000               1                   0.0   \n",
       "47            1              0.000000               2                   0.0   \n",
       "48            1              0.000000               2                   0.0   \n",
       "49            1              0.000000               1                   0.0   \n",
       "\n",
       "    name==username  description length  external URL  private  #posts  \\\n",
       "0                0                   0             0        0       2   \n",
       "1                0                   0             0        1       0   \n",
       "2                0                   8             0        1      72   \n",
       "3                0                  10             0        1       6   \n",
       "4                0                   0             0        0       0   \n",
       "5                0                  72             0        1      13   \n",
       "6                0                  19             0        1      17   \n",
       "7                0                  11             0        0      33   \n",
       "8                0                 104             0        1       6   \n",
       "9                0                  15             0        0       7   \n",
       "10               0                   0             0        0       6   \n",
       "11               0                  17             1        0      28   \n",
       "12               0                  61             0        0     159   \n",
       "13               0                  34             0        1      10   \n",
       "14               0                 127             0        0      23   \n",
       "15               0                  66             0        1      25   \n",
       "16               0                  67             1        0     141   \n",
       "17               0                  47             0        1     387   \n",
       "18               0                 142             0        1    8144   \n",
       "19               0                   4             0        1       1   \n",
       "20               0                  32             0        0      14   \n",
       "21               0                  75             1        0     353   \n",
       "22               0                   0             0        0       9   \n",
       "23               0                  29             0        1       3   \n",
       "24               0                  26             0        1      37   \n",
       "25               0                  89             1        1     243   \n",
       "26               0                 140             1        0     666   \n",
       "27               0                  20             0        0      71   \n",
       "28               0                   5             0        0     148   \n",
       "29               0                   0             0        1       2   \n",
       "30               0                  64             0        1       8   \n",
       "31               0                  29             0        1      10   \n",
       "32               0                  14             0        1       3   \n",
       "33               0                  29             0        1      43   \n",
       "34               0                  58             1        0      24   \n",
       "35               0                  25             0        1      36   \n",
       "36               0                   8             0        1      33   \n",
       "37               0                  18             0        1     202   \n",
       "38               0                   7             0        0      45   \n",
       "39               0                  76             0        0      12   \n",
       "40               0                   9             0        1       5   \n",
       "41               0                  61             0        1       9   \n",
       "42               0                   0             0        1       3   \n",
       "43               0                  82             0        0      99   \n",
       "44               0                  80             0        1      21   \n",
       "45               0                   0             0        1       0   \n",
       "46               0                  13             0        1      40   \n",
       "47               0                   0             0        1       4   \n",
       "48               0                  33             0        0      74   \n",
       "49               0                 121             0        0     958   \n",
       "\n",
       "    #followers  #follows  \n",
       "0          897       830  \n",
       "1          129       132  \n",
       "2         1157       698  \n",
       "3         1410       619  \n",
       "4         1916       731  \n",
       "5          950       649  \n",
       "6         1543      1289  \n",
       "7         1076       606  \n",
       "8          202       485  \n",
       "9         1262       679  \n",
       "10        1150       732  \n",
       "11        2442       629  \n",
       "12         556       765  \n",
       "13         531       526  \n",
       "14        1137       909  \n",
       "15         583       805  \n",
       "16        4615      1948  \n",
       "17          75       162  \n",
       "18         664      1527  \n",
       "19         466       325  \n",
       "20         419       414  \n",
       "21        1399       764  \n",
       "22         611       554  \n",
       "23        2064      1077  \n",
       "24         628       714  \n",
       "25        2316      1030  \n",
       "26        4460       492  \n",
       "27        4101       878  \n",
       "28         424       716  \n",
       "29         640       730  \n",
       "30        1141       891  \n",
       "31        1378       986  \n",
       "32         994       698  \n",
       "33         181       169  \n",
       "34        1144      1091  \n",
       "35         687       574  \n",
       "36        1846       996  \n",
       "37        1180       600  \n",
       "38        1206       676  \n",
       "39         661      3004  \n",
       "40         759       706  \n",
       "41         439       612  \n",
       "42         911       822  \n",
       "43         556       733  \n",
       "44         478       385  \n",
       "45         653       312  \n",
       "46         713       657  \n",
       "47         113       311  \n",
       "48        3564      1051  \n",
       "49         904       479  "
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_likers_data = pd.DataFrame(y_likers_table,\n",
    "                         columns = ['profile pic', \n",
    "                                    'nums/length username', \n",
    "                                    'fullname words',\n",
    "                                    'nums/length fullname',\n",
    "                                    'name==username',\n",
    "                                    'description length',\n",
    "                                    'external URL',\n",
    "                                    'private',\n",
    "                                    '#posts',\n",
    "                                    '#followers',\n",
    "                                    '#follows'])\n",
    "y_likers_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict!\n",
    "y_likers_pred = rfc_model.predict(y_likers_data)\n",
    "y_likers_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The media with the ID:YYYYY has 96.0% authentic likes.\n"
     ]
    }
   ],
   "source": [
    "# Calculate the number of fake likes\n",
    "no_fakes_yl = len([x for x in y_likers_pred if x==1])\n",
    "\n",
    "# Calculate media likes authenticity\n",
    "y_post_authenticity = (len(y_random_likers) - no_fakes_yl) * 100 / len(y_random_likers)\n",
    "print(\"The media with the ID:YYYYY has \" + str(y_post_authenticity) + \"% authentic likes.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Very high likes authenticity but very low follower authenticity? How is that possible?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use **engagement rates** to explain this phenomena further.\n",
    "\n",
    "Engagement rate = average number of engagements (likes+comments) / number of followers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['taken_at', 'pk', 'id', 'device_timestamp', 'media_type', 'code', 'client_cache_key', 'filter_type', 'carousel_media_count', 'carousel_media', 'can_see_insights_as_brand', 'location', 'lat', 'lng', 'user', 'can_viewer_reshare', 'caption_is_edited', 'comment_likes_enabled', 'comment_threading_enabled', 'has_more_comments', 'max_num_visible_preview_comments', 'preview_comments', 'can_view_more_preview_comments', 'comment_count', 'inline_composer_display_condition', 'inline_composer_imp_trigger_time', 'like_count', 'has_liked', 'top_likers', 'photo_of_you', 'caption', 'can_viewer_save', 'organic_tracking_token'])"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_posts[0].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.50268408791654"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = 0\n",
    "\n",
    "for post in y_posts:\n",
    "    count += post['comment_count']\n",
    "    count += post['like_count']\n",
    "    \n",
    "average_engagements = count / len(y_posts)\n",
    "engagement_rate = average_engagements*100 / len(y_followers)\n",
    "\n",
    "engagement_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means that only roughly 9.5% of user Y's followers engage with their content. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 8: Thoughts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Making sense of the result**\n",
    "\n",
    "So user X received an 82% follower authenticity score and a 92% media likes authenticity on one of their posts. Is that good enough? What about user Y with a 35% follower authenticity score and a 96% media likes authenticity?\n",
    "\n",
    "Since this entire notebook is an exploratory analysis, there's not really a hard line between a 'good' influencer and a 'bad' influencer. For user X, we can tell that the user has authentic and loyal followers. However for user Y, we can assume that they have a rather low authentic follower score, however their likes consist of real followers. This means that user Y might have invested on buying followers, but not likes! This causes a really low engagement rate.\n",
    "\n",
    "In fact, with a little bit more research, you can sort of establish a pattern just by observation:\n",
    "- High follower authenticity, high media authenticity, high engagement rate = authentic user\n",
    "- Low follower authenticity, high media authenticity, low engagement rate = buys followers, does not buy likes\n",
    "- Low follower authenticity, high media authenticity, high engagement rate = buys followers AND likes\n",
    "- ... and so on!\n",
    "\n",
    "**So is this influencer worth investing or not?**\n",
    "\n",
    "Remember that we used a *random sample* of 50 followers out of thousands. As objective as random sampling could be, it still isn't an *absolutely complete* picture of the user's followers. However, the follower authenticity combined with the media likes authenticity still provides an insight for brands who are planning to invest on the influencer. \n",
    "\n",
    "Personally, I feel like any number under 50% is rather suspicious, and there are other ways that you can confirm this suspicion:\n",
    "- Low engagement rates (engagement rate = average number of engagements (likes+comments) / number of followers)\n",
    "- Spikes in follower growth (uneven growth chart)\n",
    "- Comments (loyal followers acutally care about the user's content)\n",
    "\n",
    "But of course, you have to be aware of tech-savvy influencers who cheats the audit system and try to avoid getting caught, such as influencers who buys 'drip-followers' - i.e. you buy followers in bulk but they arrive slowly. This method will make their follower growth seem gradual."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion**\n",
    "\n",
    "The rapid growth of technology allows anyone with a computer to create bots to follow users and like media on any platform. However, this also means that our ability to detect fake engagements should also improve!\n",
    "\n",
    "Businesses, small or large, invest on social media influencers to reach a wider audience, especially during times of a global pandemic where everyone is constantly on their phones! Less tech-savvy and less aware ones are prone to this kind of misinformation.\n",
    "\n",
    "For brands who rely on influencers for marketing, it is highly recommended to check out services such as SocialBlade to check user authenticity and engagement. Some services are more pricey, but is definitely worth the investment!\n"
   ]
  }
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