Austin-City-Influencers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c7f6d9c1",
   "metadata": {},
   "source": [
    "## Part 1: Find Predictor's of influence from Kaggle Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5140910d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This Python 3 environment is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# Here we load several packages helpful for our analysis\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import tree\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import \n",
    "from sklearn.metrics import accuracy_score, roc_curve, roc_auc_score, classification_report, confusion_matrix\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import scikitplot as skplt\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "644a59c2",
   "metadata": {},
   "source": [
    "#### Read train set and basic EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6e3d1836",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Choice</th>\n",
       "      <th>A_follower_count</th>\n",
       "      <th>A_following_count</th>\n",
       "      <th>A_listed_count</th>\n",
       "      <th>A_mentions_received</th>\n",
       "      <th>A_retweets_received</th>\n",
       "      <th>A_mentions_sent</th>\n",
       "      <th>A_retweets_sent</th>\n",
       "      <th>A_posts</th>\n",
       "      <th>A_network_feature_1</th>\n",
       "      <th>...</th>\n",
       "      <th>B_following_count</th>\n",
       "      <th>B_listed_count</th>\n",
       "      <th>B_mentions_received</th>\n",
       "      <th>B_retweets_received</th>\n",
       "      <th>B_mentions_sent</th>\n",
       "      <th>B_retweets_sent</th>\n",
       "      <th>B_posts</th>\n",
       "      <th>B_network_feature_1</th>\n",
       "      <th>B_network_feature_2</th>\n",
       "      <th>B_network_feature_3</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>228</td>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
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       "      <td>0.100503</td>\n",
       "      <td>0.100503</td>\n",
       "      <td>0.362150</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>29808</td>\n",
       "      <td>1689</td>\n",
       "      <td>15.430498</td>\n",
       "      <td>3.984029</td>\n",
       "      <td>8.204331</td>\n",
       "      <td>0.332423</td>\n",
       "      <td>6.988815</td>\n",
       "      <td>66</td>\n",
       "      <td>75.530303</td>\n",
       "      <td>1916.893939</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>21591</td>\n",
       "      <td>1179</td>\n",
       "      <td>228</td>\n",
       "      <td>90.456506</td>\n",
       "      <td>25.798292</td>\n",
       "      <td>5.709329</td>\n",
       "      <td>1.111159</td>\n",
       "      <td>5.176620</td>\n",
       "      <td>369</td>\n",
       "      <td>...</td>\n",
       "      <td>848</td>\n",
       "      <td>1610</td>\n",
       "      <td>40.495021</td>\n",
       "      <td>8.943607</td>\n",
       "      <td>3.227677</td>\n",
       "      <td>0.564343</td>\n",
       "      <td>1.070321</td>\n",
       "      <td>163</td>\n",
       "      <td>132.030675</td>\n",
       "      <td>2931.515337</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7310</td>\n",
       "      <td>1215</td>\n",
       "      <td>101</td>\n",
       "      <td>25.503644</td>\n",
       "      <td>9.556347</td>\n",
       "      <td>5.361519</td>\n",
       "      <td>0.591206</td>\n",
       "      <td>3.589718</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>482</td>\n",
       "      <td>206</td>\n",
       "      <td>0.734696</td>\n",
       "      <td>0.354379</td>\n",
       "      <td>0.603202</td>\n",
       "      <td>0.100503</td>\n",
       "      <td>0.603202</td>\n",
       "      <td>3</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>277.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>7.690824</td>\n",
       "      <td>0.277306</td>\n",
       "      <td>1.331508</td>\n",
       "      <td>0.100503</td>\n",
       "      <td>2.830627</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>17637</td>\n",
       "      <td>278</td>\n",
       "      <td>572.874856</td>\n",
       "      <td>390.293681</td>\n",
       "      <td>27.552040</td>\n",
       "      <td>7.167557</td>\n",
       "      <td>32.101906</td>\n",
       "      <td>1762</td>\n",
       "      <td>23.469296</td>\n",
       "      <td>1395.845634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>45589</td>\n",
       "      <td>862</td>\n",
       "      <td>2641</td>\n",
       "      <td>148.854279</td>\n",
       "      <td>36.998884</td>\n",
       "      <td>27.881768</td>\n",
       "      <td>3.333492</td>\n",
       "      <td>23.861282</td>\n",
       "      <td>551</td>\n",
       "      <td>...</td>\n",
       "      <td>1711</td>\n",
       "      <td>181</td>\n",
       "      <td>21.601866</td>\n",
       "      <td>3.581661</td>\n",
       "      <td>6.764657</td>\n",
       "      <td>1.119727</td>\n",
       "      <td>4.563246</td>\n",
       "      <td>85</td>\n",
       "      <td>48.500000</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 23 columns</p>\n",
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      ],
      "text/plain": [
       "   Choice  A_follower_count  A_following_count  A_listed_count  \\\n",
       "0       0               228                302               3   \n",
       "1       0             21591               1179             228   \n",
       "2       0              7310               1215             101   \n",
       "3       0                20                  7               2   \n",
       "4       1             45589                862            2641   \n",
       "\n",
       "   A_mentions_received  A_retweets_received  A_mentions_sent  A_retweets_sent  \\\n",
       "0             0.583979             0.100503         0.100503         0.100503   \n",
       "1            90.456506            25.798292         5.709329         1.111159   \n",
       "2            25.503644             9.556347         5.361519         0.591206   \n",
       "3             7.690824             0.277306         1.331508         0.100503   \n",
       "4           148.854279            36.998884        27.881768         3.333492   \n",
       "\n",
       "     A_posts  A_network_feature_1  ...  B_following_count  B_listed_count  \\\n",
       "0   0.362150                    2  ...              29808            1689   \n",
       "1   5.176620                  369  ...                848            1610   \n",
       "2   3.589718                   95  ...                482             206   \n",
       "3   2.830627                    6  ...              17637             278   \n",
       "4  23.861282                  551  ...               1711             181   \n",
       "\n",
       "   B_mentions_received  B_retweets_received  B_mentions_sent  B_retweets_sent  \\\n",
       "0            15.430498             3.984029         8.204331         0.332423   \n",
       "1            40.495021             8.943607         3.227677         0.564343   \n",
       "2             0.734696             0.354379         0.603202         0.100503   \n",
       "3           572.874856           390.293681        27.552040         7.167557   \n",
       "4            21.601866             3.581661         6.764657         1.119727   \n",
       "\n",
       "     B_posts  B_network_feature_1  B_network_feature_2  B_network_feature_3  \n",
       "0   6.988815                   66            75.530303          1916.893939  \n",
       "1   1.070321                  163           132.030675          2931.515337  \n",
       "2   0.603202                    3            10.333333           277.333333  \n",
       "3  32.101906                 1762            23.469296          1395.845634  \n",
       "4   4.563246                   85            48.500000          1993.627907  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading the csv with training data\n",
    "train=pd.read_csv(\"train.csv\")`\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b624915e",
   "metadata": {},
   "source": [
    "The binary label represents a human judgement about which one of the two individuals is more influential. A label '1' means A is more influential than B. 0 means B is more influential than A."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "32f8bba3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Choice</th>\n",
       "      <th>A_follower_count</th>\n",
       "      <th>A_following_count</th>\n",
       "      <th>A_listed_count</th>\n",
       "      <th>A_mentions_received</th>\n",
       "      <th>A_retweets_received</th>\n",
       "      <th>A_mentions_sent</th>\n",
       "      <th>A_retweets_sent</th>\n",
       "      <th>A_posts</th>\n",
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       "      <th>...</th>\n",
       "      <th>B_following_count</th>\n",
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       "      <th>B_posts</th>\n",
       "      <th>B_network_feature_1</th>\n",
       "      <th>B_network_feature_2</th>\n",
       "      <th>B_network_feature_3</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5500.000000</td>\n",
       "      <td>5.500000e+03</td>\n",
       "      <td>5.500000e+03</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>5.500000e+03</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>5500.000000</td>\n",
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       "      <td>5500.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>5.500000e+03</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.509455</td>\n",
       "      <td>6.498840e+05</td>\n",
       "      <td>1.265895e+04</td>\n",
       "      <td>5952.453273</td>\n",
       "      <td>2.666032e+03</td>\n",
       "      <td>1032.371839</td>\n",
       "      <td>6.011873</td>\n",
       "      <td>1.109924</td>\n",
       "      <td>9.090730</td>\n",
       "      <td>5267.768000</td>\n",
       "      <td>...</td>\n",
       "      <td>12738.259818</td>\n",
       "      <td>5903.148364</td>\n",
       "      <td>2.554598e+03</td>\n",
       "      <td>997.149954</td>\n",
       "      <td>6.099658</td>\n",
       "      <td>1.106236</td>\n",
       "      <td>9.505821</td>\n",
       "      <td>5254.933636</td>\n",
       "      <td>85.024196</td>\n",
       "      <td>3745.175480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.499956</td>\n",
       "      <td>2.028787e+06</td>\n",
       "      <td>4.900867e+04</td>\n",
       "      <td>17339.141191</td>\n",
       "      <td>2.916543e+04</td>\n",
       "      <td>10954.953223</td>\n",
       "      <td>9.519797</td>\n",
       "      <td>1.910104</td>\n",
       "      <td>18.311060</td>\n",
       "      <td>28946.777345</td>\n",
       "      <td>...</td>\n",
       "      <td>50054.520874</td>\n",
       "      <td>16298.462018</td>\n",
       "      <td>2.508873e+04</td>\n",
       "      <td>9342.006880</td>\n",
       "      <td>9.729557</td>\n",
       "      <td>1.939914</td>\n",
       "      <td>19.424680</td>\n",
       "      <td>26778.820125</td>\n",
       "      <td>106.689384</td>\n",
       "      <td>5518.399590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.600000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.005034e-01</td>\n",
       "      <td>0.100503</td>\n",
       "      <td>0.100503</td>\n",
       "      <td>0.100503</td>\n",
       "      <td>0.100503</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.005034e-01</td>\n",
       "      <td>0.100503</td>\n",
       "      <td>0.100503</td>\n",
       "      <td>0.100503</td>\n",
       "      <td>0.100503</td>\n",
       "      <td>0.000000</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>2.663750e+03</td>\n",
       "      <td>3.220000e+02</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>3.453649e+00</td>\n",
       "      <td>0.716816</td>\n",
       "      <td>0.359534</td>\n",
       "      <td>0.100503</td>\n",
       "      <td>0.632440</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>3.260473e+00</td>\n",
       "      <td>0.714556</td>\n",
       "      <td>0.356943</td>\n",
       "      <td>0.100503</td>\n",
       "      <td>0.822584</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>15.176568</td>\n",
       "      <td>1206.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.558900e+04</td>\n",
       "      <td>7.780000e+02</td>\n",
       "      <td>932.000000</td>\n",
       "      <td>4.876542e+01</td>\n",
       "      <td>14.029113</td>\n",
       "      <td>2.299666</td>\n",
       "      <td>0.341936</td>\n",
       "      <td>3.555194</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>773.000000</td>\n",
       "      <td>890.000000</td>\n",
       "      <td>4.876542e+01</td>\n",
       "      <td>14.029113</td>\n",
       "      <td>2.251398</td>\n",
       "      <td>0.341936</td>\n",
       "      <td>3.342999</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>54.925234</td>\n",
       "      <td>2206.420734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.927380e+05</td>\n",
       "      <td>2.838000e+03</td>\n",
       "      <td>6734.000000</td>\n",
       "      <td>3.498196e+02</td>\n",
       "      <td>118.704407</td>\n",
       "      <td>7.198330</td>\n",
       "      <td>1.320681</td>\n",
       "      <td>10.691878</td>\n",
       "      <td>1323.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2838.000000</td>\n",
       "      <td>6734.000000</td>\n",
       "      <td>3.743699e+02</td>\n",
       "      <td>107.081021</td>\n",
       "      <td>6.866840</td>\n",
       "      <td>1.320681</td>\n",
       "      <td>10.600502</td>\n",
       "      <td>1323.000000</td>\n",
       "      <td>112.191489</td>\n",
       "      <td>4349.908608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.654319e+07</td>\n",
       "      <td>1.165830e+06</td>\n",
       "      <td>549144.000000</td>\n",
       "      <td>1.145219e+06</td>\n",
       "      <td>435825.874241</td>\n",
       "      <td>76.809514</td>\n",
       "      <td>16.290540</td>\n",
       "      <td>193.072418</td>\n",
       "      <td>920838.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>664324.000000</td>\n",
       "      <td>549144.000000</td>\n",
       "      <td>1.145219e+06</td>\n",
       "      <td>435825.874241</td>\n",
       "      <td>76.809514</td>\n",
       "      <td>16.290540</td>\n",
       "      <td>193.072418</td>\n",
       "      <td>920838.000000</td>\n",
       "      <td>1861.583333</td>\n",
       "      <td>75526.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Choice  A_follower_count  A_following_count  A_listed_count  \\\n",
       "count  5500.000000      5.500000e+03       5.500000e+03     5500.000000   \n",
       "mean      0.509455      6.498840e+05       1.265895e+04     5952.453273   \n",
       "std       0.499956      2.028787e+06       4.900867e+04    17339.141191   \n",
       "min       0.000000      1.600000e+01       0.000000e+00        0.000000   \n",
       "25%       0.000000      2.663750e+03       3.220000e+02       85.000000   \n",
       "50%       1.000000      4.558900e+04       7.780000e+02      932.000000   \n",
       "75%       1.000000      3.927380e+05       2.838000e+03     6734.000000   \n",
       "max       1.000000      3.654319e+07       1.165830e+06   549144.000000   \n",
       "\n",
       "       A_mentions_received  A_retweets_received  A_mentions_sent  \\\n",
       "count         5.500000e+03          5500.000000      5500.000000   \n",
       "mean          2.666032e+03          1032.371839         6.011873   \n",
       "std           2.916543e+04         10954.953223         9.519797   \n",
       "min           1.005034e-01             0.100503         0.100503   \n",
       "25%           3.453649e+00             0.716816         0.359534   \n",
       "50%           4.876542e+01            14.029113         2.299666   \n",
       "75%           3.498196e+02           118.704407         7.198330   \n",
       "max           1.145219e+06        435825.874241        76.809514   \n",
       "\n",
       "       A_retweets_sent      A_posts  A_network_feature_1  ...  \\\n",
       "count      5500.000000  5500.000000          5500.000000  ...   \n",
       "mean          1.109924     9.090730          5267.768000  ...   \n",
       "std           1.910104    18.311060         28946.777345  ...   \n",
       "min           0.100503     0.100503             0.000000  ...   \n",
       "25%           0.100503     0.632440            12.000000  ...   \n",
       "50%           0.341936     3.555194           195.000000  ...   \n",
       "75%           1.320681    10.691878          1323.000000  ...   \n",
       "max          16.290540   193.072418        920838.000000  ...   \n",
       "\n",
       "       B_following_count  B_listed_count  B_mentions_received  \\\n",
       "count        5500.000000     5500.000000         5.500000e+03   \n",
       "mean        12738.259818     5903.148364         2.554598e+03   \n",
       "std         50054.520874    16298.462018         2.508873e+04   \n",
       "min             0.000000        0.000000         1.005034e-01   \n",
       "25%           322.000000       75.000000         3.260473e+00   \n",
       "50%           773.000000      890.000000         4.876542e+01   \n",
       "75%          2838.000000     6734.000000         3.743699e+02   \n",
       "max        664324.000000   549144.000000         1.145219e+06   \n",
       "\n",
       "       B_retweets_received  B_mentions_sent  B_retweets_sent      B_posts  \\\n",
       "count          5500.000000      5500.000000      5500.000000  5500.000000   \n",
       "mean            997.149954         6.099658         1.106236     9.505821   \n",
       "std            9342.006880         9.729557         1.939914    19.424680   \n",
       "min               0.100503         0.100503         0.100503     0.100503   \n",
       "25%               0.714556         0.356943         0.100503     0.822584   \n",
       "50%              14.029113         2.251398         0.341936     3.342999   \n",
       "75%             107.081021         6.866840         1.320681    10.600502   \n",
       "max          435825.874241        76.809514        16.290540   193.072418   \n",
       "\n",
       "       B_network_feature_1  B_network_feature_2  B_network_feature_3  \n",
       "count          5500.000000          5500.000000          5500.000000  \n",
       "mean           5254.933636            85.024196          3745.175480  \n",
       "std           26778.820125           106.689384          5518.399590  \n",
       "min               0.000000             0.000000             0.000000  \n",
       "25%              11.000000            15.176568          1206.500000  \n",
       "50%             190.000000            54.925234          2206.420734  \n",
       "75%            1323.000000           112.191489          4349.908608  \n",
       "max          920838.000000          1861.583333         75526.083333  \n",
       "\n",
       "[8 rows x 23 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b663f509",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Choice                 0\n",
       "A_follower_count       0\n",
       "A_following_count      0\n",
       "A_listed_count         0\n",
       "A_mentions_received    0\n",
       "A_retweets_received    0\n",
       "A_mentions_sent        0\n",
       "A_retweets_sent        0\n",
       "A_posts                0\n",
       "A_network_feature_1    0\n",
       "A_network_feature_2    0\n",
       "A_network_feature_3    0\n",
       "B_follower_count       0\n",
       "B_following_count      0\n",
       "B_listed_count         0\n",
       "B_mentions_received    0\n",
       "B_retweets_received    0\n",
       "B_mentions_sent        0\n",
       "B_retweets_sent        0\n",
       "B_posts                0\n",
       "B_network_feature_1    0\n",
       "B_network_feature_2    0\n",
       "B_network_feature_3    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b42a96b0",
   "metadata": {},
   "source": [
    "We see that there are no missing values in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "44332f43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Choice', 'A_follower_count', 'A_following_count', 'A_listed_count',\n",
       "       'A_mentions_received', 'A_retweets_received', 'A_mentions_sent',\n",
       "       'A_retweets_sent', 'A_posts', 'A_network_feature_1',\n",
       "       'A_network_feature_2', 'A_network_feature_3', 'B_follower_count',\n",
       "       'B_following_count', 'B_listed_count', 'B_mentions_received',\n",
       "       'B_retweets_received', 'B_mentions_sent', 'B_retweets_sent', 'B_posts',\n",
       "       'B_network_feature_1', 'B_network_feature_2', 'B_network_feature_3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3449264b",
   "metadata": {},
   "source": [
    "#### Feature Engineering\n",
    "\n",
    "Since, we have pairs of features for two users, we performed below transformations on all pairs of attributes to get a combined value for each attribute pair-\n",
    "\n",
    "1. A + B\n",
    "2. A – B\n",
    "3. A / B\n",
    "4. (A - B) / (A + B)\n",
    "\n",
    "We will see in feature importance that (A - B) / (A + B) transformation is most important."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7d545235",
   "metadata": {},
   "outputs": [],
   "source": [
    "def feat_eng(df):\n",
    "    df.replace(0, 0.001)\n",
    "    \n",
    "    df['follower_diff'] = (df['A_follower_count'] - df['B_follower_count'])\n",
    "    df['following_diff'] = (df['A_following_count'] - df['B_following_count'])\n",
    "    df['listed_diff'] = (df['A_listed_count'] - df['B_listed_count'])\n",
    "    df['ment_rec_diff'] = (df['A_mentions_received'] - df['B_mentions_received'])\n",
    "    df['rt_rec_diff'] = (df['A_retweets_received'] - df['B_retweets_received'])\n",
    "    df['ment_sent_diff'] = (df['A_mentions_sent'] - df['B_mentions_sent'])\n",
    "    df['rt_sent_diff'] = (df['A_retweets_sent'] - df['B_retweets_sent'])\n",
    "    df['posts_diff'] = (df['A_posts'] - df['B_posts'])\n",
    "    \n",
    "    df['follower_diffrat'] = ((df['A_follower_count'] - df['B_follower_count']) / (df['A_follower_count'] + df['B_follower_count']))\n",
    "    df['following_diffrat'] = ((df['A_following_count'] - df['B_following_count']) / (df['A_following_count'] + df['B_following_count']))\n",
    "    df['listed_diffrat'] = ((df['A_listed_count'] - df['B_listed_count']) / (df['A_listed_count'] + df['B_listed_count']))\n",
    "    df['ment_rec_diffrat'] = ((df['A_mentions_received'] - df['B_mentions_received']) / (df['A_mentions_received'] + df['B_mentions_received']))\n",
    "    df['rt_rec_diffrat'] = ((df['A_retweets_received'] - df['B_retweets_received']) / (df['A_retweets_received'] + df['B_retweets_received']))\n",
    "    df['ment_sent_diffrat'] = ((df['A_mentions_sent'] - df['B_mentions_sent']) / (df['A_mentions_sent'] + df['B_mentions_sent']))\n",
    "    df['rt_sent_diffrat'] = ((df['A_retweets_sent'] - df['B_retweets_sent']) / (df['A_retweets_sent'] + df['B_retweets_sent']))\n",
    "    df['posts_diffrat'] = ((df['A_posts'] - df['B_posts']) / (df['A_posts'] + df['B_posts']))\n",
    "    \n",
    "    df['nf1_diffrat'] = ((df['A_network_feature_1'] - df['B_network_feature_1']) / (df['A_network_feature_1'] + df['B_network_feature_1']))\n",
    "    df['nf2_diffrat'] = ((df['A_network_feature_2'] - df['B_network_feature_2']) / (df['A_network_feature_2'] + df['B_network_feature_2']))\n",
    "    df['nf3_diffrat'] = ((df['A_network_feature_3'] - df['B_network_feature_3']) / (df['A_network_feature_3'] + df['B_network_feature_3']))\n",
    "    \n",
    "    df = df.replace([np.inf, -np.inf], 1)\n",
    "    df = df.fillna(0)\n",
    "    \n",
    "    return(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e73ebcdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>A_follower_count</th>\n",
       "      <th>A_following_count</th>\n",
       "      <th>A_listed_count</th>\n",
       "      <th>A_mentions_received</th>\n",
       "      <th>A_retweets_received</th>\n",
       "      <th>A_mentions_sent</th>\n",
       "      <th>A_retweets_sent</th>\n",
       "      <th>A_posts</th>\n",
       "      <th>B_follower_count</th>\n",
       "      <th>B_following_count</th>\n",
       "      <th>...</th>\n",
       "      <th>following_diffrat</th>\n",
       "      <th>listed_diffrat</th>\n",
       "      <th>ment_rec_diffrat</th>\n",
       "      <th>rt_rec_diffrat</th>\n",
       "      <th>ment_sent_diffrat</th>\n",
       "      <th>rt_sent_diffrat</th>\n",
       "      <th>posts_diffrat</th>\n",
       "      <th>nf1_diffrat</th>\n",
       "      <th>nf2_diffrat</th>\n",
       "      <th>nf3_diffrat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>228</td>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "      <td>0.583979</td>\n",
       "      <td>0.100503</td>\n",
       "      <td>0.100503</td>\n",
       "      <td>0.100503</td>\n",
       "      <td>0.362150</td>\n",
       "      <td>34463</td>\n",
       "      <td>29808</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.979940</td>\n",
       "      <td>-0.996454</td>\n",
       "      <td>-0.927069</td>\n",
       "      <td>-0.950788</td>\n",
       "      <td>-0.975796</td>\n",
       "      <td>-0.535702</td>\n",
       "      <td>-0.901469</td>\n",
       "      <td>-0.941176</td>\n",
       "      <td>0.375861</td>\n",
       "      <td>0.711135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21591</td>\n",
       "      <td>1179</td>\n",
       "      <td>228</td>\n",
       "      <td>90.456506</td>\n",
       "      <td>25.798292</td>\n",
       "      <td>5.709329</td>\n",
       "      <td>1.111159</td>\n",
       "      <td>5.176620</td>\n",
       "      <td>39262</td>\n",
       "      <td>848</td>\n",
       "      <td>...</td>\n",
       "      <td>0.163296</td>\n",
       "      <td>-0.751904</td>\n",
       "      <td>0.381527</td>\n",
       "      <td>0.485140</td>\n",
       "      <td>0.277683</td>\n",
       "      <td>0.326360</td>\n",
       "      <td>0.657330</td>\n",
       "      <td>0.387218</td>\n",
       "      <td>-0.754868</td>\n",
       "      <td>-0.375691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7310</td>\n",
       "      <td>1215</td>\n",
       "      <td>101</td>\n",
       "      <td>25.503644</td>\n",
       "      <td>9.556347</td>\n",
       "      <td>5.361519</td>\n",
       "      <td>0.591206</td>\n",
       "      <td>3.589718</td>\n",
       "      <td>3622</td>\n",
       "      <td>482</td>\n",
       "      <td>...</td>\n",
       "      <td>0.431939</td>\n",
       "      <td>-0.342020</td>\n",
       "      <td>0.943998</td>\n",
       "      <td>0.928486</td>\n",
       "      <td>0.797743</td>\n",
       "      <td>0.709406</td>\n",
       "      <td>0.712276</td>\n",
       "      <td>0.938776</td>\n",
       "      <td>0.739259</td>\n",
       "      <td>0.911638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>7.690824</td>\n",
       "      <td>0.277306</td>\n",
       "      <td>1.331508</td>\n",
       "      <td>0.100503</td>\n",
       "      <td>2.830627</td>\n",
       "      <td>19562</td>\n",
       "      <td>17637</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.999207</td>\n",
       "      <td>-0.985714</td>\n",
       "      <td>-0.973506</td>\n",
       "      <td>-0.998580</td>\n",
       "      <td>-0.907802</td>\n",
       "      <td>-0.972344</td>\n",
       "      <td>-0.837938</td>\n",
       "      <td>-0.993213</td>\n",
       "      <td>-0.842948</td>\n",
       "      <td>-0.871091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45589</td>\n",
       "      <td>862</td>\n",
       "      <td>2641</td>\n",
       "      <td>148.854279</td>\n",
       "      <td>36.998884</td>\n",
       "      <td>27.881768</td>\n",
       "      <td>3.333492</td>\n",
       "      <td>23.861282</td>\n",
       "      <td>7554</td>\n",
       "      <td>1711</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.329965</td>\n",
       "      <td>0.871722</td>\n",
       "      <td>0.746540</td>\n",
       "      <td>0.823479</td>\n",
       "      <td>0.609503</td>\n",
       "      <td>0.497116</td>\n",
       "      <td>0.678922</td>\n",
       "      <td>0.732704</td>\n",
       "      <td>0.448564</td>\n",
       "      <td>0.174050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   A_follower_count  A_following_count  A_listed_count  A_mentions_received  \\\n",
       "0               228                302               3             0.583979   \n",
       "1             21591               1179             228            90.456506   \n",
       "2              7310               1215             101            25.503644   \n",
       "3                20                  7               2             7.690824   \n",
       "4             45589                862            2641           148.854279   \n",
       "\n",
       "   A_retweets_received  A_mentions_sent  A_retweets_sent    A_posts  \\\n",
       "0             0.100503         0.100503         0.100503   0.362150   \n",
       "1            25.798292         5.709329         1.111159   5.176620   \n",
       "2             9.556347         5.361519         0.591206   3.589718   \n",
       "3             0.277306         1.331508         0.100503   2.830627   \n",
       "4            36.998884        27.881768         3.333492  23.861282   \n",
       "\n",
       "   B_follower_count  B_following_count  ...  following_diffrat  \\\n",
       "0             34463              29808  ...          -0.979940   \n",
       "1             39262                848  ...           0.163296   \n",
       "2              3622                482  ...           0.431939   \n",
       "3             19562              17637  ...          -0.999207   \n",
       "4              7554               1711  ...          -0.329965   \n",
       "\n",
       "   listed_diffrat  ment_rec_diffrat  rt_rec_diffrat  ment_sent_diffrat  \\\n",
       "0       -0.996454         -0.927069       -0.950788          -0.975796   \n",
       "1       -0.751904          0.381527        0.485140           0.277683   \n",
       "2       -0.342020          0.943998        0.928486           0.797743   \n",
       "3       -0.985714         -0.973506       -0.998580          -0.907802   \n",
       "4        0.871722          0.746540        0.823479           0.609503   \n",
       "\n",
       "   rt_sent_diffrat  posts_diffrat  nf1_diffrat  nf2_diffrat  nf3_diffrat  \n",
       "0        -0.535702      -0.901469    -0.941176     0.375861     0.711135  \n",
       "1         0.326360       0.657330     0.387218    -0.754868    -0.375691  \n",
       "2         0.709406       0.712276     0.938776     0.739259     0.911638  \n",
       "3        -0.972344      -0.837938    -0.993213    -0.842948    -0.871091  \n",
       "4         0.497116       0.678922     0.732704     0.448564     0.174050  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = feat_eng(train)\n",
    "\n",
    "X = X.drop(['Choice', 'A_network_feature_1', 'A_network_feature_2', 'A_network_feature_3', 'B_network_feature_1', 'B_network_feature_2', 'B_network_feature_3'],axis='columns')\n",
    "y = train['Choice']\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d5029ed",
   "metadata": {},
   "source": [
    "### Standardize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d0cc4578",
   "metadata": {},
   "outputs": [],
   "source": [
    "normalized_df=(X-X.mean())/X.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f3ebbdbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>A_follower_count</th>\n",
       "      <th>A_following_count</th>\n",
       "      <th>A_listed_count</th>\n",
       "      <th>A_mentions_received</th>\n",
       "      <th>A_retweets_received</th>\n",
       "      <th>A_mentions_sent</th>\n",
       "      <th>A_retweets_sent</th>\n",
       "      <th>A_posts</th>\n",
       "      <th>B_follower_count</th>\n",
       "      <th>B_following_count</th>\n",
       "      <th>...</th>\n",
       "      <th>following_diffrat</th>\n",
       "      <th>listed_diffrat</th>\n",
       "      <th>ment_rec_diffrat</th>\n",
       "      <th>rt_rec_diffrat</th>\n",
       "      <th>ment_sent_diffrat</th>\n",
       "      <th>rt_sent_diffrat</th>\n",
       "      <th>posts_diffrat</th>\n",
       "      <th>nf1_diffrat</th>\n",
       "      <th>nf2_diffrat</th>\n",
       "      <th>nf3_diffrat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.320219</td>\n",
       "      <td>-0.252138</td>\n",
       "      <td>-0.343123</td>\n",
       "      <td>-0.091391</td>\n",
       "      <td>-0.094229</td>\n",
       "      <td>-0.620955</td>\n",
       "      <td>-0.528464</td>\n",
       "      <td>-0.476683</td>\n",
       "      <td>-0.301315</td>\n",
       "      <td>0.341023</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.397746</td>\n",
       "      <td>-1.288518</td>\n",
       "      <td>-1.142857</td>\n",
       "      <td>-1.171676</td>\n",
       "      <td>-1.383924</td>\n",
       "      <td>-0.853830</td>\n",
       "      <td>-1.292933</td>\n",
       "      <td>-1.156683</td>\n",
       "      <td>0.572495</td>\n",
       "      <td>1.199667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.309689</td>\n",
       "      <td>-0.234243</td>\n",
       "      <td>-0.330146</td>\n",
       "      <td>-0.088309</td>\n",
       "      <td>-0.091883</td>\n",
       "      <td>-0.031780</td>\n",
       "      <td>0.000646</td>\n",
       "      <td>-0.213757</td>\n",
       "      <td>-0.299093</td>\n",
       "      <td>-0.237546</td>\n",
       "      <td>...</td>\n",
       "      <td>0.218767</td>\n",
       "      <td>-0.975368</td>\n",
       "      <td>0.463937</td>\n",
       "      <td>0.585509</td>\n",
       "      <td>0.387195</td>\n",
       "      <td>0.516197</td>\n",
       "      <td>0.951218</td>\n",
       "      <td>0.468038</td>\n",
       "      <td>-1.145501</td>\n",
       "      <td>-0.613329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.316728</td>\n",
       "      <td>-0.233509</td>\n",
       "      <td>-0.337471</td>\n",
       "      <td>-0.090536</td>\n",
       "      <td>-0.093366</td>\n",
       "      <td>-0.068316</td>\n",
       "      <td>-0.271566</td>\n",
       "      <td>-0.300420</td>\n",
       "      <td>-0.315589</td>\n",
       "      <td>-0.244858</td>\n",
       "      <td>...</td>\n",
       "      <td>0.598624</td>\n",
       "      <td>-0.450504</td>\n",
       "      <td>1.154584</td>\n",
       "      <td>1.128043</td>\n",
       "      <td>1.122021</td>\n",
       "      <td>1.124951</td>\n",
       "      <td>1.030322</td>\n",
       "      <td>1.142633</td>\n",
       "      <td>1.124631</td>\n",
       "      <td>1.534138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.320321</td>\n",
       "      <td>-0.258157</td>\n",
       "      <td>-0.343180</td>\n",
       "      <td>-0.091147</td>\n",
       "      <td>-0.094213</td>\n",
       "      <td>-0.491645</td>\n",
       "      <td>-0.528464</td>\n",
       "      <td>-0.341876</td>\n",
       "      <td>-0.308211</td>\n",
       "      <td>0.097868</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.424988</td>\n",
       "      <td>-1.274766</td>\n",
       "      <td>-1.199876</td>\n",
       "      <td>-1.230160</td>\n",
       "      <td>-1.287849</td>\n",
       "      <td>-1.547761</td>\n",
       "      <td>-1.201469</td>\n",
       "      <td>-1.220327</td>\n",
       "      <td>-1.279328</td>\n",
       "      <td>-1.439735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.297860</td>\n",
       "      <td>-0.240711</td>\n",
       "      <td>-0.190981</td>\n",
       "      <td>-0.086307</td>\n",
       "      <td>-0.090861</td>\n",
       "      <td>2.297307</td>\n",
       "      <td>1.164108</td>\n",
       "      <td>0.806646</td>\n",
       "      <td>-0.313769</td>\n",
       "      <td>-0.220305</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.478694</td>\n",
       "      <td>1.103712</td>\n",
       "      <td>0.912130</td>\n",
       "      <td>0.999544</td>\n",
       "      <td>0.856045</td>\n",
       "      <td>0.787570</td>\n",
       "      <td>0.982304</td>\n",
       "      <td>0.890593</td>\n",
       "      <td>0.682958</td>\n",
       "      <td>0.303724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5495</th>\n",
       "      <td>-0.299745</td>\n",
       "      <td>-0.254525</td>\n",
       "      <td>-0.265091</td>\n",
       "      <td>-0.038964</td>\n",
       "      <td>-0.068418</td>\n",
       "      <td>7.436885</td>\n",
       "      <td>2.241892</td>\n",
       "      <td>5.207120</td>\n",
       "      <td>0.055034</td>\n",
       "      <td>-0.227118</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.089665</td>\n",
       "      <td>-1.082576</td>\n",
       "      <td>-0.785806</td>\n",
       "      <td>-1.101192</td>\n",
       "      <td>0.763525</td>\n",
       "      <td>0.611887</td>\n",
       "      <td>1.012836</td>\n",
       "      <td>-0.931933</td>\n",
       "      <td>0.613940</td>\n",
       "      <td>0.738074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5496</th>\n",
       "      <td>-0.320276</td>\n",
       "      <td>-0.253342</td>\n",
       "      <td>-0.343007</td>\n",
       "      <td>-0.091361</td>\n",
       "      <td>-0.094229</td>\n",
       "      <td>-0.620955</td>\n",
       "      <td>-0.528464</td>\n",
       "      <td>-0.463521</td>\n",
       "      <td>-0.316898</td>\n",
       "      <td>-0.244918</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.474317</td>\n",
       "      <td>-0.308046</td>\n",
       "      <td>0.794143</td>\n",
       "      <td>-0.626902</td>\n",
       "      <td>-0.005160</td>\n",
       "      <td>-0.002468</td>\n",
       "      <td>-0.654279</td>\n",
       "      <td>0.809824</td>\n",
       "      <td>0.498141</td>\n",
       "      <td>-0.915614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5497</th>\n",
       "      <td>-0.312748</td>\n",
       "      <td>-0.244568</td>\n",
       "      <td>-0.300214</td>\n",
       "      <td>-0.089491</td>\n",
       "      <td>-0.092200</td>\n",
       "      <td>0.098149</td>\n",
       "      <td>-0.402066</td>\n",
       "      <td>-0.141267</td>\n",
       "      <td>-0.313769</td>\n",
       "      <td>-0.220305</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.627780</td>\n",
       "      <td>0.768462</td>\n",
       "      <td>0.539687</td>\n",
       "      <td>0.877151</td>\n",
       "      <td>0.013552</td>\n",
       "      <td>-0.848148</td>\n",
       "      <td>0.257340</td>\n",
       "      <td>0.493047</td>\n",
       "      <td>0.787548</td>\n",
       "      <td>0.421869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5498</th>\n",
       "      <td>-0.189584</td>\n",
       "      <td>-0.254036</td>\n",
       "      <td>-0.311518</td>\n",
       "      <td>-0.069744</td>\n",
       "      <td>-0.052462</td>\n",
       "      <td>-0.057868</td>\n",
       "      <td>-0.528464</td>\n",
       "      <td>-0.086975</td>\n",
       "      <td>-0.167918</td>\n",
       "      <td>0.496713</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.410478</td>\n",
       "      <td>-1.231121</td>\n",
       "      <td>0.687106</td>\n",
       "      <td>1.008346</td>\n",
       "      <td>-0.164502</td>\n",
       "      <td>-1.370729</td>\n",
       "      <td>0.385895</td>\n",
       "      <td>0.701444</td>\n",
       "      <td>-1.469128</td>\n",
       "      <td>-1.434521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5499</th>\n",
       "      <td>-0.320022</td>\n",
       "      <td>-0.239508</td>\n",
       "      <td>-0.342950</td>\n",
       "      <td>-0.091275</td>\n",
       "      <td>-0.094181</td>\n",
       "      <td>-0.130459</td>\n",
       "      <td>-0.125537</td>\n",
       "      <td>-0.335180</td>\n",
       "      <td>-0.083996</td>\n",
       "      <td>-0.192375</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.779819</td>\n",
       "      <td>-1.291857</td>\n",
       "      <td>-1.201734</td>\n",
       "      <td>-1.221329</td>\n",
       "      <td>0.038169</td>\n",
       "      <td>-0.150892</td>\n",
       "      <td>-0.339000</td>\n",
       "      <td>-1.213435</td>\n",
       "      <td>0.951725</td>\n",
       "      <td>1.348702</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5500 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      A_follower_count  A_following_count  A_listed_count  \\\n",
       "0            -0.320219          -0.252138       -0.343123   \n",
       "1            -0.309689          -0.234243       -0.330146   \n",
       "2            -0.316728          -0.233509       -0.337471   \n",
       "3            -0.320321          -0.258157       -0.343180   \n",
       "4            -0.297860          -0.240711       -0.190981   \n",
       "...                ...                ...             ...   \n",
       "5495         -0.299745          -0.254525       -0.265091   \n",
       "5496         -0.320276          -0.253342       -0.343007   \n",
       "5497         -0.312748          -0.244568       -0.300214   \n",
       "5498         -0.189584          -0.254036       -0.311518   \n",
       "5499         -0.320022          -0.239508       -0.342950   \n",
       "\n",
       "      A_mentions_received  A_retweets_received  A_mentions_sent  \\\n",
       "0               -0.091391            -0.094229        -0.620955   \n",
       "1               -0.088309            -0.091883        -0.031780   \n",
       "2               -0.090536            -0.093366        -0.068316   \n",
       "3               -0.091147            -0.094213        -0.491645   \n",
       "4               -0.086307            -0.090861         2.297307   \n",
       "...                   ...                  ...              ...   \n",
       "5495            -0.038964            -0.068418         7.436885   \n",
       "5496            -0.091361            -0.094229        -0.620955   \n",
       "5497            -0.089491            -0.092200         0.098149   \n",
       "5498            -0.069744            -0.052462        -0.057868   \n",
       "5499            -0.091275            -0.094181        -0.130459   \n",
       "\n",
       "      A_retweets_sent   A_posts  B_follower_count  B_following_count  ...  \\\n",
       "0           -0.528464 -0.476683         -0.301315           0.341023  ...   \n",
       "1            0.000646 -0.213757         -0.299093          -0.237546  ...   \n",
       "2           -0.271566 -0.300420         -0.315589          -0.244858  ...   \n",
       "3           -0.528464 -0.341876         -0.308211           0.097868  ...   \n",
       "4            1.164108  0.806646         -0.313769          -0.220305  ...   \n",
       "...               ...       ...               ...                ...  ...   \n",
       "5495         2.241892  5.207120          0.055034          -0.227118  ...   \n",
       "5496        -0.528464 -0.463521         -0.316898          -0.244918  ...   \n",
       "5497        -0.402066 -0.141267         -0.313769          -0.220305  ...   \n",
       "5498        -0.528464 -0.086975         -0.167918           0.496713  ...   \n",
       "5499        -0.125537 -0.335180         -0.083996          -0.192375  ...   \n",
       "\n",
       "      following_diffrat  listed_diffrat  ment_rec_diffrat  rt_rec_diffrat  \\\n",
       "0             -1.397746       -1.288518         -1.142857       -1.171676   \n",
       "1              0.218767       -0.975368          0.463937        0.585509   \n",
       "2              0.598624       -0.450504          1.154584        1.128043   \n",
       "3             -1.424988       -1.274766         -1.199876       -1.230160   \n",
       "4             -0.478694        1.103712          0.912130        0.999544   \n",
       "...                 ...             ...               ...             ...   \n",
       "5495          -1.089665       -1.082576         -0.785806       -1.101192   \n",
       "5496          -0.474317       -0.308046          0.794143       -0.626902   \n",
       "5497          -0.627780        0.768462          0.539687        0.877151   \n",
       "5498          -1.410478       -1.231121          0.687106        1.008346   \n",
       "5499          -0.779819       -1.291857         -1.201734       -1.221329   \n",
       "\n",
       "      ment_sent_diffrat  rt_sent_diffrat  posts_diffrat  nf1_diffrat  \\\n",
       "0             -1.383924        -0.853830      -1.292933    -1.156683   \n",
       "1              0.387195         0.516197       0.951218     0.468038   \n",
       "2              1.122021         1.124951       1.030322     1.142633   \n",
       "3             -1.287849        -1.547761      -1.201469    -1.220327   \n",
       "4              0.856045         0.787570       0.982304     0.890593   \n",
       "...                 ...              ...            ...          ...   \n",
       "5495           0.763525         0.611887       1.012836    -0.931933   \n",
       "5496          -0.005160        -0.002468      -0.654279     0.809824   \n",
       "5497           0.013552        -0.848148       0.257340     0.493047   \n",
       "5498          -0.164502        -1.370729       0.385895     0.701444   \n",
       "5499           0.038169        -0.150892      -0.339000    -1.213435   \n",
       "\n",
       "      nf2_diffrat  nf3_diffrat  \n",
       "0        0.572495     1.199667  \n",
       "1       -1.145501    -0.613329  \n",
       "2        1.124631     1.534138  \n",
       "3       -1.279328    -1.439735  \n",
       "4        0.682958     0.303724  \n",
       "...           ...          ...  \n",
       "5495     0.613940     0.738074  \n",
       "5496     0.498141    -0.915614  \n",
       "5497     0.787548     0.421869  \n",
       "5498    -1.469128    -1.434521  \n",
       "5499     0.951725     1.348702  \n",
       "\n",
       "[5500 rows x 35 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d99d4e32",
   "metadata": {},
   "source": [
    "### Influencer Modeling - Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "217db03a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(normalized_df, y, stratify=y, test_size=0.20, random_state= 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eff72bef",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(random_state=18)\n",
    "param_grid = {\n",
    "                 'n_estimators': [5,100],\n",
    "                 'max_depth': [2, 5, 7, 9]\n",
    "             }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "61738735",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=RandomForestClassifier(random_state=18),\n",
       "             param_grid={&#x27;max_depth&#x27;: [2, 5, 7, 9], &#x27;n_estimators&#x27;: [5, 100]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=RandomForestClassifier(random_state=18),\n",
       "             param_grid={&#x27;max_depth&#x27;: [2, 5, 7, 9], &#x27;n_estimators&#x27;: [5, 100]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=18)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=18)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=RandomForestClassifier(random_state=18),\n",
       "             param_grid={'max_depth': [2, 5, 7, 9], 'n_estimators': [5, 100]})"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_clf = GridSearchCV(clf, param_grid, cv=10)\n",
    "grid_clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8418cec6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 9, 'n_estimators': 100}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dd2e4f17",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf=RandomForestClassifier(**grid_clf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "83f903e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=9)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=9)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=9)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c58f4fdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c03efd19",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm=confusion_matrix(y_test, ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "15186b23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix for Validation set using RF classifer STANDARDIZED DATA:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax= plt.subplot()\n",
    "sns.heatmap(cm, annot=True, fmt='g', ax=ax);  #annot=True to annotate cells, ftm='g' to disable scientific notation\n",
    "print(\"Confusion Matrix for Validation set using RF classifer STANDARDIZED DATA:\\n\",)\n",
    "# labels, title and ticks\n",
    "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \n",
    "ax.set_title('Confusion Matrix'); \n",
    "ax.xaxis.set_ticklabels(['0', '1']); ax.yaxis.set_ticklabels(['1', '0']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1026f1f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest, AUC=0.8626\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,8))\n",
    "dt_probs = rf.predict_proba(X_test)\n",
    "dt_probs = dt_probs[:, 1]\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test, dt_probs)\n",
    "auc = round(metrics.roc_auc_score(y_test, dt_probs), 4)\n",
    "plt.plot(fpr,tpr)\n",
    "print(\"Random Forest, AUC=\"+str(auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6ebcc9c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Random Forest Feature Importance')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,20))\n",
    "sorted_idx = rf.feature_importances_.argsort()\n",
    "plt.barh(X.columns[sorted_idx], rf.feature_importances_[sorted_idx])\n",
    "plt.xlabel(\"Random Forest Feature Importance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "de280d78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score for Test set using Random Forest classifer: 77.91\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy Score for Test set using Random Forest classifer:\",round(accuracy_score(y_test, ypred)*100,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07ba4b55",
   "metadata": {},
   "source": [
    "Some of the most indicative features of being an influencer are:\n",
    "* Listed difference ratio\n",
    "* Listed Count\n",
    "* Follower Count\n",
    "* Mentions Received\n",
    "* Retweets Recieved\n",
    "* Degree\n",
    "* Betweenness\n",
    "* Closeness\n",
    "\n",
    "Followed by :\n",
    "\n",
    "* Number of Posts\n",
    "* Mentions Sent\n",
    "* Following Count\n",
    "* Retweets Sent"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39c157b4",
   "metadata": {},
   "source": [
    "To determine the weights of these predictors, we move ahead with logistic regression. First we'll check if the logistic regression give better/similar results as Random Forest Classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "63d4af3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Choice</th>\n",
       "      <th>A_follower_count</th>\n",
       "      <th>A_following_count</th>\n",
       "      <th>A_listed_count</th>\n",
       "      <th>A_mentions_received</th>\n",
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       "      <th>...</th>\n",
       "      <th>following_diffrat</th>\n",
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       "      <td>0.100503</td>\n",
       "      <td>0.100503</td>\n",
       "      <td>0.362150</td>\n",
       "      <td>2</td>\n",
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       "      <td>-0.996454</td>\n",
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       "      <td>-0.950788</td>\n",
       "      <td>-0.975796</td>\n",
       "      <td>-0.535702</td>\n",
       "      <td>-0.901469</td>\n",
       "      <td>-0.941176</td>\n",
       "      <td>0.375861</td>\n",
       "      <td>0.711135</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>21591</td>\n",
       "      <td>1179</td>\n",
       "      <td>228</td>\n",
       "      <td>90.456506</td>\n",
       "      <td>25.798292</td>\n",
       "      <td>5.709329</td>\n",
       "      <td>1.111159</td>\n",
       "      <td>5.176620</td>\n",
       "      <td>369</td>\n",
       "      <td>...</td>\n",
       "      <td>0.163296</td>\n",
       "      <td>-0.751904</td>\n",
       "      <td>0.381527</td>\n",
       "      <td>0.485140</td>\n",
       "      <td>0.277683</td>\n",
       "      <td>0.326360</td>\n",
       "      <td>0.657330</td>\n",
       "      <td>0.387218</td>\n",
       "      <td>-0.754868</td>\n",
       "      <td>-0.375691</td>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7310</td>\n",
       "      <td>1215</td>\n",
       "      <td>101</td>\n",
       "      <td>25.503644</td>\n",
       "      <td>9.556347</td>\n",
       "      <td>5.361519</td>\n",
       "      <td>0.591206</td>\n",
       "      <td>3.589718</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>0.431939</td>\n",
       "      <td>-0.342020</td>\n",
       "      <td>0.943998</td>\n",
       "      <td>0.928486</td>\n",
       "      <td>0.797743</td>\n",
       "      <td>0.709406</td>\n",
       "      <td>0.712276</td>\n",
       "      <td>0.938776</td>\n",
       "      <td>0.739259</td>\n",
       "      <td>0.911638</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>7.690824</td>\n",
       "      <td>0.277306</td>\n",
       "      <td>1.331508</td>\n",
       "      <td>0.100503</td>\n",
       "      <td>2.830627</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.999207</td>\n",
       "      <td>-0.985714</td>\n",
       "      <td>-0.973506</td>\n",
       "      <td>-0.998580</td>\n",
       "      <td>-0.907802</td>\n",
       "      <td>-0.972344</td>\n",
       "      <td>-0.837938</td>\n",
       "      <td>-0.993213</td>\n",
       "      <td>-0.842948</td>\n",
       "      <td>-0.871091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>45589</td>\n",
       "      <td>862</td>\n",
       "      <td>2641</td>\n",
       "      <td>148.854279</td>\n",
       "      <td>36.998884</td>\n",
       "      <td>27.881768</td>\n",
       "      <td>3.333492</td>\n",
       "      <td>23.861282</td>\n",
       "      <td>551</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.329965</td>\n",
       "      <td>0.871722</td>\n",
       "      <td>0.746540</td>\n",
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       "      <td>0.732704</td>\n",
       "      <td>0.448564</td>\n",
       "      <td>0.174050</td>\n",
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       "<p>5 rows × 42 columns</p>\n",
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      "text/plain": [
       "   Choice  A_follower_count  A_following_count  A_listed_count  \\\n",
       "0       0               228                302               3   \n",
       "1       0             21591               1179             228   \n",
       "2       0              7310               1215             101   \n",
       "3       0                20                  7               2   \n",
       "4       1             45589                862            2641   \n",
       "\n",
       "   A_mentions_received  A_retweets_received  A_mentions_sent  A_retweets_sent  \\\n",
       "0             0.583979             0.100503         0.100503         0.100503   \n",
       "1            90.456506            25.798292         5.709329         1.111159   \n",
       "2            25.503644             9.556347         5.361519         0.591206   \n",
       "3             7.690824             0.277306         1.331508         0.100503   \n",
       "4           148.854279            36.998884        27.881768         3.333492   \n",
       "\n",
       "     A_posts  A_network_feature_1  ...  following_diffrat  listed_diffrat  \\\n",
       "0   0.362150                    2  ...          -0.979940       -0.996454   \n",
       "1   5.176620                  369  ...           0.163296       -0.751904   \n",
       "2   3.589718                   95  ...           0.431939       -0.342020   \n",
       "3   2.830627                    6  ...          -0.999207       -0.985714   \n",
       "4  23.861282                  551  ...          -0.329965        0.871722   \n",
       "\n",
       "   ment_rec_diffrat  rt_rec_diffrat  ment_sent_diffrat  rt_sent_diffrat  \\\n",
       "0         -0.927069       -0.950788          -0.975796        -0.535702   \n",
       "1          0.381527        0.485140           0.277683         0.326360   \n",
       "2          0.943998        0.928486           0.797743         0.709406   \n",
       "3         -0.973506       -0.998580          -0.907802        -0.972344   \n",
       "4          0.746540        0.823479           0.609503         0.497116   \n",
       "\n",
       "   posts_diffrat  nf1_diffrat  nf2_diffrat  nf3_diffrat  \n",
       "0      -0.901469    -0.941176     0.375861     0.711135  \n",
       "1       0.657330     0.387218    -0.754868    -0.375691  \n",
       "2       0.712276     0.938776     0.739259     0.911638  \n",
       "3      -0.837938    -0.993213    -0.842948    -0.871091  \n",
       "4       0.678922     0.732704     0.448564     0.174050  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = feat_eng(train)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7c02a548",
   "metadata": {},
   "outputs": [
    {
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       "      <th>A_follower_count</th>\n",
       "      <th>A_following_count</th>\n",
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       "      <th>1</th>\n",
       "      <td>21591</td>\n",
       "      <td>1179</td>\n",
       "      <td>228</td>\n",
       "      <td>90.456506</td>\n",
       "      <td>25.798292</td>\n",
       "      <td>5.709329</td>\n",
       "      <td>1.111159</td>\n",
       "      <td>5.176620</td>\n",
       "      <td>39262</td>\n",
       "      <td>848</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7310</td>\n",
       "      <td>1215</td>\n",
       "      <td>101</td>\n",
       "      <td>25.503644</td>\n",
       "      <td>9.556347</td>\n",
       "      <td>5.361519</td>\n",
       "      <td>0.591206</td>\n",
       "      <td>3.589718</td>\n",
       "      <td>3622</td>\n",
       "      <td>482</td>\n",
       "      <td>...</td>\n",
       "      <td>0.431939</td>\n",
       "      <td>-0.342020</td>\n",
       "      <td>0.943998</td>\n",
       "      <td>0.928486</td>\n",
       "      <td>0.797743</td>\n",
       "      <td>0.709406</td>\n",
       "      <td>0.712276</td>\n",
       "      <td>0.938776</td>\n",
       "      <td>0.739259</td>\n",
       "      <td>0.911638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>7.690824</td>\n",
       "      <td>0.277306</td>\n",
       "      <td>1.331508</td>\n",
       "      <td>0.100503</td>\n",
       "      <td>2.830627</td>\n",
       "      <td>19562</td>\n",
       "      <td>17637</td>\n",
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       "      <td>-0.985714</td>\n",
       "      <td>-0.973506</td>\n",
       "      <td>-0.998580</td>\n",
       "      <td>-0.907802</td>\n",
       "      <td>-0.972344</td>\n",
       "      <td>-0.837938</td>\n",
       "      <td>-0.993213</td>\n",
       "      <td>-0.842948</td>\n",
       "      <td>-0.871091</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45589</td>\n",
       "      <td>862</td>\n",
       "      <td>2641</td>\n",
       "      <td>148.854279</td>\n",
       "      <td>36.998884</td>\n",
       "      <td>27.881768</td>\n",
       "      <td>3.333492</td>\n",
       "      <td>23.861282</td>\n",
       "      <td>7554</td>\n",
       "      <td>1711</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.329965</td>\n",
       "      <td>0.871722</td>\n",
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       "      <td>0.174050</td>\n",
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       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
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      ],
      "text/plain": [
       "   A_follower_count  A_following_count  A_listed_count  A_mentions_received  \\\n",
       "0               228                302               3             0.583979   \n",
       "1             21591               1179             228            90.456506   \n",
       "2              7310               1215             101            25.503644   \n",
       "3                20                  7               2             7.690824   \n",
       "4             45589                862            2641           148.854279   \n",
       "\n",
       "   A_retweets_received  A_mentions_sent  A_retweets_sent    A_posts  \\\n",
       "0             0.100503         0.100503         0.100503   0.362150   \n",
       "1            25.798292         5.709329         1.111159   5.176620   \n",
       "2             9.556347         5.361519         0.591206   3.589718   \n",
       "3             0.277306         1.331508         0.100503   2.830627   \n",
       "4            36.998884        27.881768         3.333492  23.861282   \n",
       "\n",
       "   B_follower_count  B_following_count  ...  following_diffrat  \\\n",
       "0             34463              29808  ...          -0.979940   \n",
       "1             39262                848  ...           0.163296   \n",
       "2              3622                482  ...           0.431939   \n",
       "3             19562              17637  ...          -0.999207   \n",
       "4              7554               1711  ...          -0.329965   \n",
       "\n",
       "   listed_diffrat  ment_rec_diffrat  rt_rec_diffrat  ment_sent_diffrat  \\\n",
       "0       -0.996454         -0.927069       -0.950788          -0.975796   \n",
       "1       -0.751904          0.381527        0.485140           0.277683   \n",
       "2       -0.342020          0.943998        0.928486           0.797743   \n",
       "3       -0.985714         -0.973506       -0.998580          -0.907802   \n",
       "4        0.871722          0.746540        0.823479           0.609503   \n",
       "\n",
       "   rt_sent_diffrat  posts_diffrat  nf1_diffrat  nf2_diffrat  nf3_diffrat  \n",
       "0        -0.535702      -0.901469    -0.941176     0.375861     0.711135  \n",
       "1         0.326360       0.657330     0.387218    -0.754868    -0.375691  \n",
       "2         0.709406       0.712276     0.938776     0.739259     0.911638  \n",
       "3        -0.972344      -0.837938    -0.993213    -0.842948    -0.871091  \n",
       "4         0.497116       0.678922     0.732704     0.448564     0.174050  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = feat_eng(train)\n",
    "X = X.drop(['Choice', 'A_network_feature_1', 'A_network_feature_2', 'A_network_feature_3', 'B_network_feature_1', 'B_network_feature_2', 'B_network_feature_3'],axis='columns')\n",
    "y = train['Choice']\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c210330",
   "metadata": {},
   "source": [
    "#### Standardize Feature Engineered columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e44fe7d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "normalized_df=(X-X.mean())/X.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0e18a0a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5500,)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "612d3e16",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>A_listed_count</th>\n",
       "      <th>A_mentions_received</th>\n",
       "      <th>A_retweets_received</th>\n",
       "      <th>A_mentions_sent</th>\n",
       "      <th>A_retweets_sent</th>\n",
       "      <th>A_posts</th>\n",
       "      <th>B_follower_count</th>\n",
       "      <th>B_following_count</th>\n",
       "      <th>...</th>\n",
       "      <th>following_diffrat</th>\n",
       "      <th>listed_diffrat</th>\n",
       "      <th>ment_rec_diffrat</th>\n",
       "      <th>rt_rec_diffrat</th>\n",
       "      <th>ment_sent_diffrat</th>\n",
       "      <th>rt_sent_diffrat</th>\n",
       "      <th>posts_diffrat</th>\n",
       "      <th>nf1_diffrat</th>\n",
       "      <th>nf2_diffrat</th>\n",
       "      <th>nf3_diffrat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.320219</td>\n",
       "      <td>-0.252138</td>\n",
       "      <td>-0.343123</td>\n",
       "      <td>-0.091391</td>\n",
       "      <td>-0.094229</td>\n",
       "      <td>-0.620955</td>\n",
       "      <td>-0.528464</td>\n",
       "      <td>-0.476683</td>\n",
       "      <td>-0.301315</td>\n",
       "      <td>0.341023</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.397746</td>\n",
       "      <td>-1.288518</td>\n",
       "      <td>-1.142857</td>\n",
       "      <td>-1.171676</td>\n",
       "      <td>-1.383924</td>\n",
       "      <td>-0.853830</td>\n",
       "      <td>-1.292933</td>\n",
       "      <td>-1.156683</td>\n",
       "      <td>0.572495</td>\n",
       "      <td>1.199667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.309689</td>\n",
       "      <td>-0.234243</td>\n",
       "      <td>-0.330146</td>\n",
       "      <td>-0.088309</td>\n",
       "      <td>-0.091883</td>\n",
       "      <td>-0.031780</td>\n",
       "      <td>0.000646</td>\n",
       "      <td>-0.213757</td>\n",
       "      <td>-0.299093</td>\n",
       "      <td>-0.237546</td>\n",
       "      <td>...</td>\n",
       "      <td>0.218767</td>\n",
       "      <td>-0.975368</td>\n",
       "      <td>0.463937</td>\n",
       "      <td>0.585509</td>\n",
       "      <td>0.387195</td>\n",
       "      <td>0.516197</td>\n",
       "      <td>0.951218</td>\n",
       "      <td>0.468038</td>\n",
       "      <td>-1.145501</td>\n",
       "      <td>-0.613329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.316728</td>\n",
       "      <td>-0.233509</td>\n",
       "      <td>-0.337471</td>\n",
       "      <td>-0.090536</td>\n",
       "      <td>-0.093366</td>\n",
       "      <td>-0.068316</td>\n",
       "      <td>-0.271566</td>\n",
       "      <td>-0.300420</td>\n",
       "      <td>-0.315589</td>\n",
       "      <td>-0.244858</td>\n",
       "      <td>...</td>\n",
       "      <td>0.598624</td>\n",
       "      <td>-0.450504</td>\n",
       "      <td>1.154584</td>\n",
       "      <td>1.128043</td>\n",
       "      <td>1.122021</td>\n",
       "      <td>1.124951</td>\n",
       "      <td>1.030322</td>\n",
       "      <td>1.142633</td>\n",
       "      <td>1.124631</td>\n",
       "      <td>1.534138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.320321</td>\n",
       "      <td>-0.258157</td>\n",
       "      <td>-0.343180</td>\n",
       "      <td>-0.091147</td>\n",
       "      <td>-0.094213</td>\n",
       "      <td>-0.491645</td>\n",
       "      <td>-0.528464</td>\n",
       "      <td>-0.341876</td>\n",
       "      <td>-0.308211</td>\n",
       "      <td>0.097868</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.424988</td>\n",
       "      <td>-1.274766</td>\n",
       "      <td>-1.199876</td>\n",
       "      <td>-1.230160</td>\n",
       "      <td>-1.287849</td>\n",
       "      <td>-1.547761</td>\n",
       "      <td>-1.201469</td>\n",
       "      <td>-1.220327</td>\n",
       "      <td>-1.279328</td>\n",
       "      <td>-1.439735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.297860</td>\n",
       "      <td>-0.240711</td>\n",
       "      <td>-0.190981</td>\n",
       "      <td>-0.086307</td>\n",
       "      <td>-0.090861</td>\n",
       "      <td>2.297307</td>\n",
       "      <td>1.164108</td>\n",
       "      <td>0.806646</td>\n",
       "      <td>-0.313769</td>\n",
       "      <td>-0.220305</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.478694</td>\n",
       "      <td>1.103712</td>\n",
       "      <td>0.912130</td>\n",
       "      <td>0.999544</td>\n",
       "      <td>0.856045</td>\n",
       "      <td>0.787570</td>\n",
       "      <td>0.982304</td>\n",
       "      <td>0.890593</td>\n",
       "      <td>0.682958</td>\n",
       "      <td>0.303724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   A_follower_count  A_following_count  A_listed_count  A_mentions_received  \\\n",
       "0         -0.320219          -0.252138       -0.343123            -0.091391   \n",
       "1         -0.309689          -0.234243       -0.330146            -0.088309   \n",
       "2         -0.316728          -0.233509       -0.337471            -0.090536   \n",
       "3         -0.320321          -0.258157       -0.343180            -0.091147   \n",
       "4         -0.297860          -0.240711       -0.190981            -0.086307   \n",
       "\n",
       "   A_retweets_received  A_mentions_sent  A_retweets_sent   A_posts  \\\n",
       "0            -0.094229        -0.620955        -0.528464 -0.476683   \n",
       "1            -0.091883        -0.031780         0.000646 -0.213757   \n",
       "2            -0.093366        -0.068316        -0.271566 -0.300420   \n",
       "3            -0.094213        -0.491645        -0.528464 -0.341876   \n",
       "4            -0.090861         2.297307         1.164108  0.806646   \n",
       "\n",
       "   B_follower_count  B_following_count  ...  following_diffrat  \\\n",
       "0         -0.301315           0.341023  ...          -1.397746   \n",
       "1         -0.299093          -0.237546  ...           0.218767   \n",
       "2         -0.315589          -0.244858  ...           0.598624   \n",
       "3         -0.308211           0.097868  ...          -1.424988   \n",
       "4         -0.313769          -0.220305  ...          -0.478694   \n",
       "\n",
       "   listed_diffrat  ment_rec_diffrat  rt_rec_diffrat  ment_sent_diffrat  \\\n",
       "0       -1.288518         -1.142857       -1.171676          -1.383924   \n",
       "1       -0.975368          0.463937        0.585509           0.387195   \n",
       "2       -0.450504          1.154584        1.128043           1.122021   \n",
       "3       -1.274766         -1.199876       -1.230160          -1.287849   \n",
       "4        1.103712          0.912130        0.999544           0.856045   \n",
       "\n",
       "   rt_sent_diffrat  posts_diffrat  nf1_diffrat  nf2_diffrat  nf3_diffrat  \n",
       "0        -0.853830      -1.292933    -1.156683     0.572495     1.199667  \n",
       "1         0.516197       0.951218     0.468038    -1.145501    -0.613329  \n",
       "2         1.124951       1.030322     1.142633     1.124631     1.534138  \n",
       "3        -1.547761      -1.201469    -1.220327    -1.279328    -1.439735  \n",
       "4         0.787570       0.982304     0.890593     0.682958     0.303724  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cb1a41d",
   "metadata": {},
   "source": [
    "### Influencer Modeling - Logistic Regression\n",
    "\n",
    "We use LASSO regularization to keep the feature space compact, since analysis in Part-2 requires scraping new data-points and certain features may not be available. So its best to keep the feature space as small as possible using LASSO regularization on Logistic Regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "45d7f64f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4400, 35)\n",
      "(1100, 35)\n",
      "(4400,)\n",
      "(1100,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(normalized_df, y, test_size = 0.2, random_state = 147)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7edd1bf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegressionCV(Cs=[0.01, 0.1, 0.5],\n",
       "                     cv=RepeatedKFold(n_repeats=5, n_splits=10, random_state=123),\n",
       "                     max_iter=10000, n_jobs=-1, penalty=&#x27;l1&#x27;, solver=&#x27;saga&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegressionCV</label><div class=\"sk-toggleable__content\"><pre>LogisticRegressionCV(Cs=[0.01, 0.1, 0.5],\n",
       "                     cv=RepeatedKFold(n_repeats=5, n_splits=10, random_state=123),\n",
       "                     max_iter=10000, n_jobs=-1, penalty=&#x27;l1&#x27;, solver=&#x27;saga&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegressionCV(Cs=[0.01, 0.1, 0.5],\n",
       "                     cv=RepeatedKFold(n_repeats=5, n_splits=10, random_state=123),\n",
       "                     max_iter=10000, n_jobs=-1, penalty='l1', solver='saga')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.model_selection import RepeatedKFold\n",
    "\n",
    "# Define model evaluation method\n",
    "cv = RepeatedKFold(n_splits = 10, n_repeats = 5, random_state = 123)\n",
    "\n",
    "# Initialize model with L1 regularization as hyperparameter\n",
    "model4 = LogisticRegressionCV(cv = cv, max_iter = 10000, n_jobs = -1, Cs=[0.01,0.1,0.5], penalty='l1', solver='saga')\n",
    "\n",
    "# Fitting the model\n",
    "model4.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "181589ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best inverse regularization hyperparameter fir LogisticCV with LASSO penalty:  [0.1]\n"
     ]
    }
   ],
   "source": [
    "print('Best inverse regularization hyperparameter fir LogisticCV with LASSO penalty: ', str(model4.C_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ece70ba9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most important feature from Logistic Regression\n"
     ]
    },
    {
     "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>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>listed_diffrat</th>\n",
       "      <td>0.560528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>follower_diffrat</th>\n",
       "      <td>0.399779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nf1_diffrat</th>\n",
       "      <td>0.281095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nf2_diffrat</th>\n",
       "      <td>0.236246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rt_rec_diffrat</th>\n",
       "      <td>0.235237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ment_sent_diffrat</th>\n",
       "      <td>0.185465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rt_sent_diffrat</th>\n",
       "      <td>0.120438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B_posts</th>\n",
       "      <td>0.096695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>listed_diff</th>\n",
       "      <td>0.094690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>posts_diffrat</th>\n",
       "      <td>0.022672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>following_diffrat</th>\n",
       "      <td>0.013724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nf3_diffrat</th>\n",
       "      <td>0.011322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B_follower_count</th>\n",
       "      <td>0.007840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B_following_count</th>\n",
       "      <td>0.000185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Coefficient\n",
       "listed_diffrat        0.560528\n",
       "follower_diffrat      0.399779\n",
       "nf1_diffrat           0.281095\n",
       "nf2_diffrat           0.236246\n",
       "rt_rec_diffrat        0.235237\n",
       "ment_sent_diffrat     0.185465\n",
       "rt_sent_diffrat       0.120438\n",
       "B_posts               0.096695\n",
       "listed_diff           0.094690\n",
       "posts_diffrat         0.022672\n",
       "following_diffrat     0.013724\n",
       "nf3_diffrat           0.011322\n",
       "B_follower_count      0.007840\n",
       "B_following_count     0.000185"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Most important feature from Logistic Regression')\n",
    "coeff_parameter = pd.DataFrame(model4.coef_.transpose(), X.columns, columns = ['Coefficient'])\n",
    "coeff_parameter = coeff_parameter[coeff_parameter['Coefficient'] > 0].sort_values('Coefficient', ascending=False)\n",
    "coeff_parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1f04273b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, ..., 0, 0, 1])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting the test data using the model\n",
    "predictions4 = model4.predict(X_test)\n",
    "predictions4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "488c965b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def regression_results(y_true, y_pred):\n",
    "    # Regression metrics\n",
    "    explained_variance = metrics.explained_variance_score(y_true, y_pred)\n",
    "    accuracy = metrics.accuracy_score(y_true, y_pred)\n",
    "    mean_absolute_error = metrics.mean_absolute_error(y_true, y_pred) \n",
    "    mse = metrics.mean_squared_error(y_true, y_pred) \n",
    "    median_absolute_error = metrics.median_absolute_error(y_true, y_pred)\n",
    "    r2 = metrics.r2_score(y_true, y_pred)\n",
    "    cf_mat = pd.DataFrame(metrics.confusion_matrix(y_true, y_pred))\n",
    "\n",
    "    print('Explained Variance: ', round(explained_variance, 4))\n",
    "    print('Accuracy: ', round(accuracy, 4))\n",
    "    print('R2: ', round(r2, 4))\n",
    "    print('MAE: ', round(mean_absolute_error, 4))\n",
    "    print('MSE: ', round(mse, 4))\n",
    "    print('RMSE: ', round(np.sqrt(mse), 4))\n",
    "    print('Confusion Matrix: \\n', cf_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ab95cf3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained Variance:  0.1085\n",
      "Accuracy:  0.7773\n",
      "R2:  0.1068\n",
      "MAE:  0.2227\n",
      "MSE:  0.2227\n",
      "RMSE:  0.4719\n",
      "Confusion Matrix: \n",
      "      0    1\n",
      "0  411  111\n",
      "1  134  444\n"
     ]
    }
   ],
   "source": [
    "regression_results(y_test, predictions4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a46f7fb1",
   "metadata": {},
   "source": [
    "### Financial Value of the model\n",
    "\n",
    "`A retailer wants influencers to tweet its promotion for a product. If a non-influencer\n",
    "tweets, there is no benefit to the retailer. If an influencer tweets once, there is a\n",
    "0.01% chance that his/her followers will buy one unit of a product. Assume the\n",
    "retailer has a profit margin of $10 per unit, and that one customer can buy only one\n",
    "unit. If an influencer tweets twice, the overall buying probability will be 0.015%.\n",
    "Without analytics, the retailer offers $5 to each person (A and B) to tweet once. With\n",
    "analytics, the retailer offers $10 to those identified as influencers by the model to\n",
    "send two tweets each. If the model classifies an individual as a non-influencer, s/he\n",
    "is not selected/paid by the retailer to tweet`\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "897f846e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of followers of predicted influencers:  6236454962\n",
      "Net Profit with our analytical model: $ 9299682.44\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('./train.csv')\n",
    "\n",
    "y_choice = model4.predict(normalized_df)\n",
    "y_choice = [round(num) for num in y_choice]\n",
    "df['Choice'] = y_choice\n",
    "\n",
    "cost = 0\n",
    "rev = 0\n",
    "followers = 0 \n",
    "for row in range(len(df)):\n",
    "    cost += 10\n",
    "    if df.loc[row,'Choice'] == 1:\n",
    "        rev += df.loc[row,'A_follower_count'] * 10 * 0.00015\n",
    "        followers += df.loc[row,'A_follower_count']\n",
    "    else:\n",
    "        rev += df.loc[row,'B_follower_count'] * 10 * 0.00015\n",
    "        followers += df.loc[row,'B_follower_count']\n",
    "profit = rev - cost\n",
    "print('Sum of followers of predicted influencers: ', followers)\n",
    "print('Net Profit with our analytical model: $', round(profit,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ca07d206",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of followers of labeled Influencers from training data:  5376568593\n",
      "Net Profit with perfect analytical model: $ 8009852.89\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('./train.csv')\n",
    "\n",
    "cost = 0\n",
    "rev = 0\n",
    "followers = 0\n",
    "for row in range(len(df)):\n",
    "    cost += 10\n",
    "    if df.loc[row,'Choice'] == 1:\n",
    "        rev += df.loc[row,'A_follower_count'] * 10 * 0.00015\n",
    "        followers += df.loc[row,'A_follower_count']\n",
    "    else:\n",
    "        rev += df.loc[row,'B_follower_count'] * 10 * 0.00015\n",
    "        followers += df.loc[row,'B_follower_count']\n",
    "profit = rev - cost\n",
    "print('Sum of followers of labeled Influencers from training data: ', followers)\n",
    "print('Net Profit with perfect analytical model: $', round(profit,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a64c97c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of followers of all persons:  5376568593\n",
      "Net Profit without using analytics: $ 5321568.59\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('./train.csv')\n",
    "\n",
    "cost = 0\n",
    "rev = 0\n",
    "followers = 0\n",
    "for row in range(len(df)):\n",
    "    cost += 10\n",
    "    if df.loc[row,'Choice'] == 1:\n",
    "        rev += df.loc[row,'A_follower_count'] * 10 * 0.0001\n",
    "        followers += df.loc[row,'A_follower_count']\n",
    "    else:\n",
    "        rev += df.loc[row,'B_follower_count'] * 10 * 0.0001\n",
    "        followers += df.loc[row,'B_follower_count']\n",
    "profit = rev - cost\n",
    "print('Sum of followers of all persons: ', followers)\n",
    "print('Net Profit without using analytics: $', round(profit,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92e7d1e5",
   "metadata": {},
   "source": [
    "Note that we assume a perfect analytical model is the one that can predict influencers with 100% accuracy. This means that the model does not always select the influener as person with the most followers.\n",
    "\n",
    "Net Profit with analytical model = Sum of followers of predicted influencers * Profit Margin * Buying Probability of tweeting twice - Cost for influencers * Number of predicted influencers\n",
    "\n",
    "= 6236583679 * 10 * 0.00015 - 10 * 5500\n",
    "\n",
    "= 9299875.5185\n",
    "\n",
    "\n",
    "Net Profit with perfect anaytical model = Sum of followers of labeled Influencers from training data * 10 * 0.00015\n",
    "\n",
    "= 5376568593 * 10 * 0.00015 - 10 * 5500\n",
    "\n",
    "= 8009852.89\n",
    "\n",
    "\n",
    "Net Profit without analytics = Sum of followers of all persons * Profit Margin * Buying Probability of tweeting once - Cost for each person * Number of persons\n",
    "\n",
    "= 5376568593 * 10 * 0.0001 - 5 * 11000\n",
    "\n",
    "= 5321568.59\n",
    "\n",
    "\n",
    "Lift in Net Profit from our analytical model\n",
    "\n",
    "= 9299875.5185 - 5321568.59\n",
    "\n",
    "= 3978306.9285\n",
    "\n",
    "\n",
    "Lift in Net Profit from our perfect analytical model\n",
    "\n",
    "= 8009852.89 - 5321568.59\n",
    "\n",
    "= 2688284.3\n",
    "\n",
    "Lift in Net Profit from our analytical model is more than perfect analytical model because our model's predicted influencers have more followers than labeled influencer's followers. Since Lift in Net Profit is proportional to number of followers (refer formula), our analytical model results in higher Lift in Net Profit than perfect analytical model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d037a21f",
   "metadata": {},
   "source": [
    "#### Cumulative Gains Plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "783519c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Cumulative Gains Curve'}, xlabel='Percentage of sample', ylabel='Gain'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predicting the probability and plotting lift\n",
    "predictions = model4.predict_proba(normalized_df)\n",
    "skplt.metrics.plot_cumulative_gain(y, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c92ebfc8",
   "metadata": {},
   "source": [
    "The above cumulative gains curve shows that we obtain the best gain by contacting the top 50% of the influencers that our model predicts, since we get maximum separation from the baseline around 0.5 mark."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ed4920f",
   "metadata": {},
   "source": [
    "### Network Analysis of Twitter users talking about Austin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ff3bf3cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in scraped data\n",
    "entire_data_df = pd.read_csv('df_for_graph.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "P6aeHb8dFM3p",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "P6aeHb8dFM3p",
    "outputId": "d52e3795-4295-4d06-f611-26cb318ca9db"
   },
   "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>Tweeter</th>\n",
       "      <th>Tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>@MortifiedPengu4</td>\n",
       "      <td>RT @amuse: Austin Democrats fought for the rig...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@srart25</td>\n",
       "      <td>RT @sewellchan: ICYMI: Pence, Haley, Scott, Ke...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@ITalkSportsAR</td>\n",
       "      <td>RT @AndyKHLiu: Steph saw Austin Rivers’ little...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>@xandria_austin</td>\n",
       "      <td>Read. Every to hurt you\\n؟?\\nنمشے\\n\\n🔹H4🔹\\n🔹H4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>@austin_medz</td>\n",
       "      <td>@Sha_Shox @JamesGunn This guy liked his own tw...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>@BookSpotlight</td>\n",
       "      <td>RT @katkinneywrites: 🔥Seven brothers\\n🔥Set in ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>@tuckerfan69420</td>\n",
       "      <td>@tymetwx @HKronberg he's referring to the time...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>@Ohio_Texan</td>\n",
       "      <td>@finebaum Buddy Guy and Junior Wells at Antone...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>@siphaphathnthi1</td>\n",
       "      <td>RT @asianewsteam: Charlotte Austin ชาล็อต ออสต...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>@TheFakeLiu</td>\n",
       "      <td>RT @UTAustin: UT Austin facilities in Austin w...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Tweeter                                              Tweet\n",
       "0     @MortifiedPengu4  RT @amuse: Austin Democrats fought for the rig...\n",
       "1             @srart25  RT @sewellchan: ICYMI: Pence, Haley, Scott, Ke...\n",
       "2       @ITalkSportsAR  RT @AndyKHLiu: Steph saw Austin Rivers’ little...\n",
       "3      @xandria_austin  Read. Every to hurt you\\n؟?\\nنمشے\\n\\n🔹H4🔹\\n🔹H4...\n",
       "4         @austin_medz  @Sha_Shox @JamesGunn This guy liked his own tw...\n",
       "...                ...                                                ...\n",
       "4995    @BookSpotlight  RT @katkinneywrites: 🔥Seven brothers\\n🔥Set in ...\n",
       "4996   @tuckerfan69420  @tymetwx @HKronberg he's referring to the time...\n",
       "4997       @Ohio_Texan  @finebaum Buddy Guy and Junior Wells at Antone...\n",
       "4998  @siphaphathnthi1  RT @asianewsteam: Charlotte Austin ชาล็อต ออสต...\n",
       "4999       @TheFakeLiu  RT @UTAustin: UT Austin facilities in Austin w...\n",
       "\n",
       "[5000 rows x 2 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reseting index on the read data\n",
    "df_for_graph = pd.read_csv('df_for_graph.csv')\n",
    "entire_data_df = pd.read_csv('entire_data_df.csv')\n",
    "df_for_graph.drop(['Unnamed: 0'], axis=1, inplace=True)\n",
    "entire_data_df.drop(['Unnamed: 0'], axis=1, inplace=True)\n",
    "entire_data_df.fillna(0)\n",
    "df_for_graph.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "SsYLfcV5F0fb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "SsYLfcV5F0fb",
    "outputId": "28ba966c-54cd-4e2f-f79f-592f6aacf3f8"
   },
   "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>Tweeter</th>\n",
       "      <th>Mentioned_user</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>@MortifiedPengu4</td>\n",
       "      <td>@amuse</td>\n",
       "      <td>RT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@srart25</td>\n",
       "      <td>@sewellchan</td>\n",
       "      <td>RT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@ITalkSportsAR</td>\n",
       "      <td>@AndyKHLiu</td>\n",
       "      <td>RT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>@xandria_austin</td>\n",
       "      <td></td>\n",
       "      <td>Tweet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>@austin_medz</td>\n",
       "      <td>@Sha_Shox</td>\n",
       "      <td>Tweet</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Tweeter Mentioned_user   Type\n",
       "0  @MortifiedPengu4         @amuse     RT\n",
       "1          @srart25    @sewellchan     RT\n",
       "2    @ITalkSportsAR     @AndyKHLiu     RT\n",
       "3   @xandria_austin                 Tweet\n",
       "4      @austin_medz      @Sha_Shox  Tweet"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# code for graph edges\n",
    "\n",
    "# fetch mentioned user\n",
    "df_for_graph['Mentioned_user'] = df_for_graph.apply(lambda row : \", \".join(re.findall(r'@\\w+',row['Tweet'])), axis = 1)\n",
    "df_for_graph.Mentioned_user = df_for_graph.Mentioned_user.str.split(', ')\n",
    "\n",
    "# unpivot the mentioned users\n",
    "df_for_graph = df_for_graph.explode('Mentioned_user', ignore_index=True)\n",
    "\n",
    "# mark the type i.e. Tweet, Tweet(Self) or retweet\n",
    "df_for_graph['Type'] = np.where(df_for_graph['Tweet'].astype(str).str[0:2] == \"RT\", \"RT\", np.where(df_for_graph['Tweeter'] == df_for_graph['Mentioned_user'], \"Tweet(Self)\", \"Tweet\"))\n",
    "df_for_graph = df_for_graph[['Tweeter','Mentioned_user','Type']]\n",
    "df_for_graph[:5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d986366c",
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.from_pandas_edgelist(df_for_graph, source = 'Tweeter', target = 'Mentioned_user')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f3b99d25",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1800x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the newtwork\n",
    "from matplotlib.pyplot import figure\n",
    "figure(figsize=(25, 25))\n",
    "nx.draw(G, with_labels=False, node_size=10, node_color='#00b4d9')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e61c31d",
   "metadata": {},
   "source": [
    "We can see that the most influencial accounts are highlighted in blue in the center of the graph and they have the most nodes attached to them. We also see a few black circles which represent self-retweets and multiple small clusters at the periphery of the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "qFUzl0niGCyA",
   "metadata": {
    "id": "qFUzl0niGCyA"
   },
   "outputs": [],
   "source": [
    "# Calculating the centrality, betweenness, and closeness\n",
    "degree = nx.degree_centrality(G)\n",
    "betweenness = nx.betweenness_centrality(G)\n",
    "closeness = nx.closeness_centrality(G)\n",
    "centrality_df = pd.DataFrame(degree,index=['degree']).T\n",
    "centrality_df['betweenness'] = pd.DataFrame(betweenness,index=['betweenness']).T.betweenness\n",
    "centrality_df['closeness'] = pd.DataFrame(closeness,index=['closeness']).T.closeness\n",
    "centrality_df = centrality_df.reset_index().rename(columns={'index':'name'})\n",
    "centrality_df.to_csv('centrality_df.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "O-d9fiI8Suvs",
   "metadata": {
    "id": "O-d9fiI8Suvs"
   },
   "outputs": [],
   "source": [
    "centrality_df = pd.read_csv('centrality_df.csv')\n",
    "centrality_df.drop(['Unnamed: 0'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d86e1219",
   "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>name</th>\n",
       "      <th>degree</th>\n",
       "      <th>betweenness</th>\n",
       "      <th>closeness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>@MortifiedPengu4</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.116579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@amuse</td>\n",
       "      <td>0.003547</td>\n",
       "      <td>6.509596e-03</td>\n",
       "      <td>0.150330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@srart25</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.080393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>@sewellchan</td>\n",
       "      <td>0.001520</td>\n",
       "      <td>1.401122e-03</td>\n",
       "      <td>0.095120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>@ITalkSportsAR</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.115165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5916</th>\n",
       "      <td>@tuckerfan69420</td>\n",
       "      <td>0.000338</td>\n",
       "      <td>6.932256e-06</td>\n",
       "      <td>0.119676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5917</th>\n",
       "      <td>@Ohio_Texan</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5918</th>\n",
       "      <td>@finebaum</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5919</th>\n",
       "      <td>@siphaphathnthi1</td>\n",
       "      <td>0.000338</td>\n",
       "      <td>2.024948e-08</td>\n",
       "      <td>0.096239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5920</th>\n",
       "      <td>@TheFakeLiu</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.097778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5921 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  name    degree   betweenness  closeness\n",
       "0     @MortifiedPengu4  0.000169  0.000000e+00   0.116579\n",
       "1               @amuse  0.003547  6.509596e-03   0.150330\n",
       "2             @srart25  0.000169  0.000000e+00   0.080393\n",
       "3          @sewellchan  0.001520  1.401122e-03   0.095120\n",
       "4       @ITalkSportsAR  0.000169  0.000000e+00   0.115165\n",
       "...                ...       ...           ...        ...\n",
       "5916   @tuckerfan69420  0.000338  6.932256e-06   0.119676\n",
       "5917       @Ohio_Texan  0.000169  0.000000e+00   0.000169\n",
       "5918         @finebaum  0.000169  0.000000e+00   0.000169\n",
       "5919  @siphaphathnthi1  0.000338  2.024948e-08   0.096239\n",
       "5920       @TheFakeLiu  0.000169  0.000000e+00   0.097778\n",
       "\n",
       "[5921 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centrality_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ca9e193",
   "metadata": {},
   "source": [
    "To understand the importance of nodes, we tabulate the three centrality measures - degree, betweenness and closeness. These centrality measures provide insights into the influence, popularity, and accessibility of individuals or entities in the social network."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "_jRsA-AEEZVq",
   "metadata": {
    "id": "_jRsA-AEEZVq"
   },
   "source": [
    "## Do not run the next few blocks\n",
    "### Creating some intermediatary dataframes for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "KvjL73FF1VUP",
   "metadata": {
    "id": "KvjL73FF1VUP"
   },
   "outputs": [],
   "source": [
    "# Reconfiguring data types, engineering required columns\n",
    "df_for_model=entire_data_df[['screen_name','listed_count','followers_count','Retweet_count']]\n",
    "df_for_model['listed_count'] = np.where(df_for_model['listed_count']==\"False\",0,df_for_model['listed_count'])\n",
    "df_for_model['listed_count'] = df_for_model['listed_count'].astype(float)\n",
    "df_for_model['followers_count'] = df_for_model['followers_count'].astype(float)\n",
    "df_for_model['Retweet_count'] = df_for_model['Retweet_count'].astype(float)\n",
    "df_for_model=pd.DataFrame(df_for_model.groupby(['screen_name', 'listed_count','followers_count'], as_index=False)['Retweet_count'].sum())\n",
    "df_for_model['screen_name'] = df_for_model['screen_name'].str.replace('@', '')\n",
    "centrality_df['name'] = centrality_df['name'].str.replace('@', '')\n",
    "df_for_model = df_for_model.merge(centrality_df, left_on='screen_name', right_on='name', how='left')\n",
    "df_for_model.drop(['name'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4dYToe6P3kvk",
   "metadata": {
    "id": "4dYToe6P3kvk"
   },
   "outputs": [],
   "source": [
    "# cross join and remove matching to get n*n-1 records to apply model coefficients found in part I\n",
    "df1 = df_for_model\n",
    "df2 = df_for_model\n",
    "df1['key'] = 0\n",
    "df2['key'] = 0\n",
    "df1 = df1.merge(df2, on='key')\n",
    "\n",
    "# remove columns where screen_names are equal\n",
    "df_for_model=df1[df1['screen_name_x'] != df1['screen_name_y']]\n",
    "df_for_model['listed_diffrat'] = ((df_for_model['listed_count_x'] - df_for_model['listed_count_y']) / (df_for_model['listed_count_x'] + df_for_model['listed_count_y']))\n",
    "df_for_model['follower_diffrat'] = ((df_for_model['followers_count_x'] - df_for_model['followers_count_y']) / (df_for_model['followers_count_x'] + df_for_model['followers_count_y']))\n",
    "df_for_model['listed_diff'] = (df_for_model['listed_count_x'] - df_for_model['listed_count_y'])\n",
    "df_for_model.drop(['key'], axis=1, inplace=True)\n",
    "\n",
    "df_for_model['nf1_diffrat'] = ((df_for_model['degree_x'] - df_for_model['degree_y']) / (df_for_model['degree_x'] + df_for_model['degree_y']))\n",
    "df_for_model['nf2_diffrat'] = ((df_for_model['betweenness_x'] - df_for_model['betweenness_y']) / (df_for_model['betweenness_x'] + df_for_model['betweenness_y']))\n",
    "df_for_model['nf3_diffrat'] = ((df_for_model['closeness_x'] - df_for_model['closeness_y']) / (df_for_model['closeness_x'] + df_for_model['closeness_y']))\n",
    "\n",
    "df_for_model.drop(['degree_x','degree_y','betweenness_x','betweenness_y','closeness_x','closeness_y'], axis=1, inplace=True)\n",
    "df_for_model = df_for_model.fillna(0)\n",
    "# resetting index\n",
    "df_for_model.drop(['Unnamed: 0'], axis=1, inplace=True)\n",
    "df_for_model=pd.read_csv('df_for_model.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0358b3f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing duplicates user combinations\n",
    "def dedup_users(row):\n",
    "    x = [row['screen_name_x'], row['screen_name_y']]\n",
    "    x.sort()\n",
    "    return str(x)\n",
    "\n",
    "df_for_model['combined_name'] = df_for_model.apply(dedup_users, axis=1)\n",
    "dedup_df = df_for_model.drop_duplicates(subset='combined_name',keep='last')\n",
    "dedup_df.shape\n",
    "## Final data file to generate scores\n",
    "dedup_df.to_csv('DataForScore.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc543dbc",
   "metadata": {},
   "source": [
    "## Reading csv with cleaned data to compute scores "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d0ce5f6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "dedup_df = pd.read_csv('DataForScore.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c42765e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>listed_diffrat</th>\n",
       "      <th>follower_diffrat</th>\n",
       "      <th>nf1_diffrat</th>\n",
       "      <th>nf2_diffrat</th>\n",
       "      <th>nf3_diffrat</th>\n",
       "      <th>screen_name_x</th>\n",
       "      <th>screen_name_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.251521</td>\n",
       "      <td>1.356018</td>\n",
       "      <td>-1.817197</td>\n",
       "      <td>-1.413961</td>\n",
       "      <td>1.128403</td>\n",
       "      <td>08wvdcfan</td>\n",
       "      <td>07kanamiyu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.251521</td>\n",
       "      <td>1.282430</td>\n",
       "      <td>0.008905</td>\n",
       "      <td>0.660284</td>\n",
       "      <td>0.001708</td>\n",
       "      <td>0x0000DIN0</td>\n",
       "      <td>07kanamiyu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.721720</td>\n",
       "      <td>-1.093051</td>\n",
       "      <td>1.835008</td>\n",
       "      <td>1.423208</td>\n",
       "      <td>-1.180399</td>\n",
       "      <td>0x0000DIN0</td>\n",
       "      <td>08wvdcfan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.251521</td>\n",
       "      <td>1.362903</td>\n",
       "      <td>-0.295445</td>\n",
       "      <td>0.001526</td>\n",
       "      <td>-0.298260</td>\n",
       "      <td>0xGanon</td>\n",
       "      <td>07kanamiyu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.666907</td>\n",
       "      <td>1.080341</td>\n",
       "      <td>1.652398</td>\n",
       "      <td>1.423208</td>\n",
       "      <td>-1.260316</td>\n",
       "      <td>0xGanon</td>\n",
       "      <td>08wvdcfan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7548550</th>\n",
       "      <td>-0.627756</td>\n",
       "      <td>-0.097854</td>\n",
       "      <td>-2.232220</td>\n",
       "      <td>-1.413961</td>\n",
       "      <td>-0.105901</td>\n",
       "      <td>zjones20</td>\n",
       "      <td>yweldon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7548551</th>\n",
       "      <td>0.840429</td>\n",
       "      <td>0.864182</td>\n",
       "      <td>0.008905</td>\n",
       "      <td>0.004624</td>\n",
       "      <td>-0.207825</td>\n",
       "      <td>zjones20</td>\n",
       "      <td>zak_snak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7548552</th>\n",
       "      <td>-0.187300</td>\n",
       "      <td>1.086411</td>\n",
       "      <td>0.008905</td>\n",
       "      <td>0.004624</td>\n",
       "      <td>-0.029867</td>\n",
       "      <td>zjones20</td>\n",
       "      <td>zelosq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7548553</th>\n",
       "      <td>0.840429</td>\n",
       "      <td>-0.813330</td>\n",
       "      <td>-1.360671</td>\n",
       "      <td>-1.413961</td>\n",
       "      <td>-0.078588</td>\n",
       "      <td>zjones20</td>\n",
       "      <td>zephtx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7548554</th>\n",
       "      <td>-0.489574</td>\n",
       "      <td>-0.377382</td>\n",
       "      <td>-0.904146</td>\n",
       "      <td>-1.413961</td>\n",
       "      <td>-0.207970</td>\n",
       "      <td>zjones20</td>\n",
       "      <td>zepshoops</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7548555 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         listed_diffrat  follower_diffrat  nf1_diffrat  nf2_diffrat  \\\n",
       "0              1.251521          1.356018    -1.817197    -1.413961   \n",
       "1              1.251521          1.282430     0.008905     0.660284   \n",
       "2             -0.721720         -1.093051     1.835008     1.423208   \n",
       "3              1.251521          1.362903    -0.295445     0.001526   \n",
       "4             -0.666907          1.080341     1.652398     1.423208   \n",
       "...                 ...               ...          ...          ...   \n",
       "7548550       -0.627756         -0.097854    -2.232220    -1.413961   \n",
       "7548551        0.840429          0.864182     0.008905     0.004624   \n",
       "7548552       -0.187300          1.086411     0.008905     0.004624   \n",
       "7548553        0.840429         -0.813330    -1.360671    -1.413961   \n",
       "7548554       -0.489574         -0.377382    -0.904146    -1.413961   \n",
       "\n",
       "         nf3_diffrat screen_name_x screen_name_y  \n",
       "0           1.128403     08wvdcfan    07kanamiyu  \n",
       "1           0.001708    0x0000DIN0    07kanamiyu  \n",
       "2          -1.180399    0x0000DIN0     08wvdcfan  \n",
       "3          -0.298260       0xGanon    07kanamiyu  \n",
       "4          -1.260316       0xGanon     08wvdcfan  \n",
       "...              ...           ...           ...  \n",
       "7548550    -0.105901      zjones20       yweldon  \n",
       "7548551    -0.207825      zjones20      zak_snak  \n",
       "7548552    -0.029867      zjones20        zelosq  \n",
       "7548553    -0.078588      zjones20        zephtx  \n",
       "7548554    -0.207970      zjones20     zepshoops  \n",
       "\n",
       "[7548555 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalizing the numerical values\n",
    "normalized_df = (dedup_df[['listed_diffrat', 'follower_diffrat','nf1_diffrat','nf2_diffrat','nf3_diffrat']]-dedup_df[['listed_diffrat', 'follower_diffrat','nf1_diffrat','nf2_diffrat','nf3_diffrat']].mean())/dedup_df[['listed_diffrat', 'follower_diffrat','nf1_diffrat','nf2_diffrat','nf3_diffrat']].std()\n",
    "norm_df = pd.concat([normalized_df, dedup_df['screen_name_x'],dedup_df['screen_name_y']], axis=1)\n",
    "norm_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "37946dcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>listed_diffrat</th>\n",
       "      <th>follower_diffrat</th>\n",
       "      <th>nf1_diffrat</th>\n",
       "      <th>nf2_diffrat</th>\n",
       "      <th>nf3_diffrat</th>\n",
       "      <th>screen_name_x</th>\n",
       "      <th>screen_name_y</th>\n",
       "      <th>I_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>250118</th>\n",
       "      <td>1.251521</td>\n",
       "      <td>1.363827</td>\n",
       "      <td>2.669798</td>\n",
       "      <td>1.423208</td>\n",
       "      <td>-0.000702</td>\n",
       "      <td>EvilMopacATX</td>\n",
       "      <td>DanicaPhipps14</td>\n",
       "      <td>2.333436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250234</th>\n",
       "      <td>1.251521</td>\n",
       "      <td>1.363827</td>\n",
       "      <td>2.669798</td>\n",
       "      <td>1.423208</td>\n",
       "      <td>1.354808</td>\n",
       "      <td>EvilMopacATX</td>\n",
       "      <td>Ed28540697</td>\n",
       "      <td>2.333436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249623</th>\n",
       "      <td>1.251521</td>\n",
       "      <td>1.363827</td>\n",
       "      <td>2.669798</td>\n",
       "      <td>1.423208</td>\n",
       "      <td>1.223394</td>\n",
       "      <td>EvilMopacATX</td>\n",
       "      <td>80Coepri</td>\n",
       "      <td>2.333436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249711</th>\n",
       "      <td>1.251521</td>\n",
       "      <td>1.363827</td>\n",
       "      <td>2.669798</td>\n",
       "      <td>1.423208</td>\n",
       "      <td>0.367897</td>\n",
       "      <td>EvilMopacATX</td>\n",
       "      <td>AmyS04465264</td>\n",
       "      <td>2.333436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249870</th>\n",
       "      <td>1.251521</td>\n",
       "      <td>1.363772</td>\n",
       "      <td>2.669798</td>\n",
       "      <td>1.423208</td>\n",
       "      <td>0.222029</td>\n",
       "      <td>EvilMopacATX</td>\n",
       "      <td>Beprecious913</td>\n",
       "      <td>2.333414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1711032</th>\n",
       "      <td>-1.215030</td>\n",
       "      <td>-1.322264</td>\n",
       "      <td>-2.651987</td>\n",
       "      <td>-1.413961</td>\n",
       "      <td>-0.284157</td>\n",
       "      <td>StevenWeigand2</td>\n",
       "      <td>EvilMopacATX</td>\n",
       "      <td>-2.289174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537873</th>\n",
       "      <td>-1.215030</td>\n",
       "      <td>-1.322264</td>\n",
       "      <td>-2.651987</td>\n",
       "      <td>-1.413961</td>\n",
       "      <td>-1.416936</td>\n",
       "      <td>Jm102897Justin</td>\n",
       "      <td>EvilMopacATX</td>\n",
       "      <td>-2.289174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2450498</th>\n",
       "      <td>-1.215030</td>\n",
       "      <td>-1.322264</td>\n",
       "      <td>-2.651987</td>\n",
       "      <td>-1.413961</td>\n",
       "      <td>-0.061427</td>\n",
       "      <td>alibash11834890</td>\n",
       "      <td>EvilMopacATX</td>\n",
       "      <td>-2.289174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3106985</th>\n",
       "      <td>-1.215030</td>\n",
       "      <td>-1.322264</td>\n",
       "      <td>-2.651987</td>\n",
       "      <td>-1.413961</td>\n",
       "      <td>-1.416936</td>\n",
       "      <td>burnspbesq</td>\n",
       "      <td>EvilMopacATX</td>\n",
       "      <td>-2.289174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2430617</th>\n",
       "      <td>-1.215030</td>\n",
       "      <td>-1.322264</td>\n",
       "      <td>-2.651987</td>\n",
       "      <td>-1.413961</td>\n",
       "      <td>-1.413022</td>\n",
       "      <td>alecpeterson01</td>\n",
       "      <td>EvilMopacATX</td>\n",
       "      <td>-2.289174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7548555 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         listed_diffrat  follower_diffrat  nf1_diffrat  nf2_diffrat  \\\n",
       "250118         1.251521          1.363827     2.669798     1.423208   \n",
       "250234         1.251521          1.363827     2.669798     1.423208   \n",
       "249623         1.251521          1.363827     2.669798     1.423208   \n",
       "249711         1.251521          1.363827     2.669798     1.423208   \n",
       "249870         1.251521          1.363772     2.669798     1.423208   \n",
       "...                 ...               ...          ...          ...   \n",
       "1711032       -1.215030         -1.322264    -2.651987    -1.413961   \n",
       "537873        -1.215030         -1.322264    -2.651987    -1.413961   \n",
       "2450498       -1.215030         -1.322264    -2.651987    -1.413961   \n",
       "3106985       -1.215030         -1.322264    -2.651987    -1.413961   \n",
       "2430617       -1.215030         -1.322264    -2.651987    -1.413961   \n",
       "\n",
       "         nf3_diffrat    screen_name_x   screen_name_y   I_Score  \n",
       "250118     -0.000702     EvilMopacATX  DanicaPhipps14  2.333436  \n",
       "250234      1.354808     EvilMopacATX      Ed28540697  2.333436  \n",
       "249623      1.223394     EvilMopacATX        80Coepri  2.333436  \n",
       "249711      0.367897     EvilMopacATX    AmyS04465264  2.333436  \n",
       "249870      0.222029     EvilMopacATX   Beprecious913  2.333414  \n",
       "...              ...              ...             ...       ...  \n",
       "1711032    -0.284157   StevenWeigand2    EvilMopacATX -2.289174  \n",
       "537873     -1.416936   Jm102897Justin    EvilMopacATX -2.289174  \n",
       "2450498    -0.061427  alibash11834890    EvilMopacATX -2.289174  \n",
       "3106985    -1.416936       burnspbesq    EvilMopacATX -2.289174  \n",
       "2430617    -1.413022   alecpeterson01    EvilMopacATX -2.289174  \n",
       "\n",
       "[7548555 rows x 8 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using important features found from the Part 1 to create influence scores\n",
    "norm_df['I_Score'] = coeff_parameter.iloc[0].values * norm_df[coeff_parameter.index.values[0]] + coeff_parameter.iloc[1].values * norm_df[coeff_parameter.index.values[1]] + coeff_parameter.iloc[2].values * norm_df[coeff_parameter.index.values[2]] + coeff_parameter.iloc[3].values * norm_df[coeff_parameter.index.values[3]]\n",
    "sorted_norm_df=norm_df.sort_values('I_Score',ascending=False)\n",
    "sorted_norm_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac60a88e",
   "metadata": {},
   "source": [
    "#### Average and Scale Influence Score to 0-100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9e386f87",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Average and scale Influence Scores for each user between 0-100 \n",
    "scaler = MinMaxScaler(feature_range=(0,100))\n",
    "# Listing the top 20 influencers in the network\n",
    "influencers_scores = pd.DataFrame(sorted_norm_df.groupby('screen_name_x')['I_Score'].mean().sort_values(ascending=False))\n",
    "influencers_scores = pd.DataFrame(scaler.fit_transform(influencers_scores), index=influencers_scores.index, columns=['Influence_Score']).round(2)\n",
    "influencers_scores.index.name ='Twitter Handle'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98fb0ebd",
   "metadata": {},
   "source": [
    "#### Get top-20 Austin city influencers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a27c5648",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Influence_Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Twitter Handle</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TheNBACentral</th>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KXAN_News</th>\n",
       "      <td>99.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KVUE</th>\n",
       "      <td>99.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asmongold</th>\n",
       "      <td>98.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austin_Police</th>\n",
       "      <td>98.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TexasTribune</th>\n",
       "      <td>98.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000s_WWE</th>\n",
       "      <td>98.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CreationEnt</th>\n",
       "      <td>97.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>statesman</th>\n",
       "      <td>97.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>austin_visual</th>\n",
       "      <td>97.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thedailybeast</th>\n",
       "      <td>96.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KXAN_Weather</th>\n",
       "      <td>96.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EvilMopacATX</th>\n",
       "      <td>96.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fox7austin</th>\n",
       "      <td>95.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tplohetski</th>\n",
       "      <td>95.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BrandonHuffman</th>\n",
       "      <td>94.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spann</th>\n",
       "      <td>94.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austin_Federa</th>\n",
       "      <td>94.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mattlargey</th>\n",
       "      <td>94.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCEA</th>\n",
       "      <td>93.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Influence_Score\n",
       "Twitter Handle                 \n",
       "TheNBACentral            100.00\n",
       "KXAN_News                 99.74\n",
       "KVUE                      99.09\n",
       "Asmongold                 98.79\n",
       "Austin_Police             98.78\n",
       "TexasTribune              98.73\n",
       "2000s_WWE                 98.18\n",
       "CreationEnt               97.55\n",
       "statesman                 97.45\n",
       "austin_visual             97.19\n",
       "thedailybeast             96.82\n",
       "KXAN_Weather              96.80\n",
       "EvilMopacATX              96.47\n",
       "fox7austin                95.90\n",
       "tplohetski                95.42\n",
       "BrandonHuffman            94.36\n",
       "spann                     94.33\n",
       "Austin_Federa             94.29\n",
       "mattlargey                94.17\n",
       "TCEA                      93.91"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top20_influencers = influencers_scores[:20]\n",
    "top20_influencers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "214f5656",
   "metadata": {},
   "source": [
    "Our top 20 influencers are:\n",
    "\n",
    "TheNBACentral: This Twitter account is popular among basketball fans in Austin, providing news and analysis on the National Basketball Association.\n",
    "\n",
    "KXAN_News: This is the official Twitter account of KXAN-TV, the NBC affiliate in Austin, providing local news, weather, and traffic updates.\n",
    "\n",
    "KVUE: This is the official Twitter account of KVUE-TV, the ABC affiliate in Austin, providing local news, weather, and traffic updates.\n",
    "\n",
    "Asmongold: This Twitter account belongs to a popular Twitch streamer and YouTuber who is based in Austin and focuses on World of Warcraft content.\n",
    "\n",
    "Austin_Police: This is the official Twitter account of the Austin Police Department, providing updates on local law enforcement activity and community outreach.\n",
    "\n",
    "TexasTribune: This Twitter account belongs to a non-profit, non-partisan news organization that covers Texas politics and policy.\n",
    "\n",
    "2000s_WWE: This Twitter account provides news and analysis on the World Wrestling Entertainment (WWE) events of the 2000s.\n",
    "\n",
    "CreationEnt: This Twitter account belongs to a leading independent entertainment company that provides high-quality live events in Austin and other cities.\n",
    "\n",
    "statesman: This Twitter account belongs to the Austin American-Statesman, the largest daily newspaper in Austin, providing local news and analysis.\n",
    "\n",
    "austin_visual: This Twitter account showcases the visual arts scene in Austin, including exhibitions, events, and artist profiles.\n",
    "\n",
    "thedailybeast: This Twitter account belongs to The Daily Beast, a news and opinion website that covers national and international news.\n",
    "\n",
    "KXAN_Weather: This is the official Twitter account of the weather team at KXAN-TV, providing up-to-date weather forecasts for Austin and surrounding areas.\n",
    "\n",
    "EvilMopacATX: This Twitter account provides commentary and satire on local Austin issues and events.\n",
    "\n",
    "fox7austin: This is the official Twitter account of FOX 7 Austin, providing local news, weather, and traffic updates.\n",
    "\n",
    "tplohetski: This Twitter account belongs to Tony Plohetski, an investigative reporter at the Austin American-Statesman.\n",
    "\n",
    "BrandonHuffman: This Twitter account belongs to Brandon Huffman, a journalist and editor who covers high school and college football.\n",
    "\n",
    "spann: This Twitter account belongs to James Spann, a well-known meteorologist and weather forecaster based in Austin.\n",
    "\n",
    "Austin_Federa: This Twitter account provides news and analysis on the Austin chapter of the Federal Reserve Bank of Dallas.\n",
    "\n",
    "mattlargey: This Twitter account belongs to Matt Largey, a journalist and radio host based in Austin.\n",
    "\n",
    "TCEA: This Twitter account belongs to the Texas Computer Education Association, a professional organization that supports technology education in Texas.    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f7beb65",
   "metadata": {},
   "source": [
    "We see that news channels like KXAN News, KVUE, the daily beast, texas tribune, fox 7 austin, are among the top 20 influencers in Austin. \n",
    "Among the top 20 we also have \"tplohetski\" is a news reporter and \"spann\" is a meterologist. This is expected as with the current winter storm, these channel received a lot retweets on the updates that they shared. \n",
    "UT Police and KXAN_weather has sent out multiple tweets regarding the winter storm and resources for citizens and this is being publicised among the community by other users.\n",
    "We also see some popular sports entity among our top 20 like The NBA Central and 2000s_WWE. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "e69b0cd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nf1_diffrat</th>\n",
       "      <th>nf2_diffrat</th>\n",
       "      <th>nf3_diffrat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>screen_name_x</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EvilMopacATX</th>\n",
       "      <td>2.589883</td>\n",
       "      <td>1.395346</td>\n",
       "      <td>0.656742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KXAN_News</th>\n",
       "      <td>2.527832</td>\n",
       "      <td>1.395004</td>\n",
       "      <td>0.642504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asmongold</th>\n",
       "      <td>2.520989</td>\n",
       "      <td>1.368577</td>\n",
       "      <td>0.585493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TheNBACentral</th>\n",
       "      <td>2.501808</td>\n",
       "      <td>1.387718</td>\n",
       "      <td>0.618803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CreationEnt</th>\n",
       "      <td>2.487199</td>\n",
       "      <td>1.371712</td>\n",
       "      <td>0.646771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austin_Police</th>\n",
       "      <td>2.484596</td>\n",
       "      <td>1.369357</td>\n",
       "      <td>0.646937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KVUE</th>\n",
       "      <td>2.443817</td>\n",
       "      <td>1.389333</td>\n",
       "      <td>0.651577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>austin_visual</th>\n",
       "      <td>2.441148</td>\n",
       "      <td>1.394237</td>\n",
       "      <td>0.624850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KXAN_Weather</th>\n",
       "      <td>2.430586</td>\n",
       "      <td>1.370731</td>\n",
       "      <td>0.632045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000s_WWE</th>\n",
       "      <td>2.429141</td>\n",
       "      <td>1.383104</td>\n",
       "      <td>0.701087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mattlargey</th>\n",
       "      <td>2.405520</td>\n",
       "      <td>1.396401</td>\n",
       "      <td>0.633528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austin_Federa</th>\n",
       "      <td>2.394275</td>\n",
       "      <td>1.203914</td>\n",
       "      <td>-0.233681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TexasTribune</th>\n",
       "      <td>2.369143</td>\n",
       "      <td>1.359519</td>\n",
       "      <td>0.565469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tplohetski</th>\n",
       "      <td>2.292601</td>\n",
       "      <td>1.394055</td>\n",
       "      <td>0.634676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCEA</th>\n",
       "      <td>2.246887</td>\n",
       "      <td>0.995791</td>\n",
       "      <td>-0.452658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>statesman</th>\n",
       "      <td>2.170272</td>\n",
       "      <td>1.382764</td>\n",
       "      <td>0.633476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BrandonHuffman</th>\n",
       "      <td>2.139452</td>\n",
       "      <td>1.319019</td>\n",
       "      <td>0.538014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fox7austin</th>\n",
       "      <td>2.085975</td>\n",
       "      <td>1.366919</td>\n",
       "      <td>0.627941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thedailybeast</th>\n",
       "      <td>2.055545</td>\n",
       "      <td>1.341782</td>\n",
       "      <td>0.612384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spann</th>\n",
       "      <td>1.881395</td>\n",
       "      <td>1.244697</td>\n",
       "      <td>0.452233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                nf1_diffrat  nf2_diffrat  nf3_diffrat\n",
       "screen_name_x                                        \n",
       "EvilMopacATX       2.589883     1.395346     0.656742\n",
       "KXAN_News          2.527832     1.395004     0.642504\n",
       "Asmongold          2.520989     1.368577     0.585493\n",
       "TheNBACentral      2.501808     1.387718     0.618803\n",
       "CreationEnt        2.487199     1.371712     0.646771\n",
       "Austin_Police      2.484596     1.369357     0.646937\n",
       "KVUE               2.443817     1.389333     0.651577\n",
       "austin_visual      2.441148     1.394237     0.624850\n",
       "KXAN_Weather       2.430586     1.370731     0.632045\n",
       "2000s_WWE          2.429141     1.383104     0.701087\n",
       "mattlargey         2.405520     1.396401     0.633528\n",
       "Austin_Federa      2.394275     1.203914    -0.233681\n",
       "TexasTribune       2.369143     1.359519     0.565469\n",
       "tplohetski         2.292601     1.394055     0.634676\n",
       "TCEA               2.246887     0.995791    -0.452658\n",
       "statesman          2.170272     1.382764     0.633476\n",
       "BrandonHuffman     2.139452     1.319019     0.538014\n",
       "fox7austin         2.085975     1.366919     0.627941\n",
       "thedailybeast      2.055545     1.341782     0.612384\n",
       "spann              1.881395     1.244697     0.452233"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter2 = sorted_norm_df[\"screen_name_x\"].isin(top20_influencers.index)\n",
    "df_to_verify = sorted_norm_df[['nf1_diffrat','nf2_diffrat','nf3_diffrat','screen_name_x']][filter2]\n",
    "df_to_verify.groupby(by='screen_name_x').mean().sort_values(by='nf1_diffrat', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "255b4710",
   "metadata": {},
   "source": [
    "Taking a look at the network features we see that transformations of degree, centrality and betweenness of nodes (nf1_diffrat and nf2_diffrat) are more dominating in deciding the order of influence among the users as compared to closeness (nf3_diffrat)"
   ]
  }
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