{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Selection - Instagram Top Post"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the required libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import confusion_matrix , classification_report, f1_score,\\\n",
    "                            accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "SEED = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>height</th>\n",
       "      <th>width</th>\n",
       "      <th>has_audio</th>\n",
       "      <th>video_view_count</th>\n",
       "      <th>is_video</th>\n",
       "      <th>is_verified</th>\n",
       "      <th>followers_count</th>\n",
       "      <th>comments_count</th>\n",
       "      <th>hashtag_count</th>\n",
       "      <th>like_count</th>\n",
       "      <th>tag_name</th>\n",
       "      <th>caption_length</th>\n",
       "      <th>is_top</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1349</td>\n",
       "      <td>1080</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>774304</td>\n",
       "      <td>626</td>\n",
       "      <td>0</td>\n",
       "      <td>50311</td>\n",
       "      <td>2021</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1332</td>\n",
       "      <td>1080</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>68438</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>1957</td>\n",
       "      <td>2021</td>\n",
       "      <td>382</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1350</td>\n",
       "      <td>1080</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3122</td>\n",
       "      <td>102</td>\n",
       "      <td>23</td>\n",
       "      <td>595</td>\n",
       "      <td>2021</td>\n",
       "      <td>377</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1080</td>\n",
       "      <td>1080</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>250500</td>\n",
       "      <td>123573</td>\n",
       "      <td>5</td>\n",
       "      <td>8290</td>\n",
       "      <td>2021</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1349</td>\n",
       "      <td>1080</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3578</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>365</td>\n",
       "      <td>2021</td>\n",
       "      <td>45</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2165</th>\n",
       "      <td>1085</td>\n",
       "      <td>1080</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2002</td>\n",
       "      <td>28</td>\n",
       "      <td>15</td>\n",
       "      <td>74</td>\n",
       "      <td>world</td>\n",
       "      <td>671</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2166</th>\n",
       "      <td>750</td>\n",
       "      <td>750</td>\n",
       "      <td>True</td>\n",
       "      <td>48</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>world</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2167</th>\n",
       "      <td>1080</td>\n",
       "      <td>1080</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1386</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>world</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2168</th>\n",
       "      <td>1110</td>\n",
       "      <td>1080</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>450271</td>\n",
       "      <td>260</td>\n",
       "      <td>19</td>\n",
       "      <td>8371</td>\n",
       "      <td>world</td>\n",
       "      <td>463</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2169</th>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>False</td>\n",
       "      <td>26</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>17</td>\n",
       "      <td>world</td>\n",
       "      <td>429</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2170 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      height  width  has_audio  video_view_count  is_video  is_verified  \\\n",
       "0       1349   1080      False                 0     False        False   \n",
       "1       1332   1080      False                 0     False        False   \n",
       "2       1350   1080      False                 0     False        False   \n",
       "3       1080   1080      False                 0     False        False   \n",
       "4       1349   1080      False                 0     False        False   \n",
       "...      ...    ...        ...               ...       ...          ...   \n",
       "2165    1085   1080      False                 0     False        False   \n",
       "2166     750    750       True                48      True        False   \n",
       "2167    1080   1080      False                 0     False        False   \n",
       "2168    1110   1080      False                 0     False        False   \n",
       "2169     640    640      False                26      True        False   \n",
       "\n",
       "      followers_count  comments_count  hashtag_count  like_count tag_name  \\\n",
       "0              774304             626              0       50311     2021   \n",
       "1               68438               7             11        1957     2021   \n",
       "2                3122             102             23         595     2021   \n",
       "3              250500          123573              5        8290     2021   \n",
       "4                3578              26              4         365     2021   \n",
       "...               ...             ...            ...         ...      ...   \n",
       "2165             2002              28             15          74    world   \n",
       "2166               72               0              8           7    world   \n",
       "2167             1386               0             19           4    world   \n",
       "2168           450271             260             19        8371    world   \n",
       "2169               42               0             29          17    world   \n",
       "\n",
       "      caption_length  is_top  \n",
       "0                 49       1  \n",
       "1                382       1  \n",
       "2                377       1  \n",
       "3                512       1  \n",
       "4                 45       1  \n",
       "...              ...     ...  \n",
       "2165             671       0  \n",
       "2166              51       0  \n",
       "2167             200       0  \n",
       "2168             463       0  \n",
       "2169             429       0  \n",
       "\n",
       "[2170 rows x 13 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load required data\n",
    "df = pd.read_csv(\"db.csv\")\n",
    "\n",
    "# View the data\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "height\n",
      "width\n",
      "has_audio\n",
      "video_view_count\n",
      "is_video\n",
      "is_verified\n",
      "followers_count\n",
      "comments_count\n",
      "hashtag_count\n",
      "like_count\n",
      "tag_name\n",
      "caption_length\n",
      "is_top\n"
     ]
    }
   ],
   "source": [
    "for i in df:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1099\n",
       "0    1071\n",
       "Name: is_top, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Is the data balanced?\n",
    "df['is_top'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Data Pre-processing & Exploratory Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2170 entries, 0 to 2169\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   height            2170 non-null   int64 \n",
      " 1   width             2170 non-null   int64 \n",
      " 2   has_audio         2170 non-null   bool  \n",
      " 3   video_view_count  2170 non-null   int64 \n",
      " 4   is_video          2170 non-null   bool  \n",
      " 5   is_verified       2170 non-null   bool  \n",
      " 6   followers_count   2170 non-null   int64 \n",
      " 7   comments_count    2170 non-null   int64 \n",
      " 8   hashtag_count     2170 non-null   int64 \n",
      " 9   like_count        2170 non-null   int64 \n",
      " 10  tag_name          2170 non-null   object\n",
      " 11  caption_length    2170 non-null   int64 \n",
      " 12  is_top            2170 non-null   int64 \n",
      "dtypes: bool(3), int64(9), object(1)\n",
      "memory usage: 176.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the 'has_audio' column from bool to object\n",
    "df.has_audio= df['has_audio'].apply(lambda x : 'No' if x == 0 else 'Yes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the 'is_video' from bool to object\n",
    "df.is_video= df['is_video'].apply(lambda x : 'No' if x == 0 else 'Yes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the 'is_verified' column from bool to object\n",
    "df.is_verified= df['is_verified'].apply(lambda x : 'No' if x == 0 else 'Yes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2170 entries, 0 to 2169\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   height            2170 non-null   int64 \n",
      " 1   width             2170 non-null   int64 \n",
      " 2   has_audio         2170 non-null   object\n",
      " 3   video_view_count  2170 non-null   int64 \n",
      " 4   is_video          2170 non-null   object\n",
      " 5   is_verified       2170 non-null   object\n",
      " 6   followers_count   2170 non-null   int64 \n",
      " 7   comments_count    2170 non-null   int64 \n",
      " 8   hashtag_count     2170 non-null   int64 \n",
      " 9   like_count        2170 non-null   int64 \n",
      " 10  tag_name          2170 non-null   object\n",
      " 11  caption_length    2170 non-null   int64 \n",
      " 12  is_top            2170 non-null   int64 \n",
      "dtypes: int64(9), object(4)\n",
      "memory usage: 220.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "height              False\n",
       "width               False\n",
       "has_audio           False\n",
       "video_view_count    False\n",
       "is_video            False\n",
       "is_verified         False\n",
       "followers_count     False\n",
       "comments_count      False\n",
       "hashtag_count       False\n",
       "like_count          False\n",
       "tag_name            False\n",
       "caption_length      False\n",
       "is_top              False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Any missing values to clean?\n",
    "df.isna().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizations to see how independent variables contribute to a top post"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x3960 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # Visualizations of the non-object (int64) variables\n",
    "continuous_vars = [i for i in df.columns if df[i].dtypes !='object']\n",
    "fig,ax = plt.subplots(9,1,figsize=(10,55))\n",
    "\n",
    "for i,x in enumerate(continuous_vars):\n",
    "    ax[i].hist(df[x][df.is_top==0],label='is_top=0',bins=20)\n",
    "    ax[i].hist(df[x][df.is_top==1],label='is_top=1',bins=20)\n",
    "    ax[i].set(xlabel=x,ylabel='count')\n",
    "    ax[i].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['height',\n",
       " 'width',\n",
       " 'video_view_count',\n",
       " 'followers_count',\n",
       " 'comments_count',\n",
       " 'hashtag_count',\n",
       " 'like_count',\n",
       " 'caption_length',\n",
       " 'is_top']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continuous_vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizations of the object variables\n",
    "categorical_var = [i for i in df.columns if df[i].dtypes =='object']\n",
    "catVars_isTop = categorical_var[:-1]\n",
    "\n",
    "fig ,ax = plt.subplots(1,4,figsize=(15,3))\n",
    "for axi ,var in zip(ax.flat,catVars_isTop):\n",
    "    sns.countplot(x=df.is_top,hue=df[var],ax=axi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['has_audio', 'is_video', 'is_verified', 'tag_name']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "has_audio ['No' 'Yes']\n",
      "is_video ['No' 'Yes']\n",
      "is_verified ['No' 'Yes']\n",
      "is_top [1 0]\n"
     ]
    }
   ],
   "source": [
    "# Label Encoding for features with 2 classes\n",
    "le = LabelEncoder()\n",
    "for x in [i for i in df.columns if len(df[i].unique())==2]:\n",
    "    print(x, df[x].unique())\n",
    "    df[x]= le.fit_transform(df[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One Hot Encoding for features with more than 2 classes\n",
    "df = pd.get_dummies(df, columns= [i for i in df.columns if df[i].dtypes=='object'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "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>height</th>\n",
       "      <th>width</th>\n",
       "      <th>has_audio</th>\n",
       "      <th>video_view_count</th>\n",
       "      <th>is_video</th>\n",
       "      <th>is_verified</th>\n",
       "      <th>followers_count</th>\n",
       "      <th>comments_count</th>\n",
       "      <th>hashtag_count</th>\n",
       "      <th>like_count</th>\n",
       "      <th>...</th>\n",
       "      <th>tag_name_travel</th>\n",
       "      <th>tag_name_trump</th>\n",
       "      <th>tag_name_university</th>\n",
       "      <th>tag_name_water</th>\n",
       "      <th>tag_name_wolf</th>\n",
       "      <th>tag_name_women</th>\n",
       "      <th>tag_name_word</th>\n",
       "      <th>tag_name_workathome</th>\n",
       "      <th>tag_name_workonline</th>\n",
       "      <th>tag_name_world</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1674</th>\n",
       "      <td>750</td>\n",
       "      <td>750</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>1080</td>\n",
       "      <td>1080</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5563</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>153</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1746</th>\n",
       "      <td>645</td>\n",
       "      <td>1080</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>188</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1191</th>\n",
       "      <td>658</td>\n",
       "      <td>750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>483</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>62</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>1080</td>\n",
       "      <td>1080</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1950</td>\n",
       "      <td>126</td>\n",
       "      <td>19</td>\n",
       "      <td>759</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>600</td>\n",
       "      <td>480</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>124858</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "      <td>819</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>1350</td>\n",
       "      <td>1080</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15899</td>\n",
       "      <td>65</td>\n",
       "      <td>22</td>\n",
       "      <td>528</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>699</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>750</td>\n",
       "      <td>750</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>578</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>1348</td>\n",
       "      <td>1080</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>626462</td>\n",
       "      <td>45</td>\n",
       "      <td>22</td>\n",
       "      <td>10428</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>1349</td>\n",
       "      <td>1080</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>117806</td>\n",
       "      <td>242</td>\n",
       "      <td>30</td>\n",
       "      <td>11651</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>1053</td>\n",
       "      <td>1080</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>338110</td>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>4287</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>1080</td>\n",
       "      <td>1080</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1442</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>617</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1252</th>\n",
       "      <td>1080</td>\n",
       "      <td>1080</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>714</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>614130</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>1771</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>1346</td>\n",
       "      <td>1080</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120920</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "      <td>275</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>1080</td>\n",
       "      <td>1080</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25226</td>\n",
       "      <td>32</td>\n",
       "      <td>30</td>\n",
       "      <td>557</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1100</th>\n",
       "      <td>750</td>\n",
       "      <td>750</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7045</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2121</th>\n",
       "      <td>800</td>\n",
       "      <td>640</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>905</td>\n",
       "      <td>1080</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2284</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 130 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      height  width  has_audio  video_view_count  is_video  is_verified  \\\n",
       "1674     750    750          1                50         1            0   \n",
       "1978    1080   1080          0                 0         0            0   \n",
       "1746     645   1080          0                 0         0            0   \n",
       "1191     658    750          0                 0         0            0   \n",
       "990     1080   1080          0                 0         0            0   \n",
       "1013     600    480          0                 0         0            0   \n",
       "462     1350   1080          0                 0         0            0   \n",
       "495      640    640          0                 0         0            0   \n",
       "2013     750    750          0                37         1            0   \n",
       "608     1348   1080          0                 0         0            0   \n",
       "878     1349   1080          0                 0         0            0   \n",
       "2005    1053   1080          0                 0         0            0   \n",
       "653     1080   1080          0                 0         0            0   \n",
       "1252    1080   1080          0                 0         0            0   \n",
       "548      640    640          0                 0         0            0   \n",
       "440     1346   1080          0                 0         0            0   \n",
       "158     1080   1080          0                 0         0            0   \n",
       "1100     750    750          0               108         1            0   \n",
       "2121     800    640          0                 0         0            0   \n",
       "1281     905   1080          0                 0         0            0   \n",
       "\n",
       "      followers_count  comments_count  hashtag_count  like_count  ...  \\\n",
       "1674               30               0              6           4  ...   \n",
       "1978             5563              11              0         153  ...   \n",
       "1746              188               0             19           1  ...   \n",
       "1191              483              15             14          62  ...   \n",
       "990              1950             126             19         759  ...   \n",
       "1013           124858               9             29         819  ...   \n",
       "462             15899              65             22         528  ...   \n",
       "495               699              12              0         187  ...   \n",
       "2013              578               0             17           9  ...   \n",
       "608            626462              45             22       10428  ...   \n",
       "878            117806             242             30       11651  ...   \n",
       "2005           338110             107              0        4287  ...   \n",
       "653              1442              16             16         617  ...   \n",
       "1252              714               0             27           3  ...   \n",
       "548            614130              13             29        1771  ...   \n",
       "440            120920               9             29         275  ...   \n",
       "158             25226              32             30         557  ...   \n",
       "1100             7045               0             28          16  ...   \n",
       "2121              137               2             15          12  ...   \n",
       "1281             2284               0              5           4  ...   \n",
       "\n",
       "      tag_name_travel  tag_name_trump  tag_name_university  tag_name_water  \\\n",
       "1674                0               0                    0               0   \n",
       "1978                0               0                    0               0   \n",
       "1746                0               0                    0               0   \n",
       "1191                0               0                    0               0   \n",
       "990                 0               0                    0               0   \n",
       "1013                0               0                    0               0   \n",
       "462                 0               0                    0               0   \n",
       "495                 0               0                    0               0   \n",
       "2013                0               0                    1               0   \n",
       "608                 0               0                    0               0   \n",
       "878                 0               0                    0               0   \n",
       "2005                0               1                    0               0   \n",
       "653                 0               0                    0               0   \n",
       "1252                0               0                    0               0   \n",
       "548                 0               0                    0               0   \n",
       "440                 0               0                    0               0   \n",
       "158                 0               0                    0               0   \n",
       "1100                0               0                    0               0   \n",
       "2121                0               0                    0               0   \n",
       "1281                0               0                    0               0   \n",
       "\n",
       "      tag_name_wolf  tag_name_women  tag_name_word  tag_name_workathome  \\\n",
       "1674              0               0              0                    0   \n",
       "1978              0               0              0                    0   \n",
       "1746              0               0              0                    0   \n",
       "1191              0               0              0                    0   \n",
       "990               0               0              0                    0   \n",
       "1013              0               0              0                    0   \n",
       "462               0               0              0                    0   \n",
       "495               0               0              0                    0   \n",
       "2013              0               0              0                    0   \n",
       "608               0               0              0                    0   \n",
       "878               0               0              0                    0   \n",
       "2005              0               0              0                    0   \n",
       "653               0               0              0                    0   \n",
       "1252              0               0              0                    0   \n",
       "548               0               0              0                    0   \n",
       "440               0               0              0                    0   \n",
       "158               0               0              0                    0   \n",
       "1100              0               0              0                    0   \n",
       "2121              0               0              0                    1   \n",
       "1281              0               0              0                    0   \n",
       "\n",
       "      tag_name_workonline  tag_name_world  \n",
       "1674                    0               0  \n",
       "1978                    0               0  \n",
       "1746                    0               0  \n",
       "1191                    0               0  \n",
       "990                     0               0  \n",
       "1013                    0               0  \n",
       "462                     0               0  \n",
       "495                     0               0  \n",
       "2013                    0               0  \n",
       "608                     0               0  \n",
       "878                     0               0  \n",
       "2005                    0               0  \n",
       "653                     0               0  \n",
       "1252                    0               0  \n",
       "548                     0               0  \n",
       "440                     0               0  \n",
       "158                     0               0  \n",
       "1100                    0               0  \n",
       "2121                    0               0  \n",
       "1281                    0               0  \n",
       "\n",
       "[20 rows x 130 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "height\n",
      "width\n",
      "has_audio\n",
      "video_view_count\n",
      "is_video\n",
      "is_verified\n",
      "followers_count\n",
      "comments_count\n",
      "hashtag_count\n",
      "like_count\n",
      "caption_length\n",
      "is_top\n",
      "tag_name_Afghanistan\n",
      "tag_name_Albania\n",
      "tag_name_Algeria\n",
      "tag_name_Angola\n",
      "tag_name_Argentina\n",
      "tag_name_Australia\n",
      "tag_name_Austria\n",
      "tag_name_Belgium\n",
      "tag_name_Brazil\n",
      "tag_name_Cameroon\n",
      "tag_name_Chile\n",
      "tag_name_Computer\n",
      "tag_name_Ecuador\n",
      "tag_name_Egypt\n",
      "tag_name_Greece\n",
      "tag_name_Iraq\n",
      "tag_name_Israel\n",
      "tag_name_Mexico\n",
      "tag_name_Morocco\n",
      "tag_name_NBA\n",
      "tag_name_Netherlands\n",
      "tag_name_New_Zealand\n",
      "tag_name_Oman\n",
      "tag_name_Pakistan\n",
      "tag_name_Philippines\n",
      "tag_name_Poland\n",
      "tag_name_Portugal\n",
      "tag_name_Qatar\n",
      "tag_name_Romania\n",
      "tag_name_Senegal\n",
      "tag_name_Serbia\n",
      "tag_name_Spain\n",
      "tag_name_Thailand\n",
      "tag_name_Turkey\n",
      "tag_name_USA\n",
      "tag_name_Ukraine\n",
      "tag_name_Uzbekistan\n",
      "tag_name_amazon\n",
      "tag_name_animal\n",
      "tag_name_arab\n",
      "tag_name_artwork\n",
      "tag_name_asia\n",
      "tag_name_bitcoin\n",
      "tag_name_blockchain\n",
      "tag_name_buy\n",
      "tag_name_canada\n",
      "tag_name_cat\n",
      "tag_name_ceo\n",
      "tag_name_chef\n",
      "tag_name_chigago\n",
      "tag_name_china\n",
      "tag_name_club\n",
      "tag_name_collection\n",
      "tag_name_dairyfree\n",
      "tag_name_dog\n",
      "tag_name_europe\n",
      "tag_name_films\n",
      "tag_name_fish\n",
      "tag_name_florida\n",
      "tag_name_food\n",
      "tag_name_football\n",
      "tag_name_forex\n",
      "tag_name_forexlife\n",
      "tag_name_game\n",
      "tag_name_germany\n",
      "tag_name_girl\n",
      "tag_name_girls\n",
      "tag_name_god\n",
      "tag_name_goldenretriever\n",
      "tag_name_gym\n",
      "tag_name_hard\n",
      "tag_name_home\n",
      "tag_name_house\n",
      "tag_name_iran\n",
      "tag_name_japan\n",
      "tag_name_jumper\n",
      "tag_name_lazday\n",
      "tag_name_london\n",
      "tag_name_love\n",
      "tag_name_mensstyle\n",
      "tag_name_messi\n",
      "tag_name_minesota\n",
      "tag_name_movie\n",
      "tag_name_newyear\n",
      "tag_name_newyork\n",
      "tag_name_pasta\n",
      "tag_name_perfumes\n",
      "tag_name_photography\n",
      "tag_name_play\n",
      "tag_name_programing\n",
      "tag_name_puppy\n",
      "tag_name_qoute\n",
      "tag_name_recipes\n",
      "tag_name_ronaldo\n",
      "tag_name_sale\n",
      "tag_name_sea\n",
      "tag_name_see\n",
      "tag_name_sell\n",
      "tag_name_seo\n",
      "tag_name_sky\n",
      "tag_name_snake\n",
      "tag_name_snow\n",
      "tag_name_soft\n",
      "tag_name_sport\n",
      "tag_name_stock\n",
      "tag_name_summer\n",
      "tag_name_swim\n",
      "tag_name_tattooart\n",
      "tag_name_travel\n",
      "tag_name_trump\n",
      "tag_name_university\n",
      "tag_name_water\n",
      "tag_name_wolf\n",
      "tag_name_women\n",
      "tag_name_word\n",
      "tag_name_workathome\n",
      "tag_name_workonline\n",
      "tag_name_world\n"
     ]
    }
   ],
   "source": [
    "for i in df.columns:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split data and train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split & train the model\n",
    "X=df.drop('is_top',axis=1)\n",
    "y=df['is_top']\n",
    "\n",
    "# Split the Data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scale the Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit only to the training data\n",
    "scaler.fit(X_train)\n",
    "\n",
    "StandardScaler(copy=True, with_mean=True, with_std=True)\n",
    "\n",
    "# Now apply the transformations to the data:\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Baseline model using Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 scores: [0.80177099 0.82455743 0.82104883 0.8336556  0.84865764]\n",
      "Mean & standard deviation: 0.83 0.02\n"
     ]
    }
   ],
   "source": [
    "# K-fold cross validation using F1-score as scorer\n",
    "scores = cross_val_score(logreg, \n",
    "                         X_train_scaled, \n",
    "                         y_train, \n",
    "                         cv=5, \n",
    "                         scoring='f1_macro')\n",
    "print('F1 scores:', scores)\n",
    "print('Mean & standard deviation: {:.2} {:.2f}'.format(scores.mean(), np.std(scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Selection - Train and evaluate multiple models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lists to keep track of model metrics\n",
    "models = []\n",
    "precision_list = []\n",
    "recall_list = []\n",
    "f1_score_list = []\n",
    "training_acc_list = []\n",
    "testing_acc_list = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using ML Model: Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic Regression\n",
    "logreg = LogisticRegression(n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 5.22 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(n_jobs=-1), n_jobs=-1,\n",
       "             param_grid={'C': array([3.16227766e-05, 3.43332002e-04, 3.72759372e-03, 4.04708995e-02,\n",
       "       4.39397056e-01, 4.77058270e+00, 5.17947468e+01, 5.62341325e+02,\n",
       "       6.10540230e+03, 6.62870316e+04, 7.19685673e+05, 7.81370738e+06,\n",
       "       8.48342898e+07, 9.21055318e+08, 1.00000000e+10]),\n",
       "                         'penalty': ['l1', 'l2', 'elasticnet']},\n",
       "             scoring='f1_macro')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Hyperparameter tuning using K-fold cross validation\n",
    "# ... via Grid Search method\n",
    "param_grid = {'penalty': ['l1', 'l2', 'elasticnet'],\n",
    "              'C': np.logspace(-4.5, 10, 15)}\n",
    "\n",
    "gs_logreg = GridSearchCV(logreg,\n",
    "                      param_grid, \n",
    "                      cv=5,\n",
    "                      scoring='f1_macro',\n",
    "                      n_jobs=-1)\n",
    "gs_logreg.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOGISTIC REGRESSION - FINE TUNED HYPERPARAMETERS\n",
      "\n",
      "Best Estimator: LogisticRegression(C=84834289.8244076, n_jobs=-1)\n",
      "Best Parameters: {'C': 84834289.8244076, 'penalty': 'l2'}\n",
      "Best Score: 0.8630919622213158\n"
     ]
    }
   ],
   "source": [
    "# Best model hyperparameters and score\n",
    "print('LOGISTIC REGRESSION - FINE TUNED HYPERPARAMETERS')\n",
    "print()\n",
    "print('Best Estimator:', gs_logreg.best_estimator_)\n",
    "print('Best Parameters:', gs_logreg.best_params_)\n",
    "print('Best Score:', gs_logreg.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=84834289.8244076, n_jobs=-1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using best estimator found by GridSearchCV\n",
    "logreg = gs_logreg.best_estimator_\n",
    "logreg.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = logreg.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.93      0.87       263\n",
      "           1       0.92      0.81      0.87       280\n",
      "\n",
      "    accuracy                           0.87       543\n",
      "   macro avg       0.87      0.87      0.87       543\n",
      "weighted avg       0.88      0.87      0.87       543\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "models.append('Logistic Regression')\n",
    "precision_list.append(precision_score(y_test, y_pred, average='macro'))\n",
    "recall_list.append(recall_score(y_test, y_pred, average='macro'))\n",
    "f1_score_list.append(f1_score(y_test, y_pred, average='macro'))\n",
    "training_acc_list.append(accuracy_score(y_train, logreg.predict(X_train_scaled)))\n",
    "testing_acc_list.append(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using ML Model: Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 50.8 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(n_jobs=-1), n_jobs=-1,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [None, 5, 10, 25],\n",
       "                         'min_samples_split': [5, 10, 15],\n",
       "                         'n_estimators': [100, 200, 300]},\n",
       "             scoring='f1_macro')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Hyperparameter tuning using K-fold cross validation\n",
    "# ... via Grid Search method\n",
    "param_grid = {'n_estimators': [100, 200, 300],\n",
    "              'criterion': ['gini', 'entropy'],\n",
    "              'max_depth': [None, 5, 10, 25],\n",
    "              'min_samples_split': [5, 10, 15]}\n",
    "\n",
    "gs_rf = GridSearchCV(rf,\n",
    "                    param_grid, \n",
    "                    cv=5,\n",
    "                    scoring='f1_macro',\n",
    "                    n_jobs=-1)\n",
    "gs_rf.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RANDOM FOREST - FINE TUNED HYPERPARAMETERS\n",
      "\n",
      "Best Estimator: RandomForestClassifier(criterion='entropy', max_depth=25, min_samples_split=15,\n",
      "                       n_jobs=-1)\n",
      "Best Parameters: {'criterion': 'entropy', 'max_depth': 25, 'min_samples_split': 15, 'n_estimators': 100}\n",
      "Best Score: 0.9507664705688077\n"
     ]
    }
   ],
   "source": [
    "# Best model hyperparameters and score\n",
    "print('RANDOM FOREST - FINE TUNED HYPERPARAMETERS')\n",
    "print()\n",
    "print('Best Estimator:', gs_rf.best_estimator_)\n",
    "print('Best Parameters:', gs_rf.best_params_)\n",
    "print('Best Score:', gs_rf.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', max_depth=25, min_samples_split=15,\n",
       "                       n_jobs=-1)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using best estimator found by GridSearchCV\n",
    "rf = gs_rf.best_estimator_\n",
    "rf.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rf.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.90      0.94       263\n",
      "           1       0.92      0.97      0.94       280\n",
      "\n",
      "    accuracy                           0.94       543\n",
      "   macro avg       0.94      0.94      0.94       543\n",
      "weighted avg       0.94      0.94      0.94       543\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "models.append('Random Forest')\n",
    "precision_list.append(precision_score(y_test, y_pred, average='macro'))\n",
    "recall_list.append(recall_score(y_test, y_pred, average='macro'))\n",
    "f1_score_list.append(f1_score(y_test, y_pred, average='macro'))\n",
    "training_acc_list.append(accuracy_score(y_train, rf.predict(X_train_scaled)))\n",
    "testing_acc_list.append(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_metrics = pd.DataFrame(zip(models, training_acc_list, testing_acc_list,\n",
    "                                 precision_list, recall_list, f1_score_list), \n",
    "                             columns=['Model', 'Training Acc', 'Testing Acc', 'Precision', 'Recall', 'F1 Score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Model</th>\n",
       "      <th>Training Acc</th>\n",
       "      <th>Testing Acc</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.966195</td>\n",
       "      <td>0.939227</td>\n",
       "      <td>0.941652</td>\n",
       "      <td>0.938186</td>\n",
       "      <td>0.938987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.900430</td>\n",
       "      <td>0.869245</td>\n",
       "      <td>0.873701</td>\n",
       "      <td>0.871021</td>\n",
       "      <td>0.869131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Training Acc  Testing Acc  Precision    Recall  \\\n",
       "1        Random Forest      0.966195     0.939227   0.941652  0.938186   \n",
       "0  Logistic Regression      0.900430     0.869245   0.873701  0.871021   \n",
       "\n",
       "   F1 Score  \n",
       "1  0.938987  \n",
       "0  0.869131  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_metrics.sort_values(by='F1 Score', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict & Evaluate the Best Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prediction by selected model\n",
    "y_pred = rf.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.90      0.94       263\n",
      "           1       0.92      0.97      0.94       280\n",
      "\n",
      "    accuracy                           0.94       543\n",
      "   macro avg       0.94      0.94      0.94       543\n",
      "weighted avg       0.94      0.94      0.94       543\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification Report\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(33.0, 0.5, 'Predicted')"
      ]
     },
     "execution_count": 42,
     "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": [
    "## Confusion Matrix\n",
    "\n",
    "conf_mat = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(conf_mat.T, annot=True, fmt='d', cbar=False, cmap=sns.cubehelix_palette(start=.5, rot=-.5, as_cmap=True),\n",
    "          xticklabels=['No','Yes'],\n",
    "          yticklabels=['No','Yes'] )\n",
    "plt.xlabel('Actuals')\n",
    "plt.ylabel('Predicted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get and plot features importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the feature importances dataframe\n",
    "feature_importance = np.array(rf.feature_importances_)\n",
    "feature_names = np.array(X.columns)\n",
    "\n",
    "feat_imp = pd.DataFrame({'feature_names':feature_names,'feature_importance':feature_importance})\n",
    "feat_imp_sorted = feat_imp.sort_values('feature_importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='feature_importance', ylabel='feature_names'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x5760 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,80))\n",
    "sns.barplot(x=feat_imp_sorted['feature_importance'], y=feat_imp_sorted['feature_names'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mission Completed!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}