Python Scripts/Kickstarter Analysis & Feature Engineering & Neural Networks - Part 1.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
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     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\valla\\\\Python Run Environment\\\\Data Mining Project Kickstarter'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Code Header #######\n",
    "## Notebook Name: Kickstarter Analysis\n",
    "## Objective: To learn which design decisions help kickstarter campaigns succeed and to predict the successes of any kickstarter campaigns.\n",
    "## Start Date: 11th April 2020\n",
    "## Data Sources\n",
    "    # Source 1: Webscraped kickstarter data - Scraped by Web Robots - https://webrobots.io/kickstarter-datasets/\n",
    "## Author(s):\n",
    "    # Vallabh Reddy\n",
    "    # Soumya Halder\n",
    "    # Utkarsh Singh\n",
    "## Edits to be made\n",
    "## Questions about the context\n",
    "    # Does every backer pledge a fixed amount? Or can backers decide the pledge amount?\n",
    "## Structure of the analysis\n",
    "    # Data Input: Read all the columns in the right format\n",
    "    # Run analysis for a single file first, but replicate for all files together.( Is there a way to batch execute)\n",
    "    # Feature Engineering\n",
    "        # Blurb : Brief text explaining the project\n",
    "        # Calculate campaign length\n",
    "        # Average pledge by backer\n",
    "        # Extract the categories from the dictionary.\n",
    "    # EDA\n",
    "        # Glean insights which might help design decisions\n",
    "    # SciKit Learn\n",
    "        # Logistic Regression; Glean insights which might help design decisions\n",
    "        # Trees\n",
    "        # Neural Network\n",
    "    \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import os\n",
    "import json\n",
    "import ast\n",
    "from datetime import datetime\n",
    "from sklearn.model_selection import train_test_split\n",
    "import textstat\n",
    "import math\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn import metrics as sk_metrics\n",
    "import glob\n",
    "from keras.layers import Dense\n",
    "from keras.models import Sequential\n",
    "from keras.utils import to_categorical\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.optimizers import Adam\n",
    "from keras.models import load_model\n",
    "from keras import metrics\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.ipynb_checkpoints',\n",
       " '16_Hidden_Non_Text.h5',\n",
       " '17_Hidden_Non_Text.h5',\n",
       " '17_Hidden_Text.h5',\n",
       " 'Data',\n",
       " 'Data_Mining_II_Project_Proposal_v2.docx',\n",
       " 'DT & RF results.docx',\n",
       " 'DT & RF results_v2.docx',\n",
       " 'Kickstarter Analysis.ipynb',\n",
       " 'Kickstarter Analysis_v2.ipynb',\n",
       " 'Kickstarter Text Features - Utkarsh.ipynb',\n",
       " 'Model performances report v1 _ latest.docx',\n",
       " 'Model performances report v1.docx',\n",
       " 'Neural Network Models.docx',\n",
       " 'Project Kickstarter - Shortcut.lnk',\n",
       " 'Text mining & Modeling (Logistic) with and without text features  - Utkarsh.ipynb']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = r'C:\\Users\\valla\\Python Run Environment\\Data Mining Project Kickstarter\\Data' # use your path\n",
    "all_files = glob.glob(path + \"/*.csv\")\n",
    "li = []\n",
    "for filename in all_files:\n",
    "    df = pd.read_csv(filename, index_col=None, header=0)\n",
    "    li.append(df)\n",
    "input = pd.concat(li, axis=0, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "211271"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "len(input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>backers_count</th>\n",
       "      <th>converted_pledged_amount</th>\n",
       "      <th>created_at</th>\n",
       "      <th>deadline</th>\n",
       "      <th>fx_rate</th>\n",
       "      <th>goal</th>\n",
       "      <th>id</th>\n",
       "      <th>launched_at</th>\n",
       "      <th>pledged</th>\n",
       "      <th>state_changed_at</th>\n",
       "      <th>static_usd_rate</th>\n",
       "      <th>usd_pledged</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>211271.000000</td>\n",
       "      <td>2.112700e+05</td>\n",
       "      <td>2.112710e+05</td>\n",
       "      <td>2.112710e+05</td>\n",
       "      <td>211271.000000</td>\n",
       "      <td>2.112710e+05</td>\n",
       "      <td>2.112710e+05</td>\n",
       "      <td>2.112710e+05</td>\n",
       "      <td>2.112710e+05</td>\n",
       "      <td>2.112710e+05</td>\n",
       "      <td>211271.000000</td>\n",
       "      <td>2.112700e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>150.037208</td>\n",
       "      <td>1.367009e+04</td>\n",
       "      <td>1.470048e+09</td>\n",
       "      <td>1.477090e+09</td>\n",
       "      <td>0.988962</td>\n",
       "      <td>5.135802e+04</td>\n",
       "      <td>1.074248e+09</td>\n",
       "      <td>1.474242e+09</td>\n",
       "      <td>2.234156e+04</td>\n",
       "      <td>1.476923e+09</td>\n",
       "      <td>1.004172</td>\n",
       "      <td>1.366035e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>908.844718</td>\n",
       "      <td>1.043794e+05</td>\n",
       "      <td>7.053706e+07</td>\n",
       "      <td>7.027463e+07</td>\n",
       "      <td>0.217407</td>\n",
       "      <td>1.260406e+06</td>\n",
       "      <td>6.190552e+08</td>\n",
       "      <td>7.035694e+07</td>\n",
       "      <td>6.063421e+05</td>\n",
       "      <td>7.016636e+07</td>\n",
       "      <td>0.237434</td>\n",
       "      <td>1.042839e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.240366e+09</td>\n",
       "      <td>1.243770e+09</td>\n",
       "      <td>0.006985</td>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>2.941000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.243770e+09</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.200000e+02</td>\n",
       "      <td>1.420513e+09</td>\n",
       "      <td>1.427039e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000e+03</td>\n",
       "      <td>5.383756e+08</td>\n",
       "      <td>1.424207e+09</td>\n",
       "      <td>1.240000e+02</td>\n",
       "      <td>1.426881e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.200000e+02</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.615000e+03</td>\n",
       "      <td>1.470848e+09</td>\n",
       "      <td>1.478232e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000e+03</td>\n",
       "      <td>1.074606e+09</td>\n",
       "      <td>1.475512e+09</td>\n",
       "      <td>1.652000e+03</td>\n",
       "      <td>1.478164e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.615000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>92.000000</td>\n",
       "      <td>6.782000e+03</td>\n",
       "      <td>1.533627e+09</td>\n",
       "      <td>1.541172e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000e+04</td>\n",
       "      <td>1.610673e+09</td>\n",
       "      <td>1.538411e+09</td>\n",
       "      <td>7.245000e+03</td>\n",
       "      <td>1.541110e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.795951e+03</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>105857.000000</td>\n",
       "      <td>1.214344e+07</td>\n",
       "      <td>1.579142e+09</td>\n",
       "      <td>1.584328e+09</td>\n",
       "      <td>1.888592</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>2.147467e+09</td>\n",
       "      <td>1.579151e+09</td>\n",
       "      <td>1.469102e+08</td>\n",
       "      <td>1.579153e+09</td>\n",
       "      <td>1.716408</td>\n",
       "      <td>1.214344e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "       backers_count  converted_pledged_amount    created_at      deadline  \\\n",
       "count  211271.000000              2.112700e+05  2.112710e+05  2.112710e+05   \n",
       "mean      150.037208              1.367009e+04  1.470048e+09  1.477090e+09   \n",
       "std       908.844718              1.043794e+05  7.053706e+07  7.027463e+07   \n",
       "min         0.000000              0.000000e+00  1.240366e+09  1.243770e+09   \n",
       "25%         4.000000              1.200000e+02  1.420513e+09  1.427039e+09   \n",
       "50%        28.000000              1.615000e+03  1.470848e+09  1.478232e+09   \n",
       "75%        92.000000              6.782000e+03  1.533627e+09  1.541172e+09   \n",
       "max    105857.000000              1.214344e+07  1.579142e+09  1.584328e+09   \n",
       "\n",
       "             fx_rate          goal            id   launched_at       pledged  \\\n",
       "count  211271.000000  2.112710e+05  2.112710e+05  2.112710e+05  2.112710e+05   \n",
       "mean        0.988962  5.135802e+04  1.074248e+09  1.474242e+09  2.234156e+04   \n",
       "std         0.217407  1.260406e+06  6.190552e+08  7.035694e+07  6.063421e+05   \n",
       "min         0.006985  1.000000e-02  2.941000e+03  0.000000e+00  0.000000e+00   \n",
       "25%         1.000000  1.500000e+03  5.383756e+08  1.424207e+09  1.240000e+02   \n",
       "50%         1.000000  5.000000e+03  1.074606e+09  1.475512e+09  1.652000e+03   \n",
       "75%         1.000000  1.500000e+04  1.610673e+09  1.538411e+09  7.245000e+03   \n",
       "max         1.888592  1.000000e+08  2.147467e+09  1.579151e+09  1.469102e+08   \n",
       "\n",
       "       state_changed_at  static_usd_rate   usd_pledged  \n",
       "count      2.112710e+05    211271.000000  2.112700e+05  \n",
       "mean       1.476923e+09         1.004172  1.366035e+04  \n",
       "std        7.016636e+07         0.237434  1.042839e+05  \n",
       "min        1.243770e+09         0.000000  0.000000e+00  \n",
       "25%        1.426881e+09         1.000000  1.200000e+02  \n",
       "50%        1.478164e+09         1.000000  1.615000e+03  \n",
       "75%        1.541110e+09         1.000000  6.795951e+03  \n",
       "max        1.579153e+09         1.716408  1.214344e+07  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['backers_count',\n",
       " 'blurb',\n",
       " 'category',\n",
       " 'converted_pledged_amount',\n",
       " 'country',\n",
       " 'country_displayable_name',\n",
       " 'created_at',\n",
       " 'creator',\n",
       " 'currency',\n",
       " 'currency_symbol',\n",
       " 'currency_trailing_code',\n",
       " 'current_currency',\n",
       " 'deadline',\n",
       " 'disable_communication',\n",
       " 'friends',\n",
       " 'fx_rate',\n",
       " 'goal',\n",
       " 'id',\n",
       " 'is_backing',\n",
       " 'is_starrable',\n",
       " 'is_starred',\n",
       " 'launched_at',\n",
       " 'location',\n",
       " 'name',\n",
       " 'permissions',\n",
       " 'photo',\n",
       " 'pledged',\n",
       " 'profile',\n",
       " 'slug',\n",
       " 'source_url',\n",
       " 'spotlight',\n",
       " 'staff_pick',\n",
       " 'state',\n",
       " 'state_changed_at',\n",
       " 'static_usd_rate',\n",
       " 'urls',\n",
       " 'usd_pledged',\n",
       " 'usd_type']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input.shape\n",
    "list(input.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting all date columns to a proper date time format\n",
    "input.created_at = pd.to_datetime(input.created_at, unit = 's')\n",
    "input.deadline = pd.to_datetime(input.deadline, unit = 's')\n",
    "input.launched_at = pd.to_datetime(input.launched_at, unit = 's')\n",
    "input.state_changed_at = pd.to_datetime(input.state_changed_at, unit = 's')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>created_at</th>\n",
       "      <th>deadline</th>\n",
       "      <th>launched_at</th>\n",
       "      <th>state_changed_at</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-08-19 20:36:22</td>\n",
       "      <td>2019-02-03 23:26:21</td>\n",
       "      <td>2018-12-05 23:26:21</td>\n",
       "      <td>2019-02-03 23:26:22</td>\n",
       "      <td>failed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-11-30 04:30:00</td>\n",
       "      <td>2017-01-25 13:45:21</td>\n",
       "      <td>2016-12-26 13:45:21</td>\n",
       "      <td>2017-01-25 13:45:21</td>\n",
       "      <td>failed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-11-01 20:53:37</td>\n",
       "      <td>2016-11-16 13:20:24</td>\n",
       "      <td>2016-11-02 12:20:24</td>\n",
       "      <td>2016-11-16 13:20:24</td>\n",
       "      <td>failed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-23 02:37:14</td>\n",
       "      <td>2018-05-10 18:33:16</td>\n",
       "      <td>2018-04-10 18:33:16</td>\n",
       "      <td>2018-05-10 18:33:17</td>\n",
       "      <td>failed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-04-08 03:43:56</td>\n",
       "      <td>2019-05-17 01:23:35</td>\n",
       "      <td>2019-04-17 01:23:35</td>\n",
       "      <td>2019-05-17 01:23:35</td>\n",
       "      <td>failed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           created_at            deadline         launched_at  \\\n",
       "0 2018-08-19 20:36:22 2019-02-03 23:26:21 2018-12-05 23:26:21   \n",
       "1 2016-11-30 04:30:00 2017-01-25 13:45:21 2016-12-26 13:45:21   \n",
       "2 2016-11-01 20:53:37 2016-11-16 13:20:24 2016-11-02 12:20:24   \n",
       "3 2018-01-23 02:37:14 2018-05-10 18:33:16 2018-04-10 18:33:16   \n",
       "4 2019-04-08 03:43:56 2019-05-17 01:23:35 2019-04-17 01:23:35   \n",
       "\n",
       "     state_changed_at   state  \n",
       "0 2019-02-03 23:26:22  failed  \n",
       "1 2017-01-25 13:45:21  failed  \n",
       "2 2016-11-16 13:20:24  failed  \n",
       "3 2018-05-10 18:33:17  failed  \n",
       "4 2019-05-17 01:23:35  failed  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input[['created_at','deadline', 'launched_at', 'state_changed_at', 'state']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting each value in the 'category' column to a dictionary\n",
    "input['cat_dict'] = input.category.apply(ast.literal_eval)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating new columns to store the category information\n",
    "len(input)\n",
    "input['category'] = ''\n",
    "input['sub_category'] = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining a function to read the dictionary and extract the required values\n",
    "def categorize(dictionary):\n",
    "    if 'parent_name' in dictionary:\n",
    "        return (dictionary['parent_name'], dictionary['name'])\n",
    "    else:\n",
    "        return (dictionary['name'], None)\n",
    "\n",
    "\n",
    "# Applying this function to the category dictionary field in the input dataset\n",
    "category_tuples = input.cat_dict.apply(categorize)\n",
    "\n",
    "# The result was a series of tuples, we must now split up these tuples and assign them appropriately to the required columns\n",
    "input.category = pd.DataFrame(category_tuples.tolist())[0]\n",
    "input.sub_category = pd.DataFrame(category_tuples.tolist())[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing duplicates at profile level with keeping the last source_url value\n",
    "input = input.drop_duplicates(subset = 'profile', keep = 'last')\n",
    "input.reset_index();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "186004"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#EDA \n",
    "    # Check Distribution of data \n",
    "        # Categories/ sub categories\n",
    "        # Countries\n",
    "        # dates launched at?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "input['Success'] = input.state.apply(lambda x: 1 if x =='successful' else 0)\n",
    "input['Failure'] = input.state.apply(lambda x: 0 if x =='successful' else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2880 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(6, figsize = (20, 40))\n",
    "chart1 = sns.barplot(x = 'Success', y = 'category', data = input, ax = ax[0])\n",
    "chart1.set_title('Successes by Category')\n",
    "chart1.set_ylabel('Category')\n",
    "\n",
    "chart2 = sns.countplot(y = 'category',  data = input, ax = ax[1])\n",
    "chart2.set_title('Count by Category');\n",
    "chart2.set_ylabel('Category')\n",
    "\n",
    "chart3 = sns.barplot(x = 'Success', y = 'sub_category', data = input, ax = ax[2])\n",
    "chart3.set_title('Successes by Sub Category')\n",
    "chart3.set_ylabel('Sub Category')\n",
    "\n",
    "chart4 = sns.countplot(y = 'sub_category', data = input, ax = ax[3])\n",
    "chart4.set_title('Count by Sub Category');\n",
    "chart4.set_ylabel('Sub Category')\n",
    "\n",
    "\n",
    "chart5 = sns.barplot(x = 'Success', y = 'country', data = input, ax = ax[4])\n",
    "chart5.set_title('Successes by Country')\n",
    "chart5.set_ylabel('Country');\n",
    "\n",
    "chart6 = sns.countplot(y = 'country', data = input, ax = ax[5]);\n",
    "chart6.set_title('Count by Country')\n",
    "chart6.set_ylabel('Country');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>category</th>\n",
       "      <th>Success</th>\n",
       "      <th>Failure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Art</td>\n",
       "      <td>11049</td>\n",
       "      <td>9024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Comics</td>\n",
       "      <td>5584</td>\n",
       "      <td>1404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Crafts</td>\n",
       "      <td>2357</td>\n",
       "      <td>4058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dance</td>\n",
       "      <td>2393</td>\n",
       "      <td>775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Design</td>\n",
       "      <td>3899</td>\n",
       "      <td>3046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category  Success  Failure\n",
       "0      Art    11049     9024\n",
       "1   Comics     5584     1404\n",
       "2   Crafts     2357     4058\n",
       "3    Dance     2393      775\n",
       "4   Design     3899     3046"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Grouping and aggregating success and failure numbers \n",
    "cat_agg = input.groupby(['category'],as_index = False).agg({'Success':[sum], 'Failure':[sum]})\n",
    "cat_agg.columns = cat_agg.columns.get_level_values(0)\n",
    "cat_agg.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20073"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(input.loc[input.category == 'Art'])\n",
    "# The sum checks out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
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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>country</th>\n",
       "      <th>Success</th>\n",
       "      <th>Failure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AT</td>\n",
       "      <td>160</td>\n",
       "      <td>293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AU</td>\n",
       "      <td>2061</td>\n",
       "      <td>2370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BE</td>\n",
       "      <td>211</td>\n",
       "      <td>323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA</td>\n",
       "      <td>4385</td>\n",
       "      <td>4326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CH</td>\n",
       "      <td>243</td>\n",
       "      <td>383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country  Success  Failure\n",
       "0      AT      160      293\n",
       "1      AU     2061     2370\n",
       "2      BE      211      323\n",
       "3      CA     4385     4326\n",
       "4      CH      243      383"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_agg = input.groupby(['country'], as_index = False).agg({'Success':[sum], 'Failure':[sum]})\n",
    "country_agg.columns = country_agg.columns.get_level_values(0)\n",
    "country_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7839"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the count of None subcategories\n",
    "sum(input.sub_category.apply(lambda x: x is None))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "186004"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the count of LU\n",
    "sum(input.country == 'LU')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Luxembourg'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a country code lookup dictionary\n",
    "country_code = input.country.tolist()\n",
    "country_name = input.country_displayable_name.tolist()\n",
    "\n",
    "country_dict = dict(zip(country_code, country_name))\n",
    "\n",
    "country_dict['LU']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Date Differences\n",
    "    # Time between launch and deadline\n",
    "    # Time between creation and launch\n",
    "\n",
    "input['design_duration_days'] = (input.launched_at - input.created_at).apply(lambda x: x.days)\n",
    "input['campaign_duration_days'] = (input.deadline - input.launched_at).apply(lambda x: x.days)\n",
    "\n",
    "## Extract month values and day values of launch and deadline\n",
    "input['launch_day'] = input.launched_at.apply(lambda x: x.day)\n",
    "input['launch_month'] = input.launched_at.apply(lambda x: x.month)\n",
    "input['launch_DOW'] = input.launched_at.apply(lambda x: x.weekday())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting goals of all currencies to USD\n",
    "input['goal_usd'] = input.goal * input.static_usd_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Feature engineering with blurb and name?\n",
    "\n",
    "# Blurb readability test using Flesch Reading Score ( higher = more readable)\n",
    "# There are NaNs in the blurb column, so we'll convert them to str first and remove them\n",
    "input.blurb = input.blurb.apply(str)\n",
    "input = input.loc[input.blurb != 'nan']\n",
    "input['blurb_readability_score'] = input.blurb.apply(textstat.flesch_reading_ease)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>blurb_readability_score</th>\n",
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       "      <th>0</th>\n",
       "      <td>74.53</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42.72</td>\n",
       "      <td>A web series about the soul crushing exercise ...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.44</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.24</td>\n",
       "      <td>A romantic comedy mini series following a self...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34.26</td>\n",
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       "      <td>62.34</td>\n",
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       "      <th>8</th>\n",
       "      <td>50.16</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>72.50</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>41.70</td>\n",
       "      <td>Social, environmental and cultural project tha...</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>44.41</td>\n",
       "      <td>Os presentamos nuestro próximo trabajo, AMARNA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>41.70</td>\n",
       "      <td>A semi-autobiographical, epic fantasy sitcom a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>69.79</td>\n",
       "      <td>The Warehouse brings culture through art and m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>40.69</td>\n",
       "      <td>A cyberpunk espionage comic book series with a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>73.17</td>\n",
       "      <td>In this Lovecraftian Horror story, can you tel...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>60.65</td>\n",
       "      <td>A twice-monthy webseries about video, tabletop...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>76.22</td>\n",
       "      <td>Want to open my first restaurant with deliciou...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>70.29</td>\n",
       "      <td>A hardboiled detective in H.P. Lovecraft's Ark...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>26.81</td>\n",
       "      <td>The shocking concluding chapter of the initial...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>57.61</td>\n",
       "      <td>We'll create a \"tour guide\" for analog travell...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>65.05</td>\n",
       "      <td>After a creative rebirth, Iris Divine is ready...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>69.79</td>\n",
       "      <td>...because we believe little changes can make ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>60.82</td>\n",
       "      <td>Designer socks with a pop culture twist. Featu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>-23.28</td>\n",
       "      <td>Another action comic extravaganza from the Eis...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>35.27</td>\n",
       "      <td>Meaningful Gifts: Extraordinary stories and pi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>49.15</td>\n",
       "      <td>Challenge met! Almost. Help me turn my 100 poe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>63.02</td>\n",
       "      <td>A young woman learns she is one of few women l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>86.03</td>\n",
       "      <td>Mankind is thrust to the stars in this journey...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>56.59</td>\n",
       "      <td>Control the weather to help herds of animals r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>59.64</td>\n",
       "      <td>Bloody Sunday Sessions is a music video projec...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211241</th>\n",
       "      <td>62.34</td>\n",
       "      <td>A Unique Trapeze workshop 'Rig' for children &amp;...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211242</th>\n",
       "      <td>59.64</td>\n",
       "      <td>Free yourself from Sleep Debt with Repsio Slee...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211243</th>\n",
       "      <td>40.69</td>\n",
       "      <td>To promote a hands-on musical theater experien...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211244</th>\n",
       "      <td>46.44</td>\n",
       "      <td>A superhero inspired series of pride-themed en...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211245</th>\n",
       "      <td>88.02</td>\n",
       "      <td>Ammatusk's debut CD came out in 2005. We had p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211246</th>\n",
       "      <td>50.84</td>\n",
       "      <td>A 2020 destiel-themed art calendar made possib...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211247</th>\n",
       "      <td>67.08</td>\n",
       "      <td>Connecticut based musician Asher Condit is tra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211248</th>\n",
       "      <td>84.17</td>\n",
       "      <td>Michael is 8 years old and wants to start his ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211249</th>\n",
       "      <td>43.73</td>\n",
       "      <td>Vrailler aims to empower the disabled as well ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211250</th>\n",
       "      <td>36.28</td>\n",
       "      <td>This bright LED display produces high-resoluti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211251</th>\n",
       "      <td>80.96</td>\n",
       "      <td>Partner with us in fundraising for our new alb...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211252</th>\n",
       "      <td>50.16</td>\n",
       "      <td>I will be creating a music CD containing 12 tr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211253</th>\n",
       "      <td>94.15</td>\n",
       "      <td>We're heading to the studio to record our firs...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211254</th>\n",
       "      <td>60.31</td>\n",
       "      <td>Simplistic gemstone jewelry and charms made ju...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211255</th>\n",
       "      <td>26.81</td>\n",
       "      <td>Phone2PC is a magic combination of various App...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211256</th>\n",
       "      <td>45.42</td>\n",
       "      <td>More vintage pinup style enamel pins for celeb...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211257</th>\n",
       "      <td>32.90</td>\n",
       "      <td>Up-and-Coming Style is a magazine dedicated to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211258</th>\n",
       "      <td>54.22</td>\n",
       "      <td>Iconic D&amp;D classes re-imagined as cats - in me...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211259</th>\n",
       "      <td>35.95</td>\n",
       "      <td>A collection of cute and nostalgic gaming pins...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211260</th>\n",
       "      <td>51.18</td>\n",
       "      <td>Introducing the world's first intelligent vide...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211261</th>\n",
       "      <td>21.06</td>\n",
       "      <td>\"Thank You For Choosing This Reality\" - Releas...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211262</th>\n",
       "      <td>87.72</td>\n",
       "      <td>They've splattered the stage, now they wanna s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211263</th>\n",
       "      <td>36.28</td>\n",
       "      <td>Empowering spirited girls to embrace their ind...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211264</th>\n",
       "      <td>38.32</td>\n",
       "      <td>The world's fastest recycled toy and science p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211265</th>\n",
       "      <td>33.92</td>\n",
       "      <td>Support our mission to create Upstate New York...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211266</th>\n",
       "      <td>17.00</td>\n",
       "      <td>Providing opportunity for young people aged 11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211267</th>\n",
       "      <td>61.33</td>\n",
       "      <td>A collection of enamel pins inspired by horror...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211268</th>\n",
       "      <td>53.21</td>\n",
       "      <td>A full length Bitches in the Beehive album, re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211269</th>\n",
       "      <td>53.21</td>\n",
       "      <td>Primer disco de AryaTara/ AryaTara first recor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211270</th>\n",
       "      <td>71.82</td>\n",
       "      <td>The debut album from KC's new Celtic \"Supergroup\"</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>185996 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        blurb_readability_score  \\\n",
       "0                         74.53   \n",
       "1                         42.72   \n",
       "2                          2.44   \n",
       "3                         33.24   \n",
       "4                         34.26   \n",
       "5                         59.80   \n",
       "7                         62.34   \n",
       "8                         50.16   \n",
       "9                         72.50   \n",
       "10                        41.70   \n",
       "11                        44.41   \n",
       "13                        41.70   \n",
       "15                        69.79   \n",
       "16                        40.69   \n",
       "18                        73.17   \n",
       "20                        60.65   \n",
       "21                        76.22   \n",
       "22                        70.29   \n",
       "23                        26.81   \n",
       "24                        57.61   \n",
       "25                        65.05   \n",
       "26                        69.79   \n",
       "28                        60.82   \n",
       "30                       -23.28   \n",
       "31                        35.27   \n",
       "32                        49.15   \n",
       "33                        63.02   \n",
       "34                        86.03   \n",
       "35                        56.59   \n",
       "37                        59.64   \n",
       "...                         ...   \n",
       "211241                    62.34   \n",
       "211242                    59.64   \n",
       "211243                    40.69   \n",
       "211244                    46.44   \n",
       "211245                    88.02   \n",
       "211246                    50.84   \n",
       "211247                    67.08   \n",
       "211248                    84.17   \n",
       "211249                    43.73   \n",
       "211250                    36.28   \n",
       "211251                    80.96   \n",
       "211252                    50.16   \n",
       "211253                    94.15   \n",
       "211254                    60.31   \n",
       "211255                    26.81   \n",
       "211256                    45.42   \n",
       "211257                    32.90   \n",
       "211258                    54.22   \n",
       "211259                    35.95   \n",
       "211260                    51.18   \n",
       "211261                    21.06   \n",
       "211262                    87.72   \n",
       "211263                    36.28   \n",
       "211264                    38.32   \n",
       "211265                    33.92   \n",
       "211266                    17.00   \n",
       "211267                    61.33   \n",
       "211268                    53.21   \n",
       "211269                    53.21   \n",
       "211270                    71.82   \n",
       "\n",
       "                                                    blurb  \n",
       "0       Das Good Inc goal is to make cooking easy and ...  \n",
       "1       A web series about the soul crushing exercise ...  \n",
       "2       A photographic essay on the historic, incompar...  \n",
       "3       A romantic comedy mini series following a self...  \n",
       "4       The Drums of Atlant is an original science fic...  \n",
       "5       \"...a real photographer will make you look twi...  \n",
       "7       Es nuestra ilusión, tener nuestro propio resta...  \n",
       "8       A relaxing teaching tool, this coloring book f...  \n",
       "9       Floating above the planet Ithor on Tafanda Bay...  \n",
       "10      Social, environmental and cultural project tha...  \n",
       "11      Os presentamos nuestro próximo trabajo, AMARNA...  \n",
       "13      A semi-autobiographical, epic fantasy sitcom a...  \n",
       "15      The Warehouse brings culture through art and m...  \n",
       "16      A cyberpunk espionage comic book series with a...  \n",
       "18      In this Lovecraftian Horror story, can you tel...  \n",
       "20      A twice-monthy webseries about video, tabletop...  \n",
       "21      Want to open my first restaurant with deliciou...  \n",
       "22      A hardboiled detective in H.P. Lovecraft's Ark...  \n",
       "23      The shocking concluding chapter of the initial...  \n",
       "24      We'll create a \"tour guide\" for analog travell...  \n",
       "25      After a creative rebirth, Iris Divine is ready...  \n",
       "26      ...because we believe little changes can make ...  \n",
       "28      Designer socks with a pop culture twist. Featu...  \n",
       "30      Another action comic extravaganza from the Eis...  \n",
       "31      Meaningful Gifts: Extraordinary stories and pi...  \n",
       "32      Challenge met! Almost. Help me turn my 100 poe...  \n",
       "33      A young woman learns she is one of few women l...  \n",
       "34      Mankind is thrust to the stars in this journey...  \n",
       "35      Control the weather to help herds of animals r...  \n",
       "37      Bloody Sunday Sessions is a music video projec...  \n",
       "...                                                   ...  \n",
       "211241  A Unique Trapeze workshop 'Rig' for children &...  \n",
       "211242  Free yourself from Sleep Debt with Repsio Slee...  \n",
       "211243  To promote a hands-on musical theater experien...  \n",
       "211244  A superhero inspired series of pride-themed en...  \n",
       "211245  Ammatusk's debut CD came out in 2005. We had p...  \n",
       "211246  A 2020 destiel-themed art calendar made possib...  \n",
       "211247  Connecticut based musician Asher Condit is tra...  \n",
       "211248  Michael is 8 years old and wants to start his ...  \n",
       "211249  Vrailler aims to empower the disabled as well ...  \n",
       "211250  This bright LED display produces high-resoluti...  \n",
       "211251  Partner with us in fundraising for our new alb...  \n",
       "211252  I will be creating a music CD containing 12 tr...  \n",
       "211253  We're heading to the studio to record our firs...  \n",
       "211254  Simplistic gemstone jewelry and charms made ju...  \n",
       "211255  Phone2PC is a magic combination of various App...  \n",
       "211256  More vintage pinup style enamel pins for celeb...  \n",
       "211257  Up-and-Coming Style is a magazine dedicated to...  \n",
       "211258  Iconic D&D classes re-imagined as cats - in me...  \n",
       "211259  A collection of cute and nostalgic gaming pins...  \n",
       "211260  Introducing the world's first intelligent vide...  \n",
       "211261  \"Thank You For Choosing This Reality\" - Releas...  \n",
       "211262  They've splattered the stage, now they wanna s...  \n",
       "211263  Empowering spirited girls to embrace their ind...  \n",
       "211264  The world's fastest recycled toy and science p...  \n",
       "211265  Support our mission to create Upstate New York...  \n",
       "211266  Providing opportunity for young people aged 11...  \n",
       "211267  A collection of enamel pins inspired by horror...  \n",
       "211268  A full length Bitches in the Beehive album, re...  \n",
       "211269  Primer disco de AryaTara/ AryaTara first recor...  \n",
       "211270  The debut album from KC's new Celtic \"Supergroup\"  \n",
       "\n",
       "[185996 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input[['blurb_readability_score', 'blurb']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Campaign Duration in days')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check success rate by duration\n",
    "\n",
    "plt.subplots(figsize = (20,10))\n",
    "chart2 = sns.barplot(x = 'campaign_duration_days' , y = 'Success', data = input)\n",
    "chart2.set_title('Campaign Duration Days Success Rate');\n",
    "chart2.set_xlabel('Campaign Duration in days')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.subplots(figsize = (20,10))\n",
    "chart3 = sns.countplot(x = 'launch_month', data = input)\n",
    "chart3.set_title('Launch Month Frequency Distribution')\n",
    "\n",
    "plt.subplots(figsize = (20,10))\n",
    "chart4 = sns.barplot(x = 'launch_month', y = 'Success', data = input)\n",
    "chart4.set_title('Launch Month Success Rate')\n",
    "\n",
    "plt.subplots(figsize = (20,10))\n",
    "chart5 = sns.countplot(x = 'launch_day', data = input)\n",
    "chart5.set_title('Launch Day Frequency Distribution')\n",
    "\n",
    "plt.subplots(figsize = (20,10))\n",
    "chart6 = sns.barplot(x = 'launch_day', y = 'Success', data = input)\n",
    "chart6.set_title('Launch Day Success Rate')\n",
    "\n",
    "plt.subplots(figsize = (20,10))\n",
    "chart7 = sns.countplot(x = 'launch_DOW', data = input)\n",
    "chart7.set_title('Launch Day Frequency Distribution')\n",
    "chart7.set_xlabel('Day of Week');\n",
    "\n",
    "plt.subplots(figsize = (20,10))\n",
    "chart8 = sns.barplot(x = 'launch_DOW', y = 'Success', data = input)\n",
    "chart8.set_title('Launch Day of Week Success Rate')\n",
    "chart8.set_xlabel('Day of Week');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 0 is Monday, 6 is Sunday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['backers_count',\n",
       " 'blurb',\n",
       " 'category',\n",
       " 'converted_pledged_amount',\n",
       " 'country',\n",
       " 'country_displayable_name',\n",
       " 'created_at',\n",
       " 'creator',\n",
       " 'currency',\n",
       " 'currency_symbol',\n",
       " 'currency_trailing_code',\n",
       " 'current_currency',\n",
       " 'deadline',\n",
       " 'disable_communication',\n",
       " 'friends',\n",
       " 'fx_rate',\n",
       " 'goal',\n",
       " 'id',\n",
       " 'is_backing',\n",
       " 'is_starrable',\n",
       " 'is_starred',\n",
       " 'launched_at',\n",
       " 'location',\n",
       " 'name',\n",
       " 'permissions',\n",
       " 'photo',\n",
       " 'pledged',\n",
       " 'profile',\n",
       " 'slug',\n",
       " 'source_url',\n",
       " 'spotlight',\n",
       " 'staff_pick',\n",
       " 'state',\n",
       " 'state_changed_at',\n",
       " 'static_usd_rate',\n",
       " 'urls',\n",
       " 'usd_pledged',\n",
       " 'usd_type',\n",
       " 'cat_dict',\n",
       " 'sub_category',\n",
       " 'Success',\n",
       " 'Failure',\n",
       " 'design_duration_days',\n",
       " 'campaign_duration_days',\n",
       " 'launch_day',\n",
       " 'launch_month',\n",
       " 'launch_DOW',\n",
       " 'goal_usd',\n",
       " 'blurb_readability_score']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(input.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['category',\n",
       " 'country',\n",
       " 'design_duration_days',\n",
       " 'campaign_duration_days',\n",
       " 'launch_day',\n",
       " 'launch_month',\n",
       " 'launch_DOW',\n",
       " 'goal_usd',\n",
       " 'blurb_readability_score']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating datasets for modelling \n",
    "X = input[[ 'category', 'country', 'sub_category', 'design_duration_days', 'campaign_duration_days', 'launch_day', 'launch_month', 'launch_DOW', 'goal_usd', 'blurb_readability_score']]\n",
    "X = X.drop(columns =['sub_category'])\n",
    "y  = input[['Success']]\n",
    "list(X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.get_dummies(X, columns = ['category','country'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['design_duration_days',\n",
       " 'campaign_duration_days',\n",
       " 'launch_day',\n",
       " 'launch_month',\n",
       " 'launch_DOW',\n",
       " 'goal_usd',\n",
       " 'blurb_readability_score',\n",
       " 'category_Art',\n",
       " 'category_Comics',\n",
       " 'category_Crafts',\n",
       " 'category_Dance',\n",
       " 'category_Design',\n",
       " 'category_Fashion',\n",
       " 'category_Film & Video',\n",
       " 'category_Food',\n",
       " 'category_Games',\n",
       " 'category_Journalism',\n",
       " 'category_Music',\n",
       " 'category_Photography',\n",
       " 'category_Publishing',\n",
       " 'category_Technology',\n",
       " 'category_Theater',\n",
       " 'country_AT',\n",
       " 'country_AU',\n",
       " 'country_BE',\n",
       " 'country_CA',\n",
       " 'country_CH',\n",
       " 'country_DE',\n",
       " 'country_DK',\n",
       " 'country_ES',\n",
       " 'country_FR',\n",
       " 'country_GB',\n",
       " 'country_HK',\n",
       " 'country_IE',\n",
       " 'country_IT',\n",
       " 'country_JP',\n",
       " 'country_LU',\n",
       " 'country_MX',\n",
       " 'country_NL',\n",
       " 'country_NO',\n",
       " 'country_NZ',\n",
       " 'country_SE',\n",
       " 'country_SG',\n",
       " 'country_US']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There are ~45 columns for this data file at the time of writing this comment, might change with the dataset\n",
    "list(X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Storing Column names since we will convert the factors to a matrix \n",
    "X_cols = X.columns\n",
    "#X = np.array(X)\n",
    "#y = np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test Train split with 30% as test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 710, stratify = y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37200"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TRY THIS\n",
    "# import statsmodels.api as sm\n",
    "#logit_model=sm.Logit(y,X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 1- Simple Logistic : Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training a simple logistic regression model\n",
    "logistic_mod = LogisticRegression(penalty = 'l2', solver = 'lbfgs')\n",
    "logistic_mod.fit(X_train, y_train['Success'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 1- Simple Logistic : Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6114075647194817"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In sampling accuracy score\n",
    "logistic_mod.score(X_train, y_train['Success'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6105913978494624"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Out sampling accuracy score\n",
    "logistic_mod.score(X_test, y_test['Success'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>design_duration_days</th>\n",
       "      <th>campaign_duration_days</th>\n",
       "      <th>launch_day</th>\n",
       "      <th>launch_month</th>\n",
       "      <th>launch_DOW</th>\n",
       "      <th>goal_usd</th>\n",
       "      <th>blurb_readability_score</th>\n",
       "      <th>category_Art</th>\n",
       "      <th>category_Comics</th>\n",
       "      <th>category_Crafts</th>\n",
       "      <th>...</th>\n",
       "      <th>country_IT</th>\n",
       "      <th>country_JP</th>\n",
       "      <th>country_LU</th>\n",
       "      <th>country_MX</th>\n",
       "      <th>country_NL</th>\n",
       "      <th>country_NO</th>\n",
       "      <th>country_NZ</th>\n",
       "      <th>country_SE</th>\n",
       "      <th>country_SG</th>\n",
       "      <th>country_US</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000947</td>\n",
       "      <td>-0.012642</td>\n",
       "      <td>0.008004</td>\n",
       "      <td>0.039376</td>\n",
       "      <td>0.003112</td>\n",
       "      <td>-0.000015</td>\n",
       "      <td>0.004877</td>\n",
       "      <td>0.000191</td>\n",
       "      <td>0.004024</td>\n",
       "      <td>-0.002951</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001088</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>-0.000703</td>\n",
       "      <td>-0.000462</td>\n",
       "      <td>-0.000103</td>\n",
       "      <td>-0.000029</td>\n",
       "      <td>-0.000139</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.009683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   design_duration_days  campaign_duration_days  launch_day  launch_month  \\\n",
       "0              0.000947               -0.012642    0.008004      0.039376   \n",
       "\n",
       "   launch_DOW  goal_usd  blurb_readability_score  category_Art  \\\n",
       "0    0.003112 -0.000015                 0.004877      0.000191   \n",
       "\n",
       "   category_Comics  category_Crafts  ...  country_IT  country_JP  country_LU  \\\n",
       "0         0.004024        -0.002951  ...   -0.001088    0.000012    0.000005   \n",
       "\n",
       "   country_MX  country_NL  country_NO  country_NZ  country_SE  country_SG  \\\n",
       "0   -0.000703   -0.000462   -0.000103   -0.000029   -0.000139    0.000069   \n",
       "\n",
       "   country_US  \n",
       "0    0.009683  \n",
       "\n",
       "[1 rows x 44 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coefficient values for all factors\n",
    "pd.DataFrame(list(logistic_mod.coef_), columns = X_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making predictions on the test set\n",
    "y_pred = logistic_mod.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set split: Counter({1: 20038, 0: 17162})\n",
      "Proportion of positives in test set is: 0.5386559139784947 \n",
      "\n",
      "Predictions split: Counter({1: 26602, 0: 10598})\n",
      "Proportion of positives in prediction is: 0.7151075268817204 \n",
      "\n",
      "True Negative: 6637 |False Positive: 10525 |False Negative: 3961 |True Positive: 16077 \n",
      "\n",
      "True Positive Rate: 0.8023255813953488\n",
      "True Negative Rate: 0.38672648875422444\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "import collections\n",
    "test_counter = collections.Counter(y_test['Success'])\n",
    "print( 'Test set split:', test_counter)\n",
    "print('Proportion of positives in test set is:', test_counter[1]/(test_counter[1] + test_counter[0]), '\\n')\n",
    "counter = collections.Counter(y_pred)\n",
    "\n",
    "print( 'Predictions split:', counter)\n",
    "print('Proportion of positives in prediction is:', counter[1]/(counter[1] + counter[0]), '\\n')\n",
    "tn, fp, fn, tp = sk_metrics.confusion_matrix(y_test, y_pred).ravel()\n",
    "\n",
    "print('True Negative:',tn,'|False Positive:',fp, '|False Negative:', fn,'|True Positive:', tp, '\\n')\n",
    "print('True Positive Rate:', tp/ (tp + fn))\n",
    "print('True Negative Rate:', tn/ (tn + fp))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural Networks - 68.3% accuracy on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_____________ RESULTS WITH LEARNING RATE = 0.001 _________________\n",
      "WARNING:tensorflow:From C:\\Users\\valla\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3172: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From C:\\Users\\valla\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "Train on 104157 samples, validate on 44639 samples\n",
      "Epoch 1/30\n",
      "104157/104157 [==============================] - 24s 228us/step - loss: 0.7432 - accuracy: 0.5994 - sensitivity_at_specificity_1: 0.5925 - val_loss: 0.6469 - val_accuracy: 0.6256 - val_sensitivity_at_specificity_1: 0.6599\n",
      "Epoch 2/30\n",
      "104157/104157 [==============================] - 22s 214us/step - loss: 0.6561 - accuracy: 0.6084 - sensitivity_at_specificity_1: 0.6152 - val_loss: 0.6641 - val_accuracy: 0.6062 - val_sensitivity_at_specificity_1: 0.6164\n",
      "Epoch 3/30\n",
      "104157/104157 [==============================] - 22s 214us/step - loss: 0.6451 - accuracy: 0.6265 - sensitivity_at_specificity_1: 0.6573 - val_loss: 0.6338 - val_accuracy: 0.6450 - val_sensitivity_at_specificity_1: 0.6796\n",
      "Epoch 4/30\n",
      "104157/104157 [==============================] - 22s 216us/step - loss: 0.6424 - accuracy: 0.6340 - sensitivity_at_specificity_1: 0.6672 - val_loss: 0.6239 - val_accuracy: 0.6536 - val_sensitivity_at_specificity_1: 0.7036\n",
      "Epoch 5/30\n",
      "104157/104157 [==============================] - 23s 218us/step - loss: 0.6398 - accuracy: 0.6377 - sensitivity_at_specificity_1: 0.6705 - val_loss: 0.6302 - val_accuracy: 0.6555 - val_sensitivity_at_specificity_1: 0.7017\n",
      "Epoch 6/30\n",
      "104157/104157 [==============================] - 22s 214us/step - loss: 0.6280 - accuracy: 0.6542 - sensitivity_at_specificity_1: 0.7044 - val_loss: 0.6235 - val_accuracy: 0.6562 - val_sensitivity_at_specificity_1: 0.7119\n",
      "Epoch 7/30\n",
      "104157/104157 [==============================] - 23s 216us/step - loss: 0.6231 - accuracy: 0.6588 - sensitivity_at_specificity_1: 0.7128 - val_loss: 0.6230 - val_accuracy: 0.6559 - val_sensitivity_at_specificity_1: 0.7029\n",
      "Epoch 8/30\n",
      "104157/104157 [==============================] - 22s 215us/step - loss: 0.6268 - accuracy: 0.6642 - sensitivity_at_specificity_1: 0.7246 - val_loss: 0.6254 - val_accuracy: 0.6617 - val_sensitivity_at_specificity_1: 0.7142\n",
      "Epoch 9/30\n",
      "104157/104157 [==============================] - 22s 215us/step - loss: 0.6169 - accuracy: 0.6656 - sensitivity_at_specificity_1: 0.7235 - val_loss: 0.6076 - val_accuracy: 0.6719 - val_sensitivity_at_specificity_1: 0.7380\n",
      "Epoch 10/30\n",
      "104157/104157 [==============================] - 23s 217us/step - loss: 0.6124 - accuracy: 0.6721 - sensitivity_at_specificity_1: 0.7347 - val_loss: 0.6099 - val_accuracy: 0.6695 - val_sensitivity_at_specificity_1: 0.7332\n",
      "Epoch 11/30\n",
      "104157/104157 [==============================] - 23s 217us/step - loss: 0.6092 - accuracy: 0.6728 - sensitivity_at_specificity_1: 0.7355 - val_loss: 0.6442 - val_accuracy: 0.6542 - val_sensitivity_at_specificity_1: 0.7075\n",
      "Epoch 12/30\n",
      "104157/104157 [==============================] - 22s 216us/step - loss: 0.6078 - accuracy: 0.6726 - sensitivity_at_specificity_1: 0.7374 - val_loss: 0.6067 - val_accuracy: 0.6776 - val_sensitivity_at_specificity_1: 0.7449\n",
      "Epoch 13/30\n",
      "104157/104157 [==============================] - 23s 217us/step - loss: 0.6044 - accuracy: 0.6766 - sensitivity_at_specificity_1: 0.7425 - val_loss: 0.6005 - val_accuracy: 0.6780 - val_sensitivity_at_specificity_1: 0.7502\n",
      "Epoch 14/30\n",
      "104157/104157 [==============================] - 23s 218us/step - loss: 0.6053 - accuracy: 0.6746 - sensitivity_at_specificity_1: 0.7443 - val_loss: 0.5967 - val_accuracy: 0.6800 - val_sensitivity_at_specificity_1: 0.7539\n",
      "Epoch 15/30\n",
      "104157/104157 [==============================] - 23s 219us/step - loss: 0.6045 - accuracy: 0.6768 - sensitivity_at_specificity_1: 0.7447 - val_loss: 0.5980 - val_accuracy: 0.6820 - val_sensitivity_at_specificity_1: 0.7592\n",
      "Epoch 16/30\n",
      "104157/104157 [==============================] - 24s 226us/step - loss: 0.6001 - accuracy: 0.6781 - sensitivity_at_specificity_1: 0.7472 - val_loss: 0.6005 - val_accuracy: 0.6760 - val_sensitivity_at_specificity_1: 0.7462\n",
      "Epoch 17/30\n",
      "104157/104157 [==============================] - 23s 218us/step - loss: 0.6006 - accuracy: 0.6804 - sensitivity_at_specificity_1: 0.7490 - val_loss: 0.5962 - val_accuracy: 0.6806 - val_sensitivity_at_specificity_1: 0.7520\n",
      "Epoch 18/30\n",
      "104157/104157 [==============================] - 23s 224us/step - loss: 0.6061 - accuracy: 0.6801 - sensitivity_at_specificity_1: 0.7528 - val_loss: 0.5968 - val_accuracy: 0.6790 - val_sensitivity_at_specificity_1: 0.7511\n",
      "Epoch 19/30\n",
      "104157/104157 [==============================] - 23s 217us/step - loss: 0.5982 - accuracy: 0.6788 - sensitivity_at_specificity_1: 0.7514 - val_loss: 0.5917 - val_accuracy: 0.6855 - val_sensitivity_at_specificity_1: 0.7650\n",
      "Epoch 20/30\n",
      "104157/104157 [==============================] - 23s 219us/step - loss: 0.5967 - accuracy: 0.6812 - sensitivity_at_specificity_1: 0.7542 - val_loss: 0.5936 - val_accuracy: 0.6816 - val_sensitivity_at_specificity_1: 0.7560\n",
      "Epoch 21/30\n",
      "104157/104157 [==============================] - 23s 223us/step - loss: 0.5953 - accuracy: 0.6835 - sensitivity_at_specificity_1: 0.7587 - val_loss: 0.6168 - val_accuracy: 0.6549 - val_sensitivity_at_specificity_1: 0.7147\n",
      "Epoch 22/30\n",
      "104157/104157 [==============================] - 23s 219us/step - loss: 0.5954 - accuracy: 0.6819 - sensitivity_at_specificity_1: 0.7570 - val_loss: 0.6026 - val_accuracy: 0.6749 - val_sensitivity_at_specificity_1: 0.7430\n",
      "Epoch 23/30\n",
      "104157/104157 [==============================] - 23s 220us/step - loss: 0.5972 - accuracy: 0.6812 - sensitivity_at_specificity_1: 0.7532 - val_loss: 0.5975 - val_accuracy: 0.6844 - val_sensitivity_at_specificity_1: 0.7577\n"
     ]
    }
   ],
   "source": [
    "n_cols = X_train.shape[1]\n",
    "target = to_categorical(y_train['Success'])\n",
    "early_stopping_callback = EarlyStopping(patience = 4)\n",
    "\n",
    "def get_nn_model():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(300, activation = 'relu', input_shape =(n_cols,)))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(2, activation = 'softmax'))\n",
    "    return(model)\n",
    "\n",
    "#learning_rates =[0.000001, 0.0001, 0.001, 0.01, 0.1, 1]\n",
    "# learning_rates =[0.00001, 0.0001, 0.001]\n",
    "learning_rates =[0.001]\n",
    "# Loop over learning rates\n",
    "for lr in learning_rates:\n",
    "    model = get_nn_model()\n",
    "    print('_____________ RESULTS WITH LEARNING RATE =', lr,'_________________')\n",
    "    optimizer = Adam(learning_rate = lr)\n",
    "    model.compile(optimizer = optimizer, loss = 'categorical_crossentropy', metrics = ['accuracy', metrics.SensitivityAtSpecificity(0.6)])\n",
    "    model.fit(X_train, target, validation_split = 0.3, epochs = 30, callbacks = [early_stopping_callback])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37200/37200 [==============================] - 3s 68us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5978465380719913, 0.6832258105278015, 0.762231171131134]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_test = to_categorical(y_test['Success'])\n",
    "model.evaluate(X_test, target_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"17_Hidden_Non_Text.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.70214915, 0.29785085],\n",
       "       [0.3987394 , 0.60126066],\n",
       "       [0.22621034, 0.77378964],\n",
       "       ...,\n",
       "       [0.2996707 , 0.70032936],\n",
       "       [0.260278  , 0.7397221 ],\n",
       "       [0.20262295, 0.7973771 ]], dtype=float32)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n",
    "y_pred_keras = model.predict(X_test)[:,1]\n",
    "fpr_keras, tpr_keras, thresholds_keras = sk_metrics.roc_curve(y_test['Success'], y_pred_keras)\n",
    "auc_keras = sk_metrics.auc(fpr_keras, tpr_keras)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(1)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37200/37200 [==============================] - 3s 68us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5978465380719913, 0.6832258105278015, 0.762231171131134]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_test = to_categorical(y_test['Success'])\n",
    "text_model = load_model('17_Hidden_Non_Text.h5', compile = False)\n",
    "text_model.compile(optimizer = optimizer, loss = 'categorical_crossentropy', metrics = ['accuracy', metrics.SensitivityAtSpecificity(0.6)])\n",
    "text_model.evaluate(X_test, target_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural Network including Bag-Of-Words features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading the Bag of Words features from disk\n",
    "X_text_features = np.load('Data/X_textfeatures.npy')\n",
    "X_master = np.concatenate((X, X_text_features), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test,y_train, y_test = train_test_split(X_master, y, test_size = 0.2, random_state = 710, stratify = y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_____________ RESULTS WITH LEARNING RATE = 0.001 _________________\n",
      "Train on 104157 samples, validate on 44639 samples\n",
      "Epoch 1/30\n",
      "104157/104157 [==============================] - 39s 375us/step - loss: 0.6980 - accuracy: 0.5984 - sensitivity_at_specificity_3: 0.5913 - val_loss: 0.6579 - val_accuracy: 0.6076 - val_sensitivity_at_specificity_3: 0.6101\n",
      "Epoch 2/30\n",
      "104157/104157 [==============================] - 39s 370us/step - loss: 0.6541 - accuracy: 0.6108 - sensitivity_at_specificity_3: 0.6186 - val_loss: 0.6264 - val_accuracy: 0.6539 - val_sensitivity_at_specificity_3: 0.7045\n",
      "Epoch 3/30\n",
      "104157/104157 [==============================] - 40s 381us/step - loss: 0.6286 - accuracy: 0.6506 - sensitivity_at_specificity_3: 0.7017 - val_loss: 0.6092 - val_accuracy: 0.6866 - val_sensitivity_at_specificity_3: 0.7551\n",
      "Epoch 4/30\n",
      "104157/104157 [==============================] - 40s 389us/step - loss: 0.6142 - accuracy: 0.6688 - sensitivity_at_specificity_3: 0.7288 - val_loss: 0.6108 - val_accuracy: 0.6757 - val_sensitivity_at_specificity_3: 0.7416\n",
      "Epoch 5/30\n",
      "104157/104157 [==============================] - 41s 390us/step - loss: 0.6015 - accuracy: 0.6779 - sensitivity_at_specificity_3: 0.7471 - val_loss: 0.6038 - val_accuracy: 0.6851 - val_sensitivity_at_specificity_3: 0.7619\n",
      "Epoch 6/30\n",
      "104157/104157 [==============================] - 40s 384us/step - loss: 0.6137 - accuracy: 0.6690 - sensitivity_at_specificity_3: 0.7338 - val_loss: 0.6264 - val_accuracy: 0.6766 - val_sensitivity_at_specificity_3: 0.7456\n",
      "Epoch 7/30\n",
      "104157/104157 [==============================] - 39s 378us/step - loss: 0.6190 - accuracy: 0.6578 - sensitivity_at_specificity_3: 0.7149 - val_loss: 0.6156 - val_accuracy: 0.6558 - val_sensitivity_at_specificity_3: 0.7106\n",
      "Epoch 8/30\n",
      "104157/104157 [==============================] - 39s 371us/step - loss: 0.5895 - accuracy: 0.6892 - sensitivity_at_specificity_3: 0.7676 - val_loss: 0.5814 - val_accuracy: 0.6897 - val_sensitivity_at_specificity_3: 0.7721\n",
      "Epoch 9/30\n",
      "104157/104157 [==============================] - 39s 370us/step - loss: 0.6033 - accuracy: 0.6714 - sensitivity_at_specificity_3: 0.7356 - val_loss: 0.6098 - val_accuracy: 0.6600 - val_sensitivity_at_specificity_3: 0.7226\n",
      "Epoch 10/30\n",
      "104157/104157 [==============================] - 39s 370us/step - loss: 0.5962 - accuracy: 0.6827 - sensitivity_at_specificity_3: 0.7609 - val_loss: 0.5771 - val_accuracy: 0.7030 - val_sensitivity_at_specificity_3: 0.7886\n",
      "Epoch 11/30\n",
      "104157/104157 [==============================] - 39s 375us/step - loss: 0.5694 - accuracy: 0.7047 - sensitivity_at_specificity_3: 0.7917 - val_loss: 0.5639 - val_accuracy: 0.7088 - val_sensitivity_at_specificity_3: 0.8052\n",
      "Epoch 12/30\n",
      "104157/104157 [==============================] - 39s 372us/step - loss: 0.5651 - accuracy: 0.7089 - sensitivity_at_specificity_3: 0.7992 - val_loss: 0.5895 - val_accuracy: 0.6948 - val_sensitivity_at_specificity_3: 0.7780\n",
      "Epoch 13/30\n",
      "104157/104157 [==============================] - 39s 371us/step - loss: 0.5591 - accuracy: 0.7101 - sensitivity_at_specificity_3: 0.8056 - val_loss: 0.6406 - val_accuracy: 0.6259 - val_sensitivity_at_specificity_3: 0.6514\n",
      "Epoch 14/30\n",
      "104157/104157 [==============================] - 39s 372us/step - loss: 0.5690 - accuracy: 0.7028 - sensitivity_at_specificity_3: 0.7942 - val_loss: 0.6045 - val_accuracy: 0.6791 - val_sensitivity_at_specificity_3: 0.7486\n",
      "Epoch 15/30\n",
      "104157/104157 [==============================] - 39s 372us/step - loss: 0.5548 - accuracy: 0.7144 - sensitivity_at_specificity_3: 0.8083 - val_loss: 0.5783 - val_accuracy: 0.6975 - val_sensitivity_at_specificity_3: 0.7819\n"
     ]
    }
   ],
   "source": [
    "n_cols = X_train.shape[1]\n",
    "target = to_categorical(y_train['Success'])\n",
    "early_stopping_callback = EarlyStopping(patience = 4)\n",
    "\n",
    "def get_nn_model():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(2000, activation = 'relu', input_shape =(n_cols,)))\n",
    "    model.add(Dense(700, activation = 'relu'))\n",
    "    model.add(Dense(500, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(2, activation = 'softmax'))\n",
    "    return(model)\n",
    "\n",
    "#learning_rates =[0.000001, 0.0001, 0.001, 0.01, 0.1, 1]\n",
    "# learning_rates =[0.00001, 0.0001, 0.001]\n",
    "learning_rates =[0.001]\n",
    "# Loop over learning rates\n",
    "for lr in learning_rates:\n",
    "    model = get_nn_model()\n",
    "    print('_____________ RESULTS WITH LEARNING RATE =', lr,'_________________')\n",
    "    optimizer = Adam(learning_rate = lr)\n",
    "    model.compile(optimizer = optimizer, loss = 'categorical_crossentropy', metrics = ['accuracy', metrics.SensitivityAtSpecificity(0.6)])\n",
    "    model.fit(X_train, target, validation_split = 0.3, epochs = 30, callbacks = [early_stopping_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"17_Hidden_Text.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37200/37200 [==============================] - 3s 89us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5788993996958579, 0.696854829788208, 0.7822580933570862]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_test = to_categorical(y_test['Success'])\n",
    "text_model = load_model('17_Hidden_Text.h5', compile = False)\n",
    "text_model.compile(optimizer = optimizer, loss = 'categorical_crossentropy', metrics = ['accuracy', metrics.SensitivityAtSpecificity(0.6)])\n",
    "text_model.evaluate(X_test, target_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_keras = text_model.predict(X_test)[:,1]\n",
    "fpr_keras, tpr_keras, thresholds_keras = sk_metrics.roc_curve(y_test['Success'], y_pred_keras)\n",
    "auc_keras = sk_metrics.auc(fpr_keras, tpr_keras)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(1)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_____________ RESULTS WITH LEARNING RATE = 0.001 _________________\n",
      "Train on 104157 samples, validate on 44639 samples\n",
      "Epoch 1/20\n",
      "104157/104157 [==============================] - 53s 504us/step - loss: 0.6910 - accuracy: 0.5919 - sensitivity_at_specificity_5: 0.5820 - val_loss: 0.6514 - val_accuracy: 0.6224 - val_sensitivity_at_specificity_5: 0.6388\n",
      "Epoch 2/20\n",
      "104157/104157 [==============================] - 52s 504us/step - loss: 0.6536 - accuracy: 0.6148 - sensitivity_at_specificity_5: 0.6343 - val_loss: 0.6439 - val_accuracy: 0.6249 - val_sensitivity_at_specificity_5: 0.6497\n",
      "Epoch 3/20\n",
      "104157/104157 [==============================] - 53s 511us/step - loss: 0.6628 - accuracy: 0.5975 - sensitivity_at_specificity_5: 0.5904 - val_loss: 0.6707 - val_accuracy: 0.5821 - val_sensitivity_at_specificity_5: 0.5639\n",
      "Epoch 4/20\n",
      "104157/104157 [==============================] - 52s 501us/step - loss: 0.6362 - accuracy: 0.6417 - sensitivity_at_specificity_5: 0.6818 - val_loss: 0.6532 - val_accuracy: 0.6111 - val_sensitivity_at_specificity_5: 0.6236\n",
      "Epoch 5/20\n",
      "104157/104157 [==============================] - 52s 501us/step - loss: 0.6263 - accuracy: 0.6520 - sensitivity_at_specificity_5: 0.6984 - val_loss: 0.6461 - val_accuracy: 0.6187 - val_sensitivity_at_specificity_5: 0.6402\n",
      "Epoch 6/20\n",
      "104157/104157 [==============================] - 52s 500us/step - loss: 0.6118 - accuracy: 0.6726 - sensitivity_at_specificity_5: 0.7377 - val_loss: 0.6249 - val_accuracy: 0.6348 - val_sensitivity_at_specificity_5: 0.6733ETA: 2s - loss: 0.6126 - accuracy: 0.6\n",
      "Epoch 7/20\n",
      "104157/104157 [==============================] - 52s 503us/step - loss: 0.6145 - accuracy: 0.6742 - sensitivity_at_specificity_5: 0.7452 - val_loss: 0.6553 - val_accuracy: 0.6117 - val_sensitivity_at_specificity_5: 0.6191\n",
      "Epoch 8/20\n",
      "104157/104157 [==============================] - 53s 508us/step - loss: 0.6179 - accuracy: 0.6639 - sensitivity_at_specificity_5: 0.7250 - val_loss: 0.6224 - val_accuracy: 0.6520 - val_sensitivity_at_specificity_5: 0.7111\n",
      "Epoch 9/20\n",
      "104157/104157 [==============================] - 55s 523us/step - loss: 0.6267 - accuracy: 0.6588 - sensitivity_at_specificity_5: 0.7131 - val_loss: 0.5867 - val_accuracy: 0.6999 - val_sensitivity_at_specificity_5: 0.7786\n",
      "Epoch 10/20\n",
      "104157/104157 [==============================] - 53s 508us/step - loss: 0.6099 - accuracy: 0.6705 - sensitivity_at_specificity_5: 0.7362 - val_loss: 0.5888 - val_accuracy: 0.6892 - val_sensitivity_at_specificity_5: 0.7717\n",
      "Epoch 11/20\n",
      "104157/104157 [==============================] - 53s 504us/step - loss: 0.5868 - accuracy: 0.6927 - sensitivity_at_specificity_5: 0.7744 - val_loss: 0.5734 - val_accuracy: 0.7047 - val_sensitivity_at_specificity_5: 0.7937\n",
      "Epoch 12/20\n",
      "104157/104157 [==============================] - 52s 503us/step - loss: 0.5878 - accuracy: 0.6911 - sensitivity_at_specificity_5: 0.7707 - val_loss: 0.5731 - val_accuracy: 0.7005 - val_sensitivity_at_specificity_5: 0.7905\n",
      "Epoch 13/20\n",
      "104157/104157 [==============================] - 53s 507us/step - loss: 0.5740 - accuracy: 0.7012 - sensitivity_at_specificity_5: 0.7863 - val_loss: 0.5840 - val_accuracy: 0.6886 - val_sensitivity_at_specificity_5: 0.7671\n",
      "Epoch 14/20\n",
      "104157/104157 [==============================] - 54s 521us/step - loss: 0.5769 - accuracy: 0.6970 - sensitivity_at_specificity_5: 0.7841 - val_loss: 0.5789 - val_accuracy: 0.6957 - val_sensitivity_at_specificity_5: 0.7891: 0.\n",
      "Epoch 15/20\n",
      "104157/104157 [==============================] - 55s 527us/step - loss: 0.5796 - accuracy: 0.6960 - sensitivity_at_specificity_5: 0.7815 - val_loss: 0.5796 - val_accuracy: 0.6986 - val_sensitivity_at_specificity_5: 0.7809\n",
      "Epoch 16/20\n",
      "104157/104157 [==============================] - 54s 515us/step - loss: 0.5684 - accuracy: 0.7071 - sensitivity_at_specificity_5: 0.7945 - val_loss: 0.5969 - val_accuracy: 0.6819 - val_sensitivity_at_specificity_5: 0.7546\n",
      "Epoch 17/20\n",
      "104157/104157 [==============================] - 53s 511us/step - loss: 0.5684 - accuracy: 0.7118 - sensitivity_at_specificity_5: 0.8036 - val_loss: 0.6117 - val_accuracy: 0.6613 - val_sensitivity_at_specificity_5: 0.7257\n",
      "Epoch 18/20\n",
      "104157/104157 [==============================] - 55s 526us/step - loss: 0.5532 - accuracy: 0.7167 - sensitivity_at_specificity_5: 0.8165 - val_loss: 0.5619 - val_accuracy: 0.7124 - val_sensitivity_at_specificity_5: 0.8083\n",
      "Epoch 19/20\n",
      "104157/104157 [==============================] - 54s 521us/step - loss: 0.6090 - accuracy: 0.7116 - sensitivity_at_specificity_5: 0.8044 - val_loss: 0.5592 - val_accuracy: 0.7172 - val_sensitivity_at_specificity_5: 0.8139\n",
      "Epoch 20/20\n",
      "104157/104157 [==============================] - 54s 521us/step - loss: 0.5452 - accuracy: 0.7239 - sensitivity_at_specificity_5: 0.8231 - val_loss: 0.5543 - val_accuracy: 0.7196 - val_sensitivity_at_specificity_5: 0.8197\n"
     ]
    }
   ],
   "source": [
    "early_stopping_callback = EarlyStopping(patience = 8)\n",
    "def get_nn_model():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(2000, activation = 'relu', input_shape =(n_cols,)))\n",
    "    model.add(Dense(1000, activation = 'relu'))\n",
    "    model.add(Dense(700, activation = 'relu'))\n",
    "    model.add(Dense(700, activation = 'relu'))\n",
    "    model.add(Dense(600, activation = 'relu'))\n",
    "    model.add(Dense(500, activation = 'relu'))\n",
    "    model.add(Dense(400, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(2, activation = 'softmax'))\n",
    "    return(model)\n",
    "\n",
    "#learning_rates =[0.000001, 0.0001, 0.001, 0.01, 0.1, 1]\n",
    "#learning_rates =[0.0001, 0.001]\n",
    "learning_rates =[0.001]\n",
    "# Loop over learning rates\n",
    "for lr in learning_rates:\n",
    "    model = get_nn_model()\n",
    "    print('_____________ RESULTS WITH LEARNING RATE =', lr,'_________________')\n",
    "    optimizer = Adam(learning_rate = lr)\n",
    "    model.compile(optimizer = optimizer, loss = 'categorical_crossentropy', metrics = ['accuracy', metrics.SensitivityAtSpecificity(0.6)])\n",
    "    model.fit(X_train, target, validation_split = 0.3, epochs = 20, callbacks = [early_stopping_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37200/37200 [==============================] - 4s 110us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.6901640666428432, 0.5386559367179871, 0.5386559367179871]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_test = to_categorical(y_test['Success'])\n",
    "model.save(\"24_hidden_text.h5\")\n",
    "model.evaluate(X_test, target_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_____________ RESULTS WITH LEARNING RATE = 0.001 _________________\n",
      "Train on 104157 samples, validate on 44639 samples\n",
      "Epoch 1/20\n",
      "104157/104157 [==============================] - 60s 577us/step - loss: 0.6899 - accuracy: 0.5409 - sensitivity_at_specificity_6: 0.4578 - val_loss: 0.6904 - val_accuracy: 0.5381 - val_sensitivity_at_specificity_6: 0.5381\n",
      "Epoch 2/20\n",
      "104157/104157 [==============================] - 60s 578us/step - loss: 0.6902 - accuracy: 0.5389 - sensitivity_at_specificity_6: 0.5000 - val_loss: 0.6905 - val_accuracy: 0.5381 - val_sensitivity_at_specificity_6: 0.5381\n",
      "Epoch 3/20\n",
      "104157/104157 [==============================] - 60s 576us/step - loss: 0.6912 - accuracy: 0.5387 - sensitivity_at_specificity_6: 0.4717 - val_loss: 0.6903 - val_accuracy: 0.5381 - val_sensitivity_at_specificity_6: 0.5381\n",
      "Epoch 4/20\n",
      "104157/104157 [==============================] - 59s 571us/step - loss: 0.6902 - accuracy: 0.5389 - sensitivity_at_specificity_6: 0.4993 - val_loss: 0.6902 - val_accuracy: 0.5381 - val_sensitivity_at_specificity_6: 0.5381\n",
      "Epoch 5/20\n",
      "104157/104157 [==============================] - 62s 594us/step - loss: 0.6902 - accuracy: 0.5389 - sensitivity_at_specificity_6: 0.4611 - val_loss: 0.6903 - val_accuracy: 0.5381 - val_sensitivity_at_specificity_6: 0.5381\n",
      "Epoch 6/20\n",
      "104157/104157 [==============================] - 60s 574us/step - loss: 0.6902 - accuracy: 0.5389 - sensitivity_at_specificity_6: 0.5220 - val_loss: 0.6902 - val_accuracy: 0.5381 - val_sensitivity_at_specificity_6: 0.5381\n",
      "Epoch 7/20\n",
      "104157/104157 [==============================] - 59s 569us/step - loss: 0.6902 - accuracy: 0.5389 - sensitivity_at_specificity_6: 0.5010 - val_loss: 0.6902 - val_accuracy: 0.5381 - val_sensitivity_at_specificity_6: 0.5381\n",
      "Epoch 8/20\n",
      "104157/104157 [==============================] - 59s 570us/step - loss: 0.6902 - accuracy: 0.5389 - sensitivity_at_specificity_6: 0.4792 - val_loss: 0.6902 - val_accuracy: 0.5381 - val_sensitivity_at_specificity_6: 0.5381\n",
      "Epoch 9/20\n",
      "104157/104157 [==============================] - 60s 575us/step - loss: 0.6901 - accuracy: 0.5389 - sensitivity_at_specificity_6: 0.4818 - val_loss: 0.6904 - val_accuracy: 0.5381 - val_sensitivity_at_specificity_6: 0.5381\n",
      "Epoch 10/20\n",
      "104157/104157 [==============================] - 60s 576us/step - loss: 0.6901 - accuracy: 0.5389 - sensitivity_at_specificity_6: 0.4321 - val_loss: 0.6904 - val_accuracy: 0.5381 - val_sensitivity_at_specificity_6: 0.5381\n",
      "Epoch 11/20\n",
      "104157/104157 [==============================] - 61s 584us/step - loss: 0.6902 - accuracy: 0.5389 - sensitivity_at_specificity_6: 0.5011 - val_loss: 0.6902 - val_accuracy: 0.5381 - val_sensitivity_at_specificity_6: 0.5381rac - ETA: 0s - loss: 0.6902 - accuracy: 0.5388 - sensitivity_at_specificity_\n",
      "Epoch 12/20\n",
      "104157/104157 [==============================] - 61s 581us/step - loss: 0.6901 - accuracy: 0.5389 - sensitivity_at_specificity_6: 0.4810 - val_loss: 0.6902 - val_accuracy: 0.5381 - val_sensitivity_at_specificity_6: 0.5381\n",
      "Epoch 13/20\n",
      "104157/104157 [==============================] - 59s 568us/step - loss: 0.6901 - accuracy: 0.5389 - sensitivity_at_specificity_6: 0.4485 - val_loss: 0.6904 - val_accuracy: 0.5381 - val_sensitivity_at_specificity_6: 0.5381\n",
      "Epoch 14/20\n",
      "104157/104157 [==============================] - 59s 569us/step - loss: 0.6902 - accuracy: 0.5389 - sensitivity_at_specificity_6: 0.5058 - val_loss: 0.6903 - val_accuracy: 0.5381 - val_sensitivity_at_specificity_6: 0.5381\n",
      "Epoch 15/20\n",
      "104157/104157 [==============================] - 59s 564us/step - loss: 0.6902 - accuracy: 0.5389 - sensitivity_at_specificity_6: 0.5178 - val_loss: 0.6904 - val_accuracy: 0.5381 - val_sensitivity_at_specificity_6: 0.5381\n",
      "Epoch 16/20\n",
      "104157/104157 [==============================] - 59s 567us/step - loss: 0.6902 - accuracy: 0.5389 - sensitivity_at_specificity_6: 0.5307 - val_loss: 0.6903 - val_accuracy: 0.5381 - val_sensitivity_at_specificity_6: 0.5381\n",
      "Epoch 17/20\n",
      "104157/104157 [==============================] - 59s 567us/step - loss: 0.6902 - accuracy: 0.5389 - sensitivity_at_specificity_6: 0.5188 - val_loss: 0.6903 - val_accuracy: 0.5381 - val_sensitivity_at_specificity_6: 0.5381\n",
      "Epoch 18/20\n",
      "104157/104157 [==============================] - 59s 565us/step - loss: 0.6902 - accuracy: 0.5389 - sensitivity_at_specificity_6: 0.4646 - val_loss: 0.6903 - val_accuracy: 0.5381 - val_sensitivity_at_specificity_6: 0.5381\n",
      "Epoch 19/20\n",
      "104157/104157 [==============================] - 59s 568us/step - loss: 0.6902 - accuracy: 0.5389 - sensitivity_at_specificity_6: 0.5002 - val_loss: 0.6902 - val_accuracy: 0.5381 - val_sensitivity_at_specificity_6: 0.5381\n",
      "Epoch 20/20\n",
      "104157/104157 [==============================] - 60s 571us/step - loss: 0.6902 - accuracy: 0.5389 - sensitivity_at_specificity_6: 0.5156 - val_loss: 0.6902 - val_accuracy: 0.5381 - val_sensitivity_at_specificity_6: 0.5381\n"
     ]
    }
   ],
   "source": [
    "def get_nn_model():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(2000, activation = 'relu', input_shape =(n_cols,)))\n",
    "    model.add(Dense(1000, activation = 'relu'))\n",
    "    model.add(Dense(700, activation = 'relu'))\n",
    "    model.add(Dense(700, activation = 'relu'))\n",
    "    model.add(Dense(600, activation = 'relu'))\n",
    "    model.add(Dense(500, activation = 'relu'))\n",
    "    model.add(Dense(400, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(300, activation = 'relu'))\n",
    "    model.add(Dense(2, activation = 'softmax'))\n",
    "    return(model)\n",
    "\n",
    "#learning_rates =[0.000001, 0.0001, 0.001, 0.01, 0.1, 1]\n",
    " #learning_rates =[0.0001, 0.001]\n",
    "learning_rates =[0.001]\n",
    "# Loop over learning rates\n",
    "for lr in learning_rates:\n",
    "    model = get_nn_model()\n",
    "    print('_____________ RESULTS WITH LEARNING RATE =', lr,'_________________')\n",
    "    optimizer = Adam(learning_rate = lr)\n",
    "    model.compile(optimizer = optimizer, loss = 'categorical_crossentropy', metrics = ['accuracy', metrics.SensitivityAtSpecificity(0.6)])\n",
    "    model.fit(X_train, target, validation_split = 0.3, epochs = 20, callbacks = [early_stopping_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37200/37200 [==============================] - 4s 113us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.6901640666428432, 0.5386559367179871, 0.5386559367179871]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.save(\"30_hidden_text.h5\")\n",
    "model.evaluate(X_test, target_test)\n"
   ]
  }
 ],
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