Kickstarters.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import randint\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.utils import resample\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score, KFold, cross_val_predict,RandomizedSearchCV, StratifiedKFold\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_curve, auc\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "import shap \n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from kmodes.kprototypes import KPrototypes\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>goal</th>\n",
       "      <th>pledged</th>\n",
       "      <th>state</th>\n",
       "      <th>disable_communication</th>\n",
       "      <th>country</th>\n",
       "      <th>currency</th>\n",
       "      <th>deadline</th>\n",
       "      <th>state_changed_at</th>\n",
       "      <th>...</th>\n",
       "      <th>created_at_day</th>\n",
       "      <th>created_at_yr</th>\n",
       "      <th>created_at_hr</th>\n",
       "      <th>launched_at_month</th>\n",
       "      <th>launched_at_day</th>\n",
       "      <th>launched_at_yr</th>\n",
       "      <th>launched_at_hr</th>\n",
       "      <th>create_to_launch_days</th>\n",
       "      <th>launch_to_deadline_days</th>\n",
       "      <th>launch_to_state_change_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1538064060</td>\n",
       "      <td>MAGIC PIXEL - Bluetooth full color LED display</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>5933.0</td>\n",
       "      <td>failed</td>\n",
       "      <td>False</td>\n",
       "      <td>GB</td>\n",
       "      <td>GBP</td>\n",
       "      <td>2016-03-19 09:31:29</td>\n",
       "      <td>2016-03-19 09:31:32</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>2016</td>\n",
       "      <td>9</td>\n",
       "      <td>62</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>556771080</td>\n",
       "      <td>SmartPi - Turn your Raspberry Pi into a SmartM...</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>16552.0</td>\n",
       "      <td>successful</td>\n",
       "      <td>False</td>\n",
       "      <td>DE</td>\n",
       "      <td>EUR</td>\n",
       "      <td>2016-04-03 08:05:09</td>\n",
       "      <td>2016-04-03 08:05:10</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>839314928</td>\n",
       "      <td>PlantSitter - The World's Smartest Plant Monit...</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>43234.0</td>\n",
       "      <td>canceled</td>\n",
       "      <td>False</td>\n",
       "      <td>US</td>\n",
       "      <td>USD</td>\n",
       "      <td>2016-03-29 08:01:08</td>\n",
       "      <td>2016-03-28 09:46:41</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>681077916</td>\n",
       "      <td>Digital Video LUT Box for Colorblindness Corre...</td>\n",
       "      <td>125000.0</td>\n",
       "      <td>1262.0</td>\n",
       "      <td>canceled</td>\n",
       "      <td>False</td>\n",
       "      <td>US</td>\n",
       "      <td>USD</td>\n",
       "      <td>2016-03-19 07:48:02</td>\n",
       "      <td>2016-02-23 09:30:28</td>\n",
       "      <td>...</td>\n",
       "      <td>28</td>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1315415013</td>\n",
       "      <td>help send Object Collection to Norway!</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>successful</td>\n",
       "      <td>False</td>\n",
       "      <td>US</td>\n",
       "      <td>USD</td>\n",
       "      <td>2016-03-03 17:00:00</td>\n",
       "      <td>2016-03-03 17:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id                                               name      goal  \\\n",
       "0  1538064060     MAGIC PIXEL - Bluetooth full color LED display   15000.0   \n",
       "1   556771080  SmartPi - Turn your Raspberry Pi into a SmartM...    9000.0   \n",
       "2   839314928  PlantSitter - The World's Smartest Plant Monit...   60000.0   \n",
       "3   681077916  Digital Video LUT Box for Colorblindness Corre...  125000.0   \n",
       "4  1315415013             help send Object Collection to Norway!    2000.0   \n",
       "\n",
       "   pledged       state  disable_communication country currency  \\\n",
       "0   5933.0      failed                  False      GB      GBP   \n",
       "1  16552.0  successful                  False      DE      EUR   \n",
       "2  43234.0    canceled                  False      US      USD   \n",
       "3   1262.0    canceled                  False      US      USD   \n",
       "4   2300.0  successful                  False      US      USD   \n",
       "\n",
       "             deadline    state_changed_at  ... created_at_day created_at_yr  \\\n",
       "0 2016-03-19 09:31:29 2016-03-19 09:31:32  ...             18          2015   \n",
       "1 2016-04-03 08:05:09 2016-04-03 08:05:10  ...              8          2016   \n",
       "2 2016-03-29 08:01:08 2016-03-28 09:46:41  ...             31          2016   \n",
       "3 2016-03-19 07:48:02 2016-02-23 09:30:28  ...             28          2016   \n",
       "4 2016-03-03 17:00:00 2016-03-03 17:00:00  ...             16          2016   \n",
       "\n",
       "   created_at_hr  launched_at_month  launched_at_day  launched_at_yr  \\\n",
       "0              3                  2               18            2016   \n",
       "1              9                  2               18            2016   \n",
       "2              5                  2               18            2016   \n",
       "3             11                  2               18            2016   \n",
       "4             10                  2               18            2016   \n",
       "\n",
       "  launched_at_hr  create_to_launch_days  launch_to_deadline_days  \\\n",
       "0              9                     62                       30   \n",
       "1              8                      9                       45   \n",
       "2              8                     18                       40   \n",
       "3              7                     20                       30   \n",
       "4              7                      1                       14   \n",
       "\n",
       "   launch_to_state_change_days  \n",
       "0                           30  \n",
       "1                           45  \n",
       "2                           39  \n",
       "3                            5  \n",
       "4                           14  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_excel('Kickstarter.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15474, 45)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the dimensions of the data\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\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>id</th>\n",
       "      <th>goal</th>\n",
       "      <th>pledged</th>\n",
       "      <th>deadline</th>\n",
       "      <th>state_changed_at</th>\n",
       "      <th>created_at</th>\n",
       "      <th>launched_at</th>\n",
       "      <th>backers_count</th>\n",
       "      <th>static_usd_rate</th>\n",
       "      <th>usd_pledged</th>\n",
       "      <th>...</th>\n",
       "      <th>created_at_day</th>\n",
       "      <th>created_at_yr</th>\n",
       "      <th>created_at_hr</th>\n",
       "      <th>launched_at_month</th>\n",
       "      <th>launched_at_day</th>\n",
       "      <th>launched_at_yr</th>\n",
       "      <th>launched_at_hr</th>\n",
       "      <th>create_to_launch_days</th>\n",
       "      <th>launch_to_deadline_days</th>\n",
       "      <th>launch_to_state_change_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.547400e+04</td>\n",
       "      <td>1.547400e+04</td>\n",
       "      <td>1.547400e+04</td>\n",
       "      <td>15474</td>\n",
       "      <td>15474</td>\n",
       "      <td>15474</td>\n",
       "      <td>15474</td>\n",
       "      <td>15474.000000</td>\n",
       "      <td>15474.000000</td>\n",
       "      <td>1.547400e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>15474.000000</td>\n",
       "      <td>15474.000000</td>\n",
       "      <td>15474.000000</td>\n",
       "      <td>15474.000000</td>\n",
       "      <td>15474.000000</td>\n",
       "      <td>15474.000000</td>\n",
       "      <td>15474.000000</td>\n",
       "      <td>15474.000000</td>\n",
       "      <td>15474.000000</td>\n",
       "      <td>15474.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.071135e+09</td>\n",
       "      <td>9.777089e+04</td>\n",
       "      <td>1.978704e+04</td>\n",
       "      <td>2014-11-23 17:17:49.526819328</td>\n",
       "      <td>2014-11-21 02:33:26.250096896</td>\n",
       "      <td>2014-09-02 12:28:16.682111744</td>\n",
       "      <td>2014-10-19 22:03:32.033734144</td>\n",
       "      <td>181.169833</td>\n",
       "      <td>1.051427</td>\n",
       "      <td>1.997549e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>15.531149</td>\n",
       "      <td>2014.169639</td>\n",
       "      <td>12.844772</td>\n",
       "      <td>6.615096</td>\n",
       "      <td>15.299212</td>\n",
       "      <td>2014.293460</td>\n",
       "      <td>12.589182</td>\n",
       "      <td>46.905648</td>\n",
       "      <td>34.682952</td>\n",
       "      <td>32.020098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.645550e+05</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2009-06-01 11:00:00</td>\n",
       "      <td>2009-05-05 20:41:27</td>\n",
       "      <td>2009-04-25 00:37:50</td>\n",
       "      <td>2009-04-25 10:36:21</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.112727</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.451956e+08</td>\n",
       "      <td>3.600000e+03</td>\n",
       "      <td>2.500000e+01</td>\n",
       "      <td>2014-08-10 16:53:31.750000128</td>\n",
       "      <td>2014-08-08 09:19:57.500000</td>\n",
       "      <td>2014-05-16 21:29:59</td>\n",
       "      <td>2014-07-10 10:23:14</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.486011e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.071634e+09</td>\n",
       "      <td>1.200000e+04</td>\n",
       "      <td>6.500000e+02</td>\n",
       "      <td>2015-02-19 10:54:45</td>\n",
       "      <td>2015-02-15 02:55:30</td>\n",
       "      <td>2014-11-18 21:00:56</td>\n",
       "      <td>2015-01-16 13:15:57.500000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.768430e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.604739e+09</td>\n",
       "      <td>4.500000e+04</td>\n",
       "      <td>5.727500e+03</td>\n",
       "      <td>2015-08-02 10:20:51</td>\n",
       "      <td>2015-07-31 11:47:25.249999872</td>\n",
       "      <td>2015-05-26 19:52:53.750000128</td>\n",
       "      <td>2015-06-29 11:33:10.750000128</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.896877e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.147388e+09</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>6.225355e+06</td>\n",
       "      <td>2016-04-18 04:13:25</td>\n",
       "      <td>2016-04-18 04:13:25</td>\n",
       "      <td>2016-02-17 14:49:34</td>\n",
       "      <td>2016-02-18 09:31:29</td>\n",
       "      <td>105857.000000</td>\n",
       "      <td>1.715913</td>\n",
       "      <td>6.225355e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1528.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>91.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.158555e+08</td>\n",
       "      <td>1.486588e+06</td>\n",
       "      <td>1.129384e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1287.826211</td>\n",
       "      <td>0.232488</td>\n",
       "      <td>1.144395e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>8.748463</td>\n",
       "      <td>1.129148</td>\n",
       "      <td>5.918025</td>\n",
       "      <td>3.362745</td>\n",
       "      <td>8.784290</td>\n",
       "      <td>1.115405</td>\n",
       "      <td>5.587257</td>\n",
       "      <td>101.765225</td>\n",
       "      <td>11.976346</td>\n",
       "      <td>13.674964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id          goal       pledged  \\\n",
       "count  1.547400e+04  1.547400e+04  1.547400e+04   \n",
       "mean   1.071135e+09  9.777089e+04  1.978704e+04   \n",
       "min    1.645550e+05  1.000000e+00  0.000000e+00   \n",
       "25%    5.451956e+08  3.600000e+03  2.500000e+01   \n",
       "50%    1.071634e+09  1.200000e+04  6.500000e+02   \n",
       "75%    1.604739e+09  4.500000e+04  5.727500e+03   \n",
       "max    2.147388e+09  1.000000e+08  6.225355e+06   \n",
       "std    6.158555e+08  1.486588e+06  1.129384e+05   \n",
       "\n",
       "                            deadline               state_changed_at  \\\n",
       "count                          15474                          15474   \n",
       "mean   2014-11-23 17:17:49.526819328  2014-11-21 02:33:26.250096896   \n",
       "min              2009-06-01 11:00:00            2009-05-05 20:41:27   \n",
       "25%    2014-08-10 16:53:31.750000128     2014-08-08 09:19:57.500000   \n",
       "50%              2015-02-19 10:54:45            2015-02-15 02:55:30   \n",
       "75%              2015-08-02 10:20:51  2015-07-31 11:47:25.249999872   \n",
       "max              2016-04-18 04:13:25            2016-04-18 04:13:25   \n",
       "std                              NaN                            NaN   \n",
       "\n",
       "                          created_at                    launched_at  \\\n",
       "count                          15474                          15474   \n",
       "mean   2014-09-02 12:28:16.682111744  2014-10-19 22:03:32.033734144   \n",
       "min              2009-04-25 00:37:50            2009-04-25 10:36:21   \n",
       "25%              2014-05-16 21:29:59            2014-07-10 10:23:14   \n",
       "50%              2014-11-18 21:00:56     2015-01-16 13:15:57.500000   \n",
       "75%    2015-05-26 19:52:53.750000128  2015-06-29 11:33:10.750000128   \n",
       "max              2016-02-17 14:49:34            2016-02-18 09:31:29   \n",
       "std                              NaN                            NaN   \n",
       "\n",
       "       backers_count  static_usd_rate   usd_pledged  ...  created_at_day  \\\n",
       "count   15474.000000     15474.000000  1.547400e+04  ...    15474.000000   \n",
       "mean      181.169833         1.051427  1.997549e+04  ...       15.531149   \n",
       "min         0.000000         0.112727  0.000000e+00  ...        1.000000   \n",
       "25%         2.000000         1.000000  2.486011e+01  ...        8.000000   \n",
       "50%        12.000000         1.000000  6.768430e+02  ...       15.000000   \n",
       "75%        63.000000         1.000000  5.896877e+03  ...       23.000000   \n",
       "max    105857.000000         1.715913  6.225355e+06  ...       31.000000   \n",
       "std      1287.826211         0.232488  1.144395e+05  ...        8.748463   \n",
       "\n",
       "       created_at_yr  created_at_hr  launched_at_month  launched_at_day  \\\n",
       "count   15474.000000   15474.000000       15474.000000     15474.000000   \n",
       "mean     2014.169639      12.844772           6.615096        15.299212   \n",
       "min      2009.000000       0.000000           1.000000         1.000000   \n",
       "25%      2014.000000       9.000000           4.000000         8.000000   \n",
       "50%      2014.000000      13.000000           7.000000        15.000000   \n",
       "75%      2015.000000      17.000000          10.000000        23.000000   \n",
       "max      2016.000000      23.000000          12.000000        31.000000   \n",
       "std         1.129148       5.918025           3.362745         8.784290   \n",
       "\n",
       "       launched_at_yr  launched_at_hr  create_to_launch_days  \\\n",
       "count    15474.000000    15474.000000           15474.000000   \n",
       "mean      2014.293460       12.589182              46.905648   \n",
       "min       2009.000000        0.000000               0.000000   \n",
       "25%       2014.000000        9.000000               3.000000   \n",
       "50%       2015.000000       13.000000              13.000000   \n",
       "75%       2015.000000       17.000000              43.000000   \n",
       "max       2016.000000       23.000000            1528.000000   \n",
       "std          1.115405        5.587257             101.765225   \n",
       "\n",
       "       launch_to_deadline_days  launch_to_state_change_days  \n",
       "count             15474.000000                 15474.000000  \n",
       "mean                 34.682952                    32.020098  \n",
       "min                   1.000000                     0.000000  \n",
       "25%                  30.000000                    29.000000  \n",
       "50%                  30.000000                    30.000000  \n",
       "75%                  40.000000                    35.000000  \n",
       "max                  91.000000                    91.000000  \n",
       "std                  11.976346                    13.674964  \n",
       "\n",
       "[8 rows x 33 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Exploring the descriptive statistics of the data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'name', 'goal', 'pledged', 'state', 'disable_communication',\n",
       "       'country', 'currency', 'deadline', 'state_changed_at', 'created_at',\n",
       "       'launched_at', 'staff_pick', 'backers_count', 'static_usd_rate',\n",
       "       'usd_pledged', 'category', 'spotlight', 'name_len', 'name_len_clean',\n",
       "       'blurb_len', 'blurb_len_clean', 'deadline_weekday',\n",
       "       'state_changed_at_weekday', 'created_at_weekday', 'launched_at_weekday',\n",
       "       'deadline_month', 'deadline_day', 'deadline_yr', 'deadline_hr',\n",
       "       'state_changed_at_month', 'state_changed_at_day', 'state_changed_at_yr',\n",
       "       'state_changed_at_hr', 'created_at_month', 'created_at_day',\n",
       "       'created_at_yr', 'created_at_hr', 'launched_at_month',\n",
       "       'launched_at_day', 'launched_at_yr', 'launched_at_hr',\n",
       "       'create_to_launch_days', 'launch_to_deadline_days',\n",
       "       'launch_to_state_change_days'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a column to hold the converted goal amount in USD\n",
    "df['goal_converted']=df['goal']*df['static_usd_rate']"
   ]
  },
  {
   "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>state</th>\n",
       "      <th>disable_communication</th>\n",
       "      <th>country</th>\n",
       "      <th>staff_pick</th>\n",
       "      <th>category</th>\n",
       "      <th>name_len_clean</th>\n",
       "      <th>blurb_len_clean</th>\n",
       "      <th>deadline_weekday</th>\n",
       "      <th>created_at_weekday</th>\n",
       "      <th>launched_at_weekday</th>\n",
       "      <th>...</th>\n",
       "      <th>deadline_hr</th>\n",
       "      <th>created_at_month</th>\n",
       "      <th>created_at_day</th>\n",
       "      <th>created_at_hr</th>\n",
       "      <th>launched_at_month</th>\n",
       "      <th>launched_at_day</th>\n",
       "      <th>launched_at_hr</th>\n",
       "      <th>create_to_launch_days</th>\n",
       "      <th>launch_to_deadline_days</th>\n",
       "      <th>goal_converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>failed</td>\n",
       "      <td>False</td>\n",
       "      <td>GB</td>\n",
       "      <td>False</td>\n",
       "      <td>Gadgets</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>62</td>\n",
       "      <td>30</td>\n",
       "      <td>21449.83740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>successful</td>\n",
       "      <td>False</td>\n",
       "      <td>DE</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>45</td>\n",
       "      <td>10034.45163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>canceled</td>\n",
       "      <td>False</td>\n",
       "      <td>US</td>\n",
       "      <td>False</td>\n",
       "      <td>Gadgets</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>40</td>\n",
       "      <td>60000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>canceled</td>\n",
       "      <td>False</td>\n",
       "      <td>US</td>\n",
       "      <td>False</td>\n",
       "      <td>Hardware</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>125000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>successful</td>\n",
       "      <td>False</td>\n",
       "      <td>US</td>\n",
       "      <td>False</td>\n",
       "      <td>Experimental</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>2000.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        state  disable_communication country  staff_pick      category  \\\n",
       "0      failed                  False      GB       False       Gadgets   \n",
       "1  successful                  False      DE       False           NaN   \n",
       "2    canceled                  False      US       False       Gadgets   \n",
       "3    canceled                  False      US       False      Hardware   \n",
       "4  successful                  False      US       False  Experimental   \n",
       "\n",
       "   name_len_clean  blurb_len_clean deadline_weekday created_at_weekday  \\\n",
       "0             8.0             14.0         Saturday             Friday   \n",
       "1             6.0             15.0           Sunday             Monday   \n",
       "2             8.0             12.0          Tuesday             Sunday   \n",
       "3             6.0             16.0         Saturday           Thursday   \n",
       "4             5.0             13.0         Thursday            Tuesday   \n",
       "\n",
       "  launched_at_weekday  ...  deadline_hr  created_at_month  created_at_day  \\\n",
       "0            Thursday  ...            9                12              18   \n",
       "1            Thursday  ...            8                 2               8   \n",
       "2            Thursday  ...            8                 1              31   \n",
       "3            Thursday  ...            7                 1              28   \n",
       "4            Thursday  ...           17                 2              16   \n",
       "\n",
       "   created_at_hr  launched_at_month  launched_at_day  launched_at_hr  \\\n",
       "0              3                  2               18               9   \n",
       "1              9                  2               18               8   \n",
       "2              5                  2               18               8   \n",
       "3             11                  2               18               7   \n",
       "4             10                  2               18               7   \n",
       "\n",
       "   create_to_launch_days  launch_to_deadline_days  goal_converted  \n",
       "0                     62                       30     21449.83740  \n",
       "1                      9                       45     10034.45163  \n",
       "2                     18                       40     60000.00000  \n",
       "3                     20                       30    125000.00000  \n",
       "4                      1                       14      2000.00000  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Storing the columns to drop in a list\n",
    "cols_to_drop=['id','name','currency','goal','name_len','blurb_len','spotlight','created_at','launch_to_state_change_days','launched_at','pledged','deadline','backers_count','state_changed_at','deadline_yr','created_at_yr','launched_at_yr','state_changed_at_weekday','usd_pledged','static_usd_rate','state_changed_at_month', 'state_changed_at_day', 'state_changed_at_yr','state_changed_at_hr']\n",
    "df.drop(cols_to_drop,axis=1,inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15474, 22)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the new dimensions of the data\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state                       object\n",
       "disable_communication         bool\n",
       "country                     object\n",
       "staff_pick                    bool\n",
       "category                    object\n",
       "name_len_clean             float64\n",
       "blurb_len_clean            float64\n",
       "deadline_weekday            object\n",
       "created_at_weekday          object\n",
       "launched_at_weekday         object\n",
       "deadline_month               int64\n",
       "deadline_day                 int64\n",
       "deadline_hr                  int64\n",
       "created_at_month             int64\n",
       "created_at_day               int64\n",
       "created_at_hr                int64\n",
       "launched_at_month            int64\n",
       "launched_at_day              int64\n",
       "launched_at_hr               int64\n",
       "create_to_launch_days        int64\n",
       "launch_to_deadline_days      int64\n",
       "goal_converted             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the data types of the columns\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting 'disable_communication' and 'staff_pick' columns to int64\n",
    "df['disable_communication'] = df['disable_communication'].astype('int64')\n",
    "#df['staff_pick'] = df['staff_pick'].astype('int64')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state                   4\n",
       "country                18\n",
       "category               23\n",
       "deadline_weekday        7\n",
       "created_at_weekday      7\n",
       "launched_at_weekday     7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of unique values in each categorical column\n",
    "df.select_dtypes('object').nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state                         0\n",
       "disable_communication         0\n",
       "country                       0\n",
       "staff_pick                    0\n",
       "category                   1392\n",
       "name_len_clean                4\n",
       "blurb_len_clean               4\n",
       "deadline_weekday              0\n",
       "created_at_weekday            0\n",
       "launched_at_weekday           0\n",
       "deadline_month                0\n",
       "deadline_day                  0\n",
       "deadline_hr                   0\n",
       "created_at_month              0\n",
       "created_at_day                0\n",
       "created_at_hr                 0\n",
       "launched_at_month             0\n",
       "launched_at_day               0\n",
       "launched_at_hr                0\n",
       "create_to_launch_days         0\n",
       "launch_to_deadline_days       0\n",
       "goal_converted                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking number of null values in each column\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>disable_communication</th>\n",
       "      <th>country</th>\n",
       "      <th>staff_pick</th>\n",
       "      <th>category</th>\n",
       "      <th>name_len_clean</th>\n",
       "      <th>blurb_len_clean</th>\n",
       "      <th>deadline_weekday</th>\n",
       "      <th>created_at_weekday</th>\n",
       "      <th>launched_at_weekday</th>\n",
       "      <th>...</th>\n",
       "      <th>deadline_hr</th>\n",
       "      <th>created_at_month</th>\n",
       "      <th>created_at_day</th>\n",
       "      <th>created_at_hr</th>\n",
       "      <th>launched_at_month</th>\n",
       "      <th>launched_at_day</th>\n",
       "      <th>launched_at_hr</th>\n",
       "      <th>create_to_launch_days</th>\n",
       "      <th>launch_to_deadline_days</th>\n",
       "      <th>goal_converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>successful</td>\n",
       "      <td>0</td>\n",
       "      <td>DE</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>45</td>\n",
       "      <td>10034.45163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>successful</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>500.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>failed</td>\n",
       "      <td>0</td>\n",
       "      <td>DE</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>30</td>\n",
       "      <td>6696.56310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>successful</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>57</td>\n",
       "      <td>30</td>\n",
       "      <td>28000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>successful</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>20.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15369</th>\n",
       "      <td>failed</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Friday</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>50000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15370</th>\n",
       "      <td>canceled</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15375</th>\n",
       "      <td>successful</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Monday</td>\n",
       "      <td>...</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>10000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15394</th>\n",
       "      <td>failed</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>2000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15400</th>\n",
       "      <td>successful</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Friday</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>200.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1396 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            state  disable_communication country  staff_pick category  \\\n",
       "1      successful                      0      DE       False      NaN   \n",
       "21     successful                      0      US       False      NaN   \n",
       "22         failed                      0      DE       False      NaN   \n",
       "39     successful                      0      US        True      NaN   \n",
       "40     successful                      0      US       False      NaN   \n",
       "...           ...                    ...     ...         ...      ...   \n",
       "15369      failed                      0      US       False      NaN   \n",
       "15370    canceled                      0      US       False      NaN   \n",
       "15375  successful                      0      US        True      NaN   \n",
       "15394      failed                      0      US       False      NaN   \n",
       "15400  successful                      0      US       False      NaN   \n",
       "\n",
       "       name_len_clean  blurb_len_clean deadline_weekday created_at_weekday  \\\n",
       "1                 6.0             15.0           Sunday             Monday   \n",
       "21                5.0             11.0           Sunday             Friday   \n",
       "22                2.0             14.0           Friday          Wednesday   \n",
       "39                6.0             10.0         Thursday             Sunday   \n",
       "40                4.0              7.0         Saturday            Tuesday   \n",
       "...               ...              ...              ...                ...   \n",
       "15369             8.0             13.0           Sunday           Thursday   \n",
       "15370             6.0              4.0           Monday           Thursday   \n",
       "15375             5.0             15.0         Thursday           Thursday   \n",
       "15394             7.0             11.0          Tuesday           Thursday   \n",
       "15400             3.0              8.0           Sunday             Friday   \n",
       "\n",
       "      launched_at_weekday  ...  deadline_hr  created_at_month  created_at_day  \\\n",
       "1                Thursday  ...            8                 2               8   \n",
       "21              Wednesday  ...           19                 2              12   \n",
       "22              Wednesday  ...           11                 1              20   \n",
       "39                Tuesday  ...           13                12              20   \n",
       "40                Tuesday  ...           13                 2               9   \n",
       "...                   ...  ...          ...               ...             ...   \n",
       "15369              Friday  ...            3                 6               3   \n",
       "15370            Thursday  ...            6                 6               3   \n",
       "15375              Monday  ...           22                 5              20   \n",
       "15394            Thursday  ...            4                 4              15   \n",
       "15400              Friday  ...            2                 3              12   \n",
       "\n",
       "       created_at_hr  launched_at_month  launched_at_day  launched_at_hr  \\\n",
       "1                  9                  2               18               8   \n",
       "21                17                  2               17              11   \n",
       "22                13                  2               17              11   \n",
       "39                17                  2               16              13   \n",
       "40                16                  2               16              13   \n",
       "...              ...                ...              ...             ...   \n",
       "15369             10                  6                4              12   \n",
       "15370             22                  6                3              22   \n",
       "15375             14                  5               24              15   \n",
       "15394             18                  4               15              19   \n",
       "15400             16                  3               12              19   \n",
       "\n",
       "       create_to_launch_days  launch_to_deadline_days  goal_converted  \n",
       "1                          9                       45     10034.45163  \n",
       "21                         4                       32       500.00000  \n",
       "22                        27                       30      6696.56310  \n",
       "39                        57                       30     28000.00000  \n",
       "40                         6                        4        20.00000  \n",
       "...                      ...                      ...             ...  \n",
       "15369                      1                       50     50000.00000  \n",
       "15370                      0                        3      1000.00000  \n",
       "15375                      4                       45     10000.00000  \n",
       "15394                      0                       39      2000.00000  \n",
       "15400                      0                       15       200.00000  \n",
       "\n",
       "[1396 rows x 22 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view rows with null values\n",
    "df[df.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping the null values\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# storing all categorical columns except the target in a list\n",
    "cat_cols = df.select_dtypes('object').columns.tolist()\n",
    "cat_cols.remove('state')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a second dataframe with dummy variables for all categorical columns; the original one will be used for  clustering\n",
    "df2=pd.get_dummies(df,columns=cat_cols,drop_first=True, dtype='int')\n",
    "# dropping staff pick column from df2 because it will be used in the clustering model and it is not a feature in the classification model\n",
    "df2.drop('staff_pick',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14078, 73)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the new dimensions of the data\n",
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 14078 entries, 0 to 15473\n",
      "Data columns (total 73 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   state                          14078 non-null  object \n",
      " 1   disable_communication          14078 non-null  int64  \n",
      " 2   name_len_clean                 14078 non-null  float64\n",
      " 3   blurb_len_clean                14078 non-null  float64\n",
      " 4   deadline_month                 14078 non-null  int64  \n",
      " 5   deadline_day                   14078 non-null  int64  \n",
      " 6   deadline_hr                    14078 non-null  int64  \n",
      " 7   created_at_month               14078 non-null  int64  \n",
      " 8   created_at_day                 14078 non-null  int64  \n",
      " 9   created_at_hr                  14078 non-null  int64  \n",
      " 10  launched_at_month              14078 non-null  int64  \n",
      " 11  launched_at_day                14078 non-null  int64  \n",
      " 12  launched_at_hr                 14078 non-null  int64  \n",
      " 13  create_to_launch_days          14078 non-null  int64  \n",
      " 14  launch_to_deadline_days        14078 non-null  int64  \n",
      " 15  goal_converted                 14078 non-null  float64\n",
      " 16  country_AU                     14078 non-null  int64  \n",
      " 17  country_BE                     14078 non-null  int64  \n",
      " 18  country_CA                     14078 non-null  int64  \n",
      " 19  country_CH                     14078 non-null  int64  \n",
      " 20  country_DE                     14078 non-null  int64  \n",
      " 21  country_DK                     14078 non-null  int64  \n",
      " 22  country_ES                     14078 non-null  int64  \n",
      " 23  country_FR                     14078 non-null  int64  \n",
      " 24  country_GB                     14078 non-null  int64  \n",
      " 25  country_IE                     14078 non-null  int64  \n",
      " 26  country_IT                     14078 non-null  int64  \n",
      " 27  country_LU                     14078 non-null  int64  \n",
      " 28  country_NL                     14078 non-null  int64  \n",
      " 29  country_NO                     14078 non-null  int64  \n",
      " 30  country_NZ                     14078 non-null  int64  \n",
      " 31  country_SE                     14078 non-null  int64  \n",
      " 32  country_US                     14078 non-null  int64  \n",
      " 33  category_Apps                  14078 non-null  int64  \n",
      " 34  category_Blues                 14078 non-null  int64  \n",
      " 35  category_Experimental          14078 non-null  int64  \n",
      " 36  category_Festivals             14078 non-null  int64  \n",
      " 37  category_Flight                14078 non-null  int64  \n",
      " 38  category_Gadgets               14078 non-null  int64  \n",
      " 39  category_Hardware              14078 non-null  int64  \n",
      " 40  category_Immersive             14078 non-null  int64  \n",
      " 41  category_Makerspaces           14078 non-null  int64  \n",
      " 42  category_Musical               14078 non-null  int64  \n",
      " 43  category_Places                14078 non-null  int64  \n",
      " 44  category_Plays                 14078 non-null  int64  \n",
      " 45  category_Restaurants           14078 non-null  int64  \n",
      " 46  category_Robots                14078 non-null  int64  \n",
      " 47  category_Shorts                14078 non-null  int64  \n",
      " 48  category_Software              14078 non-null  int64  \n",
      " 49  category_Sound                 14078 non-null  int64  \n",
      " 50  category_Spaces                14078 non-null  int64  \n",
      " 51  category_Thrillers             14078 non-null  int64  \n",
      " 52  category_Wearables             14078 non-null  int64  \n",
      " 53  category_Web                   14078 non-null  int64  \n",
      " 54  category_Webseries             14078 non-null  int64  \n",
      " 55  deadline_weekday_Monday        14078 non-null  int64  \n",
      " 56  deadline_weekday_Saturday      14078 non-null  int64  \n",
      " 57  deadline_weekday_Sunday        14078 non-null  int64  \n",
      " 58  deadline_weekday_Thursday      14078 non-null  int64  \n",
      " 59  deadline_weekday_Tuesday       14078 non-null  int64  \n",
      " 60  deadline_weekday_Wednesday     14078 non-null  int64  \n",
      " 61  created_at_weekday_Monday      14078 non-null  int64  \n",
      " 62  created_at_weekday_Saturday    14078 non-null  int64  \n",
      " 63  created_at_weekday_Sunday      14078 non-null  int64  \n",
      " 64  created_at_weekday_Thursday    14078 non-null  int64  \n",
      " 65  created_at_weekday_Tuesday     14078 non-null  int64  \n",
      " 66  created_at_weekday_Wednesday   14078 non-null  int64  \n",
      " 67  launched_at_weekday_Monday     14078 non-null  int64  \n",
      " 68  launched_at_weekday_Saturday   14078 non-null  int64  \n",
      " 69  launched_at_weekday_Sunday     14078 non-null  int64  \n",
      " 70  launched_at_weekday_Thursday   14078 non-null  int64  \n",
      " 71  launched_at_weekday_Tuesday    14078 non-null  int64  \n",
      " 72  launched_at_weekday_Wednesday  14078 non-null  int64  \n",
      "dtypes: float64(3), int64(69), object(1)\n",
      "memory usage: 7.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='state',data=df2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Excluding the canceled and suspended projects\n",
    "df2 = df2[~df2['state'].isin(['canceled', 'suspended'])]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12181, 73)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the new dimensions of the data\n",
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n"
     ]
    },
    {
     "data": {
      "image/png": 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GAABgGQINAADAMgQaAACAZQg0AAAAyxBoAAAAliHQAAAALEOgAQAAWIZAAwAAsAyBBgAAYBkCDQAAwDIEGgAAgGUINAAAAMsQaAAAAJYh0AAAACxDoAEAAFiGQAMAALAMgQYAAGAZAg0AAMAyBBoAAIBlCDQAAADLEGgAAACWIdAAAAAsQ6ABAABYhkADAACwDIEGAABgGQINAADAMgQaAACAZQg0AAAAyxBoAAAAliHQAAAALEOgAQAAWIZAAwAAsAyBVs9OnDihIUOGyO12y+FwqKKi4jsfc+DAATkcDhUWFl7z+QEAAPuF+HoC/uYPf/iDPvzwQ3388cdq3ry5IiMjfT0lAADQwBBo9Wz//v1KTU1Vhw4dfD0VAADQQPn0Jc63335baWlpcrlcio6OVlZWlo4fP66+fftq/PjxXmMHDRqknJwcz8+nTp1SXl6eEhIS5HQ6lZycrNdff92z/ZNPPtE999wjt9utiIgIZWZmav/+/Z7tr732mlJTUxUWFqb27dvr5Zdf9mw7ffq0xo4dq5YtWyosLEytWrXS9OnTJUnGGD399NNKTEyU0+lUfHy8xo0bJ0nq27evZs6cqQ0bNsjhcKhv376SJIfDoWXLlnmtJyoqSgsWLLjsf6tTp06pqqrK6wYAAPyTz86glZWVadiwYXr22Wd17733qrq6Wh9++KGMMZf1+BEjRmjTpk2aPXu2OnXqpJKSEv3rX/+SJH3xxRfq3bu3+vbtqzVr1sjtduujjz7S2bNnJUmLFi3SlClTNGfOHKWnp2vHjh0aNWqUGjdurOzsbM2ePVvvvPOOFi9erMTERB06dEiHDh2SJP35z3/WCy+8oPz8fN188806fPiwdu7cKUlasmSJJk6cqKKiIi1ZskShoaH19u81ffp0TZs2rd72BwAA7OXTQDt79qwGDx6sVq1aSZLS0tIu67F79+7V4sWLtWrVKmVlZUmS2rRp49n+0ksvKTIyUvn5+WrUqJEk6Xvf+55n+9SpUzVz5kwNHjxYktS6dWvt2bNHr7zyirKzs1VaWqp27dqpV69ecjgcnvlJUmlpqeLi4pSVlaVGjRopMTFRXbt2lSQ1a9ZM4eHhCg0NVVxc3FX861xo0qRJys3N9fxcVVWlhISEen0OAABgB5+9xNmpUyf169dPaWlpuu+++zRv3jx99dVXl/XYwsJCBQcHq0+fPpfcnpmZ6Ymz8x0/flz79+/Xz372MzVp0sRz+9WvfuV5CTQnJ0eFhYVKSUnRuHHj9P7773sef9999+nkyZNq06aNRo0apaVLl3rOzF1LTqdTbrfb6wYAAPyTzwItODhYq1at0nvvvaebbrpJL774olJSUlRSUqKgoKALXuo8c+aM579dLte37vvbth87dkySNG/ePBUWFnpuRUVF2rx5sySpc+fOKikp0S9/+UudPHlSP/7xj/WjH/1IkpSQkKDi4mK9/PLLcrlcGj16tHr37u01v29yOBzfuh4AAIDz+fQiAYfDoZ49e2ratGnasWOHQkNDtXTpUrVo0UJlZWWecTU1NSoqKvL8nJaWptraWq1fv/6i++3YsaM+/PDDi0ZQbGys4uPj9fnnnys5Odnr1rp1a884t9utoUOHat68efrTn/6kP//5zzp69KikrwNw4MCBmj17ttatW6dNmzZp9+7dl1znN9ezb98+nThx4vL/oQAAQEDx2XvQtmzZotWrV+uOO+5QTEyMtmzZon/+859KTU1V48aNlZubq+XLl6tt27Z6/vnnvT7wNSkpSdnZ2Ro5cqTnIoGDBw+qvLxcP/7xjzV27Fi9+OKL+slPfqJJkyYpMjJSmzdvVteuXZWSkqJp06Zp3LhxioyM1IABA3Tq1Clt375dX331lXJzc/X888+rZcuWSk9PV1BQkN566y3FxcV5rrysqalRt27dFB4erjfeeEMul8vrfWrfdPvtt2vOnDnKyMhQTU2N8vLyLvryKwAAgOTDQHO73dqwYYN++9vfqqqqSq1atdLMmTN155136syZM9q5c6dGjBihkJAQPf7447rtttu8Hj937lw99dRTGj16tI4cOaLExEQ99dRTkqTo6GitWbNGEyZMUJ8+fRQcHKzvf//76tmzpyTpgQceUHh4uJ577jlNmDBBjRs3VlpamuejPSIiIvTss89q3759Cg4O1q233qp3331XQUFBioqK0owZM5Sbm6uamhqlpaXpr3/9q6Kjoy+51pkzZ+qnP/2pMjMzFR8fr1mzZqmgoODa/MMCAIAGz2Eu93MtYJWqqipFRkaqaFKqIsKCfT0dIOAlTrn02xwA4Jxzf78rKyu/9YI/vosTAADAMgQaAACAZQg0AAAAyxBoAAAAliHQAAAALEOgAQAAWIZAAwAAsAyBBgAAYBkCDQAAwDIEGgAAgGUINAAAAMsQaAAAAJYh0AAAACxDoAEAAFiGQAMAALAMgQYAAGAZAg0AAMAyBBoAAIBlCDQAAADLEGgAAACWIdAAAAAsQ6ABAABYhkADAACwDIEGAABgGQINAADAMgQaAACAZQg0AAAAyxBoAAAAliHQAAAALEOgAQAAWIZAAwAAsAyBBgAAYBkCDQAAwDIEGgAAgGUINAAAAMsQaAAAAJYh0AAAACxDoAEAAFiGQAMAALAMgQYAAGAZAg0AAMAyBBoAAIBlCDQAAADLEGgAAACWIdAAAAAsQ6ABAABYhkADAACwDIEGAABgGQINAADAMgQaAACAZQg0AAAAyxBoAAAAliHQAAAALEOgAQAAWIZAAwAAsAyBBgAAYBkCDQAAwDIEGgAAgGUINAAAAMsQaAAAAJYh0AAAACxDoAEAAFiGQAMAALAMgQYAAGAZAg0AAMAyBBoAAIBlQnw9AVydhImb5Xa7fT0NAABQjziDBgAAYBkCDQAAwDIEGgAAgGUINAAAAMsQaAAAAJYh0AAAACxDoAEAAFiGQAMAALAMgQYAAGAZAg0AAMAyBBoAAIBlCDQAAADLEGgAAACWIdAAAAAsQ6ABAABYhkADAACwDIEGAABgmRBfTwB1Y4yRJFVVVfl4JgAA4HKd+7t97u/4pRBoDdSRI0ckSQkJCT6eCQAAuFLV1dWKjIy85HYCrYFq1qyZJKm0tPRbD3BDVlVVpYSEBB06dEhut9vX07lmAmGdgbBGKTDWGQhrlAJjnYGwRsm+dRpjVF1drfj4+G8dR6A1UEFBX799MDIy0or/wV1Lbrfb79coBcY6A2GNUmCsMxDWKAXGOgNhjZJd67ycEytcJAAAAGAZAg0AAMAyBFoD5XQ6NXXqVDmdTl9P5ZoJhDVKgbHOQFijFBjrDIQ1SoGxzkBYo9Rw1+kw33WdJwAAAK4rzqABAABYhkADAACwDIEGAABgGQINAADAMgRaA/TSSy8pKSlJYWFh6tatm7Zu3errKV3Shg0bNHDgQMXHx8vhcGjZsmVe240xmjJlilq2bCmXy6WsrCzt27fPa8zRo0c1fPhwud1uRUVF6Wc/+5mOHTvmNWbXrl3KzMxUWFiYEhIS9Oyzz17rpXlMnz5dt956qyIiIhQTE6NBgwapuLjYa8y///1vjRkzRtHR0WrSpImGDBmiL7/80mtMaWmp7r77boWHhysmJkYTJkzQ2bNnvcasW7dOnTt3ltPpVHJyshYsWHCtl+cxd+5cdezY0fNhjxkZGXrvvfc82/1hjd80Y8YMORwOjR8/3nOfP6zz6aeflsPh8Lq1b9/es90f1ihJX3zxhf7rv/5L0dHRcrlcSktL0/bt2z3b/eH3T1JS0gXH0uFwaMyYMZL841jW1NRo8uTJat26tVwul9q2batf/vKXXt9l6Q/H8gIGDUp+fr4JDQ01v//9780nn3xiRo0aZaKiosyXX37p66ld1Lvvvmt+/vOfmyVLlhhJZunSpV7bZ8yYYSIjI82yZcvMzp07zQ9+8APTunVrc/LkSc+YAQMGmE6dOpnNmzebDz/80CQnJ5thw4Z5tldWVprY2FgzfPhwU1RUZN58803jcrnMK6+8cl3W2L9/fzN//nxTVFRkCgsLzV133WUSExPNsWPHPGMefvhhk5CQYFavXm22b99uunfvbnr06OHZfvbsWdOhQweTlZVlduzYYd59913TvHlzM2nSJM+Yzz//3ISHh5vc3FyzZ88e8+KLL5rg4GCzYsWK67LOd955xyxfvtzs3bvXFBcXm6eeeso0atTIFBUV+c0az7d161aTlJRkOnbsaB577DHP/f6wzqlTp5qbb77ZlJWVeW7//Oc//WqNR48eNa1atTI5OTlmy5Yt5vPPPzcrV640n332mWeMP/z+KS8v9zqOq1atMpLM2rVrjTH+cSyfeeYZEx0dbf72t7+ZkpIS89Zbb5kmTZqYWbNmecb4w7H8JgKtgenatasZM2aM5+eamhoTHx9vpk+f7sNZXZ5vBlptba2Ji4szzz33nOe+iooK43Q6zZtvvmmMMWbPnj1Gktm2bZtnzHvvvWccDof54osvjDHGvPzyy6Zp06bm1KlTnjF5eXkmJSXlGq/o4srLy40ks379emPM12tq1KiReeuttzxjPv30UyPJbNq0yRjzdcgGBQWZw4cPe8bMnTvXuN1uz7qefPJJc/PNN3s919ChQ03//v2v9ZIuqWnTpua1117zuzVWV1ebdu3amVWrVpk+ffp4As1f1jl16lTTqVOni27zlzXm5eWZXr16XXK7v/7+eeyxx0zbtm1NbW2t3xzLu+++24wcOdLrvsGDB5vhw4cbY/z3WPISZwNy+vRpFRQUKCsry3NfUFCQsrKytGnTJh/OrG5KSkp0+PBhr/VERkaqW7dunvVs2rRJUVFR6tKli2dMVlaWgoKCtGXLFs+Y3r17KzQ01DOmf//+Ki4u1ldffXWdVvMflZWVkv7zhfYFBQU6c+aM1zrbt2+vxMREr3WmpaUpNjbWM6Z///6qqqrSJ5984hlz/j7OjfHFsa+pqVF+fr6OHz+ujIwMv1vjmDFjdPfdd18wF39a5759+xQfH682bdpo+PDhKi0tleQ/a3znnXfUpUsX3XfffYqJiVF6errmzZvn2e6Pv39Onz6tN954QyNHjpTD4fCbY9mjRw+tXr1ae/fulSTt3LlTGzdu1J133inJP4+lxHvQGpR//etfqqmp8fo/kiTFxsbq8OHDPppV3Z2b87et5/Dhw4qJifHaHhISombNmnmNudg+zn+O66W2tlbjx49Xz5491aFDB88cQkNDFRUVdcEcr2QNlxpTVVWlkydPXovlXGD37t1q0qSJnE6nHn74YS1dulQ33XSTX60xPz9ff//73zV9+vQLtvnLOrt166YFCxZoxYoVmjt3rkpKSpSZmanq6mq/WePnn3+uuXPnql27dlq5cqUeeeQRjRs3Tn/4wx+85ulPv3+WLVumiooK5eTkeJ7fH47lxIkT9ZOf/ETt27dXo0aNlJ6ervHjx2v48OFe8/SnYylJIdf9GQE/NmbMGBUVFWnjxo2+nso1kZKSosLCQlVWVurtt99Wdna21q9f7+tp1ZtDhw7pscce06pVqxQWFubr6Vwz5848SFLHjh3VrVs3tWrVSosXL5bL5fLhzOpPbW2tunTpol//+teSpPT0dBUVFel3v/udsrOzfTy7a+P111/XnXfeqfj4eF9PpV4tXrxYixYt0h//+EfdfPPNKiws1Pjx4xUfH++3x1LiDFqD0rx5cwUHB19wBc6XX36puLg4H82q7s7N+dvWExcXp/Lycq/tZ8+e1dGjR73GXGwf5z/H9TB27Fj97W9/09q1a3XjjTd67o+Li9Pp06dVUVFxwRyvZA2XGuN2u6/bH9XQ0FAlJyfrlltu0fTp09WpUyfNmjXLb9ZYUFCg8vJyde7cWSEhIQoJCdH69es1e/ZshYSEKDY21i/W+U1RUVH63ve+p88++8xvjmXLli110003ed2XmprqeSnX337/HDx4UB988IEeeOABz33+ciwnTJjgOYuWlpam+++/X48//rjnLLe/HctzCLQGJDQ0VLfccotWr17tua+2tlarV69WRkaGD2dWN61bt1ZcXJzXeqqqqrRlyxbPejIyMlRRUaGCggLPmDVr1qi2tlbdunXzjNmwYYPOnDnjGbNq1SqlpKSoadOm13wdxhiNHTtWS5cu1Zo1a9S6dWuv7bfccosaNWrktc7i4mKVlpZ6rXP37t1ev0BWrVolt9vt+SOTkZHhtY9zY3x57Gtra3Xq1Cm/WWO/fv20e/duFRYWem5dunTR8OHDPf/tD+v8pmPHjmn//v1q2bKl3xzLnj17XvBxN3v37lWrVq0k+c/vn3Pmz5+vmJgY3X333Z77/OVYnjhxQkFB3rkSHBys2tpaSf53LD18cmkC6iw/P984nU6zYMECs2fPHvPggw+aqKgorytwbFJdXW127NhhduzYYSSZ559/3uzYscMcPHjQGPP1pdFRUVHmL3/5i9m1a5f54Q9/eNFLo9PT082WLVvMxo0bTbt27bwuja6oqDCxsbHm/vvvN0VFRSY/P9+Eh4dft0ujH3nkERMZGWnWrVvndbn7iRMnPGMefvhhk5iYaNasWWO2b99uMjIyTEZGhmf7uUvd77jjDlNYWGhWrFhhWrRocdFL3SdMmGA+/fRT89JLL13XS90nTpxo1q9fb0pKSsyuXbvMxIkTjcPhMO+//77frPFizr+K0xj/WOcTTzxh1q1bZ0pKSsxHH31ksrKyTPPmzU15ebnfrHHr1q0mJCTEPPPMM2bfvn1m0aJFJjw83LzxxhueMf7w+8eYr6/mT0xMNHl5eRds84djmZ2dbW644QbPx2wsWbLENG/e3Dz55JOeMf5yLM9HoDVAL774oklMTDShoaGma9euZvPmzb6e0iWtXbvWSLrglp2dbYz5+vLoyZMnm9jYWON0Ok2/fv1McXGx1z6OHDlihg0bZpo0aWLcbrf56U9/aqqrq73G7Ny50/Tq1cs4nU5zww03mBkzZlyvJV50fZLM/PnzPWNOnjxpRo8ebZo2bWrCw8PNvffea8rKyrz2c+DAAXPnnXcal8tlmjdvbp544glz5swZrzFr16413//+901oaKhp06aN13NcayNHjjStWrUyoaGhpkWLFqZfv36eODPGP9Z4Md8MNH9Y59ChQ03Lli1NaGioueGGG8zQoUO9Ph/MH9ZojDF//etfTYcOHYzT6TTt27c3r776qtd2f/j9Y4wxK1euNJIumLsx/nEsq6qqzGOPPWYSExNNWFiYadOmjfn5z3/u9XEY/nIsz+cw5ryP4gUAAIDP8R40AAAAyxBoAAAAliHQAAAALEOgAQAAWIZAAwAAsAyBBgAAYBkCDQAAwDIEGgAAgGUINAAAAMsQaADgRw4cOCCHw6HCwkJfTwXAVSDQAAAALEOgAUA9qq2t1bPPPqvk5GQ5nU4lJibqmWeekSTt3r1bt99+u1wul6Kjo/Xggw/q2LFjnsf27dtX48eP99rfoEGDlJOT4/k5KSlJv/71rzVy5EhFREQoMTFRr776qmd769atJUnp6elyOBzq27fvNVsrgGuHQAOAejRp0iTNmDFDkydP1p49e/THP/5RsbGxOn78uPr376+mTZtq27Zteuutt/TBBx9o7NixV/wcM2fOVJcuXbRjxw6NHj1ajzzyiIqLiyVJW7dulSR98MEHKisr05IlS+p1fQCujxBfTwAA/EV1dbVmzZqlOXPmKDs7W5LUtm1b9erVS/PmzdO///1vLVy4UI0bN5YkzZkzRwMHDtRvfvMbxcbGXvbz3HXXXRo9erQkKS8vTy+88ILWrl2rlJQUtWjRQpIUHR2tuLi4el4hgOuFM2gAUE8+/fRTnTp1Sv369bvotk6dOnniTJJ69uyp2tpaz9mvy9WxY0fPfzscDsXFxam8vLzuEwdgHQINAOqJy+W6qscHBQXJGON135kzZy4Y16hRI6+fHQ6Hamtrr+q5AdiFQAOAetKuXTu5XC6tXr36gm2pqanauXOnjh8/7rnvo48+UlBQkFJSUiRJLVq0UFlZmWd7TU2NioqKrmgOoaGhnscCaLgINACoJ2FhYcrLy9OTTz6phQsXav/+/dq8ebNef/11DR8+XGFhYcrOzlZRUZHWrl2rRx99VPfff7/n/We33367li9fruXLl+t//ud/9Mgjj6iiouKK5hATEyOXy6UVK1boyy+/VGVl5TVYKYBrjUADgHo0efJkPfHEE5oyZYpSU1M1dOhQlZeXKzw8XCtXrtTRo0d166236kc/+pH69eunOXPmeB47cuRIZWdna8SIEerTp4/atGmj22677YqePyQkRLNnz9Yrr7yi+Ph4/fCHP6zvJQK4Dhzmm294AAAAgE9xBg0AAMAyBBoAAIBlCDQAAADLEGgAAACWIdAAAAAsQ6ABAABYhkADAACwDIEGAABgGQINAADAMgQaAACAZQg0AAAAy/x/qgZnBhJ5magAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='state',data=df2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df2.drop('state',axis=1)\n",
    "y=df2['state']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      failed       0.76      0.90      0.82      8218\n",
      "  successful       0.66      0.42      0.51      3963\n",
      "\n",
      "    accuracy                           0.74     12181\n",
      "   macro avg       0.71      0.66      0.67     12181\n",
      "weighted avg       0.73      0.74      0.72     12181\n",
      "\n"
     ]
    }
   ],
   "source": [
    "random_forest = RandomForestClassifier()\n",
    "# creating kfold cross validation\n",
    "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "\n",
    "y_pred = cross_val_predict(random_forest, X, y, cv=kf)\n",
    "\n",
    "# Printing the classification report\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      failed       0.78      0.87      0.83      8218\n",
      "  successful       0.65      0.50      0.56      3963\n",
      "\n",
      "    accuracy                           0.75     12181\n",
      "   macro avg       0.72      0.68      0.69     12181\n",
      "weighted avg       0.74      0.75      0.74     12181\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gbc=GradientBoostingClassifier()\n",
    "\n",
    "# Creating k-fold cross-validation\n",
    "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "\n",
    "y_pred = cross_val_predict(gbc, X, y, cv=kf)\n",
    "\n",
    "# Printing the classification report\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyperparameters: {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 157, 'subsample': 1.0}\n",
      "Best Score: 0.7436377102773706\n",
      "Classification Report on Full Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      failed       0.80      0.86      0.83      1652\n",
      "  successful       0.64      0.54      0.59       785\n",
      "\n",
      "    accuracy                           0.75      2437\n",
      "   macro avg       0.72      0.70      0.71      2437\n",
      "weighted avg       0.75      0.75      0.75      2437\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "# Instantiating a Gradient Boosting classifier\n",
    "gbc = GradientBoostingClassifier(max_features='sqrt', random_state=42)\n",
    "\n",
    "# Defining the hyperparameter grid\n",
    "gbc = GradientBoostingClassifier()\n",
    "\n",
    "# Defining the hyperparameter grid\n",
    "param_dist = {\n",
    "    'n_estimators': randint(50, 200),\n",
    "    'learning_rate': [0.001, 0.01, 0.1, 0.2, 0.3],\n",
    "    'subsample': [0.8, 0.9, 1.0],\n",
    "    'max_depth': [3, 4, 5, 6, 7, 8, 9, 10,11]\n",
    "}\n",
    "\n",
    "# Creating KFold cross-validation\n",
    "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "\n",
    "# Creating RandomizedSearchCV with KFold\n",
    "random_search = RandomizedSearchCV(gbc, param_distributions=param_dist, n_iter=10, scoring='accuracy', cv=kf, random_state=42)\n",
    "\n",
    "# Fitting the RandomizedSearchCV on the full dataset\n",
    "random_search.fit(X_train, y_train)\n",
    "\n",
    "# Printing the best hyperparameters\n",
    "print(\"Best Hyperparameters:\", random_search.best_params_)\n",
    "print('Best Score:', random_search.best_score_)\n",
    "\n",
    "# Getting the best model\n",
    "best_model = random_search.best_estimator_\n",
    "\n",
    "# Making predictions on the full dataset\n",
    "y_pred = best_model.predict(X_test)\n",
    "\n",
    "# Printing the classification report\n",
    "print(\"Classification Report on Full Dataset:\")\n",
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Best Hyperparameters: {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 157, 'subsample': 1.0}\n",
    "tuned_params=random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Best Threshold: 0.492\n",
      "Average Best Accuracy: 0.7540428317408211\n",
      "------------------------------------------------------------\n",
      "Final Model Performance with Threshold of 0.492\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      failed       0.85      0.91      0.88      8218\n",
      "  successful       0.77      0.67      0.72      3963\n",
      "\n",
      "    accuracy                           0.83     12181\n",
      "   macro avg       0.81      0.79      0.80     12181\n",
      "weighted avg       0.83      0.83      0.83     12181\n",
      "\n"
     ]
    }
   ],
   "source": [
    "n_splits = 5\n",
    "skf = StratifiedKFold(n_splits=n_splits, random_state=42, shuffle=True)\n",
    "\n",
    "# Placeholder for the best threshold\n",
    "best_thresholds = []\n",
    "best_accuracies = []\n",
    "\n",
    "# Looping over each fold\n",
    "for train_index, test_index in skf.split(X, y):\n",
    "    X_train_th, X_test_th = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_train_th, y_test_th = y.iloc[train_index], y.iloc[test_index]\n",
    "\n",
    "    # Training the model\n",
    "    model = GradientBoostingClassifier(**tuned_params)\n",
    "    model.fit(X_train_th, y_train_th)\n",
    "\n",
    "    # Finding the best threshold for this fold\n",
    "    best_accuracy = 0\n",
    "    best_threshold = 0.5\n",
    "    thresholds = np.linspace(0, 1, 101)\n",
    "    for th in thresholds:\n",
    "        preds = np.where(model.predict_proba(X_test_th)[:, 1] >= th, 'successful', 'failed')\n",
    "        acc = accuracy_score(y_test_th, preds)\n",
    "        if acc > best_accuracy:\n",
    "            best_accuracy = acc\n",
    "            best_threshold = th\n",
    "\n",
    "    best_thresholds.append(best_threshold)\n",
    "    best_accuracies.append(best_accuracy)\n",
    "\n",
    "# Calculate the average best threshold\n",
    "final_best_threshold = np.mean(best_thresholds)\n",
    "final_best_accuracy = np.mean(best_accuracies)\n",
    "\n",
    "print(f'Average Best Threshold: {final_best_threshold}')\n",
    "print(f'Average Best Accuracy: {final_best_accuracy}')\n",
    "\n",
    "# Retraining the model on the entire dataset\n",
    "final_model = GradientBoostingClassifier(**tuned_params)\n",
    "final_model.fit(X, y)\n",
    "\n",
    "# Final prediction \n",
    "final_preds = np.where(final_model.predict_proba(X)[:, 1] >= final_best_threshold, 'successful', 'failed')\n",
    "\n",
    "# Final evaluation report\n",
    "print('------------------------------------------------------------')\n",
    "print(f'Final Model Performance with Threshold of {final_best_threshold}')\n",
    "print(classification_report(y, final_preds, target_names=['failed', 'successful']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>goal_converted</th>\n",
       "      <td>0.219480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_Web</th>\n",
       "      <td>0.121220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>create_to_launch_days</th>\n",
       "      <td>0.104198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_Software</th>\n",
       "      <td>0.070837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>launch_to_deadline_days</th>\n",
       "      <td>0.057357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name_len_clean</th>\n",
       "      <td>0.048279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>launched_at_hr</th>\n",
       "      <td>0.042981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deadline_hr</th>\n",
       "      <td>0.030473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deadline_day</th>\n",
       "      <td>0.026866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>launched_at_day</th>\n",
       "      <td>0.022213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         importance\n",
       "goal_converted             0.219480\n",
       "category_Web               0.121220\n",
       "create_to_launch_days      0.104198\n",
       "category_Software          0.070837\n",
       "launch_to_deadline_days    0.057357\n",
       "name_len_clean             0.048279\n",
       "launched_at_hr             0.042981\n",
       "deadline_hr                0.030473\n",
       "deadline_day               0.026866\n",
       "launched_at_day            0.022213"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = pd.DataFrame(model.feature_importances_, index=X.columns, columns=['importance']).sort_values('importance', ascending=False)\n",
    "feature_importances.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the top features based on thier importance\n",
    "(pd.Series(final_model.feature_importances_, index=X.columns)\n",
    "    .nlargest(10)  \n",
    "    .plot(kind='barh', figsize=[16,10])\n",
    "    .invert_yaxis()) \n",
    "plt.yticks(size=15)\n",
    "plt.title('Most Important Features', size=18);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|===================| 12155/12181 [00:54<00:00]        "
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x950 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explainer = shap.Explainer(final_model, X, seed=42)\n",
    "shap_values = explainer.shap_values(X)\n",
    "# Summary plot\n",
    "shap.summary_plot(shap_values, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['state', 'disable_communication', 'country', 'staff_pick', 'category',\n",
       "       'name_len_clean', 'blurb_len_clean', 'deadline_weekday',\n",
       "       'created_at_weekday', 'launched_at_weekday', 'deadline_month',\n",
       "       'deadline_day', 'deadline_hr', 'created_at_month', 'created_at_day',\n",
       "       'created_at_hr', 'launched_at_month', 'launched_at_day',\n",
       "       'launched_at_hr', 'create_to_launch_days', 'launch_to_deadline_days',\n",
       "       'goal_converted'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##  Clustering\n",
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Excluding the canceled and suspended projects\n",
    "df = df[~df['state'].isin(['canceled', 'suspended'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>goal_converted</th>\n",
       "      <th>create_to_launch_days</th>\n",
       "      <th>name_len_clean</th>\n",
       "      <th>launch_to_deadline_days</th>\n",
       "      <th>blurb_len_clean</th>\n",
       "      <th>category</th>\n",
       "      <th>staff_pick</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.049827</td>\n",
       "      <td>0.150504</td>\n",
       "      <td>1.198746</td>\n",
       "      <td>-0.377655</td>\n",
       "      <td>0.298116</td>\n",
       "      <td>Gadgets</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.065493</td>\n",
       "      <td>-0.447896</td>\n",
       "      <td>-0.045171</td>\n",
       "      <td>-1.70864</td>\n",
       "      <td>-0.017133</td>\n",
       "      <td>Experimental</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.064687</td>\n",
       "      <td>-0.251699</td>\n",
       "      <td>-0.45981</td>\n",
       "      <td>2.117941</td>\n",
       "      <td>0.298116</td>\n",
       "      <td>Plays</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.038913</td>\n",
       "      <td>-0.23208</td>\n",
       "      <td>-1.289088</td>\n",
       "      <td>0.204651</td>\n",
       "      <td>0.613364</td>\n",
       "      <td>Gadgets</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.06596</td>\n",
       "      <td>-0.447896</td>\n",
       "      <td>1.613385</td>\n",
       "      <td>-0.377655</td>\n",
       "      <td>-0.332381</td>\n",
       "      <td>Gadgets</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  goal_converted create_to_launch_days name_len_clean launch_to_deadline_days  \\\n",
       "0      -0.049827              0.150504       1.198746               -0.377655   \n",
       "1      -0.065493             -0.447896      -0.045171                -1.70864   \n",
       "2      -0.064687             -0.251699       -0.45981                2.117941   \n",
       "3      -0.038913              -0.23208      -1.289088                0.204651   \n",
       "4       -0.06596             -0.447896       1.613385               -0.377655   \n",
       "\n",
       "  blurb_len_clean      category staff_pick  \n",
       "0        0.298116       Gadgets      False  \n",
       "1       -0.017133  Experimental      False  \n",
       "2        0.298116         Plays      False  \n",
       "3        0.613364       Gadgets      False  \n",
       "4       -0.332381       Gadgets      False  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_features = ['goal_converted', 'create_to_launch_days', 'name_len_clean', 'launch_to_deadline_days', 'blurb_len_clean']\n",
    "\n",
    "# Selecting the numeric features to be scaled and the other columns to be clustered\n",
    "X_cluster_num = df[num_features]\n",
    "category_column = df[['category','staff_pick']]\n",
    "\n",
    "# Initializng the StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Transforming the numeric features\n",
    "X_scaled_array = scaler.fit_transform(X_cluster_num)\n",
    "\n",
    "# Concatenating the scaled numeric features with the other columns\n",
    "X_combined = np.column_stack((X_scaled_array, category_column))\n",
    "X_scaled = pd.DataFrame(X_combined, columns=num_features + ['category','staff_pick'])\n",
    "X_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbr_clusters_list = list(range(1, 10))\n",
    "cat_cols = X_scaled.select_dtypes('object').columns.tolist()\n",
    "\n",
    "cost_for_each_nbr_cluster = []\n",
    "\n",
    "for nbr_clusters in nbr_clusters_list:\n",
    "    \n",
    "    \n",
    "    # Initializing the KPrototypes model\n",
    "    kproto = KPrototypes(n_clusters=nbr_clusters, random_state=42)\n",
    "\n",
    "    # Fitting the model to the data\n",
    "    kproto.fit(X_scaled, categorical=5)\n",
    "\n",
    "    # Accessing the cost (sum of within cluster dissimilarities) of the model\n",
    "    cost_for_each_nbr_cluster.append(kproto.cost_)\n",
    "\n",
    "# Plotting the cost\n",
    "plt.figure(figsize=(16, 6))\n",
    "plt.plot(nbr_clusters_list, cost_for_each_nbr_cluster, marker='o')\n",
    "plt.title(\"Determine the Number of Clusters: Elbow Method for k-Prototypes Clustering\")\n",
    "plt.xlabel(\"Number of Clusters\")\n",
    "plt.ylabel(\"Cost\")\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KPrototypes(gamma=0.443095719727341, n_clusters=5, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KPrototypes</label><div class=\"sk-toggleable__content\"><pre>KPrototypes(gamma=0.443095719727341, n_clusters=5, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KPrototypes(gamma=0.443095719727341, n_clusters=5, random_state=42)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kproto = KPrototypes(n_clusters=5, random_state=42,n_init=10)\n",
    "kproto.fit(X_scaled, categorical=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>goal_converted</th>\n",
       "      <th>create_to_launch_days</th>\n",
       "      <th>name_len_clean</th>\n",
       "      <th>launch_to_deadline_days</th>\n",
       "      <th>blurb_len_clean</th>\n",
       "      <th>staff_pick</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>74296.4443028004</td>\n",
       "      <td>475.00941176470417</td>\n",
       "      <td>5.710588235294118</td>\n",
       "      <td>35.698823529411754</td>\n",
       "      <td>12.840000000000048</td>\n",
       "      <td>0.13176470588235295</td>\n",
       "      <td>Hardware</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50125.67408502953</td>\n",
       "      <td>25.51502504173643</td>\n",
       "      <td>3.3232470784641666</td>\n",
       "      <td>29.321786310517634</td>\n",
       "      <td>11.539232053422293</td>\n",
       "      <td>0.08806343906510851</td>\n",
       "      <td>Web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45335.850539654624</td>\n",
       "      <td>36.96196868008962</td>\n",
       "      <td>6.990644702054034</td>\n",
       "      <td>30.51698189953236</td>\n",
       "      <td>14.597315436241516</td>\n",
       "      <td>0.15334553589587147</td>\n",
       "      <td>Hardware</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>143768.73858022684</td>\n",
       "      <td>30.54429760156675</td>\n",
       "      <td>4.644640234948608</td>\n",
       "      <td>56.209985315712174</td>\n",
       "      <td>12.944199706314224</td>\n",
       "      <td>0.07342143906020558</td>\n",
       "      <td>Web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56606002.60000002</td>\n",
       "      <td>11.999999999999964</td>\n",
       "      <td>4.5</td>\n",
       "      <td>39.75</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Hardware</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       goal_converted create_to_launch_days      name_len_clean  \\\n",
       "0    74296.4443028004    475.00941176470417   5.710588235294118   \n",
       "1   50125.67408502953     25.51502504173643  3.3232470784641666   \n",
       "2  45335.850539654624     36.96196868008962   6.990644702054034   \n",
       "3  143768.73858022684     30.54429760156675   4.644640234948608   \n",
       "4   56606002.60000002    11.999999999999964                 4.5   \n",
       "\n",
       "  launch_to_deadline_days     blurb_len_clean           staff_pick  category  \n",
       "0      35.698823529411754  12.840000000000048  0.13176470588235295  Hardware  \n",
       "1      29.321786310517634  11.539232053422293  0.08806343906510851       Web  \n",
       "2       30.51698189953236  14.597315436241516  0.15334553589587147  Hardware  \n",
       "3      56.209985315712174  12.944199706314224  0.07342143906020558       Web  \n",
       "4                   39.75                10.5                  0.0  Hardware  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "cluster_centroids_scaled = kproto.cluster_centroids_\n",
    "\n",
    "# Defining the number of numeric features\n",
    "num_numeric_features = len(num_features)\n",
    "\n",
    "# Separating numeric and categorical parts\n",
    "cluster_centroids_numeric = cluster_centroids_scaled[:, :num_numeric_features]\n",
    "cluster_centroids_categorical = cluster_centroids_scaled[:, num_numeric_features:]\n",
    "\n",
    "# Reversing the scaling for the numeric features\n",
    "cluster_centroids_original_numeric = scaler.inverse_transform(cluster_centroids_numeric)\n",
    "\n",
    "# Concatenating the reversed numeric features with the categorical features\n",
    "cluster_centroids_combined = np.column_stack((cluster_centroids_original_numeric, cluster_centroids_categorical))\n",
    "\n",
    "# Creating a DataFrame with the correct column order\n",
    "cluster_centroids_df = pd.DataFrame(cluster_centroids_combined, columns=num_features + ['staff_pick', 'category'])\n",
    "\n",
    "# Displaying the DataFrame\n",
    "cluster_centroids_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a column with cluster labels \n",
    "df['cluster'] = kproto.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster\n",
       "2    0.403661\n",
       "1    0.393400\n",
       "3    0.167720\n",
       "0    0.034890\n",
       "4    0.000328\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  checking the number of projects in each cluster\n",
    "df['cluster'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of Project Success in Cluster 0\n",
      "state\n",
      "failed        0.698824\n",
      "successful    0.301176\n",
      "Name: proportion, dtype: float64\n",
      "-------------------------------------------\n",
      "Percentage of Project Success in Cluster 1\n",
      "state\n",
      "failed        0.718698\n",
      "successful    0.281302\n",
      "Name: proportion, dtype: float64\n",
      "-------------------------------------------\n",
      "Percentage of Project Success in Cluster 2\n",
      "state\n",
      "failed        0.581655\n",
      "successful    0.418345\n",
      "Name: proportion, dtype: float64\n",
      "-------------------------------------------\n",
      "Percentage of Project Success in Cluster 3\n",
      "state\n",
      "failed        0.789525\n",
      "successful    0.210475\n",
      "Name: proportion, dtype: float64\n",
      "-------------------------------------------\n",
      "Percentage of Project Success in Cluster 4\n",
      "state\n",
      "failed    1.0\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculating the success rate for each cluster\n",
    "print('Percentage of Project Success in Cluster 0')\n",
    "print(df[df['cluster']==0]['state'].value_counts(normalize=True))\n",
    "print('-------------------------------------------')\n",
    "print('Percentage of Project Success in Cluster 1')\n",
    "print(df[df['cluster']==1]['state'].value_counts(normalize=True))\n",
    "print('-------------------------------------------')    \n",
    "print('Percentage of Project Success in Cluster 2')\n",
    "print(df[df['cluster']==2]['state'].value_counts(normalize=True))\n",
    "print('-------------------------------------------')\n",
    "print('Percentage of Project Success in Cluster 3')\n",
    "print(df[df['cluster']==3]['state'].value_counts(normalize=True))\n",
    "print('-------------------------------------------')\n",
    "print('Percentage of Project Success in Cluster 4')\n",
    "print(df[df['cluster']==4]['state'].value_counts(normalize=True))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n"
     ]
    },
    {
     "data": {
      "image/png": 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eew8JCQmIiYmp0JaTk4PS0lK1a+3RowcAVLhCrfzIW0BAgNrLVJeurm6F79mWLVtw584dlWmBgYH466+/8M0331RYxtOO/FTXqFGjYGNjg/Hjx+Pq1asV2rOzs8WwUpnyq8D+/d+CUqmscGorLy+vwv5yd3eHjo6Oyu0OKvsu6OrqIjAwENu2bav0Hw737t178gYSVYFHiIieYPTo0Xj06BHeeecdODs7o7i4GMePH8fmzZvh4OCAoUOHin2HDx+OuXPnYvjw4Wjbti3i4+Mr/UGZM2cODhw4gM6dO4uXy2dkZGDLli04duwYzMzMMGHCBGzduhXvvvsuhg0bBi8vL9y/fx+7du3CypUr4enpiQ8++AC//PILRo0ahcOHD6NDhw5QKpW4cuUKfvnlF/FeObNmzUJ8fDwCAgJgb2+P7OxsLF++HI0bN8Ybb7wBAPDz84O1tTU6dOgAKysrXL58Gd988w0CAgKeeYDtJ598gkWLFmHu3LnYtGkT/Pz8IJfL0atXL3z44YfIz8/HmjVrYGlpKR5pK+fl5YUVK1bgiy++gJOTEywtLfHmm29iwoQJ2LVrF3r27IkhQ4bAy8sLBQUFSE5OxtatW3Hz5k2VIyrV4enpieDgYKxevRo5OTno3LkzTp06hfXr16NPnz7o2rXrM21/udLSUvz000+Vtr3zzjswNDREz549MWvWLAwdOhT/+9//kJycjA0bNqBp06Yq/YOCgvDDDz8gPDwcp06dQseOHVFQUICDBw/i448/Ru/evZ+rVuCfIzw7duxAjx490Lp1a5U7VZ89exY///wzvL29nzi/m5sbXn/9dUREROD+/fswNzfHpk2bKoSfQ4cOISwsDO+++y5atGiB0tJS/Pjjj2LYKefl5YWDBw9iwYIFsLW1haOjI9q3b4+5c+fi8OHDaN++PUaMGAFXV1fcv38fZ8+excGDByv9hwDRU2nr8jaium7//v3CsGHDBGdnZ8HIyEiQy+WCk5OTMHr0aCErK0ul76NHj4SQkBDB1NRUMDY2Ft577z0hOzu7wmX3giAIt27dEoKCggQLCwtBoVAITZs2FUJDQ4WioiKxz99//y2EhYUJr7zyiiCXy4XGjRsLwcHBKpcZFxcXC1999ZXg5uYmKBQKoUGDBoKXl5cwc+ZMITc3VxAEQYiLixN69+4t2NraCnK5XLC1tRUGDhyocun6qlWrhE6dOgkNGzYUFAqF0KxZM2HChAniMp6k/BLv+fPnV9o+ZMgQQVdXV7h+/bogCIKwa9cuwcPDQ9DT0xMcHByEr776SryU/N+XaWdmZgoBAQGCsbGxAEDlsu6HDx8KERERgpOTkyCXy4VGjRoJ//vf/4Svv/66wu0Q/quyy+4FQRBKSkqEmTNnCo6OjkL9+vUFOzs7ISIiQigsLFTpZ29vLwQEBFS5jv+uD0+47P7f21xYWCiMHz9esLGxEfT19YUOHToICQkJFS5pF4R/vmdTpkwRa7W2thb69esnpKamCoJQ9T6p7Lv4JHfv3hXGjRsntGjRQtDT0xMMDAwELy8v4csvv1T5XlRWY2pqquDr6ysoFArByspK+Oyzz4TY2FiVy+5v3LghDBs2TGjWrJmgp6cnmJubC127dhUOHjyosqwrV64InTp1EvT19QUAKpfgZ2VlCaGhoYKdnZ34Wfj4+AirV68W+5Rfdv+0y/uJBEEQZIKgoWOtRERERC8ojiEiIiIiyWMgIiIiIsljICIiIiLJYyAiIiIiyWMgIiIiIsljICIiIiLJ440Zq6GsrAx3796FsbFxtZ95RERERNolCAIePnwIW1tb8YHWT8JAVA13794VH5ZJREREL5bbt29X+dBggIGoWsofX3D79m2YmJhouRoiIiKqjry8PNjZ2VXrMUQMRNVQfprMxMSEgYiIiOgFU53hLhxUTURERJLHQERERESSx0BEREREkscxREREJAlKpRIlJSXaLoM0TC6XP/WS+upgICIiopeaIAjIzMxETk6OtkuhGqCjowNHR0fI5fLnWo5WA9GKFSuwYsUK3Lx5EwDg5uaGadOmoXv37gCALl264OjRoyrzfPjhh1i5cqX4Pj09HR999BEOHz4MIyMjBAcHIzIyEvXq/d+mHTlyBOHh4bh48SLs7OwwdepUDBkypMa3j4iItK88DFlaWsLAwIA32H2JlN84OSMjA02aNHmufavVQNS4cWPMnTsXzZs3hyAIWL9+PXr37o0//vgDbm5uAIARI0Zg1qxZ4jwGBgbi30qlEgEBAbC2tsbx48eRkZGBoKAg1K9fH3PmzAEApKWlISAgAKNGjcKGDRsQFxeH4cOHw8bGBv7+/rW7wUREVKuUSqUYhho2bKjtcqgGWFhY4O7duygtLUX9+vWfeTkyQRAEDdb13MzNzTF//nyEhISgS5cuaN26NRYtWlRp3/3796Nnz564e/curKysAAArV67EpEmTcO/ePcjlckyaNAl79+7FhQsXxPkGDBiAnJwcREdHV6umvLw8mJqaIjc3l/chIiJ6gRQWFiItLQ0ODg7Q19fXdjlUAx4/foybN2/C0dERenp6Km3q/H7XmavMlEolNm3ahIKCAnh7e4vTN2zYgEaNGqFVq1aIiIjAo0ePxLaEhAS4u7uLYQgA/P39kZeXh4sXL4p9fH19Vdbl7++PhISEJ9ZSVFSEvLw8lRcREb24eJrs5aWpfav1QdXJycnw9vZGYWEhjIyMsGPHDri6ugIA3n//fdjb28PW1hbnz5/HpEmTkJKSgu3btwP457zwv8MQAPF9ZmZmlX3y8vLw+PHjSv/FEBkZiZkzZ2p8W4mIiKhu0nogatmyJZKSkpCbm4utW7ciODgYR48ehaurK0aOHCn2c3d3h42NDXx8fJCamopmzZrVWE0REREIDw8X35c/C4WIiIheTlo/ZSaXy+Hk5AQvLy9ERkbC09MTixcvrrRv+/btAQDXr18HAFhbWyMrK0ulT/l7a2vrKvuYmJg88XyyQqEQn1vG55cREVFdMWTIEPTp00ft+WbMmIHWrVtrvJ6XidYD0X+VlZWhqKio0rakpCQAgI2NDQDA29sbycnJyM7OFvvExsbCxMREPO3m7e2NuLg4leXExsaqjFMiIiIiiRO0aPLkycLRo0eFtLQ04fz588LkyZMFmUwmHDhwQLh+/bowa9Ys4cyZM0JaWprw66+/Ck2bNhU6deokzl9aWiq0atVK8PPzE5KSkoTo6GjBwsJCiIiIEPvcuHFDMDAwECZMmCBcvnxZWLZsmaCrqytER0dXu87c3FwBgJCbm6vR7Sciopr1+PFj4dKlS8Ljx4+1XYpatmzZIrRq1UrQ09MTzM3NBR8fH+HTTz8VAKi8Dh8+LAiCIEycOFFo3ry5oK+vLzg6OgpTp04ViouLBUEQhLVr11aYb+3atYIgCMKDBw+EkJAQoVGjRoKxsbHQtWtXISkpSUtb/Wyq2sfq/H5rdQxRdnY2goKCkJGRAVNTU3h4eCAmJgZvvfUWbt++jYMHD2LRokUoKCiAnZ0dAgMDMXXqVHF+XV1d7NmzBx999BG8vb1haGiI4OBglfsWOTo6Yu/evRg3bhwWL16Mxo0b49tvv+U9iIiIqE7KyMjAwIEDMW/ePLzzzjt4+PAhfvvtNwQFBSE9PR15eXlYu3YtgH9uVQMAxsbGWLduHWxtbZGcnIwRI0bA2NgYEydORP/+/XHhwgVER0fj4MGDAABTU1MAwLvvvgt9fX3s378fpqamWLVqFXx8fHD16lVx2VJR5+5DVBfxPkQvLq8JP2i7hCdKnB+k7RKIXnrl9yGq7B41ddXZs2fh5eWFmzdvwt7eXqVtyJAhyMnJwc6dO6tcxtdff41NmzbhzJkzAP4ZQ7Rz505x6AkAHDt2DAEBAcjOzoZCoRCnOzk5YeLEiSoXNtVlVe1jdX6/tX6VGREREf0fT09P+Pj4wN3dHf7+/vDz80O/fv3QoEGDJ86zefNmLFmyBKmpqcjPz0dpaelTA8C5c+eQn59f4Q7ejx8/Rmpqqka25UXCQERERFSH6OrqIjY2FsePH8eBAwewdOlSTJkyBSdPnqy0f0JCAgYNGoSZM2fC398fpqam2LRpE6KioqpcT35+PmxsbHDkyJEKbWZmZhrYkhcLAxEREVEdI5PJ0KFDB3To0AHTpk2Dvb09duzYAblcDqVSqdL3+PHjsLe3x5QpU8Rpt27dUulT2Xxt2rRBZmYm6tWrBwcHhxrblhcFAxEREVEdcvLkScTFxcHPzw+WlpY4efIk7t27BxcXFxQWFiImJgYpKSlo2LAhTE1N0bx5c6Snp2PTpk1o164d9u7dix07dqgs08HBAWlpaUhKSkLjxo1hbGwMX19feHt7o0+fPpg3bx5atGiBu3fvYu/evXjnnXfQtm1bLX0C2lHn7kNEREQkZSYmJoiPj0ePHj3QokULTJ06FVFRUejevTtGjBiBli1bom3btrCwsMDvv/+Ot99+G+PGjUNYWBhat26N48eP4/PPP1dZZmBgILp164auXbvCwsICP//8M2QyGfbt24dOnTph6NChaNGiBQYMGIBbt25VeOSVFPAqs2rgVWYvLl5lRiRtL+JVZqQeTV1lxiNEREREJHkMRERERCR5DEREREQkeQxEREREJHkMRERERCR5DEREREQkeQxEREREJHkMRERERCR5DEREREQkeQxEREREdZAgCBg5ciTMzc0hk8mQlJRUZf+bN2+q9Dty5AhkMhlycnKeqw4HBwcsWrTouZbxIuDDXYmISJJq89E+z/KonujoaKxbtw5HjhxB06ZN0ahRoyr729nZISMj46n9qHIMRERERHVQamoqbGxs8L///a9a/XV1dWFtbV3DVb28eMqMiIiojhkyZAhGjx6N9PR0yGQyODg4IDo6Gm+88QbMzMzQsGFD9OzZE6mpqeI8/z1lVpljx46hY8eO0NfXh52dHcaMGYOCggKxPTs7G7169YK+vj4cHR2xYcOGmtzMOoWBiIiIqI5ZvHgxZs2ahcaNGyMjIwOnT59GQUEBwsPDcebMGcTFxUFHRwfvvPMOysrKqrXM1NRUdOvWDYGBgTh//jw2b96MY8eOISwsTOwzZMgQ3L59G4cPH8bWrVuxfPlyZGdn19Rm1ik8ZUZERFTHmJqawtjYWOU0WGBgoEqf77//HhYWFrh06RJatWr11GVGRkZi0KBBGDt2LACgefPmWLJkCTp37owVK1YgPT0d+/fvx6lTp9CuXTsAwHfffQcXFxfNblwdxSNEREREL4Br165h4MCBaNq0KUxMTODg4AAASE9Pr9b8586dw7p162BkZCS+/P39UVZWhrS0NFy+fBn16tWDl5eXOI+zszPMzMxqYGvqHh4hIiIiegH06tUL9vb2WLNmDWxtbVFWVoZWrVqhuLi4WvPn5+fjww8/xJgxYyq0NWnSBFevXtV0yS8UBiIiIqI67u+//0ZKSgrWrFmDjh07AvhngLQ62rRpg0uXLsHJyanSdmdnZ5SWliIxMVE8ZZaSkvLc9zF6UfCUGRERUR3XoEEDNGzYEKtXr8b169dx6NAhhIeHq7WMSZMm4fjx4wgLC0NSUhKuXbuGX3/9VRxU3bJlS3Tr1g0ffvghTp48icTERAwfPhz6+vo1sUl1DgMRERFRHaejo4NNmzYhMTERrVq1wrhx4zB//ny1luHh4YGjR4/i6tWr6NixI1599VVMmzYNtra2Yp+1a9fC1tYWnTt3Rt++fTFy5EhYWlpqenPqJJkgCIK2i6jr8vLyYGpqitzcXJiYmGi7HFJDbd6JVl3PcudaIlJPYWEh0tLS4OjoCD09PW2XQzWgqn2szu83jxARERGR5DEQERERkeQxEBEREZHkMRARERGR5DEQERERkeQxEBEREZHkMRARERGR5DEQERERkeQxEBEREZHkMRARERFRrXj06BECAwNhYmICmUxWrQfH3rx5EzKZDElJSTVaG592T0REkpQ+y73W1tVkWnKtrasuW79+PX777TccP34cjRo1gqmpqbZLEjEQERERUa1ITU2Fi4sLWrVqpe1SKuApMyIiojpo69atcHd3h76+Pho2bAhfX18UFBSgS5cuGDt2rErfPn36YMiQIeL7oqIiTJo0CXZ2dlAoFHBycsJ3330ntl+8eBE9e/aEiYkJjI2N0bFjR6Smport3377LVxcXKCnpwdnZ2csX75cbCsuLkZYWBhsbGygp6cHe3t7REZGAgAEQcCMGTPQpEkTKBQK2NraYsyYMQCALl26ICoqCvHx8ZDJZOjSpQsAQCaTYefOnSrbY2ZmhnXr1j3/h6gGrQaiFStWwMPDAyYmJjAxMYG3tzf2798vthcWFiI0NBQNGzaEkZERAgMDkZWVpbKM9PR0BAQEwMDAAJaWlpgwYQJKS0tV+hw5cgRt2rQRvxS1/SETERGpIyMjAwMHDsSwYcNw+fJlHDlyBH379oUgCNWaPygoCD///DOWLFmCy5cvY9WqVTAyMgIA3LlzB506dYJCocChQ4eQmJiIYcOGib+dGzZswLRp0/Dll1/i8uXLmDNnDj7//HOsX78eALBkyRLs2rULv/zyC1JSUrBhwwY4ODgAALZt24aFCxdi1apVuHbtGnbu3Al3939OTW7fvh0jRoyAt7c3MjIysH37dg1/as9Hq6fMGjdujLlz56J58+YQBAHr169H79698ccff8DNzQ3jxo3D3r17sWXLFpiamiIsLAx9+/bF77//DgBQKpUICAiAtbU1jh8/joyMDAQFBaF+/fqYM2cOACAtLQ0BAQEYNWoUNmzYgLi4OAwfPhw2Njbw9/fX5uYTERFVKiMjA6Wlpejbty/s7e0BQAwWT3P16lX88ssviI2Nha+vLwCgadOmYvuyZctgamqKTZs2oX79+gCAFi1aiO3Tp09HVFQU+vbtCwBwdHTEpUuXsGrVKgQHByM9PR3NmzfHG2+8AZlMJtYH/HOQwtraGr6+vqhfvz6aNGmC1157DQBgbm4OAwMDyOVyWFtbP8enUzO0eoSoV69e6NGjB5o3b44WLVrgyy+/hJGREU6cOIHc3Fx89913WLBgAd588014eXlh7dq1OH78OE6cOAEAOHDgAC5duoSffvoJrVu3Rvfu3TF79mwsW7YMxcXFAICVK1fC0dERUVFRcHFxQVhYGPr164eFCxdqc9OJiIieyNPTEz4+PnB3d8e7776LNWvW4MGDB9WaNykpCbq6uujcufMT2zt27CiGoX8rKChAamoqQkJCYGRkJL6++OIL8ZTakCFDkJSUhJYtW2LMmDE4cOCAOP+7776Lx48fo2nTphgxYgR27NhR4axNXVVnxhAplUps2rQJBQUF8Pb2RmJiIkpKSsR0CwDOzs5o0qQJEhISAAAJCQlwd3eHlZWV2Mff3x95eXm4ePGi2OffyyjvU76MyhQVFSEvL0/lRUREVFt0dXURGxuL/fv3w9XVFUuXLkXLli2RlpYGHR2dCqfOSkpKxL/19fWrXHZV7fn5+QCANWvWICkpSXxduHBBPBjRpk0bpKWlYfbs2Xj8+DHee+899OvXDwBgZ2eHlJQULF++HPr6+vj444/RqVMnlfr+SyaTVbk9tUXrgSg5ORlGRkZQKBQYNWoUduzYAVdXV2RmZkIul8PMzEylv5WVFTIzMwEAmZmZKmGovL28rao+eXl5ePz4caU1RUZGwtTUVHzZ2dlpYlOJiIiqTSaToUOHDpg5cyb++OMPyOVy7NixAxYWFsjIyBD7KZVKXLhwQXzv7u6OsrIyHD16tNLlenh44Lfffqs0dFhZWcHW1hY3btyAk5OTysvR0VHsZ2Jigv79+2PNmjXYvHkztm3bhvv37wP4J3D16tULS5YswZEjR5CQkIDk5CffduC/23Pt2jU8evSo+h+Uhmj9svuWLVsiKSkJubm52Lp1K4KDg5+4E2tLREQEwsPDxfd5eXkMRUREVGtOnjyJuLg4+Pn5wdLSEidPnsS9e/fg4uICQ0NDhIeHY+/evWjWrBkWLFigcoNDBwcHBAcHY9iwYViyZAk8PT1x69YtZGdn47333kNYWBiWLl2KAQMGICIiAqampjhx4gRee+01tGzZEjNnzsSYMWNgamqKbt26oaioCGfOnMGDBw8QHh6OBQsWwMbGBq+++ip0dHSwZcsWWFtbi1eGKZVKtG/fHgYGBvjpp5+gr6+vMs7ov958801888038Pb2hlKpxKRJkyo9nVfTtB6I5HI5nJycAABeXl44ffo0Fi9ejP79+6O4uBg5OTkqR4mysrLEwVjW1tY4deqUyvLKr0L7d5//XpmWlZUFExOTJx42VCgUUCgUGtk+IiIidZmYmCA+Ph6LFi1CXl4e7O3tERUVhe7du6OkpATnzp1DUFAQ6tWrh3HjxqFr164q869YsQKfffYZPv74Y/z9999o0qQJPvvsMwBAw4YNcejQIUyYMAGdO3eGrq4uWrdujQ4dOgAAhg8fDgMDA8yfPx8TJkyAoaEh3N3dxUv9jY2NMW/ePFy7dg26urpo164d9u3bBx0dHZiZmWHu3LkIDw+HUqmEu7s7du/ejYYNGz5xW6OiojB06FB07NgRtra2WLx4MRITE2vmg62CTKjuNXy15M0330STJk2wePFiWFhY4Oeff0ZgYCAAICUlBc7OzkhISMDrr7+O/fv3o2fPnsjIyIClpSUAYPXq1ZgwYQKys7OhUCgwadIk7Nu3T+Vw3fvvv4/79+8jOjq6WjXl5eXB1NQUubm5MDEx0fxGU43xmvCDtkt4osT5QdougeilV1hYiLS0NDg6OkJPT0/b5VANqGofq/P7rdUjRBEREejevTuaNGmChw8fYuPGjThy5AhiYmJgamqKkJAQhIeHw9zcHCYmJhg9ejS8vb3x+uuvAwD8/Pzg6uqKDz74APPmzUNmZiamTp2K0NBQ8QjPqFGj8M0332DixIkYNmwYDh06hF9++QV79+7V5qYTERFRHaLVQJSdnY2goCBkZGTA1NQUHh4eiImJwVtvvQUAWLhwIXR0dBAYGIiioiL4+/ur3C1TV1cXe/bswUcffQRvb28YGhoiODgYs2bNEvs4Ojpi7969GDduHBYvXozGjRvj22+/5T2IiIiISFTnTpnVRTxl9uLiKTMiaeMps5efpk6Zaf2yeyIiIiJtYyAiIqKXHk+GvLw0tW8ZiIiI6KVVfj8bbdzoj2pH+aO6dHV1n2s5Wr8PERERUU3R1dWFmZkZsrOzAQAGBgaQyWRaroo0paysDPfu3YOBgQHq1Xu+SMNAREREL7XyG/WWhyJ6uejo6KBJkybPHXQZiIiI6KUmk8lgY2MDS0tLrTw0lGqWXC6Hjs7zjwBiICIiIknQ1dV97nEm9PJiICKiWsF7QhFRXcarzIiIiEjyGIiIiIhI8hiIiIiISPIYiIiIiEjyGIiIiIhI8hiIiIiISPIYiIiIiEjyGIiIiIhI8hiIiIiISPIYiIiIiEjyGIiIiIhI8hiIiIiISPIYiIiIiEjyGIiIiIhI8hiIiIiISPIYiIiIiEjyGIiIiIhI8hiIiIiISPIYiIiIiEjyGIiIiIhI8hiIiIiISPIYiIiIiEjyGIiIiIhI8hiIiIiISPIYiIiIiEjyGIiIiIhI8hiIiIiISPIYiIiIiEjyGIiIiIhI8hiIiIiISPIYiIiIiEjyGIiIiIhI8rQaiCIjI9GuXTsYGxvD0tISffr0QUpKikqfLl26QCaTqbxGjRql0ic9PR0BAQEwMDCApaUlJkyYgNLSUpU+R44cQZs2baBQKODk5IR169bV9OYRERHRC0Krgejo0aMIDQ3FiRMnEBsbi5KSEvj5+aGgoECl34gRI5CRkSG+5s2bJ7YplUoEBASguLgYx48fx/r167Fu3TpMmzZN7JOWloaAgAB07doVSUlJGDt2LIYPH46YmJha21YiIiKqu+ppc+XR0dEq79etWwdLS0skJiaiU6dO4nQDAwNYW1tXuowDBw7g0qVLOHjwIKysrNC6dWvMnj0bkyZNwowZMyCXy7Fy5Uo4OjoiKioKAODi4oJjx45h4cKF8Pf3r7kNJCIiohdCnRpDlJubCwAwNzdXmb5hwwY0atQIrVq1QkREBB49eiS2JSQkwN3dHVZWVuI0f39/5OXl4eLFi2IfX19flWX6+/sjISGh0jqKioqQl5en8iIiIqKXl1aPEP1bWVkZxo4diw4dOqBVq1bi9Pfffx/29vawtbXF+fPnMWnSJKSkpGD79u0AgMzMTJUwBEB8n5mZWWWfvLw8PH78GPr6+iptkZGRmDlzpsa3kYiIiOqmOhOIQkNDceHCBRw7dkxl+siRI8W/3d3dYWNjAx8fH6SmpqJZs2Y1UktERATCw8PF93l5ebCzs6uRdREREZH21YlTZmFhYdizZw8OHz6Mxo0bV9m3ffv2AIDr168DAKytrZGVlaXSp/x9+bijJ/UxMTGpcHQIABQKBUxMTFReRERE9PLSaiASBAFhYWHYsWMHDh06BEdHx6fOk5SUBACwsbEBAHh7eyM5ORnZ2dlin9jYWJiYmMDV1VXsExcXp7Kc2NhYeHt7a2hLiIiI6EWm1UAUGhqKn376CRs3boSxsTEyMzORmZmJx48fAwBSU1Mxe/ZsJCYm4ubNm9i1axeCgoLQqVMneHh4AAD8/Pzg6uqKDz74AOfOnUNMTAymTp2K0NBQKBQKAMCoUaNw48YNTJw4EVeuXMHy5cvxyy+/YNy4cVrbdiIiIqo7tBqIVqxYgdzcXHTp0gU2Njbia/PmzQAAuVyOgwcPws/PD87Ozhg/fjwCAwOxe/ducRm6urrYs2cPdHV14e3tjcGDByMoKAizZs0S+zg6OmLv3r2IjY2Fp6cnoqKi8O233/KSeyIiIgKggUHVSqUSycnJsLe3R4MGDdSaVxCEKtvt7Oxw9OjRpy7H3t4e+/btq7JPly5d8Mcff6hVHxEREUmD2keIxo4di++++w7AP2Goc+fOaNOmDezs7HDkyBFN10dERERU49QORFu3boWnpycAYPfu3UhLS8OVK1cwbtw4TJkyReMFEhEREdU0tQPRX3/9JV7Ovm/fPrz77rto0aIFhg0bhuTkZI0XSERERFTT1A5EVlZWuHTpEpRKJaKjo/HWW28BAB49egRdXV2NF0hERERU09QeVD106FC89957sLGxgUwmE58RdvLkSTg7O2u8QCIiIqKapnYgmjFjBlq1aoXbt2/j3XffFe/1o6uri8mTJ2u8QCIiIqKapnYg+uGHH9C/f38xCJUbOHAgNm3apLHCiIiIiGqL2mOIhg4ditzc3ArTHz58iKFDh2qkKCIiIqLapHYgEgQBMpmswvQ///wTpqamGimKiIiIqDZV+5TZq6++CplMBplMBh8fH9Sr93+zKpVKpKWloVu3bjVSJBEREVFNqnYg6tOnD4B/njbv7+8PIyMjsU0ul8PBwQGBgYEaL5CIiIioplU7EE2fPh0A4ODggAEDBlQYVE1ERET0olJ7DJGrqyuSkpIqTD958iTOnDmjiZqIiIiIapXagSg0NBS3b9+uMP3OnTsIDQ3VSFFEREREtUntQHTp0iW0adOmwvRXX30Vly5d0khRRERERLVJ7UCkUCiQlZVVYXpGRobKlWdERERELwq1A5Gfnx8iIiJUbs6Yk5ODzz77THzQKxEREdGLRO1DOl9//TU6deoEe3t7vPrqqwD+uRTfysoKP/74o8YLJCIiIqppageiV155BefPn8eGDRtw7tw56OvrY+jQoRg4cCDq169fEzUSERER1ahnGvRjaGiIkSNHaroWIiIiIq1QewwRAPz444944403YGtri1u3bgEAFi5ciF9//VWjxRERERHVBrUD0YoVKxAeHo7u3bvjwYMHUCqVAIAGDRpg0aJFmq6PiIiIqMapHYiWLl2KNWvWYMqUKSqX2bdt2xbJyckaLY6IiIioNqgdiNLS0sSry/5NoVCgoKBAI0URERER1Sa1A5Gjo2OlzzKLjo6Gi4uLJmoiIiIiqlVqX2UWHh6O0NBQFBYWQhAEnDp1Cj///DMiIyPx7bff1kSNRERERDVK7UA0fPhw6OvrY+rUqXj06BHef/992NraYvHixRgwYEBN1EhERERUo57pPkSDBg3CoEGD8OjRI+Tn58PS0lLTdRERERHVmud6GquBgQEMDAw0VQsRERGRVlQrELVp0wZxcXFo0KABXn31Vchksif2NTIygpubGz777DPY2dlprFAiIiKimlKtQNS7d28oFAoAQJ8+farsW1RUhLi4OAwePBhHjx597gKJiIiIalq1AtH06dMr/ftJUlNT4ebm9uxVEREREdWiZx5DdO/ePaSkpAAAWrZsCQsLC7GtWbNmyMrKev7qiIiIiGqB2jdmLCgowLBhw2Bra4tOnTqhU6dOsLW1RUhICB49eiT2MzU11WihRERERDVF7UAUHh6Oo0ePYteuXcjJyUFOTg5+/fVXHD16FOPHj6+JGomIiIhqlNqnzLZt24atW7eiS5cu4rQePXpAX18f7733HlasWKHJ+oiIiIhqnNpHiB49egQrK6sK0y0tLVVOmRERERG9KNQORN7e3pg+fToKCwvFaY8fP8bMmTPh7e2t0eKIiIiIaoPap8wWLVqEbt26oXHjxvD09AQAnDt3Dnp6eoiJidF4gURENS19lru2S6hSk2nJ2i6B6KWndiByd3fHtWvXsGHDBly5cgUAMHDgQAwaNAj6+voaL5CIiIiopql1yqykpATNmjXDrVu3MGLECERFRSEqKgrDhw9/pjAUGRmJdu3awdjYGJaWlujTp494b6NyhYWFCA0NRcOGDWFkZITAwMAK9zhKT09HQEAADAwMYGlpiQkTJqC0tFSlz5EjR9CmTRsoFAo4OTlh3bp1atdLRERELye1AlH9+vVVxg49r6NHjyI0NBQnTpxAbGwsSkpK4Ofnh4KCArHPuHHjsHv3bmzZsgVHjx7F3bt30bdvX7FdqVQiICAAxcXFOH78ONavX49169Zh2rRpYp+0tDQEBASga9euSEpKwtixYzF8+HCe4iMiIiIAgEwQBEGdGebMmYOrV6/i22+/Rb16z3yj60rdu3cPlpaWOHr0KDp16oTc3FxYWFhg48aN6NevHwDgypUrcHFxQUJCAl5//XXs378fPXv2xN27d8Wr31auXIlJkybh3r17kMvlmDRpEvbu3YsLFy6I6xowYABycnIQHR391Lry8vJgamqK3NxcmJiYaHSbqWZ5TfhB2yU8UeL8IG2XUKvq8r7YYTxf2yVUiWOIiJ6NOr/faiea06dPIy4uDgcOHIC7uzsMDQ1V2rdv367uIkW5ubkAAHNzcwBAYmIiSkpK4OvrK/ZxdnZGkyZNxECUkJAAd3d3lVsB+Pv746OPPsLFixfx6quvIiEhQWUZ5X3Gjh1baR1FRUUoKioS3+fl5T3zNhEREVHdp3YgMjMzQ2BgoMYLKSsrw9ixY9GhQwe0atUKAJCZmQm5XA4zMzOVvlZWVsjMzBT7/Pe+SOXvn9YnLy8Pjx8/rjD+KTIyEjNnztTYthEREVHdpnYgWrt2bU3UgdDQUFy4cAHHjh2rkeWrIyIiAuHh4eL7vLw82NnZabEiIiIiqknVHlRdVlaGr776Ch06dEC7du0wefJkPH78WCNFhIWFYc+ePTh8+DAaN24sTre2tkZxcTFycnJU+mdlZcHa2lrs89+rzsrfP62PiYlJpVfHKRQKmJiYqLyIiIjo5VXtQPTll1/is88+g5GREV555RUsXrwYoaGhz7VyQRAQFhaGHTt24NChQ3B0dFRp9/LyQv369REXFydOS0lJQXp6unhXbG9vbyQnJyM7O1vsExsbCxMTE7i6uop9/r2M8j68szYREREBagSiH374AcuXL0dMTAx27tyJ3bt3Y8OGDSgrK3vmlYeGhuKnn37Cxo0bYWxsjMzMTGRmZopHnkxNTRESEoLw8HAcPnwYiYmJGDp0KLy9vfH6668DAPz8/ODq6ooPPvgA586dQ0xMDKZOnYrQ0FAoFAoAwKhRo3Djxg1MnDgRV65cwfLly/HLL79g3Lhxz1w7ERERvTyqHYjS09PRo0cP8b2vry9kMhnu3r37zCtfsWIFcnNz0aVLF9jY2IivzZs3i30WLlyInj17IjAwEJ06dYK1tbXKlWy6urrYs2cPdHV14e3tjcGDByMoKAizZs0S+zg6OmLv3r2IjY2Fp6cnoqKi8O2338Lf3/+ZayciIqKXR7UHVZeWlkJPT09lWv369VFSUvLMK6/OLZD09PSwbNkyLFu27Il97O3tsW/fviqX06VLF/zxxx9q10hEREQvv2oHIkEQMGTIEPE0FPDPYzVGjRqlci+i57kPEREREZE2VDsQBQcHV5g2ePBgjRZDREREpA3VDkQ1df8hIiIiIm1T6+GuRERERC8jBiIiIiKSPAYiIiIikjwGIiIiIpK8agWiNm3a4MGDBwCAWbNm4dGjRzVaFBEREVFtqlYgunz5MgoKCgAAM2fORH5+fo0WRURERFSbqnXZfevWrTF06FC88cYbEAQBX3/9NYyMjCrtO23aNI0WSERERFTTqhWI1q1bh+nTp2PPnj2QyWTYv38/6tWrOKtMJmMgIiIiohdOtQJRy5YtsWnTJgCAjo4O4uLiYGlpWaOFEREREdWWat+pulxZWVlN1EFERESkNWoHIgBITU3FokWLcPnyZQCAq6srPvnkEzRr1kyjxRERERHVBrXvQxQTEwNXV1ecOnUKHh4e8PDwwMmTJ+Hm5obY2NiaqJGIiIioRql9hGjy5MkYN24c5s6dW2H6pEmT8NZbb2msOCIiIqLaoPYRosuXLyMkJKTC9GHDhuHSpUsaKYqIiIioNqkdiCwsLJCUlFRhelJSEq88IyIioheS2qfMRowYgZEjR+LGjRv43//+BwD4/fff8dVXXyE8PFzjBRIRERHVNLUD0eeffw5jY2NERUUhIiICAGBra4sZM2ZgzJgxGi+QiIiIqKapHYhkMhnGjRuHcePG4eHDhwAAY2NjjRdGREREVFue6T5E5RiEiIiI6GWg9qBqIiIiopcNAxERERFJHgMRERERSZ5agaikpAQ+Pj64du1aTdVDREREVOvUCkT169fH+fPna6oWIiIiIq1Q+5TZ4MGD8d1339VELURERERaofZl96Wlpfj+++9x8OBBeHl5wdDQUKV9wYIFGiuOiIiIqDaoHYguXLiANm3aAACuXr2q0iaTyTRTFREREVEtUjsQHT58uCbqICIiItKaZ77s/vr164iJicHjx48BAIIgaKwoIiIiotqkdiD6+++/4ePjgxYtWqBHjx7IyMgAAISEhGD8+PEaL5CIiIiopql9ymzcuHGoX78+0tPT4eLiIk7v378/wsPDERUVpdECiYhIs7wm/KDtEqqUOD9I2yWQBKkdiA4cOICYmBg0btxYZXrz5s1x69YtjRVGREREVFvUPmVWUFAAAwODCtPv378PhUKhkaKIiIiIapPagahjx4744Yf/O9wqk8lQVlaGefPmoWvXrhotjoiIiKg2qH3KbN68efDx8cGZM2dQXFyMiRMn4uLFi7h//z5+//33mqiRiIiIqEapfYSoVatWuHr1Kt544w307t0bBQUF6Nu3L/744w80a9asJmokIiIiqlFqHyECAFNTU0yZMkXTtRARERFpxTPdmPHBgwf4+uuvERISgpCQEERFReH+/ftqLyc+Ph69evWCra0tZDIZdu7cqdI+ZMgQyGQylVe3bt1U+ty/fx+DBg2CiYkJzMzMEBISgvz8fJU+58+fR8eOHaGnpwc7OzvMmzdP7VqJiIjo5aV2IIqPj4eDgwOWLFmCBw8e4MGDB1iyZAkcHR0RHx+v1rIKCgrg6emJZcuWPbFPt27dkJGRIb5+/vlnlfZBgwbh4sWLiI2NxZ49exAfH4+RI0eK7Xl5efDz84O9vT0SExMxf/58zJgxA6tXr1Zvw4mIiOilpfYps9DQUPTv3x8rVqyArq4uAECpVOLjjz9GaGgokpOTq72s7t27o3v37lX2USgUsLa2rrTt8uXLiI6OxunTp9G2bVsAwNKlS9GjRw98/fXXsLW1xYYNG1BcXIzvv/8ecrkcbm5uSEpKwoIFC1SCExEREUmX2keIrl+/jvHjx4thCAB0dXURHh6O69eva7Q4ADhy5AgsLS3RsmVLfPTRR/j777/FtoSEBJiZmYlhCAB8fX2ho6ODkydPin06deoEuVwu9vH390dKSgoePHhQ6TqLioqQl5en8iIiIqKXl9qBqE2bNrh8+XKF6ZcvX4anp6dGiirXrVs3/PDDD4iLi8NXX32Fo0ePonv37lAqlQCAzMxMWFpaqsxTr149mJubIzMzU+xjZWWl0qf8fXmf/4qMjISpqan4srOz0+h2ERERUd1SrVNm58+fF/8eM2YMPvnkE1y/fh2vv/46AODEiRNYtmwZ5s6dq9HiBgwYIP7t7u4ODw8PNGvWDEeOHIGPj49G1/VvERERCA8PF9/n5eUxFBEREb3EqhWIWrduDZlMBkEQxGkTJ06s0O/9999H//79NVfdfzRt2hSNGjXC9evX4ePjA2tra2RnZ6v0KS0txf3798VxR9bW1sjKylLpU/7+SWOTFAoFH0NCREQkIdUKRGlpaTVdR7X8+eef+Pvvv2FjYwMA8Pb2Rk5ODhITE+Hl5QUAOHToEMrKytC+fXuxz5QpU1BSUoL69esDAGJjY9GyZUs0aNBAOxtCREREdUq1ApG9vX2NrDw/P19lIHZaWhqSkpJgbm4Oc3NzzJw5E4GBgbC2tkZqaiomTpwIJycn+Pv7AwBcXFzQrVs3jBgxAitXrkRJSQnCwsIwYMAA2NraAvjnqNXMmTMREhKCSZMm4cKFC1i8eDEWLlxYI9tEREREL55nulP13bt3cezYMWRnZ6OsrEylbcyYMdVezpkzZ1QeCFs+bic4OBgrVqzA+fPnsX79euTk5MDW1hZ+fn6YPXu2yumsDRs2ICwsDD4+PtDR0UFgYCCWLFkitpuamuLAgQMIDQ2Fl5cXGjVqhGnTpvGSeyIiIhKpHYjWrVuHDz/8EHK5HA0bNoRMJhPbZDKZWoGoS5cuKuOS/ismJuapyzA3N8fGjRur7OPh4YHffvut2nURERGRtKgdiD7//HNMmzYNERER0NF5pid/EBEREdUpaieaR48eYcCAAQxDRERE9NJQO9WEhIRgy5YtNVELERERkVaofcosMjISPXv2RHR0NNzd3cVL2cstWLBAY8URERER1YZnCkQxMTFo2bIlAFQYVE1ERET0olE7EEVFReH777/HkCFDaqAcIiIiotqn9hgihUKBDh061EQtRERERFqhdiD65JNPsHTp0pqohYiIiEgr1D5ldurUKRw6dAh79uyBm5tbhUHV27dv11hxRERERLVB7UBkZmaGvn371kQtRJKSPstd2yVUqcm0ZG2XQERUa9QORGvXrq2JOoiIiIi0hrebJiIiIslT+wiRo6NjlfcbunHjxnMVRERERFTb1A5EY8eOVXlfUlKCP/74A9HR0ZgwYYKm6iIiIiKqNWoHok8++aTS6cuWLcOZM2eeuyAiIiKi2qaxMUTdu3fHtm3bNLU4IiIiolqjsUC0detWmJuba2pxRERERLVG7VNmr776qsqgakEQkJmZiXv37mH58uUaLY6IiIioNqgdiPr06aPyXkdHBxYWFujSpQucnZ01VRcRERFRrVE7EE2fPr0m6iAiIiLSGt6YkYiIiCSv2keIdHR0qrwhIwDIZDKUlpY+d1FEREREtanagWjHjh1PbEtISMCSJUtQVlamkaKIiIiIalO1A1Hv3r0rTEtJScHkyZOxe/duDBo0CLNmzdJocURERES14ZnGEN29excjRoyAu7s7SktLkZSUhPXr18Pe3l7T9RERERHVOLUCUW5uLiZNmgQnJydcvHgRcXFx2L17N1q1alVT9RERERHVuGqfMps3bx6++uorWFtb4+eff670FBoRERHRi6jagWjy5MnQ19eHk5MT1q9fj/Xr11fab/v27RorjoiIiKg2VDsQBQUFPfWyeyIiIqIXUbUD0bp162qwDCIiIiLt4Z2qiYiISPIYiIiIiEjyGIiIiIhI8hiIiIiISPIYiIiIiEjyGIiIiIhI8hiIiIiISPIYiIiIiEjyGIiIiIhI8hiIiIiISPK0Goji4+PRq1cv2NraQiaTYefOnSrtgiBg2rRpsLGxgb6+Pnx9fXHt2jWVPvfv38egQYNgYmICMzMzhISEID8/X6XP+fPn0bFjR+jp6cHOzg7z5s2r6U0jIiKiF4hWA1FBQQE8PT2xbNmyStvnzZuHJUuWYOXKlTh58iQMDQ3h7++PwsJCsc+gQYNw8eJFxMbGYs+ePYiPj8fIkSPF9ry8PPj5+cHe3h6JiYmYP38+ZsyYgdWrV9f49hEREdGLodoPd60J3bt3R/fu3SttEwQBixYtwtSpU9G7d28AwA8//AArKyvs3LkTAwYMwOXLlxEdHY3Tp0+jbdu2AIClS5eiR48e+Prrr2Fra4sNGzaguLgY33//PeRyOdzc3JCUlIQFCxaoBCciIiKSrjo7higtLQ2ZmZnw9fUVp5mamqJ9+/ZISEgAACQkJMDMzEwMQwDg6+sLHR0dnDx5UuzTqVMnyOVysY+/vz9SUlLw4MGDWtoaIiIiqsu0eoSoKpmZmQAAKysrlelWVlZiW2ZmJiwtLVXa69WrB3Nzc5U+jo6OFZZR3tagQYMK6y4qKkJRUZH4Pi8v7zm3hoiIiOqyOnuESJsiIyNhamoqvuzs7LRdEhEREdWgOhuIrK2tAQBZWVkq07OyssQ2a2trZGdnq7SXlpbi/v37Kn0qW8a/1/FfERERyM3NFV+3b99+/g0iIiKiOqvOBiJHR0dYW1sjLi5OnJaXl4eTJ0/C29sbAODt7Y2cnBwkJiaKfQ4dOoSysjK0b99e7BMfH4+SkhKxT2xsLFq2bFnp6TIAUCgUMDExUXkRERHRy0urgSg/Px9JSUlISkoC8M9A6qSkJKSnp0Mmk2Hs2LH44osvsGvXLiQnJyMoKAi2trbo06cPAMDFxQXdunXDiBEjcOrUKfz+++8ICwvDgAEDYGtrCwB4//33IZfLERISgosXL2Lz5s1YvHgxwsPDtbTVREREVNdodVD1mTNn0LVrV/F9eUgJDg7GunXrMHHiRBQUFGDkyJHIycnBG2+8gejoaOjp6YnzbNiwAWFhYfDx8YGOjg4CAwOxZMkSsd3U1BQHDhxAaGgovLy80KhRI0ybNo2X3BMREZFIq4GoS5cuEAThie0ymQyzZs3CrFmzntjH3NwcGzdurHI9Hh4e+O233565TiIiInq51dkxRERERES1hYGIiIiIJI+BiIiIiCSPgYiIiIgkj4GIiIiIJI+BiIiIiCSPgYiIiIgkj4GIiIiIJI+BiIiIiCSPgYiIiIgkj4GIiIiIJI+BiIiIiCSPgYiIiIgkj4GIiIiIJI+BiIiIiCSPgYiIiIgkj4GIiIiIJI+BiIiIiCSPgYiIiIgkj4GIiIiIJI+BiIiIiCSPgYiIiIgkj4GIiIiIJI+BiIiIiCSPgYiIiIgkj4GIiIiIJI+BiIiIiCSPgYiIiIgkj4GIiIiIJI+BiIiIiCSPgYiIiIgkj4GIiIiIJI+BiIiIiCSPgYiIiIgkj4GIiIiIJI+BiIiIiCSPgYiIiIgkj4GIiIiIJI+BiIiIiCSPgYiIiIgkr04HohkzZkAmk6m8nJ2dxfbCwkKEhoaiYcOGMDIyQmBgILKyslSWkZ6ejoCAABgYGMDS0hITJkxAaWlpbW8KERER1WH1tF3A07i5ueHgwYPi+3r1/q/kcePGYe/evdiyZQtMTU0RFhaGvn374vfffwcAKJVKBAQEwNraGsePH0dGRgaCgoJQv359zJkzp9a3hYiIiOqmOh+I6tWrB2tr6wrTc3Nz8d1332Hjxo148803AQBr166Fi4sLTpw4gddffx0HDhzApUuXcPDgQVhZWaF169aYPXs2Jk2ahBkzZkAul9f25hAREVEdVKdPmQHAtWvXYGtri6ZNm2LQoEFIT08HACQmJqKkpAS+vr5iX2dnZzRp0gQJCQkAgISEBLi7u8PKykrs4+/vj7y8PFy8eLF2N4SIiIjqrDp9hKh9+/ZYt24dWrZsiYyMDMycORMdO3bEhQsXkJmZCblcDjMzM5V5rKyskJmZCQDIzMxUCUPl7eVtT1JUVISioiLxfV5enoa2iIiIiOqiOh2IunfvLv7t4eGB9u3bw97eHr/88gv09fVrbL2RkZGYOXNmjS2fiIiI6pY6f8rs38zMzNCiRQtcv34d1tbWKC4uRk5OjkqfrKwsccyRtbV1havOyt9XNi6pXEREBHJzc8XX7du3NbshREREVKe8UIEoPz8fqampsLGxgZeXF+rXr4+4uDixPSUlBenp6fD29gYAeHt7Izk5GdnZ2WKf2NhYmJiYwNXV9YnrUSgUMDExUXkRERHRy6tOnzL79NNP0atXL9jb2+Pu3buYPn06dHV1MXDgQJiamiIkJATh4eEwNzeHiYkJRo8eDW9vb7z++usAAD8/P7i6uuKDDz7AvHnzkJmZialTpyI0NBQKhULLW0dERER1RZ0ORH/++ScGDhyIv//+GxYWFnjjjTdw4sQJWFhYAAAWLlwIHR0dBAYGoqioCP7+/li+fLk4v66uLvbs2YOPPvoI3t7eMDQ0RHBwMGbNmqWtTSIiIqI6qE4Hok2bNlXZrqenh2XLlmHZsmVP7GNvb499+/ZpujQiIiJ6ibxQY4iIiIiIakKdPkJERETSkz7LXdslPFGTacnaLoFqCI8QERERkeQxEBEREZHkMRARERGR5DEQERERkeQxEBEREZHkMRARERGR5DEQERERkeQxEBEREZHkMRARERGR5DEQERERkeQxEBEREZHkMRARERGR5PHhrhrmNeEHbZdQpcT5QdougYiIqM7hESIiIiKSPAYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikrx62i6Aalf6LHdtl1ClJtOStV0CERFJEI8QERERkeQxEBEREZHkMRARERGR5DEQERERkeQxEBEREZHkMRARERGR5DEQERERkeRJKhAtW7YMDg4O0NPTQ/v27XHq1Cltl0RERER1gGQC0ebNmxEeHo7p06fj7Nmz8PT0hL+/P7Kzs7VdGhEREWmZZALRggULMGLECAwdOhSurq5YuXIlDAwM8P3332u7NCIiItIySQSi4uJiJCYmwtfXV5ymo6MDX19fJCQkaLEyIiIiqgsk8Syzv/76C0qlElZWVirTrayscOXKlQr9i4qKUFRUJL7Pzc0FAOTl5T11Xcqix89Zbc16WF+p7RKqVJ3PWB11eX9wX9Qd3Bd1S13eH5reF52m/qzR5Wla/BcDtV3CcynfX4IgPLWvJAKRuiIjIzFz5swK0+3s7LRQjWa10nYBTxNpqu0Kag33Rd3BfVG31On9IbF9Ybp0lLZL0IiHDx/C1LTqfSeJQNSoUSPo6uoiKytLZXpWVhasra0r9I+IiEB4eLj4vqysDPfv30fDhg0hk8lqvN6akpeXBzs7O9y+fRsmJibaLkfSuC/qDu6LuoX7o+54GfaFIAh4+PAhbG1tn9pXEoFILpfDy8sLcXFx6NOnD4B/Qk5cXBzCwsIq9FcoFFAoFCrTzMzMaqHS2mFiYvLCfrlfNtwXdQf3Rd3C/VF3vOj74mlHhspJIhABQHh4OIKDg9G2bVu89tprWLRoEQoKCjB06FBtl0ZERERaJplA1L9/f9y7dw/Tpk1DZmYmWrdujejo6AoDrYmIiEh6JBOIACAsLKzSU2RSoVAoMH369AqnA6n2cV/UHdwXdQv3R90htX0hE6pzLRoRERHRS0wSN2YkIiIiqgoDEREREUkeAxERERFJHgMRERERSR4DkYQsW7YMDg4O0NPTQ/v27XHq1CltlyQ58fHx6NWrF2xtbSGTybBz505tlyRZkZGRaNeuHYyNjWFpaYk+ffogJSVF22VJ0ooVK+Dh4SHeANDb2xv79+/XdlkEYO7cuZDJZBg7dqy2S6lxDEQSsXnzZoSHh2P69Ok4e/YsPD094e/vj+zsbG2XJikFBQXw9PTEsmXLtF2K5B09ehShoaE4ceIEYmNjUVJSAj8/PxQUFGi7NMlp3Lgx5s6di8TERJw5cwZvvvkmevfujYsXL2q7NEk7ffo0Vq1aBQ8PD22XUit42b1EtG/fHu3atcM333wD4J9Hl9jZ2WH06NGYPHmylquTJplMhh07doiPkyHtunfvHiwtLXH06FF06tRJ2+VInrm5OebPn4+QkBBtlyJJ+fn5aNOmDZYvX44vvvgCrVu3xqJFi7RdVo3iESIJKC4uRmJiInx9fcVpOjo68PX1RUJCghYrI6o7cnNzAfzzQ0zao1QqsWnTJhQUFMDb21vb5UhWaGgoAgICVH43XnaSulO1VP31119QKpUVHlNiZWWFK1euaKkqorqjrKwMY8eORYcOHdCqVSttlyNJycnJ8Pb2RmFhIYyMjLBjxw64urpquyxJ2rRpE86ePYvTp09ru5RaxUBERJIXGhqKCxcu4NixY9ouRbJatmyJpKQk5ObmYuvWrQgODsbRo0cZimrZ7du38cknnyA2NhZ6enraLqdWMRBJQKNGjaCrq4usrCyV6VlZWbC2ttZSVUR1Q1hYGPbs2YP4+Hg0btxY2+VIllwuh5OTEwDAy8sLp0+fxuLFi7Fq1SotVyYtiYmJyM7ORps2bcRpSqUS8fHx+Oabb1BUVARdXV0tVlhzOIZIAuRyOby8vBAXFydOKysrQ1xcHM/Rk2QJgoCwsDDs2LEDhw4dgqOjo7ZLon8pKytDUVGRtsuQHB8fHyQnJyMpKUl8tW3bFoMGDUJSUtJLG4YAHiGSjPDwcAQHB6Nt27Z47bXXsGjRIhQUFGDo0KHaLk1S8vPzcf36dfF9WloakpKSYG5ujiZNmmixMukJDQ3Fxo0b8euvv8LY2BiZmZkAAFNTU+jr62u5OmmJiIhA9+7d0aRJEzx8+BAbN27EkSNHEBMTo+3SJMfY2LjCODpDQ0M0bNjwpR9fx0AkEf3798e9e/cwbdo0ZGZmonXr1oiOjq4w0Jpq1pkzZ9C1a1fxfXh4OAAgODgY69at01JV0rRixQoAQJcuXVSmr127FkOGDKn9giQsOzsbQUFByMjIgKmpKTw8PBATE4O33npL26WRhPA+RERERCR5HENEREREksdARERERJLHQERERESSx0BEREREksdARERERJLHQERERESSx0BEREREksdAREQvFZlMhp07d2q7DCJ6wTAQEdELJTMzE6NHj0bTpk2hUChgZ2eHXr16qTyrT1OOHDkCmUyGnJwcjS+biOoWPrqDiF4YN2/eRIcOHWBmZob58+fD3d0dJSUliImJQWhoKK5cuaLtEislCAKUSiXq1eP/conqKh4hIqIXxscffwyZTIZTp04hMDAQLVq0gJubG8LDw3HixIkK/Ss7wpOUlASZTIabN28CAG7duoVevXqhQYMGMDQ0hJubG/bt24ebN2+Kz51r0KABZDKZ+IyzsrIyREZGwtHREfr6+vD09MTWrVsrrHf//v3w8vKCQqHAsWPHauxzIaLnx3+uENEL4f79+4iOjsaXX34JQ0PDCu1mZmbPtNzQ0FAUFxcjPj4ehoaGuHTpEoyMjGBnZ4dt27YhMDAQKSkpMDExgb6+PgAgMjISP/30E1auXInmzZsjPj4egwcPhoWFBTp37iwue/Lkyfj666/RtGlTNGjQ4JnqI6LawUBERC+E69evQxAEODs7a3S56enpCAwMhLu7OwCgadOmYpu5uTkAwNLSUgxcRUVFmDNnDg4ePAhvb29xnmPHjmHVqlUqgWjWrFl8YjvRC4KBiIheCIIg1Mhyx4wZg48++ggHDhyAr68vAgMD4eHh8cT+169fx6NHjyoEneLiYrz66qsq09q2bVsjNROR5jEQEdELoXnz5pDJZGoNnNbR+WeY5L/DVElJiUqf4cOHw9/fH3v37sWBAwcQGRmJqKgojB49utJl5ufnAwD27t2LV155RaVNoVCovK/s1B4R1U0cVE1ELwRzc3P4+/tj2bJlKCgoqNBe2aXxFhYWAICMjAxxWlJSUoV+dnZ2GDVqFLZv347x48djzZo1AAC5XA4AUCqVYl9XV1coFAqkp6fDyclJ5WVnZ/c8m0hEWsRAREQvjGXLlkGpVOK1117Dtm3bcO3aNVy+fBlLliwRx/P8W3lImTFjBq5du4a9e/ciKipKpc/YsWMRExODtLQ0nD17FocPH4aLiwsAwN7eHjKZDHv27MG9e/eQn58PY2NjfPrppxg3bhzWr1+P1NRUnD17FkuXLsX69etr5XMgIs1jICKiF0bTpk1x9uxZdO3aFePHj0erVq3w1ltvIS4uDitWrKjQv379+vj5559x5coVeHh44KuvvsIXX3yh0kepVCI0NBQuLi7o1q0bWrRogeXLlwMAXnnlFcycOROTJ0+GlZUVwsLCAACzZ8/G559/jsjISHG+vXv3wtHRseY/BCKqETKhpkYqEhEREb0geISIiIiIJI+BiIiIiCSPgYiIiIgkj4GIiIiIJI+BiIiIiCSPgYiIiIgkj4GIiIiIJI+BiIiIiCSPgYiIiIgkj4GIiIiIJI+BiIiIiCSPgYiIiIgk7/8BHFSUH7W9WIoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a grouped bar chart to visualize the success rate for each cluster\n",
    "\n",
    "\n",
    "sns.countplot(x='cluster', hue='state', data=df)\n",
    "plt.title('Success Rate for Each Cluster')\n",
    "plt.xlabel('Cluster')\n",
    "plt.ylabel('Number of Projects');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}