notebooks/Preprocessing I_Variable_Selection.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "11f48415",
   "metadata": {},
   "source": [
    "# Preparation and Variable Selection\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ec3f4243",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# import warnings\n",
    "# warnings.filterwarnings(\"ignore\")\n",
    "%matplotlib inline\n",
    "sns.set() # to change the theme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "89bd9982",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>Unnamed: 0</th>\n",
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       "      <th>name</th>\n",
       "      <th>currency</th>\n",
       "      <th>launched_at</th>\n",
       "      <th>backers_count</th>\n",
       "      <th>blurb</th>\n",
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       "      <th>slug</th>\n",
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       "      <td>785.82065</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>60.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1639965256</td>\n",
       "      <td>Reflections on the LHC Safety Report</td>\n",
       "      <td>USD</td>\n",
       "      <td>2014-11-21 10:42:26</td>\n",
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       "      <td>2014-12-15 10:42:26</td>\n",
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       "      <td>successful</td>\n",
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       "      <td>Academic</td>\n",
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       "      <td>93.00000</td>\n",
       "      <td>San Diego</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>Ultra-Custom Chocolate Bars</td>\n",
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       "      <td>2020-10-19 05:26:59</td>\n",
       "      <td>99</td>\n",
       "      <td>Ultra-customized bean to bar chocolate. Any fl...</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-11-18 05:26:59</td>\n",
       "      <td>ultra-custom-chocolate-bars-0</td>\n",
       "      <td>successful</td>\n",
       "      <td>4697.000000</td>\n",
       "      <td>food</td>\n",
       "      <td>Small Batch</td>\n",
       "      <td>205122366</td>\n",
       "      <td>122</td>\n",
       "      <td>1500.00000</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1888984372</td>\n",
       "      <td>GlassWeb: Internet Monitoring</td>\n",
       "      <td>USD</td>\n",
       "      <td>2019-05-29 16:01:34</td>\n",
       "      <td>3</td>\n",
       "      <td>A module device to monitor internet connection...</td>\n",
       "      <td>US</td>\n",
       "      <td>2019-07-28 16:01:34</td>\n",
       "      <td>glassweb-internet-monitoring</td>\n",
       "      <td>failed</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>technology</td>\n",
       "      <td>Gadgets</td>\n",
       "      <td>1458852038</td>\n",
       "      <td>69</td>\n",
       "      <td>25000.00000</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1409770586</td>\n",
       "      <td>Dreaming of Streaming - 96.9 FM KMRD-LP Madrid...</td>\n",
       "      <td>USD</td>\n",
       "      <td>2015-11-19 20:36:01</td>\n",
       "      <td>100</td>\n",
       "      <td>Madrid's low power FM community radio station,...</td>\n",
       "      <td>US</td>\n",
       "      <td>2015-12-19 20:36:01</td>\n",
       "      <td>dreaming-of-streaming-969-fm-kmrd-lp-madrid-ne...</td>\n",
       "      <td>successful</td>\n",
       "      <td>5410.000000</td>\n",
       "      <td>publishing</td>\n",
       "      <td>Radio &amp; Podcasts</td>\n",
       "      <td>1845213665</td>\n",
       "      <td>131</td>\n",
       "      <td>5000.00000</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>217240</th>\n",
       "      <td>217248</td>\n",
       "      <td>1438234172</td>\n",
       "      <td>Adventures of a Lost Poet</td>\n",
       "      <td>USD</td>\n",
       "      <td>2017-05-31 17:37:07</td>\n",
       "      <td>1</td>\n",
       "      <td>My VERY FIRST book. Chronicling my journey dea...</td>\n",
       "      <td>US</td>\n",
       "      <td>2017-07-15 17:37:07</td>\n",
       "      <td>adventures-of-a-lost-poet</td>\n",
       "      <td>failed</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>publishing</td>\n",
       "      <td>Poetry</td>\n",
       "      <td>1049486300</td>\n",
       "      <td>125</td>\n",
       "      <td>500.00000</td>\n",
       "      <td>Hollywood</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217241</th>\n",
       "      <td>217249</td>\n",
       "      <td>906168648</td>\n",
       "      <td>Lomak - Animated Movie / Series</td>\n",
       "      <td>GBP</td>\n",
       "      <td>2015-01-28 16:43:38</td>\n",
       "      <td>6</td>\n",
       "      <td>About a powerful soldier who is nurtured from ...</td>\n",
       "      <td>GB</td>\n",
       "      <td>2015-03-19 15:43:38</td>\n",
       "      <td>lomak-animated-movie-series</td>\n",
       "      <td>failed</td>\n",
       "      <td>244.588849</td>\n",
       "      <td>film &amp; video</td>\n",
       "      <td>Animation</td>\n",
       "      <td>2096189858</td>\n",
       "      <td>90</td>\n",
       "      <td>40621.01400</td>\n",
       "      <td>London</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217242</th>\n",
       "      <td>217250</td>\n",
       "      <td>708119891</td>\n",
       "      <td>Growing with our people - Gastronomy (Canceled)</td>\n",
       "      <td>MXN</td>\n",
       "      <td>2017-07-29 03:07:34</td>\n",
       "      <td>5</td>\n",
       "      <td>We give Mexican producers an opportunity to pr...</td>\n",
       "      <td>MX</td>\n",
       "      <td>2017-09-15 15:00:00</td>\n",
       "      <td>orocafoods-with-mexican-love</td>\n",
       "      <td>canceled</td>\n",
       "      <td>287.559221</td>\n",
       "      <td>food</td>\n",
       "      <td>Farmer's Markets</td>\n",
       "      <td>2013704740</td>\n",
       "      <td>109</td>\n",
       "      <td>17662.55400</td>\n",
       "      <td>Mexico City</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217243</th>\n",
       "      <td>217251</td>\n",
       "      <td>239852131</td>\n",
       "      <td>Carmel - Second Wife Blues | Sad Situation CD ...</td>\n",
       "      <td>GBP</td>\n",
       "      <td>2015-08-30 21:17:44</td>\n",
       "      <td>51</td>\n",
       "      <td>A limited edition CD run of 1000 copies of Car...</td>\n",
       "      <td>GB</td>\n",
       "      <td>2015-09-24 21:17:44</td>\n",
       "      <td>carmel-second-wife-blues-sad-situation-cd-single</td>\n",
       "      <td>successful</td>\n",
       "      <td>1683.874823</td>\n",
       "      <td>music</td>\n",
       "      <td>Blues</td>\n",
       "      <td>197599039</td>\n",
       "      <td>110</td>\n",
       "      <td>1354.03380</td>\n",
       "      <td>London</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217244</th>\n",
       "      <td>217252</td>\n",
       "      <td>1126079486</td>\n",
       "      <td>Tick-Tock</td>\n",
       "      <td>USD</td>\n",
       "      <td>2015-02-04 05:08:07</td>\n",
       "      <td>34</td>\n",
       "      <td>A passion project focusing on bullying and its...</td>\n",
       "      <td>US</td>\n",
       "      <td>2015-03-03 20:00:00</td>\n",
       "      <td>tick-tock</td>\n",
       "      <td>successful</td>\n",
       "      <td>3505.000000</td>\n",
       "      <td>film &amp; video</td>\n",
       "      <td>Thrillers</td>\n",
       "      <td>1667603717</td>\n",
       "      <td>61</td>\n",
       "      <td>2800.00000</td>\n",
       "      <td>Hollywood</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>217245 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0          id  \\\n",
       "0                0  1714728788   \n",
       "1                1  1639965256   \n",
       "2                2  1582062943   \n",
       "3                3  1888984372   \n",
       "4                4  1409770586   \n",
       "...            ...         ...   \n",
       "217240      217248  1438234172   \n",
       "217241      217249   906168648   \n",
       "217242      217250   708119891   \n",
       "217243      217251   239852131   \n",
       "217244      217252  1126079486   \n",
       "\n",
       "                                                     name currency  \\\n",
       "0           Urban Farm Kits: Our Solution to City Farming      CAD   \n",
       "1                    Reflections on the LHC Safety Report      USD   \n",
       "2                             Ultra-Custom Chocolate Bars      USD   \n",
       "3                           GlassWeb: Internet Monitoring      USD   \n",
       "4       Dreaming of Streaming - 96.9 FM KMRD-LP Madrid...      USD   \n",
       "...                                                   ...      ...   \n",
       "217240                          Adventures of a Lost Poet      USD   \n",
       "217241                    Lomak - Animated Movie / Series      GBP   \n",
       "217242    Growing with our people - Gastronomy (Canceled)      MXN   \n",
       "217243  Carmel - Second Wife Blues | Sad Situation CD ...      GBP   \n",
       "217244                                          Tick-Tock      USD   \n",
       "\n",
       "                launched_at  backers_count  \\\n",
       "0       2018-02-10 22:47:52             16   \n",
       "1       2014-11-21 10:42:26              7   \n",
       "2       2020-10-19 05:26:59             99   \n",
       "3       2019-05-29 16:01:34              3   \n",
       "4       2015-11-19 20:36:01            100   \n",
       "...                     ...            ...   \n",
       "217240  2017-05-31 17:37:07              1   \n",
       "217241  2015-01-28 16:43:38              6   \n",
       "217242  2017-07-29 03:07:34              5   \n",
       "217243  2015-08-30 21:17:44             51   \n",
       "217244  2015-02-04 05:08:07             34   \n",
       "\n",
       "                                                    blurb country  \\\n",
       "0       Earn $1,000 - $20,000+ by transforming idle re...      CA   \n",
       "1                     Need Peer Review, Open Source style      US   \n",
       "2       Ultra-customized bean to bar chocolate. Any fl...      US   \n",
       "3       A module device to monitor internet connection...      US   \n",
       "4       Madrid's low power FM community radio station,...      US   \n",
       "...                                                   ...     ...   \n",
       "217240  My VERY FIRST book. Chronicling my journey dea...      US   \n",
       "217241  About a powerful soldier who is nurtured from ...      GB   \n",
       "217242  We give Mexican producers an opportunity to pr...      MX   \n",
       "217243  A limited edition CD run of 1000 copies of Car...      GB   \n",
       "217244  A passion project focusing on bullying and its...      US   \n",
       "\n",
       "                   deadline  \\\n",
       "0       2018-04-11 21:47:52   \n",
       "1       2014-12-15 10:42:26   \n",
       "2       2020-11-18 05:26:59   \n",
       "3       2019-07-28 16:01:34   \n",
       "4       2015-12-19 20:36:01   \n",
       "...                     ...   \n",
       "217240  2017-07-15 17:37:07   \n",
       "217241  2015-03-19 15:43:38   \n",
       "217242  2017-09-15 15:00:00   \n",
       "217243  2015-09-24 21:17:44   \n",
       "217244  2015-03-03 20:00:00   \n",
       "\n",
       "                                                     slug      status  \\\n",
       "0            urban-farm-kits-our-solution-to-city-farming      failed   \n",
       "1       reflections-on-the-lhc-safety-report-review-an...  successful   \n",
       "2                           ultra-custom-chocolate-bars-0  successful   \n",
       "3                            glassweb-internet-monitoring      failed   \n",
       "4       dreaming-of-streaming-969-fm-kmrd-lp-madrid-ne...  successful   \n",
       "...                                                   ...         ...   \n",
       "217240                          adventures-of-a-lost-poet      failed   \n",
       "217241                        lomak-animated-movie-series      failed   \n",
       "217242                       orocafoods-with-mexican-love    canceled   \n",
       "217243   carmel-second-wife-blues-sad-situation-cd-single  successful   \n",
       "217244                                          tick-tock  successful   \n",
       "\n",
       "        usd_pledged  sub_category     main_category  creator_id  blurb_length  \\\n",
       "0        577.902078          food             Farms    41046209           122   \n",
       "1        211.000000    publishing          Academic  1877377173            35   \n",
       "2       4697.000000          food       Small Batch   205122366           122   \n",
       "3        222.000000    technology           Gadgets  1458852038            69   \n",
       "4       5410.000000    publishing  Radio & Podcasts  1845213665           131   \n",
       "...             ...           ...               ...         ...           ...   \n",
       "217240    10.000000    publishing            Poetry  1049486300           125   \n",
       "217241   244.588849  film & video         Animation  2096189858            90   \n",
       "217242   287.559221          food  Farmer's Markets  2013704740           109   \n",
       "217243  1683.874823         music             Blues   197599039           110   \n",
       "217244  3505.000000  film & video         Thrillers  1667603717            61   \n",
       "\n",
       "           goal_usd          city  duration  \n",
       "0         785.82065      Victoria      60.0  \n",
       "1          93.00000     San Diego      24.0  \n",
       "2        1500.00000  Philadelphia      30.0  \n",
       "3       25000.00000       Chicago      60.0  \n",
       "4        5000.00000        Madrid      30.0  \n",
       "...             ...           ...       ...  \n",
       "217240    500.00000     Hollywood      45.0  \n",
       "217241  40621.01400        London      50.0  \n",
       "217242  17662.55400   Mexico City      48.0  \n",
       "217243   1354.03380        London      25.0  \n",
       "217244   2800.00000     Hollywood      28.0  \n",
       "\n",
       "[217245 rows x 19 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path=\".../GitHub/MAY23_BDA_INT_Crowdfunding/data/kaggle\"\n",
    "file=f\"{path}/Kaggle_Dataset.csv\"\n",
    "df=pd.read_csv(file)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5b7069e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 217245 entries, 0 to 217244\n",
      "Data columns (total 19 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   Unnamed: 0     217245 non-null  int64  \n",
      " 1   id             217245 non-null  int64  \n",
      " 2   name           217245 non-null  object \n",
      " 3   currency       217245 non-null  object \n",
      " 4   launched_at    217245 non-null  object \n",
      " 5   backers_count  217245 non-null  int64  \n",
      " 6   blurb          217245 non-null  object \n",
      " 7   country        217245 non-null  object \n",
      " 8   deadline       217245 non-null  object \n",
      " 9   slug           217245 non-null  object \n",
      " 10  status         217245 non-null  object \n",
      " 11  usd_pledged    217245 non-null  float64\n",
      " 12  sub_category   217245 non-null  object \n",
      " 13  main_category  217245 non-null  object \n",
      " 14  creator_id     217245 non-null  int64  \n",
      " 15  blurb_length   217245 non-null  int64  \n",
      " 16  goal_usd       217245 non-null  float64\n",
      " 17  city           217245 non-null  object \n",
      " 18  duration       217245 non-null  float64\n",
      "dtypes: float64(3), int64(5), object(11)\n",
      "memory usage: 31.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# general\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "436a94ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "successful    0.623145\n",
       "failed        0.376855\n",
       "Name: status, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reduce to successful and failed projects, only\n",
    "df.state=df.status[(df.status==\"successful\")|(df.status==\"failed\")]\n",
    "df.state.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7463d479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24357"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for duplicates\n",
    "df.duplicated().sum() # 0\n",
    "df.id.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fd42c86e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# convert date variables\n",
    "import datetime\n",
    "df.launched_at.value_counts()[:5] # type object\n",
    "df['dead']=pd.to_datetime(df['deadline'])\n",
    "df['launch']=pd.to_datetime(df['launched_at'])\n",
    "def get_year(x):\n",
    "    return x.year\n",
    "df['launched_year']=df['launch'].apply(get_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3932f298",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 217245 entries, 0 to 217244\n",
      "Data columns (total 26 columns):\n",
      " #   Column         Non-Null Count   Dtype         \n",
      "---  ------         --------------   -----         \n",
      " 0   Unnamed: 0     217245 non-null  int64         \n",
      " 1   id             217245 non-null  int64         \n",
      " 2   name           217245 non-null  object        \n",
      " 3   currency       217245 non-null  object        \n",
      " 4   launched_at    217245 non-null  object        \n",
      " 5   backers_count  217245 non-null  int64         \n",
      " 6   blurb          217245 non-null  object        \n",
      " 7   country        217245 non-null  object        \n",
      " 8   deadline       217245 non-null  object        \n",
      " 9   slug           217245 non-null  object        \n",
      " 10  status         217245 non-null  object        \n",
      " 11  usd_pledged    217245 non-null  float64       \n",
      " 12  sub_category   217245 non-null  object        \n",
      " 13  main_category  217245 non-null  object        \n",
      " 14  creator_id     217245 non-null  int64         \n",
      " 15  blurb_length   217245 non-null  int64         \n",
      " 16  goal_usd       217245 non-null  float64       \n",
      " 17  city           217245 non-null  object        \n",
      " 18  duration       217245 non-null  float64       \n",
      " 19  dead           217245 non-null  datetime64[ns]\n",
      " 20  launch         217245 non-null  datetime64[ns]\n",
      " 21  launched_year  217245 non-null  int64         \n",
      " 22  duration_s     217245 non-null  float64       \n",
      " 23  duration_min   217245 non-null  float64       \n",
      " 24  duration_mon   217245 non-null  float64       \n",
      " 25  duration_y     217245 non-null  float64       \n",
      "dtypes: datetime64[ns](2), float64(7), int64(6), object(11)\n",
      "memory usage: 43.1+ MB\n",
      "status           failed  successful\n",
      "launched_year                      \n",
      "2009           0.176744    0.823256\n",
      "2010           0.230943    0.769057\n",
      "2011           0.204227    0.795773\n",
      "2012           0.209902    0.790098\n",
      "2013           0.189312    0.810688\n",
      "2014           0.464462    0.535538\n",
      "2015           0.535037    0.464963\n",
      "2016           0.491094    0.508906\n",
      "2017           0.467173    0.532827\n",
      "2018           0.394025    0.605975\n",
      "2019           0.293471    0.706529\n",
      "2020           0.177622    0.822378\n"
     ]
    }
   ],
   "source": [
    "# derive duration of a campaign in different units\n",
    "from datetime import timedelta\n",
    "df['duration_s']=(df['dead']-df['launch'])/timedelta(seconds=1)\n",
    "df['duration_min']=(df['dead']-df['launch'])/timedelta(hours=1)\n",
    "# df['duration_d']=(df['dead']-df['launch'])/timedelta(days=1) duration in days is what (already existing variable) duration contains\n",
    "df['duration_mon']=df['duration_s']/86400/30 # derive duration in months\n",
    "df['duration_y']=df['duration_s']/86400/30/12 # derive duration in years\n",
    "df[['duration','duration_mon','duration_y']].describe() # duration is sufficient\n",
    "df.info()\n",
    "df.launched_year.value_counts() # outliers for 1970 not present in this dataset (was an issue in webrobots dataset)\n",
    "print(pd.crosstab(df.launched_year,df.state,normalize=\"index\"))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "40ba6e1a",
   "metadata": {},
   "source": [
    "#### Deleting duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "685e6182",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "202847"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Only Successful and failed projects are important for us\n",
    "df_final=df[(df['status']=='successful')|(df['status']=='failed')]\n",
    "len(df_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6afa21a2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180677"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.drop_duplicates(keep='first',inplace=True,subset='id')\n",
    "len(df_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "77c1b678",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1714728788    1\n",
       "574454559     1\n",
       "1447919771    1\n",
       "575248545     1\n",
       "1443118213    1\n",
       "             ..\n",
       "2119307936    1\n",
       "1853136422    1\n",
       "1378288601    1\n",
       "607196300     1\n",
       "1126079486    1\n",
       "Name: id, Length: 180677, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['id'].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8d621785",
   "metadata": {},
   "source": [
    "#### Variable Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "360f8e5c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heatmap for the identification of redundant numerical variables\n",
    "sns.heatmap(df_final.corr(),cmap='winter');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "27d313a2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# deleting of numerical variables\n",
    "df_final.drop(columns=['duration_s','duration_min','duration_mon','duration_y','Unnamed: 0','blurb_length'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f875d545",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# deleting of some time variables\n",
    "df_final.drop(columns=['dead','launch','deadline','launched_at'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "73a5fd9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df_final.corr(),cmap='winter',annot=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "62c1d280",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_list=['id','backers_count',\n",
    "       'usd_pledged','creator_id', 'goal_usd',\n",
    "       'launched_year', 'duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9d94a4d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_list=['currency','blurb',\n",
    "       'country','slug', 'status', 'usd_pledged','sub_category',\n",
    "       'main_category', 'creator_id','city']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e535151c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##### id\n",
      "                df        sum_sq       mean_sq         F    PR(>F)\n",
      "status         1.0  7.357709e+16  7.357709e+16  0.192003  0.661255\n",
      "Residual  180675.0  6.923618e+22  3.832084e+17       NaN       NaN\n",
      "##### backers_count\n",
      "                df        sum_sq       mean_sq            F  PR(>F)\n",
      "status         1.0  2.517713e+09  2.517713e+09  2777.435608     0.0\n",
      "Residual  180675.0  1.637798e+11  9.064883e+05          NaN     NaN\n",
      "##### usd_pledged\n",
      "                df        sum_sq       mean_sq            F  PR(>F)\n",
      "status         1.0  2.082107e+13  2.082107e+13  1799.415355     0.0\n",
      "Residual  180675.0  2.090594e+15  1.157102e+10          NaN     NaN\n",
      "##### creator_id\n",
      "                df        sum_sq       mean_sq         F    PR(>F)\n",
      "status         1.0  7.249285e+12  7.249285e+12  0.000019  0.996539\n",
      "Residual  180675.0  6.960304e+22  3.852389e+17       NaN       NaN\n",
      "##### goal_usd\n",
      "                df        sum_sq       mean_sq           F        PR(>F)\n",
      "status         1.0  2.372236e+14  2.372236e+14  193.978484  4.537199e-44\n",
      "Residual  180675.0  2.209543e+17  1.222938e+12         NaN           NaN\n",
      "##### launched_year\n",
      "                df        sum_sq     mean_sq          F        PR(>F)\n",
      "status         1.0  3.981126e+02  398.112595  67.452092  2.172726e-16\n",
      "Residual  180675.0  1.066372e+06    5.902153        NaN           NaN\n",
      "##### duration\n",
      "                df        sum_sq        mean_sq          F  PR(>F)\n",
      "status         1.0  8.083369e+05  808336.888964  5635.1445     0.0\n",
      "Residual  180675.0  2.591704e+07     143.445636        NaN     NaN\n"
     ]
    }
   ],
   "source": [
    "# Anova Test for the selection of interesting numerical variables\n",
    "# Interpretationsschwirigkeiten, daher numerische manuell ausgewählt\n",
    "import statsmodels.api\n",
    "for item in num_list:\n",
    "    print('#####',item)\n",
    "    result=statsmodels.formula.api.ols(f'{item} ~ status',data=df_final).fit()\n",
    "    print(statsmodels.api.stats.anova_lm(result))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8e81ab1d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "currency : 0.0\n",
      "blurb : 0.1455364113516466\n",
      "country : 0.0\n",
      "slug : 0.49889390478627377\n",
      "status : 0.0\n",
      "usd_pledged : 0.0\n",
      "sub_category : 0.0\n",
      "main_category : 0.0\n",
      "creator_id : 3.271221184161235e-232\n",
      "city : 0.0\n"
     ]
    }
   ],
   "source": [
    "# Correlation test for the selecting of interessting categorical variables\n",
    "import scipy.stats as sc\n",
    "cat_relevant=[]\n",
    "cat_drop=[]\n",
    "for item in cat_list:\n",
    "    chi2=sc.chi2_contingency(pd.crosstab(df_final['status'],df_final[item]))[1]\n",
    "    print(item,':',chi2)\n",
    "    if chi2 <=0.05:\n",
    "        cat_relevant.append(item)\n",
    "    else:\n",
    "        cat_drop.append(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "89c36f88",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "159"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Categories of the Project\n",
    "display(len(pd.DataFrame(df_final['main_category'].value_counts())))\n",
    "display(len(pd.DataFrame(df_final['sub_category'].value_counts())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f3e55f3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop project name\n",
    "# cat_drop.append('sub_category')\n",
    "cat_drop.append('name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c63bc5a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reverse column names of category variables correctly\n",
    "new={\"main_category\":\"sub_category2\",\n",
    "\"sub_category\":\"main_category\"}\n",
    "df_final.rename(columns=new,inplace=True)\n",
    "df_final.rename(columns={\"sub_category2\":\"sub_category\"},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b0cba320",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_final.drop(columns=cat_drop,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a0cf65db",
   "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>creator_id</th>\n",
       "      <th>creator_project_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2104052526</th>\n",
       "      <td>2104052526</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957058942</th>\n",
       "      <td>957058942</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077465647</th>\n",
       "      <td>1077465647</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897079804</th>\n",
       "      <td>897079804</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172204344</th>\n",
       "      <td>172204344</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2073233486</th>\n",
       "      <td>2073233486</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726159289</th>\n",
       "      <td>726159289</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174809538</th>\n",
       "      <td>174809538</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1174362408</th>\n",
       "      <td>1174362408</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1667603717</th>\n",
       "      <td>1667603717</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154987 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            creator_id  creator_project_counts\n",
       "2104052526  2104052526                      60\n",
       "957058942    957058942                      46\n",
       "1077465647  1077465647                      36\n",
       "897079804    897079804                      35\n",
       "172204344    172204344                      32\n",
       "...                ...                     ...\n",
       "2073233486  2073233486                       1\n",
       "726159289    726159289                       1\n",
       "174809538    174809538                       1\n",
       "1174362408  1174362408                       1\n",
       "1667603717  1667603717                       1\n",
       "\n",
       "[154987 rows x 2 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count projects per creator \n",
    "df_creator=pd.DataFrame(df_final['creator_id'].value_counts())\n",
    "df_creator['id']=df_creator.index\n",
    "df_creator['creator_project_counts']=df_creator['creator_id']\n",
    "df_creator.drop(\"creator_id\",axis=1,inplace=True)\n",
    "df_creator.rename(columns={\"id\":\"creator_id\"},inplace=True)\n",
    "df_creator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "67c95056",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>creator_projects</th>\n",
       "      <th>1</th>\n",
       "      <th>16-30</th>\n",
       "      <th>2-5</th>\n",
       "      <th>31+</th>\n",
       "      <th>6-15</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>creator_project_counts</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>140295</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10306</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>861</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>454</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "creator_projects             1  16-30    2-5  31+  6-15\n",
       "creator_project_counts                                 \n",
       "1                       140295      0      0    0     0\n",
       "2                            0      0  10306    0     0\n",
       "3                            0      0   2300    0     0\n",
       "4                            0      0    861    0     0\n",
       "5                            0      0    454    0     0\n",
       "6                            0      0      0    0   234\n",
       "7                            0      0      0    0   151\n",
       "8                            0      0      0    0   102\n",
       "9                            0      0      0    0    63\n",
       "10                           0      0      0    0    42\n",
       "11                           0      0      0    0    33\n",
       "12                           0      0      0    0    31\n",
       "13                           0      0      0    0    16\n",
       "14                           0      0      0    0    18\n",
       "15                           0      0      0    0     9\n",
       "16                           0     12      0    0     0\n",
       "17                           0     11      0    0     0\n",
       "18                           0      7      0    0     0\n",
       "19                           0      6      0    0     0\n",
       "20                           0      4      0    0     0\n",
       "21                           0      2      0    0     0\n",
       "22                           0      4      0    0     0\n",
       "23                           0      7      0    0     0\n",
       "24                           0      4      0    0     0\n",
       "25                           0      4      0    0     0\n",
       "26                           0      2      0    0     0\n",
       "27                           0      2      0    0     0\n",
       "29                           0      1      0    0     0\n",
       "32                           0      0      0    2     0\n",
       "35                           0      0      0    1     0\n",
       "36                           0      0      0    1     0\n",
       "46                           0      0      0    1     0\n",
       "60                           0      0      0    1     0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building groups of (un)experienced creators\n",
    "df_creator.loc[df_creator['creator_project_counts']==1,'creator_projects']='1'\n",
    "df_creator.loc[(df_creator['creator_project_counts']>=2) & (df_creator['creator_project_counts']<=5),'creator_projects']='2-5'\n",
    "df_creator.loc[(df_creator['creator_project_counts']>=6) & (df_creator['creator_project_counts']<=15),'creator_projects']='6-15'\n",
    "df_creator.loc[(df_creator['creator_project_counts']>=16) & (df_creator['creator_project_counts']<=30),'creator_projects']='16-30'\n",
    "df_creator.loc[(df_creator['creator_project_counts']>=31),'creator_projects']='31+'\n",
    "pd.crosstab(df_creator['creator_project_counts'],df_creator['creator_projects'],dropna=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2917ed61",
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge to main dataframe\n",
    "df_final=df_final.merge(right=df_creator,on=\"creator_id\",how=\"inner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "71da9f37",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1        140295\n",
       "2-5       33226\n",
       "6-15       5594\n",
       "16-30      1321\n",
       "31+         241\n",
       "Name: creator_projects, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.drop(['creator_id','creator_project_counts'],axis=1,inplace=True)\n",
    "df_final['creator_projects'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "80bbfe54",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>currency</th>\n",
       "      <th>backers_count</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
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       "      <th>goal_usd</th>\n",
       "      <th>city</th>\n",
       "      <th>duration</th>\n",
       "      <th>launched_year</th>\n",
       "      <th>creator_projects</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1714728788</td>\n",
       "      <td>CAD</td>\n",
       "      <td>16</td>\n",
       "      <td>CA</td>\n",
       "      <td>failed</td>\n",
       "      <td>577.902078</td>\n",
       "      <td>food</td>\n",
       "      <td>Farms</td>\n",
       "      <td>785.820650</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2018</td>\n",
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       "    <tr>\n",
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       "      <td>1639965256</td>\n",
       "      <td>USD</td>\n",
       "      <td>7</td>\n",
       "      <td>US</td>\n",
       "      <td>successful</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>publishing</td>\n",
       "      <td>Academic</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1582062943</td>\n",
       "      <td>USD</td>\n",
       "      <td>99</td>\n",
       "      <td>US</td>\n",
       "      <td>successful</td>\n",
       "      <td>4697.000000</td>\n",
       "      <td>food</td>\n",
       "      <td>Small Batch</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1888984372</td>\n",
       "      <td>USD</td>\n",
       "      <td>3</td>\n",
       "      <td>US</td>\n",
       "      <td>failed</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>technology</td>\n",
       "      <td>Gadgets</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1409770586</td>\n",
       "      <td>USD</td>\n",
       "      <td>100</td>\n",
       "      <td>US</td>\n",
       "      <td>successful</td>\n",
       "      <td>5410.000000</td>\n",
       "      <td>publishing</td>\n",
       "      <td>Radio &amp; Podcasts</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180672</th>\n",
       "      <td>70127099</td>\n",
       "      <td>HKD</td>\n",
       "      <td>324</td>\n",
       "      <td>HK</td>\n",
       "      <td>successful</td>\n",
       "      <td>12671.335268</td>\n",
       "      <td>games</td>\n",
       "      <td>Video Games</td>\n",
       "      <td>10113.056914</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180673</th>\n",
       "      <td>1527815363</td>\n",
       "      <td>EUR</td>\n",
       "      <td>0</td>\n",
       "      <td>NL</td>\n",
       "      <td>failed</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>games</td>\n",
       "      <td>Live Games</td>\n",
       "      <td>60823.921000</td>\n",
       "      <td>Waalwijk</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180674</th>\n",
       "      <td>1438234172</td>\n",
       "      <td>USD</td>\n",
       "      <td>1</td>\n",
       "      <td>US</td>\n",
       "      <td>failed</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>publishing</td>\n",
       "      <td>Poetry</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>Hollywood</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180675</th>\n",
       "      <td>239852131</td>\n",
       "      <td>GBP</td>\n",
       "      <td>51</td>\n",
       "      <td>GB</td>\n",
       "      <td>successful</td>\n",
       "      <td>1683.874823</td>\n",
       "      <td>music</td>\n",
       "      <td>Blues</td>\n",
       "      <td>1354.033800</td>\n",
       "      <td>London</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180676</th>\n",
       "      <td>1126079486</td>\n",
       "      <td>USD</td>\n",
       "      <td>34</td>\n",
       "      <td>US</td>\n",
       "      <td>successful</td>\n",
       "      <td>3505.000000</td>\n",
       "      <td>film &amp; video</td>\n",
       "      <td>Thrillers</td>\n",
       "      <td>2800.000000</td>\n",
       "      <td>Hollywood</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180677 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                id currency  backers_count country      status   usd_pledged  \\\n",
       "0       1714728788      CAD             16      CA      failed    577.902078   \n",
       "1       1639965256      USD              7      US  successful    211.000000   \n",
       "2       1582062943      USD             99      US  successful   4697.000000   \n",
       "3       1888984372      USD              3      US      failed    222.000000   \n",
       "4       1409770586      USD            100      US  successful   5410.000000   \n",
       "...            ...      ...            ...     ...         ...           ...   \n",
       "180672    70127099      HKD            324      HK  successful  12671.335268   \n",
       "180673  1527815363      EUR              0      NL      failed      0.000000   \n",
       "180674  1438234172      USD              1      US      failed     10.000000   \n",
       "180675   239852131      GBP             51      GB  successful   1683.874823   \n",
       "180676  1126079486      USD             34      US  successful   3505.000000   \n",
       "\n",
       "       main_category      sub_category      goal_usd          city  duration  \\\n",
       "0               food             Farms    785.820650      Victoria      60.0   \n",
       "1         publishing          Academic     93.000000     San Diego      24.0   \n",
       "2               food       Small Batch   1500.000000  Philadelphia      30.0   \n",
       "3         technology           Gadgets  25000.000000       Chicago      60.0   \n",
       "4         publishing  Radio & Podcasts   5000.000000        Madrid      30.0   \n",
       "...              ...               ...           ...           ...       ...   \n",
       "180672         games       Video Games  10113.056914     Hong Kong      30.0   \n",
       "180673         games        Live Games  60823.921000      Waalwijk      14.0   \n",
       "180674    publishing            Poetry    500.000000     Hollywood      45.0   \n",
       "180675         music             Blues   1354.033800        London      25.0   \n",
       "180676  film & video         Thrillers   2800.000000     Hollywood      28.0   \n",
       "\n",
       "        launched_year creator_projects  \n",
       "0                2018                1  \n",
       "1                2014                1  \n",
       "2                2020                1  \n",
       "3                2019                1  \n",
       "4                2015                1  \n",
       "...               ...              ...  \n",
       "180672           2019                1  \n",
       "180673           2014                1  \n",
       "180674           2017                1  \n",
       "180675           2015                1  \n",
       "180676           2015                1  \n",
       "\n",
       "[180677 rows x 13 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a666a635",
   "metadata": {},
   "source": [
    "#### Save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "67560a68",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save\n",
    "filename=f'{path}/Kaggle_deduplicated.csv'\n",
    "f=open(filename,'w',encoding='utf-8')\n",
    "f.write(df_final.to_csv())\n",
    "f.close()"
   ]
  }
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