feature-selection-generation-score-0-95705.ipynb

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   "source": [
    "# Analyzing the Kickstarter Dataset\n",
    "Goal of this notebook is to generate new features using the given ones to find correlation between features and the success rate of the kickstarter projects"
   ]
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  {
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   },
   "source": [
    "# **Loading necessary libraries and the data**"
   ]
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  {
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   "execution_count": 1,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "['failed', 'canceled', 'successful', 'live', 'undefined', 'suspended']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import lightgbm as lgb\n",
    "from sklearn import metrics\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "data = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv',\n",
    "                 parse_dates=['deadline', 'launched'])\n",
    "#Printing kickstarter \n",
    "list(data.state.unique())"
   ]
  },
  {
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   "source": [
    "There are 6 different states for projects, we are not interested in \"live\" projects for our analysis (they are yet to be labeled as successful or failed)"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# Manipulating the data to give it more meaning\n",
    "\n",
    "Grouping projects into Successful and Unsuccessful projects\n",
    "\n",
    "Dropping live projects to focus on the finished projects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
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   "source": [
    "\n",
    "data = data.query('state != \"live\"')\n",
    "data = data.assign(isSuccessful = (data['state']=='successful').astype(int))"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
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   "source": [
    "## Calculating the time from launch until deadline\n",
    "Idea is to find a correlation between life time of the project and their success rate"
   ]
  },
  {
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   "execution_count": 3,
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   "outputs": [],
   "source": [
    "data = data.assign(lhour=data.launched.dt.hour,\n",
    "               lday=data.launched.dt.day,\n",
    "               lmonth=data.launched.dt.month,\n",
    "               lyear=data.launched.dt.year)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
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   "source": [
    "### Kickstarter is founded in 2009\n",
    "This means any project launched before 28.04.2009(DD/MM/YYYY, sorry I don't use weird date formats) will be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "data = data[(data['launched'] >= '2009-04-28')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
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   "source": [
    "data = data.assign(dhour=data.deadline.dt.hour,\n",
    "               dday=data.deadline.dt.day,\n",
    "               dmonth=data.deadline.dt.month,\n",
    "               dyear=data.deadline.dt.year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-20T11:28:15.032974Z",
     "iopub.status.busy": "2020-08-20T11:28:15.031900Z",
     "iopub.status.idle": "2020-08-20T11:28:15.214229Z",
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   "outputs": [],
   "source": [
    "data = data.assign(total_year= data.dyear-data.lyear)\n",
    "data = data.assign(total_month = data.total_year*12 + (data.dmonth-data.lmonth))\n",
    "data = data.assign(total_day = data.total_month * 30 + (data.dday - data.lday))\n",
    "data = data.assign(total_hour = data.total_day * 24 + (data.dhour - data.lhour))"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
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    "tags": []
   },
   "source": [
    "## Can you have negative lifespan?\n",
    "No. Then remove it."
   ]
  },
  {
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   "execution_count": 7,
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       "      <td>0</td>\n",
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       "      <td>30</td>\n",
       "      <td>702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378660</th>\n",
       "      <td>999988282</td>\n",
       "      <td>Nou Renmen Ayiti!  We Love Haiti!</td>\n",
       "      <td>Performance Art</td>\n",
       "      <td>Art</td>\n",
       "      <td>USD</td>\n",
       "      <td>2011-08-16</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2011-07-19 09:07:47</td>\n",
       "      <td>524.0</td>\n",
       "      <td>failed</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2011</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>2011</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>639</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>375849 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID                                               name  \\\n",
       "0       1000002330                    The Songs of Adelaide & Abullah   \n",
       "1       1000003930      Greeting From Earth: ZGAC Arts Capsule For ET   \n",
       "2       1000004038                                     Where is Hank?   \n",
       "3       1000007540  ToshiCapital Rekordz Needs Help to Complete Album   \n",
       "4       1000011046  Community Film Project: The Art of Neighborhoo...   \n",
       "...            ...                                                ...   \n",
       "378656   999976400  ChknTruk Nationwide Charity Drive 2014 (Canceled)   \n",
       "378657   999977640                                          The Tribe   \n",
       "378658   999986353  Walls of Remedy- New lesbian Romantic Comedy f...   \n",
       "378659   999987933                           BioDefense Education Kit   \n",
       "378660   999988282                  Nou Renmen Ayiti!  We Love Haiti!   \n",
       "\n",
       "               category main_category currency   deadline     goal  \\\n",
       "0                Poetry    Publishing      GBP 2015-10-09   1000.0   \n",
       "1        Narrative Film  Film & Video      USD 2017-11-01  30000.0   \n",
       "2        Narrative Film  Film & Video      USD 2013-02-26  45000.0   \n",
       "3                 Music         Music      USD 2012-04-16   5000.0   \n",
       "4          Film & Video  Film & Video      USD 2015-08-29  19500.0   \n",
       "...                 ...           ...      ...        ...      ...   \n",
       "378656      Documentary  Film & Video      USD 2014-10-17  50000.0   \n",
       "378657   Narrative Film  Film & Video      USD 2011-07-19   1500.0   \n",
       "378658   Narrative Film  Film & Video      USD 2010-08-16  15000.0   \n",
       "378659       Technology    Technology      USD 2016-02-13  15000.0   \n",
       "378660  Performance Art           Art      USD 2011-08-16   2000.0   \n",
       "\n",
       "                  launched  pledged     state  ...  lmonth lyear  dhour  dday  \\\n",
       "0      2015-08-11 12:12:28      0.0    failed  ...       8  2015      0     9   \n",
       "1      2017-09-02 04:43:57   2421.0    failed  ...       9  2017      0     1   \n",
       "2      2013-01-12 00:20:50    220.0    failed  ...       1  2013      0    26   \n",
       "3      2012-03-17 03:24:11      1.0    failed  ...       3  2012      0    16   \n",
       "4      2015-07-04 08:35:03   1283.0  canceled  ...       7  2015      0    29   \n",
       "...                    ...      ...       ...  ...     ...   ...    ...   ...   \n",
       "378656 2014-09-17 02:35:30     25.0  canceled  ...       9  2014      0    17   \n",
       "378657 2011-06-22 03:35:14    155.0    failed  ...       6  2011      0    19   \n",
       "378658 2010-07-01 19:40:30     20.0    failed  ...       7  2010      0    16   \n",
       "378659 2016-01-13 18:13:53    200.0    failed  ...       1  2016      0    13   \n",
       "378660 2011-07-19 09:07:47    524.0    failed  ...       7  2011      0    16   \n",
       "\n",
       "        dmonth  dyear  total_year  total_month  total_day  total_hour  \n",
       "0           10   2015           0            2         58        1380  \n",
       "1           11   2017           0            2         59        1412  \n",
       "2            2   2013           0            1         44        1056  \n",
       "3            4   2012           0            1         29         693  \n",
       "4            8   2015           0            1         55        1312  \n",
       "...        ...    ...         ...          ...        ...         ...  \n",
       "378656      10   2014           0            1         30         718  \n",
       "378657       7   2011           0            1         27         645  \n",
       "378658       8   2010           0            1         45        1061  \n",
       "378659       2   2016           0            1         30         702  \n",
       "378660       8   2011           0            1         27         639  \n",
       "\n",
       "[375849 rows x 28 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.query('total_hour >= 0')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.008768,
     "end_time": "2020-08-20T11:28:15.556543",
     "exception": false,
     "start_time": "2020-08-20T11:28:15.547775",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Adding a new column to turn \"goal\" data into categorical data\n",
    "Turning \"goal\" and \"backers\" into categorical data helps us to analyze their effects on the success rate with ease because we will have a more generic opinion about them.\n",
    "For example we could have two projects with just 1 dollar goal difference, this small change could feint our model into making wrong decisions and could lead to some over-fitting problems. Grouping numeric features with high range and non-uniform distribution will lead to clear understanding of their effects on success rate."
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.008478,
     "end_time": "2020-08-20T11:28:15.573975",
     "exception": false,
     "start_time": "2020-08-20T11:28:15.565497",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Selecting 30th, 60th and 90th percentiles to let us fit our numeric daha into categories.\n",
    "Any value which is lower  from 30th percentile of the values will be labeled as \"low\"\n",
    "And value which is between 30th and 60th percentile of our data will be labeled as \"medium\"\n",
    "And value which have between 60th and 90th percentile of our data will be labeled as \"high\"\n",
    "And any value higher than this will be labeled as \"extreme\""
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.00876,
     "end_time": "2020-08-20T11:28:15.591440",
     "exception": false,
     "start_time": "2020-08-20T11:28:15.582680",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Changing percentiles and creating more labels could lead to better predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-20T11:28:15.618570Z",
     "iopub.status.busy": "2020-08-20T11:28:15.617228Z",
     "iopub.status.idle": "2020-08-20T11:28:15.809741Z",
     "shell.execute_reply": "2020-08-20T11:28:15.809193Z"
    },
    "papermill": {
     "duration": 0.209762,
     "end_time": "2020-08-20T11:28:15.809870",
     "exception": false,
     "start_time": "2020-08-20T11:28:15.600108",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "low = data['goal'].quantile(0.3)\n",
    "mid = data['goal'].quantile(0.6)\n",
    "high = data['goal'].quantile(0.9)\n",
    "\n",
    "conditions = [(data['goal']<low),((data['goal']>=low) & (data['goal']<mid)),((data['goal']>=mid) & (data['goal']<high)),(data['goal']>=high)]\n",
    "tags = ['t0_low', 't1_medium','t2_high','t3_extreme']\n",
    "\n",
    "data = data.assign(categorical_goal = np.select(conditions,tags))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.008517,
     "end_time": "2020-08-20T11:28:15.827657",
     "exception": false,
     "start_time": "2020-08-20T11:28:15.819140",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Adding another column to turn \"backers\" into categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-20T11:28:15.855116Z",
     "iopub.status.busy": "2020-08-20T11:28:15.854148Z",
     "iopub.status.idle": "2020-08-20T11:28:16.080581Z",
     "shell.execute_reply": "2020-08-20T11:28:16.080037Z"
    },
    "papermill": {
     "duration": 0.244228,
     "end_time": "2020-08-20T11:28:16.080722",
     "exception": false,
     "start_time": "2020-08-20T11:28:15.836494",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>name</th>\n",
       "      <th>category</th>\n",
       "      <th>main_category</th>\n",
       "      <th>currency</th>\n",
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       "      <th>dday</th>\n",
       "      <th>dmonth</th>\n",
       "      <th>dyear</th>\n",
       "      <th>total_year</th>\n",
       "      <th>total_month</th>\n",
       "      <th>total_day</th>\n",
       "      <th>total_hour</th>\n",
       "      <th>categorical_goal</th>\n",
       "      <th>categorical_backers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000002330</td>\n",
       "      <td>The Songs of Adelaide &amp; Abullah</td>\n",
       "      <td>Poetry</td>\n",
       "      <td>Publishing</td>\n",
       "      <td>GBP</td>\n",
       "      <td>2015-10-09</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2015-08-11 12:12:28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>failed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>58</td>\n",
       "      <td>1380</td>\n",
       "      <td>t0_low</td>\n",
       "      <td>t0_low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000003930</td>\n",
       "      <td>Greeting From Earth: ZGAC Arts Capsule For ET</td>\n",
       "      <td>Narrative Film</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>USD</td>\n",
       "      <td>2017-11-01</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>2017-09-02 04:43:57</td>\n",
       "      <td>2421.0</td>\n",
       "      <td>failed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>59</td>\n",
       "      <td>1412</td>\n",
       "      <td>t2_high</td>\n",
       "      <td>t1_medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000004038</td>\n",
       "      <td>Where is Hank?</td>\n",
       "      <td>Narrative Film</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>USD</td>\n",
       "      <td>2013-02-26</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>2013-01-12 00:20:50</td>\n",
       "      <td>220.0</td>\n",
       "      <td>failed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>2013</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>1056</td>\n",
       "      <td>t2_high</td>\n",
       "      <td>t1_medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000007540</td>\n",
       "      <td>ToshiCapital Rekordz Needs Help to Complete Album</td>\n",
       "      <td>Music</td>\n",
       "      <td>Music</td>\n",
       "      <td>USD</td>\n",
       "      <td>2012-04-16</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>2012-03-17 03:24:11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>failed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>2012</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>693</td>\n",
       "      <td>t1_medium</td>\n",
       "      <td>t0_low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000011046</td>\n",
       "      <td>Community Film Project: The Art of Neighborhoo...</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>USD</td>\n",
       "      <td>2015-08-29</td>\n",
       "      <td>19500.0</td>\n",
       "      <td>2015-07-04 08:35:03</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>canceled</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>1312</td>\n",
       "      <td>t2_high</td>\n",
       "      <td>t1_medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID                                               name  \\\n",
       "0  1000002330                    The Songs of Adelaide & Abullah   \n",
       "1  1000003930      Greeting From Earth: ZGAC Arts Capsule For ET   \n",
       "2  1000004038                                     Where is Hank?   \n",
       "3  1000007540  ToshiCapital Rekordz Needs Help to Complete Album   \n",
       "4  1000011046  Community Film Project: The Art of Neighborhoo...   \n",
       "\n",
       "         category main_category currency   deadline     goal  \\\n",
       "0          Poetry    Publishing      GBP 2015-10-09   1000.0   \n",
       "1  Narrative Film  Film & Video      USD 2017-11-01  30000.0   \n",
       "2  Narrative Film  Film & Video      USD 2013-02-26  45000.0   \n",
       "3           Music         Music      USD 2012-04-16   5000.0   \n",
       "4    Film & Video  Film & Video      USD 2015-08-29  19500.0   \n",
       "\n",
       "             launched  pledged     state  ...  dhour dday  dmonth  dyear  \\\n",
       "0 2015-08-11 12:12:28      0.0    failed  ...      0    9      10   2015   \n",
       "1 2017-09-02 04:43:57   2421.0    failed  ...      0    1      11   2017   \n",
       "2 2013-01-12 00:20:50    220.0    failed  ...      0   26       2   2013   \n",
       "3 2012-03-17 03:24:11      1.0    failed  ...      0   16       4   2012   \n",
       "4 2015-07-04 08:35:03   1283.0  canceled  ...      0   29       8   2015   \n",
       "\n",
       "   total_year  total_month  total_day  total_hour  categorical_goal  \\\n",
       "0           0            2         58        1380            t0_low   \n",
       "1           0            2         59        1412           t2_high   \n",
       "2           0            1         44        1056           t2_high   \n",
       "3           0            1         29         693         t1_medium   \n",
       "4           0            1         55        1312           t2_high   \n",
       "\n",
       "   categorical_backers  \n",
       "0               t0_low  \n",
       "1            t1_medium  \n",
       "2            t1_medium  \n",
       "3               t0_low  \n",
       "4            t1_medium  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low = data['backers'].quantile(0.3)\n",
    "mid = data['backers'].quantile(0.6)\n",
    "high = data['backers'].quantile(0.9)\n",
    "\n",
    "\n",
    "conditions = [(data['backers']<low),((data['backers']>=low) & (data['backers']<mid)),((data['backers']>=mid) & (data['backers']<high)),(data['backers']>=high)]\n",
    "tags = ['t0_low', 't1_medium','t2_high','t3_extreme']\n",
    "\n",
    "data = data.assign(categorical_backers = np.select(conditions,tags))\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.009222,
     "end_time": "2020-08-20T11:28:16.099710",
     "exception": false,
     "start_time": "2020-08-20T11:28:16.090488",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "> # Getting rid of some columns which are not relevant"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.009037,
     "end_time": "2020-08-20T11:28:16.118491",
     "exception": false,
     "start_time": "2020-08-20T11:28:16.109454",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- \"Name\" of the project _could_ have a effect on the success of the project but it would need high resources to identify (NLP/semantic analysis)\n",
    "- \"Goal\" will be changed with \"categorical_goal\" so it is dropped.\n",
    "- \"Backers\" will be changed with \"categorical_backers\" so it is dropped.\n",
    "- Any date info will be changed with lifetime of the project so they are dropped.\n",
    "- Total pledged money means success for finished projects but it can be used to make predictions about ongoing projects, which is not our case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-20T11:28:16.145019Z",
     "iopub.status.busy": "2020-08-20T11:28:16.144321Z",
     "iopub.status.idle": "2020-08-20T11:28:16.238736Z",
     "shell.execute_reply": "2020-08-20T11:28:16.238173Z"
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     "start_time": "2020-08-20T11:28:16.127706",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Poetry</td>\n",
       "      <td>Publishing</td>\n",
       "      <td>GBP</td>\n",
       "      <td>GB</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>58</td>\n",
       "      <td>1380</td>\n",
       "      <td>t0_low</td>\n",
       "      <td>t0_low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Narrative Film</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>USD</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>59</td>\n",
       "      <td>1412</td>\n",
       "      <td>t2_high</td>\n",
       "      <td>t1_medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Narrative Film</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>USD</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>1056</td>\n",
       "      <td>t2_high</td>\n",
       "      <td>t1_medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Music</td>\n",
       "      <td>Music</td>\n",
       "      <td>USD</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>693</td>\n",
       "      <td>t1_medium</td>\n",
       "      <td>t0_low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>USD</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>1312</td>\n",
       "      <td>t2_high</td>\n",
       "      <td>t1_medium</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378656</th>\n",
       "      <td>Documentary</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>USD</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>718</td>\n",
       "      <td>t3_extreme</td>\n",
       "      <td>t0_low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378657</th>\n",
       "      <td>Narrative Film</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>USD</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>645</td>\n",
       "      <td>t0_low</td>\n",
       "      <td>t1_medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378658</th>\n",
       "      <td>Narrative Film</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>USD</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>1061</td>\n",
       "      <td>t2_high</td>\n",
       "      <td>t0_low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378659</th>\n",
       "      <td>Technology</td>\n",
       "      <td>Technology</td>\n",
       "      <td>USD</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>702</td>\n",
       "      <td>t2_high</td>\n",
       "      <td>t1_medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378660</th>\n",
       "      <td>Performance Art</td>\n",
       "      <td>Art</td>\n",
       "      <td>USD</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>639</td>\n",
       "      <td>t0_low</td>\n",
       "      <td>t1_medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>375849 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               category main_category currency country  isSuccessful  \\\n",
       "0                Poetry    Publishing      GBP      GB             0   \n",
       "1        Narrative Film  Film & Video      USD      US             0   \n",
       "2        Narrative Film  Film & Video      USD      US             0   \n",
       "3                 Music         Music      USD      US             0   \n",
       "4          Film & Video  Film & Video      USD      US             0   \n",
       "...                 ...           ...      ...     ...           ...   \n",
       "378656      Documentary  Film & Video      USD      US             0   \n",
       "378657   Narrative Film  Film & Video      USD      US             0   \n",
       "378658   Narrative Film  Film & Video      USD      US             0   \n",
       "378659       Technology    Technology      USD      US             0   \n",
       "378660  Performance Art           Art      USD      US             0   \n",
       "\n",
       "        total_year  total_month  total_day  total_hour categorical_goal  \\\n",
       "0                0            2         58        1380           t0_low   \n",
       "1                0            2         59        1412          t2_high   \n",
       "2                0            1         44        1056          t2_high   \n",
       "3                0            1         29         693        t1_medium   \n",
       "4                0            1         55        1312          t2_high   \n",
       "...            ...          ...        ...         ...              ...   \n",
       "378656           0            1         30         718       t3_extreme   \n",
       "378657           0            1         27         645           t0_low   \n",
       "378658           0            1         45        1061          t2_high   \n",
       "378659           0            1         30         702          t2_high   \n",
       "378660           0            1         27         639           t0_low   \n",
       "\n",
       "       categorical_backers  \n",
       "0                   t0_low  \n",
       "1                t1_medium  \n",
       "2                t1_medium  \n",
       "3                   t0_low  \n",
       "4                t1_medium  \n",
       "...                    ...  \n",
       "378656              t0_low  \n",
       "378657           t1_medium  \n",
       "378658              t0_low  \n",
       "378659           t1_medium  \n",
       "378660           t1_medium  \n",
       "\n",
       "[375849 rows x 11 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupableData = data.drop(columns = ['ID', 'name','goal','backers', 'deadline','dyear','lyear','dmonth','lmonth','dday','lday','dhour','lhour','launched','usd_goal_real','pledged','state','usd_pledged_real','usd pledged'])\n",
    "groupableData"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.009497,
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     "start_time": "2020-08-20T11:28:16.248982",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Checking which main categories are more likely to succeed\n",
    "\n",
    "As it can be observed below, projects related to entertaintment have higher chances to succeed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-20T11:28:16.292423Z",
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     "start_time": "2020-08-20T11:28:16.269237",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "      <td>0.373486</td>\n",
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       "      <td>0.358230</td>\n",
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       "      <th>Publishing</th>\n",
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       "      <th>Photography</th>\n",
       "      <td>0.307986</td>\n",
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       "      <td>0.242185</td>\n",
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       "      <th>Journalism</th>\n",
       "      <td>0.214225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>0.199838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               isSuccessful\n",
       "main_category              \n",
       "Dance              0.623467\n",
       "Theater            0.601049\n",
       "Comics             0.543796\n",
       "Music              0.468597\n",
       "Art                0.411668\n",
       "Film & Video       0.373486\n",
       "Games              0.358230\n",
       "Design             0.354455\n",
       "Publishing         0.310810\n",
       "Photography        0.307986\n",
       "Food               0.249201\n",
       "Fashion            0.247873\n",
       "Crafts             0.242185\n",
       "Journalism         0.214225\n",
       "Technology         0.199838"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mainCategorySuccess = groupableData[['main_category','isSuccessful']]\n",
    "mainCategorySuccess = mainCategorySuccess.groupby(['main_category']).mean().sort_values(by=['isSuccessful'], ascending = False)\n",
    "mainCategorySuccess"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.014958,
     "end_time": "2020-08-20T11:28:16.391845",
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     "start_time": "2020-08-20T11:28:16.376887",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Checking which categories are more likely to succeed\n",
    "\n",
    "Data below somewhat contradict our main categories. This could help us to define some feature which we could not define only with our main categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-20T11:28:16.428909Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isSuccessful</th>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>Chiptune</th>\n",
       "      <td>0.771429</td>\n",
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       "      <th>Residencies</th>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "<p>159 rows × 1 columns</p>\n",
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      ],
      "text/plain": [
       "              isSuccessful\n",
       "category                  \n",
       "Chiptune          0.771429\n",
       "Residencies       0.724638\n",
       "Anthologies       0.672258\n",
       "Dance             0.665516\n",
       "Letterpress       0.645833\n",
       "...                    ...\n",
       "Software          0.122638\n",
       "Video             0.119718\n",
       "Web               0.086914\n",
       "Mobile Games      0.086538\n",
       "Apps              0.060326\n",
       "\n",
       "[159 rows x 1 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorySuccess = groupableData[['category','isSuccessful']]\n",
    "categorySuccess = categorySuccess.groupby(['category']).mean().sort_values(by=['isSuccessful'], ascending = False)\n",
    "categorySuccess"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
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     "start_time": "2020-08-20T11:28:16.524963",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Is Currency relevant to success?\n",
    "\n",
    "Some currencies have higher chances of success but does this mean anything?\n",
    "\n",
    "Currencies like Euro and USD are valid in wide regions, which does not tell much, we could go into a little bit more detail without forgetting about currencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "      <th>AUD</th>\n",
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       "      <th>CHF</th>\n",
       "      <td>0.248340</td>\n",
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       "      <td>0.325247</td>\n",
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       "      <th>EUR</th>\n",
       "      <td>0.227337</td>\n",
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       "      <th>GBP</th>\n",
       "      <td>0.356866</td>\n",
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       "      <th>MXN</th>\n",
       "      <td>0.240729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOK</th>\n",
       "      <td>0.228291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NZD</th>\n",
       "      <td>0.306011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEK</th>\n",
       "      <td>0.287896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGD</th>\n",
       "      <td>0.337761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USD</th>\n",
       "      <td>0.372521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          isSuccessful\n",
       "currency              \n",
       "AUD           0.255203\n",
       "CAD           0.278913\n",
       "CHF           0.248340\n",
       "DKK           0.325247\n",
       "EUR           0.227337\n",
       "GBP           0.356866\n",
       "HKD           0.370497\n",
       "JPY           0.225806\n",
       "MXN           0.240729\n",
       "NOK           0.228291\n",
       "NZD           0.306011\n",
       "SEK           0.287896\n",
       "SGD           0.337761\n",
       "USD           0.372521"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "currencySuccess = groupableData[['currency','isSuccessful']]\n",
    "currencySuccess = currencySuccess.groupby(['currency']).mean()\n",
    "currencySuccess"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.016677,
     "end_time": "2020-08-20T11:28:16.669238",
     "exception": false,
     "start_time": "2020-08-20T11:28:16.652561",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### What about countries?\n",
    "\n",
    "Some countries in EU-Zone have higher tendency to provide/get support for kickstarter projects than others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-20T11:28:16.712387Z",
     "iopub.status.busy": "2020-08-20T11:28:16.710923Z",
     "iopub.status.idle": "2020-08-20T11:28:16.772413Z",
     "shell.execute_reply": "2020-08-20T11:28:16.773141Z"
    },
    "papermill": {
     "duration": 0.089485,
     "end_time": "2020-08-20T11:28:16.773418",
     "exception": false,
     "start_time": "2020-08-20T11:28:16.683933",
     "status": "completed"
    },
    "tags": []
   },
   "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>isSuccessful</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AT</th>\n",
       "      <td>0.183849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AU</th>\n",
       "      <td>0.258721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>0.251240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>0.282637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>0.250670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>0.228760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DK</th>\n",
       "      <td>0.328168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES</th>\n",
       "      <td>0.221223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR</th>\n",
       "      <td>0.314513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GB</th>\n",
       "      <td>0.361363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HK</th>\n",
       "      <td>0.370497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IE</th>\n",
       "      <td>0.258750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>0.156674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP</th>\n",
       "      <td>0.225806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LU</th>\n",
       "      <td>0.311475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MX</th>\n",
       "      <td>0.240729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N,0\"</th>\n",
       "      <td>0.027661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL</th>\n",
       "      <td>0.217790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>0.231429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NZ</th>\n",
       "      <td>0.311978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>0.293034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG</th>\n",
       "      <td>0.337761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>0.375751</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         isSuccessful\n",
       "country              \n",
       "AT           0.183849\n",
       "AU           0.258721\n",
       "BE           0.251240\n",
       "CA           0.282637\n",
       "CH           0.250670\n",
       "DE           0.228760\n",
       "DK           0.328168\n",
       "ES           0.221223\n",
       "FR           0.314513\n",
       "GB           0.361363\n",
       "HK           0.370497\n",
       "IE           0.258750\n",
       "IT           0.156674\n",
       "JP           0.225806\n",
       "LU           0.311475\n",
       "MX           0.240729\n",
       "N,0\"         0.027661\n",
       "NL           0.217790\n",
       "NO           0.231429\n",
       "NZ           0.311978\n",
       "SE           0.293034\n",
       "SG           0.337761\n",
       "US           0.375751"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countrySuccess = groupableData[['country','isSuccessful']]\n",
    "countrySuccess = countrySuccess.groupby(['country']).mean()\n",
    "countrySuccess"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.020496,
     "end_time": "2020-08-20T11:28:16.813717",
     "exception": false,
     "start_time": "2020-08-20T11:28:16.793221",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Setting up high goals is a good thing?\n",
    "\n",
    "There is a obvious negative correlation between amount of cash aimed and success rate. So don't be too _greedy_ when creating a kickstarter project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-20T11:28:16.869008Z",
     "iopub.status.busy": "2020-08-20T11:28:16.866557Z",
     "iopub.status.idle": "2020-08-20T11:28:17.245821Z",
     "shell.execute_reply": "2020-08-20T11:28:17.244727Z"
    },
    "papermill": {
     "duration": 0.411944,
     "end_time": "2020-08-20T11:28:17.246030",
     "exception": false,
     "start_time": "2020-08-20T11:28:16.834086",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2e95e2df90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "goalSuccess = groupableData[['categorical_goal','isSuccessful']]\n",
    "goalSuccess = goalSuccess.groupby(['categorical_goal']).mean()\n",
    "goalSuccess.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.017999,
     "end_time": "2020-08-20T11:28:17.281040",
     "exception": false,
     "start_time": "2020-08-20T11:28:17.263041",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Quantity > Quality\n",
    "\n",
    "A lot of people giving some money is better than some people giving alot of money. Because people attract more people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-20T11:28:17.310711Z",
     "iopub.status.busy": "2020-08-20T11:28:17.309944Z",
     "iopub.status.idle": "2020-08-20T11:28:17.490686Z",
     "shell.execute_reply": "2020-08-20T11:28:17.490002Z"
    },
    "papermill": {
     "duration": 0.197751,
     "end_time": "2020-08-20T11:28:17.490812",
     "exception": false,
     "start_time": "2020-08-20T11:28:17.293061",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2e95d5ad90>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "backerSuccess = groupableData[['categorical_backers','isSuccessful']]\n",
    "backerSuccess = backerSuccess.groupby(['categorical_backers']).mean()\n",
    "backerSuccess.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.011293,
     "end_time": "2020-08-20T11:28:17.513476",
     "exception": false,
     "start_time": "2020-08-20T11:28:17.502183",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Plotting success rate related to total time passed"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.011206,
     "end_time": "2020-08-20T11:28:17.536090",
     "exception": false,
     "start_time": "2020-08-20T11:28:17.524884",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Will I succeed after 40 years?\n",
    "No."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-20T11:28:17.569784Z",
     "iopub.status.busy": "2020-08-20T11:28:17.568821Z",
     "iopub.status.idle": "2020-08-20T11:28:17.734996Z",
     "shell.execute_reply": "2020-08-20T11:28:17.734284Z"
    },
    "papermill": {
     "duration": 0.187551,
     "end_time": "2020-08-20T11:28:17.735118",
     "exception": false,
     "start_time": "2020-08-20T11:28:17.547567",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2e95636710>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yearSuccess = groupableData[['total_year','isSuccessful']]\n",
    "yearSuccess = yearSuccess.groupby(['total_year']).mean()\n",
    "yearSuccess.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-20T11:28:17.769087Z",
     "iopub.status.busy": "2020-08-20T11:28:17.768405Z",
     "iopub.status.idle": "2020-08-20T11:28:17.940245Z",
     "shell.execute_reply": "2020-08-20T11:28:17.939701Z"
    },
    "papermill": {
     "duration": 0.193207,
     "end_time": "2020-08-20T11:28:17.940385",
     "exception": false,
     "start_time": "2020-08-20T11:28:17.747178",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2e955adf10>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9MkjUCwvhbyN70yYmgsmfbWTnoVyeua4vUfX0VyxS1+mKPoiYGXcP7safr+rB5+t2ce30xew5nOd1LBHxmAp9ELrhnA5MH53Iup05DJuykM17jngdSUQ8pEIfpC7u3opZEwdwJK+AYVMWsOSH/V5HEhGPqNAHsT7tYph7SwqNI8O5dvpiPlq9w+tIIuIBFfogF9+8AXNvSaFbm0ak/iOTVxZt8TqSiFQzFfo6oFnD+sy6+Rwu7NaSP761moffX6utjkXqEBX6OiKyXijTbujH9f3bM+3fG7lr9jKOF6jXXqQuUJN1HRIWGsKfr+pBbJNI/vrBOnbl5DFtdD8aRYR7HU1EqpCu6OsYM+PW88/giVG9Sd+yj6unLiL7wDGvY4lIFVKhr6OG9o3jpfHJZB84xtApC/g2+5DXkUSkiqjQ12EDz2jOnFsGEGLGyGe11bFIsFKhr+O6tW7E/FsHEtckkhtfSmd2xlavI4lIJVOhF1o39m11PKBzM36XtoInPl6vrY5FgogKvQD/t9Xx1f3i+Pun33F32gq1X4oECbVXygnhoSH8dUQv4ppE8cQn69lxMJepNyQQrfZLkVpNV/TyE2bGby7uwmNX92bxpr1cPW0R2w+q/VKkNlOhl1KN6Odrv8zaf4yhkxeyZrvaL0Vqq4AKvZldambrzGyDmd1TyvEhZrbCzJaZWYaZnVvs2BYzW/njscoML1Xr3C7NmZM6AICrpy3iy+/UfilSG5Vb6M0sFJgMXAZ0B641s+4lhn0K9HbO9QFuBGaUOH6Bc66Pcy6xEjJLNTqrTSPm35ZCXJNIxr+Yzhy1X4rUOoFc0ScDG5xzm5xzx4FZwJDiA5xzh93/9eM1ANSbF0TaNI5kduoAzunUjLvTVvD3T75T+6VILRJIoY8Fil/GZfmf+wkzG2pma4F38V3V/8gBH5lZpplNLOtFzGyif9knY/duLRHUNI0iwnlxfBIj+sXxxCfr+V3aCvIL1X4pUhsEUuitlOd+djnnnJvvnOsGXAU8WOzQQOdcAr6ln9vM7LzSXsQ5N905l+icS2zRokUAsaS6hYeG8OiIXvzmoi7MyczixpfSycnN9zqWiJQjkEKfBbQr9jgOyC5rsHPuC6CzmTX3P872/3cXMB/fUpDUUmbGby/pyl9H9GLRRl/75Y6DuV7HEpGTCKTQpwNdzKyjmdUDrgHeLj7AzM4wM/N/nADUA/aaWQMzi/Y/3wAYBKyqzC9AvDEysR0vjEvytV9OWcDaHWq/FKmpyi30zrkC4HbgQ2ANMNs5t9rMUs0s1T9sOLDKzJbh69AZ5X9zthXwlZktB74B3nXOfVAVX4hUv/O6tmD2fw6gyDmunrqIBRv2eB1JREphNbF7IjEx0WVkqOW+tsg+cIzxL6azcfdhHhnei+H94ryOJFLnmFlmWS3s+s1YOW1tYyKZc8sA+ndqyn/NWc5Tn6r9UqQmUaGXStEoIpwXxyUzLCGWxz9ezz1zV6r9UqSG0O6VUmnqhYXwt6t7E9ckiqc+/Y7th3KZcn0CDevr20zES7qil0plZtx1SVceGd6TBRv2MHLaInYeUvuliJdU6KVKjEpqzwvjkvh+7xGGTl7Auh05XkcSqbNU6KXK/KJrC2anDqCgyDFi2kIWqv1SxBMq9FKlzm7bmPm3DaRN4wjGvvgN85dmeR1JpM5RoZcqFxsTyZzUFBI7NOW3byznmX+p/VKkOqnQS7VoHBnOyzcmM7RvLI99tJ7/nqf2S5Hqor43qTb1wkJ4fGRv4ppE8vS/NrD9YC6T1X4pUuV0RS/Vysz4r0Fn8tCwnnyl9kuRaqFCL564Nrk9M8Ym8v3eIwybspD1O9V+KVJVVOjFMxec2ZI3/nMAxwuLGD51IQs3qv1SpCqo0IunesQ2Zv6tKbRuFMHYF77hzaXbvI4kEnRU6MVzcU2iSEtNoV+HJtz5xjImf7ZB7ZcilUiFXmqExlG+9sshfdry6IfruHf+SgrUfilSKdTXJjVG/bBQnhzVh7gmkUz+bKOv/fK6BBqo/VLktOiKXmoUM+Puwd34f0N78uV3exg1fRG71H4pclpU6KVGuq5/e2aMSWTT7iMMnbKQ79R+KXLKVOilxrqgW0tmF2u/XLxpr9eRRGolFXqp0XrENmbeLSm0bBTBmOe/4a1lar+s6bYfPMbkzzZw0d8+Z/jUhRw7Xuh1pDpPhV5qvHZNo5ibmkLf9jH8ZtYypnyu9suaJje/kLeWbWP081+T8vC/ePTDdTSMCGfJD/u5O225/r48pnYGqRUaR4Uz86Zk7p6zgr9+sI6s/cf431+dTViorlW84pxjyQ/7ScvM4p3l28nJKyA2JpI7LuzC8IRYOjRrwNTPN/LIB2s5s1U0d1zUxevIdZYKvdQaP7ZfxjaJZOrnG9l+4BjPqP2y2mUfOMb8pdtIy8xi854jRIaHclnP1ozoF8c5HZsREmInxqb+ohPrd+bwt4/X06VVNJf2aO1h8rpL/4dIrRISYvz+0m7ExkRy31uruGb6Yp4fl0jL6AivowW1Y8cL+XD1DtIys1iwcQ/OQf+OTbnl/M5c3rNNmVtNmxkPDevJpj1HuGv2Mjo0S+GsNo2qOb1YTVw7S0xMdBkZGV7HkBru0zU7uf21pTRtUI+Xb0zijJbRXkcKKs45Mr7fT1pGFu+u3M7hvALimkQyPCGO4QlxtG8WFfDn2nUol189s4DQEOOt2wfSvGH9KkxeN5lZpnMusdRjKvRSm63IOsCNL2VwvKCQ58Yk0r9TM68j1XrbDhxjXmYWc5dksWXvUaLqhXJ5zzaM6BdHcnzTnyzNVMSKrANcPW0RveNi+MeE/tQL0/srlUmFXoLa1n1HGffiN2zdd4zHRvbmV73beh2p1jl6vIAPVu1g7pIsFm7ci3MwoFMzhveL47IerSvtfZB/Ls/mjteXMiqxHQ8P74nZqf2jIT93skIf0N+emV0K/B0IBWY45x4ucXwI8CBQBBQAdzrnvgrkXJHT1a5pFHNvSWHizEx+/fpStu0/RuovOqmIlMM5R/qW/aRlbuXdFds5cryQdk0jufOirgxLiKVd08CXZgJ1Ze+2rNuRwzOfbeDM1tHceG7HSn8N+blyr+jNLBRYD1wCZAHpwLXOuW+LjWkIHHHOOTPrBcx2znUL5NzS6IpeTkVufiF3p63gn8uzueGc9jxwpdovS7N131HmLdnG3CVZ/LDvKA2KLc0kncbSTKCKihyp/8jkkzU7eWl8Mud1bVGlr1dXnO4VfTKwwTm3yf/JZgFDgBPF2jl3uNj4BoAL9FyRyhIRHsrfR/UhNiaSaf/eyPYDuTx9XV+i6qm57OjxAt5f6euaWeTfSiKlczPuvLgLl/ZoXa1zFBJiPDGqD8OnLuT215bw5m0D6dSiYbW9fl0UyN9uLLC12OMsoH/JQWY2FHgIaAlcUZFz/edPBCYCtG/fPoBYIj8XEmLcc1k3YmMiuP/t1b72y7FJtIiue10eRUWOb7bsIy0zi/dX+pZmOjSL4q5LfEszcU0qf2kmUA3qh/HcmESGTF7AhJczmH/bQBpHhnuWJ9gFUuhL+znuZ+s9zrn5wHwzOw/fev3FgZ7rP386MB18SzcB5BIp0+gB8bRpHMkdry9l6JQFvDQ+mTNa1o2rxq37jjJ3ia9rZuu+YzSsH8Yve7VlRGIciR2a1Jj3Lto1jWLq9Qnc8PzX3PH6Ul4Ym6iltioSSKHPAtoVexwHZJc12Dn3hZl1NrPmFT1XpDJd3L0Vsyaew00vpzN86kKeG5NIcsemXseqEkfyCnhv5XbSMrP4evM+zGBg5+b81yVnMvjs1kTWC/U6Yqn6d2rGg0N6cM+8lTz0/lr++MvuXkcKSoEU+nSgi5l1BLYB1wDXFR9gZmcAG/1vxiYA9YC9wIHyzhWpSr3bxTD/1oGMffEbbpjxNX8b2Zsrg6T9sqjIsXjzXuZmbuP9Vds5eryQ+GZRTBrUlaEJccTGRHodMSDXJLdn7Y4cnv9qM2e2imZkUrvyT5IKKbfQO+cKzOx24EN8LZIvOOdWm1mq//g0YDgwxszygWPAKOdr5yn13Cr6WkRK9ePulxNfyeCO15eSfeAYE8+rve2X3+89wtwl25ibmcW2A8eIrh/GkD5tGdEvjoT2NWdppiL+54qz2Lj7MH94cyWdWjQgMT44f/Lyin5hSuqM3PxC/mvOct5dsZ3R53TggV+dTWgVtxJWlsN5Bby3YjtpS7L4xr80c+4ZzRnRL45B3Wvu0kxFHDyaz1VTFpCTm8+btw309M3i2ki/GSviV1TkeOSDtTz7xSYuPqslT11bc9svi4ocizft9XXNrNrBsfxCOjVvwPB+cQztG0vbWrI0UxEbdh1m6JQFxDWJIi11gHYmrQAVepESZi7awgNvr6ZnbGNm1LD2yy17jjB3SRbzlmzzLc1E+Ltm+sWR0D6mVi7NVMTn63Zx40vpDOreminXJ1T5L3AFCxV6kVJ8tHoHv561lBbR9XlpfDKdPfylnZzc/BNdM+lb9hNicG6XFv6lmVZEhNf+pZmKmPHlJv787hp+fVEX7rqkq9dxagUVepEyLNt6gJteSqfQOZ4bk0hSNb4JWFjkWLRxL2mZW/lg9Q5y84vo3KIBI/q1Y2jfWFo3rrt77Dvn+F3aCuZkZjH5ugSu6NXG60g1ngq9yEn8sNe3+2XWgWM8MbJPlReVzXuOMDczi3lLssg+mEt0RBi/6u1bmunTLviXZgKVV1DIdc99zersg6SlptAjtrHXkWo0FXqRcuw/cpwJMzPI/H4/917ejZv/o3LbLw/l5vPuCt/STOb3vqWZ87r6lmYuPqvuLc0EandOHkOe+QoHvHX7QN1J7CRU6EUCkJtfyF2zl/Heyh2MHdCB+648vfbLwiLHwo17SMvM4oNVO8grKOKMlg0Z4e+aadVIRSsQq7MPMmLqIs5qE83rE8+hfpj+USzNae9HL1IXRISH8sy1CTwUs4bnvtxM9sFcnrqmb4V71DfuPuxfmtnGjkO5NIoIY2RiO0b0i6NXXGMtzVTQ2W0b8/jI3tzy6hL+e95K/nZ1b81hBanQixQTEmL84YruxMZE8qd3vuWa5xbz/NjEcu9xevDYj0szW1nywwFCDH7RtQV//GV3LjqrpZZmTtNlPdtw58VdePKT7+jWOpqJ53X2OlKtokIvUopxAzvSJiaSX7++lGFTFvLS+KSf7ZleWOT4aoNvaebD1Ts4XlBE11YNuffyblzVJ5aWWpqpVL++sAvrd+bw0Ptr6dIymgu6tfQ6Uq2hNXqRk1jyw34mvJxBkXPMGJNIYnxTNuzKIS1zG/OXZrHzUB4xUeEM6d2W4f3i6BmrpZmqdPR4ASOmLmLrvqPMvy2FM1pGex2pxtCbsSKn4fu9Rxj3YjrbDhyjW+toVmQdJDTEON/fNXPhWS31BmE12nbgGEOeWUCD+qG8ddtAYqLqeR2pRlChFzlN+44c567Zy9h1KI+hfWMZ0retWv08lPn9fq6dvpikjk14aXwy4bphiQq9iASftMwsJs1ZztgBHfjTkB5ex/Gc2itFJOiM6BfHuh2HeO7LzZzZuhHX9de9psuin3dEpNa657KzOP/MFtz31ioWb9rrdZwaS4VeRGqt0BDjqWv70qFZFLf8I5Ot+456HalGUqEXkVqtUUQ4M8YmUeRgwssZHM4r8DpSjaNCLyK1XsfmDZh8XQIbdh/mzlnLKCqqeU0mXlKhF5GgcG6X5vzxirP4ZM1OHvtonddxahR13YhI0BibEs+6nYeZ8vlGzmwdzZA+sV5HqhF0RS8iQcPM+NOvzia5Y1N+l7aC5VsPeB2pRlChF5GgUi8shGk39KNFdH1unpnBzkO5XkfynAq9iASdpg3qMWNsIkfyCpg4M4Pc/EKvI3lKhV5EglK31o14YlQfVmw7yO/nrqAmbvdSXVToRSRoDTq7NZMGnclby7KZ8vlGr+N4Rl03IhLUbj2/M+t25PDYR+vo2iqaS7q38jpStQvoit7MLjWzdWa2wczuKeX49Wa2wv9noZn1LnZsi5mtNLNlZqYtKUWkWpkZfx3Ri56xjblz1lLW7jjkdaRqV26hN7NQYDJwGdAduNbMupcYthn4hXOuF/AgML3E8XAlkjwAAA2FSURBVAucc33K2kJTRKQqRYSHMn10Ig3qhzHh5Qz2HTnudaRqFcgVfTKwwTm3yTl3HJgFDCk+wDm30Dm33/9wMRBXuTFFRE5P68YRTB+TyK6cPG75RybHC4q8jlRtAin0scDWYo+z/M+V5Sbg/WKPHfCRmWWa2cSyTjKziWaWYWYZu3fvDiCWiEjF9GkXw6MjevH15n088M/VdaYTJ5A3Y0u703Gps2NmF+Ar9OcWe3qgcy7bzFoCH5vZWufcFz/7hM5Nx7/kk5iYWDdmX0Sq3ZA+sazdkcPUzzfSrXU0YwbEex2pygVyRZ8FtCv2OA7ILjnIzHoBM4AhzrkTdwBwzmX7/7sLmI9vKUhExDN3DzqTi89qyZ/++S0LNuzxOk6VC6TQpwNdzKyjmdUDrgHeLj7AzNoD84DRzrn1xZ5vYGbRP34MDAJWVVZ4EZFTERJiPHlNXzq3aMCtry5h854jXkeqUuUWeudcAXA78CGwBpjtnFttZqlmluofdh/QDJhSoo2yFfCVmS0HvgHedc59UOlfhYhIBTWsH8aMMUmEGEx4OZ1DufleR6oyVhPfjEhMTHQZGWq5F5Gqt2jjXkY//zXndmnO82OTCA0p7W3Jms/MMstqYdcWCCJSpw3o3Iw/DTmbz9ft5pEP1nodp0poCwQRqfOu79+BdTtymP7FJrq2imZEv+D6VSBd0YuIAH/8ZXdSOjfj3nkryfx+f/kn1CIq9CIiQHhoCFOuT6BNTAT/+Uom2QeOeR2p0qjQi4j4xUTVY8aYRHLzC5n4SgbHjgfHDUtU6EVEiunSKpqnr+3L6uxDTJqzPCi2SVChFxEp4YJuLbnn0m68u3I7T326wes4p01dNyIipZh4XifW7cjhiU/W07VVQy7r2cbrSKdMV/QiIqUwM/7fsJ70bR/DXbOXszr7oNeRTpkKvYhIGSLCQ3l2dD9iosKZODOTPYfzvI50SlToRUROomV0BM+NSWTvkTxSX8kkr6D2deKo0IuIlKNHbGMeu7o3Gd/v549vrqp1nTh6M1ZEJAC/7NWW9TtyeOpfGzizdSNuOrej15ECpit6EZEA3XlxVwaf3Yq/vPst/15fe255qkIvIhKgkBDj8ZF96NoqmttfW8LG3Ye9jhQQFXoRkQpoUD+MGWMTqRcawoSXMzh4tObfsESFXkSkguKaRDFtdD+y9h/l9teXUFBY5HWkk1KhFxE5BUnxTfnLVT358rs9/OW9NV7HOSl13YiInKKRSe1YuyOHFxZsplvraEYltfc6Uql0RS8ichruvbwb53Vtwf+8uYr0Lfu8jlMqFXoRkdMQFhrC09f2pV2TKFJfySRr/1GvI/2MCr2IyGlqHBnOc2MTOV5YxISXMziSV+B1pJ9QoRcRqQSdWzRk8nUJrN+Zw12zl1FUVHO2SVChFxGpJOd1bcEfrujOh6t38sQn672Oc4K6bkREKtGNA+NZvyOHp/+1ga6tormyd1uvI+mKXkSkMpkZD17Vg6T4Jkyas5yVWd7fsESFXkSkktULC2HqDf1o3rA+N8/MYNehXE/zqNCLiFSB5g3r89yYRA7l5jPxlUxy8727YUlAhd7MLjWzdWa2wczuKeX49Wa2wv9noZn1DvRcEZFg1b1tIx4f2YdlWw9w77yVnt2wpNxCb2ahwGTgMqA7cK2ZdS8xbDPwC+dcL+BBYHoFzhURCVqX9mjNXZd0Zd7SbUz/YpMnGQK5ok8GNjjnNjnnjgOzgCHFBzjnFjrn9vsfLgbiAj1XRCTY3XHhGVzRqw0Pf7CWf63dWe2vH0ihjwW2Fnuc5X+uLDcB71f0XDObaGYZZpaxe3ftuXOLiEh5zIzHRvTm7LaN+PXry1i/M6daXz+QQm+lPFfqQpOZXYCv0P++ouc656Y75xKdc4ktWrQIIJaISO0RWS+U6aMTiQgPZcLLGew/crzaXjuQQp8FtCv2OA7ILjnIzHoBM4Ahzrm9FTlXRKQuaBsTyfQx/dhxKJdbX11CfjXdsCSQQp8OdDGzjmZWD7gGeLv4ADNrD8wDRjvn1lfkXBGRuiShfRMeHtaTRZv28r///LZaXrPcLRCccwVmdjvwIRAKvOCcW21mqf7j04D7gGbAFDMDKPAvw5R6bhV9LSIitcKwhDjW7cjh2S82cWbraG44p0OVvp551dd5MomJiS4jI8PrGCIiVaawyHHzzAy+WL+bV27qz4DOzU7r85lZpnMusbRj+s1YEREPhIYYf7+mD/HNG3DLq5n8sLfqbliiQi8i4pHoiHBmjEnEOZgwM52c3PwqeR0VehERD8U3b8DU6xPYuPsId85aRmEV3LBEhV5ExGMpZzTn/iu7Y2YcL6j8lkvdeEREpAYYfU4HbujfgZCQ0n7P9PSo0IuI1ABmhlV+jQe0dCMiEvRU6EVEgpwKvYhIkFOhFxEJcir0IiJBToVeRCTIqdCLiAS5Grl7pZntBr4/xdObA3sqMU5lUa6KUa6KUa6KCcZcHZxzpd6er0YW+tNhZhllbdXpJeWqGOWqGOWqmLqWS0s3IiJBToVeRCTIBWOhn+51gDIoV8UoV8UoV8XUqVxBt0YvIiI/FYxX9CIiUowKvYhIkKuVhd7MLjWzdWa2wczuKeW4mdlT/uMrzCyhhuQ638wOmtky/5/7qinXC2a2y8xWlXHcq/kqL5dX89XOzD4zszVmttrMflPKmGqfswBzVfucmVmEmX1jZsv9uf5Uyhgv5iuQXJ58j/lfO9TMlprZO6Ucq9z5cs7Vqj9AKLAR6ATUA5YD3UuMuRx4HzDgHODrGpLrfOAdD+bsPCABWFXG8WqfrwBzeTVfbYAE/8fRwPoa8j0WSK5qnzP/HDT0fxwOfA2cUwPmK5BcnnyP+V/7LuC10l6/suerNl7RJwMbnHObnHPHgVnAkBJjhgAznc9iIMbM2tSAXJ5wzn0B7DvJEC/mK5BcnnDObXfOLfF/nAOsAWJLDKv2OQswV7Xzz8Fh/8Nw/5+SXR5ezFcguTxhZnHAFcCMMoZU6nzVxkIfC2wt9jiLn3+zBzLGi1wAA/w/Sr5vZmdXcaZAeTFfgfJ0vswsHuiL72qwOE/n7CS5wIM58y9DLAN2AR8752rEfAWQC7z5HnsS+B1Q1p3AK3W+amOhL+2uiiX/lQ5kTGUL5DWX4NuPojfwNPBmFWcKlBfzFQhP58vMGgJzgTudc4dKHi7llGqZs3JyeTJnzrlC51wfIA5INrMeJYZ4Ml8B5Kr2+TKzXwK7nHOZJxtWynOnPF+1sdBnAe2KPY4Dsk9hTLXncs4d+vFHSefce0C4mTWv4lyB8GK+yuXlfJlZOL5i+qpzbl4pQzyZs/Jyef095pw7AHwOXFrikKffY2Xl8mi+BgK/MrMt+JZ4LzSzf5QYU6nzVRsLfTrQxcw6mlk94Brg7RJj3gbG+N+5Pgc46Jzb7nUuM2tt5rvPu5kl45v/vVWcKxBezFe5vJov/2s+D6xxzj1exrBqn7NAcnkxZ2bWwsxi/B9HAhcDa0sM82K+ys3lxXw55/7bORfnnIvHVyf+5Zy7ocSwSp2vsFOP6w3nXIGZ3Q58iK/T5QXn3GozS/Ufnwa8h+9d6w3AUWB8Dck1ArjFzAqAY8A1zv8We1Uys9fxdRc0N7Ms4H58b0x5Nl8B5vJkvvBdcY0GVvrXdwHuBdoXy+bFnAWSy4s5awO8bGah+ArlbOfcO17/PxlgLq++x36mKudLWyCIiAS52rh0IyIiFaBCLyIS5FToRUSCnAq9iEiQU6EXEQlyKvQiIkFOhV5qJTOLMbNbyxkTb2bXBfC54q2MrZKrkpn1MbPLiz1+wMwmVXcOCX4q9FJbxQAnLfRAPFBuofdQH3y/FCNSpfQLU1IrmdmP20CvAz72P30Zvo2f/uyce8PMFgNnAZuBl4H5wCtAA//4251zC/07Qb7jnCu54dWPrzUOuArfbzz3AP6G754Do4E84HLn3D4z6wNMA6Lw3ZvgRufcfjP7HN8ukxfg+wfqJv/jDUAksA14yJ+1Pb57GrQHnnTOPXU68yQCuqKX2useYKN/Z8LF+K6Oe+Pbz+RR/97d9wBfOuf6OOeewLdV7SXOuQRgFFCRItoD308HycBfgKPOub7AImCMf8xM4PfOuV7ASnxbOvwozDmXDNwJ3O+/Z8F9wBv+fG/4x3UDBvtf537/JmYip6XW7XUjUopzgdedc4XATjP7N5AElNzCNxx4xn/lXQh0rcBrfOa/2UeOmR0E/ul/fiXQy8waAzHOuX/7n38ZmFPs/B93mszEt6RUlnedc3lAnpntAlrh28lQ5JSp0EswKG3v7tL8FtiJ78o/BMitwGvkFfu4qNjjIgL7/+jH8YXljC/+OuWNFQmIlm6ktsrBd99UgC+AUf67CbXAdy/ab0qMAWgMbHfOFeFbXw+trDDOuYPAfjP7D/9To4F/n+QUSsknUiV0tSC1knNur5kt8LdFvg+swHdDdgf8zjm3w8z2AgVmthx4CZgCzDWzq4HPgCOVHGssMM3MooBNlL+17GfAPf4thx+q5CwiJ6jrRkQkyGnpRkQkyGnpRsTPzAYDj5R4erNzbqgXeUQqi5ZuRESCnJZuRESCnAq9iEiQU6EXEQlyKvQiIkHu/wN9I5xP43tQrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "monthSuccess = groupableData[['total_month','isSuccessful']]\n",
    "monthSuccess = monthSuccess.groupby(['total_month']).mean()\n",
    "monthSuccess.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-20T11:28:17.973740Z",
     "iopub.status.busy": "2020-08-20T11:28:17.973065Z",
     "iopub.status.idle": "2020-08-20T11:28:18.221726Z",
     "shell.execute_reply": "2020-08-20T11:28:18.221040Z"
    },
    "papermill": {
     "duration": 0.26944,
     "end_time": "2020-08-20T11:28:18.221858",
     "exception": false,
     "start_time": "2020-08-20T11:28:17.952418",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2e955259d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "daySuccess = groupableData[['total_day','isSuccessful']]\n",
    "daySuccess = daySuccess.groupby(['total_day']).mean()\n",
    "daySuccess.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-20T11:28:18.258850Z",
     "iopub.status.busy": "2020-08-20T11:28:18.258039Z",
     "iopub.status.idle": "2020-08-20T11:28:18.442813Z",
     "shell.execute_reply": "2020-08-20T11:28:18.442209Z"
    },
    "papermill": {
     "duration": 0.207957,
     "end_time": "2020-08-20T11:28:18.442939",
     "exception": false,
     "start_time": "2020-08-20T11:28:18.234982",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2e95493f90>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hourSuccess = groupableData[['total_hour','isSuccessful']]\n",
    "hourSuccess = hourSuccess.groupby(['total_hour']).mean()\n",
    "hourSuccess.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.012897,
     "end_time": "2020-08-20T11:28:18.469706",
     "exception": false,
     "start_time": "2020-08-20T11:28:18.456809",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Encoding categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-20T11:28:18.501646Z",
     "iopub.status.busy": "2020-08-20T11:28:18.500916Z",
     "iopub.status.idle": "2020-08-20T11:28:19.108803Z",
     "shell.execute_reply": "2020-08-20T11:28:19.108221Z"
    },
    "papermill": {
     "duration": 0.625901,
     "end_time": "2020-08-20T11:28:19.108925",
     "exception": false,
     "start_time": "2020-08-20T11:28:18.483024",
     "status": "completed"
    },
    "tags": []
   },
   "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>category</th>\n",
       "      <th>main_category</th>\n",
       "      <th>currency</th>\n",
       "      <th>country</th>\n",
       "      <th>isSuccessful</th>\n",
       "      <th>total_year</th>\n",
       "      <th>total_month</th>\n",
       "      <th>total_day</th>\n",
       "      <th>total_hour</th>\n",
       "      <th>categorical_goal</th>\n",
       "      <th>categorical_backers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>108</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>57</td>\n",
       "      <td>1379</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>93</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>58</td>\n",
       "      <td>1411</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>93</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>692</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>1311</td>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378656</th>\n",
       "      <td>39</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>717</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378657</th>\n",
       "      <td>93</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>644</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378658</th>\n",
       "      <td>93</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>1060</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378659</th>\n",
       "      <td>138</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>701</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378660</th>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>638</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>375849 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        category  main_category  currency  country  isSuccessful  total_year  \\\n",
       "0            108             12         5        9             0           0   \n",
       "1             93              6        13       22             0           0   \n",
       "2             93              6        13       22             0           0   \n",
       "3             90             10        13       22             0           0   \n",
       "4             55              6        13       22             0           0   \n",
       "...          ...            ...       ...      ...           ...         ...   \n",
       "378656        39              6        13       22             0           0   \n",
       "378657        93              6        13       22             0           0   \n",
       "378658        93              6        13       22             0           0   \n",
       "378659       138             13        13       22             0           0   \n",
       "378660        98              0        13       22             0           0   \n",
       "\n",
       "        total_month  total_day  total_hour  categorical_goal  \\\n",
       "0                 2         57        1379                 0   \n",
       "1                 2         58        1411                 2   \n",
       "2                 1         43        1055                 2   \n",
       "3                 1         28         692                 1   \n",
       "4                 1         54        1311                 2   \n",
       "...             ...        ...         ...               ...   \n",
       "378656            1         29         717                 3   \n",
       "378657            1         26         644                 0   \n",
       "378658            1         44        1060                 2   \n",
       "378659            1         29         701                 2   \n",
       "378660            1         26         638                 0   \n",
       "\n",
       "        categorical_backers  \n",
       "0                         0  \n",
       "1                         1  \n",
       "2                         1  \n",
       "3                         0  \n",
       "4                         1  \n",
       "...                     ...  \n",
       "378656                    0  \n",
       "378657                    1  \n",
       "378658                    0  \n",
       "378659                    1  \n",
       "378660                    1  \n",
       "\n",
       "[375849 rows x 11 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder = LabelEncoder()\n",
    "data = groupableData.apply(encoder.fit_transform)\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.012941,
     "end_time": "2020-08-20T11:28:19.135705",
     "exception": false,
     "start_time": "2020-08-20T11:28:19.122764",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Splitting data into test, train, validation sets"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.012865,
     "end_time": "2020-08-20T11:28:19.161700",
     "exception": false,
     "start_time": "2020-08-20T11:28:19.148835",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "80% of the total data is selected as \"trainSet\"\n",
    "10% of the total data is selected as \"validationSet\"\n",
    "10% of the total data is selected as \"testSet\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-20T11:28:19.195243Z",
     "iopub.status.busy": "2020-08-20T11:28:19.194252Z",
     "iopub.status.idle": "2020-08-20T11:28:19.196995Z",
     "shell.execute_reply": "2020-08-20T11:28:19.196340Z"
    },
    "papermill": {
     "duration": 0.022003,
     "end_time": "2020-08-20T11:28:19.197113",
     "exception": false,
     "start_time": "2020-08-20T11:28:19.175110",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "frac = 0.1\n",
    "size = int(len(data)*frac)\n",
    "\n",
    "trainSet = data[:-2*size]\n",
    "validationSet = data[-2*size: -size]\n",
    "testSet = data[-size:]"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.012888,
     "end_time": "2020-08-20T11:28:19.223153",
     "exception": false,
     "start_time": "2020-08-20T11:28:19.210265",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Training a model"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.012747,
     "end_time": "2020-08-20T11:28:19.248891",
     "exception": false,
     "start_time": "2020-08-20T11:28:19.236144",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Dropping \"isSuccessful\" column to seperate features (isSuccessful is the result, not a feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-20T11:28:19.289975Z",
     "iopub.status.busy": "2020-08-20T11:28:19.289249Z",
     "iopub.status.idle": "2020-08-20T11:28:21.827403Z",
     "shell.execute_reply": "2020-08-20T11:28:21.828082Z"
    },
    "papermill": {
     "duration": 2.56655,
     "end_time": "2020-08-20T11:28:21.828283",
     "exception": false,
     "start_time": "2020-08-20T11:28:19.261733",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "features = trainSet.columns.drop('isSuccessful')\n",
    "\n",
    "dtrain = lgb.Dataset(trainSet[features], label=trainSet['isSuccessful'])\n",
    "dvalid = lgb.Dataset(validationSet[features], label=validationSet['isSuccessful'])\n",
    "\n",
    "param = {'num_leaves': 64, 'objective': 'binary'}\n",
    "param['metric'] = 'auc'\n",
    "num_round = 1000\n",
    "bst = lgb.train(param, dtrain, num_round, valid_sets=[dvalid], early_stopping_rounds=10, verbose_eval=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.017739,
     "end_time": "2020-08-20T11:28:21.864950",
     "exception": false,
     "start_time": "2020-08-20T11:28:21.847211",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Make predictions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-20T11:28:21.907711Z",
     "iopub.status.busy": "2020-08-20T11:28:21.906951Z",
     "iopub.status.idle": "2020-08-20T11:28:22.007773Z",
     "shell.execute_reply": "2020-08-20T11:28:22.008833Z"
    },
    "papermill": {
     "duration": 0.126344,
     "end_time": "2020-08-20T11:28:22.009147",
     "exception": false,
     "start_time": "2020-08-20T11:28:21.882803",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test AUC score: 0.9570498031623396\n"
     ]
    }
   ],
   "source": [
    "ypred = bst.predict(testSet[features])\n",
    "score = metrics.roc_auc_score(testSet['isSuccessful'], ypred)\n",
    "\n",
    "print(f\"Test AUC score: {score}\")"
   ]
  }
 ],
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