EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This notebook is about the EDA of the Kickstarter Dataset.**\n",
    "\n",
    "In the following you can see what we have done so far and what will follow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [x] Import the data, which is split into 56 individual csv-files\n",
    "- [x] Clean the data\n",
    "- [x] Save and export\n",
    "- [ ] Exploratory Data Analysis\n",
    "- [ ] Try at least 3 different machine learning algorithms\n",
    "- [ ] Give recommendations base upon findings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "sns.set()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dataframe as \"df\"\n",
    "df = pd.read_csv(\"Kickstarter_Train.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quick Data Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>backers_count</th>\n",
       "      <th>category</th>\n",
       "      <th>converted_pledged_amount</th>\n",
       "      <th>country</th>\n",
       "      <th>created_at</th>\n",
       "      <th>currency</th>\n",
       "      <th>deadline</th>\n",
       "      <th>launched_at</th>\n",
       "      <th>staff_pick</th>\n",
       "      <th>state_changed_at</th>\n",
       "      <th>days_until_launch</th>\n",
       "      <th>days_until_success</th>\n",
       "      <th>days_total</th>\n",
       "      <th>days_diff_total_success</th>\n",
       "      <th>converted_goal_amount</th>\n",
       "      <th>blurb_length</th>\n",
       "      <th>name_length</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>crafts</td>\n",
       "      <td>139</td>\n",
       "      <td>GB</td>\n",
       "      <td>2014-11-24</td>\n",
       "      <td>GBP</td>\n",
       "      <td>2014-12-24</td>\n",
       "      <td>2014-11-24</td>\n",
       "      <td>0</td>\n",
       "      <td>2014-12-24</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1308</td>\n",
       "      <td>129</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>journalism</td>\n",
       "      <td>0</td>\n",
       "      <td>CA</td>\n",
       "      <td>2014-12-04</td>\n",
       "      <td>CAD</td>\n",
       "      <td>2015-01-17</td>\n",
       "      <td>2014-12-18</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-17</td>\n",
       "      <td>14</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>751</td>\n",
       "      <td>135</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>technology</td>\n",
       "      <td>585</td>\n",
       "      <td>US</td>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>USD</td>\n",
       "      <td>2015-11-07</td>\n",
       "      <td>2015-10-07</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-11-07</td>\n",
       "      <td>37</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>2500</td>\n",
       "      <td>135</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2125</td>\n",
       "      <td>technology</td>\n",
       "      <td>357769</td>\n",
       "      <td>US</td>\n",
       "      <td>2017-02-08</td>\n",
       "      <td>USD</td>\n",
       "      <td>2017-07-27</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-07-27</td>\n",
       "      <td>139</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>100000</td>\n",
       "      <td>124</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75</td>\n",
       "      <td>food</td>\n",
       "      <td>3091</td>\n",
       "      <td>US</td>\n",
       "      <td>2017-10-27</td>\n",
       "      <td>USD</td>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>2017-10-29</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "      <td>76</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   backers_count    category  converted_pledged_amount country  created_at  \\\n",
       "0              7      crafts                       139      GB  2014-11-24   \n",
       "1              0  journalism                         0      CA  2014-12-04   \n",
       "2              8  technology                       585      US  2015-08-31   \n",
       "3           2125  technology                    357769      US  2017-02-08   \n",
       "4             75        food                      3091      US  2017-10-27   \n",
       "\n",
       "  currency    deadline launched_at  staff_pick state_changed_at  \\\n",
       "0      GBP  2014-12-24  2014-11-24           0       2014-12-24   \n",
       "1      CAD  2015-01-17  2014-12-18           0       2015-01-17   \n",
       "2      USD  2015-11-07  2015-10-07           0       2015-11-07   \n",
       "3      USD  2017-07-27  2017-06-27           0       2017-07-27   \n",
       "4      USD  2017-11-29  2017-10-29           0       2017-11-29   \n",
       "\n",
       "   days_until_launch  days_until_success  days_total  days_diff_total_success  \\\n",
       "0                  0                  30          30                        0   \n",
       "1                 14                  30          30                        0   \n",
       "2                 37                  31          31                        0   \n",
       "3                139                  30          30                        0   \n",
       "4                  2                  31          31                        0   \n",
       "\n",
       "   converted_goal_amount  blurb_length  name_length  state  \n",
       "0                   1308           129           46      0  \n",
       "1                    751           135           29      0  \n",
       "2                   2500           135           30      0  \n",
       "3                 100000           124           53      1  \n",
       "4                   3000            76           40      1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>backers_count</th>\n",
       "      <th>converted_pledged_amount</th>\n",
       "      <th>staff_pick</th>\n",
       "      <th>days_until_launch</th>\n",
       "      <th>days_until_success</th>\n",
       "      <th>days_total</th>\n",
       "      <th>days_diff_total_success</th>\n",
       "      <th>converted_goal_amount</th>\n",
       "      <th>blurb_length</th>\n",
       "      <th>name_length</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>144496.0</td>\n",
       "      <td>144496.0</td>\n",
       "      <td>144496.0</td>\n",
       "      <td>144496.0</td>\n",
       "      <td>144496.0</td>\n",
       "      <td>144496.0</td>\n",
       "      <td>144496.0</td>\n",
       "      <td>144496.0</td>\n",
       "      <td>144496.0</td>\n",
       "      <td>144496.0</td>\n",
       "      <td>144496.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>152.0</td>\n",
       "      <td>13407.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>38822.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>937.0</td>\n",
       "      <td>87959.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1135388.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>30.0</td>\n",
       "      <td>1774.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4924.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>94.0</td>\n",
       "      <td>7077.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>105857.0</td>\n",
       "      <td>8596474.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2593.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132567974.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       backers_count  converted_pledged_amount  staff_pick  days_until_launch  \\\n",
       "count       144496.0                  144496.0    144496.0           144496.0   \n",
       "mean           152.0                   13407.0         0.0               47.0   \n",
       "std            937.0                   87959.0         0.0              130.0   \n",
       "min              0.0                       0.0         0.0                0.0   \n",
       "25%              4.0                     150.0         0.0                3.0   \n",
       "50%             30.0                    1774.0         0.0               11.0   \n",
       "75%             94.0                    7077.0         0.0               37.0   \n",
       "max         105857.0                 8596474.0         1.0             2593.0   \n",
       "\n",
       "       days_until_success  days_total  days_diff_total_success  \\\n",
       "count            144496.0    144496.0                 144496.0   \n",
       "mean                 32.0        32.0                     -0.0   \n",
       "std                  12.0        12.0                      0.0   \n",
       "min                   1.0         1.0                    -38.0   \n",
       "25%                  30.0        30.0                      0.0   \n",
       "50%                  30.0        30.0                      0.0   \n",
       "75%                  34.0        34.0                      0.0   \n",
       "max                  93.0        93.0                      0.0   \n",
       "\n",
       "       converted_goal_amount  blurb_length  name_length     state  \n",
       "count               144496.0      144496.0     144496.0  144496.0  \n",
       "mean                 38822.0         112.0         35.0       1.0  \n",
       "std                1135388.0          27.0         16.0       0.0  \n",
       "min                      0.0           1.0          1.0       0.0  \n",
       "25%                   1500.0         101.0         22.0       0.0  \n",
       "50%                   4924.0         124.0         35.0       1.0  \n",
       "75%                  12000.0         132.0         49.0       1.0  \n",
       "max              132567974.0         151.0         85.0       1.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2009-04-29\n",
      "2019-03-06\n"
     ]
    }
   ],
   "source": [
    "#Data Timespan: When was the first and last project launch? \n",
    "print(df.launched_at.min())\n",
    "print(df.launched_at.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Distribution of successful & non-successful projects**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What is the % of successful projects?\n",
    "df.state.value_counts()\n",
    "(df.state.value_counts()[1] / (df.state.value_counts()[1] + df.state.value_counts()[0]) * 100).round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution of the state \n",
    "\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "plt.hist(df[\"state\"], weights=np.ones(len(df[\"state\"])) / len(df[\"state\"]), bins=2, label=df[\"state\"])#\n",
    "plt.gca().yaxis.set_major_formatter(PercentFormatter(1))\n",
    "plt.tick_params(labelbottom=False)\n",
    "plt.xlabel(\"not successful                            succesful\")\n",
    "#plt.xticks(df[\"state\"],['not successful','succesful'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Timeline**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the frequencies of the project launches per year \n",
    "\n",
    "# Transform the dataframe to datetime and only show the year of the date \n",
    "df['launched'] = pd.to_datetime(df['launched_at'])\n",
    "df['launched_year'] = df.launched.dt.year\n",
    "\n",
    "plt.subplots(figsize = (15,6))\n",
    "sns.countplot(x=\"launched_year\", data=df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Almost the same plot. Only this time the respective state is included.\n",
    "\n",
    "plt.subplots(figsize = (15,6))\n",
    "sns.countplot(x=\"launched_year\", hue=\"state\", data=df);\n",
    "plt.legend(title='State', loc='upper left', labels=['not successful', 'successful']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# similar to the previous plot, but this time with the whole date \n",
    "launched_at_count = df.groupby(\"launched_at\")[\"launched_at\"].count()\n",
    "\n",
    "fig = launched_at_count.plot(kind=\"line\", figsize=(20,7));\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Strong increase to be seen from around 2013 onwards. Stagnation from 2014.\n",
    "\n",
    "Around October conspicuities are to be recognized, since it breaks down strongly there."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Category overview**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "music           19429\n",
       "film & video    19259\n",
       "technology      14541\n",
       "art             14360\n",
       "publishing      14131\n",
       "food            11181\n",
       "games            9318\n",
       "fashion          8049\n",
       "comics           6127\n",
       "design           5799\n",
       "photography      5540\n",
       "crafts           4949\n",
       "theater          4869\n",
       "journalism       4010\n",
       "dance            2934\n",
       "Name: category, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of projects from each category \n",
    "df.category.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Countplot of the categories \n",
    "plt.subplots(figsize = (15,6))\n",
    "sns.countplot(df.category);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Also a countplot but now with sorted order \n",
    "\n",
    "plt.subplots(figsize = (15,7))\n",
    "sns.countplot(y=df.category, order = df['category'].value_counts().index);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Countries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the number of countries \n",
    "len(df.country.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show us the top 10 Countries (Distribution)\n",
    "plt.subplots(figsize = (15,7))\n",
    "sns.countplot(y=df.country, order = df['country'].value_counts().head(10).index);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By far the most projects come from the USA.\n",
    "\n",
    "One reason could be that Kickstarter comes from the USA or perhaps the Americans have more entrepreneurial spirit. Or maybe it is much more difficult to get bank loans there.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--- "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Money**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70409     132567974\n",
       "96230     113209128\n",
       "107016    100000000\n",
       "109076    100000000\n",
       "144011    100000000\n",
       "105919    100000000\n",
       "52026     100000000\n",
       "130742    100000000\n",
       "38106     100000000\n",
       "99839     100000000\n",
       "Name: converted_goal_amount, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Top 10 Goals \n",
    "df.converted_goal_amount.sort_values(ascending=False)[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There a lot of unrealistic/\"fun\" goals, especially when we see how much money was really collected from the top projects --> Outlier?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100517    8596474\n",
       "73124     6333295\n",
       "140119    6256244\n",
       "43936     6256244\n",
       "135840    5702153\n",
       "98125     5545991\n",
       "20842     5408916\n",
       "126552    5333792\n",
       "93003     5333792\n",
       "52958     4236618\n",
       "Name: converted_pledged_amount, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Top 10 Pledges\n",
    "df.converted_pledged_amount.sort_values(ascending=False)[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       144496.0\n",
       "mean        -25415.0\n",
       "std        1138222.0\n",
       "min     -132567974.0\n",
       "25%          -3538.0\n",
       "50%             95.0\n",
       "75%            968.0\n",
       "max        7646474.0\n",
       "Name: goal_usd_pledged_diff, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the differences between goal & pledged\n",
    "df[\"goal_usd_pledged_diff\"] = df.converted_pledged_amount - df.converted_goal_amount\n",
    "df[\"goal_usd_pledged_diff\"].describe().round()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you look at the median (50%), you will quickly see that at least more money was collected than the set goal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Avg. usd spent per backer (per catergory)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How much money spent a backer per category? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>dance</th>\n",
       "      <th>film &amp; video</th>\n",
       "      <th>technology</th>\n",
       "      <th>theater</th>\n",
       "      <th>design</th>\n",
       "      <th>music</th>\n",
       "      <th>photography</th>\n",
       "      <th>food</th>\n",
       "      <th>fashion</th>\n",
       "      <th>publishing</th>\n",
       "      <th>art</th>\n",
       "      <th>crafts</th>\n",
       "      <th>games</th>\n",
       "      <th>journalism</th>\n",
       "      <th>comics</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>avg_usd_per_backer</th>\n",
       "      <td>69.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    dance  film & video  technology  theater  design  music  \\\n",
       "avg_usd_per_backer   69.0          69.0        66.0     63.0    60.0   56.0   \n",
       "\n",
       "                    photography  food  fashion  publishing   art  crafts  \\\n",
       "avg_usd_per_backer         55.0  54.0     51.0        48.0  46.0    38.0   \n",
       "\n",
       "                    games  journalism  comics  \n",
       "avg_usd_per_backer   37.0        35.0    34.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average usd pledged per backer')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"avg_usd_per_backer\"] = df.converted_pledged_amount / df.backers_count\n",
    "\n",
    "# Get a list of all categories \n",
    "cat_list = list(df['category'].unique())\n",
    "\n",
    "# Create a table of the average usd spent per backer for every category\n",
    "pledge_table = dict()\n",
    "for category in cat_list:\n",
    "    cat_df = df[df['category'] == category]\n",
    "    pledge_table.update({category : cat_df['avg_usd_per_backer'].median()})\n",
    "\n",
    "# Visualize the results\n",
    "# Table\n",
    "pledge_df = pd.DataFrame(pledge_table, index=['avg_usd_per_backer'])\n",
    "pledge_df.sort_values('avg_usd_per_backer', axis=1, inplace=True, ascending=False)\n",
    "display(pledge_df.head().round())\n",
    "\n",
    "# Plot\n",
    "ax = pledge_df.plot(kind='bar', \n",
    "                    title='Average pledged per backer by category', \n",
    "                    rot=0, figsize=(14,5))\n",
    "ax.set_xlabel('Categories')\n",
    "ax.set_ylabel('Average usd pledged per backer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check staff_pick** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deeper insights into staff picked projects\n",
    "\n",
    "staff_pick = How can I get my project featured by Kickstarter?\n",
    "\n",
    "\"These are questions we hear often from our creator community. Our team is always on the lookout for exceptional projects, and when we find one, we share our enthusiasm by giving it a Projects We Love badge, and by showcasing select projects on our homepage and in our newsletters.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only view projects that were picked by kickstater staff -> got featured\n",
    "staff_pick1_state = df.query(\"staff_pick == 1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>backers_count</th>\n",
       "      <th>converted_pledged_amount</th>\n",
       "      <th>staff_pick</th>\n",
       "      <th>days_until_launch</th>\n",
       "      <th>days_until_success</th>\n",
       "      <th>days_total</th>\n",
       "      <th>days_diff_total_success</th>\n",
       "      <th>converted_goal_amount</th>\n",
       "      <th>blurb_length</th>\n",
       "      <th>name_length</th>\n",
       "      <th>state</th>\n",
       "      <th>avg_usd_per_backer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>19385.0</td>\n",
       "      <td>19385.0</td>\n",
       "      <td>19385.0</td>\n",
       "      <td>19385.0</td>\n",
       "      <td>19385.0</td>\n",
       "      <td>19385.0</td>\n",
       "      <td>19385.0</td>\n",
       "      <td>19385.0</td>\n",
       "      <td>19385.0</td>\n",
       "      <td>19385.0</td>\n",
       "      <td>19385.0</td>\n",
       "      <td>19376.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>509.0</td>\n",
       "      <td>44773.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>20724.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2228.0</td>\n",
       "      <td>193151.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89259.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>71.0</td>\n",
       "      <td>4288.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>151.0</td>\n",
       "      <td>10835.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8630.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>349.0</td>\n",
       "      <td>28618.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>105857.0</td>\n",
       "      <td>8596474.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2593.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       backers_count  converted_pledged_amount  staff_pick  days_until_launch  \\\n",
       "count        19385.0                   19385.0     19385.0            19385.0   \n",
       "mean           509.0                   44773.0         1.0               64.0   \n",
       "std           2228.0                  193151.0         0.0              153.0   \n",
       "min              0.0                       0.0         1.0                0.0   \n",
       "25%             71.0                    4288.0         1.0                8.0   \n",
       "50%            151.0                   10835.0         1.0               22.0   \n",
       "75%            349.0                   28618.0         1.0               56.0   \n",
       "max         105857.0                 8596474.0         1.0             2593.0   \n",
       "\n",
       "       days_until_success  days_total  days_diff_total_success  \\\n",
       "count             19385.0     19385.0                  19385.0   \n",
       "mean                 32.0        32.0                     -0.0   \n",
       "std                   9.0         9.0                      0.0   \n",
       "min                   1.0         1.0                     -5.0   \n",
       "25%                  30.0        30.0                      0.0   \n",
       "50%                  30.0        30.0                      0.0   \n",
       "75%                  33.0        33.0                      0.0   \n",
       "max                  91.0        91.0                      0.0   \n",
       "\n",
       "       converted_goal_amount  blurb_length  name_length    state  \\\n",
       "count                19385.0       19385.0      19385.0  19385.0   \n",
       "mean                 20724.0         114.0         38.0      1.0   \n",
       "std                  89259.0          24.0         16.0      0.0   \n",
       "min                      1.0           1.0          3.0      0.0   \n",
       "25%                   3500.0         104.0         25.0      1.0   \n",
       "50%                   8630.0         124.0         39.0      1.0   \n",
       "75%                  20000.0         132.0         51.0      1.0   \n",
       "max               10000000.0         150.0         85.0      1.0   \n",
       "\n",
       "       avg_usd_per_backer  \n",
       "count             19376.0  \n",
       "mean                 96.0  \n",
       "std                 133.0  \n",
       "min                   1.0  \n",
       "25%                  41.0  \n",
       "50%                  65.0  \n",
       "75%                 106.0  \n",
       "max                4100.0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "staff_pick1_state.describe().round()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.4942"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.staff_pick.value_counts()[1] / df.staff_pick.value_counts()[0] * 100).round(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "15% of the projects were picked by kickstarter staff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VfVi48AXy81/nscceYeHCF8jLG83SpcFCj+vXf8kll5zfCN+txCVtZOLu75jZXcCbQDrw38BvgJXAc0ALgkComFwfCsw2szbAUmBmWB8HPGZmE4DPgMvD+kRgnpl9DGwN9xcRqdFLL/3hgGXcq3P77RMZO/Za+vbtx+9//yy/+91T9OhxPH/724f89rdPUVJSwrhxV3PWWT9m6tRJ3HDDTXTr1p01a/6PW275JU8++TyzZ/+Ghx9+lLZtD+eBB2bw2Wefkp//+gF9GDJkGMuXL6NPnxM555zzeeWVRQ34XYkuqfeZuPtcgkuB470G9Kqi7YfAKVXU1xLMu1SubwaSe9JQRL51qlrG/fnnnzmg3datWyks3ETfvv0AuOiiSwCYPv1OfvSjH5ORkUFGRgbz5j3BN998w9//voJp0ybv3X/nzp18/fVW+vbtx9ixV3HGGf0588wfcdxxxs6dOw/oQ1OnhR5FpFnp2bP3Acu4xy/RXrE8e1paGrHYvut8du/ezaZNBWF93+etX/8lrVu3ISMjk3nznthb37jxK9q0yeL663/JJ59cyNtvv8mvfz2RkSNHc/bZ51S5lHy8+K9dsYz9oUzLqYhIs1LVMu7BsvHBMvD5+a8D0KpVK3Jzj+Cvfw1uf3vllUU88shD9Op1An/5y58pKSlh165d/OIX17J5cyFHHXX03lNT7767hPHjR1NaWspll11EdnY2w4ZdyU9/ei6rVnmVfagsfin7/Py/NMB3JhqNTESkWbn44sEHLOMei8E999zNo4/O5pRTTt3b9j/+49f853/ewaxZM8nKymbixMnk5OSwcuUKRo4cSllZOYMGXU6nTp2ZNGkKd989jSee+C1paelMnjyNtLQ0rrrqGq6/fjyZmZm0bduWW2+9jeLi4gP6UNnQocOZOvU2Xn75Rfr169+A36G60RL0EpmWoN9HS9BXrfJy5m2zMkjLqP9Hy5YU72bL18X1/rnNUW2XoNfIREQaXPALX7/0v000ZyIiIpEpTEREJDKFiYg0iGY4P9tklZeXceDyhzVTmIhI0qWlZbBjxzYFyiGuvLyckpI9bN26iYyMFrXaVxPwIpJ0bdvmsmVLAUVFWxu7K3IQKSmptGzZilatsmq1n8Kkjlq3aUGLTD2LSyQRqalp5OR0OHhDabIUJnXUIjOdITctOHjDZuCJu7TGpkhzpzkTERGJTGEiIiKRKUxERCQyhYmIiESmMBERkcgUJiIiEpnCREREIlOYiIhIZAoTERGJLKl3wJvZ/wBHAHvC0jVAa2A60BJ42t0nhG17A3OANsAbwBh3LzGzTsD88HMcGOruRWaWDSwAugAFwKXuviGZxyMiIlVL2sjEzGJAN6CXu/d2997AcmAucCHQAzjZzAaGu8wH8ty9G8Hax6PC+ixglrt3B94DJob1KUC+u/cAZgMzknUsIiJSs2Se5rLw31fN7EMzywNOAVa7+xp3LyEIkEFm1hlo6e5Lwn3mhfV04Azg2fh6+PpcgpEJwJPAwLC9iIg0sGSe5moLvAZcC6QDfwHuBNbHtVkPHAV0rKaeA2wLgye+Tvw+4emwbUAu8GUinWvXrlWtD0gkEbm5rRu7CyINLmlh4u5vA29XvDezR4DJwJtxzWJAGcEIqTyBOmG9ok28WNy2gyosLKKsrO4P6tEvDKlOQcH2xu6CSL1LSYnV+Ed4MudMTjezs+JKMeBTIP6hBu0JRhLrqqlvBLLMLDWsd2DfyOOLsB1mlkYwsV9Yv0chIiKJSOacSTZwt5m1MLPWwM+AWwAzs65hQAwBFrv7WmCXmfUN9x0W1vcA+cDgsD4cWBy+XhS+J9yeH7YXEZEGlrQwcfeXgJeBD4D3gbnhqa8RwHPACmAl+ybXhwL3mNlKoBUwM6yPA0ab2QqgHzAhrE8ETjWzj8M245N1LCIiUrOk3mfi7hPZdylvRe01oFcVbT8kuNqrcn0t0L+K+mbggvrqq4iI1J3ugBcRkcgUJiIiEpnCREREIlOYiIhIZAoTERGJTGEiIiKRKUxERCQyhYmIiESmMBERkcgUJiIiEpnCREREIlOYiIhIZAoTERGJTGEiIiKRKUxERCQyhYmIiESmMBERkcgUJiIiEpnCREREIlOYiIhIZGmJNDKzI939i0q177v7igT2/U8gx91HmNkAYDrQEnja3SeEbXoDc4A2wBvAGHcvMbNOwHzgCMCBoe5eZGbZwAKgC1AAXOruGxI7ZBERqW81jkzM7HAzOxxYZGZtK96b2XeB5w/24WZ2FvCz8HVLYC5wIdADONnMBoZN5wN57t4NiAGjwvosYJa7dwfeAyaG9SlAvrv3AGYDMxI+YhERqXcHO831JLAJ+CFQGL7eBHwOLK1pxzCEpgLTwtIpwGp3X+PuJQQBMsjMOgMt3X1J2G5eWE8HzgCeja+Hr88lGJlU9HFg2F5ERBpBjae53P1sADOb6+4ja/nZDwG3AkeH7zsC6+O2rweOqqGeA2wLgye+vt9nhafDtgG5wJe17KOIiNSDhOZM3H1kOII4nOA0VEW9ytGJmV0NfO7ur5nZiLCcApTHNYsBZbWoE9Yr2sSLxW1LSLt2rWrTXCRhubmtG7sLIg0u0Qn424EbgY3s+wVfTjABXpXBQAczW0YQQK2AzkBpXJv2BCOJdUCHKuobgSwzS3X30rBNxcjji7DdOjNLA1oTnIZLWGFhEWVllbMqcfqFIdUpKNje2F0QqXcpKbEa/whPKEyA4UBXd0/oNJK7/7jidTgy6Q+MAVabWVdgDTAEmOvua81sl5n1dff/BYYBi919j5nlEwTTE2EfFocfuyh8Py3cnu/uexI8FhERqWeJ3mfyeaJBUh133wWMAJ4DVgAr2Te5PhS4x8xWEoxiZob1ccBoM1sB9AMmhPWJwKlm9nHYZnyUvomISDSJjkxeM7O7gD8AOyuK1c2ZxHP3eQRXYuHurwG9qmjzIcHVXpXrawlGNZXrm4ELEuy7iIgkWaJhMiL8d1BcraY5ExERaUYSvZrre8nuiIiINF2JXs31b1XV3X16/XZHRESaokRPc/0w7nUGcCbwWv13R0REmqJET3NdGf/ezDoCjySlRyIi0uTUaQn68DLhY+q3KyIi0lTVZc4kBpxEcIe6iIhIneZMyoHPCJZXERERqd2cSbjYY7q7f5LUXomISJOS6GmurgR3v3cEUsxsE3Ceu/89mZ0TEZGmIdEJ+PuBu9y9rbtnETzp8IHkdUtERJqSRMPku+7+WMUbd3+U4GFUIiIiCYdJWvgYXgDMLIcDH1wlIiLNVKJXc90HLDGzpwlC5DLgnqT1SkREmpRERyaLCEIkA/g+cCTw+2R1SkREmpZEw2Qe8IC73wxcAdwKzE1Wp0REpGlJNExy3H0mBE9MdPd72f+57SIi0ozVZgK+Y8UbM/suwbIqIiIiCU/ATweWmdkfCeZOBqDlVEREJJTQyMTd5xIEyAfAe8DZ7v5EMjsmIiJNR6IjE9x9ObC8Nh9uZpOBSwhGM4+4+3QzG0Aw0mkJPO3uE8K2vYE5QBvgDWCMu5eYWSdgPnAE4MBQdy8ys2xgAcFz6AuAS919Q236JyIi9aNOzzNJhJmdCfwI6EmwZP21ZtaL4CqwC4EewMlmNjDcZT6Q5+7dCOZjRoX1WcAsd+9OMCqaGNanAPnu3gOYDcxI1rGIiEjNkhYm7v468M/uXkIwqkgDsoHV7r4mrM8HBoWrEbd09yXh7vPCejpwBvBsfD18fS7ByATgSWBg2F5ERBpY0sIEwN33mNntwAqCZ8Z3BNbHNVkPHFVDPQfYFgZPfJ34fcLt29B6YSIijSLhOZO6cvdJZnYnsBDoxv5resWAMoJQS6ROWK9oEy8Wt+2g2rVrlWhTkVrJzW3d2F0QaXBJCxMz6w60cPdl7v6NmT1PMBlfGtesPfAlsI79b4KsqG8Essws1d1LwzZfhm2+CNutM7M0oDVQmGj/CguLKCur+1qV+oUh1Sko2N7YXRCpdykpsRr/CE/maa4uwGwzyzSzDIJJ94cAM7OuZpYKDAEWu/taYJeZ9Q33HRbW9wD5wOCwPhxYHL5eFL4n3J4fthcRkQaWzAn4RcDLBPemvA+85e5PASOA5wjmUVayb3J9KHCPma0EWgEzw/o4YLSZrQD6ARPC+kTgVDP7OGwzPlnHIiIiNUvqnIm73wbcVqn2GtCrirYfAqdUUV8L9K+ivhm4oH56KiIiUST1ai4REWkeFCYiIhKZwkRERCJTmIiISGQKExERiUxhIiIikSlMREQkMoWJiIhEpjAREZHIFCYiIhKZwkRERCJTmIiISGQKExERiUxhIiIikSlMREQkMoWJiIhEpjAREZHIFCYiIhKZwkRERCJTmIiISGQKExERiSwtmR9uZpOAS8O3L7v7TWY2AJgOtASedvcJYdvewBygDfAGMMbdS8ysEzAfOAJwYKi7F5lZNrAA6AIUAJe6+4ZkHo+IiFQtaSOTMDR+AvQBegMnmtnlwFzgQqAHcLKZDQx3mQ/kuXs3IAaMCuuzgFnu3h14D5gY1qcA+e7eA5gNzEjWsYiISM2SeZprPfALdy929z3A34FuwGp3X+PuJQQBMsjMOgMt3X1JuO+8sJ4OnAE8G18PX59LMDIBeBIYGLYXEZEGlrTTXO7+ccVrMzuO4HTXfQQhU2E9cBTQsZp6DrAtDJ74OvH7hKfDtgG5wJeJ9K9du1a1PCKRxOTmtm7sLog0uKTOmQCY2fHAy8CNQAnB6KRCDCgjGCGVJ1AnrFe0iReL23ZQhYVFlJVV/ujE6ReGVKegYHtjd0Gk3qWkxGr8IzypV3OZWV/gNeDf3f0xYB3QIa5Je4KRRHX1jUCWmaWG9Q7sG3l8EbbDzNKA1kBhco5ERERqkswJ+KOBF4Ah7v5UWH4n2GRdw4AYAix297XArjB8AIaF9T1APjA4rA8HFoevF4XvCbfnh+1FRKSBJfM01y+BFsB0M6uoPQiMAJ4Lty1i3+T6UGC2mbUBlgIzw/o44DEzmwB8Blwe1icC88zsY2BruL+IiDSCZE7AXwdcV83mXlW0/xA4pYr6WqB/FfXNwAXReikiIvVBd8CLiEhkSb+aS0SksbTNyiAtI7Oxu3FIKCnezZavi5P2+QoTEfnWSsvI5P27rm7sbhwSTrxpDpC8MNFpLhERiUxhIiIikSlMREQkMoWJiIhEpjAREZHIFCYiIhKZwkRERCJTmIiISGQKExERiUxhIiIikSlMREQkMoWJiIhEpjAREZHIFCYiIhKZwkRERCJTmIiISGQKExERiSypT1o0szbAW8B57v6pmQ0ApgMtgafdfULYrjcwB2gDvAGMcfcSM+sEzAeOABwY6u5FZpYNLAC6AAXApe6+IZnHIiIi1UvayMTM/gl4E+gWvm8JzAUuBHoAJ5vZwLD5fCDP3bsBMWBUWJ8FzHL37sB7wMSwPgXId/cewGxgRrKOQ0REDi6Zp7lGAeOBL8P3pwCr3X2Nu5cQBMggM+sMtHT3JWG7eWE9HTgDeDa+Hr4+l2BkAvAkMDBsLyIijSBpYeLuV7t7flypI7A+7v164Kga6jnAtjB44uv7fVa4fRuQW9/HICIiiUnqnEklKUB53PsYUFaLOmG9ok28WNy2hLRr16o2zUUSlpvburG7IFKlZP5sNmSYrAM6xL1vT3AKrLr6RiDLzFLdvTRsU3HK7Iuw3TozSwNaA4W16UxhYRFlZZWzKnH6hSHVKSjY3thdkJD+P91flJ/NlJRYjX+EN+Slwe8AZmZdzSwVGAIsdve1wC4z6xu2GxbW9wD5wOCwPhxYHL5eFL4n3J4fthcRkUbQYGHi7ruAEcBzwApgJfsm14cC95jZSqAVMDOsjwNGm9kKoB8wIaxPBE41s4/DNuMb4hhERKRqST/N5e7HxL1+DehVRZsPCa72qlxfC/Svor4ZuKA++ykiInWnO+BFRCQyhYmIiESmMBERkcgUJiIiEpnCREREIlOYiIhIZAoTERGJTGEiIiKRKUxERCQyhYmIiESmMBERkcgUJiIiEpnCREREIlOYiIhIZAoTERGJTGEiIiKRKUxERCQyhYmIiESmMBERkciS/gx4EWlYrdu0oEVmemN3Q5oZhYnIt0yLzM0rjw0AAAR8SURBVHSG3LSgsbtxSHjirqGN3YVmo0mHiZkNASYA6cC97v5AI3dJRKRZarJzJmZ2JDAVOB3oDYw2s+83bq9ERJqnpjwyGQD82d03A5jZs8AlwOSD7JcKkJISi9yBnLaHRf6Mb4uMNu0auwuHjPr42YpKP5v76Gdznyg/m3H7pla1PVZeXl7nD29MZvYr4DB3nxC+vxo4xd1HH2TX04H8ZPdPRORbqh/wZuViUx6ZpADxSRgDyhLY712Cb8Z6oDQJ/RIR+TZKBToQ/A49QFMOk3UEoVChPfBlAvvtpopUFRGRg/pHdRuacpj8CbjNzHKBHcDFwMFOcYmISBI02au53P0L4Fbgf4BlwBPu/tfG7ZWISPPUZCfgRUTk0NFkRyYiInLoUJiIiEhkChMREYlMYSIiIpE15UuDpZFpoU051JlZG+At4Dx3/7SRu/OtppGJ1IkW2pRDnZn9E8ENyt0auy/NgcJE6mrvQpvuvgOoWGhT5FAxChhPYitjSEQ6zSV11ZFgfbMK64FTGqkvIgdw96sBzKyxu9IsaGQidVXXhTZF5FtIYSJ1tY5gBdEKiS60KSLfQjrNJXWlhTZFZC+NTKROtNCmiMTTQo8iIhKZRiYiIhKZwkRERCJTmIiISGQKExERiUxhIiIikek+E5F6YGavAkPcfdNB2v0G+CnwBPANcA3wJ3cfWYevOQbIdvf/V0ObT4FL3P292n6+SG0oTETqx48TbHcN0Mnd15nZ/xEE0Jt1+YLu/mBd9hNJBt1nIlILZtYKeBQ4jmAtsvcJ/igbAXwEnAP0Am4BMoAjgMfcfaKZ5RMs2f8RsBHoB6wB/sPdn67m6x0DvA78EfgngjXQ8tw938xuA3LcPc/MugEPhV+vDJji7k9XjEyAlcAi4G13v7n+viMiAc2ZiNTORUBrd+8NnBzWpob//jPBmmW/AH7m7icBpwK/MrMcd+9X0c7dzyJYy2xodUESpxPwevg1/x142szSK7V5Cvidux9PEGjTwgdDAWQBrwAvK0gkWRQmIrXzJnC8mf2F4Bf7ve7+ScVGdy8HzgdONLNJwHSC0cRhEb7mFnd/Ivz8xUAp0LNio5kdTjAamhO2+dzdj3X3bWGTx4GjgZkR+iBSI4WJSC24+xqgK3AH0Ab4k5mdX7HdzA4DPgBOAJYCNwJ7CAKlrkoqvU8hCJTK2/ees7ZAy/DtFOAd4K4IfRCpkcJEpBbMbCzBnMmr4SmjVwiCoxRIJ5hLaQNMcPeFQH8gE0iN8GVzzeyn4dc/nyCc/laxMRyBvA/8LGxzNPC/BKe3AP4KjAUGmdlPIvRDpFoKE5Ha+S1BMKwws/cJfmHPBH5HMFFeBrwErDSzvxOc8lpBMJqpq13AMDP7kGCl5n9x99JKbYYAl4ZtFgJXu/uGio3hJcvjgLlm1jZCX0SqpKu5RA5h4dVcH7l7q8bui0hNdJ+JSCMzs3sIrgSryoyG7ItIXWlkIiIikWnOREREIlOYiIhIZAoTERGJTGEiIiKRKUxERCQyhYmIiET2/wGNsCLk+3dFKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How many projects will be successful if they are featured on Kickstarter?\n",
    "sns.countplot(x=df.staff_pick, hue=df.state);\n",
    "plt.legend(title='State', loc='upper right', labels=['not successful', 'successful']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(staff_pick1_state.state.value_counts()[0] / staff_pick1_state.state.value_counts()[1] * 100).round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ~10% of the projects which were featured failed -> so ~90% will succeed \n",
    "((1 - staff_pick1_state.state.value_counts()[0] / staff_pick1_state.state.value_counts()[1]) * 100).round()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>backers_count</th>\n",
       "      <th>converted_pledged_amount</th>\n",
       "      <th>staff_pick</th>\n",
       "      <th>days_until_launch</th>\n",
       "      <th>days_until_success</th>\n",
       "      <th>days_total</th>\n",
       "      <th>days_diff_total_success</th>\n",
       "      <th>converted_goal_amount</th>\n",
       "      <th>blurb_length</th>\n",
       "      <th>name_length</th>\n",
       "      <th>state</th>\n",
       "      <th>avg_usd_per_backer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17670.00</td>\n",
       "      <td>17670.00</td>\n",
       "      <td>17670.0</td>\n",
       "      <td>17670.00</td>\n",
       "      <td>17670.00</td>\n",
       "      <td>17670.00</td>\n",
       "      <td>17670.00</td>\n",
       "      <td>17670.00</td>\n",
       "      <td>17670.00</td>\n",
       "      <td>17670.00</td>\n",
       "      <td>17670.0</td>\n",
       "      <td>17670.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>550.31</td>\n",
       "      <td>48307.49</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64.21</td>\n",
       "      <td>31.71</td>\n",
       "      <td>31.71</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>18665.06</td>\n",
       "      <td>114.32</td>\n",
       "      <td>37.66</td>\n",
       "      <td>1.0</td>\n",
       "      <td>96.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2328.29</td>\n",
       "      <td>201821.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>153.93</td>\n",
       "      <td>9.32</td>\n",
       "      <td>9.32</td>\n",
       "      <td>0.04</td>\n",
       "      <td>49358.94</td>\n",
       "      <td>23.91</td>\n",
       "      <td>15.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.00</td>\n",
       "      <td>26.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>81.00</td>\n",
       "      <td>5093.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3300.00</td>\n",
       "      <td>104.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>167.00</td>\n",
       "      <td>11892.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>124.00</td>\n",
       "      <td>39.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>65.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>381.00</td>\n",
       "      <td>31052.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56.00</td>\n",
       "      <td>33.00</td>\n",
       "      <td>33.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20000.00</td>\n",
       "      <td>132.00</td>\n",
       "      <td>52.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>106.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>105857.00</td>\n",
       "      <td>8596474.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2593.00</td>\n",
       "      <td>91.00</td>\n",
       "      <td>91.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2000000.00</td>\n",
       "      <td>150.00</td>\n",
       "      <td>85.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4100.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       backers_count  converted_pledged_amount  staff_pick  days_until_launch  \\\n",
       "count       17670.00                  17670.00     17670.0           17670.00   \n",
       "mean          550.31                  48307.49         1.0              64.21   \n",
       "std          2328.29                 201821.08         0.0             153.93   \n",
       "min             2.00                     26.00         1.0               0.00   \n",
       "25%            81.00                   5093.75         1.0               8.00   \n",
       "50%           167.00                  11892.00         1.0              22.00   \n",
       "75%           381.00                  31052.25         1.0              56.00   \n",
       "max        105857.00                8596474.00         1.0            2593.00   \n",
       "\n",
       "       days_until_success  days_total  days_diff_total_success  \\\n",
       "count            17670.00    17670.00                 17670.00   \n",
       "mean                31.71       31.71                    -0.00   \n",
       "std                  9.32        9.32                     0.04   \n",
       "min                  1.00        1.00                    -5.00   \n",
       "25%                 30.00       30.00                     0.00   \n",
       "50%                 30.00       30.00                     0.00   \n",
       "75%                 33.00       33.00                     0.00   \n",
       "max                 91.00       91.00                     0.00   \n",
       "\n",
       "       converted_goal_amount  blurb_length  name_length    state  \\\n",
       "count               17670.00      17670.00     17670.00  17670.0   \n",
       "mean                18665.06        114.32        37.66      1.0   \n",
       "std                 49358.94         23.91        15.48      0.0   \n",
       "min                     1.00          1.00         3.00      1.0   \n",
       "25%                  3300.00        104.00        25.00      1.0   \n",
       "50%                  8000.00        124.00        39.00      1.0   \n",
       "75%                 20000.00        132.00        52.00      1.0   \n",
       "max               2000000.00        150.00        85.00      1.0   \n",
       "\n",
       "       avg_usd_per_backer  \n",
       "count            17670.00  \n",
       "mean                96.84  \n",
       "std                135.17  \n",
       "min                  1.16  \n",
       "25%                 42.16  \n",
       "50%                 65.45  \n",
       "75%                106.64  \n",
       "max               4100.29  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If state = 1 & staff_pick = 1, do we get more money than we have set as our goal? \n",
    "staff_pick1_state1 = df.query(\"staff_pick == 1 and state ==1\")\n",
    "staff_pick1_state1.describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      17670.00\n",
       "mean       29642.43\n",
       "std       175503.94\n",
       "min        -5284.00\n",
       "25%          534.00\n",
       "50%         1896.50\n",
       "75%         7861.25\n",
       "max      7646474.00\n",
       "Name: goal_usd_pledged_diff, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# update the query with the new column \"goal_usd_pledged_diff\"\n",
    "staff_pick1_state1 = df.query(\"staff_pick == 1 & state ==1\")\n",
    "staff_pick1_state1.describe().round(2)\n",
    "staff_pick1_state1.goal_usd_pledged_diff.describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "137.0  more backers\n",
      "13.689370968631124  $ more spending per backer\n"
     ]
    }
   ],
   "source": [
    "# Differences between backers_count & avg_usd_per_backer when state = 1 & staff_pick = 1 and with the normal dataset\n",
    "print(staff_pick1_state1.backers_count.median() - df.backers_count.median(),\" more backers\")\n",
    "print((staff_pick1_state1.avg_usd_per_backer.median() - df.avg_usd_per_backer.median()),\" $ more spending per backer\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 15% of the projects will be picked by staff to get featured \n",
    "* 90% of featured projects succeed \n",
    "* On average, these projects received 25,000 dollar more than expected (goal = 21,000 dollar) \n",
    "* Also on average: 137 more backers and $14 more per backer\n",
    "\n",
    "-> Staff_pick is a strong indicator for success "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--- "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check successful projects**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next step we concentrate on the successful projects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>backers_count</th>\n",
       "      <th>converted_pledged_amount</th>\n",
       "      <th>staff_pick</th>\n",
       "      <th>days_until_launch</th>\n",
       "      <th>days_until_success</th>\n",
       "      <th>days_total</th>\n",
       "      <th>days_diff_total_success</th>\n",
       "      <th>converted_goal_amount</th>\n",
       "      <th>blurb_length</th>\n",
       "      <th>name_length</th>\n",
       "      <th>state</th>\n",
       "      <th>avg_usd_per_backer</th>\n",
       "      <th>goal_usd_pledged_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>88098.0</td>\n",
       "      <td>88098.0</td>\n",
       "      <td>88098.0</td>\n",
       "      <td>88098.0</td>\n",
       "      <td>88098.0</td>\n",
       "      <td>88098.0</td>\n",
       "      <td>88098.0</td>\n",
       "      <td>88098.0</td>\n",
       "      <td>88098.0</td>\n",
       "      <td>88098.0</td>\n",
       "      <td>88098.0</td>\n",
       "      <td>88098.0</td>\n",
       "      <td>88098.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>242.0</td>\n",
       "      <td>21286.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>8915.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>12372.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1190.0</td>\n",
       "      <td>111843.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25336.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>99753.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-5284.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.0</td>\n",
       "      <td>1746.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>69.0</td>\n",
       "      <td>4894.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>161.0</td>\n",
       "      <td>12652.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9812.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>2486.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>105857.0</td>\n",
       "      <td>8596474.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2593.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000000.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9606.0</td>\n",
       "      <td>7646474.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       backers_count  converted_pledged_amount  staff_pick  days_until_launch  \\\n",
       "count        88098.0                   88098.0     88098.0            88098.0   \n",
       "mean           242.0                   21286.0         0.0               51.0   \n",
       "std           1190.0                  111843.0         0.0              132.0   \n",
       "min              1.0                       1.0         0.0                0.0   \n",
       "25%             32.0                    1746.0         0.0                5.0   \n",
       "50%             69.0                    4894.0         0.0               15.0   \n",
       "75%            161.0                   12652.0         0.0               42.0   \n",
       "max         105857.0                 8596474.0         1.0             2593.0   \n",
       "\n",
       "       days_until_success  days_total  days_diff_total_success  \\\n",
       "count             88098.0     88098.0                  88098.0   \n",
       "mean                 31.0        31.0                     -0.0   \n",
       "std                  11.0        11.0                      0.0   \n",
       "min                   1.0         1.0                    -38.0   \n",
       "25%                  29.0        29.0                      0.0   \n",
       "50%                  30.0        30.0                      0.0   \n",
       "75%                  32.0        32.0                      0.0   \n",
       "max                  93.0        93.0                      0.0   \n",
       "\n",
       "       converted_goal_amount  blurb_length  name_length    state  \\\n",
       "count                88098.0       88098.0      88098.0  88098.0   \n",
       "mean                  8915.0         112.0         37.0      1.0   \n",
       "std                  25336.0          26.0         15.0      0.0   \n",
       "min                      0.0           1.0          2.0      1.0   \n",
       "25%                   1000.0         100.0         24.0      1.0   \n",
       "50%                   3500.0         123.0         37.0      1.0   \n",
       "75%                   9812.0         132.0         51.0      1.0   \n",
       "max                2000000.0         151.0         85.0      1.0   \n",
       "\n",
       "       avg_usd_per_backer  goal_usd_pledged_diff  \n",
       "count             88098.0                88098.0  \n",
       "mean                 92.0                12372.0  \n",
       "std                 134.0                99753.0  \n",
       "min                   1.0                -5284.0  \n",
       "25%                  39.0                  159.0  \n",
       "50%                  62.0                  610.0  \n",
       "75%                 102.0                 2486.0  \n",
       "max                9606.0              7646474.0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Query for the successful projects\n",
    "state1 = df.query(\"state == 1\")\n",
    "state1.describe().round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>backers_count</th>\n",
       "      <th>converted_pledged_amount</th>\n",
       "      <th>staff_pick</th>\n",
       "      <th>days_until_launch</th>\n",
       "      <th>days_until_success</th>\n",
       "      <th>days_total</th>\n",
       "      <th>days_diff_total_success</th>\n",
       "      <th>converted_goal_amount</th>\n",
       "      <th>blurb_length</th>\n",
       "      <th>name_length</th>\n",
       "      <th>state</th>\n",
       "      <th>avg_usd_per_backer</th>\n",
       "      <th>goal_usd_pledged_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>56398.0</td>\n",
       "      <td>56398.0</td>\n",
       "      <td>56398.0</td>\n",
       "      <td>56398.0</td>\n",
       "      <td>56398.0</td>\n",
       "      <td>56398.0</td>\n",
       "      <td>56398.0</td>\n",
       "      <td>56398.0</td>\n",
       "      <td>56398.0</td>\n",
       "      <td>56398.0</td>\n",
       "      <td>56398.0</td>\n",
       "      <td>45121.0</td>\n",
       "      <td>56398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.0</td>\n",
       "      <td>1099.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>85539.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>-84440.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>49.0</td>\n",
       "      <td>5828.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1816106.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>1816060.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-132567974.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-19999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-6176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.0</td>\n",
       "      <td>443.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>-2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4435.0</td>\n",
       "      <td>607628.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2126.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132567974.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>7891.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       backers_count  converted_pledged_amount  staff_pick  days_until_launch  \\\n",
       "count        56398.0                   56398.0     56398.0            56398.0   \n",
       "mean            12.0                    1099.0         0.0               42.0   \n",
       "std             49.0                    5828.0         0.0              125.0   \n",
       "min              0.0                       0.0         0.0                0.0   \n",
       "25%              1.0                       1.0         0.0                2.0   \n",
       "50%              3.0                      56.0         0.0                7.0   \n",
       "75%              9.0                     443.0         0.0               27.0   \n",
       "max           4435.0                  607628.0         1.0             2126.0   \n",
       "\n",
       "       days_until_success  days_total  days_diff_total_success  \\\n",
       "count             56398.0     56398.0                  56398.0   \n",
       "mean                 35.0        35.0                     -0.0   \n",
       "std                  13.0        13.0                      0.0   \n",
       "min                   1.0         1.0                    -18.0   \n",
       "25%                  30.0        30.0                      0.0   \n",
       "50%                  30.0        30.0                      0.0   \n",
       "75%                  40.0        40.0                      0.0   \n",
       "max                  92.0        92.0                      0.0   \n",
       "\n",
       "       converted_goal_amount  blurb_length  name_length    state  \\\n",
       "count                56398.0       56398.0      56398.0  56398.0   \n",
       "mean                 85539.0         113.0         33.0      0.0   \n",
       "std                1816106.0          27.0         16.0      0.0   \n",
       "min                      1.0           1.0          1.0      0.0   \n",
       "25%                   2500.0         103.0         19.0      0.0   \n",
       "50%                   7000.0         125.0         31.0      0.0   \n",
       "75%                  22000.0         132.0         46.0      0.0   \n",
       "max              132567974.0         150.0         85.0      0.0   \n",
       "\n",
       "       avg_usd_per_backer  goal_usd_pledged_diff  \n",
       "count             45121.0                56398.0  \n",
       "mean                 58.0               -84440.0  \n",
       "std                 142.0              1816060.0  \n",
       "min                   0.0           -132567974.0  \n",
       "25%                  10.0               -19999.0  \n",
       "50%                  30.0                -6176.0  \n",
       "75%                  62.0                -2000.0  \n",
       "max               10000.0                 7891.0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Query for the non-successful projects\n",
    "state0 = df.query(\"state == 0\")\n",
    "state0.describe().round()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How does a successful project on kickstarter looks like? \n",
    "* 3,500 dollar goal (not successful = 7,000 dollar) \n",
    "* 4,900 dollar reached\n",
    "* 69 backers \n",
    "* 62 dollar per backer\n",
    "\n",
    "→ 50% of the project goals are between 1,000 - 10,000 dollar\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49.838815864151286"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How much % of the projects goals are between 1k - 10k\n",
    "state1.query(\"converted_goal_amount > 1000 and converted_goal_amount < 10000\").shape[0] / state1.shape[0] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'state1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-11-e6e249dc2ce7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#How much % of the projects got more than 100k?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mstate1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"converted_pledged_amount > 100000\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'state1' is not defined"
     ]
    }
   ],
   "source": [
    "# How much % of the projects got more than 100k?\n",
    "state1.query(\"converted_pledged_amount > 100000\").shape[0] / df.shape[0] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>backers_count</th>\n",
       "      <th>category</th>\n",
       "      <th>converted_pledged_amount</th>\n",
       "      <th>country</th>\n",
       "      <th>created_at</th>\n",
       "      <th>currency</th>\n",
       "      <th>deadline</th>\n",
       "      <th>launched_at</th>\n",
       "      <th>staff_pick</th>\n",
       "      <th>state_changed_at</th>\n",
       "      <th>days_until_launch</th>\n",
       "      <th>days_until_success</th>\n",
       "      <th>days_total</th>\n",
       "      <th>days_diff_total_success</th>\n",
       "      <th>converted_goal_amount</th>\n",
       "      <th>blurb_length</th>\n",
       "      <th>name_length</th>\n",
       "      <th>state</th>\n",
       "      <th>avg_usd_per_backer</th>\n",
       "      <th>goal_usd_pledged_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100517</th>\n",
       "      <td>63416</td>\n",
       "      <td>games</td>\n",
       "      <td>8596474</td>\n",
       "      <td>US</td>\n",
       "      <td>2012-05-15</td>\n",
       "      <td>USD</td>\n",
       "      <td>2012-08-09</td>\n",
       "      <td>2012-07-10</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-08-09</td>\n",
       "      <td>56</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>950000</td>\n",
       "      <td>127</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>135.556863</td>\n",
       "      <td>7646474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73124</th>\n",
       "      <td>69320</td>\n",
       "      <td>games</td>\n",
       "      <td>6333295</td>\n",
       "      <td>US</td>\n",
       "      <td>2015-06-02</td>\n",
       "      <td>USD</td>\n",
       "      <td>2015-07-18</td>\n",
       "      <td>2015-06-16</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-07-18</td>\n",
       "      <td>14</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>2000000</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>91.363171</td>\n",
       "      <td>4333295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140119</th>\n",
       "      <td>41939</td>\n",
       "      <td>games</td>\n",
       "      <td>6256244</td>\n",
       "      <td>GB</td>\n",
       "      <td>2018-11-09</td>\n",
       "      <td>GBP</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>2018-12-05</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>26</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>53027</td>\n",
       "      <td>133</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>149.174849</td>\n",
       "      <td>6203217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43936</th>\n",
       "      <td>41939</td>\n",
       "      <td>games</td>\n",
       "      <td>6256244</td>\n",
       "      <td>GB</td>\n",
       "      <td>2018-11-09</td>\n",
       "      <td>GBP</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>2018-12-05</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>26</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>53027</td>\n",
       "      <td>133</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>149.174849</td>\n",
       "      <td>6203217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135840</th>\n",
       "      <td>91585</td>\n",
       "      <td>film &amp; video</td>\n",
       "      <td>5702153</td>\n",
       "      <td>US</td>\n",
       "      <td>2011-11-04</td>\n",
       "      <td>USD</td>\n",
       "      <td>2013-04-13</td>\n",
       "      <td>2013-03-13</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-04-13</td>\n",
       "      <td>495</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>2000000</td>\n",
       "      <td>125</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>62.260774</td>\n",
       "      <td>3702153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98125</th>\n",
       "      <td>64867</td>\n",
       "      <td>games</td>\n",
       "      <td>5545991</td>\n",
       "      <td>US</td>\n",
       "      <td>2015-05-02</td>\n",
       "      <td>USD</td>\n",
       "      <td>2015-06-13</td>\n",
       "      <td>2015-05-11</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-06-13</td>\n",
       "      <td>9</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>500000</td>\n",
       "      <td>110</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>85.497880</td>\n",
       "      <td>5045991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20842</th>\n",
       "      <td>105857</td>\n",
       "      <td>technology</td>\n",
       "      <td>5408916</td>\n",
       "      <td>US</td>\n",
       "      <td>2014-04-23</td>\n",
       "      <td>USD</td>\n",
       "      <td>2014-07-02</td>\n",
       "      <td>2014-05-28</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-07-02</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>1000000</td>\n",
       "      <td>135</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>51.096441</td>\n",
       "      <td>4408916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126552</th>\n",
       "      <td>26828</td>\n",
       "      <td>technology</td>\n",
       "      <td>5333792</td>\n",
       "      <td>US</td>\n",
       "      <td>2017-03-06</td>\n",
       "      <td>USD</td>\n",
       "      <td>2017-04-27</td>\n",
       "      <td>2017-03-23</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-04-27</td>\n",
       "      <td>17</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>50000</td>\n",
       "      <td>131</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>198.814373</td>\n",
       "      <td>5283792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93003</th>\n",
       "      <td>26828</td>\n",
       "      <td>technology</td>\n",
       "      <td>5333792</td>\n",
       "      <td>US</td>\n",
       "      <td>2017-03-06</td>\n",
       "      <td>USD</td>\n",
       "      <td>2017-04-27</td>\n",
       "      <td>2017-03-23</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-04-27</td>\n",
       "      <td>17</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>50000</td>\n",
       "      <td>131</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>198.814373</td>\n",
       "      <td>5283792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52958</th>\n",
       "      <td>5946</td>\n",
       "      <td>technology</td>\n",
       "      <td>4236618</td>\n",
       "      <td>US</td>\n",
       "      <td>2016-12-09</td>\n",
       "      <td>USD</td>\n",
       "      <td>2017-11-03</td>\n",
       "      <td>2017-09-19</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-11-03</td>\n",
       "      <td>284</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>200000</td>\n",
       "      <td>129</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>712.515641</td>\n",
       "      <td>4036618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        backers_count      category  converted_pledged_amount country  \\\n",
       "100517          63416         games                   8596474      US   \n",
       "73124           69320         games                   6333295      US   \n",
       "140119          41939         games                   6256244      GB   \n",
       "43936           41939         games                   6256244      GB   \n",
       "135840          91585  film & video                   5702153      US   \n",
       "98125           64867         games                   5545991      US   \n",
       "20842          105857    technology                   5408916      US   \n",
       "126552          26828    technology                   5333792      US   \n",
       "93003           26828    technology                   5333792      US   \n",
       "52958            5946    technology                   4236618      US   \n",
       "\n",
       "        created_at currency    deadline launched_at  staff_pick  \\\n",
       "100517  2012-05-15      USD  2012-08-09  2012-07-10           1   \n",
       "73124   2015-06-02      USD  2015-07-18  2015-06-16           1   \n",
       "140119  2018-11-09      GBP  2018-12-28  2018-12-05           1   \n",
       "43936   2018-11-09      GBP  2018-12-28  2018-12-05           1   \n",
       "135840  2011-11-04      USD  2013-04-13  2013-03-13           1   \n",
       "98125   2015-05-02      USD  2015-06-13  2015-05-11           1   \n",
       "20842   2014-04-23      USD  2014-07-02  2014-05-28           1   \n",
       "126552  2017-03-06      USD  2017-04-27  2017-03-23           0   \n",
       "93003   2017-03-06      USD  2017-04-27  2017-03-23           0   \n",
       "52958   2016-12-09      USD  2017-11-03  2017-09-19           1   \n",
       "\n",
       "       state_changed_at  days_until_launch  days_until_success  days_total  \\\n",
       "100517       2012-08-09                 56                  30          30   \n",
       "73124        2015-07-18                 14                  32          32   \n",
       "140119       2018-12-28                 26                  23          23   \n",
       "43936        2018-12-28                 26                  23          23   \n",
       "135840       2013-04-13                495                  31          31   \n",
       "98125        2015-06-13                  9                  33          33   \n",
       "20842        2014-07-02                 35                  35          35   \n",
       "126552       2017-04-27                 17                  35          35   \n",
       "93003        2017-04-27                 17                  35          35   \n",
       "52958        2017-11-03                284                  45          45   \n",
       "\n",
       "        days_diff_total_success  converted_goal_amount  blurb_length  \\\n",
       "100517                        0                 950000           127   \n",
       "73124                         0                2000000            76   \n",
       "140119                        0                  53027           133   \n",
       "43936                         0                  53027           133   \n",
       "135840                        0                2000000           125   \n",
       "98125                         0                 500000           110   \n",
       "20842                         0                1000000           135   \n",
       "126552                        0                  50000           131   \n",
       "93003                         0                  50000           131   \n",
       "52958                         0                 200000           129   \n",
       "\n",
       "        name_length  state  avg_usd_per_backer  goal_usd_pledged_diff  \n",
       "100517           38      1          135.556863                7646474  \n",
       "73124             9      1           91.363171                4333295  \n",
       "140119           33      1          149.174849                6203217  \n",
       "43936            33      1          149.174849                6203217  \n",
       "135840           31      1           62.260774                3702153  \n",
       "98125            33      1           85.497880                5045991  \n",
       "20842            55      1           51.096441                4408916  \n",
       "126552           60      1          198.814373                5283792  \n",
       "93003            60      1          198.814373                5283792  \n",
       "52958            38      1          712.515641                4036618  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 10 projects with the most money \n",
    "state1.sort_values(by=\"converted_pledged_amount\", ascending=False)[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Success per Category** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We continue to look at the successful projects and how they relate to the different categories in percentage terms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>dance</th>\n",
       "      <th>comics</th>\n",
       "      <th>theater</th>\n",
       "      <th>publishing</th>\n",
       "      <th>design</th>\n",
       "      <th>music</th>\n",
       "      <th>games</th>\n",
       "      <th>fashion</th>\n",
       "      <th>film &amp; video</th>\n",
       "      <th>art</th>\n",
       "      <th>photography</th>\n",
       "      <th>crafts</th>\n",
       "      <th>technology</th>\n",
       "      <th>food</th>\n",
       "      <th>journalism</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cat_success_percent</th>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     dance  comics  theater  publishing  design  music  games  \\\n",
       "cat_success_percent   84.0    84.0     74.0        73.0    71.0   69.0   66.0   \n",
       "\n",
       "                     fashion  film & video   art  photography  crafts  \\\n",
       "cat_success_percent     65.0          61.0  61.0         55.0    52.0   \n",
       "\n",
       "                     technology  food  journalism  \n",
       "cat_success_percent        42.0  37.0        34.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'success_per_cat')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the amount of projects from each category \n",
    "cat_count = df[\"category\"].value_counts()\n",
    "\n",
    "# only show the amount of successful projects per category \n",
    "cat_success = df[df[\"state\"] == 1]\n",
    "cat_success_count = cat_success[\"category\"].value_counts()\n",
    "\n",
    "# get the successful rate in percent for each category \n",
    "cat_success_percent = (cat_success_count / cat_count) * 100\n",
    "\n",
    "# Get a list of all categories \n",
    "cat_list_sorted = list(df['category'].unique())\n",
    "cat_list_sorted.sort()\n",
    "cat_list_sorted\n",
    "\n",
    "# Create a table of the average usd spent per backer for every category\n",
    "cat_success_table = dict()\n",
    "for idx, category in enumerate(cat_list_sorted):\n",
    "    cat_df = df[df['category'] == category]\n",
    "    cat_success_table.update({category : cat_success_percent[idx]})\n",
    "\n",
    "# Visualize the results\n",
    "# Table\n",
    "cat_success_df = pd.DataFrame(cat_success_table, index=['cat_success_percent'])\n",
    "cat_success_df.sort_values('cat_success_percent', axis=1, inplace=True, ascending=False)\n",
    "display(cat_success_df.head().round())\n",
    "\n",
    "# Plot\n",
    "ax = cat_success_df.plot(kind='bar', \n",
    "                    title='success_per_cat', \n",
    "                    rot=0, figsize=(14,5))\n",
    "ax.set_xlabel('Categories')\n",
    "ax.set_ylabel('success_per_cat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dance and Comics have the highest successrate with around 84%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Goal & Pledged per Category**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we continue to look at the individual categories by looking at the different goals and the money collected, based on the median. In the last step we calculate the differences between the two values to see what the median distance is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Median goal per Category of succesful projects\n",
    "# get the amount of projects from each category \n",
    "cat_count = df[\"category\"].value_counts()\n",
    "\n",
    "# only show the amount of successful projects per category \n",
    "cat_success = df[df[\"state\"] == 1]\n",
    "cat_success_count = cat_success[\"category\"].value_counts()\n",
    "\n",
    "# get the median goal for each category \n",
    "cat_success_mgoal = (cat_success_count / cat_count) * 100\n",
    "# cat_success_mgoal.sort_values(ascending=False).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>technology</th>\n",
       "      <th>food</th>\n",
       "      <th>film &amp; video</th>\n",
       "      <th>design</th>\n",
       "      <th>games</th>\n",
       "      <th>journalism</th>\n",
       "      <th>fashion</th>\n",
       "      <th>music</th>\n",
       "      <th>photography</th>\n",
       "      <th>publishing</th>\n",
       "      <th>dance</th>\n",
       "      <th>theater</th>\n",
       "      <th>comics</th>\n",
       "      <th>art</th>\n",
       "      <th>crafts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>median_goal</th>\n",
       "      <td>15000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>3827.0</td>\n",
       "      <td>3532.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3260.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>1549.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_pledged</th>\n",
       "      <td>1628.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2086.0</td>\n",
       "      <td>4850.0</td>\n",
       "      <td>3498.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>1676.0</td>\n",
       "      <td>2345.0</td>\n",
       "      <td>987.0</td>\n",
       "      <td>2476.0</td>\n",
       "      <td>3004.0</td>\n",
       "      <td>2191.0</td>\n",
       "      <td>2852.0</td>\n",
       "      <td>922.0</td>\n",
       "      <td>447.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                technology     food  film & video  design   games  journalism  \\\n",
       "median_goal        15000.0  10000.0        5500.0  5000.0  5000.0      5000.0   \n",
       "median_pledged      1628.0    550.0        2086.0  4850.0  3498.0       117.0   \n",
       "\n",
       "                fashion   music  photography  publishing   dance  theater  \\\n",
       "median_goal      4000.0  3827.0       3532.0      3500.0  3260.0   3000.0   \n",
       "median_pledged   1676.0  2345.0        987.0      2476.0  3004.0   2191.0   \n",
       "\n",
       "                comics     art  crafts  \n",
       "median_goal     2600.0  2250.0  1549.0  \n",
       "median_pledged  2852.0   922.0   447.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Median goal per Category')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get a list of all categories\n",
    "cat_list_sorted = list(df['category'].unique())\n",
    "cat_list_sorted.sort()\n",
    "cat_list_sorted\n",
    "\n",
    "# Create a table with categories & converted_goal_amount median\n",
    "cat_mgoal_table = dict()\n",
    "for idx, category in enumerate(cat_list_sorted):\n",
    "    cat_df = df[df['category'] == category]\n",
    "    cat_mgoal_table.update({category : cat_df.converted_goal_amount.median()})\n",
    "    \n",
    "cat_mpledged_table = dict()\n",
    "for idx, category in enumerate(cat_list_sorted):\n",
    "    cat_df = df[df['category'] == category]\n",
    "    cat_mpledged_table.update({category : cat_df.converted_pledged_amount.median()})\n",
    "\n",
    "# Visualize the results\n",
    "# Table\n",
    "cat_mgoal_df = pd.DataFrame(cat_mgoal_table, index=['median_goal'])\n",
    "cat_mpledged_df = pd.DataFrame(cat_mpledged_table, index=['median_pledged'])\n",
    "cat_mgoal_df.sort_values('median_goal', axis=1, inplace=True, ascending=False)\n",
    "\n",
    "# join both dataframes together\n",
    "cat_medians_df = cat_mgoal_df.append(cat_mpledged_df)\n",
    "display(cat_medians_df.head().round())\n",
    "\n",
    "# Plot median gaol\n",
    "ax = cat_mgoal_df.plot(kind='bar', \n",
    "                    title='Median goal per Category', \n",
    "                    rot=0, figsize=(14,5))\n",
    "ax.set_xlabel('Categories')\n",
    "ax.set_ylabel('Median goal per Category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Median pledged per Category')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot median pledged\n",
    "cat_mpledged_df.sort_values('median_pledged', axis=1, inplace=True, ascending=False)\n",
    "\n",
    "ax = cat_mpledged_df.plot(kind='bar', \n",
    "                    title='Median pledged per Category', \n",
    "                    rot=0, figsize=(14,5))\n",
    "ax.set_xlabel('Categories')\n",
    "ax.set_ylabel('Median pledged per Category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>technology</th>\n",
       "      <th>food</th>\n",
       "      <th>film &amp; video</th>\n",
       "      <th>design</th>\n",
       "      <th>games</th>\n",
       "      <th>journalism</th>\n",
       "      <th>fashion</th>\n",
       "      <th>music</th>\n",
       "      <th>photography</th>\n",
       "      <th>publishing</th>\n",
       "      <th>dance</th>\n",
       "      <th>theater</th>\n",
       "      <th>comics</th>\n",
       "      <th>art</th>\n",
       "      <th>crafts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>median_goal</th>\n",
       "      <td>15000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>3827.0</td>\n",
       "      <td>3532.5</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3260.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>1549.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_pledged</th>\n",
       "      <td>1628.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2086.0</td>\n",
       "      <td>4850.0</td>\n",
       "      <td>3498.5</td>\n",
       "      <td>117.0</td>\n",
       "      <td>1676.0</td>\n",
       "      <td>2345.0</td>\n",
       "      <td>987.0</td>\n",
       "      <td>2476.0</td>\n",
       "      <td>3004.5</td>\n",
       "      <td>2191.0</td>\n",
       "      <td>2852.0</td>\n",
       "      <td>922.5</td>\n",
       "      <td>447.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_diff</th>\n",
       "      <td>-13372.0</td>\n",
       "      <td>-9450.0</td>\n",
       "      <td>-3414.0</td>\n",
       "      <td>-150.0</td>\n",
       "      <td>-1501.5</td>\n",
       "      <td>-4883.0</td>\n",
       "      <td>-2324.0</td>\n",
       "      <td>-1482.0</td>\n",
       "      <td>-2545.5</td>\n",
       "      <td>-1024.0</td>\n",
       "      <td>-255.5</td>\n",
       "      <td>-809.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>-1327.5</td>\n",
       "      <td>-1102.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                technology     food  film & video  design   games  journalism  \\\n",
       "median_goal        15000.0  10000.0        5500.0  5000.0  5000.0      5000.0   \n",
       "median_pledged      1628.0    550.0        2086.0  4850.0  3498.5       117.0   \n",
       "median_diff       -13372.0  -9450.0       -3414.0  -150.0 -1501.5     -4883.0   \n",
       "\n",
       "                fashion   music  photography  publishing   dance  theater  \\\n",
       "median_goal      4000.0  3827.0       3532.5      3500.0  3260.0   3000.0   \n",
       "median_pledged   1676.0  2345.0        987.0      2476.0  3004.5   2191.0   \n",
       "median_diff     -2324.0 -1482.0      -2545.5     -1024.0  -255.5   -809.0   \n",
       "\n",
       "                comics     art  crafts  \n",
       "median_goal     2600.0  2250.0  1549.0  \n",
       "median_pledged  2852.0   922.5   447.0  \n",
       "median_diff      252.0 -1327.5 -1102.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a table for the differences between goal & pledged \n",
    "cat_medians_diff_table = []\n",
    "for i in range(len(cat_list_sorted)):\n",
    "    cat_medians_diff_table.append(cat_medians_df.iloc[1,i] - cat_medians_df.iloc[0,i])\n",
    "    \n",
    "cat_medians_diff_table_dic = dict()\n",
    "for idx, category in enumerate(list(cat_medians_df.columns)):\n",
    "    cat_df = df[df['category'] == category]\n",
    "    cat_medians_diff_table_dic.update({category : cat_medians_diff_table[idx]})\n",
    "\n",
    "\n",
    "cat_medians_diff_df = pd.DataFrame(cat_medians_diff_table_dic, index=[\"median_diff\"])\n",
    "cat_medians_df = cat_medians_df.append(cat_medians_diff_df)\n",
    "cat_medians_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Considering the median, only comics get more money than their stated goal. Food and technology show by far the strongest differences/deviations. In the next graphic this is shown visually once again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Median diff. per Category')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the diff. between goal & pledged \n",
    "# cat_medians_diff_df.sort_values('median_diff', axis=1, inplace=True, ascending=False)\n",
    "\n",
    "ax = cat_medians_diff_df.plot(kind='bar', \n",
    "                    title='Median diff. per Category', \n",
    "                    rot=0, figsize=(14,5))\n",
    "ax.set_xlabel('Categories')\n",
    "ax.set_ylabel('Median diff. per Category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recommendations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the EDA we can say the following: \n",
    "* Don’t expect too much → just 25% of the projects got more than 7,000 dollar\n",
    "* Set a realistic goal - not too high (median of succesful projects was 3,500 dollar)\n",
    "* Be aware of the different chances of success per category \n",
    "* Try to get featured (hints: https://www.kickstarter.com/blog/how-to-get-featured-on-kickstarter?lang=en) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Next Steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [x] Import the data, which is split into 56 individual csv-files\n",
    "- [x] Clean the data\n",
    "- [x] Save and export\n",
    "- [x] Exploratory Data Analysis\n",
    "- [ ] Try at least 3 different machine learning algorithms\n",
    "- [ ] Give recommendations base upon findings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next step we will focus on the machine learning models to predict the \"state\" --> Will a project be successful? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}