Kickstarter_dataproject.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Why analyze Kickstarter data\n",
    "<p>\n",
    "Kickstarter is a fundraising platform for enterprise projects. Understanding the determinants of success for a Kickstarter fundraiser can help aspiring innovators make decisions that will maximize their chances of success. </p>\n",
    "\n",
    "# Methodology\n",
    "\n",
    "<p> This project assumes that the Kickstarter data best fits nonparametric assumptions and does not fit parametric assumptions. This data fits nonparametric assumptions better than parametric assumptions because the data is not normally or (at least) symmetrically distributed and it is subject to extreme outliers. Nonparametric methods do not require assumptions about the population probability distribution functions. There are fewer nonparametric assumptions. Nonparametric assumptions include randomness and independence.\n",
    "</p>\n",
    "\n",
    "# Primary Questions\n",
    "<p> \n",
    "Is there an optimal time of year to post a KickStarter campaign? <br>\n",
    "Are there any particular days of the month or week that are better to post a KickStarter campaign?\n",
    "\n",
    "</p>\n",
    "\n",
    "# Steps in analysis\n",
    "<ol> \n",
    "    <li>Import Data </li>\n",
    "    <li>Clean for Duplicates </li>\n",
    "        <ol>\n",
    "            <li>Drop Cancelled or Live Projects on the <strong><em>status</em></strong> variable. We cannot use cancelled or live projects in this analysis. Live projects have yet to conclude and cancelled projects won't yield relevent insights into the population under consideration. </li>\n",
    "        </ol>\n",
    "    <li>Fix Inconsistent Data Entry </li>\n",
    "    <li>Identify Outliers and choose approach </li>\n",
    "    <ol> \n",
    "        <li> It might be worth examining the outliers for common features or comparisons against other data points. </li>\n",
    "    </ol>\n",
    "    <li>Create new features </li>\n",
    "    <li>Measure Skewness </li>\n",
    "    <li>Test for Normality </li>\n",
    "    <li>Check Probability Distribution of Variables </li>\n",
    "    <ol>\n",
    "        <li>Are users equally likely to post on any given day of the week or month? </li>\n",
    "        <li>Do the probability distributions of variables segmented by <strong><em>status</em></strong> have the same variance? </li>\n",
    "    </ol>\n",
    "    <li>Test the independence of variables </li>\n",
    "    <li>Test for Joint Significance </li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import concurrent.futures\n",
    "from scipy import stats\n",
    "from scipy.stats import skew\n",
    "from scipy.stats import chisquare\n",
    "from scipy.stats import kruskal\n",
    "import scikit_posthocs\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load Data\n",
    "df = pd.read_excel(r\"C:\\Users\\rayde\\Desktop\\DSI_kickstarterscrape_dataset.xlsx\", parse_dates=['funded date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['project id', 'name', 'url', 'category', 'subcategory', 'location',\n",
       "       'status', 'goal', 'pledged', 'funded percentage', 'backers',\n",
       "       'funded date', 'levels', 'reward levels', 'updates', 'comments',\n",
       "       'duration'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect columns: Should only choose features from the data set that we know prior to the outcome variable. \n",
    "# Possible X values - location, category, subcategory, goal, funded date (if decided at the onset o the project)\n",
    "# Possible Y values - pledged, funded percentage, backers, status\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>project id</th>\n",
       "      <th>goal</th>\n",
       "      <th>pledged</th>\n",
       "      <th>funded percentage</th>\n",
       "      <th>backers</th>\n",
       "      <th>levels</th>\n",
       "      <th>updates</th>\n",
       "      <th>comments</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.595700e+04</td>\n",
       "      <td>4.595700e+04</td>\n",
       "      <td>4.594500e+04</td>\n",
       "      <td>45957.000000</td>\n",
       "      <td>45957.000000</td>\n",
       "      <td>45957.000000</td>\n",
       "      <td>45957.00000</td>\n",
       "      <td>45957.000000</td>\n",
       "      <td>45957.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.080800e+09</td>\n",
       "      <td>1.194271e+04</td>\n",
       "      <td>4.980750e+03</td>\n",
       "      <td>1.850129</td>\n",
       "      <td>69.973192</td>\n",
       "      <td>8.004939</td>\n",
       "      <td>4.08508</td>\n",
       "      <td>8.379529</td>\n",
       "      <td>39.995547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.218057e+08</td>\n",
       "      <td>1.887583e+05</td>\n",
       "      <td>5.674162e+04</td>\n",
       "      <td>88.492706</td>\n",
       "      <td>688.628479</td>\n",
       "      <td>4.233907</td>\n",
       "      <td>6.43922</td>\n",
       "      <td>174.015737</td>\n",
       "      <td>17.414458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.940900e+04</td>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.438962e+08</td>\n",
       "      <td>1.800000e+03</td>\n",
       "      <td>1.960000e+02</td>\n",
       "      <td>0.044000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.078345e+09</td>\n",
       "      <td>4.000000e+03</td>\n",
       "      <td>1.310000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.621596e+09</td>\n",
       "      <td>9.862000e+03</td>\n",
       "      <td>4.165000e+03</td>\n",
       "      <td>1.115640</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>48.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.147460e+09</td>\n",
       "      <td>2.147484e+07</td>\n",
       "      <td>1.026684e+07</td>\n",
       "      <td>15066.000000</td>\n",
       "      <td>87142.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>149.00000</td>\n",
       "      <td>19311.000000</td>\n",
       "      <td>91.960000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         project id          goal       pledged  funded percentage  \\\n",
       "count  4.595700e+04  4.595700e+04  4.594500e+04       45957.000000   \n",
       "mean   1.080800e+09  1.194271e+04  4.980750e+03           1.850129   \n",
       "std    6.218057e+08  1.887583e+05  5.674162e+04          88.492706   \n",
       "min    3.940900e+04  1.000000e-02  0.000000e+00           0.000000   \n",
       "25%    5.438962e+08  1.800000e+03  1.960000e+02           0.044000   \n",
       "50%    1.078345e+09  4.000000e+03  1.310000e+03           1.000000   \n",
       "75%    1.621596e+09  9.862000e+03  4.165000e+03           1.115640   \n",
       "max    2.147460e+09  2.147484e+07  1.026684e+07       15066.000000   \n",
       "\n",
       "            backers        levels      updates      comments      duration  \n",
       "count  45957.000000  45957.000000  45957.00000  45957.000000  45957.000000  \n",
       "mean      69.973192      8.004939      4.08508      8.379529     39.995547  \n",
       "std      688.628479      4.233907      6.43922    174.015737     17.414458  \n",
       "min        0.000000      0.000000      0.00000      0.000000      1.000000  \n",
       "25%        5.000000      5.000000      0.00000      0.000000     30.000000  \n",
       "50%       23.000000      7.000000      2.00000      0.000000     32.000000  \n",
       "75%       59.000000     10.000000      6.00000      3.000000     48.390000  \n",
       "max    87142.000000     80.000000    149.00000  19311.000000     91.960000  "
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View basic stats on data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fill missing values\n",
    "df.fillna(value=np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Browse Duplicates\n",
    "<p> Verify that duplicates are not just the result of a project running multiple campaigns. Multiple campaigns are acceptable but the same campaign should not be included twice. Drop these. </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>project id</th>\n",
       "      <th>name</th>\n",
       "      <th>url</th>\n",
       "      <th>category</th>\n",
       "      <th>subcategory</th>\n",
       "      <th>location</th>\n",
       "      <th>status</th>\n",
       "      <th>goal</th>\n",
       "      <th>pledged</th>\n",
       "      <th>funded percentage</th>\n",
       "      <th>backers</th>\n",
       "      <th>funded date</th>\n",
       "      <th>levels</th>\n",
       "      <th>reward levels</th>\n",
       "      <th>updates</th>\n",
       "      <th>comments</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45651</th>\n",
       "      <td>2140333236</td>\n",
       "      <td>Imagined Family Heirlooms: An Archive of Inher...</td>\n",
       "      <td>http://www.kickstarter.com/projects/andersonst...</td>\n",
       "      <td>Photography</td>\n",
       "      <td>Photography</td>\n",
       "      <td>Little Rock, AR</td>\n",
       "      <td>successful</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>8521.0</td>\n",
       "      <td>1.469138</td>\n",
       "      <td>110</td>\n",
       "      <td>2011-07-29 00:53:53+00:00</td>\n",
       "      <td>16</td>\n",
       "      <td>$10,$20,$35,$50,$75,$100,$150,$200,$225,$300,$...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>67.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45652</th>\n",
       "      <td>2140333236</td>\n",
       "      <td>Imagined Family Heirlooms: An Archive of Inher...</td>\n",
       "      <td>http://www.kickstarter.com/projects/andersonst...</td>\n",
       "      <td>Photography</td>\n",
       "      <td>Photography</td>\n",
       "      <td>Little Rock, AR</td>\n",
       "      <td>successful</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>8521.0</td>\n",
       "      <td>1.469138</td>\n",
       "      <td>110</td>\n",
       "      <td>2011-07-29 00:53:53+00:00</td>\n",
       "      <td>16</td>\n",
       "      <td>$10,$20,$35,$50,$75,$100,$150,$200,$225,$300,$...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>67.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45653</th>\n",
       "      <td>2140344109</td>\n",
       "      <td>Help Fund Thana Alexa's Debut Album &amp;quot;Ode ...</td>\n",
       "      <td>http://www.kickstarter.com/projects/1024906052...</td>\n",
       "      <td>Music</td>\n",
       "      <td>Jazz</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>successful</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>16678.0</td>\n",
       "      <td>1.111867</td>\n",
       "      <td>142</td>\n",
       "      <td>2012-05-20 20:36:57+00:00</td>\n",
       "      <td>16</td>\n",
       "      <td>$1,$5,$10,$15,$25,$50,$75,$100,$200,$250,$300,...</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>32.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45654</th>\n",
       "      <td>2140344109</td>\n",
       "      <td>Help Fund Thana Alexa's Debut Album &amp;quot;Ode ...</td>\n",
       "      <td>http://www.kickstarter.com/projects/1024906052...</td>\n",
       "      <td>Music</td>\n",
       "      <td>Jazz</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>successful</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>16678.0</td>\n",
       "      <td>1.111867</td>\n",
       "      <td>142</td>\n",
       "      <td>2012-05-20 20:36:57+00:00</td>\n",
       "      <td>16</td>\n",
       "      <td>$1,$5,$10,$15,$25,$50,$75,$100,$200,$250,$300,...</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>32.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45655</th>\n",
       "      <td>2140513451</td>\n",
       "      <td>The Cookie Chew - Changing the Way We Eat a Co...</td>\n",
       "      <td>http://www.kickstarter.com/projects/1650956569...</td>\n",
       "      <td>Food</td>\n",
       "      <td>Food</td>\n",
       "      <td>San Diego, CA</td>\n",
       "      <td>successful</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>5540.0</td>\n",
       "      <td>1.231111</td>\n",
       "      <td>89</td>\n",
       "      <td>2010-08-24 04:43:00+00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>$10,$50,$75,$100,$500</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>43.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       project id                                               name  \\\n",
       "45651  2140333236  Imagined Family Heirlooms: An Archive of Inher...   \n",
       "45652  2140333236  Imagined Family Heirlooms: An Archive of Inher...   \n",
       "45653  2140344109  Help Fund Thana Alexa's Debut Album &quot;Ode ...   \n",
       "45654  2140344109  Help Fund Thana Alexa's Debut Album &quot;Ode ...   \n",
       "45655  2140513451  The Cookie Chew - Changing the Way We Eat a Co...   \n",
       "\n",
       "                                                     url     category  \\\n",
       "45651  http://www.kickstarter.com/projects/andersonst...  Photography   \n",
       "45652  http://www.kickstarter.com/projects/andersonst...  Photography   \n",
       "45653  http://www.kickstarter.com/projects/1024906052...        Music   \n",
       "45654  http://www.kickstarter.com/projects/1024906052...        Music   \n",
       "45655  http://www.kickstarter.com/projects/1650956569...         Food   \n",
       "\n",
       "       subcategory         location      status     goal  pledged  \\\n",
       "45651  Photography  Little Rock, AR  successful   5800.0   8521.0   \n",
       "45652  Photography  Little Rock, AR  successful   5800.0   8521.0   \n",
       "45653         Jazz     New York, NY  successful  15000.0  16678.0   \n",
       "45654         Jazz     New York, NY  successful  15000.0  16678.0   \n",
       "45655         Food    San Diego, CA  successful   4500.0   5540.0   \n",
       "\n",
       "       funded percentage  backers               funded date  levels  \\\n",
       "45651           1.469138      110 2011-07-29 00:53:53+00:00      16   \n",
       "45652           1.469138      110 2011-07-29 00:53:53+00:00      16   \n",
       "45653           1.111867      142 2012-05-20 20:36:57+00:00      16   \n",
       "45654           1.111867      142 2012-05-20 20:36:57+00:00      16   \n",
       "45655           1.231111       89 2010-08-24 04:43:00+00:00       5   \n",
       "\n",
       "                                           reward levels  updates  comments  \\\n",
       "45651  $10,$20,$35,$50,$75,$100,$150,$200,$225,$300,$...        5         4   \n",
       "45652  $10,$20,$35,$50,$75,$100,$150,$200,$225,$300,$...        5         4   \n",
       "45653  $1,$5,$10,$15,$25,$50,$75,$100,$200,$250,$300,...        5         2   \n",
       "45654  $1,$5,$10,$15,$25,$50,$75,$100,$200,$250,$300,...        5         2   \n",
       "45655                              $10,$50,$75,$100,$500       14        17   \n",
       "\n",
       "       duration  \n",
       "45651     67.00  \n",
       "45652     67.00  \n",
       "45653     32.00  \n",
       "45654     32.00  \n",
       "45655     43.27  "
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['project id'].duplicated(keep=False)].sort_values(by='project id').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop Duplicates on Project Id / Drop Cancelled or Live Projects\n",
    "new_df = df.drop_duplicates(subset=['project id'])\n",
    "new_df = new_df.query('status == \"successful\" | status ==\"failed\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['successful', 'failed'], dtype=object)"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['status'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fix inconsistent data entry / importation errors\n",
    "<ol>\n",
    "    <li>Look at the unique values of each column to see if there are any data entry errors</li>\n",
    "    <li>Replace data entry errors using the replace method of the pandas dataframe</li>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Short Film', 'Board & Card Games', 'Documentary', 'Fashion',\n",
       "       'Music', 'Illustration', 'Open Software', 'Indie Rock', 'Dance',\n",
       "       'Fiction', 'Nonfiction', 'Film & Video', 'Theater', 'Games',\n",
       "       'Art Book', 'Animation', 'Country & Folk', 'Comics', 'Webseries',\n",
       "       'Technology', 'Performance Art', 'Narrative Film', 'Video Games',\n",
       "       'Product Design', 'Painting', 'Photography', 'Conceptual Art',\n",
       "       'Rock', 'Jazz', 'Classical Music', 'Food', 'Art', 'Pop',\n",
       "       'Journalism', 'Poetry', 'World Music', 'Sculpture', 'Publishing',\n",
       "       'Electronic Music', \"Children's Book\", 'Open Hardware',\n",
       "       'Graphic Design', 'Hip-Hop', 'Public Art', 'Mixed Media',\n",
       "       'Periodical', 'Crafts', 'Design', 'Digital Art'], dtype=object)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.subcategory.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Film & Video', 'Games', 'Fashion', 'Music', 'Art', 'Technology',\n",
       "       'Dance', 'Publishing', 'Theater', 'Comics', 'Design',\n",
       "       'Photography', 'Food'], dtype=object)"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.category.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = new_df.replace(to_replace=\"Film &amp; Video\", value=\"Film & Video\")\n",
    "new_df = new_df.replace(to_replace='Board &amp; Card Games', value=\"Board & Card Games\")\n",
    "new_df = new_df.replace(to_replace='Country &amp; Folk', value=\"Country & Folk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Short Film', 'Board & Card Games', 'Documentary', 'Fashion',\n",
       "       'Music', 'Illustration', 'Open Software', 'Indie Rock', 'Dance',\n",
       "       'Fiction', 'Nonfiction', 'Film & Video', 'Theater', 'Games',\n",
       "       'Art Book', 'Animation', 'Country & Folk', 'Comics', 'Webseries',\n",
       "       'Technology', 'Performance Art', 'Narrative Film', 'Video Games',\n",
       "       'Product Design', 'Painting', 'Photography', 'Conceptual Art',\n",
       "       'Rock', 'Jazz', 'Classical Music', 'Food', 'Art', 'Pop',\n",
       "       'Journalism', 'Poetry', 'World Music', 'Sculpture', 'Publishing',\n",
       "       'Electronic Music', \"Children's Book\", 'Open Hardware',\n",
       "       'Graphic Design', 'Hip-Hop', 'Public Art', 'Mixed Media',\n",
       "       'Periodical', 'Crafts', 'Design', 'Digital Art'], dtype=object)"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.subcategory.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Film & Video', 'Games', 'Fashion', 'Music', 'Art', 'Technology',\n",
       "       'Dance', 'Publishing', 'Theater', 'Comics', 'Design',\n",
       "       'Photography', 'Food'], dtype=object)"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.category.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['name'] = new_df.name.str.replace(\"&quot;\", '\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Dinner Party - a short film about friendship... and revenge'"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.name.iloc[4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Identify outliers in potential outcome variables\n",
    "<p> It actually might be worth examining the outliers. What factors may have contributed to the values becoming so extreme? But, we are going to focus less on this and more on what we should do with these outliers. </p>\n",
    "<p> Options \n",
    "<ol>\n",
    "    <li>Drop all outliers</li>\n",
    "    <li>Drop some outliers</li>\n",
    "    <li>Change the values of the outliers</li>\n",
    "</ol>   \n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25202293b08>"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eLdxo5mltAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#It looks like we could drop the most extreme one - three data points from pledged. \n",
    "# There are some other outliers but that's not necessarily a bad thing. In this particular data set \n",
    "# (given that the data is skewed and not normally distributed) \n",
    "# dropping too many outliers might homogenize the data set too much. In fact, it could be useful to analyze the outliers \n",
    "# on their own.\n",
    "sns.boxplot(x=df['pledged'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x252022be3c8>"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#It looks like we might want to drop at least two data points from pledged\n",
    "sns.boxplot(x=df['funded percentage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x252022fc988>"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uk/QBSd/Yc4cG7Bh3QwOAILwCBoAgBDAABCGAASAIAQwAQQhgAAhCAANAEAIYAIL8DzateejUH8uaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We should probably drop at least the three data points above 60,000. \n",
    "sns.boxplot(x=df['backers'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10710    10266845.0\n",
       "41151     3336372.0\n",
       "42674     2933252.0\n",
       "11231     1836447.0\n",
       "35458     1464706.0\n",
       "Name: pledged, dtype: float64"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pledged'].sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11231    1836447.0\n",
       "35458    1464706.0\n",
       "4086     1254120.0\n",
       "17468    1192793.0\n",
       "4114      942578.0\n",
       "Name: pledged, dtype: float64"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_columns = df['pledged'].sort_values(ascending=False).head(3).index\n",
    "df.drop(drop_columns, axis=0, inplace=True)\n",
    "df['pledged'].sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "project id           1.077617e+09\n",
      "goal                 8.050000e+03\n",
      "pledged              3.964750e+03\n",
      "funded percentage    1.071608e+00\n",
      "backers              5.400000e+01\n",
      "levels               5.000000e+00\n",
      "updates              6.000000e+00\n",
      "comments             3.000000e+00\n",
      "duration             1.839000e+01\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Use the Inter Quartile Range to identify outliers\n",
    "Q1 = df.quantile(0.25)\n",
    "Q3 = df.quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "print(IQR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "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>pledged</th>\n",
       "      <th>funded percentage</th>\n",
       "      <th>backers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>31047.0</td>\n",
       "      <td>3.104700</td>\n",
       "      <td>426.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>14366.0</td>\n",
       "      <td>9.577607</td>\n",
       "      <td>386.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>273725.0</td>\n",
       "      <td>3.649674</td>\n",
       "      <td>5661.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>11578.0</td>\n",
       "      <td>4.631200</td>\n",
       "      <td>383.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>32121.0</td>\n",
       "      <td>3.212100</td>\n",
       "      <td>261.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45492</th>\n",
       "      <td>16423.0</td>\n",
       "      <td>5.474333</td>\n",
       "      <td>172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45661</th>\n",
       "      <td>12800.0</td>\n",
       "      <td>4.266770</td>\n",
       "      <td>332.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45662</th>\n",
       "      <td>12800.0</td>\n",
       "      <td>4.266770</td>\n",
       "      <td>332.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45783</th>\n",
       "      <td>17764.0</td>\n",
       "      <td>3.552800</td>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45784</th>\n",
       "      <td>17764.0</td>\n",
       "      <td>3.552800</td>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>452 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        pledged  funded percentage  backers\n",
       "85      31047.0           3.104700    426.0\n",
       "253     14366.0           9.577607    386.0\n",
       "443    273725.0           3.649674   5661.0\n",
       "452     11578.0           4.631200    383.0\n",
       "503     32121.0           3.212100    261.0\n",
       "...         ...                ...      ...\n",
       "45492   16423.0           5.474333    172.0\n",
       "45661   12800.0           4.266770    332.0\n",
       "45662   12800.0           4.266770    332.0\n",
       "45783   17764.0           3.552800    235.0\n",
       "45784   17764.0           3.552800    235.0\n",
       "\n",
       "[452 rows x 3 columns]"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In this particular data set there are a lot of data points that qualify as outliers or projects that have unusually \n",
    "# high performance. Thus, the IQR might not be the best strategy for this data set.\n",
    "df[['pledged', 'funded percentage', 'backers']][(df[['pledged', 'funded percentage', 'backers']] < (Q1 - 1.5 * IQR)) |(df[['pledged', 'funded percentage', 'backers']] > (Q3 + 1.5 * IQR))].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identify outliers by Z-score\n",
    "for each in ['funded percentage','backers', 'pledged']:\n",
    "    df[f'z_{each}'] = np.abs(stats.zscore(df[each]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_indices = df[df['z_funded percentage']> 3].sort_values('z_funded percentage', ascending=False).head(2).index\n",
    "df.drop(drop_indices, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_indices = df[df['z_backers']> 3].sort_values('z_backers', ascending=False).head(3).index\n",
    "df.drop(drop_indices, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_indices = df[df['z_pledged']> 3].sort_values('z_pledged', ascending=False).head(3).index\n",
    "df.drop(drop_indices, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create new features out of funded date and location\n",
    "<p>\n",
    "    <ol> \n",
    "        <li>Posting Date is the funded date minus duration</li>\n",
    "        <li>Extract Year, Month, Day, Time, and Day of Week from the dates</li>\n",
    "        <li>Split location into City, State, and Country</li>\n",
    "        </ol>\n",
    "        </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration               float64\n",
       "duration_td    timedelta64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['duration_td'] = new_df.duration.apply(lambda x: datetime.timedelta(days=x))\n",
    "new_df[['duration','duration_td']].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Posting_Date</th>\n",
       "      <th>duration</th>\n",
       "      <th>duration_td</th>\n",
       "      <th>funded date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-07-20 19:28:17+00:00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>30 days 00:00:00</td>\n",
       "      <td>2011-08-19 19:28:17+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-06-15 23:39:48+00:00</td>\n",
       "      <td>47.18</td>\n",
       "      <td>47 days 04:19:12</td>\n",
       "      <td>2010-08-02 03:59:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-03-06 20:57:12+00:00</td>\n",
       "      <td>32.22</td>\n",
       "      <td>32 days 05:16:48</td>\n",
       "      <td>2012-04-08 02:14:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-05-02 15:25:39+00:00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>30 days 00:00:00</td>\n",
       "      <td>2011-06-01 15:25:39+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011-06-01 03:13:48+00:00</td>\n",
       "      <td>21.43</td>\n",
       "      <td>21 days 10:19:12</td>\n",
       "      <td>2011-06-22 13:33:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Posting_Date  duration      duration_td  \\\n",
       "0 2011-07-20 19:28:17+00:00     30.00 30 days 00:00:00   \n",
       "1 2010-06-15 23:39:48+00:00     47.18 47 days 04:19:12   \n",
       "3 2012-03-06 20:57:12+00:00     32.22 32 days 05:16:48   \n",
       "4 2011-05-02 15:25:39+00:00     30.00 30 days 00:00:00   \n",
       "5 2011-06-01 03:13:48+00:00     21.43 21 days 10:19:12   \n",
       "\n",
       "                funded date  \n",
       "0 2011-08-19 19:28:17+00:00  \n",
       "1 2010-08-02 03:59:00+00:00  \n",
       "3 2012-04-08 02:14:00+00:00  \n",
       "4 2011-06-01 15:25:39+00:00  \n",
       "5 2011-06-22 13:33:00+00:00  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['Posting_Date'] = new_df['funded date'] - new_df['duration_td']\n",
    "new_df[['Posting_Date','duration','duration_td', 'funded date']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_funded</th>\n",
       "      <th>month_funded</th>\n",
       "      <th>day_funded</th>\n",
       "      <th>time_funded</th>\n",
       "      <th>dayOfWeek_funded</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>19:28:17</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>03:59:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>02:14:00</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>15:25:39</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>13:33:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>45952</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>13:57:00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45953</th>\n",
       "      <td>2012</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>02:12:10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45954</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>05:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45955</th>\n",
       "      <td>2012</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>19:44:47</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45956</th>\n",
       "      <td>2011</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>04:30:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41841 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year_funded  month_funded  day_funded time_funded  dayOfWeek_funded\n",
       "0             2011             8          19    19:28:17                 4\n",
       "1             2010             8           2    03:59:00                 0\n",
       "3             2012             4           8    02:14:00                 6\n",
       "4             2011             6           1    15:25:39                 2\n",
       "5             2011             6          22    13:33:00                 2\n",
       "...            ...           ...         ...         ...               ...\n",
       "45952         2012             3          30    13:57:00                 4\n",
       "45953         2012             4          22    02:12:10                 6\n",
       "45954         2012             1          16    05:00:00                 0\n",
       "45955         2012             5          19    19:44:47                 5\n",
       "45956         2011             8           4    04:30:00                 3\n",
       "\n",
       "[41841 rows x 5 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['year_funded'] = new_df['funded date'].dt.year\n",
    "new_df['month_funded'] = new_df['funded date'].dt.month\n",
    "new_df['day_funded'] = new_df['funded date'].dt.day\n",
    "new_df['time_funded'] = new_df['funded date'].dt.time\n",
    "new_df['dayOfWeek_funded'] = new_df['funded date'].dt.dayofweek\n",
    "new_df[['year_funded','month_funded', 'day_funded', 'time_funded', 'dayOfWeek_funded']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_posted</th>\n",
       "      <th>month_posted</th>\n",
       "      <th>day_posted</th>\n",
       "      <th>time_posted</th>\n",
       "      <th>dayOfWeek_posted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>19:28:17</td>\n",
       "      <td>2</td>\n",
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       "      <th>1</th>\n",
       "      <td>2010</td>\n",
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       "      <td>23:39:48</td>\n",
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       "      <td>2012</td>\n",
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       "      <td>6</td>\n",
       "      <td>20:57:12</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>2011</td>\n",
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       "      <td>15:25:39</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>03:13:48</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>45952</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>18:01:48</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45953</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>03:09:46</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45954</th>\n",
       "      <td>2011</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>21:19:12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45955</th>\n",
       "      <td>2012</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>19:44:47</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45956</th>\n",
       "      <td>2011</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>19:22:48</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41841 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year_posted  month_posted  day_posted time_posted  dayOfWeek_posted\n",
       "0             2011             7          20    19:28:17                 2\n",
       "1             2010             6          15    23:39:48                 1\n",
       "3             2012             3           6    20:57:12                 1\n",
       "4             2011             5           2    15:25:39                 0\n",
       "5             2011             6           1    03:13:48                 2\n",
       "...            ...           ...         ...         ...               ...\n",
       "45952         2012             2          21    18:01:48                 1\n",
       "45953         2012             2          22    03:09:46                 2\n",
       "45954         2011            12          19    21:19:12                 0\n",
       "45955         2012             4          19    19:44:47                 3\n",
       "45956         2011             6          24    19:22:48                 4\n",
       "\n",
       "[41841 rows x 5 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['year_posted'] = new_df['Posting_Date'].dt.year\n",
    "new_df['month_posted'] = new_df['Posting_Date'].dt.month\n",
    "new_df['day_posted'] = new_df['Posting_Date'].dt.day\n",
    "new_df['time_posted'] = new_df['Posting_Date'].dt.time\n",
    "new_df['dayOfWeek_posted'] = new_df['Posting_Date'].dt.dayofweek\n",
    "new_df[['year_posted','month_posted', 'day_posted', 'time_posted', 'dayOfWeek_posted']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Columbia</td>\n",
       "      <td>MO</td>\n",
       "      <td>Columbia, MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maplewood</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Maplewood, NJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Novi</td>\n",
       "      <td>MI</td>\n",
       "      <td>Novi, MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Portland</td>\n",
       "      <td>OR</td>\n",
       "      <td>Portland, OR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45952</th>\n",
       "      <td>Austin</td>\n",
       "      <td>TX</td>\n",
       "      <td>Austin, TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45953</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45954</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45955</th>\n",
       "      <td>Vero Beach</td>\n",
       "      <td>FL</td>\n",
       "      <td>Vero Beach, FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45956</th>\n",
       "      <td>Bozeman</td>\n",
       "      <td>MT</td>\n",
       "      <td>Bozeman, MT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41841 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              city state         location\n",
       "0         Columbia    MO     Columbia, MO\n",
       "1        Maplewood    NJ    Maplewood, NJ\n",
       "3      Los Angeles    CA  Los Angeles, CA\n",
       "4             Novi    MI         Novi, MI\n",
       "5         Portland    OR     Portland, OR\n",
       "...            ...   ...              ...\n",
       "45952       Austin    TX       Austin, TX\n",
       "45953  Los Angeles    CA  Los Angeles, CA\n",
       "45954  Los Angeles    CA  Los Angeles, CA\n",
       "45955   Vero Beach    FL   Vero Beach, FL\n",
       "45956      Bozeman    MT      Bozeman, MT\n",
       "\n",
       "[41841 rows x 3 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['city'], new_df['state'] = new_df['location'].str.split(', ', 1).str\n",
    "new_df[['city', 'state', 'location']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Columbia', 'Maplewood', 'Los Angeles', ..., 'Peaks',\n",
       "       'Lumberville', 'Lake Oswego'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['city'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['MO', 'NJ', 'CA', 'MI', 'OR', 'TN', 'IL', nan, 'NY', 'DC', 'ID',\n",
       "       'FL', 'TX', 'CO', 'ME', 'Taiwan', 'IN', 'Norway', 'MN', 'NC', 'MA',\n",
       "       'PA', 'CT', 'Chile', 'MD', 'HI', 'VA', 'WA', 'AZ', 'OH', 'NV',\n",
       "       'Haiti', 'GA', 'AL', 'UT', 'Canada', 'LA', 'SC', 'Ecuador', 'WI',\n",
       "       'Jamaica', 'Argentina', 'Hong Kong', 'Germany', 'NM', 'NE',\n",
       "       'Guatemala', 'IA', 'WY', 'Australia', 'RI', 'Sweden', 'OK', 'DE',\n",
       "       'South Africa', 'AK', 'WV', 'Nepal', 'MT', 'KY', 'VT', 'Kenya',\n",
       "       'Bosnia and Herzegovina', 'Iceland', 'Mexico', 'KS', 'Hungary',\n",
       "       'Indonesia', 'China', 'SD', 'Cuba', 'Peru', 'Italy', 'Netherlands',\n",
       "       'Singapore', 'Ethiopia', 'NH', 'New Zealand', 'France',\n",
       "       'United Kingdom', 'Austria', 'Turkey', 'AR', 'Mt', 'Congo',\n",
       "       'Colombia', 'India', 'Mongolia', 'MS', 'Israel', 'Dominica',\n",
       "       'Finland', 'Czech Republic', 'Japan', 'Virgin Islands, U.S.',\n",
       "       'Lebanon', 'Armenia', 'Portugal', 'Qatar', 'Morocco', 'Martinique',\n",
       "       'Puerto Rico', 'Afghanistan', 'Denmark', 'Tanzania', 'Kazakhstan',\n",
       "       'Zambia', 'Switzerland', 'Egypt', 'Svalbard and Jan Mayen', 'ND',\n",
       "       'Liberia', 'Ghana', 'Nicaragua', 'Russia', 'Ukraine', 'Bulgaria',\n",
       "       'South Korea', 'Brazil', 'Rwanda', 'Spain', 'Costa Rica',\n",
       "       'Philippines', 'Pakistan', 'El Salvador', 'Panama',\n",
       "       'United Arab Emirates', 'Honduras', 'Palestinian Territories',\n",
       "       'Poland', 'Georgia', 'Syrian Arab Republic', 'Cambodia', 'Nigeria',\n",
       "       'Mali', 'Saudi Arabia', 'Argent', 'Iraq', 'Cameroon', 'Namibia',\n",
       "       'Ireland', 'Bolivia', 'Venezuela', 'Thailand', 'Jordan',\n",
       "       'Zimbabwe', 'Iran', 'Tunisia', 'Greece', 'Sierra Leone',\n",
       "       'Isle of Man', 'Central African Republic', 'Serbia', 'Belize',\n",
       "       'Uganda', 'Malta', 'Slovenia', 'Sudan', 'Belgium', 'Myanmar',\n",
       "       'Malaysia', 'Greenland', 'Senegal', 'Bahamas', 'Malawi', 'Romania',\n",
       "       'Mozambique', 'Bhutan', 'Lithuania', 'Micronesia', 'Tajikistan',\n",
       "       'Sri Lanka', 'Libyan Arab Jamahiriya', 'Guinea', 'Kyoto, Japan',\n",
       "       'Bermuda', 'Belarus', 'Estonia', 'Suriname', 'Dominican Republic',\n",
       "       'Vanuatu', 'Trinidad and Tobago', 'Cyprus', 'Macedonia',\n",
       "       'Viet Nam', 'Swaziland', 'Falkland Islands', 'Kyrgyzstan',\n",
       "       'Bangladesh', 'Laos', 'Middleburg, MD', 'Papua New Guinea',\n",
       "       'Uruguay', 'Guam', 'Dominican Re', 'Saint Lucia'], dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['state'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = new_df.replace(to_replace='Virginia', value=\"VA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x, y = new_df[new_df.state == 'Middleburg, MD']['state'].str.split(', ', 1).str\n",
    "row_val = list(new_df[new_df.state == 'Middleburg, MD'].index)[0]\n",
    "\n",
    "new_df.loc[[row_val],['city']] = x\n",
    "new_df.loc[[row_val], ['state']]= y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>project id</th>\n",
       "      <th>name</th>\n",
       "      <th>url</th>\n",
       "      <th>category</th>\n",
       "      <th>subcategory</th>\n",
       "      <th>location</th>\n",
       "      <th>status</th>\n",
       "      <th>goal</th>\n",
       "      <th>pledged</th>\n",
       "      <th>funded percentage</th>\n",
       "      <th>...</th>\n",
       "      <th>time_funded</th>\n",
       "      <th>dayOfWeek_funded</th>\n",
       "      <th>year_posted</th>\n",
       "      <th>month_posted</th>\n",
       "      <th>day_posted</th>\n",
       "      <th>time_posted</th>\n",
       "      <th>dayOfWeek_posted</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>727286</td>\n",
       "      <td>Offline Wikipedia iPhone app</td>\n",
       "      <td>http://www.kickstarter.com/projects/dphiffer/o...</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Open Software</td>\n",
       "      <td>NaN</td>\n",
       "      <td>successful</td>\n",
       "      <td>99.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>1.464646</td>\n",
       "      <td>...</td>\n",
       "      <td>06:59:59</td>\n",
       "      <td>1</td>\n",
       "      <td>2009</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>15:38:23</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>893085</td>\n",
       "      <td>Esperanza Farm: A Novel</td>\n",
       "      <td>http://www.kickstarter.com/projects/JesusMaria...</td>\n",
       "      <td>Publishing</td>\n",
       "      <td>Fiction</td>\n",
       "      <td>NaN</td>\n",
       "      <td>failed</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>765.0</td>\n",
       "      <td>0.117692</td>\n",
       "      <td>...</td>\n",
       "      <td>03:59:00</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>19:49:24</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1512678</td>\n",
       "      <td>Eyes and Ears on English - comedy/English educ...</td>\n",
       "      <td>http://www.kickstarter.com/projects/1787808582...</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>Taipei, Taiwan</td>\n",
       "      <td>failed</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>1502.0</td>\n",
       "      <td>0.125167</td>\n",
       "      <td>...</td>\n",
       "      <td>05:15:04</td>\n",
       "      <td>6</td>\n",
       "      <td>2011</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>05:15:04</td>\n",
       "      <td>3</td>\n",
       "      <td>Taipei</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>Taiwan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2117470</td>\n",
       "      <td>\" Hugo's There \" - Real News. Unbelievably.</td>\n",
       "      <td>http://www.kickstarter.com/projects/mastermind...</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>Webseries</td>\n",
       "      <td>Oslo, Norway</td>\n",
       "      <td>failed</td>\n",
       "      <td>8400.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.002976</td>\n",
       "      <td>...</td>\n",
       "      <td>01:20:25</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>01:20:25</td>\n",
       "      <td>0</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Norway</td>\n",
       "      <td>Norway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2442649</td>\n",
       "      <td>Dream with Me--A documentary about one year in...</td>\n",
       "      <td>http://www.kickstarter.com/projects/1800556280...</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>successful</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5615.0</td>\n",
       "      <td>1.123000</td>\n",
       "      <td>...</td>\n",
       "      <td>01:08:00</td>\n",
       "      <td>4</td>\n",
       "      <td>2009</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>03:46:24</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    project id                                               name  \\\n",
       "11      727286                       Offline Wikipedia iPhone app   \n",
       "14      893085                            Esperanza Farm: A Novel   \n",
       "29     1512678  Eyes and Ears on English - comedy/English educ...   \n",
       "42     2117470        \" Hugo's There \" - Real News. Unbelievably.   \n",
       "49     2442649  Dream with Me--A documentary about one year in...   \n",
       "\n",
       "                                                  url      category  \\\n",
       "11  http://www.kickstarter.com/projects/dphiffer/o...    Technology   \n",
       "14  http://www.kickstarter.com/projects/JesusMaria...    Publishing   \n",
       "29  http://www.kickstarter.com/projects/1787808582...  Film & Video   \n",
       "42  http://www.kickstarter.com/projects/mastermind...  Film & Video   \n",
       "49  http://www.kickstarter.com/projects/1800556280...  Film & Video   \n",
       "\n",
       "      subcategory        location      status     goal  pledged  \\\n",
       "11  Open Software             NaN  successful     99.0    145.0   \n",
       "14        Fiction             NaN      failed   6500.0    765.0   \n",
       "29   Film & Video  Taipei, Taiwan      failed  12000.0   1502.0   \n",
       "42      Webseries    Oslo, Norway      failed   8400.0     25.0   \n",
       "49    Documentary             NaN  successful   5000.0   5615.0   \n",
       "\n",
       "    funded percentage  ...  time_funded dayOfWeek_funded  year_posted  \\\n",
       "11           1.464646  ...     06:59:59                1         2009   \n",
       "14           0.117692  ...     03:59:00                4         2010   \n",
       "29           0.125167  ...     05:15:04                6         2011   \n",
       "42           0.002976  ...     01:20:25                0         2011   \n",
       "49           1.123000  ...     01:08:00                4         2009   \n",
       "\n",
       "   month_posted  day_posted  time_posted  dayOfWeek_posted    city   state  \\\n",
       "11            4          25     15:38:23                 5     NaN     NaN   \n",
       "14            5          16     19:49:24                 6     NaN     NaN   \n",
       "29            4           7     05:15:04                 3  Taipei  Taiwan   \n",
       "42            7          25     01:20:25                 0    Oslo  Norway   \n",
       "49            9          15     03:46:24                 1     NaN     NaN   \n",
       "\n",
       "    country  \n",
       "11      NaN  \n",
       "14      NaN  \n",
       "29   Taiwan  \n",
       "42   Norway  \n",
       "49      NaN  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['country'] = new_df['state'].replace(to_replace=r'^..{1}$', value=\"USA\", regex=True)\n",
    "new_df[new_df.country != 'USA'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "series_1 = new_df[new_df.country != 'USA']['state'].apply(lambda x: np.nan)\n",
    "indices= list(new_df[new_df.country != 'USA'].index)\n",
    "new_df.loc[indices, ['state']] = series_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['MO', 'NJ', 'CA', 'MI', 'OR', 'TN', 'IL', nan, 'NY', 'DC', 'ID',\n",
       "       'FL', 'TX', 'CO', 'ME', 'IN', 'MN', 'NC', 'MA', 'PA', 'CT', 'MD',\n",
       "       'HI', 'VA', 'WA', 'AZ', 'OH', 'NV', 'GA', 'AL', 'UT', 'LA', 'SC',\n",
       "       'WI', 'NM', 'NE', 'IA', 'WY', 'RI', 'OK', 'DE', 'AK', 'WV', 'MT',\n",
       "       'KY', 'VT', 'KS', 'SD', 'NH', 'AR', 'Mt', 'MS', 'ND'], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['state'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['USA', nan, 'Taiwan', 'Norway', 'Chile', 'Haiti', 'Canada',\n",
       "       'Ecuador', 'Jamaica', 'Argentina', 'Hong Kong', 'Germany',\n",
       "       'Guatemala', 'Australia', 'Sweden', 'South Africa', 'Nepal',\n",
       "       'Kenya', 'Bosnia and Herzegovina', 'Iceland', 'Mexico', 'Hungary',\n",
       "       'Indonesia', 'China', 'Cuba', 'Peru', 'Italy', 'Netherlands',\n",
       "       'Singapore', 'Ethiopia', 'New Zealand', 'France', 'United Kingdom',\n",
       "       'Austria', 'Turkey', 'Congo', 'Colombia', 'India', 'Mongolia',\n",
       "       'Israel', 'Dominica', 'Finland', 'Czech Republic', 'Japan',\n",
       "       'Virgin Islands, U.S.', 'Lebanon', 'Armenia', 'Portugal', 'Qatar',\n",
       "       'Morocco', 'Martinique', 'Puerto Rico', 'Afghanistan', 'Denmark',\n",
       "       'Tanzania', 'Kazakhstan', 'Zambia', 'Switzerland', 'Egypt',\n",
       "       'Svalbard and Jan Mayen', 'Liberia', 'Ghana', 'Nicaragua',\n",
       "       'Russia', 'Ukraine', 'Bulgaria', 'South Korea', 'Brazil', 'Rwanda',\n",
       "       'Spain', 'Costa Rica', 'Philippines', 'Pakistan', 'El Salvador',\n",
       "       'Panama', 'United Arab Emirates', 'Honduras',\n",
       "       'Palestinian Territories', 'Poland', 'Georgia',\n",
       "       'Syrian Arab Republic', 'Cambodia', 'Nigeria', 'Mali',\n",
       "       'Saudi Arabia', 'Argent', 'Iraq', 'Cameroon', 'Namibia', 'Ireland',\n",
       "       'Bolivia', 'Venezuela', 'Thailand', 'Jordan', 'Zimbabwe', 'Iran',\n",
       "       'Tunisia', 'Greece', 'Sierra Leone', 'Isle of Man',\n",
       "       'Central African Republic', 'Serbia', 'Belize', 'Uganda', 'Malta',\n",
       "       'Slovenia', 'Sudan', 'Belgium', 'Myanmar', 'Malaysia', 'Greenland',\n",
       "       'Senegal', 'Bahamas', 'Malawi', 'Romania', 'Mozambique', 'Bhutan',\n",
       "       'Lithuania', 'Micronesia', 'Tajikistan', 'Sri Lanka',\n",
       "       'Libyan Arab Jamahiriya', 'Guinea', 'Kyoto, Japan', 'Bermuda',\n",
       "       'Belarus', 'Estonia', 'Suriname', 'Dominican Republic', 'Vanuatu',\n",
       "       'Trinidad and Tobago', 'Cyprus', 'Macedonia', 'Viet Nam',\n",
       "       'Swaziland', 'Falkland Islands', 'Kyrgyzstan', 'Bangladesh',\n",
       "       'Laos', 'Papua New Guinea', 'Uruguay', 'Guam', 'Dominican Re',\n",
       "       'Saint Lucia'], dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>location</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    location country\n",
       "11       NaN     nan\n",
       "14       NaN     nan\n",
       "49       NaN     nan\n",
       "98       NaN     nan\n",
       "100      NaN     nan"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['country'] = new_df['country'].astype('str')\n",
    "new_df[new_df.country == 'nan'][['location', 'country']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "series1 = new_df[new_df['country']=='nan']['country'].apply(lambda x: np.nan)\n",
    "indices = series1.index\n",
    "new_df.loc[indices, ['country']] = series_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country\n",
       "11      NaN\n",
       "14      NaN\n",
       "49      NaN\n",
       "98      NaN\n",
       "100     NaN"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.loc[indices, ['country']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Dummy Variables Using LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj_cols = ['category', 'subcategory', 'status', 'year_posted', 'year_funded', 'month_posted', 'month_funded']\n",
    "label_encoder = LabelEncoder()\n",
    "for col in obj_cols:\n",
    "    try:\n",
    "        new_df[col+\"_dummy\"] = label_encoder.fit_transform(new_df[col])\n",
    "    except:\n",
    "        print('problem with' + col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "countries = new_df.country\n",
    "country_counts = countries.value_counts()\n",
    "\n",
    "mask = countries.isin(country_counts[country_counts < 10].index)\n",
    "\n",
    "# Label all other categories as Other\n",
    "countries[mask] = 'Other'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['country'] = new_df['country'].astype('str')\n",
    "new_df.country\n",
    "new_df['country_dummy'] = label_encoder.fit_transform(new_df['country'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>status</th>\n",
       "      <th>status_dummy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>successful</td>\n",
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       "      <th>1</th>\n",
       "      <td>failed</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>successful</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>failed</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>successful</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>45952</th>\n",
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       "    <tr>\n",
       "      <th>45953</th>\n",
       "      <td>failed</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>45954</th>\n",
       "      <td>successful</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45955</th>\n",
       "      <td>failed</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45956</th>\n",
       "      <td>successful</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41841 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           status  status_dummy\n",
       "0      successful             1\n",
       "1          failed             0\n",
       "3      successful             1\n",
       "4          failed             0\n",
       "5      successful             1\n",
       "...           ...           ...\n",
       "45952  successful             1\n",
       "45953      failed             0\n",
       "45954  successful             1\n",
       "45955      failed             0\n",
       "45956  successful             1\n",
       "\n",
       "[41841 rows x 2 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[['status','status_dummy']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>project id</th>\n",
       "      <th>goal</th>\n",
       "      <th>pledged</th>\n",
       "      <th>funded percentage</th>\n",
       "      <th>backers</th>\n",
       "      <th>levels</th>\n",
       "      <th>updates</th>\n",
       "      <th>comments</th>\n",
       "      <th>duration</th>\n",
       "      <th>duration_td</th>\n",
       "      <th>...</th>\n",
       "      <th>day_posted</th>\n",
       "      <th>dayOfWeek_posted</th>\n",
       "      <th>category_dummy</th>\n",
       "      <th>subcategory_dummy</th>\n",
       "      <th>status_dummy</th>\n",
       "      <th>year_posted_dummy</th>\n",
       "      <th>year_funded_dummy</th>\n",
       "      <th>month_posted_dummy</th>\n",
       "      <th>month_funded_dummy</th>\n",
       "      <th>country_dummy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.184100e+04</td>\n",
       "      <td>4.184100e+04</td>\n",
       "      <td>4.184100e+04</td>\n",
       "      <td>41841.000000</td>\n",
       "      <td>41841.000000</td>\n",
       "      <td>41841.000000</td>\n",
       "      <td>41841.000000</td>\n",
       "      <td>41841.000000</td>\n",
       "      <td>41841.000000</td>\n",
       "      <td>41841</td>\n",
       "      <td>...</td>\n",
       "      <td>41841.000000</td>\n",
       "      <td>41841.000000</td>\n",
       "      <td>41841.000000</td>\n",
       "      <td>41841.000000</td>\n",
       "      <td>41841.000000</td>\n",
       "      <td>41841.000000</td>\n",
       "      <td>41841.000000</td>\n",
       "      <td>41841.000000</td>\n",
       "      <td>41841.000000</td>\n",
       "      <td>41841.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.078276e+09</td>\n",
       "      <td>1.041084e+04</td>\n",
       "      <td>5.128665e+03</td>\n",
       "      <td>1.980354</td>\n",
       "      <td>71.098731</td>\n",
       "      <td>7.950646</td>\n",
       "      <td>4.335580</td>\n",
       "      <td>8.327287</td>\n",
       "      <td>40.232153</td>\n",
       "      <td>40 days 05:34:17.990965799</td>\n",
       "      <td>...</td>\n",
       "      <td>15.222413</td>\n",
       "      <td>2.577735</td>\n",
       "      <td>6.258072</td>\n",
       "      <td>26.105423</td>\n",
       "      <td>0.547358</td>\n",
       "      <td>2.025501</td>\n",
       "      <td>2.123205</td>\n",
       "      <td>4.918358</td>\n",
       "      <td>5.079993</td>\n",
       "      <td>49.872493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.197736e+08</td>\n",
       "      <td>1.480363e+05</td>\n",
       "      <td>5.916179e+04</td>\n",
       "      <td>92.728555</td>\n",
       "      <td>713.994952</td>\n",
       "      <td>4.192497</td>\n",
       "      <td>6.628569</td>\n",
       "      <td>175.451017</td>\n",
       "      <td>17.786219</td>\n",
       "      <td>17 days 18:52:09.326775168</td>\n",
       "      <td>...</td>\n",
       "      <td>8.819130</td>\n",
       "      <td>1.820043</td>\n",
       "      <td>3.160544</td>\n",
       "      <td>13.910777</td>\n",
       "      <td>0.497758</td>\n",
       "      <td>0.754904</td>\n",
       "      <td>0.744352</td>\n",
       "      <td>3.488458</td>\n",
       "      <td>3.331319</td>\n",
       "      <td>6.445682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.940900e+04</td>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1 days 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.426141e+08</td>\n",
       "      <td>1.650000e+03</td>\n",
       "      <td>2.200000e+02</td>\n",
       "      <td>0.051667</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30 days 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.077801e+09</td>\n",
       "      <td>4.000000e+03</td>\n",
       "      <td>1.450000e+03</td>\n",
       "      <td>1.005167</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.080000</td>\n",
       "      <td>32 days 01:55:12</td>\n",
       "      <td>...</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.616992e+09</td>\n",
       "      <td>8.800000e+03</td>\n",
       "      <td>4.400000e+03</td>\n",
       "      <td>1.130000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>49.080000</td>\n",
       "      <td>49 days 01:55:12</td>\n",
       "      <td>...</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.147460e+09</td>\n",
       "      <td>2.147484e+07</td>\n",
       "      <td>1.026684e+07</td>\n",
       "      <td>15066.000000</td>\n",
       "      <td>87142.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>19311.000000</td>\n",
       "      <td>91.960000</td>\n",
       "      <td>91 days 23:02:24</td>\n",
       "      <td>...</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>54.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         project id          goal       pledged  funded percentage  \\\n",
       "count  4.184100e+04  4.184100e+04  4.184100e+04       41841.000000   \n",
       "mean   1.078276e+09  1.041084e+04  5.128665e+03           1.980354   \n",
       "std    6.197736e+08  1.480363e+05  5.916179e+04          92.728555   \n",
       "min    3.940900e+04  1.000000e-02  0.000000e+00           0.000000   \n",
       "25%    5.426141e+08  1.650000e+03  2.200000e+02           0.051667   \n",
       "50%    1.077801e+09  4.000000e+03  1.450000e+03           1.005167   \n",
       "75%    1.616992e+09  8.800000e+03  4.400000e+03           1.130000   \n",
       "max    2.147460e+09  2.147484e+07  1.026684e+07       15066.000000   \n",
       "\n",
       "            backers        levels       updates      comments      duration  \\\n",
       "count  41841.000000  41841.000000  41841.000000  41841.000000  41841.000000   \n",
       "mean      71.098731      7.950646      4.335580      8.327287     40.232153   \n",
       "std      713.994952      4.192497      6.628569    175.451017     17.786219   \n",
       "min        0.000000      0.000000      0.000000      0.000000      1.000000   \n",
       "25%        5.000000      5.000000      0.000000      0.000000     30.000000   \n",
       "50%       25.000000      7.000000      2.000000      1.000000     32.080000   \n",
       "75%       61.000000     10.000000      6.000000      3.000000     49.080000   \n",
       "max    87142.000000     80.000000    149.000000  19311.000000     91.960000   \n",
       "\n",
       "                      duration_td  ...    day_posted  dayOfWeek_posted  \\\n",
       "count                       41841  ...  41841.000000      41841.000000   \n",
       "mean   40 days 05:34:17.990965799  ...     15.222413          2.577735   \n",
       "std    17 days 18:52:09.326775168  ...      8.819130          1.820043   \n",
       "min               1 days 00:00:00  ...      1.000000          0.000000   \n",
       "25%              30 days 00:00:00  ...      8.000000          1.000000   \n",
       "50%              32 days 01:55:12  ...     15.000000          2.000000   \n",
       "75%              49 days 01:55:12  ...     23.000000          4.000000   \n",
       "max              91 days 23:02:24  ...     31.000000          6.000000   \n",
       "\n",
       "       category_dummy  subcategory_dummy  status_dummy  year_posted_dummy  \\\n",
       "count    41841.000000       41841.000000  41841.000000       41841.000000   \n",
       "mean         6.258072          26.105423      0.547358           2.025501   \n",
       "std          3.160544          13.910777      0.497758           0.754904   \n",
       "min          0.000000           0.000000      0.000000           0.000000   \n",
       "25%          5.000000          14.000000      0.000000           2.000000   \n",
       "50%          6.000000          27.000000      1.000000           2.000000   \n",
       "75%          8.000000          41.000000      1.000000           3.000000   \n",
       "max         12.000000          48.000000      1.000000           3.000000   \n",
       "\n",
       "       year_funded_dummy  month_posted_dummy  month_funded_dummy  \\\n",
       "count       41841.000000        41841.000000        41841.000000   \n",
       "mean            2.123205            4.918358            5.079993   \n",
       "std             0.744352            3.488458            3.331319   \n",
       "min             0.000000            0.000000            0.000000   \n",
       "25%             2.000000            2.000000            2.000000   \n",
       "50%             2.000000            4.000000            4.000000   \n",
       "75%             3.000000            8.000000            8.000000   \n",
       "max             3.000000           11.000000           11.000000   \n",
       "\n",
       "       country_dummy  \n",
       "count   41841.000000  \n",
       "mean       49.872493  \n",
       "std         6.445682  \n",
       "min         0.000000  \n",
       "25%        51.000000  \n",
       "50%        51.000000  \n",
       "75%        51.000000  \n",
       "max        54.000000  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['goal', 'pledged', 'funded percentage', 'backers', 'levels', \n",
    "           'updates', 'comments', 'duration', 'Posting_Date', 'month_posted', \n",
    "          'day_posted', 'dayOfWeek_posted','category_dummy', 'subcategory_dummy']\n",
    "describe_by_status = pd.DataFrame()\n",
    "for column in columns:\n",
    "    for status in [0, 1]:\n",
    "        describe_by_status[f'{column}_{status}'] = new_df[new_df['status_dummy']==status][column].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>goal_0</th>\n",
       "      <th>goal_1</th>\n",
       "      <th>pledged_0</th>\n",
       "      <th>pledged_1</th>\n",
       "      <th>funded percentage_0</th>\n",
       "      <th>funded percentage_1</th>\n",
       "      <th>backers_0</th>\n",
       "      <th>backers_1</th>\n",
       "      <th>levels_0</th>\n",
       "      <th>levels_1</th>\n",
       "      <th>...</th>\n",
       "      <th>month_posted_0</th>\n",
       "      <th>month_posted_1</th>\n",
       "      <th>day_posted_0</th>\n",
       "      <th>day_posted_1</th>\n",
       "      <th>dayOfWeek_posted_0</th>\n",
       "      <th>dayOfWeek_posted_1</th>\n",
       "      <th>category_dummy_0</th>\n",
       "      <th>category_dummy_1</th>\n",
       "      <th>subcategory_dummy_0</th>\n",
       "      <th>subcategory_dummy_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.893900e+04</td>\n",
       "      <td>22902.000000</td>\n",
       "      <td>18939.000000</td>\n",
       "      <td>2.290200e+04</td>\n",
       "      <td>18939.000000</td>\n",
       "      <td>22902.000000</td>\n",
       "      <td>18939.000000</td>\n",
       "      <td>22902.000000</td>\n",
       "      <td>18939.000000</td>\n",
       "      <td>22902.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>18939.000000</td>\n",
       "      <td>22902.000000</td>\n",
       "      <td>18939.000000</td>\n",
       "      <td>22902.000000</td>\n",
       "      <td>18939.000000</td>\n",
       "      <td>22902.000000</td>\n",
       "      <td>18939.000000</td>\n",
       "      <td>22902.000000</td>\n",
       "      <td>18939.000000</td>\n",
       "      <td>22902.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.636516e+04</td>\n",
       "      <td>5486.856735</td>\n",
       "      <td>897.392471</td>\n",
       "      <td>8.627751e+03</td>\n",
       "      <td>0.104839</td>\n",
       "      <td>3.531326</td>\n",
       "      <td>12.570885</td>\n",
       "      <td>119.498821</td>\n",
       "      <td>7.287608</td>\n",
       "      <td>8.498952</td>\n",
       "      <td>...</td>\n",
       "      <td>5.942605</td>\n",
       "      <td>5.898306</td>\n",
       "      <td>15.335921</td>\n",
       "      <td>15.128548</td>\n",
       "      <td>2.638576</td>\n",
       "      <td>2.527421</td>\n",
       "      <td>6.175088</td>\n",
       "      <td>6.326696</td>\n",
       "      <td>25.589471</td>\n",
       "      <td>26.532093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.194686e+05</td>\n",
       "      <td>12379.293920</td>\n",
       "      <td>2843.505734</td>\n",
       "      <td>7.975562e+04</td>\n",
       "      <td>0.150140</td>\n",
       "      <td>125.316534</td>\n",
       "      <td>27.160113</td>\n",
       "      <td>962.078858</td>\n",
       "      <td>3.740884</td>\n",
       "      <td>4.458296</td>\n",
       "      <td>...</td>\n",
       "      <td>3.492812</td>\n",
       "      <td>3.484802</td>\n",
       "      <td>8.868666</td>\n",
       "      <td>8.777041</td>\n",
       "      <td>1.831379</td>\n",
       "      <td>1.809111</td>\n",
       "      <td>3.104164</td>\n",
       "      <td>3.204864</td>\n",
       "      <td>13.802958</td>\n",
       "      <td>13.985239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.999830</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.500000e+03</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.656000e+03</td>\n",
       "      <td>0.002857</td>\n",
       "      <td>1.033750</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000e+03</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>3.500000e+03</td>\n",
       "      <td>0.037388</td>\n",
       "      <td>1.109823</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.200000e+04</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>740.000000</td>\n",
       "      <td>7.259500e+03</td>\n",
       "      <td>0.148536</td>\n",
       "      <td>1.320002</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.147484e+07</td>\n",
       "      <td>900000.000000</td>\n",
       "      <td>112287.000000</td>\n",
       "      <td>1.026684e+07</td>\n",
       "      <td>1.079700</td>\n",
       "      <td>15066.000000</td>\n",
       "      <td>882.000000</td>\n",
       "      <td>87142.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             goal_0         goal_1      pledged_0     pledged_1  \\\n",
       "count  1.893900e+04   22902.000000   18939.000000  2.290200e+04   \n",
       "mean   1.636516e+04    5486.856735     897.392471  8.627751e+03   \n",
       "std    2.194686e+05   12379.293920    2843.505734  7.975562e+04   \n",
       "min    5.000000e-01       0.010000       0.000000  1.000000e+00   \n",
       "25%    2.500000e+03    1300.000000      20.000000  1.656000e+03   \n",
       "50%    5.000000e+03    3000.000000     170.000000  3.500000e+03   \n",
       "75%    1.200000e+04    6000.000000     740.000000  7.259500e+03   \n",
       "max    2.147484e+07  900000.000000  112287.000000  1.026684e+07   \n",
       "\n",
       "       funded percentage_0  funded percentage_1     backers_0     backers_1  \\\n",
       "count         18939.000000         22902.000000  18939.000000  22902.000000   \n",
       "mean              0.104839             3.531326     12.570885    119.498821   \n",
       "std               0.150140           125.316534     27.160113    962.078858   \n",
       "min               0.000000             0.999830      0.000000      1.000000   \n",
       "25%               0.002857             1.033750      1.000000     29.000000   \n",
       "50%               0.037388             1.109823      4.000000     52.000000   \n",
       "75%               0.148536             1.320002     14.000000     96.000000   \n",
       "max               1.079700         15066.000000    882.000000  87142.000000   \n",
       "\n",
       "           levels_0      levels_1  ...  month_posted_0  month_posted_1  \\\n",
       "count  18939.000000  22902.000000  ...    18939.000000    22902.000000   \n",
       "mean       7.287608      8.498952  ...        5.942605        5.898306   \n",
       "std        3.740884      4.458296  ...        3.492812        3.484802   \n",
       "min        0.000000      0.000000  ...        1.000000        1.000000   \n",
       "25%        5.000000      6.000000  ...        3.000000        3.000000   \n",
       "50%        7.000000      8.000000  ...        5.000000        5.000000   \n",
       "75%        9.000000     10.000000  ...        9.000000        9.000000   \n",
       "max       70.000000     80.000000  ...       12.000000       12.000000   \n",
       "\n",
       "       day_posted_0  day_posted_1  dayOfWeek_posted_0  dayOfWeek_posted_1  \\\n",
       "count  18939.000000  22902.000000        18939.000000        22902.000000   \n",
       "mean      15.335921     15.128548            2.638576            2.527421   \n",
       "std        8.868666      8.777041            1.831379            1.809111   \n",
       "min        1.000000      1.000000            0.000000            0.000000   \n",
       "25%        8.000000      8.000000            1.000000            1.000000   \n",
       "50%       15.000000     15.000000            3.000000            2.000000   \n",
       "75%       23.000000     22.000000            4.000000            4.000000   \n",
       "max       31.000000     31.000000            6.000000            6.000000   \n",
       "\n",
       "      category_dummy_0 category_dummy_1  subcategory_dummy_0  \\\n",
       "count     18939.000000     22902.000000         18939.000000   \n",
       "mean          6.175088         6.326696            25.589471   \n",
       "std           3.104164         3.204864            13.802958   \n",
       "min           0.000000         0.000000             0.000000   \n",
       "25%           5.000000         5.000000            14.000000   \n",
       "50%           5.000000         7.000000            27.000000   \n",
       "75%           8.000000         8.000000            39.000000   \n",
       "max          12.000000        12.000000            48.000000   \n",
       "\n",
       "       subcategory_dummy_1  \n",
       "count         22902.000000  \n",
       "mean             26.532093  \n",
       "std              13.985239  \n",
       "min               0.000000  \n",
       "25%              14.000000  \n",
       "50%              27.000000  \n",
       "75%              41.000000  \n",
       "max              48.000000  \n",
       "\n",
       "[8 rows x 28 columns]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Look at the difference in standard deviation of the successful and unsuccessful projects\n",
    "describe_by_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    41841.000000\n",
       "mean        26.054994\n",
       "std          8.005827\n",
       "min          0.000000\n",
       "25%         21.000000\n",
       "50%         27.000000\n",
       "75%         33.000000\n",
       "max         37.000000\n",
       "Name: year_month_posted, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create interaction between month and year\n",
    "new_df['year_month_posted'] = new_df['year_posted'].astype('str') + \"_\" + new_df['month_posted_dummy'].astype('str')\n",
    "new_df['year_month_posted'] = label_encoder.fit_transform(new_df['year_month_posted'])\n",
    "new_df['year_month_posted'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    41841.000000\n",
       "mean        26.390956\n",
       "std          7.909144\n",
       "min          0.000000\n",
       "25%         22.000000\n",
       "50%         28.000000\n",
       "75%         33.000000\n",
       "max         37.000000\n",
       "Name: year_month_funded, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create interaction between month and year\n",
    "new_df['year_month_funded'] = new_df['year_funded'].astype('str') + \"_\" + new_df['month_funded_dummy'].astype('str')\n",
    "new_df['year_month_funded'] = label_encoder.fit_transform(new_df['year_month_funded'])\n",
    "new_df['year_month_funded'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check Skewness and test for normal distribution\n",
    "<p> Notes: \n",
    "    <ul> \n",
    "        <li>mean greater than median, positively skewed</li> \n",
    "        <li>mean less than median, negatively skewed</li> \n",
    "        <li>mean = median, symmetrical distribution </li>\n",
    "    </ul>\n",
    "        </p>\n",
    "\n",
    "<p> The normal test tests if the distribution of the variable differs from the normal distribution - This is important because the results indicate whether parametric or nonparametric statistical tests should be used. </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_vars = ['goal', 'updates', 'comments', 'duration', 'year_posted_dummy', 'year_funded_dummy', \n",
    "          'month_posted_dummy', 'month_funded_dummy','day_posted', 'day_funded', 'dayOfWeek_posted', 'dayOfWeek_funded', 'category_dummy', \n",
    "          'subcategory_dummy', 'year_month_posted', 'year_month_funded']\n",
    "y_vars = ['pledged', 'funded percentage', 'backers', 'status_dummy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "project id             -0.008691\n",
       "goal                  132.451022\n",
       "pledged               134.091965\n",
       "funded percentage     134.417943\n",
       "backers                85.971300\n",
       "levels                  2.607894\n",
       "updates                 3.907637\n",
       "comments               71.633083\n",
       "duration                1.066485\n",
       "year_funded            -0.445769\n",
       "month_funded            0.288752\n",
       "day_funded              0.057872\n",
       "dayOfWeek_funded       -0.108342\n",
       "year_posted            -0.385792\n",
       "month_posted            0.287381\n",
       "day_posted              0.052742\n",
       "dayOfWeek_posted        0.233772\n",
       "category_dummy         -0.281979\n",
       "subcategory_dummy      -0.074178\n",
       "status_dummy           -0.190294\n",
       "year_posted_dummy      -0.385792\n",
       "year_funded_dummy      -0.445769\n",
       "month_posted_dummy      0.287381\n",
       "month_funded_dummy      0.288752\n",
       "country_dummy          -5.658267\n",
       "year_month_posted      -0.773060\n",
       "year_month_funded      -0.832694\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.skew(axis = 0, skipna = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "goal: NormaltestResult(statistic=181221.97474867204, pvalue=0.0)\n",
      "updates: NormaltestResult(statistic=35981.269196937435, pvalue=0.0)\n",
      "comments: NormaltestResult(statistic=148916.91323430266, pvalue=0.0)\n",
      "duration: NormaltestResult(statistic=6185.700723610547, pvalue=0.0)\n",
      "year_posted_dummy: NormaltestResult(statistic=1166.7593889551026, pvalue=4.3794324700114634e-254)\n",
      "year_funded_dummy: NormaltestResult(statistic=1611.3934272908994, pvalue=0.0)\n",
      "month_posted_dummy: NormaltestResult(statistic=48094.14473316772, pvalue=0.0)\n",
      "month_funded_dummy: NormaltestResult(statistic=14727.479581701287, pvalue=0.0)\n",
      "day_posted: NormaltestResult(statistic=30782.809488430856, pvalue=0.0)\n",
      "day_funded: NormaltestResult(statistic=36756.83174117571, pvalue=0.0)\n",
      "dayOfWeek_posted: NormaltestResult(statistic=9129.33885408962, pvalue=0.0)\n",
      "dayOfWeek_funded: NormaltestResult(statistic=63622.4356716953, pvalue=0.0)\n",
      "category_dummy: NormaltestResult(statistic=1072.5833590653822, pvalue=1.2344766744891414e-233)\n",
      "subcategory_dummy: NormaltestResult(statistic=50316.776119196045, pvalue=0.0)\n",
      "year_month_posted: NormaltestResult(statistic=3307.224471499674, pvalue=0.0)\n",
      "year_month_funded: NormaltestResult(statistic=3773.7823995172466, pvalue=0.0)\n",
      "pledged: NormaltestResult(statistic=182011.5493629134, pvalue=0.0)\n",
      "funded percentage: NormaltestResult(statistic=182069.71940144463, pvalue=0.0)\n",
      "backers: NormaltestResult(statistic=158220.52349264896, pvalue=0.0)\n",
      "status_dummy: NormaltestResult(statistic=146072.00431160198, pvalue=0.0)\n"
     ]
    }
   ],
   "source": [
    "#Reject the null that they are normally distributed\n",
    "for each in x_vars:\n",
    "    print(f\"{each}:\", stats.normaltest(new_df[each],axis = 0, nan_policy='omit'))\n",
    "for each in y_vars:\n",
    "    print(f\"{each}:\", stats.normaltest(new_df[each],axis = 0, nan_policy='omit'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Look at Relationship between outcome variables and duration\n",
    "<p> There seems to be a consistent jump in pledged, percent funded, and backers at around 30 days duration. </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y1 = new_df[new_df['status_dummy']==0]['pledged']\n",
    "y2 = new_df[new_df['status_dummy']==1]['pledged']\n",
    "\n",
    "x1 = new_df[new_df['status_dummy']==0]['duration']\n",
    "x2 = new_df[new_df['status_dummy']==1]['duration']\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(x1, y1)\n",
    "plt.xlabel('Duration')\n",
    "plt.ylabel('Pledged')\n",
    "plt.title('Unsucessful Projects')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(x2, y2)\n",
    "plt.title('Sucessful Projects')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Independent Variance\n",
      "----------\n",
      "Unsuccessful =  349.886916767045\n",
      "Successful =  277.23816741709203\n",
      "\n",
      "Dependent Variance\n",
      "----------\n",
      "Unsuccessful =  8085524.857734167\n",
      "Successful =  6360958490.897638\n"
     ]
    }
   ],
   "source": [
    "print(\"Independent Variance\\n----------\")\n",
    "print(\"Unsuccessful = \", x1.var())\n",
    "print(\"Successful = \", x2.var())\n",
    "print(\"\\nDependent Variance\\n----------\")\n",
    "print(\"Unsuccessful = \",y1.var())\n",
    "print(\"Successful = \", y2.var())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y1 = new_df[new_df['status_dummy']==0]['backers']\n",
    "y2 = new_df[new_df['status_dummy']==1]['backers']\n",
    "\n",
    "x1 = new_df[new_df['status_dummy']==0]['duration']\n",
    "x2 = new_df[new_df['status_dummy']==1]['duration']\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(x1, y1)\n",
    "plt.xlabel('Duration')\n",
    "plt.ylabel('Backers')\n",
    "plt.title('Unsucessful Projects')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(x2, y2)\n",
    "plt.title('Sucessful Projects')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Independent Variance\n",
      "----------\n",
      "Unsuccessful =  349.886916767045\n",
      "Successful =  277.23816741709203\n",
      "\n",
      "Dependent Variance\n",
      "----------\n",
      "Unsuccessful =  737.6717491947761\n",
      "Successful =  925595.728634911\n"
     ]
    }
   ],
   "source": [
    "print(\"Independent Variance\\n----------\")\n",
    "print(\"Unsuccessful = \", x1.var())\n",
    "print(\"Successful = \", x2.var())\n",
    "print(\"\\nDependent Variance\\n----------\")\n",
    "print(\"Unsuccessful = \",y1.var())\n",
    "print(\"Successful = \", y2.var())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dlKN03FHBcZxGxlMdNRhpSqfQJdIdFRzHaXRcQTUYaV5zZ8+e6o4KjuM0FW7iazDKWRerp/RIjuM45eIKqsaMRIkkrYsVHueEQydy++oeLyroOE7D4gqqhlSqMm3ScZas2JCaI6/SCspnao7j5IGvQdWQSqVSSjpOWpxUpV3Pa51qyXGc5sUVVA2pVCqlWrqee/l3x3HywhVUDdm3va2s9jRq6Xru2cwdx8kLV1A1REn53Any6E1buJxpC5cz87K7SprLaul67tnMHcfJC3eSqCG92/pL9tm6rZ8Lb13LomXreKmvP9EJoZYpmaqRr9BxnLGJK6gaMqmjPVNevYGdRm9foMzSPP1qlZKpnvIVOo7TXLiJr4accOjIii/WmxNCVP796jOOAuCCpWuqXtnXcZzmw2dQIbWI5bn38ZGXr09zQki6Dsh3hnNx18MsWbmBeIm8agYGexyW4zQnrqCoXMBsuYzG0y3JCSHpOs5fumZIn0pf28VdD3Pjig2J2/IKDI5Tq+/OcZz8cRMftYvlyeLptudurbS1DHX3S3NCSLqOJCp5bTet3Fh0e97u5o0ShyXpOkkvSnok1naVpMclPSTp3yV1xLZdJGm9pCckzYm1zw3b1ktaGGs/WNJKSU9KWippt+pdnePkw5hSUF3dPRy/+B4OXrh8yBpJrWJ5iq1Btbe18vUzjmLdZXO56vQjM7mLlyNvpa5twIrX9s3b3byB4rCuB+YWtN0NvMXM3gr8D3ARgKTDgDOBw8N9vi2pVVIr8C3gZOAw4KywL8BXgavNbDqwFTgv38txnPwZMya+YqagNG+6vAfX5Q+9kNjeIoYooaweelm9AqO+eVMNd/NafXflYma/kDStoO2u2NsVwIfD16cBN5vZ68AzktYDx4bb1pvZ0wCSbgZOk/QY8G7gI2GfG4BFwHcqfyWOUz3GzAyqmCkoLdA178F1a0oc1E4bun5ycdfDHHLRHUxbuJxDLrqDi7seTtwv6TrSGKkHYTlUoyZVrb67HPgL4Gfh604gbjvdFLaltb8B6DWzHQXtw5A0X9IqSas2bx65k47jVIMxM4MqZgrKGstTC2+xQieEATNuXLGBG1dsoLNAhuj/omXrBuOm0hiNB2GczpQZTGdHe1WcFJohDkvSF4EdwJKoKaGbkfxAaUX6D280uwa4BmDWrFnF7bOOU2PGjIIqZQoqZUarlbdYMSeEJBmi64iUaZrJr1JrNPWQSaJWQcqVQNK5wPuBE80GF/Q2AVNi3SYDz4evk9p/C3RIGhfOouL9HadhGTMmvtGagmrlLVbKCSFNhih4tjPnXHnzZnZy5QeP8HLzI0DSXOALwKlmti22aRlwpqTdJR0MTAd+DTwATA899nYjcKRYFiq2e9m1hnUu8ONqXYfj5MWYmUGN1hRUK2+xVqmkkurp7ePghcuxsP9Zx03h8nlHAIFiXnDbWvoHdh2jrVUVneE08gymWki6CXgXsL+kTcAlBF57uwN3K8gcvMLMPmlm6yTdAjxKYPr7lJkNhMf5NHAn0ApcZ2brwlN8AbhZ0uVAN3Bt1S7OcXJizCgoGN1AWitvsbOOm5IaCBsnUj/RGhUwqKSGrUb4ykMiea4xmtlZCc2pSsTMrgCuSGi/A7gjof1pdnn6OU5TMGZMfCMlip3q6e2reH2lNPNbvP3yeUdwzuyptKbV5kghWru66s4n6N85VCP177S6C2StNV4Z2HHqD1dQRYgPWjDUXaoSay0L5sygrXWo4mkRbNu+Y0gw8eXzjuCpK9/Hs4tP4ethQtZSRGbBBgpkrSmNkpHCccYSrqCKkDRoGYFyun/huyti/omvDUEQA7V1W3/qU/y8mZ2pM684UXYkLyiYDVfkjlN/5Kqg0vKGxbZPlXSvpO4wH9n78pSnXMoZtNLSKBXjoh89VLJP0lP8gjkzEgNf4uw+rmWwb+EsrdJOEs2AK3LHqT9yU1Al8oZFXAzcYmYzCVxmv52XPCMh66A10vWLvv6dmeQoVIjzZnaW9HN4LX7snJ0kRqKc6420zBrVyLjhOE4yec6gjiXMG2Zm24GbCXKMxTFgn/D1vtRZcGHW2Km81y+SFGUpM1+0T95OEs3iXJCWWaNSGTccxymfPBVUWt6wOIuAc8K4kDuAv006UK3yh2UNQs1z/SLNU7BY3r34PmmZJLImlS1FMzgXdHX35J5xw3Gc8ikZByVpPHAhMNXMPiFpOjDDzH5aateEtkLj0lnA9Wb2T5LeDvxQ0lvMbIjtq5b5w7LETuUVI9UqpXoKxgOPe3r7BgN6OzvamfaGdi68Ze2wYoWFxx4NpVIpVUoB5k00A0zD16Acp3ZkCdT9V2A18Pbw/SbgVqCUgiqWTyziPMIaOWb2K0l7APsDL2aQKxPVSPCaVz66AbOisiYpz2IVbguPPVIK8xImMVoFWC2KFXls0KzojtM0ZFFQh5jZGZLOAjCzPinT6DOYNwzoIXCC+EhBnw3AicD1kv4I2AOomA2vWgley02jFCnNUiQN8qUUbqkKt8WOnZUslXtHowCrSTETnucUdJzakkVBbZfUTmiek3QI8HqpncxsR1LeMEmXAavMbBmB6fB7ki4Ij/+xWEbnUVNsfSTLwFPO7CtrGqUss4+IwkE+i8LNqhhGo0CyrMs0ygwqzTxbrXIhjuOkk8VJ4hLgP4ApkpYAPwc+n+XgZnaHmf2hmR0S5hbDzL4UKifM7FEzO97MjjSzowoqjI6atIG0p7evpDt0Xt5pWWYfEYWeelkcErIqhizBvmlkWZcZMGsI9/MmKnjoOE1HyRmUmd0t6UFgNoHjw2fM7Le5S1YBipVAjxTOque2cO/jm4fNkkY7+0ojq1eYYNggmcVbMEty2dEG6iatuSVxwdI1g14x1aqfVS7NUPDQcZqVLF58R4cvXwj/T5W0L/BcrMR0XVJqIO3rH2DJig2Jg2heruPFlGYcIxjgo5L082Z2ZvIWjDKY37RyIwNmtChInzTs4DHKdSQp9CDU8EMmnaYiCj4PvFyI49QnWUx83wZWELh5fw/4FUHQ7f9IOilH2UZNFMfU0d6W2idtEE0zY3WMTz9WForFLyXJ1tPbxwVL1zBt4XK2bd9BW8tQE16SOSqeXPbAfYdfRzxQdySmzLhC6+xo5+zZUzObDD2uyHGcrGRRUM8CM81slpkdA8wEHgHeA/xDjrJVjNd3ZEspFNHT28errydPDl95bceQwbvcdZZ5Mzv50DGdJXPpxYmU6NZt/fTvNMa3tWSuXltqJlhuoG2SQrt9dU/mlEAeV+Q4TlayePEdGqvaiZk9KmmmmT2dzdu8thRzSkgzTQno7etP3Ceafcyb2TliN/Z7H988qnR4ff07ufqMozKZpUqZBcs1ZaYptCyxV+584DhOOWSZQT0h6TuS/iT8+zaBeW93IHkUryOKmZTOnj11mLktTWklHTNtsL7wlrVFZ1KjNXNZeO4sLJgzY5hZsK1ll5NEuVm8R5ohohL1sxoZSddJelHSI7G2/STdLenJ8P+EsF2SvhFWAXgotg6MpHPD/k9KOjfWfoykh8N9vpExVrFiNILHptN4ZFFQHwPWA+cDFwBPh239wAl5CVYp0gbazo52Lp93xLBce1lmNtEx0wbrAbOi6ziVMHOVo+QKDZzx9+W4WY900InqZwFjeRC7njBrSoyFwM/NbDpB+EZUkuZkYHr4Nx/4DgQKjSDs4ziCZMyXREot7DM/tl/huXKjWRIGO/VHFjfzPuCfwr9CXqm4RBXmhEMnJpqfojWTQg+uqLx7GvHBO8p/l0Qxj7WsbtrFmNTRPiQfXjwXX9wL79KfrGOgwI1vYKfxxX9/eMi1Z/HiG0kC2Gi2Vq2sHvWKmf1C0rSC5tOAd4WvbwDuA74Qtv8gDFpfIalD0oFh37vNbAuApLuBuZLuA/Yxs1+F7T8A5gE/y++KdpFXSIbjZHEznw5cSVDTaY+o3czelKNcFaPcMgpJyiMy+xUO/qWyMaTNcqL9iyVzjc579uypQ1zhIVCSJxw6cYickSyFA//WbclW2Fe3D9DV3TOopLIMJOWaJjva21h06uHMm9nJ8Yvv8UFsOG80sxcAzOwFSQeE7WmVAIq1b0poH4ak+QQzLaZOnVqBS/BqxE5+ZE0WewlwNYFJ7+MkZyqvS8q9ecqZUXSWiGkqZsqbN7OzpIKaFJohZx203zB5ijl/ZC13Ua5yyBrDFbHmkl1RCPU+iFUjqXAZpFUCKLd9eGMOlQHyyubvOFkUVLuZ/VySzOw5YJGk/yJQWnXPSG6erDOKYqa6yBQ40oEvnu0hSZ4LSii3KH6qGIXKoZSs5ZgmC1Mu1dMgVnidJxw6kdtX99TC/PgbSQeGs6cD2ZXFP60SwCZ2mQSj9vvC9skJ/atCXtn8HSeLk8RrklqAJyV9WtKfAgeU2qleyDPXWrygIewalCOPNWDEi8fjWsRVdz6R6lCQZWAv9XjcItHV3UNXdw8zL7uL85euGSJrFCAcnT+pgGMaUS6+iHrJeZe0oL9kxYZaFV1cBkSeeOcCP461fzT05psNvBSaAu8ETpI0IXSOOAm4M9z2e0mzQ++9j8aOlTtZC3s6TrlkmUGdD4wH/g74MoGZ76N5ClVJqpVrTcAf7LvHkGPPvOyuEQ98ff07B2ccSU/0lXC0GDBjwW1rwRhWFh5IzaMX/+ymLVyeevykfWptRksyjaYp8kqaHyXdRDD72T+sIH0JsBi4RdJ5BKVnTg+73wG8j8B7dhuBWR0z2yLpywSlbAAuixwmgL8m8BRsJ3COqIqDRISni3LyQKWqW0g63cxuLdVWLWbNmmWrVq3K/TxZTHNJpTPa21oHZ0/F1piyxFsVMmF8G91f2rWu09Xdw4W3rK1a7aW4u3hEMQWVtk8tOXjh8syfezHZJa02s1mVk6z6VOtecpxSpN1PWUx8F2VsaxqyxnUUc68tNUsaiUrZuq1/iAzzZnays4qFAUcyo6i30u9pptFCLwNfQ3Gc2pNq4pN0MoGZoVPSN2Kb9gHqOot5OSTNlLLGddTCM61QhnI960ZD0uDe1gL9RVId1lvhwrQF/Q8d05lYdsVxnNpRbAb1PLAKeA1YHftbBszJX7T8SZsppQ34hYqnWJqgvLzTCmVIy45eabWQNqMolVGn3kq/py3ozzpov1qL5jhOAakzKDNbC6yV9G9mVvc590ZC2kwpLUNEodJJexo/4dCJLH/ohcLdK4IRZLuInvDnzexk1XNbhgXzVkItpAUox9k+UPxMo6ncmxeFC/pjPcuF49QrWbz4jpW0CDgo7C/AGiWTRDHSTHEDZrS3tQ7LJlFYUiLJM60wpiYP4gMowO2reyqikAqJlNNInRwaZR3HU/U4Tn2SRUFdS5AkdjWQ36hbA9LWbzpDRROflRiBIph10H5DBq2kXH55KqeIuLt6nucbzXpaNWJhRpsBoqu7J9WkW28OHo4z1sjixfeSmf3MzF40s99Ff7lLVgWS1m9EMDDdtHJjarXdYlQzdU9Pb1/FBtH2tuSfQqm1tN3HJe+3526tVVFOo8miHe2fRr05eDjOWCOLgrpX0lWS3i7p6Ogvd8mqQGEmiHhsUtrifikFVE/5x7KWlofkqsOlTHRd3T2p1Yq3bc9/FlluNeAs+8epNwcPxxlrZDHxHRf+jwdRGVA/0ZejIDLRlSqzEVFKAS2YM4MLlq7JZU2oXHYf15LZ/FeYSCKeiTyNYoogD0VdaM7L6m2ZRql+9ejg4ThjiSz1oOq+KGElyDqo9fT2DfGii4jXZqoX0srWZ2HP3ceVNNEV+8wq7RyR5GmXlo0jq3IspuTiyXodx6kNJU18kt4o6VpJPwvfHxbmDmsqynniL1zr6OruYcGta+tKOY2WLAq72Gf2rXufTN2WVB68VMnwtBx6o8kAkRZDBtA/YKx6bkviNsdxqkOWNajrCbIoTwrf/w9BAtmmYsGcGbS1Zl8Uj691LFq2LjHZar0j0h0BsijsYorgyRdfTWxPcmxYcOtaFty2tqizQ5rCjFzhy8miHSnDC5auYfdxLbSkfO1LVmzwsuWOU0OyrEHtb2a3SLoIwMx2SGoad/P4uka5RPuMxpRWS55ZfEpqwtsss5AsRRdh6GfckhAEnaTcC3NUDnQAACAASURBVOOQioUElBOnVXi9xb47o/yijo7jVI4sM6hXJb2B0Nwf1afJcnBJcyU9IWm9pIUpff5M0qOS1kn6t8ySV4DCp/ly50D15LFXLtHMqZxaPqXMcEl0dfcMmR2V4xkXf2ioVD2pUp57xWRwHKe6ZJlBfZYg/94hku4HJgIfLrWTpFbgW8B7CSp+PiBpmZk9GusznSAz+vFmtlVSVQshljtYxWlraexF9LiiyFLLp1g6oGJc+pN19JdIh5RG/AEgSz2pLEG75SqcRn4IcZxGJ4sX34OS/gSYQbBs8UTG3HzHAuvN7GkASTcDpwGPxvp8AviWmW0Nz/XisKPkSJbBKrVuU5PHcBYO9r3btifGHJVi67bSP5W2FoEYosiSZkfFFGnWfHrlZn9v5IcQx2l0snjxfQrYy8zWmdkjwF6S/ibDsTuBjbH3m8K2OH8I/KGk+yWtkDQ3RYb5klZJWrV58+YMp85G2tNxqzRo7rr6jKMS42H6B6waJcFzZdrC5cy87K5hprokR4ZXcwi8jT7jq04/kqs+fOSoSoZnDdot5rmXhK8/OU7tyGLi+4SZfSt6E5riPgF8u8R+SXOMwsnIOGA6QSnsycB/SXqLmfUO2cnsGuAaCKqAZpA5E2nZyAsHxwtSHAGimKiRVMfNyvi2FibsuXtuLuxbt/UHZd8ZakarVH6/jva2REeEjvY21lxy0pC20SiDrLW5kkyFxT7bru4eV1KOUyOyOEm0KFb0J1xb2i3DfpuAKbH3kwlqTBX2+bGZ9ZvZM8ATBAorFwoX+YFMDgLF1iGigNG86OvfmbuZqXA2WEnHgEWnHh6Y8GK0tYhFpx5esXNA8dpchcyb2cn9C9/NM4tPYcGcGbSm+ZlTPFtGpZB0Qegk9IikmyTtIelgSSslPSlpqaTdwr67h+/Xh9unxY5zUdj+hKSmqNnmjG2yKKi7gFsknSjp3cBNwH9k2O8BYHp4o+0GnEngbBGnCzgBQNL+BCa/p7MKXw5piUWBwcHq/oXvTnxaLiyzUchOgmSreSQXrdYifVwpZT1nlqudN7OTq04far676vQjM89KsnoOjtTL76o7n2CgSAxb3l58kjqBvwNmmdlbgFaCe+WrwNVmNh3YCkTB8ecBW83szcDVYT8kHRbudzgwF/h2+DDpOA1LFgX1eeDnwF8Dnwpff77UTma2A/g0QZDvY8AtZrZO0mWSTg273Qn8TtKjwL3AgrwypY8msei9j5de93qtfydPXfk+OtrbRixjIVENqmo8xceVUtZ1mqxmzfiMJe0hIIlyspWX4y4fp5TptEoPCOOAdknjgPHACwS5Lm8Lt98AzAtfnxa+J9x+YmjhOA242cxeD60R6wkclRynYSm6BhU+gd1gZucA3y334GZ2B3BHQduXYq+NwI39s+Ueu1yyrlFExL3YsgzE+7a3cfzieyoatGvA0l9vzD1LRYtg2/YdHLxw+aB79pUfPGJYIcabVm6saobvcgsJZnGXLyStejJUJx+fmfVI+kdgA9BHYLFYDfSGD3kw1MFo0PkoDJp/CXhD2L4idugkpyQkzQfmA0ydOrXi1+M4laSogjKzAUkTJe1mZturJVQepC2GJz0hX9z18LAS6qXo7esvqZxG4kxRjRRKkgbdwXt6+1hw21qu+vCRwzI0LFmxIXdZ4pT7UDESiincthbl7iAhaQLB7OdgoBe4FTg5oWskaJrzURanpNwcjhwnD7KY+J4F7pf0fyV9NvrLWa6Kk3WNoqu7J5NyGslqU72OBoVrMP0DxqU/WTesX7nmrtHmsSvH8WGkFCupsa1/ZzVy8b0HeMbMNofxhT8C3gF0hCY/GOpgNOh8FG7fF9hCNqckx2kosiio54Gfhn33jv01FFnXKK6684miiiQeHzVhfOXWm+qNrdv6hzkllBtDVE512yQqld6oGKUcYKqw/rcBmC1pfLiWdCJBMPu97MrYci7w4/D1svA94fZ7QlP5MuDM0MvvYAJv2F/nLbzj5EmWTBKXAkja08ySU1Q3CFnWKIqZjwoTk2ZJlFrPtLe1Fo13ipwSVj23hXsf38zzvX10jG9j93Etmdbaiq0XFVIsTVGp9EUjOWZEKQeYvL34zGylpNuAB4EdQDeBCW45cLOky8O2a8NdrgV+KGk9wczpzPA46yTdQqDcdgCfMrOmSersjE1KKihJbye4KfYCpko6EvgrM8uSTaKuyDJgpa1VieZLe/OhYzr56doXiiqbvv6BISbPrdv6aW9r5ZzZU7l9dU/JgN4sAcal0hSNZB0oa+qjevDiM7NLgEsKmp8mwQvPzF4DTk85zhXAFRUX0HFqRBYT39eBOcDvAMxsLfDHeQqVB1ldlpPMSgLOnh14PB2/+B6mLVzOIRcNcU5sSJb+eiOHTyptrS00efb1D3Dv45u58oNHlNw3HgObFtM0mhCANLIes1jsWoua76HEcRqJLKmOMLONGnojN5zpIKvLcppZCRjyRF5Nd+u86N9p3P/UyKrGPt/bl6keVOR/UWxGU8pbr9jMN21bVg/AYt/jToNVz23xVEeOUyOyKKiNkt4BWJgR4u8IAm8biiyD4KU/WTck+3ZHe9tgoGwzlXOvBOWavtIeEC68ZS37puTrm9TRnqjYLli6hvOXrmHC+DZeeW3HoCt+XOllDSsoFgcFcNPKjVw+r/RM0XGcypPFxPdJggwSnQSurEeF7xuKYi7LUVG9wtIQvX393Lhiw5hXToVGsJF40qU9IAyY8er2HcPy9UXnSFJs8fWwwjixaFac1QOw1Ey4GWbKjtOoZFFQmNnZZvZGMzvAzM7JKx1RnqS5SEdP5CMtqjcWiEeBjqQUBhSfcfUPGHvtMS4xBGAkXnSR+TFLWEGp1FR55Fd0HCcbqSY+SR8ArgN2SBoA/szMflk1ySpMNDB98d8fHlbbyFVTaYzhbvblkFTaJE7vtn66v3TSsPZyCwxG+0A2D8BSXoiz3zShrHM7jlM5is2grgD+j5kdCHwIuLI6IuXHvJmdvNa/s9ZiNCyFs5mLu0qXfB/fFvzEohlN2owkbYZVbnBwOebHru4eXt9R/Pfw7O/GtnnXcWpJMSeJHWb2OAwGEzZc9ogkfE1h5ERKJPKcyzKz2da/k+MX3zPE8y6pSGSaUon2KeYt2NHexkt9/WUH8mZxY887UNdxnHSKKagDCnLuDXlvZl/LT6z8KOW1lTfj21r44DGTWbJyA7XWleV8FpESKfSqy0JSkGw52SHmzexMVYhJlXmzkkX5VKsel+M4wylm4vseQ3PvFb5vSM46bkrpTjmyrX8nN67YwKR9az/wlfosOtrbhjkYjLQcfDxIdiT1odK88kZTmTeL8vFAXcepHakzqCgHX7MRxbSUW06j0tSD6/rtq3uYfsCePPlicopFKagU3NPbx/lL13DB0jWj+sxGYy4bbV6+JBbMmVHUdNje1uJBuo5TQzJlkmg2Lp93xKCiOnjh8jHrxdfXP8C27elOAoVxYaP9nErNWErlShxpXr5itABpn8D2AaOru8eVlOPUiExxUM3MWF9jeL63r2hNpEpRyruunPLuleKqO59IVU4Q1MlKqovlOE51GPMKasGcGcOyGIwlDHj19R0l+42GCePbSgb35pEwthRZTI6Fs0jHcapHsUDdolVzG9WLr5Bo0Fy0bN1gPjiJinnY7blbKx3jd6Ont29EJd+rQZbaTiOlo70tMQC3kGqUdy9kJEHAjuNUj2JrUJGn3gzgbQQVOwE+APwiT6EqycVdD3PTyo0MmAWL/uNa6OvfOWSNI2lt4/jF91Rk8GprbRk0o51w6MTBwn/1qKjy4PBJew+Jm4pc2zsL1pjSlIXBsDiqSlHKSQJKp0JyHCc/Uk18ZnZp6Mm3P3C0mV1oZhcCxwCTqyXgaLi462FuXLFhMNbHLHDzLrbGEdUsqtSTdW9f/+D5lqzYwLQ3tDfMuldb6+hNn798esvg2hLsCpQu/PyLZYzIaz0qi8J7/5EHVvScjuNkJ8sa1FRge+z9dmBaLtJUmJtWbiy6va9/gEXL1g0W0Zt52V0suHVtUeUUDdlRyp5yHAwMuP+pLQ1hVpowvo0z3jZlWCbziLaMq5dm6fnuCmOjouSupfpWkgnji8+QSpWErwSSOiTdJulxSY9Jeruk/STdLenJ8P+EsK8kfUPSekkPSTo6dpxzw/5PSjo3d8EdJ2eyDDM/BH4taZGkS4CVwA/yFasyZMmS0NvXP+g5llS+oZDxu7Xy7OJTeOrK9/Hs4lOaNpBz/G7juPfxzYmmSAF77VEZ01d8jSkK4E1TinmsRx12YPGY8yo9TPwz8B9mdihwJEG9tYXAz81sOvDz8D3AycD08G8+8B0ASfsRlI0/jqBU/CWRUnOcRqWkgjKzK4CPA1uBXuDjZvaVvAWrBHmUSohnQu/q7mHBrWsrfo56oKe3L1UhGEH28UoQrTHFzXdpJlADZl52VyZTX1p5+UJWPL216HHyLrchaR/gj4FrAcxsu5n1AqcBN4TdbgDmha9PA35gASuADkkHAnOAu81si5ltBe4G5uYqvOPkTFY38/HAy2b2z8AmSQfnKFPFyCutUVd3D13dPUEdqRIzrmaks6Oy62jlrEdt3dbPgtvWFlVS5cRU1UHBwjcBm4F/ldQt6fuS9gTeaGYvAIT/Dwj7dwJx2/WmsC2tfQiS5ktaJWnV5s35my8dZzSUVFChWe8LwEVhUxtwY55CVYrL5x3BObOnDj4FS7vKP4zmyfiCpWu48Na1Te+Jl3R9UcBtpU2bSetRad9R/4AlrkdFs6bzl64pGlMVn12VogoFC8cBRwPfMbOZwKvsMuclkSSQFWkf2mB2jZnNMrNZEydOHIm8jlM1ssyg/hQ4leDGwcyeJ2OyWElzJT0RLuim3nSSPizJJM3KctxyuHzeEYPrRc9ceQpf+eBbaW9rHdWTsRFkGRhrtEpDAm4rHd/c09s3aI6bN7OTnUW+o0LzY3zWVGyfwtlVKaowg9oEbDKzleH72wgU1m9C0x3h/xdj/eOmgcnA80XaHadhyaKgtpuZET6NheaHkkhqBb5FsKh7GHCWpMMS+u0N/B2B80XujDQbtxMM1ouWrWPawuWcv3QNeejouDmumBmxcFuW73VSR3vZ33/eaaDM7H+BjZKiKemJwKMEcYeRJ965wI/D18uAj4befLOBl0IT4J3ASZImhM4RJ4VtjtOwZFFQt0j6F4LF2E8A/x9B6Y1SHAusN7OnzWw7cDPBAm8hXwb+AXgto8yjwgvQjY48s05EROa4Ew5NNkG1tmiYibHU9xqZJsv9/qvkpfm3wBJJDwFHAV8BFgPvlfQk8N7wPcAdwNPAeoL78G8AzGwLwb30QPh3WdjmOA1LyWzmZvaPkt4LvEyQVeJLZnZ3hmMnLdoeF+8gaSYwxcx+KulzaQeSNJ/ApZapU6dmOHU6nt6mMXi+ty81Bmnv3ccNC7It9r3Gs1ZkrQQcUY1M5ma2Bkgyb5+Y0NeAT6Uc5zrguspK5zi1I5MXn5ndbWYLzOxzGZUTlFi0ldQCXA1cmOH8FVvYLeYh5iQz2qWmCePbyl6vmtTRnjrbeSlhFpdW0PDrZxw1pCiif/+O0zikKihJv5f0ctpfhmOXWrTdG3gLcJ+kZ4HZwLI8HCXiFGYsGLt5zLMz2qWmSz5weFnrVZE5Lm0NKqk9/r0WVgFO6uc59hyn/ilWUXdvAEmXAf9LkFFCwNlk8+J7AJgexkz1AGcCH4kd/yWCPH+E57kP+JyZrSr7Ksoknhy2q7unZMLQSlOvWc3zYt7MzqKf8YTxbZgFM6N929uQAlf+jvFttLVoSKyZYMjaVKkih2nyzJvZyczL7ipZTsMLFjpO7chSUXeOmcXXjr4jaSWBY0MqZrZD0qcJPIlagevMbF2o8FaZ2bJi++dNfGCLMmxXi7GknFoUZIpII16OI3IBj7zskpSHEZSqn3XQfgBD+kcegJBt7eiSDxzOglvXFg22vurOJ1xBOU6NyKKgBiSdTeCFZ8BZQCY/XTO7g8DrKN72pZS+78pyzEpwcdfDLFmxYVBRVFM5QWB+GislN3Za8Xx2vX39g+U0srqA9/UPcOEtaxO/t8gDsJhSiT+clLLxuten49SOLE4SHwH+DPhN+Hc6MVNdo9HV3TNEOVWbttbARXosKKesRDOfcrzrij1U9PT2pebgKwzULfVs0iilURynGcniZv4syfFLDUP8iblFqqly6B8wLv3JuhpKUJ/09Q9U1NQaz8EHu0x+5QbqNmu2esdpBEoqKEkTgU8Q1IAa7G9mf5GfWJWjcF2j2ua8JEotzI9V8vhuCk1+5ZrsVj23xdegHKdGZDHx/RjYlyCDxPLYX0Nw6U/WeWqjBqGzo71kAcGREFdK5ZrsShW9dBwnP7IoqPFm9gUzu8XMbo/+cpesAnR19/hspUGIYp8u+cDhZQfSRgG5aXnz4kqp3EDdephxO85YJYuC+qmk9+UuSQ7kUSI8wgN8K0erxIeO6RyMT4oH3JaaUcUDctOyScTXkebN7ORDx2Q32VWh3IbjOClkUVCfIVBSfWEWid9nzCRRc/LMuffM4lNyO/ZYY8CM21f3DHrcRaXfn1l8CuN3S18mFQxJY1SYJaRVGlyDinvzpeX4S2L2m7xquuPUiixefJlqP9UjeQbgFgs+dconLX6p2ENG3HTX1d3DpT9ZN8SkG333hd585ThKPLjhJc8m4Tg1IktF3T9O+quGcKMlz/UDz4heeZIURzETW2S66+ruYcFta4uuN8Yr6pbjKBHfz3Gc6pIlk8SC2Os9COo8rQbenYtEFSCKe3IaiyTFUewhIx7b1D9Q+mGkJ6you2DOjLLyL3o2CcepDSVnUGb2gdjfewkykP8mf9FGRpbS3079IZKDYtM88+Lt5XzXkamvHN8HzybhOLUhUz2oAjYRKKm6pJxMAZ0d7Zwze6p75NUBZ8+emrjOU8wzr6u7h6Muvaus80Qmu7OPy1740rNJOE5tyJJJ4v+xKwF3C0FJ6rV5CjUasppjRPDkfe/jmzl79lRuWrnRY15yQiqe8+6c2VO5fN4RidsipRV3gNh9XAurntvC7at7RhSE/Xxv3+D5blyxoWR/d5BwnNqQZQa1imDNaTXwK+ALZnZOrlKNgmLmmPiCezRe9vT2cfvqHldOOVKqOGCacorzWv/Owde9ff0sWbFhxBlCOsa3cfzie1iyYkNZpr48kdQqqVvST8P3B0taKelJSUsl7Ra27x6+Xx9unxY7xkVh+xOS5tTmShynchSrqDsVwMxuiP0tMbP7qyde+RTLFJCmhPr6B9zMlyPFvOuiQNyu7h6OX3xPYhbyJLNtlseJthbR1jr8m926rT9zNvMq8hngsdj7rwJXm9l0YCtwXth+HrDVzN4MXB32Q9JhBEVBDwfmAt+W5LXtnYam2AyqK3ohqSFSG8HwYM2s1M84Nba45AOHDyuB0dPbx4Lb1nLUpXdx8MLlI3J46Whv44xjp7BnkUDfekHSZOAU4PvhexF4yd4WdrkBmBe+Pi18T7j9xLD/acDNZva6mT0DrCfwuHWchqWYgoo/er4pb0EqSZSJwGdF9UPhrFYEa0/zZnYmzpD6B4zevv6yHxz23K2Vjva2QTNgb19D5GL8OvB5ILJjvgHoNbMd4ftNQLQQ1glshKBqNfBS2H+wPWGfQSTNl7RK0qrNm7Nn1HCcWlBMQVnK64bB3YPrh6jeEwTek1efcdTg2lMl4owEHH/Ifuw0BpVSI/xoJb0feNHMVsebE7paiW3F9tnVYHaNmc0ys1kTJ04sW17HqSbFFNSRUe494K3h64bKxVdu5monXwbMBl3E455x7W0jiXYYigErnt5a8dIqLflPw48HTpX0LHAzgWnv60CHpMg+ORl4Pny9CZgCEG7fF9gSb0/Yx3EaktSRwcxazWwfM9vbzMaFr6P3+1RTyJFSmBk7inuK3num6upTmDro4q6H2Rbz0BsNeXhiHjJxz4ofM46ZXWRmk81sGoGTwz1mdjZwL/DhsNu5BHXZAJaF7wm332NmFrafGXr5HQxMB36dq/COkzP1v4I8SqISDkl0dfdwwdI1DWEKqjdKxTYVo6e3j4MXLmdSR3tF0wiVSg7c1iJ2G9fCq9uzz7Ke3rytEqKNhC8AN0u6HOgGrg3brwV+KGk9wczpTAAzWyfpFuBRYAfwKTPzSp1OQzN620oDM29mJ+84ZL9ai9GQjHayEnnrVerhoL2tlbOOm5LqGNMqcdXpR7Lusrl8/YyjEt3Pk6hmfJyZ3Wdm7w9fP21mx5rZm83sdDN7PWx/LXz/5nD707H9rzCzQ8xshpn9rGqCO05ONP0Mqhhd3T08uOGlWovhjJLOjvYh61pLVmwYovja21q58oOBQ8bxi+8JZm1u3XWcuqcpFVSUzfz53j4mFQxeccrJ2+fUJ1HRwojL5x3BrIP2G/b9Q5AodvD7druu49Q9TaegoqDPaCAqLFYXx8soND5JoQRJ647HL77HH0Ycp8FoujWopFlRWtG5YnFSbgFqDE44dGLRNEkRXn7FcRqPplNQabOieHs0oKUNWu1tLW4BahBuXLGB85euGZIm6aIfPTxESXV194z4gaNFJCo8x3HyJ1cFJWlumFl5vaSFCds/K+lRSQ9J+rmkg0Z7zrRZ0aSO9sH6QdGAlsbu4zy4t5EpnDFfdecTI37g2GkMU3iO41SH3BRUmEn5W8DJwGHAWWHG5TjdwCwzeytB4st/GO150wrcnXDoRC760cOZcrM1SP42pwjxGfNo1xrTTMRO/ZHF3Os0DnnOoI4F1ofxHNsJ0ricFu9gZveaWRQJuYIgPcuoSMoeceUHj+Dexzf7InmdsudulZ+xxmfSlcjJ6A419U9SVvyxOPttJiWdpxdfUnbl44r0Pw9IDC6UNB+YDzB1aulS3UleXBcsXVNyP6c2lJPZIQttrRpSpn3BnBlDXcxHgCcern+KOUiNlarI5XgxNwJ5zqAyZVcGkHQOMAu4Kml7JTIw+wAzhij4lc2b2cmHjukcsaNElODWqW+yOEg1O+V4MTcCec6gMmVXlvQe4IvAn0TpXCpBYbDuCYdO5PbVPW7mq0Oi+k2Von+nDXlq7uru4aaVG0fsKHHlB49oyKfPscakjvZE56ex9HDabEo6TwX1ADA9zKzcQ5DU8iPxDpJmAv8CzDWzF0d7wkgp9fT2IXY9SPf09nH76h4+dEwn9z6+med7+9i3vY2XX+tnp/uT15xFpx7O+RU2wfb09jFt4fIhv4OR4sqpMUgy5Y612W+zKencFJSZ7ZD0aeBOoBW4Lsy4fBmwysyWEZj09gJuDapWs8HMTh3J+Qptr4WDUl//APc+vnlIWpzCfZzqIwIFsOq5Ldy4YkPZ+5fKYD5a5ZQxp6xTB0QPElnSnDUrzaakc011ZGZ3AHcUtH0p9vo9lTpXlrx6hdPc6Id76U/WsXWbu5bXAgMOXricdxyyHy2i7BntWcdNydV0O2DBg8xYGuQamWLldcYCzaakmyYXXxYba3yaW7hGtf9eu/Hki6/mKaKTggH3P7VlRPsWJofNw2LbyF5QztijmZR00yioNNtrRHyam+SK6TQm0xYup6O9LSigmNM5xpqrsuPUC02Tiy8pg0REq8SHjgkGl+MX38P5S9f4ulMT0dvXn7uJtlG9oBynkWkaBRXPIAFDg7AGzFj6640suG2tz5acEZGXF5SkKZLulfSYpHWSPhO27yfpbklPhv8nhO2S9I0wv+VDko6OHevcsP+Tks7NRWDHqSJNo6AgUFL3L3w3nR3tw8w9/TuN/gH3KXfKJ2cvqB3AhWb2R8Bs4FNhzsqFwM/NbDrw8/A9BLktp4d/84HvQKDQgEsIsrUcC1wSKTXHaVSaSkFFuDnGqSR5Buqa2Qtm9mD4+vfAYwRpwk4Dbgi73QDMC1+fBvzAAlYAHZIOBOYAd5vZFjPbCtwNzM1FaMepEk2poBo1KM0Z20iaBswEVgJvNLMXIFBiwAFht6Qcl51F2gvPMV/SKkmrNm/eXOlLcJyK0pQKqpjDhOOUSzUyYkvaC7gdON/MXi7WNaHNirQPbahAXstmppkygTcDTamgCh0mHGc05J1sU1IbgXJaYmY/Cpt/E5ruCP9HqcDSclxmyn3ppOPlOuqPplNQ0RPQBUvXsG37DtpaPFeNM3ryWtdUkOPrWuAxM/tabNMyIPLEOxf4caz9o6E332zgpdAEeCdwkqQJoXPESWHbmGOks6BmywTeDDRNoC4MD8D19EVOpchxXfN44M+BhyVFGXP/HlgM3CLpPGADcHq47Q7gfcB6YBvwcQAz2yLpywRJmgEuM7ORpedoYEZTD6nZMoE3A02loLLk43OcciksgFhJzOy/SV4/Ajgxob8Bn0o51nXAdZWTrr4pTFe2YM6MURUtbLZM4M1AU5n4/EnHyQUPn6s70taL0gLxs4wNSc5VjZwJvFZU0tGkqRSUP+k4eRAVQHTqh7SZUquSJ6NZxoa4c5WAzo52L1ZZJpV2NGkqE19SLRTHqQQ+O68v0r6PATPa21pHXA+pmTKB14LRmFiTaCoFVVgLpaVEMTvHycq+7W21FmFMU7je1DG+LdEJqrOjnRMOnchNKzcyYDaYKLpSSidp3csV2i4q7WjSVAoKhj4BHbxweY2lcZqFFMuRUwWSPPPaWkRbq4bk12xva2XaG9pZsmLD4LLhgBm3r+5h1kH7jVqRjMZDcKxQaUeTplqDKsTXpJxK0eshCzUjyWzUv9PYc7dxQ9aLPnRMJ798asswn5ZKxTJ5nFRpKu1o0nQzqIiu7h62bd9RazGcJsEfdmpHmnnopb5+1lxy0uD74xffk+pwWYk1RI+TKk2lS843pYIqnIo7zmhwV+PaktVsVExRVOIBI02OFomu7h4384VU0tGkKU18HrDrVBJ3Na4tWc1GaUpI4THyAfxtVQAACahJREFUkAOCda5q5uwbSwltm0pBRV+cV811Kokrp9qSNT4pSYEIOHv21Ip8h5EcSbFW1VqLGmsJbZvGxOdmPcdpXrKYjebN7GTVc1uGuJifddwULp93REXluGDpmsRt1ViLqnScUb3TNDMoN+s5ztimq7uH21f3DMY+Ri7mlZ5dpJkSq+FIM9YcNZpGQblZz3HGNtVyA69lzr5aKsda0BQKqqu7JzUdtOOMlou7Hq61CHVNvSzap80iKv3wmmfOvlKf5VhLaNsUa1BX3fmEJ5x2cuPGFRsAKrqW0SzUOrtCPPVQWmozETxk3Pv45oqlKMojZ19Xdw8Lbl1L/87gGnp6+1hw69rB88X/j5V0S7kqKElzgX8GWoHvm9nigu27Az8AjgF+B5xhZs+Wex437zl5s2TlBldQCaSZ1S68ZejAmgeFyjEt76bBkPRH9ZqiaNGydYPKKaJ/p7Fo2bohco6lhLa5KShJrcC3gPcCm4AHJC0zs0dj3c4DtprZmyWdCXwVOKOc80TmPZ9BOXniOYeTKZZVvFAJVDrRajmOUWnpj+bN7CxLrjyTxfb2JafTSmsfC+Q5gzoWWG9mTwNIuhk4DYgrqNOAReHr24BvSlJYNTQTbt5znMpSziCcll0BhiqBs7/3K+5/alcF+p7ePs5fuobzl66hNTTNRZnIs5riRuu51tPbx7SChNJxuToLzl9rc2alyEPJxo/Z3tZC346dmDFqV3+VoQvKO7D0YWCumf1l+P7PgePM7NOxPo+EfTaF758K+/y24FjzgfkAU6dOPea5554b3HbwwuWuoJyq8OziUwZfS1ptZrNqKE4ipczqcWbNmmWrVq0a0tbV3cP5KXE+jjNaph+wJ3d/9l3D2tPupzy9+JIc6wp1SZY+mNk1ZjbLzGZNnDhxyLZmda906ouOBqgHFTOrnwwcBpwl6bByjuHKycmTJ198lfd+7b7M/fNUUJuAKbH3k4Hn0/pIGgfsC2yhDNLyYzlOpWgBFp16eK3FyMKgWd3MtgORWd1x6oYnX3w1c988FdQDwHRJB0vaDTgTWFbQZxlwbvj6w8A95aw/wdCYBMepNB3tbXztjKMaZY2hE9gYe78pbBtE0nxJqySt2rx5c1WFc5xyyc1Jwsx2SPo0cCeBPfw6M1sn6TJglZktA64FfihpPcHM6cyRnGssuV06ThFKmszN7BrgGgjWoKohlOOMlFzjoMzsDuCOgrYvxV6/BpyepwyOM4bIYlYvyj67t/Ly657T0smP6QfsmblvU6Q6chwHyGZWL8pDl85ln919TdfJhzQvvjSaItWR4zjpZvVyj/PQpXMrLpvjjARXUI7TRCSZ1R2nUXETn+M4jlOXuIJyHMdx6hJXUI7jOE5d4grKcRzHqUtySxabF5I2A88lbNof+G1Ce71Q7/JB/ctYT/IdZGYTS3erXxr4XoL6l7He5YP6kjHxfmo4BZWGpFX1mF06ot7lg/qXsd7laxYa4XOudxnrXT5oDBndxOc4juPUJa6gHMdxnLqkmRTUNbUWoAT1Lh/Uv4z1Ll+z0Aifc73LWO/yQQPI2DRrUI7jOE5z0UwzKMdxHKeJcAXlOI7j1CUNr6AkzZX0hKT1khbWWh4ASVMk3SvpMUnrJH0mbF8kqUfSmvDvfTWU8VlJD4dyrArb9pN0t6Qnw/8TaijfjNjntEbSy5LOr6fPsBmpt/upEe6lUJ66vZ8a+V5q6DUoSa3A/wDvJSjW9gBwlpk9WmO5DgQONLMHJe0NrAbmAX8GvGJm/1hL+SC4oYBZZvbbWNs/AFvMbHE4OE0wsy/USsaYXK1AD3Ac8HHq5DNsNurxfmqEewka535qtHup0WdQxwLrzexpM9sO3AycVmOZMLMXzOzB8PXvgceARqhJfxpwQ/j6BoKBoB44EXjKzJKyHjiVo+7upwa+l6A+76eGupcaXUF1Ahtj7zdRZz9eSdOAmcDKsOnTkh6SdF0tTWiAAXdJWi1pftj2RjN7AYKBATigZtIN5Uzgptj7evkMm426vp/q+F6CxrmfGupeanQFpYS2urFZStoLuB0438xeBr4DHAIcBbwA/FMNxTvezI4GTgY+JemPayhLKmHp8lOBW8OmevoMm426vZ/q/F6CBrifGvFeanQFtQmYEns/GXi+RrIMQVIbwQ21xMx+BGBmvzGzATPbCXyPwKRSE8zs+fD/i8C/h7L8JrT5R7b/F2slX4yTgQfN7DdQX59hE1KX91O930uhPI1wPzXcvdToCuoBYLqkg8OngzOBZTWWCUkCrgUeM7OvxdoPjHX7U+CRassWyrFnuOCMpD2Bk0JZlgHnht3OBX5cC/kKOIuYSaJePsMmpe7up3q/l0JZGuV+arh7qaG9+ABC18ivA63AdWZ2RY1FQtI7gf8CHgZ2hs1/T/ADOYrAbPIs8FeRjbrK8r2J4CkPYBzwb2Z2haQ3ALcAU4ENwOlmtqXa8kVIGk+wJvImM3spbPshdfAZNiv1dj/V+70Uylj391Oj3ksNr6Acx3Gc5qTRTXyO4zhOk+IKynEcx6lLXEE5juM4dYkrKMdxHKcucQXlOI7j1CWuoOocSQNhpuF1ktZK+qykin1vkj4maVLs/fclHVap4zuO44wUdzOvcyS9YmZ7ha8PAP4NuN/MLinjGK1mNpCy7T7gc2a2qhLyOo7jVAqfQTUQYRqV+QQJHhXOfr4ZbZf0U0nvCl+/IukySSuBt0v6kqQHJD0i6Zpw/w8Ds4Al4SytXdJ9kmaFxzhLQY2bRyR9NXaeVyRdEc7oVkh6YzU/B8dxxgauoBoMM3ua4HsrlRl5T+ARMzvOzP4b+KaZvc3M3gK0A+83s9uAVcDZZnaUmfVFO4dmv68C7yaINn+bpHmxY68wsyOBXwCfqOAlOo7jAK6gGpWkrNOFDBAk2Iw4QdJKSQ8TKJ3DS+z/NuA+M9tsZjuAJUCUoXk78NPw9WpgWlbBHcdxsjKu1gI45RHm/RogyIy8g6EPGXvEXr8WrTtJ2gP4NkHFz42SFhX0TTxVkW39tmvxcgD/HTmOkwM+g2ogJE0EvktgrosSPB4lqUXSFNLT5UfK6LdhXZ0Px7b9Htg7YZ+VwJ9I2j8sE30W8J8VuAzHcZxM+JNv/dMuaQ3QRjBj+iEQlR24H3iGINPzI8CDSQcws15J3wv7PUtQViHieuC7kvqAt8f2eUHSRcC9BLOpO8ys1uUCHMcZQ7ibueM4jlOXuInPcRzHqUtcQTmO4zh1iSsox3Ecpy5xBeU4juPUJa6gHMdxnLrEFZTjOI5Tl7iCchzHceqS/x8D++bfIkIu4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y1 = new_df[new_df['status_dummy']==0]['funded percentage']\n",
    "y2 = new_df[new_df['status_dummy']==1]['funded percentage']\n",
    "\n",
    "x1 = new_df[new_df['status_dummy']==0]['duration']\n",
    "x2 = new_df[new_df['status_dummy']==1]['duration']\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(x1, y1)\n",
    "plt.xlabel('Duration')\n",
    "plt.ylabel('Funded Percentage')\n",
    "plt.title('Unsucessful Projects')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(x2, y2)\n",
    "plt.title('Sucessful Projects')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Independent Variance\n",
      "----------\n",
      "Unsuccessful =  349.886916767045\n",
      "Successful =  277.23816741709203\n",
      "\n",
      "Dependent Variance\n",
      "----------\n",
      "Unsuccessful =  0.022541962935768396\n",
      "Successful =  15704.233585570006\n"
     ]
    }
   ],
   "source": [
    "print(\"Independent Variance\\n----------\")\n",
    "print(\"Unsuccessful = \", x1.var())\n",
    "print(\"Successful = \", x2.var())\n",
    "print(\"\\nDependent Variance\\n----------\")\n",
    "print(\"Unsuccessful = \",y1.var())\n",
    "print(\"Successful = \", y2.var())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = new_df[['duration',\"status_dummy\"]].groupby('status_dummy').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2527d363d88>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check to see if project listing date is independent to month, day, year, and day of week\n",
    "<p> The Chi-Square test is a statistical hypothesis test to check independence of categorical variables. In this context, we use this test to see if projects are more or less likely to be posted in certain months, on certain days, or in particular years. </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=2776.5209005520896, pvalue=0.0)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We Reject the null that there is no difference between means - Month posted is jointly significant.\n",
    "month_posted = new_df['project id'].groupby(new_df['month_posted_dummy']).count()\n",
    "stats.chisquare(month_posted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x219f5a5cf88>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# It looks like most projects are posted in March (dummy = 2) and April (dummy = 3).\n",
    "month_posted.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=3348.6238617623862, pvalue=0.0)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_funded = new_df['project id'].groupby(new_df['month_funded_dummy']).count()\n",
    "stats.chisquare(month_funded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x219f58fa4c8>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Most projects end in March, April, and May.\n",
    "month_funded.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=664.074950407495, pvalue=1.4887333283985904e-120)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_posted = new_df['project id'].groupby(new_df['day_posted']).count()\n",
    "stats.chisquare(day_posted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x219f5857488>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Most projects are posted on the first of the month\n",
    "day_posted.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=2642.9375492937547, pvalue=0.0)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_funded = new_df['project id'].groupby(new_df['day_funded']).count()\n",
    "stats.chisquare(day_funded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x219f9787d08>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Most projects end on the first of the month\n",
    "day_funded.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=19780.878540187856, pvalue=0.0)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_posted = new_df['project id'].groupby(new_df['year_posted']).count()\n",
    "stats.chisquare(year_posted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x219f9c2a388>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2011 saw the highest number of fundraisers on the platform (in the sample)\n",
    "year_posted.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=20089.310747831074, pvalue=0.0)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_funded = new_df['project id'].groupby(new_df['year_funded']).count()\n",
    "stats.chisquare(year_funded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x219f56549c8>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "year_funded.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=2879.8277765827775, pvalue=0.0)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekDay_posted = new_df['project id'].groupby(new_df['dayOfWeek_posted']).count()\n",
    "stats.chisquare(weekDay_posted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x219f56b9ec8>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fundraisers are less likely to be posted on Fridays and Saturdays\n",
    "weekDay_posted.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=295.2817571281757, pvalue=8.387785658465541e-61)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekDay_funded = new_df['project id'].groupby(new_df['dayOfWeek_funded']).count()\n",
    "stats.chisquare(weekDay_funded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x219f9781588>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fundraisers are less likely to end on Mondays and Tuesdays\n",
    "weekDay_funded.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check for joint significance of month, day of week, and year \n",
    "<p> Note: The F-test tests multiple hypothesis. The null hypothesis is that all slopes/coefficients are zero. Unfortunately the data isn't normally distributed so a parametric test probably isn't appropriate. </p>\n",
    "<p> Month dummies: Jointly significant; Reject Null <br>\n",
    "Year dummies: Jointly significant; Reject Null <br>\n",
    "Day of week dummies: Not Jointly Significant; Do not reject the null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Posted: F_onewayResult(statistic=2.7794177318667224, pvalue=0.001290621161661944)\n",
      "Funded: F_onewayResult(statistic=4.211975575580128, pvalue=2.8486971326095864e-06)\n"
     ]
    }
   ],
   "source": [
    "for each in ['posted', 'funded']:\n",
    "    print(f'{each.capitalize()}:', stats.f_oneway(new_df['status_dummy'][new_df[f'month_{each}'] == 1], \n",
    "               new_df['status_dummy'][new_df[f'month_{each}'] == 2], \n",
    "               new_df['status_dummy'][new_df[f'month_{each}'] == 3],\n",
    "              new_df['status_dummy'][new_df[f'month_{each}'] == 4],\n",
    "              new_df['status_dummy'][new_df[f'month_{each}'] == 5],\n",
    "              new_df['status_dummy'][new_df[f'month_{each}'] == 6],\n",
    "              new_df['status_dummy'][new_df[f'month_{each}'] == 7],\n",
    "              new_df['status_dummy'][new_df[f'month_{each}'] == 8],\n",
    "              new_df['status_dummy'][new_df[f'month_{each}'] == 9],\n",
    "              new_df['status_dummy'][new_df[f'month_{each}'] == 10],\n",
    "              new_df['status_dummy'][new_df[f'month_{each}'] == 11],\n",
    "              new_df['status_dummy'][new_df[f'month_{each}'] == 12]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Posted: F_onewayResult(statistic=6.31445909442635, pvalue=0.0002814123156969091)\n",
      "Funded: F_onewayResult(statistic=15.196937914090293, pvalue=7.012146438986875e-10)\n"
     ]
    }
   ],
   "source": [
    "for each in ['posted', 'funded']:\n",
    "    print(f'{each.capitalize()}:', stats.f_oneway(new_df['status_dummy'][new_df[f'year_{each}'] == 2009], \n",
    "               new_df['status_dummy'][new_df[f'year_{each}'] == 2010], \n",
    "               new_df['status_dummy'][new_df[f'year_{each}'] == 2011],\n",
    "              new_df['status_dummy'][new_df[f'year_{each}'] == 2012]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Posted: F_onewayResult(statistic=7.054726074512105, pvalue=1.5977152142137403e-07)\n",
      "Funded: F_onewayResult(statistic=1.4494149509388263, pvalue=0.1914102900357102)\n"
     ]
    }
   ],
   "source": [
    "for each in ['posted', 'funded']:\n",
    "    print(f'{each.capitalize()}:', stats.f_oneway(new_df['status_dummy'][new_df[f'dayOfWeek_{each}'] == 0], \n",
    "               new_df['status_dummy'][new_df[f'dayOfWeek_{each}'] == 1], \n",
    "               new_df['status_dummy'][new_df[f'dayOfWeek_{each}'] == 2],\n",
    "             new_df['status_dummy'][new_df[f'dayOfWeek_{each}'] == 3], \n",
    "              new_df['status_dummy'][new_df[f'dayOfWeek_{each}'] == 4],  \n",
    "               new_df['status_dummy'][new_df[f'dayOfWeek_{each}'] == 5],\n",
    "               new_df['status_dummy'][new_df[f'dayOfWeek_{each}'] == 6]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kruskal-Wallis Test\n",
    "#### Kruskal-Wallis test is a nonparametric version of the ANOVA test ; Interprets the MEDIAN difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Posted Statistics=30.559, p=0.001\n",
      "Different distributions (reject H0)\n",
      "Funded Statistics=46.293, p=0.000\n",
      "Different distributions (reject H0)\n"
     ]
    }
   ],
   "source": [
    "# Check for joint significance of Month \n",
    "for each in ['posted', 'funded']:\n",
    " \n",
    "    stat, p = kruskal(new_df['status_dummy'][new_df[f'month_{each}'] == 1], \n",
    "               new_df['status_dummy'][new_df[f'month_{each}'] == 2], \n",
    "               new_df['status_dummy'][new_df[f'month_{each}'] == 3],\n",
    "              new_df['status_dummy'][new_df[f'month_{each}'] == 4],\n",
    "              new_df['status_dummy'][new_df[f'month_{each}'] == 5],\n",
    "              new_df['status_dummy'][new_df[f'month_{each}'] == 6],\n",
    "              new_df['status_dummy'][new_df[f'month_{each}'] == 7],\n",
    "              new_df['status_dummy'][new_df[f'month_{each}'] == 8],\n",
    "              new_df['status_dummy'][new_df[f'month_{each}'] == 9],\n",
    "              new_df['status_dummy'][new_df[f'month_{each}'] == 10],\n",
    "              new_df['status_dummy'][new_df[f'month_{each}'] == 11],\n",
    "              new_df['status_dummy'][new_df[f'month_{each}'] == 12])\n",
    "    print(f'{each.capitalize()} Statistics=%.3f, p=%.3f' % (stat, p))\n",
    "    # interpret\n",
    "    alpha = 0.01\n",
    "    if p > alpha:\n",
    "        print('Same distributions (fail to reject H0)')\n",
    "    else:\n",
    "        print('Different distributions (reject H0)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Posted Statistics=18.936, p=0.000\n",
      "Different distributions (reject H0)\n",
      "Funded Statistics=45.544, p=0.000\n",
      "Different distributions (reject H0)\n"
     ]
    }
   ],
   "source": [
    "# Check for joint significance of Year\n",
    "for each in ['posted', 'funded']:\n",
    "    stat, p = kruskal(new_df['status_dummy'][new_df[f'year_{each}'] == 2009], \n",
    "                    new_df['status_dummy'][new_df[f'year_{each}'] == 2010],\n",
    "                    new_df['status_dummy'][new_df[f'year_{each}'] == 2011],\n",
    "                    new_df['status_dummy'][new_df[f'year_{each}'] == 2012])\n",
    "    print(f'{each.capitalize()} Statistics=%.3f, p=%.3f' % (stat, p))\n",
    "    # interpret\n",
    "    alpha = 0.01\n",
    "    if p > alpha:\n",
    "        print('Same distributions (fail to reject H0)')\n",
    "    else:\n",
    "        print('Different distributions (reject H0)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Posted Statistics=42.292, p=0.000\n",
      "Different distributions (reject H0)\n",
      "Funded Statistics=8.696, p=0.191\n",
      "Same distributions (fail to reject H0)\n"
     ]
    }
   ],
   "source": [
    "# Check for joint significance of day of week\n",
    "for each in ['posted', 'funded']:\n",
    "    stat, p = kruskal(new_df['status_dummy'][new_df[f'dayOfWeek_{each}'] == 0], \n",
    "                   new_df['status_dummy'][new_df[f'dayOfWeek_{each}'] == 1], \n",
    "                   new_df['status_dummy'][new_df[f'dayOfWeek_{each}'] == 2],\n",
    "                 new_df['status_dummy'][new_df[f'dayOfWeek_{each}'] == 3], \n",
    "                  new_df['status_dummy'][new_df[f'dayOfWeek_{each}'] == 4],  \n",
    "                   new_df['status_dummy'][new_df[f'dayOfWeek_{each}'] == 5],\n",
    "                   new_df['status_dummy'][new_df[f'dayOfWeek_{each}'] == 6])\n",
    "    print(f'{each.capitalize()} Statistics=%.3f, p=%.3f' % (stat, p))\n",
    "    # interpret\n",
    "    alpha = 0.01\n",
    "    if p > alpha:\n",
    "        print('Same distributions (fail to reject H0)')\n",
    "    else:\n",
    "        print('Different distributions (reject H0)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dunn's Test\n",
    "<p>Note: Dunn's test pinpoints which means are significantly different than the others </p>\n",
    "<p> This section conducts the Dunn's test between 'status_dummy' and 'month', 'year', 'day of week' and 'year_month.' </p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It looks like the means of January and February are statistically different at 95% confidence\n",
    "scikit_posthocs.posthoc_dunn(new_df, val_col='status_dummy', group_col='month_posted', p_adjust='bonferroni')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
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       "      <th>9</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.228951</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034919</td>\n",
       "      <td>0.627222</td>\n",
       "      <td>0.080245</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.007404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005146</td>\n",
       "      <td>0.000948</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.007404</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1    2         3         4         5         6         7    8    9   \\\n",
       "1   1.000000  1.0  0.269449  0.083151  1.000000  1.000000  1.000000  1.0  1.0   \n",
       "2   1.000000  1.0  1.000000  1.000000  1.000000  1.000000  1.000000  1.0  1.0   \n",
       "3   0.269449  1.0  1.000000  1.000000  0.023152  0.838899  0.085376  1.0  1.0   \n",
       "4   0.083151  1.0  1.000000  1.000000  0.003695  0.311561  0.023809  1.0  1.0   \n",
       "5   1.000000  1.0  0.023152  0.003695  1.000000  1.000000  1.000000  1.0  1.0   \n",
       "6   1.000000  1.0  0.838899  0.311561  1.000000  1.000000  1.000000  1.0  1.0   \n",
       "7   1.000000  1.0  0.085376  0.023809  1.000000  1.000000  1.000000  1.0  1.0   \n",
       "8   1.000000  1.0  1.000000  1.000000  1.000000  1.000000  1.000000  1.0  1.0   \n",
       "9   1.000000  1.0  1.000000  1.000000  1.000000  1.000000  1.000000  1.0  1.0   \n",
       "10  1.000000  1.0  1.000000  1.000000  1.000000  1.000000  1.000000  1.0  1.0   \n",
       "11  0.228951  1.0  1.000000  1.000000  0.034919  0.627222  0.080245  1.0  1.0   \n",
       "12  1.000000  1.0  0.005146  0.000948  1.000000  1.000000  1.000000  1.0  1.0   \n",
       "\n",
       "     10        11        12  \n",
       "1   1.0  0.228951  1.000000  \n",
       "2   1.0  1.000000  1.000000  \n",
       "3   1.0  1.000000  0.005146  \n",
       "4   1.0  1.000000  0.000948  \n",
       "5   1.0  0.034919  1.000000  \n",
       "6   1.0  0.627222  1.000000  \n",
       "7   1.0  0.080245  1.000000  \n",
       "8   1.0  1.000000  1.000000  \n",
       "9   1.0  1.000000  1.000000  \n",
       "10  1.0  1.000000  1.000000  \n",
       "11  1.0  1.000000  0.007404  \n",
       "12  1.0  0.007404  1.000000  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The mean success between April and December are statistically significant at 99% confidence\n",
    "# March and December at 99% confidence\n",
    "# March and May at 95% confidence\n",
    "# April and May at 99% confidence\n",
    "# April and July at 95% confidence\n",
    "# April and December at 99% confidence\n",
    "# May and November at 95% confidence\n",
    "# November and December at 99% confidence\n",
    "scikit_posthocs.posthoc_dunn(new_df, val_col='status_dummy', group_col='month_funded', p_adjust='bonferroni')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.007564</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.007564</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000930</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      2009      2010      2011     2012\n",
       "2009   1.0  1.000000  1.000000  1.00000\n",
       "2010   1.0  1.000000  0.007564  1.00000\n",
       "2011   1.0  0.007564  1.000000  0.00093\n",
       "2012   1.0  1.000000  0.000930  1.00000"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2010 and 2011 are significantly different \n",
    "# 2011 and 2012 are significantly different\n",
    "scikit_posthocs.posthoc_dunn(new_df, val_col='status_dummy', group_col='year_posted', p_adjust='bonferroni')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.511020e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.095175e-03</td>\n",
       "      <td>9.969441e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>0.451102</td>\n",
       "      <td>0.001095</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>6.314071e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996944</td>\n",
       "      <td>6.314071e-10</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          2009      2010          2011          2012\n",
       "2009  1.000000  1.000000  4.511020e-01  1.000000e+00\n",
       "2010  1.000000  1.000000  1.095175e-03  9.969441e-01\n",
       "2011  0.451102  0.001095  1.000000e+00  6.314071e-10\n",
       "2012  1.000000  0.996944  6.314071e-10  1.000000e+00"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2011 and 2010\n",
    "# 2011 and 2012\n",
    "scikit_posthocs.posthoc_dunn(new_df, val_col='status_dummy', group_col='year_funded', p_adjust='bonferroni')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.628016</td>\n",
       "      <td>0.067526</td>\n",
       "      <td>0.000672</td>\n",
       "      <td>0.002054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.340379</td>\n",
       "      <td>0.029403</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>0.000875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.149575</td>\n",
       "      <td>0.001643</td>\n",
       "      <td>0.004611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.628016</td>\n",
       "      <td>0.340379</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.410119</td>\n",
       "      <td>0.548987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.067526</td>\n",
       "      <td>0.029403</td>\n",
       "      <td>0.149575</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000672</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>0.001643</td>\n",
       "      <td>0.410119</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.002054</td>\n",
       "      <td>0.000875</td>\n",
       "      <td>0.004611</td>\n",
       "      <td>0.548987</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6\n",
       "0  1.000000  1.000000  1.000000  0.628016  0.067526  0.000672  0.002054\n",
       "1  1.000000  1.000000  1.000000  0.340379  0.029403  0.000233  0.000875\n",
       "2  1.000000  1.000000  1.000000  1.000000  0.149575  0.001643  0.004611\n",
       "3  0.628016  0.340379  1.000000  1.000000  1.000000  0.410119  0.548987\n",
       "4  0.067526  0.029403  0.149575  1.000000  1.000000  1.000000  1.000000\n",
       "5  0.000672  0.000233  0.001643  0.410119  1.000000  1.000000  1.000000\n",
       "6  0.002054  0.000875  0.004611  0.548987  1.000000  1.000000  1.000000"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It may be better to post on a Sunday, Monday, or Tuesday than a Friday or Saturday \n",
    "scikit_posthocs.posthoc_dunn(new_df, val_col='status_dummy', group_col='dayOfWeek_posted', p_adjust='bonferroni')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2527aed1408>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekDayPosted = new_df['status_dummy'].groupby(new_df['dayOfWeek_posted']).mean()\n",
    "weekDayPosted.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.405313</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.864544</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.405313</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.872227</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.872227</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.864544</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1    2         3         4    5    6\n",
       "0  1.000000  0.405313  1.0  1.000000  0.864544  1.0  1.0\n",
       "1  0.405313  1.000000  1.0  0.872227  1.000000  1.0  1.0\n",
       "2  1.000000  1.000000  1.0  1.000000  1.000000  1.0  1.0\n",
       "3  1.000000  0.872227  1.0  1.000000  1.000000  1.0  1.0\n",
       "4  0.864544  1.000000  1.0  1.000000  1.000000  1.0  1.0\n",
       "5  1.000000  1.000000  1.0  1.000000  1.000000  1.0  1.0\n",
       "6  1.000000  1.000000  1.0  1.000000  1.000000  1.0  1.0"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scikit_posthocs.posthoc_dunn(new_df, val_col='status_dummy', group_col='dayOfWeek_funded', p_adjust='bonferroni')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2527d368688>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "month_posted = new_df['status_dummy'].groupby(new_df['month_posted']).mean()\n",
    "month_posted.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2527ee34a08>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "month_funded = new_df['status_dummy'].groupby(new_df['month_funded']).mean()\n",
    "month_funded.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2527e2a8508>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "year_posted = new_df['status_dummy'].groupby(new_df['year_posted']).mean()\n",
    "year_posted.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2527f042fc8>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "year_funded = new_df['status_dummy'].groupby(new_df['year_funded']).mean()\n",
    "year_funded.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2520213f988>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "year_month_posted = new_df['status_dummy'].groupby(new_df['year_month_posted']).mean()\n",
    "year_month_posted.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2527ebc3108>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEKCAYAAAACS67iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAbVUlEQVR4nO3de7wcZZ3n8c+XxCgMCJgcLuZCMhrEeAM5BB1RcEQ3yEq8xCG4uuKOk2V3QhBnR+PKghNHRdyV0TG6ZhAYcTEiDnhGg1EBwQuXBAiXJERiBHI2KhG5vhgNwd/88TyHVIo+p+skfS558n2/XvXqqqd+p/rpPtXfrq7uqlJEYGZmu749RroDZmbWGQ50M7NCONDNzArhQDczK4QD3cysEA50M7NCjB2pO54wYUJMnTp1pO7ezGyXdMstt/w2IrpazRuxQJ86dSorV64cqbs3M9slSbqvv3ne5WJmVggHuplZIRzoZmaFcKCbmRXCgW5mVggHuplZIRzoZmaFcKCbmRWi0YFFkmYBnwPGABdExLm1+ecDr8+TewEHRMR+nezocJm68LvbTd977okj1BMzs8FpG+iSxgCLgTcCvcAKST0RsaavJiLOrNSfDhwxBH01M7MBNNnlMhNYHxEbImILsBSYPUD9KcDXO9E5MzNrrkmgTwQ2VqZ7c9szSDoEmAZc08/8eZJWSlq5efPmwfbVzMwG0CTQ1aKtvytLzwUuj4inWs2MiCUR0R0R3V1dLU8WZmZmO6hJoPcCkyvTk4BN/dTOxbtbzMxGRJNAXwFMlzRN0jhSaPfUiyS9CNgfuKGzXTQzsybaBnpEbAXmA8uBtcBlEbFa0iJJJ1VKTwGWRkR/u2PMzGwINfodekQsA5bV2s6uTX+sc90yM7PB8pGiZmaFcKCbmRXCgW5mVggHuplZIRp9KVoKn3jLzErmLXQzs0I40M3MCuFANzMrhAPdzKwQDnQzs0I40M3MCrFb/WzRhpZ/Fmo2sryFbmZWCAe6mVkhHOhmZoVwoJuZFcKBbmZWCAe6mVkhHOhmZoXw79CtEf/G3Gz0axTokmYBnwPGABdExLktav4C+BgQwO0R8a4O9nPUqAcbONxs1+I353K1DXRJY4DFwBuBXmCFpJ6IWFOpmQ58BHhNRDwk6YCh6rCZmbXWZB/6TGB9RGyIiC3AUmB2reavgMUR8RBARDzQ2W6amVk7TQJ9IrCxMt2b26oOBQ6V9FNJN+ZdNGZmNoya7ENXi7ZosZzpwHHAJODHkl4aEQ9vtyBpHjAPYMqUKYPurJmZ9a9JoPcCkyvTk4BNLWpujIgngV9KWkcK+BXVoohYAiwB6O7urr8p2A7wF1xW5fVh99Zkl8sKYLqkaZLGAXOBnlrNlcDrASRNIO2C2dDJjpqZ2cDabqFHxFZJ84HlpJ8tXhgRqyUtAlZGRE+e9yZJa4CngL+NiAeHsuO7A29t7T78c1jrhEa/Q4+IZcCyWtvZlfEAPpiH3Z5fnGY2Enzov5lZIXzov9kQ8yc2Gy7eQjczK4QD3cysEN7lYmbb8S6iXZcD3YZVaT/FdPjtnNLWh5HmQB8hXpHNrNMc6IXzFqTZ7sOBbmajWrtPs95o2caBbn5B2KA1WWdK3K042h+Tf7ZoZlYIb6GbmXXQSG7FewvdzKwQDnQzs0KM+C4XfyFnVV4fzHbciAe62Wg22n/VYFblQDczo4w3bwe67bZ2tRfwrtZfG37+UtTMrBDeQjez4u0uX7Y32kKXNEvSOknrJS1sMf9USZslrcrD+zvfVTMzG0jbLXRJY4DFwBuBXmCFpJ6IWFMr/UZEzB+CPpqZFWMoPy002eUyE1gfERsAJC0FZgP1QDcbNfwFou2OmuxymQhsrEz35ra6d0i6Q9LlkiZ3pHdmZtZYk0BXi7aoTf8rMDUiXg78EPjnlguS5klaKWnl5s2bB9dTMzMbUJNA7wWqW9yTgE3Vgoh4MCL+kCf/CTiy1YIiYklEdEdEd1dX147018zM+tEk0FcA0yVNkzQOmAv0VAskHVyZPAlY27kumplZE22/FI2IrZLmA8uBMcCFEbFa0iJgZUT0AAsknQRsBX4HnDqEfTYzsxYaHVgUEcuAZbW2syvjHwE+0tmumZnZYPjQfzOzQvjQf9vl7C6Hcdvua0fXcW+hm5kVwoFuZlYIB7qZWSEc6GZmhXCgm5kVwoFuZlYIB7qZWSEc6GZmhXCgm5kVwoFuZlYIB7qZWSEc6GZmhXCgm5kVYpc426Kv4G5m1p630M3MCuFANzMrhAPdzKwQDnQzs0I0CnRJsyStk7Re0sIB6uZICkndneuimZk10TbQJY0BFgMnADOAUyTNaFG3D7AAuKnTnTQzs/aabKHPBNZHxIaI2AIsBWa3qPs4cB7w+w72z8zMGmoS6BOBjZXp3tz2NElHAJMj4jsd7JuZmQ1Ck0BXi7Z4eqa0B3A+8DdtFyTNk7RS0srNmzc376WZmbXVJNB7gcmV6UnApsr0PsBLgR9Juhd4FdDT6ovRiFgSEd0R0d3V1bXjvTYzs2doEugrgOmSpkkaB8wFevpmRsQjETEhIqZGxFTgRuCkiFg5JD02M7OW2gZ6RGwF5gPLgbXAZRGxWtIiSScNdQfNzKyZRifniohlwLJa29n91B63890yM7PB8pGiZmaFcKCbmRXCgW5mVggHuplZIRzoZmaFcKCbmRXCgW5mVggHuplZIRzoZmaFcKCbmRXCgW5mVggHuplZIRzoZmaFcKCbmRXCgW5mVggHuplZIRzoZmaFcKCbmRXCgW5mVggHuplZIRoFuqRZktZJWi9pYYv5p0m6U9IqST+RNKPzXTUzs4G0DXRJY4DFwAnADOCUFoF9aUS8LCIOB84DPtvxnpqZ2YCabKHPBNZHxIaI2AIsBWZXCyLi0crknwDRuS6amVkTYxvUTAQ2VqZ7gaPrRZL+GvggMA748470zszMGmsS6GrR9owt8IhYDCyW9C7gLOC9z1iQNA+YBzBlypTB9bSNqQu/u930veee2NHlm5mNdk12ufQCkyvTk4BNA9QvBd7aakZELImI7ojo7urqat5LMzNrq0mgrwCmS5omaRwwF+ipFkiaXpk8Ebinc100M7Mm2u5yiYitkuYDy4ExwIURsVrSImBlRPQA8yUdDzwJPESL3S1mZja0muxDJyKWActqbWdXxs/ocL/MzGyQfKSomVkhHOhmZoVwoJuZFcKBbmZWCAe6mVkhHOhmZoVwoJuZFcKBbmZWCAe6mVkhHOhmZoVwoJuZFcKBbmZWCAe6mVkhHOhmZoVwoJuZFcKBbmZWCAe6mVkhHOhmZoVwoJuZFcKBbmZWiEaBLmmWpHWS1kta2GL+ByWtkXSHpKslHdL5rpqZ2UDaBrqkMcBi4ARgBnCKpBm1stuA7oh4OXA5cF6nO2pmZgNrsoU+E1gfERsiYguwFJhdLYiIayPiiTx5IzCps900M7N2mgT6RGBjZbo3t/XnL4GrdqZTZmY2eGMb1KhFW7QslN4NdAPH9jN/HjAPYMqUKQ27aGZmTTTZQu8FJlemJwGb6kWSjgc+CpwUEX9otaCIWBIR3RHR3dXVtSP9NTOzfjQJ9BXAdEnTJI0D5gI91QJJRwBfJoX5A53vppmZtdM20CNiKzAfWA6sBS6LiNWSFkk6KZd9Btgb+KakVZJ6+lmcmZkNkSb70ImIZcCyWtvZlfHjO9wvMzMbJB8pamZWCAe6mVkhHOhmZoVwoJuZFcKBbmZWCAe6mVkhHOhmZoVwoJuZFcKBbmZWCAe6mVkhHOhmZoVwoJuZFcKBbmZWCAe6mVkhHOhmZoVwoJuZFcKBbmZWCAe6mVkhHOhmZoVwoJuZFcKBbmZWiEaBLmmWpHWS1kta2GL+6yTdKmmrpDmd76aZmbXTNtAljQEWAycAM4BTJM2old0PnApc2ukOmplZM2Mb1MwE1kfEBgBJS4HZwJq+goi4N8/74xD00czMGmiyy2UisLEy3ZvbBk3SPEkrJa3cvHnzjizCzMz60STQ1aItduTOImJJRHRHRHdXV9eOLMLMzPrRJNB7gcmV6UnApqHpjpmZ7agmgb4CmC5pmqRxwFygZ2i7ZWZmg9U20CNiKzAfWA6sBS6LiNWSFkk6CUDSUZJ6gXcCX5a0eig7bWZmz9TkVy5ExDJgWa3t7Mr4CtKuGDMzGyE+UtTMrBAOdDOzQjjQzcwK4UA3MyuEA93MrBAOdDOzQjjQzcwK4UA3MyuEA93MrBAOdDOzQjjQzcwK4UA3MyuEA93MrBAOdDOzQjjQzcwK4UA3MyuEA93MrBAOdDOzQjjQzcwK4UA3MytEo0CXNEvSOknrJS1sMf/Zkr6R598kaWqnO2pmZgNrG+iSxgCLgROAGcApkmbUyv4SeCgiXgicD3y60x01M7OBNdlCnwmsj4gNEbEFWArMrtXMBv45j18OvEGSOtdNMzNrRxExcIE0B5gVEe/P0+8Bjo6I+ZWau3JNb57+Ra75bW1Z84B5efJFwLrK7AnAdvUt7Go1o6kvTWpGU1+a1IymvnSqZjT1pUnNaOpLk5rR1JcmNa3mHxIRXS2rI2LAAXgncEFl+j3AP9ZqVgOTKtO/AMa3W3ZtGStLqxlNfXF/d42a0dQX93fka5osozo02eXSC0yuTE8CNvVXI2kssC/wuwbLNjOzDmkS6CuA6ZKmSRoHzAV6ajU9wHvz+BzgmshvL2ZmNjzGtiuIiK2S5gPLgTHAhRGxWtIi0seBHuArwCWS1pO2zOfuQF+WFFgzmvrSpGY09aVJzWjqS6dqRlNfmtSMpr40qRlNfWlS02QZT2v7paiZme0afKSomVkhHOhmZoVwoJuZFaLtl6JDRdJhpCNMJwJB+ilkT0Ss3YHlTARuiojHK+2zIuJ7eXwmEBGxIp+2YBZwd0Qs62eZX42I/zzAfR5DOoL2roj4fm47GlgbEY9K2hNYCLwSWAN8MiIekbQAuCIiNg6w7L5fEm2KiB9KehfwZ8BaYElEPCnpBcDbSD8V3QrcA3w9Ih5p8JTZbkDSARHxwE4uY3xEPNipPtnQG5EtdEkfJp1CQMDNpJ9GCvh6q5N/tfj79+XbBcC3gdOBuyRVT0nwyVxzDvB54EuSPgV8AdgbWCjpo5J6asO/Am/vm87LuLly33+Vl7EPcE6lvxcCT+Txz5F+i//p3HZRbv84cJOkH0v675JaHe11EXAicIakS0gHdt0EHAVckB/z/wWek9v2JAX7DZKOa/fcjRaSDujQcsZ3YjmDvM99JZ0r6W5JD+ZhbW7br8HfX5VvnyvpU5IuyW/c1Zov5tuDJH1J0mJJ4yV9TNKdki6TdHCueV5tGA/cLGn/PD2r1vevSLpD0qWSDszt50qakMe7JW0grav3STo2t98q6ay8QdHfY+uWdK2kr0maLOkHkh6RtELSEblmb0mLJK3O8zZLulHSqZXljJX0XyV9L/f1dklXSTpN0rPaPL9L8u2YvIyPS3pNreasfLuXpA9J+ltJz5F0an7tnydp7wHu4+e16ZdXxp+Vn6ceSZ/M9zG/8vy+UNL1kh5WOpnhy3L7v0h690D329ZgjkLq1AD8HHhWi/ZxwD0N/v7+fHsnsHcenwqsBM7I07dVasYAewGPAs/N7XsCdwC3Al8DjgOOzbe/yuPHVpeVx1cAXXn8T4A78/jaSs2ttf6u6lsO6U30TaSfem4Gvkf6Df8+ueaOfDsW+A0wJk8r9/fOSttewI/y+JRaP/cFzgXuBh7Mw9rctl+D5/gq4LnAp4BLgHfV5n8x3x4EfIl0ArfxwMdyHy8DDs41z6sN44F7gf2B5+WaWbW+fyU/3kuBA3P7ucCEPN4NbADWA/fl/9etwFnACwZ4XN3Atfl/Phn4AfBI/r8ekWv2BhaRjoB+JP+fbgROzfOXAx8GDqos96Dc9oM8/cp+hiOBX+Wab+XH9FbSsRzfAp5dXYfy+nE66RPfHfk+puS2b+eaPwK/rA1P5tsNVNZH4ALg74FDgDOBK/teJ5Waa4Gj8vih5KMV8/L+N3A/aUPsTOD5tef3ZtKJ/E4BNgJzcvsbgBvy+LeBU0kHKX4Q+F/AdNL5oD6Za75OWq9elesm5fEvAd/gmetUdd3qrTzWS4EPALcAn62/Rknr6f8BvghcTdpYex3wGeCSXPMYKTsezeOPAU/1tddf83l5F5PWyfOBrwKrK/O/C7wtjx8H/DSP/3/SubB+l/v1NmDcoLJ1KIN7gBfV3aTzEdTbDwHW9QVbP8OdwB9yzZra3+9NegF8lkqIVubfVqtfRQrYM0kv7MNz+4Za3e2k8BlP7VBctr1xfBN4Xx6/COiuvCBW1P/pefpZwEl55d2c2+4ivbHtn1eYvsB7DimQ72Tbi35/4JbK8u6qjO906DBMgdPiBbFDocMwBQ55He1n3e5bf58Crsn9rA//1rf+1f72o8BPSetZ3/NbXX/vr6+/+fZ/5P/DyyrzflkPr37us28ZdwNj8/iNtZo7WyzntaQQ/HV+TPMa9LfvtXJ7rb3v9bEHaVfo089jP8/xz/Pzu6G2TvVNb+nLkMrfjCX9pvtfgGdX+tL3+JUfiyrTfRtX/0gK5QNbPb8tHvcq8gYr2zbE1tUfb2X6juoySJ/+3wMsI21MXAS8qb/nY7tlNSnq9EDah72etBW4JA/fy22zcs1vgMNJL+rqMJW0fxnSC+bw2rLH5if/qTx9E7BX3wpTqdu3toJOIoXyF1qsiPdWVpYN5JAkvYGsqizvYtJ5bG4iBdYG4DrgFfV/eovnZM98e2b+u/uABaSthn8iBfk5wBl5BVlCehH2vYl0AddXlrfTocMwBU6e3unQYZgCB/g+8CG2f4EfSHoz+2GevguY3s/zvzHfrq2uk7ntvaRPBvfV+wL8ff0xt1h/P0sKhA2Veb2kN6a/yeuWKvP6wuT0/Lj+nPQp6x9IW6p/x7Yt1VtbPJYxpNfzRXn6BtIn0HeS1uG35vZj2bal/zPgmDz+FmB5i3XzxryM6mt2D+Bk0uvrHmBKm+f37hbzziGtw/fU1zXSQZPV2upzfyTptbIg96O+0bcBeDvwDiqf1vuWA3yClA9/CvxP0qeGKcD7gO8M8Pw+DziNdPT96Az0yj/nVfkJmJPHx1Tmf6Xvn97iby+trMQH9VPzmnz77H7mT6ASMJX2E8kf+xo8hr2AabW2fYBX5BXgwNq8Qxsu9/nkLUtgv/z8zKzMf0luO2yAZex06DBMgZPn73To9POC6HjgkD4ZfZoU7g+RPiKvzW19n6jmAC/q57ntu8/zgONbzJ/FtsBZRN6tWKt5IXB5i/a3kMLw15W2c2pD3y7Dg4CvVuqOI+3OuI30BrmMdHbUvq3NpQ3W3VeQPh1eBRxG+j7p4bzO/Fml5ubc/pO+54m0UbIgj0/NfXmAtEX+8zz+DWAa8NfkDaUWfTg9336Nyq68yvz3A0/m8Qv6eX5fAPyk1rYHKdB/TN6orMy7qDb07SY8CLg6j59KejP6LenT9xrSJ7598/zrWz2ewQwdCWcPo2+ohc7vaqGzf64ZMHSGK3By+86GztgOBs7La4FzaG6vBs5hwPH1x8723wUcRtqVsyM1J+zockjfD720WrOTfRlszYsb1rR7/o4m/ZpsPHAM6ZPemyvzZ7Jtt9sM0gbBm2vL29GaE9l+o6Ja81rg7BbLOXqg+6ot4yWkjZdB93fA9btpoYdyBvJump2p2Zll1AJnp/sy1P2t15C20tYBV5J2x82uzO/bFdWk5vQO1Qx4X51YxiD7cncHas4hvfGvJH0xfzUpRK8n7fqrz7+mOr+fZXSq5urB1uzgMp7Rl7brZycCwsOuNVDbd7wjNZ1YxnDWdPJ+aP7rqlFRM5r6MsiagX6dNuD8JssYzppO3U+7YcQOLLKhJemO/maR9qW3renEMoazZhj78njkg9gi4t78+//LJR2SayB9HzRaakZTX5rWbI2Ip4AnJP0iIh7N9f8m6Y9pdMD5TZYxnDWd6u+AHOjlOhD4D6Qv7apE+tKvSU0nljGcNcN1P7+WdHhErAKIiMcl/UfSwWUvy7Wjqea6UdSXpjVbJO0VEU+QfmCQ/gHSvqSfwT7ZZn6TZQxnTaf6O7Amm/Eedr2BZr8SGrCmE8sYzpphvJ8mv64aNTWjqS+DqBnw12nt5jdZxnDWdOp+2g0+H7qZWSF8tkUzs0I40M3MCuFANzMrhAPdrEbSByTtVZl+fKD62t925VOi3ibptR3oy8WS5gyifqqku3b2fm3X5EC3UU/SmGG+yw+QDu7YEW8gnRTqiIj4cQf7ZNaWA906Kl9M4IzK9CckLcgXEFihdLGCv6vMv1LSLUoXO5hXaX9c6SIINwGv7ue+7s0XELhB0kpJr5S0XNIvJJ2WayTpM5LuUrowxMm5/ThJP5J0udKFKv5frl1AOjnatZKurT2O25UuxHBgP/05nHT+mzdLWiVpz+rWvaQ5ki7O4xdL+rykn0na0LcVnvvwBUlrJH0XOKDy90dKui4/X8u17QIXR+a+3UA6aZXtrpr8ttGDh6YD6VDuvvNx7EE6nfDJpNP9Krd9B3hdruk7O+GepLM/js/TAfxFm/u6F/hvefx80iHW+5BOovVAbn8H6Vz3Y0gHDN0PHEw6ydcjpN9E70E6C+MxleVOqNxPAG/J4+cBZw3Qp1OBL1SmH6+MzwEuzuMXk84+uQfpJEzrc/vbK/19PukEYXNI587/GdtOWnYy+XSv+XEfm8c/Q+W8+B52r8FHilpHRTqU+0Gly40dSDqXyFGkU9belsv2Jl0w4npggaS35fbJuf1B0rnav9XgLnvybd/5QR4DHpP0e6XLwR1Dut7qU8BvJF2X+/MocHNE9AJIWkV6M/pJi/vYQnoTgnTlmzc26FcTV0bEH4E1la3+11X6u0nSNbn9RcBLgR9IghT4v8pHEe4XEdfluktIF/Cw3ZAD3YbCBaQt1YNIh3O/AfhURHy5WqR0Do/jgVdHxBOSfkS6MhPA73OotfOHfPvHynjf9Fi2nRtkoL+F9AbS3+vhyYiIBnWtVI/ce05tXvX+q/1sdbSfSJcx2273U37T8tGBBngfug2NK0jnTD+KdO7x5cB/Ub74raSJSheJ3hd4KIf5YaSLnHTa9cDJShcM7iJtAd/c5m8eI+266YTfSHqxpD1I14hs53pgbu7vwcDrc/s6oEvSq+HpCxG/JCIeBh6RdEyu+08d6rftgryFbh0XEVvyF4oP563s70t6MXBD3l3wOPBu0mXpTlM6u+E60rmgO+0K0peqt5O2ZD8UEb/ObyD9WQJcJelXEfH6AeqaWEjaXbOR9B1Buyu6X0G6ItOdpKv0XAdPP6dzgM/n3SxjSVdsWk06R/uFkp4gvXnabsrncrGOy1ujtwLvjIh7Rro/ZrsL73KxjpI0g3Sx76sd5mbDy1voNupJuoJ0YeCqD0fEiO1ekPRR0oWmq74ZEZ8Yif6YgQPdzKwY3uViZlYIB7qZWSEc6GZmhXCgm5kVwoFuZlaIfwfjymCL5PoeeAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "year_month_funded = new_df['status_dummy'].groupby(new_df['year_month_funded']).mean()\n",
    "year_month_funded.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2527aa42e48>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekDayFunded = new_df['status_dummy'].groupby(new_df['dayOfWeek_funded']).mean()\n",
    "weekDayFunded.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look at the distribution of variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = new_df[['year_funded_dummy', 'year_posted_dummy', 'month_funded_dummy', 'month_posted_dummy', \n",
    "            'year_month_funded', 'year_month_posted', 'category_dummy', 'subcategory_dummy','country_dummy', 'duration', \n",
    "           'day_funded', 'day_posted','dayOfWeek_posted', 'dayOfWeek_funded', 'goal']]\n",
    "y = new_df['status_dummy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2520225d288>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEECAYAAADK0VhyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAXvElEQVR4nO3de7DfdX3n8eerBAQBJUJkMQESa5Q7LR4g3XqpUiF4IXihA+su0TKb0cW2drdbYZ0xKjIrK7O4jEonSiBYJFJkBxSUZhGKdhUJcgmBItlg4QiVSAIFM+Ei7/3j9zn018Pv5FyTk+Q8HzNnft/v+/v5fn/vXyY5r3xvv2+qCknS1PZbk92AJGnyGQaSJMNAkmQYSJIwDCRJGAaSJGDaZDcwVvvss0/Nnj17stuQpO3K7bff/quqmjG4vt2GwezZs1m5cuVktyFJ25Uk/9ir7mEiSZJhIEkyDCRJbMfnDCRprJ577jn6+/vZtGnTZLeyxey6667MmjWLnXfeeUTjDQNJU05/fz977rkns2fPJslktzPhqorHH3+c/v5+5syZM6J1PEwkacrZtGkTe++99w4ZBABJ2HvvvUe152MYSJqSdtQgGDDaz2cYSNJ27NOf/jTnn3/+uLfjOYMtbPZZ1012CzuMn3/+XZPdgnZQE/3vdHv8uzrsnkGSpUkeS3LPoPqfJLk/yeok/6OrfnaSNW3ZCV31+a22JslZXfU5SW5N8kCSbybZZaI+nCRty8455xwOOugg3vGOd3Daaadx/vnnc+eddzJv3jyOOOII3vve97JhwwYAvvrVr3L00Udz5JFH8v73v5+NGzdOaC8jOUx0KTC/u5DkbcAC4IiqOhQ4v9UPAU4FDm3rfCXJTkl2Ar4MnAgcApzWxgKcB1xQVXOBDcAZ4/1QkrStW7lyJd/61re44447uPrqq1/8ep3TTz+d8847j7vvvpvDDz+cz3zmMwC8733v47bbbuOuu+7i4IMP5uKLL57QfoYNg6q6BVg/qPxR4PNV9Uwb81irLwCWV9UzVfUgsAY4pv2sqaq1VfUssBxYkM4ZjrcDV7X1lwEnj/MzSdI274c//CELFixgt912Y8899+Q973kPv/71r3niiSd461vfCsDChQu55ZZbALjnnnt485vfzOGHH87ll1/O6tWrJ7SfsZ5Afj3w5nZ45++SHN3qM4GHu8b1t9pQ9b2BJ6rq+UF1SdqhVdWoxn/oQx/iS1/6EqtWrWLx4sUTfsPcWMNgGjAdmAf8V+DK9r/8Xtcy1RjqPSVZlGRlkpXr1q0bfdeStI1405vexLe//W02bdrE008/zXXXXcfuu+/O9OnT+cEPfgDA17/+9Rf3Ep566in2228/nnvuOS6//PIJ72esVxP1A1dXJ9p+kuQFYJ9W379r3CzgkTbdq/4rYK8k09reQff4l6iqJcASgL6+vtHFqiRtQ44++mhOOukkjjzySA488ED6+vp45StfybJly/jIRz7Cxo0bee1rX8sll1wCdE42H3vssRx44IEcfvjhPPXUUxPaT0ayq5JkNvCdqjqszX8EeE1VfSrJ64EbgQPonBz+Bp1zBK9p9bl09gB+BhwH/AK4Dfh3VbU6yd8A36qq5Un+Cri7qr4yXE99fX21PTzPwEtLJ872eLmetk333XcfBx988GS3wdNPP80ee+zBxo0bectb3sKSJUs46qijJmz7vT5nkturqm/w2GH3DJJcAfwBsE+SfmAxsBRY2i43fRZY2PYSVie5ErgXeB44s6p+07bzMeAGYCdgaVUNnP34BLA8yeeAO4CJPUUuSduoRYsWce+997Jp0yYWLlw4oUEwWsOGQVWdNsSifz/E+HOBc3vUrweu71FfS2dPQpKmlG984xuT3cKL/DoKSZJhIGlqGu2lndub0X4+w0DSlLPrrrvy+OOP77CBMPA8g1133XXE6/hFdZKmnFmzZtHf38+OfL/SwJPORsowkDTl7LzzziN+AthU4WEiSZJhIEkyDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSYwgDJIsTfJYe6rZ4GV/kaSS7NPmk+TCJGuS3J3kqK6xC5M80H4WdtXfmGRVW+fCJJmoDydJGpmR7BlcCswfXEyyP/AO4KGu8ol0nnk8F1gEXNTGvorO4zKPpfNUs8VJprd1LmpjB9Z7yXtJkrasYcOgqm4B1vdYdAHwl0D3F4IvAC6rjh8DeyXZDzgBWFFV66tqA7ACmN+WvaKqftSeoXwZcPL4PpIkabTGdM4gyUnAL6rqrkGLZgIPd833t9rm6v096pKkrWjUzzNI8nLgk8DxvRb3qNUY6kO99yI6h5Q44IADhu1VkjQyY9kz+G1gDnBXkp8Ds4CfJvk3dP5nv3/X2FnAI8PUZ/Wo91RVS6qqr6r6ZsyYMYbWJUm9jDoMqmpVVb26qmZX1Ww6v9CPqqp/Aq4FTm9XFc0DnqyqR4EbgOOTTG8njo8HbmjLnkoyr11FdDpwzQR9NknSCI3k0tIrgB8Bb0jSn+SMzQy/HlgLrAG+CvwngKpaD5wD3NZ+PttqAB8FvtbW+X/Ad8f2USRJYzXsOYOqOm2Y5bO7pgs4c4hxS4GlPeorgcOG60OStOV4B7IkyTCQJBkGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIY2WMvlyZ5LMk9XbUvJPmHJHcn+d9J9upadnaSNUnuT3JCV31+q61JclZXfU6SW5M8kOSbSXaZyA8oSRreSPYMLgXmD6qtAA6rqiOAnwFnAyQ5BDgVOLSt85UkOyXZCfgycCJwCHBaGwtwHnBBVc0FNgCbe8ayJGkLGDYMquoWYP2g2t9W1fNt9sfArDa9AFheVc9U1YN0HnJ/TPtZU1Vrq+pZYDmwIEmAtwNXtfWXASeP8zNJkkZpIs4Z/DHw3TY9E3i4a1l/qw1V3xt4oitYBuqSpK1oXGGQ5JPA88DlA6Uew2oM9aHeb1GSlUlWrlu3brTtSpKGMOYwSLIQeDfwwaoa+AXeD+zfNWwW8Mhm6r8C9koybVC9p6paUlV9VdU3Y8aMsbYuSRpkTGGQZD7wCeCkqtrYteha4NQkL0syB5gL/AS4DZjbrhzahc5J5mtbiNwEfKCtvxC4ZmwfRZI0ViO5tPQK4EfAG5L0JzkD+BKwJ7AiyZ1J/gqgqlYDVwL3At8Dzqyq37RzAh8DbgDuA65sY6ETKv85yRo65xAuntBPKEka1rThBlTVaT3KQ/7CrqpzgXN71K8Hru9RX0vnaiNJ0iTxDmRJkmEgSTIMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgSWJkTzpbmuSxJPd01V6VZEWSB9rr9FZPkguTrElyd5KjutZZ2MY/0J6fPFB/Y5JVbZ0Lk2SiP6QkafNGsmdwKTB/UO0s4Maqmgvc2OYBTqTz3OO5wCLgIuiEB7AYOJbOU80WDwRIG7Ooa73B7yVJ2sKGDYOqugVYP6i8AFjWppcBJ3fVL6uOHwN7JdkPOAFYUVXrq2oDsAKY35a9oqp+VFUFXNa1LUnSVjLWcwb7VtWjAO311a0+E3i4a1x/q22u3t+jLknaiib6BHKv4/01hnrvjSeLkqxMsnLdunVjbFGSNNhYw+CX7RAP7fWxVu8H9u8aNwt4ZJj6rB71nqpqSVX1VVXfjBkzxti6JGmwsYbBtcDAFUELgWu66qe3q4rmAU+2w0g3AMcnmd5OHB8P3NCWPZVkXruK6PSubUmStpJpww1IcgXwB8A+SfrpXBX0eeDKJGcADwGntOHXA+8E1gAbgQ8DVNX6JOcAt7Vxn62qgZPSH6VzxdJuwHfbjyRpKxo2DKrqtCEWHddjbAFnDrGdpcDSHvWVwGHD9SFJ2nK8A1mSZBhIkgwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkhhnGCT58ySrk9yT5IokuyaZk+TWJA8k+WaSXdrYl7X5NW357K7tnN3q9yc5YXwfSZI0WmMOgyQzgT8F+qrqMGAn4FTgPOCCqpoLbADOaKucAWyoqtcBF7RxJDmkrXcoMB/4SpKdxtqXJGn0xnuYaBqwW5JpwMuBR4G3A1e15cuAk9v0gjZPW35ckrT68qp6pqoepPP85GPG2ZckaRTGHAZV9QvgfOAhOiHwJHA78ERVPd+G9QMz2/RM4OG27vNt/N7d9R7rSJK2gvEcJppO53/1c4DXALsDJ/YYWgOrDLFsqHqv91yUZGWSlevWrRt905KknsZzmOgPgQeral1VPQdcDfxbYK922AhgFvBIm+4H9gdoy18JrO+u91jnX6mqJVXVV1V9M2bMGEfrkqRu4wmDh4B5SV7ejv0fB9wL3AR8oI1ZCFzTpq9t87Tl36+qavVT29VGc4C5wE/G0ZckaZSmDT+kt6q6NclVwE+B54E7gCXAdcDyJJ9rtYvbKhcDX0+yhs4ewaltO6uTXEknSJ4Hzqyq34y1L0nS6I05DACqajGweFB5LT2uBqqqTcApQ2znXODc8fQiSRo770CWJBkGkiTDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kS4wyDJHsluSrJPyS5L8nvJXlVkhVJHmiv09vYJLkwyZokdyc5qms7C9v4B5IsHPodJUlbwnj3DP4X8L2qOgg4ErgPOAu4sarmAje2eYAT6Tzsfi6wCLgIIMmr6Dw681g6j8tcPBAgkqStY8xhkOQVwFtoD7yvqmer6glgAbCsDVsGnNymFwCXVcePgb2S7AecAKyoqvVVtQFYAcwfa1+SpNEbz57Ba4F1wCVJ7kjytSS7A/tW1aMA7fXVbfxM4OGu9ftbbai6JGkrGU8YTAOOAi6qqt8Ffs2/HBLqJT1qtZn6SzeQLEqyMsnKdevWjbZfSdIQxhMG/UB/Vd3a5q+iEw6/bId/aK+PdY3fv2v9WcAjm6m/RFUtqaq+quqbMWPGOFqXJHUbcxhU1T8BDyd5QysdB9wLXAsMXBG0ELimTV8LnN6uKpoHPNkOI90AHJ9kejtxfHyrSZK2kmnjXP9PgMuT7AKsBT5MJ2CuTHIG8BBwSht7PfBOYA2wsY2lqtYnOQe4rY37bFWtH2dfkqRRGFcYVNWdQF+PRcf1GFvAmUNsZymwdDy9SJLGzjuQJUnjPkwkaTs1+6zrJruFHcrPP/+uyW5hXNwzkCQZBpIkw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkpiAMEiyU5I7knynzc9JcmuSB5J8sz0FjSQva/Nr2vLZXds4u9XvT3LCeHuSJI3OROwZ/BlwX9f8ecAFVTUX2ACc0epnABuq6nXABW0cSQ4BTgUOBeYDX0my0wT0JUkaoXGFQZJZwLuAr7X5AG8HrmpDlgEnt+kFbZ62/Lg2fgGwvKqeqaoH6Twj+Zjx9CVJGp3x7hl8EfhL4IU2vzfwRFU93+b7gZlteibwMEBb/mQb/2K9xzqSpK1gzGGQ5N3AY1V1e3e5x9AaZtnm1hn8nouSrEyyct26daPqV5I0tPHsGfw+cFKSnwPL6Rwe+iKwV5KBZyvPAh5p0/3A/gBt+SuB9d31Huv8K1W1pKr6qqpvxowZ42hdktRtzGFQVWdX1ayqmk3nBPD3q+qDwE3AB9qwhcA1bfraNk9b/v2qqlY/tV1tNAeYC/xkrH1JkkZv2vBDRu0TwPIknwPuAC5u9YuBrydZQ2eP4FSAqlqd5ErgXuB54Myq+s0W6EuSNIQJCYOquhm4uU2vpcfVQFW1CThliPXPBc6diF4kSaPnHciSJMNAkmQYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAksQ4wiDJ/kluSnJfktVJ/qzVX5VkRZIH2uv0Vk+SC5OsSXJ3kqO6trWwjX8gycKh3lOStGWMZ8/geeC/VNXBwDzgzCSHAGcBN1bVXODGNg9wIp3nG88FFgEXQSc8gMXAsXSekLZ4IEAkSVvHmMOgqh6tqp+26aeA+4CZwAJgWRu2DDi5TS8ALquOHwN7JdkPOAFYUVXrq2oDsAKYP9a+JEmjNyHnDJLMBn4XuBXYt6oehU5gAK9uw2YCD3et1t9qQ9UlSVvJuMMgyR7At4CPV9U/b25oj1ptpt7rvRYlWZlk5bp160bfrCSpp3GFQZKd6QTB5VV1dSv/sh3+ob0+1ur9wP5dq88CHtlM/SWqaklV9VVV34wZM8bTuiSpy3iuJgpwMXBfVf3PrkXXAgNXBC0Erumqn96uKpoHPNkOI90AHJ9kejtxfHyrSZK2kmnjWPf3gf8ArEpyZ6v9N+DzwJVJzgAeAk5py64H3gmsATYCHwaoqvVJzgFua+M+W1Xrx9GXJGmUxhwGVfVDeh/vBziux/gCzhxiW0uBpWPtRZI0Pt6BLEkyDCRJhoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJLahMEgyP8n9SdYkOWuy+5GkqWSbCIMkOwFfBk4EDgFOS3LI5HYlSVPHNhEGwDHAmqpaW1XPAsuBBZPckyRNGdtKGMwEHu6a7281SdJWMG2yG2jSo1YvGZQsAha12aeT3L9Fu5o69gF+NdlNDCfnTXYHmiT+/ZxYB/Yqbith0A/s3zU/C3hk8KCqWgIs2VpNTRVJVlZV32T3IfXi38+tY1s5THQbMDfJnCS7AKcC105yT5I0ZWwTewZV9XySjwE3ADsBS6tq9SS3JUlTxjYRBgBVdT1w/WT3MUV56E3bMv9+bgWpesl5WknSFLOtnDOQJE0iw0CStO2cM9DWk+QgOnd4z6RzP8cjwLVVdd+kNiZp0rhnMMUk+QSdr/sI8BM6l/UGuMIvCNS2LMmHJ7uHHZknkKeYJD8DDq2q5wbVdwFWV9XcyelM2rwkD1XVAZPdx47Kw0RTzwvAa4B/HFTfry2TJk2Su4daBOy7NXuZagyDqefjwI1JHuBfvhzwAOB1wMcmrSupY1/gBGDDoHqA/7v125k6DIMppqq+l+T1dL42fCadf2T9wG1V9ZtJbU6C7wB7VNWdgxckuXnrtzN1eM5AkuTVRJIkw0CShGEgScIw0A4qyceTvHyixo2jj5uT+GAWbfMMA+2oPg6M5Jf8SMdJOzTDQNu9JLsnuS7JXUnuSbKYzo11NyW5qY25KMnKJKuTfKbV/rTHuKe7tvuBJJe26VPatu9KcstmetktyfIkdyf5JrBb17Khtn1p6++mJGuTvDXJ0iT3DYwZWD/JeUluT/J/khzT9jzWJjmpjflBkt/pWufvkxwx9j9dTRWGgXYE84FHqurIqjoM+CKdL997W1W9rY35ZHuO7hHAW5McUVUX9hg3lE8BJ1TVkcBJmxn3UWBjVR0BnAu8cYSfYTrwduDPgW8DFwCHAod3/XLfHbi5qt4IPAV8DngH8F7gs23M14APAbT7SV5WVUPd1Su9yDDQjmAV8Iftf81vrqone4z5oyQ/Be6g80v2kFG+x98Dlyb5j3QezTqUtwB/DdB+CY/0F/G3q3PTzyrgl1W1qqpeAFYDs9uYZ4HvtelVwN+175ha1TXmb4B3J9kZ+GPg0hG+v6Y470DWdq+qfpbkjcA7gf+e5G+7lyeZA/wFcHRVbWiHXnYdanNd0y+OqaqPJDkWeBdwZ5LfqarHR7CNYbfdPNNeX+iaHpgf+Hf6XP3LXaIvjquqF5JMa9Mbk6yg8xXlfwR48loj4p6BtntJXkPn0MxfA+cDR9E5jLJnG/IK4NfAk0n2BU7sWr17HMAvkxyc5LfoHH4ZeI/frqpbq+pTwK+A/Ydo5xbgg22dw+gcltrstreArwEX0vmKkfVb8H20A3HPQDuCw4EvJHkBeI7OcfvfA76b5NGqeluSO+gccllL55DPgCXd44Cz6Hw/zsPAPcAebdwXksyl811ONwJ3DdHLRcAl7ds376TzzIgBQ217QlXV7Un+GbhkS2xfOya/m0jawbQ9pZuBg9p5B2lYHiaSdiBJTgdupXP1lEGgEXPPQBqDJCcA5w0qP1hVW/JcgLTFGAaSJA8TSZIMA0kShoEkCcNAkoRhIEkC/j/F0j5SFUNQdgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#On average, successful projects set lower goals\n",
    "new_df[['status_dummy','goal']].groupby('status_dummy').mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    22902\n",
       "0    18939\n",
       "Name: status_dummy, dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['status_dummy'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([18939., 22902.]), array([0. , 0.5, 1. ]), <a list of 2 Patch objects>)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oejyYPsf7h8CHgD9v18lfq6qrR9bpAfU55tNKn2PeDaxN8iLwfeC3qurbo+v1YPoc82eBryT5DTrTJZ+Zx/+xI8n9dKb5lrTrIFuB9wNU1ZfpXBdZD0wA7wA3DXzMefznJUmaJafzNJEkqU+GgSTJMJAkGQaSJAwDSRKGgSQJw0CSBPwf5Dk1QiE7LWkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(new_df['status_dummy'].T, bins=len(new_df['status_dummy'].value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  700.,  7231., 20124., 13786.]),\n",
       " array([0.  , 0.75, 1.5 , 2.25, 3.  ]),\n",
       " <a list of 4 Patch objects>)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(new_df['year_funded_dummy'].T, bins=len(new_df['year_funded_dummy'].value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1031.,  8309., 21063., 11438.]),\n",
       " array([0.  , 0.75, 1.5 , 2.25, 3.  ]),\n",
       " <a list of 4 Patch objects>)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(new_df['year_posted_dummy'].T, bins=len(new_df['year_posted_dummy'].value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3093., 3224., 4539., 5108., 5696., 2840., 2841., 3037., 2748.,\n",
       "        2789., 2634., 3292.]),\n",
       " array([ 0.        ,  0.91666667,  1.83333333,  2.75      ,  3.66666667,\n",
       "         4.58333333,  5.5       ,  6.41666667,  7.33333333,  8.25      ,\n",
       "         9.16666667, 10.08333333, 11.        ]),\n",
       " <a list of 12 Patch objects>)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(new_df['month_funded_dummy'].T, bins=len(new_df['month_funded_dummy'].value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3749., 4475., 5337., 4995., 3082., 2781., 2776., 2644., 2840.,\n",
       "        2982., 3280., 2900.]),\n",
       " array([ 0.        ,  0.91666667,  1.83333333,  2.75      ,  3.66666667,\n",
       "         4.58333333,  5.5       ,  6.41666667,  7.33333333,  8.25      ,\n",
       "         9.16666667, 10.08333333, 11.        ]),\n",
       " <a list of 12 Patch objects>)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(new_df['month_posted_dummy'].T, bins=len(new_df['month_posted_dummy'].value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 163.,  223.,   10.,   24.,   52.,   49.,   65.,  114.,  168.,\n",
       "         173.,  844.,  975.,  241.,  312.,  448.,  603.,  882.,  890.,\n",
       "         847.,  848.,  957., 1017., 1627., 2094., 1400., 1649., 1889.,\n",
       "        1823., 1907., 2098., 1836., 1827., 1968., 2034., 2898., 3147.,\n",
       "        3349.,  390.]),\n",
       " array([ 0.        ,  0.97368421,  1.94736842,  2.92105263,  3.89473684,\n",
       "         4.86842105,  5.84210526,  6.81578947,  7.78947368,  8.76315789,\n",
       "         9.73684211, 10.71052632, 11.68421053, 12.65789474, 13.63157895,\n",
       "        14.60526316, 15.57894737, 16.55263158, 17.52631579, 18.5       ,\n",
       "        19.47368421, 20.44736842, 21.42105263, 22.39473684, 23.36842105,\n",
       "        24.34210526, 25.31578947, 26.28947368, 27.26315789, 28.23684211,\n",
       "        29.21052632, 30.18421053, 31.15789474, 32.13157895, 33.10526316,\n",
       "        34.07894737, 35.05263158, 36.02631579, 37.        ]),\n",
       " <a list of 38 Patch objects>)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(new_df['year_month_funded'].T, bins=len(new_df['year_month_funded'].value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 182.,  163.,   16.,   55.,   52.,   58.,   88.,  270.,  147.,\n",
       "         233.,  295., 1007.,  980.,  412.,  536.,  592.,  886.,  839.,\n",
       "         741.,  832.,  956., 1241., 1450., 2091., 1757., 1688., 1837.,\n",
       "        1845., 1843., 1879., 1815., 1738., 1879., 2275., 2730., 3237.,\n",
       "        2606.,  590.]),\n",
       " array([ 0.        ,  0.97368421,  1.94736842,  2.92105263,  3.89473684,\n",
       "         4.86842105,  5.84210526,  6.81578947,  7.78947368,  8.76315789,\n",
       "         9.73684211, 10.71052632, 11.68421053, 12.65789474, 13.63157895,\n",
       "        14.60526316, 15.57894737, 16.55263158, 17.52631579, 18.5       ,\n",
       "        19.47368421, 20.44736842, 21.42105263, 22.39473684, 23.36842105,\n",
       "        24.34210526, 25.31578947, 26.28947368, 27.26315789, 28.23684211,\n",
       "        29.21052632, 30.18421053, 31.15789474, 32.13157895, 33.10526316,\n",
       "        34.07894737, 35.05263158, 36.02631579, 37.        ]),\n",
       " <a list of 38 Patch objects>)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(new_df['year_month_posted'].T, bins=len(new_df['year_month_posted'].value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 3684.,   965.,   704.,  1561.,  1018., 12550.,  1291.,  1460.,\n",
       "        10031.,  1380.,  4150.,   732.,  2315.]),\n",
       " array([ 0.        ,  0.92307692,  1.84615385,  2.76923077,  3.69230769,\n",
       "         4.61538462,  5.53846154,  6.46153846,  7.38461538,  8.30769231,\n",
       "         9.23076923, 10.15384615, 11.07692308, 12.        ]),\n",
       " <a list of 13 Patch objects>)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(new_df['category_dummy'].T, bins=len(new_df['category_dummy'].value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 376., 1008.,  301.,  471.,  542.,  430.,  965.,  167.,  987.,\n",
       "         228.,  704.,  260.,  134., 3674.,  269., 1018.,  943., 2333.,\n",
       "        1291.,  239.,  163.,  420.,  187., 1812.,  414.,  404.,  407.,\n",
       "        3000., 1408.,  881.,  170.,  234.,  483.,  460.,  263., 1380.,\n",
       "         200.,  690.,  910.,  509.,  616., 1627.,  329., 3735.,  328.,\n",
       "        2315.,  750., 1024.,  382.]),\n",
       " array([ 0.        ,  0.97959184,  1.95918367,  2.93877551,  3.91836735,\n",
       "         4.89795918,  5.87755102,  6.85714286,  7.83673469,  8.81632653,\n",
       "         9.79591837, 10.7755102 , 11.75510204, 12.73469388, 13.71428571,\n",
       "        14.69387755, 15.67346939, 16.65306122, 17.63265306, 18.6122449 ,\n",
       "        19.59183673, 20.57142857, 21.55102041, 22.53061224, 23.51020408,\n",
       "        24.48979592, 25.46938776, 26.44897959, 27.42857143, 28.40816327,\n",
       "        29.3877551 , 30.36734694, 31.34693878, 32.32653061, 33.30612245,\n",
       "        34.28571429, 35.26530612, 36.24489796, 37.2244898 , 38.20408163,\n",
       "        39.18367347, 40.16326531, 41.14285714, 42.12244898, 43.10204082,\n",
       "        44.08163265, 45.06122449, 46.04081633, 47.02040816, 48.        ]),\n",
       " <a list of 49 Patch objects>)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(new_df['subcategory_dummy'].T, bins=len(new_df['subcategory_dummy'].value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2978., 1916., 1321., 1301., 1317., 1254., 1252., 1207., 1157.,\n",
       "        1333., 1321., 1292., 1180., 1265., 1601., 1648., 1232., 1344.,\n",
       "        1273., 1341., 1386., 1192., 1207., 1238., 1316., 1278., 1168.,\n",
       "        1110., 1076., 1223., 1114.]),\n",
       " array([ 1.        ,  1.96774194,  2.93548387,  3.90322581,  4.87096774,\n",
       "         5.83870968,  6.80645161,  7.77419355,  8.74193548,  9.70967742,\n",
       "        10.67741935, 11.64516129, 12.61290323, 13.58064516, 14.5483871 ,\n",
       "        15.51612903, 16.48387097, 17.4516129 , 18.41935484, 19.38709677,\n",
       "        20.35483871, 21.32258065, 22.29032258, 23.25806452, 24.22580645,\n",
       "        25.19354839, 26.16129032, 27.12903226, 28.09677419, 29.06451613,\n",
       "        30.03225806, 31.        ]),\n",
       " <a list of 31 Patch objects>)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(new_df['day_funded'].T, bins=len(new_df['day_funded'].value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1727., 1646., 1515., 1362., 1338., 1372., 1409., 1338., 1514.,\n",
       "        1368., 1317., 1322., 1449., 1361., 1442., 1480., 1371., 1218.,\n",
       "        1447., 1395., 1396., 1377., 1366., 1249., 1198., 1239., 1321.,\n",
       "        1293., 1142., 1158.,  711.]),\n",
       " array([ 1.        ,  1.96774194,  2.93548387,  3.90322581,  4.87096774,\n",
       "         5.83870968,  6.80645161,  7.77419355,  8.74193548,  9.70967742,\n",
       "        10.67741935, 11.64516129, 12.61290323, 13.58064516, 14.5483871 ,\n",
       "        15.51612903, 16.48387097, 17.4516129 , 18.41935484, 19.38709677,\n",
       "        20.35483871, 21.32258065, 22.29032258, 23.25806452, 24.22580645,\n",
       "        25.19354839, 26.16129032, 27.12903226, 28.09677419, 29.06451613,\n",
       "        30.03225806, 31.        ]),\n",
       " <a list of 31 Patch objects>)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(new_df['day_posted'].T, bins=len(new_df['day_posted'].value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([6521., 7395., 7183., 7148., 6415., 4084., 3095.]),\n",
       " array([0.        , 0.85714286, 1.71428571, 2.57142857, 3.42857143,\n",
       "        4.28571429, 5.14285714, 6.        ]),\n",
       " <a list of 7 Patch objects>)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(new_df['dayOfWeek_posted'].T, bins=len(new_df['dayOfWeek_posted'].value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([6111., 5151., 5376., 5950., 6312., 6698., 6243.]),\n",
       " array([0.        , 0.85714286, 1.71428571, 2.57142857, 3.42857143,\n",
       "        4.28571429, 5.14285714, 6.        ]),\n",
       " <a list of 7 Patch objects>)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(new_df['dayOfWeek_funded'].T, bins=len(new_df['dayOfWeek_funded'].value_counts()))"
   ]
  }
 ],
 "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}