kickstarter-consulting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kickstarter, the crowdfunding platform, is thinking about providing a consulting service to project founders to help its customers create more successful crowdfunding campaigns. You’ve been asked to do some initial analysis. \n",
    "\n",
    "In order to complete this assignment, please follow the link and download the following Kickstarter dataset: http://bit.ly/2cgMGDm. Use the dataset to complete this task.\n",
    "\n",
    "A short and simple Google Slide of the results can be seen here:http://bit.ly/2y760Nx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Loading  and Inspecting the Kickstarter Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.set_autosave_interval(180000)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Autosaving every 180 seconds\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# Set autosave to 3 minutes\n",
    "%autosave 180\n",
    "# ipython magic\n",
    "%pylab inline\n",
    "# Import modules\n",
    "import os\n",
    "import datetime as dt\n",
    "from scipy import stats\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.style.use('seaborn-paper')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# The following code reads all the Kickstarter data into Pandas DataFrames\n",
    "# empty data will be filled in as NaN\n",
    "# The data can be found at http://bit.ly/2cgMGDm\n",
    "kickstarter_df = pd.read_csv('DSI_kickstarterscrape_dataset.csv', keep_default_na=True)\n",
    "# Rename columns with spaces to _\n",
    "kickstarter_df.rename(columns={'project id': 'project_id', 'funded percentage': 'funded_percentage', 'funded date': 'funded_date', 'reward levels': 'reward_levels'}, inplace=True)\n",
    "# Set index as 'project_id'\n",
    "kickstarter_df.set_index(['project_id'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read all the Kickstarter csv data into Pandas DataFrames. Empty data will be filled in as NaN. The data can be found at http://bit.ly/2cgMGDm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows: 45957\n",
      "Columns: 16\n",
      "12.92 MB\n"
     ]
    }
   ],
   "source": [
    "# Return shape and size to see how much data is being worked with\n",
    "rows, columns = kickstarter_df.shape\n",
    "print 'Rows:', rows\n",
    "print 'Columns:', columns\n",
    "print round(float(os.stat('DSI_kickstarterscrape_dataset.csv').st_size / 1000000.0), 2), 'MB'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset is 45,957 rows long, 18 columns wide, and 12.9 MB. Not a very large dataset; but certainly  large enough to justify Python over Excel..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 45957 entries, 39409 to 2147460119\n",
      "Data columns (total 16 columns):\n",
      "name                 45957 non-null object\n",
      "url                  45957 non-null object\n",
      "category             45957 non-null object\n",
      "subcategory          45957 non-null object\n",
      "location             44635 non-null object\n",
      "status               45957 non-null object\n",
      "goal                 45957 non-null float64\n",
      "pledged              45945 non-null float64\n",
      "funded_percentage    45957 non-null float64\n",
      "backers              45957 non-null int64\n",
      "funded_date          45957 non-null object\n",
      "levels               45957 non-null int64\n",
      "reward_levels        45898 non-null object\n",
      "updates              45957 non-null int64\n",
      "comments             45957 non-null int64\n",
      "duration             45957 non-null float64\n",
      "dtypes: float64(4), int64(4), object(8)\n",
      "memory usage: 6.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# Show counts and data types for all columns\n",
    "kickstarter_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing data in each Kickstarter dataframe column:\n",
      "location : 1322 missing values is 2.877 % of total\n",
      "pledged : 12 missing values is 0.026 % of total\n",
      "reward_levels : 59 missing values is 0.128 % of total\n"
     ]
    }
   ],
   "source": [
    "# Find how many missing values and percentage missing in each column\n",
    "print 'Missing data in each Kickstarter dataframe column:'\n",
    "for c in kickstarter_df.columns:\n",
    "    missing_data = len(kickstarter_df) - kickstarter_df[c].count()\n",
    "    if (missing_data > 0 or missing_data =='NaN'):\n",
    "        print c, ':', missing_data, 'missing values is', str(round(float(missing_data / float(len(kickstarter_df))) * \n",
    "                                                            100, 3)), '% of total'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All columns have data except 'location' and 'reward levels'. But still very complete with very little missing values as percentage of whole."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
       "    <tr>\n",
       "      <th>project_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39409</th>\n",
       "      <td>WHILE THE TREES SLEEP</td>\n",
       "      <td>http://www.kickstarter.com/projects/emiliesaba...</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>Short Film</td>\n",
       "      <td>Columbia, MO</td>\n",
       "      <td>successful</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>11545.0</td>\n",
       "      <td>1.099524</td>\n",
       "      <td>66</td>\n",
       "      <td>Fri, 19 Aug 2011 19:28:17 -0000</td>\n",
       "      <td>7</td>\n",
       "      <td>$25,$50,$100,$250,$500,$1,000,$2,500</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126581</th>\n",
       "      <td>Educational Online Trading Card Game</td>\n",
       "      <td>http://www.kickstarter.com/projects/972789543/...</td>\n",
       "      <td>Games</td>\n",
       "      <td>Board &amp; Card Games</td>\n",
       "      <td>Maplewood, NJ</td>\n",
       "      <td>failed</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>2</td>\n",
       "      <td>Mon, 02 Aug 2010 03:59:00 -0000</td>\n",
       "      <td>5</td>\n",
       "      <td>$1,$5,$10,$25,$50</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>47.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138119</th>\n",
       "      <td>STRUM</td>\n",
       "      <td>http://www.kickstarter.com/projects/185476022/...</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>Animation</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "      <td>live</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.002800</td>\n",
       "      <td>3</td>\n",
       "      <td>Fri, 08 Jun 2012 00:00:31 -0000</td>\n",
       "      <td>10</td>\n",
       "      <td>$1,$10,$25,$40,$50,$100,$250,$1,000,$1,337,$9,001</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237090</th>\n",
       "      <td>GETTING OVER - One son's search to finally kno...</td>\n",
       "      <td>http://www.kickstarter.com/projects/charnick/g...</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "      <td>successful</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>6535.0</td>\n",
       "      <td>1.089167</td>\n",
       "      <td>100</td>\n",
       "      <td>Sun, 08 Apr 2012 02:14:00 -0000</td>\n",
       "      <td>13</td>\n",
       "      <td>$1,$10,$25,$30,$50,$75,$85,$100,$110,$250,$500...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>32.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246101</th>\n",
       "      <td>The Launch of FlyeGrlRoyalty &amp;quot;The New Nam...</td>\n",
       "      <td>http://www.kickstarter.com/projects/flyegrlroy...</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>Novi, MI</td>\n",
       "      <td>failed</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>Wed, 01 Jun 2011 15:25:39 -0000</td>\n",
       "      <td>6</td>\n",
       "      <td>$10,$25,$50,$100,$150,$250</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         name  \\\n",
       "project_id                                                      \n",
       "39409                                   WHILE THE TREES SLEEP   \n",
       "126581                   Educational Online Trading Card Game   \n",
       "138119                                                  STRUM   \n",
       "237090      GETTING OVER - One son's search to finally kno...   \n",
       "246101      The Launch of FlyeGrlRoyalty &quot;The New Nam...   \n",
       "\n",
       "                                                          url      category  \\\n",
       "project_id                                                                    \n",
       "39409       http://www.kickstarter.com/projects/emiliesaba...  Film & Video   \n",
       "126581      http://www.kickstarter.com/projects/972789543/...         Games   \n",
       "138119      http://www.kickstarter.com/projects/185476022/...  Film & Video   \n",
       "237090      http://www.kickstarter.com/projects/charnick/g...  Film & Video   \n",
       "246101      http://www.kickstarter.com/projects/flyegrlroy...       Fashion   \n",
       "\n",
       "                   subcategory         location      status     goal  pledged  \\\n",
       "project_id                                                                      \n",
       "39409               Short Film     Columbia, MO  successful  10500.0  11545.0   \n",
       "126581      Board & Card Games    Maplewood, NJ      failed   4000.0     20.0   \n",
       "138119               Animation  Los Angeles, CA        live  20000.0     56.0   \n",
       "237090             Documentary  Los Angeles, CA  successful   6000.0   6535.0   \n",
       "246101                 Fashion         Novi, MI      failed   3500.0      0.0   \n",
       "\n",
       "            funded_percentage  backers                      funded_date  \\\n",
       "project_id                                                                \n",
       "39409                1.099524       66  Fri, 19 Aug 2011 19:28:17 -0000   \n",
       "126581               0.005000        2  Mon, 02 Aug 2010 03:59:00 -0000   \n",
       "138119               0.002800        3  Fri, 08 Jun 2012 00:00:31 -0000   \n",
       "237090               1.089167      100  Sun, 08 Apr 2012 02:14:00 -0000   \n",
       "246101               0.000000        0  Wed, 01 Jun 2011 15:25:39 -0000   \n",
       "\n",
       "            levels                                      reward_levels  \\\n",
       "project_id                                                              \n",
       "39409            7               $25,$50,$100,$250,$500,$1,000,$2,500   \n",
       "126581           5                                  $1,$5,$10,$25,$50   \n",
       "138119          10  $1,$10,$25,$40,$50,$100,$250,$1,000,$1,337,$9,001   \n",
       "237090          13  $1,$10,$25,$30,$50,$75,$85,$100,$110,$250,$500...   \n",
       "246101           6                         $10,$25,$50,$100,$150,$250   \n",
       "\n",
       "            updates  comments  duration  \n",
       "project_id                               \n",
       "39409            10         2     30.00  \n",
       "126581            6         0     47.18  \n",
       "138119            1         0     28.00  \n",
       "237090            4         0     32.22  \n",
       "246101            2         0     30.00  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect the data with .head\n",
    "kickstarter_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The .head function gives us a preview of the data without having to load the entire dataset. It shows all the 'columns' of data for each project 'row'.  One thing I notice is that the 'status' column is a string of either 'successful, failed, live'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>4.594500e+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>1.194271e+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>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>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>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>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>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>2.147484e+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": [
       "               goal       pledged  funded_percentage       backers  \\\n",
       "count  4.595700e+04  4.594500e+04       45957.000000  45957.000000   \n",
       "mean   1.194271e+04  4.980750e+03           1.850129     69.973192   \n",
       "std    1.887583e+05  5.674162e+04          88.492706    688.628479   \n",
       "min    1.000000e-02  0.000000e+00           0.000000      0.000000   \n",
       "25%    1.800000e+03  1.960000e+02           0.044000      5.000000   \n",
       "50%    4.000000e+03  1.310000e+03           1.000000     23.000000   \n",
       "75%    9.862000e+03  4.165000e+03           1.115640     59.000000   \n",
       "max    2.147484e+07  1.026684e+07       15066.000000  87142.000000   \n",
       "\n",
       "             levels      updates      comments      duration  \n",
       "count  45957.000000  45957.00000  45957.000000  45957.000000  \n",
       "mean       8.004939      4.08508      8.379529     39.995547  \n",
       "std        4.233907      6.43922    174.015737     17.414458  \n",
       "min        0.000000      0.00000      0.000000      1.000000  \n",
       "25%        5.000000      0.00000      0.000000     30.000000  \n",
       "50%        7.000000      2.00000      0.000000     32.000000  \n",
       "75%       10.000000      6.00000      3.000000     48.390000  \n",
       "max       80.000000    149.00000  19311.000000     91.960000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get some quick and dirty statistics with pd.describe\n",
    "kickstarter_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: Foundational Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  1.1: What is the mean (total) pledge that projects get? (not per backer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$ 4980.75\n"
     ]
    }
   ],
   "source": [
    "# Find mean of 'pledged' column\n",
    "print \"$\", round(kickstarter_df.pledged.mean(), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean (total) pledge that projects receive is $4980.75."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2: Create a historgram that shows the distribution for number of backers. What is the skew of the distribution?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BX3zxhex2uwYNGqQHHnjA3N6sWbO0dOlS2Ww2devWTaNHj66QvgEAgHep8FD1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112db9650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of 'backers' column\n",
    "plt.hist(kickstarter_df['backers'])\n",
    "plt.xlabel('Backers')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Project Backers (outliers included)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YCEYAAAAmghEAAICJYAQAAGAiGAEAAJgIRgAAACaCEQAAgIlgBAAAYCIYAQAA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112400510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution plot of 'backers' column\n",
    "sns.distplot(kickstarter_df['backers'])\n",
    "plt.xlabel('Backers')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Frequency of Project Backers (outliers included)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plots are a histogram and a distribution plot of kickstarter backers. It's not very clear. There is **huge** number of projects that receive almost no backers.  This would indicate that distribution of kickstarter backers is strongly positive (right-skewed & left leaning). Let's look more into this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAMAKIgMAAFhBZAAAACuIDAAAYAWRAQAArCAyAACAFUQGAACwgsgAAABWEBkA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\nIgMAAFjx/wCXaJyWlN1bIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a841050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot of'backers' column\n",
    "sns.boxplot(x=kickstarter_df['backers'])\n",
    "plt.xlabel('Backers')\n",
    "plt.title('Boxplot of Project Backers (outliers included)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The boxplot above further proves this skewness. The box (quartiles) is almost not even visible. It appears as a black smudge to the far left of the chart. The extreme outliers are making this data extremely hard to see. Let's look at the numbers and see just how bad the skew is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    45957.000000\n",
       "mean        69.973192\n",
       "std        688.628479\n",
       "min          0.000000\n",
       "25%          5.000000\n",
       "50%         23.000000\n",
       "75%         59.000000\n",
       "max      87142.000000\n",
       "Name: backers, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get quick and dirty starts for 'backers'\n",
    "kickstarter_df['backers'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The quick and dirty statistics summary shows some insight as to why.  Look at the quartiles. THe lower 25% is 5, the median is 23, the 75% is 59 and the max is a whopping 87142! A standard deviation of 688 show us that the uniform is far from uniform. Let's quickly clean up the outliers so we can see the distribution more clearly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    24979.000000\n",
       "mean        46.829016\n",
       "std         29.133568\n",
       "min         12.000000\n",
       "25%         23.000000\n",
       "50%         39.000000\n",
       "75%         65.000000\n",
       "max        128.000000\n",
       "Name: backers, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter the original kickstarter dataframe to remove lots of outliers\n",
    "filtered_df = kickstarter_df[((kickstarter_df.backers - kickstarter_df.backers.mean()) / kickstarter_df.backers.std()).abs() < .085]\n",
    "filtered_df['backers'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above summary statistics show that the data has been significantly filtered from statistical outliers. Let's check a box plot next."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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J0o8//qjHH39cSUlJSk5O1qZNm4p5tMVn4cKFioyM1EcffSSJuZHyj50xY8Yo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1175f5a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=filtered_df['backers'])\n",
    "plt.xlabel('Backers')\n",
    "plt.title('Boxplot of Project Backers (outliers excluded)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The box plot above shows the extent of the filtering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UkJCgtLQ0JSYmKisrS3v37lV0dLQkyel0Ki0tTW3atFFubq42bdqkyZMnW/db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\nkCRxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117997d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of filtered 'backers' column\n",
    "plt.hist(filtered_df['backers'])\n",
    "plt.xlabel('Backers')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Project Backers (outliers removed)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5XK4634/e1ND2tyrom7LRN2Wjb0pHv5ytJuaz+jQYPfbYY6W+//7775f5mcDA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1172a2a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution plot of filtered 'backers' column\n",
    "sns.distplot(filtered_df['backers'], kde=False)\n",
    "plt.xlabel('Backers')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of Project Backers (outliers removed)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram above now shows the strongly positive (right-skewed & left leaning) distribution in a way that's easier to understand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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I8AqIFzpfdHS0Xn31Va1bt0433HCD7rnnHvXt21fTp0+/YB9RUVFauXKlPv74YyUmJnre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\nABMITQAAACb8fw0CxElBO6jXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112e27ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Countplot\n",
    "sns.countplot(x='backers', data=kickstarter_df)\n",
    "plt.xlabel('Project')\n",
    "plt.ylabel('Backers')\n",
    "plt.title('Number of Backers (all projects)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although not a histogram; the countplot above also shows that very few kickstarter projects get massive amount of backers while the vast majorty receive very little, if any, backers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3a: Is the ‘duration’ variable normally distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    45957.000000\n",
       "mean        39.995547\n",
       "std         17.414458\n",
       "min          1.000000\n",
       "25%         30.000000\n",
       "50%         32.000000\n",
       "75%         48.390000\n",
       "max         91.960000\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe 'duration' column\n",
    "kickstarter_df['duration'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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J6tu3rxwOh7KystSkSRNJ0oABA7R161YlJSXJbrdr5MiRatu2rSQpPj5eqampuv/++2Wz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\nGKKYmBh/l4KbEMHJSwcr7Tp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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112e27b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histogram of 'duration' column\n",
    "plt.hist(kickstarter_df['duration'])\n",
    "plt.xlabel('Duration')\n",
    "plt.ylabel('Frequecny')\n",
    "plt.title('Histogram of Project Duration')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4PN7pFotFu3bt8llxAAAAZtKo4LRgwQI98MADGjt2rIKCgnxdEwAAgCk1KjiV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\nAAAABhGcAADA/98oIBIAAMZwtSAnsfAiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a867a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot distribution plot of 'duration' column\n",
    "sns.distplot(kickstarter_df['duration'], kde=False)\n",
    "plt.xlabel('Duration')\n",
    "plt.ylabel('Frequecny')\n",
    "plt.title('Distribution of Project Duration')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9FbNmzQIAfPjhh3jllVckbRwR+cbgSjJpiqcBjgwRkXJ4FIaeeOIJHDlyBEVF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\nAm4kofe5UiioAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116d7aed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot kde of 'duration' column\n",
    "sns.kdeplot(kickstarter_df['duration'])\n",
    "plt.xlabel('Duration')\n",
    "plt.ylabel('Frequecny')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'duration' column in the dataset looks to be a bit normally distributed looking at it graphically.  Let's test it mathematically:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YdIiIiEixGHSIiIhIsRh0iIiISLEYdIiIiEixGHSIiIhI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\nhxBCCCHc+h8+7i7vLCYaYwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a8671d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create normal distribution to overlay on top of actual distribution\n",
    "duration_rand_normal = np.random.normal(loc=kickstarter_df['duration'].mean(), scale=kickstarter_df['duration'].std(), size=len(kickstarter_df['duration']))\n",
    "# Overlay standard normal distribution over actual distribution\n",
    "pyplot.hist(kickstarter_df['duration'], alpha=0.5, label='Actual Distribution')\n",
    "pyplot.hist(duration_rand_normal, alpha=0.5, label='Random Standard Normal')\n",
    "pyplot.legend(loc='upper right')\n",
    "pyplot.title('Project Duration Distribution vs Standard Normal')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above chart shows a Gaussian random numbers generated to create a standard normal distribution overlay (green) on top of the actual data (blue). This gives us another idea of how close the actual data is to being standard normal. It's still not conclusive, so let's test it mathematically:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P-value = 0.0 \n",
      "Z-score = 6985.5754355 \n",
      "Distribution is not normal.\n"
     ]
    }
   ],
   "source": [
    "# Convert 'duration' column to NumPy array\n",
    "duration_test = np.array(kickstarter_df['duration'])\n",
    "# k = z-score, p = p-value\n",
    "k,p = stats.mstats.normaltest(duration_test)\n",
    "# If statement to print results of p-test\n",
    "if p < 0.05:\n",
    "    print 'P-value =', p, '\\nZ-score =', k, '\\nDistribution is not normal.'\n",
    "else:\n",
    "    print 'P-value =', p, '\\nZ-score =', k, '\\nDistribution is normal'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3b: If you could collect data on another attribute of these projects, what would it be and why?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although I have not done a very detailed analysis of the data, the one thing that catches my eye is a lack of some sort of way to distinguish which individual backers have contributed to each project. It would be interesting to see  what the distribution looks like of each backer to see how many projects they have backed. I imagine this information is in a separate database as it would be very combersome to try and present that information in this .csv. If that was in a separate database or csv some SQL joints could be used for analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2:  Qualitative Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a presentation using Google Slides (max. 5 slides) using the data above (and additional data from those tables) that make clear recommendations on how people can create a successful Kickstarter campaign."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Map 'status' column into boolean and make new column\n",
    "status_bool = {'successful': True, 'failed': False, 'suspended': False, 'canceled':False}\n",
    "kickstarter_df['status_bool'] = kickstarter_df['status'].map(status_bool)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's convert the many 'status' options into a boolean of either True or False."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1: What's the best length of time to run a campaign?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SxQAgIpIoBgARkUQxAIiIJIoBQEQkUQwAIiKJqjUBsH37dnh4eODQoUMAgKysLEyYMAH+\n/v4ICAhAQkKCmSskIrIstSIAUlJSsG3bNnh5eenmRUdHw8vLCwcOHEBERARmzJgBrVZrxiqJiCyL\n2QNACIGwsDDMmTMHNjY2uvn79u3DiBEjAABt27aFs7Mz4uPjzVUmEZHFMXsArFu3Dt7e3vD09NTN\ny87OhlarhVKp1M1zdXVFWlqaOUokIrJIZv1JyGvXruHnn39GTExMtW5Xq9XydFEpljge/DsTlU8u\nl1faxqwBkJCQgJSUFLz99tsQQiAjIwNz5szB1KlTIZfLkZmZqTsKSElJgYuLi0HbvXr1qinLfi6p\nVCpzl1DtEhMToVarzV0GUa3k7e1daRuzBsCIESN05/kBICgoCOPGjYOfnx8uXryIzZs3Y+rUqbh4\n8SLS09Ph6+tr0Hbd3Nxgb29vqrKfSwqFAkg0dxXVy93dHe7u7uYug+i5ZdYAKE0mk0EIAQCYOXMm\nQkJC4O/vD1tbW0RHRxt0SAMUH/oY2lYqLHE8+Hcmeja1KgA2bNig+79SqcSaNWvMWA0RkWUz+6eA\niIjIPBgAREQSVatOARGR6RQWFiIpKcncZVSr1q1bw9qaL2NVxZEjkoikpCRELYxB/ReczF1Ktch5\nkIFZYaP4SbBnwAAgkpD6LzhB2dCw79OQ5eM1ACIiiWIAEBFJFAOAiEiiGABERBIliYvA/PgbEdHT\nJPEKkpSUhA/mrofiBWXljZ8D6geZ+GruWH78jYieiSQCAAAULyhRt0ETc5dBRFRrSCYASNp4GpDo\nadx7SBKSkpKw/aPpaKSwjN+JuKd+jEFffMbTgPRMGAAkGY0U9mjq4GDuMohqDX4MlIhIohgAREQS\nxQAgIpIoBgARkUQxAIiIJIoBQEQkUQwAIiKJYgAQEUkUA4CISKIYAEREEsUAICKSKAYAEZFEmf1m\ncOPHj0dGRgZkMhkcHBwQFhYGDw8PBAUFITU1FfXq1QMABAYGYuzYsWaulojIcpg9AJYsWQKH/7tD\nY1xcHD7++GPs2rULABAaGgo/Pz9zlkdEZLHMfgrI4Ynb8+bm5kIul+umi4qKzFESEZEkmP0IAAA+\n/vhjnD59GjKZDKtWrdLNj46Oxpdffom//e1vCA4ORvPmzQ3anlarhVar1Zu2NKX7aEh7S2PMGEi9\n/yXtLY2xYyAlT76ZLk+tCIDIyEgAQGxsLKKiorBq1SpER0fD2dkZABATE4PJkydj7969Bm3v6tWr\netMqlap6C64FEhMToVarDW4v9TGQev8BjoHUeHt7V9qmVgRAicDAQISHhyMnJ0f34g8Ao0aNQmRk\nJHJyclC/fv1Kt+Pm5gZ7+//+9J9CoQD2JZukZnNxd3c36ucAFQoFkGjCgszAmDFQKBTIMHE9Na0q\n+8Cxg7dMWFHNM3YMSJ9ZA+DBgwdQq9Vo3LgxgOKLwA0bNoSDgwMyMzOhVCoBAAcOHICTk5NBL/5A\n8aHPk4c/hhwKPW9K99GQ9pbGmDGQev9L2lsaY8eA9Jk9AKZNm4b8/HzIZDIolUqsXLkSBQUFmDhx\nIjQaDWQyGRwdHbF8+XJzlkpEZHHMGgCurq7Ytm1bmcu2b99ew9UQEUmL2T8GSkRE5sEAICKSKAYA\nEZFEMQCIiCSKAUBEJFEMACIiiWIAEBFJFAOAiEiiGABERBLFACAikigGABGRRDEAiIgkigFARCRR\nDAAiIoliABARSRQDgIhIohgAREQSxQAgIpIoBgARkUQxAIiIJIoBQEQkUQwAIiKJYgAQEUkUA4CI\nSKIYAEREEsUAICKSKGtzFzB+/HhkZGRAJpPBwcEBoaGhaNOmDbKyshASEoJbt27B1tYW4eHh8PHx\nMXe5REQWw+wBsGTJEjg4OAAA4uLiMHv2bOzatQtRUVHw8vLC6tWr8fvvv2Pq1Kk4fPgw5HK5mSsm\nIrIMZj8FVPLiDwC5ubm6F/j9+/djxIgRAIC2bdvC2dkZ8fHxZqmRiMgSmf0IAAA+/vhjnD59GjKZ\nDKtWrUJ2dja0Wi2USqWujaurK9LS0gzanlarhVar1Zu2NKX7aEh7S2PMGEi9/yXtLY0xY1BYWIik\npCQTV1SzWrduDWvrsl/GDTlbUisCIDIyEgAQGxuLqKgo/Pvf/4YQosrbu3r1qt60SqV6pvpqo8TE\nRKjVaoPbS30MpN5/gGOgUqlw/Y9dcG5cz8RV1Yy76bl46eX+aNGiRZnLvb29K91GrQiAEoGBgZg7\ndy4AwNraGpmZmbqjgJSUFLi4uBi0HTc3N9jb2+umFQoFsC+52us1J3d3d7i7uxvcXqFQAIkmLMgM\njBkDhUKBDBPXU9Oqsg8cO3jLhBXVPGP3gcf36qG5q6OJq6o5xu4DpZk1AB48eAC1Wo3GjRsDKL4I\n3KBBAzRo0AC9e/fG5s2bMXXqVFy8eBHp6enw9fU1aLtyuVzv8McSLxyX7qMh7S2NMWMg9f6XtLc0\n3AeM2wdKM3sATJs2Dfn5+ZDJZFAqlVixYgUAYObMmQgJCYG/vz9sbW0RHR1tkX9AIiJzMWsAuLq6\nYtu2bWUuUyqVWLNmTQ1XREQkHWb/GCgREZkHA4CISKIYAEREEsUAICKSKAYAEZFEMQCIiCSKAUBE\nJFEMACIiiWIAEBFJFAOAiEiiGABERBLFACAikigGABGRRDEAiIgkigFARCRRDAAiIoliABARSRQD\ngIhIohgAREQSxQAgIpIoBgARkUQxAIiIJIoBQEQkUQwAIiKJYgAQEUkUA4CISKKsq7rirVu3cPny\nZTx+/FhvfmBgoMHbKCgoQHBwMJKSklCnTh0olUrMnTsXzZs3R1BQEFJTU1GvXj3ddseOHVvVcomI\nqJQqBcCaNWvw+eefo1WrVlAoFLr5MpnMqAAAgGHDhuGNN94AAMTExCA0NBQbNmwAAISGhsLPz68q\nJRIRUSWqFABr167Fd999h3bt2j3Tg9va2upe/AHg1Vdfxdq1a3XTRUVFz7R9IiIqX5WuAchkMnh6\nelZ3LdiwYQN69uypm46Ojka/fv0wffp03Lp1q9ofj4hIyqp0BDB58mQsWbIEH330EeRyebUUsmLF\nCqhUKixYsABA8Yu/s7MzgOJTQ5MnT8bevXsN2pZWq4VWq9WbtjSl+2hIe0tjzBhIvf8l7S0N94Hy\n+2/Ia3OVAmDFihW4f/8+vv32W9SvX19v2bFjx4ze3po1axAXF4dvv/0WdnZ2AKB78QeAUaNGITIy\nEjk5OU89XlmuXr2qN61SqYyuqbZLTEyEWq02uL3Ux0Dq/Qc4BlLrv7e3d6XrVykAPvvss6qsVqZ1\n69Zh7969WL9+PRwcHAAUp1p2djaUSiUA4MCBA3BycjLoxR8A3NzcYG9vr5tWKBTAvuRqq7k2cHd3\nh7u7u8HtFQoFkGjCgszAmDFQKBTIMHE9Na0q+8Cxg5Z1KtXYfeD8vf+YuKKaZew+UFqVAsDX17fK\nD/iku3fvIjIyEi1atMCYMWMghICdnR2+/fZbTJw4ERqNBjKZDI6Ojli+fLnB25XL5XqHP9V1mqo2\nKd1HQ9pbGmPGQOr9L2lvabgPGLcPlFbl7wFs27YNu3btwt27d+Hs7Iz+/ftjyJAhRm3D2dkZV65c\nKXPZ9u3bq1oaEREZoEoBsHLlSmzfvh3vvfcemjZtitu3b2P16tXIzMzE5MmTq7tGIiIygSoFwLZt\n27B27Vq0aNFCN69r16549913GQBERM+JKn0P4OHDh2jSpInePGdnZzx69KhaiiIiItOrUgD4+vpi\n3rx5ePjwIYDiQFi4cCE6duxYrcUREZHpVCkA5syZA5VKBV9fX3Tq1Am+vr64ceMGwsPDq7s+IiIy\nkSpdA3BycsLGjRtx584dpKenw9nZWe+LW0REVPsZHAAajQY2NjYAim/jDACOjo5wdHTUm2dra1vd\nNRIRkQkYHACdOnXCuXPnAADt2rWDTCbTWy6EgEwmw+XLl6u3QiIiMgmDA+DJG7EdOnTIJMUQEVHN\nMfgisIuLi+7/J06cQNOmTZ/6d+LECZMUSURE1a9KnwJatGhRmfOjoqKeqRgiIqo5Rn0K6Pr16wCK\nz/ffuHEDQgjdMpVKxQvARETPEaMC4B//+Ifu4m/v3r31ljk5OeHDDz+svsqIiMikjAqAkjt3Dh8+\nHFu2bDFJQUREVDOqdA2AL/5ERM+/Kn0TWKPRYNOmTYiPj8f9+/f1ljEciIieD1U6AoiIiMCOHTvQ\nuXNnJCYmok+fPsjNzUXXrl2ruz4iIjKRKgXAwYMHsWrVKowZMwZyuRxjxozB0qVLcfr06equj4iI\nTKRKAZCfn6/7PYA6derg8ePHeOmll/Dnn39Wa3FERGQ6VboG0Lp1a/z222/w8vLCK6+8giVLlsDB\nwYF3BCUieo5U6QggLCwMderUAQDMnj0biYmJOHr0KBYsWFCtxRERkekYfQSg1WqhUqnQs2dPAEDL\nli3x7bffVnddRERkYkYfAcjlcoSFhfG2D0REz7kqnQLq0qULzpw5U921EBFRDarSRWClUolJkybh\nrbfegqurq96Pw0yfPr3aiiMiItOpUgAUFBTA398fAJCRkVGtBRERUc2oUgCU93sARET0/KhSAKSm\nppa7zNXV1eDtFBQUIDg4GElJSahTpw6USiXCw8PRokULZGVlISQkBLdu3YKtrS3Cw8Ph4+NTlXKJ\niKgMVQoAPz8/yGQy3Q/CPHkNwNgfhR82bBjeeOMNAEBMTAxCQ0OxceNGREVFwcvLC6tXr8bvv/+O\nqVOn4vDhw5DL5VUpmYiISqlSAFy8eFFvOj09HUuXLjX6ZnC2tra6F38AePXVV7F27VoAwP79+xEX\nFwcAaNu2LZydnREfH4/OnTtXpWQiIiqlSgFQ+jsAzZo1w7/+9S/069cPffv2rXIxGzZsQM+ePZGd\nnQ2tVgulUqlb5urqirS0NIO2o9VqodVq9aYtTek+GtLe0hgzBlLvf0l7S8N9oPz+G3K2pEoBUJbM\nzEzk5uZWef0VK1ZApVJhwYIFUKvVz1TL1atX9aZVKtUzba82SkxMNGqcpD4GUu8/wDGQWv+9vb0r\nXb9KATB9+nS98/5qtRpnz55FYGBgVTaHNWvWIC4uDt9++y3s7OxgZ2cHuVyOzMxM3VFASkoKXFxc\nDNqem5sb7O3tddMKhQLYl1yl2mord3d3uLu7G9xeoVAAiSYsyAyMGQOFQgFL+8ByVfaBYwdvmbCi\nmmfsPnD+3n9MXFHNMnYfKK1KAdCqVSu9aXt7ewQFBVXp/Py6deuwd+9erF+/Hg4ODrr5vXv3xubN\nmzF16lRcvHgR6enp8PX1NWibcrlc7/DHEi8cl+6jIe0tjTFjIPX+l7S3NNwHjNsHSjM6APLz8+Ho\n6IiEhATk5OSgfv368PHxQYcOHYx+8Lt37yIyMhItWrTAmDFjIISAnZ0dvv/+e8ycORMhISHw9/eH\nra0toqOjLfIPSERkLkYFwKNHjzBixAhkZWWhS5cuaNOmDdLT07Fs2TJs2bIF3333nd67+Mo4Ozvj\nypUrZS5TKpVYs2aNMeUREZERjAqA1atXo1GjRk+90D98+BDBwcFYvXo1Pvroo2ovkoiIqp9RdwM9\nfPgwZs2a9dS7fAcHB8yYMQP/+Y9lXWAhIrJkRgVAamoqPDw8ylzm4eFR4S0iiIiodjEqAIqKip5p\nORER1R5GXQPQaDTYunWr7h5ApRUWFlZLUUREZHpGBcCrr76KPXv2lLu8Xbt2z1wQERHVDKMCYOPG\njaaqg4iIaliVfhOYiIiefwwAIiKJYgAQEUkUA4CISKIYAEREEsUAICKSKAYAEZFEMQCIiCSKAUBE\nJFEMACIiiWIAEBFJFAOAiEiiGABERBLFACAikigGABGRRDEAiIgkigFARCRRDAAiIoliABARSZRR\nvwlc3RYuXIjDhw8jNTUVsbGx8PDwAAAEBQUhNTUV9erVAwAEBgZi7Nix5iyViMjimDUAevfujfff\nfx8jR458alloaCj8/PzMUBURkTSYNQB8fHwAAEKIp5YVFRXVdDlERJJi1gCoSHR0NL788kv87W9/\nQ3BwMJo3b27wulqtFlqtVm/a0pTuoyHtLY0xYyD1/pe0tzTcB8rvv1wur3T9WhkA0dHRcHZ2BgDE\nxMRg8uSsIWu9AAAWNElEQVTJ2Lt3r8HrX716VW9apVJVa321QWJiItRqtcHtpT4GUu8/wDGQWv+9\nvb0rXb9WBkDJiz8AjBo1CpGRkcjJyUH9+vUNWt/NzQ329va6aYVCAexLrvY6zcnd3R3u7u4Gt1co\nFECiCQsyA2PGQKFQIMPE9dS0quwDxw7eMmFFNc/YfeD8vf+YuKKaZew+UFqtCwCtVovs7GwolUoA\nwIEDB+Dk5GTwiz9QfOjz5OGPIYdCz5vSfTSkvaUxZgyk3v+S9paG+4Bx+0BpZg2AOXPm4MiRI8jM\nzMSECRNQt25d7Nq1CxMnToRGo4FMJoOjoyOWL19uzjKJiCySWQNg/vz5Zc7fvn17DVdCRCQ9/CYw\nEZFEMQCIiCSKAUBEJFEMACIiiWIAEBFJFAOAiEiiGABERBLFACAikigGABGRRDEAiIgkigFARCRR\nDAAiIoliABARSRQDgIhIohgAREQSxQAgIpIoBgARkUQxAIiIJIoBQEQkUQwAIiKJYgAQEUkUA4CI\nSKIYAEREEsUAICKSKAYAEZFEMQCIiCTKrAGwcOFC+Pn5wcPDA1euXNHNz8rKwoQJE+Dv74+AgAAk\nJCSYsUoiIstk1gDo3bs3Nm/ejKZNm+rNj46OhpeXFw4cOICIiAjMmDEDWq3WTFUSEVkmswaAj48P\nnJ2dIYTQm79v3z6MGDECANC2bVs4OzsjPj7eHCUSEVmsWncNIDs7G1qtFkqlUjfP1dUVaWlpZqyK\niMjyWJu7AFPQarV6p4ws8fRR6T4a0t7SGDMGUu9/SXtLw32g/P7L5fJK1691AdCgQQPI5XJkZmbq\njgJSUlLg4uJi8DauXr2qN61Sqaq1xtogMTERarXa4PZSHwOp9x/gGEit/97e3pWuX+sCAPjvxeGp\nU6fi4sWLSE9Ph6+vr8Hru7m5wd7eXjetUCiAfcmmKNVs3N3d4e7ubnB7hUIBJJqwIDMwZgwUCgUy\nTFxPTavKPnDs4C0TVlTzjN0Hzt/7j4krqlnG7gOlmTUA5syZgyNHjiAzMxMTJkxA3bp1ceDAAcyc\nORMhISHw9/eHra0toqOjDTqcKSGXy/XaG7Pu86J0Hw1pb2mMGQOp97+kvaXhPmDcPlCaWQNg/vz5\nZc5XKpVYs2ZNDVdDRCQtte5TQEREVDMYAEREEsUAICKSKAYAEZFEMQCIiCSKAUBEJFEMACIiiWIA\nEBFJFAOAiEiiGABERBLFACAikigGABGRRDEAiIgkigFARCRRDAAiIoliABARSRQDgIhIohgAREQS\nxQAgIpIoBgARkUQxAIiIJIoBQEQkUQwAIiKJYgAQEUkUA4CISKIYAEREEsUAICKSKGtzF1ARPz8/\n2NnZwc7ODjKZDBMnTsQ//vEPc5dFRGQRanUAyGQyfPHFF3B3dzd3KUREFqdWnwISQkAIYe4yiIgs\nUq0+AgCAkJAQAEC7du0wffp0ODo6VrqOVquFVqvVm7Y0pftoSHtLY8wYSL3/Je0tDfeB8vsvl8sr\nXb9WB8B3332HJk2aQKvV4vPPP8fs2bOxatWqSte7evWq3rRKpTJViWaTmJgItVptcHupj4HU+w9w\nDKTWf29v70rXr9UB0KRJEwDFSTZ27Fj07t3boPXc3Nxgb2+vm1YoFMC+ZJPUaC7u7u5GXRtRKBRA\nogkLMgNjxkChUCDDxPXUtKrsA8cO3jJhRTXP2H3g/L3/mLiimmXsPlBarQ0AtVqNwsJCvPDCCwCA\nH3/8EZ6engatK5fL9Q5/DDkUet6U7qMh7S2NMWMg9f6XtLc03AeM2wdKq7UBkJGRgQ8//BBFRUUQ\nQqB58+aIjIw0d1lERBaj1gZA8+bNsXPnTnOXQURksWr1x0CJiMh0GABERBLFACAikigGABGRRDEA\niIgkigFARCRRDAAiIoliABARSRQDgIhIohgAREQSxQAgIpIoBgARkUQxAIiIJIoBQEQkUQwAIiKJ\nYgAQEUkUA4CISKIYAEREEsUAICKSKAYAEZFEMQCIiCSKAUBEJFEMACIiiWIAEBFJFAOAiEiiGABE\nRBJVqwPg5s2bGD58OPz9/TFkyBAkJSWZuyQiIotRqwNgzpw5GD58OA4cOIAJEybg448/NndJREQW\no9YGQFZWFv744w/069cPAODv7487d+7g1q1bZq6MiMgyWJu7gPKkpaWhUaNGsLL6b0a5uLggNTUV\nzZs3L3OdoqIiAMCjR4+g1Wp18zUaDRoqtLCzfmjaomuIrUILjUaDBw8eGLyORqNBfeGAOo9tTVhZ\nzZELB6PGQKPRIK9BfWTVqWPiympGnrW8SvuATR0NiqxyTVhZzbGpozF6H3iUZ4f0+yYurIY8yrOr\nsP9yuRx16tTRew0tTSaEEKYq8Fn88ccfmDlzJvbt26ebN2TIEMycOROdOnUqc53MzEzcuHGjhiok\nIqrd2rRpA3t7+3KX19ojABcXF9y7dw9FRUW6BEtLS4Orq2u569SvXx8tW7aEnZ1dhalHRCQFdSo5\n4q21AeDo6AhPT0/s2rULAwYMwP79+9GkSZNyT/8AgLW1NZRKZQ1WSUT0/Kq1p4AA4Pr16/jkk09w\n//59vPDCC1i0aBH+/ve/m7ssIiKLUKsDgIiITIcnyomIJIoBQEQkUQwAIiKJYgAQEUkUA6AW0Wg0\nmDRpEvr164cFCxZU2Hbnzp2YMmWKyWo5dOgQ+vTpgwEDBuDatWtltklPT8fo0aN10x4eHnj40Phv\nW7/22mtITU2tcq016ck+Tpo0iV88rCI/Pz9cuXLF6PUGDRqEM2fOmKAi8/vll18QFBRUY+sBtfh7\nAFL0559/QqVS6X37uSIymcxktWzZsgVTp05Fnz59ym3TuHFjbNq06ZnrMWU/qtuTta5cudKMlZAl\nqunnkMUHQH5+PmbPno1r167pvig2adIkREREIDY2FgBw7do1TJo0CYcPHwZQnKhff/01NBoNrKys\nMG/ePLRr1w7nz59HVFQUHj16BACYNm0a/Pz8cPPmTURERCArKwsFBQUYOnQoRo0aVeZjr1mzBjdv\n3sTs2bOhVqtRVFSEHj16oG/fvpg1axbS09MxYMAAvPvuu7h16xZyc3Pxz3/+EwAQExODS5cuYdGi\nRSYds4ULFyIhIQHXr1/Hxo0b0bRpU9y4cQMajQYuLi749NNPoVQqkZKSgsDAQN07sic/UVzemADF\nRxeLFy+GjY0NXn/9dZP2pbo92Uc/Pz8sW7YMjx8/Rnh4OPbs2aNbFhQUhHHjxsHPzw/Hjh3DsmXL\nkJ+fD7lcjhkzZpR7O5OaUta+fObMGSQkJKCwsBAODg5YsGABWrZsCaD4yCc4OBhxcXG4f/8+/t//\n+38YOHAgACApKQkRERG4d+8eZDIZRo4ciWHDhiEjIwMLFy5Eamoq8vLy0KNHD0ybNu2pWipqd+7c\nOcyfPx9arRavvPKK7n5fhjL2+Z+VlYVZs2bp+vLyyy8jIiICO3fuRGxsLOzt7aFSqeDo6IjIyEjd\nnQnWrl2L/fv3o7CwEEqlEvPnz4eLiwu+/vprJCUlQa1W49atW2jUqBG+/PJL1KtXD4WFhVi4cCFO\nnjyJ+vXrw9vbW6/2Xbt2ISYmBoWFhahbty5CQ0Ph4eFR6XpGERbu4MGDYvz48brpnJwccfr0aREY\nGKibd/XqVeHn5yeEECI5OVl07txZXL9+XQghRGFhoXjw4IHIzs4WXbp0EWfPntXbllarFQMHDhTJ\nyclCCCHUarUICAgQv//+e5mPLYQQCxcuFCtXrnxqfum6vvrqKxEREaGb3rRpk5g9e7YQQogdO3aI\nKVOmPNvgVGD06NEiLi5OCCFEVlaWbv7KlSvFnDlzhBBC3L59W3Ts2FG3zN3dXTx48KDCMcnMzBS+\nvr4iKSlJCCHE999/Lzw8PERKSorJ+lKdSvoohBBvvfWWuHz5shBCCH9/f3Hp0iUhhBAqlUq8/vrr\nQqvVCpVKJYYNGyYePnwohBDi5s2bomvXrqKgoMA8HRCi3H35yb/z3r179fZdd3d3sW7dOiGEEElJ\nSaJ9+/ZCq9WKwsJC8fbbb4u9e/fq2t6/f18IIcR7770nzpw5I4Qofh6NHz9e7N+/XwihP3bltSso\nKBDdu3cXJ0+eFEIIcezYMeHu7i7i4+MN7quxz/9169bp9u+S9kIUP9/atm2r26e/+eYb8d577wkh\nhNizZ48ICwsTRUVFQgghYmNjxcSJE4UQxc9hPz8/3XaCg4N1z/1NmzaJd999VxQWFgqNRiOCgoJE\nUFCQEEKIs2fPivfff1+3n5w5c0a88847la5nLIs/AnB3d0dycjLmz58PHx8fdO/evcL2J06cwBtv\nvKF75yOXy+Hg4IAjR46gVatW6NChg65tvXr1kJSUhL/++gvTp0/XvTt8/Pgx/vrrL3h7e5f52B07\ndtS9+/L19UWXLl1M0/lqsnv3buzevRsFBQUoKChAw4YNK2x//fr1csckPT0dHh4eaNWqFQBg8ODB\nlV7veB4MHDgQO3bswMsvv4zY2FgEBATAysoKv/76K1QqFUaPHq0bC2tra6SlpaFFixZmqfXChQtl\n7st79uxBTEwMHj16hKKiIuTk5OitFxAQAABo1aoVbGxscO/ePTx48AAajUbvVGGDBg2gVqtx6tQp\nZGVl6fqtVqtx/fp1vW1W1C45ORnW1tZ47bXXAABdu3at8FYwZTH2+e/l5YUNGzYgMjISPj4+6Nat\nm25Z+/bt8dJLLwEAhg0bhiVLlkAIgbi4OFy6dAkDBgwAUHxX4idPyXTr1g316tXTbb/kmtqpU6fQ\nv39/yOVyAMXXN7Zv3w6g+Cg5MTERQ4cO1Y1Lbm4uCgoKKlzPWBYfAM2bN8fevXtx6tQpHD9+HNHR\n0fj3v/+NwsJCXZv8/Pwqb18IgQYNGmDnzp1lLi/92Lt27cLbb7+NDh064Pjx49i0aRPWr1+PVatW\nPbWuXC7Xu631s9RZVWfPnsWmTZuwdetWNGzYEIcPH8ZXX31VZtuSnb6iMSk5zWZpAgMDERgYiJCQ\nEMTGxur+nkIIdOnSBdHR0WausGJpaWlYuHAhtm/fjmbNmiExMVHvwqJMJoOdnZ3e9JP7ZmlCCMhk\nMmzduhU2NjZVapeYmPhUe2PPdRv7/Pfy8kJsbCxOnDiBgwcPYsmSJdi1a1eZdT/5/0mTJmHIkCFl\n1vDkuMnlcr3HLq9vQggEBgYiODi40j4+yzU0i/8U0N27dwEAb731FkJCQnTz09LScP9+8Y3Bn/wD\nd+vWDceOHdO9UyksLMTDhw/Rvn173LhxA2fPngVQ/AfKycnBSy+9BAcHB+zYsUO3DZVKhZycHL3H\nLvk1s7S0NNy8eRNKpRL9+/fHrFmz8Ntvv5VZ+4svvohLly6hqKgIarUaP//8c3UNi8Fyc3Ph4OCA\n+vXro6CgAN9//73e8tJPBADljklubi68vLyQmJioG9/t27eX+4R4njRu3Bht27ZFREQEnJyc0Lp1\nawDF+9PJkyf1XswuXrxorjIBoMx9+fbt27CxsUGjRo0ghNC7uF/SpiwvvfQS6tSpg59++kk37/79\n+7C3t0enTp30LpSnp6frnhMlKmrXqlUraLVaxMfHAyg+OlepVEb11djn/+3bt6FQKNC7d2+EhYXh\n5s2buuskFy5c0O2327ZtQ6dOnSCTydCzZ09s3rxZd8RUWFiIy5cvV1pb586dsWfPHhQWFqKgoEDv\n+eLn54c9e/YgLS0NQPH4X7p0qdL1jGXxRwCJiYn47LPPAABarRb9+/eHj48Pxo8fj8GDB6NRo0Z6\nh3ktWrRAREQEZs6cicLCQsjlcsybNw9t27bF0qVLsWjRIjx69AhyuRzTpk3Dm2++iRUrVuDTTz/F\n+vXrodVq0bBhQyxevLjMx3Zzc8OqVauwe/du2NjYQAiB+fPnl1l7r169sH//fvTp0wdNmjSBp6cn\n1Gq16QcN/31X0a1bN+zevRu9e/dGw4YN0blzZ6Snpz/V7sn/y+XycsekcePGiIiIwJQpU2Bra4tu\n3bqhQYMGNdKn6lBWf0sMGDAAwcHBmDdvnm5eixYtEB0djfDwcOTl5UGj0aBNmzZmPSKoV69emfty\nnz590KdPHzRs2BA9e/bUW6d0X5/8Wy9btgwLFizA8uXLIZfLMXLkSAwdOhRRUVFYtGiR7tRR3bp1\nMW/ePDg7O+ttr6J2n3/+OebOnYuioiK0bdsWbdq0Maqvxj7/4+PjsW7dOt3Rd0hICBwcHAAUB2d0\ndDRu3ryJhg0bIjIyEkDxqbGcnByMGTNGd2Q0aNCgSmsdOnQorl27hnfeeUd3MfePP/4AAPj4+GDm\nzJmYMmUKioqKoNFo0L17d7zyyisVrmcs3gyOiKgSO3fuxKFDh/D111+bu5RqZfGngIiIqGw8AiAi\nkigeARARSRQDgIhIohgAREQSxQAgIpIoBgARkUQxAIiIJIoBQEQkUQwAov9z7tw5jBo1Ch07doSv\nry8GDx6Mo0ePIj4+3ujfLSj5HQCi2szi7wVEZIiHDx9i8uTJCA0NRUBAALRaLS5evAiZTAaNRmPu\n8ohMgt8EJgJw6dIljBkzBufOndOb//DhQ3Tt2hUajQZ2dnaQyWT44YcfYGtri7CwMFy+fBlWVlbo\n0qUL5s6dixdeeAEzZszATz/9BDs7O1hZWWHMmDEYMmQIevTogYsXL8LW1hYAMH36dLRq1QpTp05F\ndnY2/vnPfyIhIQFA8V02V65c+VzdKI+ePzwFRASgZcuWsLOzw4wZM/DLL78gKysLAODg4IBvvvkG\njo6OOH/+PM6dO4dWrVpBCIGJEyfi+PHj2LdvH+7du4clS5YAABYvXgwXFxcsXboU586dw0cffQSg\n4vu2r127FkIIHDt2DKdPn0Z4eLjefeSJTIEBQITiF/otW7bA3t4e8+fPR7du3TB27Nhy7z/fvHlz\ndOnSBdbW1mjQoAHGjh2ru299CWMOrm1sbJCdnY3r169DJpPB09MTCoXimfpEVBleAyD6Py+++KLu\n5ynT0tIQFhaGkJAQTJ8+/am2mZmZ+PTTT5GQkIDHjx9Dq9Xq7htfFePHj0d+fj4+/PBDqNVq9OvX\nD8HBwbqf/SMyBR4BEJXBxcUFQUFBuHbtWpmnbj777DMIIfDjjz8iISEB0dHReu/4S69jb28PAMjL\ny9PNu3fvnt7ymTNn4sCBA4iJicHBgwcRGxtb3d0i0sMAIAKQnJyMNWvW6H6CLzMzE9u2bUP79u2h\nVCqRm5uL3NxcXftHjx5BoVCgbt26uHv3LtauXau3PScnJ73TRw0bNkSTJk2wc+dOFBUVIS4uDhcu\nXNAt/+WXX3Djxg0IIVC3bl3Y2Njw3T+ZHAOACMU/Q3jp0iUMHz4c7du3x4ABA1C3bl387//+L1q1\naoV+/fqhV69e8PX1RXJyMqZOnYorV66gY8eOmDx5Mnr16qW3vYkTJ2L16tXw9fXFl19+CQBYuHAh\nNm3ahE6dOuHo0aPw8/PTtb958yYmTJiADh06oH///ujWrRv69+9fo2NA0sOPgRIRSRSPAIiIJIoB\nQEQkUQwAIiKJYgAQEUkUA4CISKIYAEREEsUAICKSKAYAEZFEMQCIiCSKAUBEJFEMACIiifr/RfXe\n5DsKucsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1120bad90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot showing rates of success by duration\n",
    "sns.factorplot(x='status', y='duration', data=kickstarter_df, kind='bar', ci=None)\n",
    "plt.xlabel('Status')\n",
    "plt.ylabel('Duration')\n",
    "plt.title('Project Status by Duration')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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URNOmTRk4cCBOpxM/Pz/ef/99xowZw9ixY+natSu+vr6kpKToPw4REQ+qVACc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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112433790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot showing rates of success by duration\n",
    "sns.factorplot(y='duration', x='status_bool', data=kickstarter_df, kind='bar', ci=None)\n",
    "plt.xlabel('Funded')\n",
    "plt.ylabel('Duration')\n",
    "plt.title('Project Sucess by Duration')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There does not seem to be any correlation between  length of time to run a campaign and success. About a month."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2: What's the ideal pledge goal?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tbfTihBBC2I/NATBy5Ej27t0LwNNPP81TTz3F4MGDiYuLa/TihBBC2I/N+wAS\nEhLU/w8ZMoTg4GAqKiro2LFjoxYmhBDCvq77YnCFhYUcPXoUb29v+fAXQohmyOYAKC0t5YUXXiAn\nJwcPDw/KysoICAhg0aJFeHt726NGIYQQdmDzPoDZs2fTuXNndu3axc6dO/n222/x9/dn1qxZNj94\nVVUVzz33HFFRUQwZMoSEhAQKCwsBiI+P5+GHHyY2NpbY2Fg++OADdbnKykomT55MREQEUVFRbNmy\nRR1TFIU5c+YQHh5OZGQkq1evtnrMJUuWEB4eTkREBIsWLbK5ZiGEuFXYvAawe/duduzYgdFoBKBF\nixa88sorPPjgg9dVwMiRI9VlV69ezbRp0/jwww8BmDZtGmFhYVcsk5qaitFoJDMzkyNHjjBixAhC\nQkIwmUykp6eTn59PVlYW5eXlxMbGEhISQseOHcnOzsZsNpORkYFOpyMuLo5evXrRr1+/66pdCCGa\nM5vXANzd3SkuLraaVlJSgvt1/GiG0Wi0Co7u3btTVFSk3q7v0FKz2aweddS+fXuCg4PJyspSx4YP\nHw6AyWQiOjqajIwMdSwmJgYXFxeMRiPDhg1j06ZNNtcthBC3gus6DDQhIYFVq1axbds2Vq1axZgx\nYxrlMhAffvgh/fv3V2+npKQQExPDiy++qG4aAiguLsbX11e97efnR0lJiTrm5+dn85gQQmiNzZuA\nxo4di6enJxs3blSPApowYcINXzH03XffpaCggDlz5gAXP/wv7VRevXo1zz77rEO/rVssFiwWi8Me\nX9x8t8Lf25bXrfTbPNXXs8FguOay13UY6NChQxk6dOj1LFqn1NRUtm7dyvvvv4+LiwuA1RFFo0aN\nYsGCBZSXl2MymfDz86O4uBgvLy8AioqKCA0NBcDX15eioiK6d++ujvn4+FiNXXL52LUcPHjwxhsV\nzUpBQYGjS7hhubm5VFRUNGhe6bd5qq/nwMDAay7b4AAoKyvjhx9+oG/fvgC8/fbb1NTUqOPPPPMM\nrVq1aujdqVauXMmmTZv44IMP1P0IFouFsrIyPD09AdiyZQteXl6YTCYAIiMjWbNmDQEBARQWFpKd\nnc3MmTMBiIqKYu3atURFRXH69GnMZjPLli1Tx+bMmUN8fDx6vZ60tDSef/75BtXZuXNn3NzcbO5P\nNF+urq4cd3QRN8jf3x9/f/8Gzevq6srOrMJrz9iE2drv3mNf2Lki+7Ol599qcACsXLkSZ2dnNQBW\nrlxJVFTRk5kQAAAgAElEQVQUcPGkMJ1OR2Jiok0PfvToURYsWMCdd97Jk08+iaIouLi48P777zNu\n3Diqq6vR6XS0bt2apUuXqsslJCQwdepUwsPDMRgMJCcn4+HhAcDgwYPZv38/ERER6PV6Ro8eTadO\nnQAIDg4mOjqagQMHotPpGDBgQIOPADIYDA1apRK3jlvh723L61b6bZ5u5LOpwQHw+eef89577/1v\nQScn5s+fD8Cvv/7K2LFjbQ4Ab29vfvzxxzrH0tLS6l3O1dW13mP49Xo906dPZ/r06XWOT5gwgQkT\nJthUpxBC3IoafBRQaWmp1fbywYMHq/9v164dR48ebdzKhBBC2FWDA6C2tpaysjL19muvvab+v6ys\nTC4HLYQQzUyDAyAgIACz2Vzn2ObNm+nWrVujFSWEEML+GrwPYNy4cUyYMIHz588TGRlJ27ZtKS0t\nJTMzkyVLlvDOO+/Ys04hhBCNrMEBEBISwl//+lfmz59PSkqKOr1du3bMnTuXPn362KVAIYQQ9mHT\niWARERFERESQn5/PqVOnaNWqFXfffbe9ahNCCGFH13UmsHzoCyFE82fzxeCEEELcGiQAhBBCoyQA\nhBBCo65rH0B+fj6bN2/m2LFjzJgxg7y8PKqrq+nSpUtj1yeEEMJObF4DyMrKIi4ujqKiItLT0wE4\nd+4cf/3rXxu9OCGEEPZj8xrAW2+9xXvvvUdAQACZmZkAdOnShdzc3EYvTgghhP3YvAZw7Ngx9bIP\nOp0OuHg5UrkWkBBCNC82B0Dnzp354gvrH1HYsWMHXbt2bbSihBBC2J/Nm4CSkpIYO3Ys999/P5WV\nlbz66qv8+9//Zvny5faoTwghhJ3YHAABAQFs3LiRDRs2YDKZ8PHx4YUXXrD6DV8hhBBN33UdBtq2\nbVvGjBnT2LUIIYS4iWwOgFdffbXO6UajER8fH8LDw+nYseMNFyaEEMK+bN4JbDQa2bx5M+fOnaNl\ny5acO3eOzZs3Y7FYyMnJITY2lk2bNtmjViGEEI3I5jWAo0ePsnTpUkJCQtRp3377LStXruTdd9/l\n888/58033+SRRx5p1EKFEEI0LpvXALKzs+ndu7fVtKCgIHbt2gXAQw89RHFxceNUJ4QQwm5sDoAO\nHTrw/vvvW0374IMP6NChA3DxRLEWLVo0SnFCCCHsx+ZNQHPnzuW5555j5cqV6u8CG41GlixZAkBh\nYSGJiYmNXqgQQojGZXMAdOnShS1btrB3716OHTtG27Zt6dGjB05OF+8qKCiIoKCgRi9UCCFE47qu\n8wCcnJyu2A8ghBCieWlQAISFhakXfruazz///IYLEkIIcXM0KABmzZql/v/AgQOkpaUxatQofH19\nKS4u5p///CfDhg2z+cGrqqpITEwkLy+P2267DU9PT2bMmMGdd97JyZMnSUpKorCwEKPRyIwZM9RN\nS5WVlUybNo19+/ah1+tJTEwkMjISAEVRmDt3Ljt27ECv1/Pkk08yatQo9TGXLFnCunXr0Ol0REdH\ny/4KIYRmNSgAHnjgAfX/KSkprFixgjvuuEOd9tBDDzFx4kTGjh1rcwEjR47kwQcfBGD16tVMmzaN\nVatWsXDhQnr06MGKFSvYt28fEydOZNu2bRgMBlJTUzEajWRmZnLkyBFGjBhBSEgIJpOJ9PR08vPz\nycrKory8nNjYWEJCQujYsSPZ2dmYzWYyMjLQ6XTExcXRq1cv+vXrZ3PdQgjR3Nl8GGhRURFeXl5W\n01q3bk1RUZHND240GtUPf4Du3bur5xBs3ryZuLg4ALp164a3t7d6roHZbFbH2rdvT3BwMFlZWerY\n8OHDATCZTERHR5ORkaGOxcTE4OLigtFoZNiwYXLWshBCs2zeCRwSEsKUKVNITEykXbt2lJSU8Pbb\nb3PffffdcDEffvgh/fv3p6ysDIvFgqenpzrm6+tLSUkJAMXFxfj6+qpjfn5+VmN+fn5WYzk5OerY\n5Uco+fn5YTabG1SbxWLBYrFcf3Oi2bkV/t62vG6l3+apvp4NBsM1l7U5AObNm8esWbMYMmQINTU1\nODs7ExERwfTp0229KyvvvvsuBQUFzJkzh4qKihu6L3s4ePCgo0sQN1lBQYGjS7hhubm5DX4/Sb/N\nU309BwYGXnNZmwOgZcuWvPHGGyxcuJCTJ0/SunVr9HqbtyRZSU1NZevWrbz//vu4uLjg4uKCwWDg\nxIkT6lpAUVERPj4+wMVv7sXFxeqmqKKiIkJDQ4GLawpFRUV07979iuUujV1y+di1dO7cGTc3txvq\nUzQvrq6uHHd0ETfI398ff3//Bs3r6urKzqxCO1dkX7b2u/fYF9eesYmzpeffuq5P7vz8fN59912W\nLFmCXq8nPz+fH3/88boKWLlyJZs2bWLlypW4u7ur06OiolizZg0AOTk5lJaWEhwcDEBkZKQ6VlhY\nSHZ2Nv3791eXW7t2LbW1tZSVlWE2mxkwYIA6tmHDBiorK6mqqiItLa3BF60zGAzyT4P/mjvp99bu\nF+rvuSFsDoCsrCzi4uIoKipi/fr1AJw9e5a//vWvtt4VR48eZcGCBZw9e5Ynn3ySIUOGMHLkSACm\nTJnC3r17iYyMZNq0aaSkpKhNJSQkUFlZSXh4OGPHjiU5ORkPDw8ABg8ezN13301ERAQjRoxg9OjR\ndOrUCYDg4GCio6MZOHAgjzzyCKGhoXIEkBBCs2zeBPTWW2/x3nvvERAQQGZmJnDx8hC5ubk2P7i3\nt3e9aw6enp6kpqbWOebq6sqiRYvqHNPr9UyfPr3efRITJkxgwoQJNtcqhBC3GpvXAI4dO0a3bt0A\n1LODDQYDtbW1jVuZEEIIu7I5ADp37swXX1jvONmxYwddu3ZttKKEEELYn82bgJKSkhg7diz3338/\nlZWVvPrqq/z73/9m+fLl9qhPCCGEndgcAAEBAWzcuJENGzZgMpnw8fHhhRdewNvb2x71CSGEsJPr\nuhx027ZtGTNmTGPXIoQQ4iZqUAC8+OKLDboc9BtvvHHDBQkhhLg5GhQAd999t73rEEIIcZM1KAAm\nTpxo7zqEEELcZA0+DPTw4cM89thj9OrVi/j4+Ou6/LMQQoimo8EBMHfuXPz8/Fi0aBFt2rS5rks/\nCCGEaDoafBTQ/v37+eKLL3B1dSUwMFC9wJoQQojmqcFrAFVVVbi6ugLg7u7OhQsX7FaUEEII+2vw\nGkBNTQ3/+te/1NtVVVVWtwH1Sp5CCCGavgYHQPfu3dXf1gW49957rW7rdDoJACGEaEYaHACrVq2y\nZx1CCCFushv7LUchhBDNlgSAEEJolASAEEJolASAEEJolASAEEJo1HX9HoDQppqaGvLy8hxdxg3p\n2LEjTk7yshcCJACEDfLy8khMfQ231i0cXcp1OX/yHIsS5uLv7+/oUoRoEiQAhE3cWrfA3dvk6DKE\nEI1A9gEIIYRGSQAIIYRGSQAIIYRGOTQA5s6dS1hYGF26dOHHH39Up8fHx/Pwww8TGxtLbGwsH3zw\ngTpWWVnJ5MmTiYiIICoqii1btqhjiqIwZ84cwsPDiYyMZPXq1VaPt2TJEsLDw4mIiGDRokX2b1AI\nIZowh+4EjoqKYuzYsTz++ONXjE2bNo2wsLArpqempmI0GsnMzOTIkSOMGDGCkJAQTCYT6enp5Ofn\nk5WVRXl5ObGxsYSEhNCxY0eys7Mxm81kZGSg0+mIi4ujV69e9OvX72a0KoQQTY5D1wCCgoLw9vZG\nUZQrxmpra+tcxmw2ExcXB0D79u0JDg4mKytLHRs+fDgAJpOJ6Oho9ZLVZrOZmJgYXFxcMBqNDBs2\njE2bNtmjLSGEaBaa7GGgKSkpvP322/z+978nMTGRO+64A4Di4mJ8fX3V+fz8/CgpKVHH/Pz8rMZy\ncnLUsaCgIKsxs9nc4HosFgsWi+WGemruboX+bfk7Sr/Nj9b6hfp7NhgM11y2SQZASkoK3t7eAKxe\nvZpnn33W4d/WDx486NDHbwoKCgocXcINy83NpaKiokHzSr/Nj9b6hfp7DgwMvOayTTIALn34A4wa\nNYoFCxZQXl6OyWTCz8+P4uJivLy8ACgqKiI0NBQAX19fioqK6N69uzrm4+NjNXbJ5WMN0blzZ9zc\n3G64t+bM1dUVch1dxY3x9/dv8JnArq6uHLdzPfZma787swrtXJF92drv3mNf2Lki+7Ol599qcoeB\nWiwWTpw4od7esmULXl5emEwXzz6NjIxkzZo1ABQWFpKdnU3//v2BizuV165dS21tLWVlZZjNZgYM\nGKCObdiwgcrKSqqqqkhLS+ORRx5pcF0Gg0H+NWCVsqmTfqXfW6lfqL/nhnDoGkBycjLbt2/nxIkT\njBkzhhYtWpCens64ceOorq5Gp9PRunVrli5dqi6TkJDA1KlTCQ8Px2AwkJycjIeHBwCDBw9m//79\nREREoNfrGT16NJ06dQIgODiY6OhoBg4ciE6nY8CAAXIEkBBC0xwaALNnz65zelpaWr3LuLq61nsM\nv16vZ/r06UyfPr3O8QkTJjBhwgTbCxVCiFtQk9sEJIQQ4uaQABBCCI2SABBCCI2SABBCCI2SABBC\nCI2SABBCCI2SABBCCI2SABBCCI2SABBCCI2SABBCCI2SABBCCI2SABBCCI2SABBCCI2SABBCCI2S\nABBCCI2SABBCCI2SABBCCI2SABBCCI2SABBCCI2SABBCCI2SABBCCI2SABBCCI2SABBCCI2SABBC\nCI2SABBCCI2SABBCCI1yaADMnTuXsLAwunTpwo8//qhOP3nyJGPGjCEyMpJBgwaxe/dudayyspLJ\nkycTERFBVFQUW7ZsUccURWHOnDmEh4cTGRnJ6tWrrR5vyZIlhIeHExERwaJFi+zfoBBCNGEODYCo\nqCjWrFmDn5+f1fSUlBR69OjBli1bmDdvHpMnT8ZisQCQmpqK0WgkMzOTFStWMGvWLMrLywFIT08n\nPz+frKwsPvnkE1JTU8nLywMgOzsbs9lMRkYGGRkZ7Ny5k+3bt9/choUQoglxaAAEBQXh7e2NoihW\n081mM3FxcQB069YNb29vdu3adcVY+/btCQ4OJisrSx0bPnw4ACaTiejoaDIyMtSxmJgYXFxcMBqN\nDBs2jE2bNt2UPoUQoilqcvsAysrKsFgseHp6qtN8fX0pKSkBoLi4GF9fX3XMz8/PauzytYmGjgkh\nhBY5ObqA5sJisaibobTqVujflr+j9Nv8aK1fqL9ng8FwzWWbXAB4eHhgMBg4ceKEuhZQVFSEj48P\ncPGbe3FxMV5eXupYaGgocHFNoaioiO7du1+x3KWxSy4fa4iDBw/eeHPNXEFBgaNLuGG5ublUVFQ0\naF7pt/nRWr9Qf8+BgYHXXLbJBQD8b+fwxIkTycnJobS0lODgYAAiIyNZs2YNAQEBFBYWkp2dzcyZ\nM9Xl1q5dS1RUFKdPn8ZsNrNs2TJ1bM6cOcTHx6PX60lLS+P5559vcE2dO3fGzc2t0XttTlxdXSHX\n0VXcGH9/f/z9/Rs0r6urK8ftXI+92drvzqxCO1dkX7b2u/fYF3auyP5s6fm3HBoAycnJbN++nRMn\nTjBmzBhatGjBli1bmDJlCklJSURGRmI0GklJSVFXZxISEpg6dSrh4eEYDAaSk5Px8PAAYPDgwezf\nv5+IiAj0ej2jR4+mU6dOAAQHBxMdHc3AgQPR6XQMGDCAfv36NbhWg8HQoFWqW9mt0L8tf0fpt/nR\nWr9wY59NDg2A2bNn1znd09OT1NTUOsdcXV3rPYZfr9czffp0pk+fXuf4hAkTmDBhwvUVK4QQt5gm\ndxSQEEKIm0MCQAghNEoCQAghNEoCQAghNEoCQAghNEoCQAghNEoCQAghNEoCQAghNEoCQAghNEoC\nQAghNEoCQAghNEoCQAghNEoCQAghNEoCQAghNEoCQAghNEoCQAghNEoCQAghNEoCQAghNEoCQAgh\nNEoCQAghNEoCQAghNEoCQAghNEoCQAghNEoCQAghNEoCQAghNEoCQAghNEoCQAghNMrJ0QVcTVhY\nGC4uLri4uKDT6Rg3bhzR0dGcPHmSpKQkCgsLMRqNzJgxg6CgIAAqKyuZNm0a+/btQ6/Xk5iYSGRk\nJACKojB37lx27NiBXq/nySefZNSoUddVW01NDXl5eY3Wq6N07NgRJ6cm/TIQQthJk37n63Q63nrr\nLfz9/a2mp6Sk0KNHD1asWMG+ffuYOHEi27Ztw2AwkJqaitFoJDMzkyNHjjBixAhCQkIwmUykp6eT\nn59PVlYW5eXlxMbGEhISQseOHW2uLS8vjz/P/ADX2z0bq92bruLMCd6Z+dQVz68QQhuadAAoioKi\nKFdMN5vNbN26FYBu3brh7e3Nrl276NOnD2azmXnz5gHQvn17goODycrK4tFHH8VsNjN8+HAATCYT\n0dHRZGRkMGnSpOuqz/V2T1p4tLvO7oQQwrGadAAAJCUlARAQEMDkyZPR6XRYLBY8Pf/3zdvX15eS\nkhIAiouL8fX1Vcf8/Pysxvz8/KzGcnJyGlSHxWLBYrFY3b4V/Lava83b3Em/V5+3udNav1B/zwaD\n4ZrLNukA+Oc//0m7du2wWCwsWrSIV155hQULFtS5VmBvBw8etLpdUFBw02uwh9zcXCoqKho0763Q\ns/RbP+m3eaqv58DAwGsu26QDoF27i5tXDAYDTz31FFFRUXh4eODk5MSJEyfUtYCioiJ8fHyAi9/q\ni4uL8fLyUsdCQ0OBi2sKRUVFdO/e/YrlrqVz5864ubmpt11dXcGc3ziNOpC/v3+D9wG4urpCrp0L\nsjNb+z1u53rszdZ+d2YV2rki+7K1373HvrBzRfZnS8+/1WQPA62oqODMmTPq7YyMDO655x4AoqKi\nWLNmDQA5OTmUlpYSHBwMQGRkpDpWWFhIdnY2/fv3V5dbu3YttbW1lJWVYTabGTBgQIPqMRgMV/y7\nFdTV19X+NXfSr/R7K/UL9ffcEE12DeD48eM8//zz1NbWoigKd9xxBwsWLABgypQpJCUlERkZidFo\nJCUlRW04ISGBqVOnEh4ejsFgIDk5GQ8PDwAGDx7M/v37iYiIQK/XM3r0aDp16uSwHoUQwpGabADc\ncccdrFu3rs4xT09PUlNT6xxzdXVl0aJFdY7p9XqmT5/O9OnTG61OIYRorprsJiAhhBD2JQEghBAa\nJQEghBAaJQEghBAaJQEghBAaJQEghBAaJQEghBAaJQEghBAaJQEghBAaJQEghBAaJQEghBAaJQEg\nhBAaJQEghBAaJQEghBAaJQEghBAaJQEghBAaJQEghBAaJQEghBAaJQEghBAaJQEghBAaJQEghBAa\nJQEghBAaJQEghBAaJQEghBAaJQEghBAaJQEghBAapbkA+OWXX3jssceIjIxk+PDh5OXlObokIYRw\nCM0FQHJyMo899hhbtmxhzJgxvPzyy44uSQghHEJTAXDy5Em+//57YmJiAIiMjOTXX3+lsLDQwZUJ\nIcTN5+ToAm6mkpIS2rRpg17/v9zz8fGhuLiYO+64o85lamtrATh37hwWi0WdXl1dTStXCy5OZ+1b\ntB0ZXS1UV1dz5syZBs1fXV2NSXHntvNGO1dmHwbF3eZ+Kz1MnLztNjtXZh+VTgab+3W+rZpa/Wk7\nV2YfzrdV29zvuUoXSk/ZuTA7OlfpUm/PBoOB2267zerz7rd0iqIo9iywKfn++++ZMmUKZrNZnTZ8\n+HCmTJnCfffdV+cyJ06c4PDhwzepQiGEaDxdu3bFzc2t3nFNrQH4+Phw7Ngxamtr1VQsKSnB19e3\n3mVMJhN33XUXLi4uV01SIYRoam67xtqrpgKgdevW3HPPPaSnpxMbG8vmzZtp165dvZt/AJycnPD0\n9LyJVQohxM2hqU1AAIcOHeLVV1/l1KlT3H777cyfP59OnTo5uiwhhLjpNBcAQgghLpKN2kIIoVES\nAEIIoVESAEIIoVESAEIIoVESALeo6upqxo8fT0xMDHPmzLnqvOvWreO5555rtMf+/PPPGTBgALGx\nsfz00091zlNaWsoTTzyh3u7SpQtnz9p+VnVISAjFxcXXXas9Xd7T+PHj5YTCBgoLC+PHH3+0eblh\nw4aRnZ1th4puvi+//JL4+Hi7L6ep8wC05IcffqCgoMDqrOer0el0jfbYH3/8MRMnTmTAgAH1ztO2\nbVs++uijG378xqy7sV1e27JlyxxYiWiObsZ7QgLABhcuXOCVV17hp59+Uk8QGz9+PPPmzWP9+vUA\n/PTTT4wfP55t27YBFxP5b3/7G9XV1ej1embNmkVAQAB79+5l4cKFnDt3DoBJkyYRFhbGL7/8wrx5\n8zh58iRVVVWMGDGCUaNG1fnYqamp/PLLL7zyyitUVFRQW1vLww8/zMCBA3nppZcoLS0lNjaWp59+\nmsLCQk6fPs3UqVMBWL16Nfv372f+/PmN+hzNnTuX3bt3c+jQIVatWoWfnx+HDx+muroaHx8f/vKX\nv+Dp6UlRURFDhgxRv7FdfjRyfc8BXFy7eOONN3B2diY0NLRRa29sl/cUFhbGkiVLOH/+PDNmzGDj\nxo3qWHx8PM888wxhYWHs3LmTJUuWcOHCBQwGA5MnT673MiX2UtdrMzs7m927d1NTU4O7uztz5szh\nrrvuAi6u6SQmJrJ161ZOnTrFhAkTGDp0KAB5eXnMmzePY8eOodPpePzxxxk5ciTHjx9n7ty5FBcX\nU1lZycMPP8ykSZOuqOVq8/33v/9l9uzZWCwW7r33XvW6XfWx9f178uRJXnrpJbX2P/zhD8ybN491\n69axfv163NzcKCgooHXr1ixYsEC9osA//vEPNm/eTE1NDZ6ensyePRsfHx/+9re/kZeXR0VFBYWF\nhbRp04a3336bli1bUlNTw9y5c/n6668xmUwEBgZa1Z6ens7q1aupqamhRYsWTJs2jS5dulxzuWtS\nRINlZWUpCQkJ6u3y8nLl22+/VYYMGaJOO3jwoBIWFqYoiqLk5+crffr0UQ4dOqQoiqLU1NQoZ86c\nUcrKypS+ffsqe/bssbovi8WiDB06VMnPz1cURVEqKiqUQYMGKfv27avzsRVFUebOnassW7bsium/\nreudd95R5s2bp97+6KOPlFdeeUVRFEX59NNPleeee+7GnpzLPPHEE8rWrVsVRVGUkydPqtOXLVum\nJCcnK4qiKEeOHFF69+6tjvn7+ytnzpy56nNw4sQJJTg4WMnLy1MURVH+9a9/KV26dFGKiooarfbG\ndKknRVGUhx56SDlw4ICiKIoSGRmp7N+/X1EURSkoKFBCQ0MVi8WiFBQUKCNHjlTOnj2rKIqi/PLL\nL8r999+vVFVV3bSa63ttXv533LRpk9Vr0d/fX1m5cqWiKIqSl5en9OzZU7FYLEpNTY0SERGhbNq0\nSZ331KlTiqIoyujRo5Xs7GxFUS6+LxISEpTNmzcrimL9XNU3X1VVldKvXz/l66+/VhRFUXbu3Kn4\n+/sru3btqrc3W9+/K1euVF+vl+ZXlIvvl27duqmv0ffee08ZPXq0oiiKsnHjRuW1115TamtrFUVR\nlPXr1yvjxo1TFOXiezAsLEy9n8TERPW9+9FHHylPP/20UlNTo1RXVyvx8fFKfHy8oiiKsmfPHmXs\n2LHq6yA7O1t55JFHrrlcQ8gagA38/f3Jz89n9uzZBAUF0a9fv6vO/9VXX/Hggw+q35QMBgPu7u5s\n376du+++m169eqnztmzZkry8PH7++WdefPFF9dvj+fPn+fnnnwkMDKzzsXv37q1+WwsODqZv3772\naf46bdiwgQ0bNlBVVUVVVRWtWrW66vyHDh2q9zkoLS2lS5cu3H333QA8+uij19y/0RQNHTqUTz/9\nlD/84Q+sX7+eQYMGodfr+fe//01BQQFPPPGE2ruTkxMlJSXceeedN6W2//u//6vztblx40ZWr17N\nuXPnqK2tpby83Gq5QYMGAXD33Xfj7OzMsWPHOHPmDNXV1VabAj08PKioqOCbb77h5MmTap8VFRUc\nOnTI6j6vNl9+fj5OTk6EhIQAcP/991/1ki5g+/u3R48efPjhhyxYsICgoCAeeOABdaxnz5506NAB\ngJEjR7J48WIURWHr1q3s37+f2NhY4OLVhC/fJPPAAw/QsmVL9f4v7SP75ptvGDx4MAaDAbi4PyMt\nLQ24uNabm5vLiBEj1Ofh9OnTVFVVXXW5hpAAsMEdd9zBpk2b+Oabb/jPf/5DSkoKr7/+OjU1Neo8\nFy5cuO77VxQFDw8P1q1bV+f4bx87PT2diIgIevXqxX/+8x8++ugjPvjgA5YvX37FsgaDwepy1jdS\nZ0Pt2bOHjz76iE8++YRWrVqxbds23nnnnTrnvfQmudpzcGmzWnM3ZMgQhgwZQlJSEuvXr1f/Xoqi\n0LdvX1JSUhxcobWSkhLmzp1LWloa7du3Jzc312pHo06nw8XFxer25a+131IUBZ1OxyeffIKzs/N1\nzZebm3vF/Nfa9m3r+7dHjx6sX7+er776iqysLBYvXkx6enqddV7+//HjxzN8+PA6a7j8eTIYDFaP\nXV8viqIwZMgQEhMTr9rfb5drCDkKyAZHjx4F4KGHHiIpKUmdXlJSwqlTFy8qfvkL5IEHHmDnzp3q\nN5uamhrOnj1Lz549OXz4MHv27AEu/oHLy8vp0KED7u7ufPrpp+p9FBQUUF5ebvXYl37FrKSkhF9+\n+QVPT08GDx7MSy+9xHfffVdn7b/73e/Yv38/tbW1VFRUkJmZ2VhPS71Onz6Nu7s7JpOJqqoq/vWv\nf1mN//aNA9T7HJw+fZoePXqQm5urPp9paWn1voGasrZt29KtWzfmzZuHl5cXHTt2BC6+Xr7++mur\nD7ecnJybWltdr80jR47g7OxMmzZtUBTFauf9pXnq0qFDB2677TY+++wzddqpU6dwc3Pjvvvus9ox\nXlpaqr7GL7nafHfffTcWi4Vdu3YBF9e2CwoKrtqbre/fI0eO4OrqSlRUFK+99hq//PKLul/k//7v\n/1p7ns0AAAXYSURBVNTX4dq1a7nvvvvQ6XT079+fNWvWqGtINTU1HDhw4Kp1AfTp04eNGzdSU1ND\nVVWV1es/LCyMjRs3UlJSAlx8vvfv33/N5RpC1gBskJuby5tvvgmAxWJh8ODBBAUFkZCQwKOPPkqb\nNm2sVhPvvPNO5s2bx5QpU6ipqcFgMDBr1iy6devG3//+d+bPn8+5c+cwGAxMmjSJP/7xj7z77rv8\n5S9/4YMPPsBisdCqVSveeOONOh+7c+fOLF++nA0bNuDs7IyiKMyePbvO2sPDw9m8eTMDBgygXbt2\n3HPPPVRUVNjlebr0LeSBBx5gw4YNREVF0apVK/r06UNpaekV813+f4PBUO9z0LZtW+bNm8dzzz2H\n0WjkgQcewMPDwy49NIa6+rskNjaWxMREZs2apU678847SUlJYcaMGVRWVlJdXU3Xrl1v6hpBy5Yt\n63xtDhgwgAEDBtCqVSv69+9vtcxve7v8b7lkyRLmzJnD0qVLMRgMPP7444wYMYKF/7+9+wmJoo/j\nOP5eURedFbQC8yDF3hQ9KLmCIUHhSVqJUgRxPRQiJJF/8KBCKopFVhh2iMouCoKCHkQRC0TqkCxr\niJGwsOVJRDZUTIvF3ecQDezzx8e1h4pnPq/jzG+GmYXlM9/f7H5/d+/S29trTh0ZhkFnZyfp6elR\n5zto3IMHD+jo6CAcDpObm0tWVtaB9xbr93dhYYHnz5+b1XNLSwsOhwP4FpR9fX2srq6SlpbGnTt3\ngG9TYVtbW3g8HrMSunz58r9eW0VFBX6/n9LSUvNl7rt37wA4c+YMzc3NXL9+nXA4TCgU4ty5c+Tk\n5Bx43GGoGZyISAzGx8d5+fIlAwMDv/pSfpimgERELEoVgIiIRakCEBGxKAWAiIhFKQBERCxKASAi\nYlEKABERi1IAiIhYlAJARMSiFAAiR+Dz+aiqqqKgoACXy8WVK1eYn59nYWEh5nUKvq8DIPKzqReQ\nSIx2dnaoq6ujra2Nixcvsr+/z9LSEjabjVAo9KsvT+TQ9E9gkRgtLy/j8Xjw+XxR23d2djh79iyh\nUAi73Y7NZmNsbIzExETa29t5//49cXFxFBUV0dHRQUpKCk1NTUxNTWG324mLi8Pj8VBeXs6FCxdY\nWloiMTERgMbGRpxOJ/X19WxubtLa2orX6wW+dd18/Pjxb90YT35PmgISidHp06ex2+00NTUxNzfH\np0+fAHA4HDx58oRjx46xuLiIz+fD6XQSiUSora3l9evXTE9Ps7GxQX9/PwD37t0jIyODR48e4fP5\nuHnzJnBwX/fBwUEikQivXr3izZs33Lp1K6rPvMhhKQBEYuRwOBgZGSE5OZmuri6Ki4upqan5x370\nmZmZFBUVER8fT2pqKjU1NWYf++9iKcQTEhLY3Nzkw4cP2Gw2srOzSUpK+qF7EmvSOwCRIzh16pS5\nHOXa2hrt7e20tLTQ2Nj4l7HBYJCenh68Xi+7u7vs7++bfeWP4urVq3z9+pUbN26wt7eH2+2moaHB\nXBZQ5LBUAYj8oIyMDKqrq/H7/X87dXP//n0ikQiTk5N4vV76+vqinvj/fExycjIAX758MbdtbGxE\n7W9ubmZmZobh4WFmZ2eZmJj4r29LLEABIBKjQCDAs2fPzCX6gsEgo6Oj5OXlcfz4cba3t9ne3jbH\nf/78maSkJAzDYH19ncHBwajznThxImr6KC0tjZMnTzI+Pk44HObFixe8ffvW3D83N8fHjx+JRCIY\nhkFCQoKe/uVIFAAiMTIMg+XlZSorK8nLy+PSpUsYhsHt27dxOp243W5KSkpwuVwEAgHq6+tZWVmh\noKCAuro6SkpKos5XW1vL06dPcblcPHz4EIDu7m6GhoYoLCxkfn6e8+fPm+NXV1e5du0a+fn5lJWV\nUVxcTFlZ2U/9DOT/QT8DFRGxKFUAIiIWpQAQEbEoBYCIiEUpAERELEoBICJiUQoAERGLUgCIiFiU\nAkBExKIUACIiFqUAEBGxKAWAiIhF/QFMZ203gLYVkAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a34fe90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot showing rates of success by duration\n",
    "sns.factorplot(x='status', y='goal', data=kickstarter_df, kind='bar', ci=None)\n",
    "plt.xlabel('Status')\n",
    "plt.ylabel('Pledge Goal')\n",
    "plt.title('Pledge Goals by Status')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kMoSHh2PhwoVdP2gJ2L9/P5YuXYqWlhaoVCqsWLHiuteN344uXLiARx55BH369MHKlSut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\nwu1GVfIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118ded510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot showing rates of success by duration\n",
    "sns.factorplot(x='status_bool', y='goal', data=kickstarter_df, kind='bar', ci=None)\n",
    "plt.xlabel('Funded')\n",
    "plt.ylabel('Pledge Goal')\n",
    "plt.title('Pledge Goal by Project Success')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot shots the number of projects organized by status based on their pledge goal. Here one can see that the lower amount the project asks for; the higher chances there are for success."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3: What type of projects would be most successful at getting funded?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Film & Video',\n",
       " 'Games',\n",
       " 'Fashion',\n",
       " 'Music',\n",
       " 'Art',\n",
       " 'Technology',\n",
       " 'Dance',\n",
       " 'Publishing',\n",
       " 'Theater',\n",
       " 'Comics',\n",
       " 'Design',\n",
       " 'Photography',\n",
       " 'Food',\n",
       " 'Film &amp; Video']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print categories\n",
    "kickstarter_df['category'].unique().tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above list of unique categories shows there's a category that's showing up twice due to bad data. This needs to be fixed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Film & Video',\n",
       " 'Games',\n",
       " 'Fashion',\n",
       " 'Music',\n",
       " 'Art',\n",
       " 'Technology',\n",
       " 'Dance',\n",
       " 'Publishing',\n",
       " 'Theater',\n",
       " 'Comics',\n",
       " 'Design',\n",
       " 'Photography',\n",
       " 'Food']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace bad data values in 'category' column\n",
    "kickstarter_df['category'] = kickstarter_df['category'].replace(['Film &amp; Video'], 'Film & Video')\n",
    "kickstarter_df['category'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6dWt++OEH4N63/SFDhjB27Fh69erFqlWrOHfuHAsWLMDX15eff/5Z5ayFEMZC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\nzpw58Pb2xq1btxAdHS1q/tq1a7Fy5coujwnzpPk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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1166368d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot showing rates of success by duration\n",
    "sns.factorplot(y='category', x='status_bool', data=kickstarter_df, kind='bar', ci=None)\n",
    "plt.xlabel('Percent Success')\n",
    "plt.ylabel('Category')\n",
    "plt.title('Success by Category')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The types of projects most successful to be funded are dance, theater, music, and art. The types of projets least succesful to be funded are fashion, technology, and publishing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Is there an ideal month/day/time to launch a campaign?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Create 'funded_month' column based on datetime conversion from 'funded_date' column\n",
    "kickstarter_df['funded_month'] = kickstarter_df['funded_date'].apply(lambda x: dt.datetime.strptime(x, '%a, %d %b %Y %X -%f').strftime('%B'))\n",
    "# Create 'funded_weekday' column based on datetime conversion from 'funded_date' column\n",
    "kickstarter_df['funded_weekday'] = kickstarter_df['funded_date'].apply(lambda x: dt.datetime.strptime(x, '%a, %d %b %Y %X -%f').strftime('%a'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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l4datW3j33XfFGmQyGVJTU3H16lX07NkTjRs3BvAolNavX//CdRERSYHGAfD3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\nUQwAIiKJ+v/3nepmVM+yZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1179b1d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot showing rates of success by weekday\n",
    "sns.factorplot(y='funded_weekday', x='status_bool', data=kickstarter_df, kind='bar', ci=None)\n",
    "plt.xlabel('Percent Succesful')\n",
    "plt.ylabel('Day of Funding')\n",
    "plt.title('Success by Funding Weekday')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There does not appear to be any correlation between success and week day or funding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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B1wICAqhWrRoXL16kffv2dO/enapVqxISEsIvv/wCwMMPP8zBgweJjIzE19eX0NBQKlSo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1170a9bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot showing rates of success by weekday\n",
    "sns.factorplot(y='funded_month', x='status_bool', data=kickstarter_df, kind='bar', ci=None)\n",
    "plt.xlabel('Funded Percentage')\n",
    "plt.ylabel('Month')\n",
    "plt.title('Success by Funding Month')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There does not appear to be any correlation between success and month of funding."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:DAND]",
   "language": "python",
   "name": "conda-env-DAND-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
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