kickstart.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "#import cufflinks as cf\n",
    "#import plotly.graph_objs as go \n",
    "#from plotly.offline import init_notebook_mode,plot,iplot\n",
    "#cf.go_offline()\n",
    "#init_notebook_mode(connected=True)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "        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>ID</th>\n",
       "      <th>name</th>\n",
       "      <th>category</th>\n",
       "      <th>main_category</th>\n",
       "      <th>currency</th>\n",
       "      <th>deadline</th>\n",
       "      <th>goal</th>\n",
       "      <th>launched</th>\n",
       "      <th>pledged</th>\n",
       "      <th>state</th>\n",
       "      <th>backers</th>\n",
       "      <th>country</th>\n",
       "      <th>usd pledged</th>\n",
       "      <th>usd_pledged_real</th>\n",
       "      <th>usd_goal_real</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000002330</td>\n",
       "      <td>The Songs of Adelaide &amp; Abullah</td>\n",
       "      <td>Poetry</td>\n",
       "      <td>Publishing</td>\n",
       "      <td>GBP</td>\n",
       "      <td>2015-10-09</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2015-08-11 12:12:28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>failed</td>\n",
       "      <td>0</td>\n",
       "      <td>GB</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1533.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000003930</td>\n",
       "      <td>Greeting From Earth: ZGAC Arts Capsule For ET</td>\n",
       "      <td>Narrative Film</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>USD</td>\n",
       "      <td>2017-11-01</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>2017-09-02 04:43:57</td>\n",
       "      <td>2421.0</td>\n",
       "      <td>failed</td>\n",
       "      <td>15</td>\n",
       "      <td>US</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2421.0</td>\n",
       "      <td>30000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000004038</td>\n",
       "      <td>Where is Hank?</td>\n",
       "      <td>Narrative Film</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>USD</td>\n",
       "      <td>2013-02-26</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>2013-01-12 00:20:50</td>\n",
       "      <td>220.0</td>\n",
       "      <td>failed</td>\n",
       "      <td>3</td>\n",
       "      <td>US</td>\n",
       "      <td>220.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>45000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000007540</td>\n",
       "      <td>ToshiCapital Rekordz Needs Help to Complete Album</td>\n",
       "      <td>Music</td>\n",
       "      <td>Music</td>\n",
       "      <td>USD</td>\n",
       "      <td>2012-04-16</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>2012-03-17 03:24:11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>failed</td>\n",
       "      <td>1</td>\n",
       "      <td>US</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000011046</td>\n",
       "      <td>Community Film Project: The Art of Neighborhoo...</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>USD</td>\n",
       "      <td>2015-08-29</td>\n",
       "      <td>19500.0</td>\n",
       "      <td>2015-07-04 08:35:03</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>canceled</td>\n",
       "      <td>14</td>\n",
       "      <td>US</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>19500.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID                                               name  \\\n",
       "0  1000002330                    The Songs of Adelaide & Abullah   \n",
       "1  1000003930      Greeting From Earth: ZGAC Arts Capsule For ET   \n",
       "2  1000004038                                     Where is Hank?   \n",
       "3  1000007540  ToshiCapital Rekordz Needs Help to Complete Album   \n",
       "4  1000011046  Community Film Project: The Art of Neighborhoo...   \n",
       "\n",
       "         category main_category currency    deadline     goal  \\\n",
       "0          Poetry    Publishing      GBP  2015-10-09   1000.0   \n",
       "1  Narrative Film  Film & Video      USD  2017-11-01  30000.0   \n",
       "2  Narrative Film  Film & Video      USD  2013-02-26  45000.0   \n",
       "3           Music         Music      USD  2012-04-16   5000.0   \n",
       "4    Film & Video  Film & Video      USD  2015-08-29  19500.0   \n",
       "\n",
       "              launched  pledged     state  backers country  usd pledged  \\\n",
       "0  2015-08-11 12:12:28      0.0    failed        0      GB          0.0   \n",
       "1  2017-09-02 04:43:57   2421.0    failed       15      US        100.0   \n",
       "2  2013-01-12 00:20:50    220.0    failed        3      US        220.0   \n",
       "3  2012-03-17 03:24:11      1.0    failed        1      US          1.0   \n",
       "4  2015-07-04 08:35:03   1283.0  canceled       14      US       1283.0   \n",
       "\n",
       "   usd_pledged_real  usd_goal_real  \n",
       "0               0.0        1533.95  \n",
       "1            2421.0       30000.00  \n",
       "2             220.0       45000.00  \n",
       "3               1.0        5000.00  \n",
       "4            1283.0       19500.00  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('ks-projects-201801.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>main_category</th>\n",
       "      <th>usd_goal_real</th>\n",
       "      <th>country</th>\n",
       "      <th>backers</th>\n",
       "      <th>state</th>\n",
       "      <th>usd_pledged_real</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Publishing</td>\n",
       "      <td>1533.95</td>\n",
       "      <td>GB</td>\n",
       "      <td>0</td>\n",
       "      <td>failed</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>US</td>\n",
       "      <td>15</td>\n",
       "      <td>failed</td>\n",
       "      <td>2421.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>45000.00</td>\n",
       "      <td>US</td>\n",
       "      <td>3</td>\n",
       "      <td>failed</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Music</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>failed</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>19500.00</td>\n",
       "      <td>US</td>\n",
       "      <td>14</td>\n",
       "      <td>canceled</td>\n",
       "      <td>1283.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  main_category  usd_goal_real country  backers     state  usd_pledged_real\n",
       "0    Publishing        1533.95      GB        0    failed               0.0\n",
       "1  Film & Video       30000.00      US       15    failed            2421.0\n",
       "2  Film & Video       45000.00      US        3    failed             220.0\n",
       "3         Music        5000.00      US        1    failed               1.0\n",
       "4  Film & Video       19500.00      US       14  canceled            1283.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[[\"main_category\",\"usd_goal_real\",\"country\",\"backers\",\"state\",\"usd_pledged_real\"]]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>usd_goal_real</th>\n",
       "      <th>backers</th>\n",
       "      <th>usd_pledged_real</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.786610e+05</td>\n",
       "      <td>378661.000000</td>\n",
       "      <td>3.786610e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.545440e+04</td>\n",
       "      <td>105.617476</td>\n",
       "      <td>9.058924e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.152950e+06</td>\n",
       "      <td>907.185035</td>\n",
       "      <td>9.097334e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000e+03</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.100000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.500000e+03</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>6.243300e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.550000e+04</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>4.050000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.663614e+08</td>\n",
       "      <td>219382.000000</td>\n",
       "      <td>2.033899e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       usd_goal_real        backers  usd_pledged_real\n",
       "count   3.786610e+05  378661.000000      3.786610e+05\n",
       "mean    4.545440e+04     105.617476      9.058924e+03\n",
       "std     1.152950e+06     907.185035      9.097334e+04\n",
       "min     1.000000e-02       0.000000      0.000000e+00\n",
       "25%     2.000000e+03       2.000000      3.100000e+01\n",
       "50%     5.500000e+03      12.000000      6.243300e+02\n",
       "75%     1.550000e+04      56.000000      4.050000e+03\n",
       "max     1.663614e+08  219382.000000      2.033899e+07"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Count plot of the state of kickstarter projects')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.countplot(df['state'])\n",
    "plt.xlabel('State')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Count plot of the state of kickstarter projects')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['failed', 'canceled', 'successful', 'live', 'undefined',\n",
       "       'suspended'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"state\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['failed', 'canceled', 'successful'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[(df[\"state\"] != \"live\")&(df[\"state\"] != \"undefined\")&(df[\"state\"] != \"suspended\")]\n",
    "df[\"state\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 370454 entries, 0 to 378660\n",
      "Data columns (total 6 columns):\n",
      "main_category       370454 non-null object\n",
      "usd_goal_real       370454 non-null float64\n",
      "country             370454 non-null object\n",
      "backers             370454 non-null int64\n",
      "state               370454 non-null object\n",
      "usd_pledged_real    370454 non-null float64\n",
      "dtypes: float64(2), int64(1), object(3)\n",
      "memory usage: 19.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df[\"state\"] = np.where((df[\"state\"] == \"canceled\"),\"failed\",df[\"state\"])\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfsuccess = df[df['state']=='successful']\n",
    "dffail = df[df['state']=='failed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Count plot of the failed kickstarter projects')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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S1DuGYUmSJElS7xiGJUmSJEm9YxiWJEmSJPWOYViSJEmS1DuGYUmSJElS7xiGJUmSJEm9YxiWJEmSJPWOYViSJEmS1DuGYUmSJElS7xiGJUmSJEm9YxiWJEmSJPWOYViSJEmS1DsjC8NJNkxyfpLvJ7ksyT+28uOTXJXkovbYuZUnyZFJliW5OMljBpZ1QJIft8cBA+V/nOSSNs+RSTKq9kiSJEmS1h3zR7jsO4AnVdXtSdYDvpnk823c66rqlHHT7wXs2B67Ah8Edk2yGXAYsBgo4MIkZ1TVLW2alwHnAWcBS4DPI0mSJEnSJEbWM1yd29vT9dqjJpllH+DENt+5wIIkWwFPBc6pqhUtAJ8DLGnjNqmqc6uqgBOBfUfVHkmSJEnSumOkvxlOMi/JRcBNdIH2vDbq7e1S6Pcm2aCVbQ1cOzD7da1ssvLrhpQPq8fBSZYmWbp8+fLVbpckSZIkae020jBcVXdV1c7ANsAuSR4JvAl4OPBYYDPgDaOsQ6vH0VW1uKoWL1y4cNSrkyRJkiSt4WblbtJVdSvwFWBJVd3QLoW+A/gwsEub7Hpg24HZtmllk5VvM6RckiRJkqRJjfJu0guTLGjDGwFPAX7QfutLu/PzvsClbZYzgBe3u0rvBvy8qm4Azgb2TLJpkk2BPYGz27jbkuzWlvVi4PRRtUeSJEmStO4Y5d2ktwJOSDKPLnR/sqrOTPLlJAuBABcBL2/TnwXsDSwDfgW8BKCqViR5G3BBm+6tVbWiDb8COB7YiO4u0t5JWpIkSZI0pZGF4aq6GHj0kPInTTB9AYdMMO444Lgh5UuBR65eTSVJkiRJfTMrvxmWJEmSJGlNYhiWJEmSJPWOYViSJEmS1DuGYUmSJElS7xiGJUmSJEm9YxiWJEmSJPWOYViSJEmS1DuGYUmSJElS7xiGJUmSJEm9YxiWJEmSJPWOYViSJEmS1DuGYUmSJElS7xiGJUmSJEm9YxiWJEmSJPWOYViSJEmS1DuGYUmSJElS7xiGJUmSJEm9YxiWJEmSJPWOYViSJEmS1DuGYUmSJElS7xiGJUmSJEm9YxiWJEmSJPWOYViSJEmS1DuGYUmSJElS7xiGJUmSJEm9YxiWJEmSJPWOYViSJEmS1DuGYUmSJElS7xiGJUmSJEm9YxiWJEmSJPWOYViSJEmS1DuGYUmSJElS7xiGJUmSJEm9YxiWJEmSJPWOYViSJEmS1DuGYUmSJElS7xiGJUmSJEm9M7IwnGTDJOcn+X6Sy5L8YyvfPsl5SZYl+USS9Vv5Bu35sjZ+0cCy3tTKf5jkqQPlS1rZsiRvHFVbJEmSJEnrllH2DN8BPKmqHgXsDCxJshvwTuC9VbUDcAtwUJv+IOCWVv7eNh1JdgL2Bx4BLAE+kGReknnAUcBewE7A89u0kiRJkiRNamRhuDq3t6frtUcBTwJOaeUnAPu24X3ac9r4JydJKz+5qu6oqquAZcAu7bGsqq6sqt8CJ7dpJUmSJEma1Eh/M9x6cC8CbgLOAX4C3FpVd7ZJrgO2bsNbA9cCtPE/BzYfLB83z0TlkiRJkiRNaqRhuKruqqqdgW3oenIfPsr1TSTJwUmWJlm6fPnyuaiCJEmSJGkNMit3k66qW4GvAI8DFiSZ30ZtA1zfhq8HtgVo4x8I3DxYPm6eicqHrf/oqlpcVYsXLlw4I22SJEmSJK29Rnk36YVJFrThjYCnAFfQheL92mQHAKe34TPac9r4L1dVtfL9292mtwd2BM4HLgB2bHenXp/uJltnjKo9kiRJkqR1x/ypJ1llWwEntLs+3w/4ZFWdmeRy4OQkRwDfA45t0x8LfCTJMmAFXbilqi5L8kngcuBO4JCqugsgyaHA2cA84LiqumyE7ZEkSZIkrSNGFoar6mLg0UPKr6T7/fD48t8Az5lgWW8H3j6k/CzgrNWurCRJkiSpV2blN8OSJEmSJK1JDMOSJEmSpN4xDEuSJEmSescwLEmSJEnqHcOwJEmSJKl3DMOSJEmSpN4xDEuSJEmSescwLEmSJEnqHcOwJEmSJKl3DMOSJEmSpN4xDEuSJEmSescwLEmSJEnqHcOwJEmSJKl3DMOSJEmSpN4xDEuSJEmSescwLEmSJEnqHcOwJEmSJKl3DMOSJEmSpN4xDEuSJEmSescwLEmSJEnqHcOwJEmSJKl3DMOSJEmSpN4xDEuSJEmSescwLEmSJEnqHcOwJEmSJKl35s91BaRR+87RT5/rKqyyxx185lxXQZIkSVon2TMsSZIkSeodw7AkSZIkqXcMw5IkSZKk3jEMS5IkSZJ6xzAsSZIkSeodw7AkSZIkqXcMw5IkSZKk3jEMS5IkSZJ6xzAsSZIkSeodw7AkSZIkqXcMw5IkSZKk3jEMS5IkSZJ6Z/6oFpxkW+BEYEuggKOr6t+SHA68DFjeJn1zVZ3V5nkTcBBwF/DKqjq7lS8B/g2YBxxTVe9o5dsDJwObAxcCL6qq346qTZLWLEd+7KlzXYVV9soXnj3XVZAkSeq1UfYM3wm8pqp2AnYDDkmyUxv33qrauT3GgvBOwP7AI4AlwAeSzEsyDzgK2AvYCXj+wHLe2Za1A3ALXZCWJEmSJGlSIwvDVXVDVX23Df8CuALYepJZ9gFOrqo7quoqYBmwS3ssq6orW6/vycA+SQI8CTilzX8CsO9oWiNJkiRJWpeM7DLpQUkWAY8GzgOeABya5MXAUrre41vogvK5A7Ndxz3h+dpx5bvSXRp9a1XdOWT68es/GDgYYLvttlv9Bq3l/vuoV891FVbLgw95z1xXQZIkSdJabuQ30EryAODTwKuq6jbgg8DDgJ2BG4B3j7oOVXV0VS2uqsULFy4c9eokSZIkSWu4kfYMJ1mPLgh/rKpOBaiqGwfGfwg4sz29Hth2YPZtWhkTlN8MLEgyv/UOD04vSZIkSdKERtYz3H7TeyxwRVW9Z6B8q4HJngVc2obPAPZPskG7S/SOwPnABcCOSbZPsj7dTbbOqKoCvgLs1+Y/ADh9VO2RJEmSJK07Rtkz/ATgRcAlSS5qZW+muxv0znT/bulq4K8BquqyJJ8ELqe7E/UhVXUXQJJDgbPp/rXScVV1WVveG4CTkxwBfI8ufEuSJEmSNKmRheGq+iaQIaPOmmSetwNvH1J+1rD5qupKurtNS5IkSZI0bSO/gZYkSZIkSWsaw7AkSZIkqXcMw5IkSZKk3jEMS5IkSZJ6xzAsSZIkSeodw7AkSZIkqXcMw5IkSZKk3jEMS5IkSZJ6xzAsSZIkSeodw7AkSZIkqXcMw5IkSZKk3jEMS5IkSZJ6xzAsSZIkSeodw7AkSZIkqXcMw5IkSZKk3jEMS5IkSZJ6xzAsSZIkSeodw7AkSZIkqXcMw5IkSZKk3jEMS5IkSZJ6xzAsSZIkSeqd+XNdAUkz55QPL5nrKqyW/V7yhbmugiRJknrCnmFJkiRJUu8YhiVJkiRJvWMYliRJkiT1jmFYkiRJktQ70wrDSZ4wnTJJkiRJktYG0+0Z/vdplkmSJEmStMab9F8rJXkc8HhgYZJXD4zaBJg3yopJkiRJkjQqU/2f4fWBB7TpNh4ovw3Yb1SVkiRJkiRplCYNw1X1NeBrSY6vqmtmqU6SJEmSJI3UVD3DYzZIcjSwaHCeqnrSKColSZIkSdIoTTcMfwr4D+AY4K7RVUeSJEmSpNGbbhi+s6o+ONKaSJIkSZI0S6b7r5U+m+QVSbZKstnYY6Q1kyRJkiRpRKbbM3xA+/u6gbICHjqz1ZEkSZIkafSmFYaravtRV0SSJEmSpNkyrTCc5MXDyqvqxJmtjiRJkiRJozfd3ww/duDxJ8DhwDMnmyHJtkm+kuTyJJcl+btWvlmSc5L8uP3dtJUnyZFJliW5OMljBpZ1QJv+x0kOGCj/4ySXtHmOTJKVar0kSZIkqZeme5n03w4+T7IAOHmK2e4EXlNV302yMXBhknOAA4EvVdU7krwReCPwBmAvYMf22BX4ILBru1HXYcBiut8pX5jkjKq6pU3zMuA84CxgCfD56bRJkiRJktRf0+0ZHu+XwKS/I66qG6rqu234F8AVwNbAPsAJbbITgH3b8D7AidU5F1iQZCvgqcA5VbWiBeBzgCVt3CZVdW5VFXDiwLIkSZIkSZrQdH8z/Fm6XlmAecAfAJ+c7kqSLAIeTdeDu2VV3dBG/RTYsg1vDVw7MNt1rWyy8uuGlA9b/8HAwQDbbbfddKstSZIkSVpHTfdfK71rYPhO4Jqqum6iiQcleQDwaeBVVXXb4M96q6qS1IQzz5CqOho4GmDx4sUjX58kSZIkac02rcukq+prwA+AjYFNgd9OZ74k69EF4Y9V1amt+MZ2iTPt702t/Hpg24HZt2llk5VvM6RckiRJkqRJTSsMJ3kucD7wHOC5wHlJ9ptingDHAldU1XsGRp0BjN0R+gDg9IHyF7e7Su8G/LxdTn02sGeSTdudp/cEzm7jbkuyW1vXiweWJUmSJEnShKZ7mfRbgMdW1U0ASRYCXwROmWSeJwAvAi5JclErezPwDuCTSQ4CrqEL19DdDXpvYBnwK+AlAFW1IsnbgAvadG+tqhVt+BXA8cBGdHeR9k7SkiRJkqQpTTcM328sCDc3M0WvclV9E5jo//4+ecj0BRwywbKOA44bUr4UeORk9ZAkSZIkabzphuEvJDkbOKk9fx5dT64kSZIkSWudScNwkh3o/hXS65L8BbB7G/Ud4GOjrpwkSZIkSaMwVc/w+4A3AbS7QZ8KkOQP27hnjLR2kiRJkiSNwFR3k96yqi4ZX9jKFo2kRpIkSZIkjdhUPcMLJhm30UxWRJI0sZectmSuq7BaPvysL8x1FSRJku5lqp7hpUleNr4wyUuBC0dTJUmSJEmSRmuqnuFXAacleSH3hN/FwPrAs0ZZMUmSJEmSRmXSMFxVNwKPT/JE7vl/vp+rqi+PvGaSJEmSJI3ItP7PcFV9BfjKiOsiSZIkSdKsmOo3w5IkSZIkrXMMw5IkSZKk3pnWZdKSJGk0nvbpY+a6Cqvlc89+6VxXQZKkVWLPsCRJkiSpdwzDkiRJkqTeMQxLkiRJknrHMCxJkiRJ6h3DsCRJkiSpdwzDkiRJkqTeMQxLkiRJknrHMCxJkiRJ6h3DsCRJkiSpdwzDkiRJkqTeMQxLkiRJknrHMCxJkiRJ6p35c10BSZLUH8845dS5rsIq++x+fzHXVZAkzSB7hiVJkiRJvWMYliRJkiT1jmFYkiRJktQ7hmFJkiRJUu94Ay1J0hpn79OOmOsqrLKznvX3c10FSZI0DfYMS5IkSZJ6xzAsSZIkSeodw7AkSZIkqXcMw5IkSZKk3jEMS5IkSZJ6xzAsSZIkSeodw7AkSZIkqXcMw5IkSZKk3hlZGE5yXJKbklw6UHZ4kuuTXNQeew+Me1OSZUl+mOSpA+VLWtmyJG8cKN8+yXmt/BNJ1h9VWyRJkiRJ65ZR9gwfDywZUv7eqtq5Pc4CSLITsD/wiDbPB5LMSzIPOArYC9gJeH6bFuCdbVk7ALcAB42wLZIkSZKkdcjIwnBVfR1YMc3J9wFOrqo7quoqYBmwS3ssq6orq+q3wMnAPkkCPAk4pc1/ArDvjDZAkiRJkrTOmovfDB+a5OJ2GfWmrWxr4NqBaa5rZROVbw7cWlV3jisfKsnBSZYmWbp8+fKZaockSZIkaS0122H4g8DDgJ2BG4B3z8ZKq+roqlpcVYsXLlw4G6uUJEmSJK3B5s/myqrqxrHhJB8CzmxPrwe2HZh0m1bGBOU3AwuSzG+9w4PTS5IkSZI0qVntGU6y1cDTZwFjd5o+A9g/yQZJtgd2BM4HLgB2bHeOXp/uJltnVFUBXwH2a/MfAJw+G22QJEmSJK39RtYznOQkYA9giyTXAYcBeyTZGSjgauCvAarqsiSfBC4H7gQOqaq72nIOBc4G5gHHVdVlbRVvAE5OcgTwPeDYUbVFkiRJkrRuGVkYrqrnDymeMLBW1duBtw8pPws4a0j5lXR3m5YkSZIkaaXMxd2kJUmSJEmaU4ZhSZIkSVLvGIYlSZIkSb1jGJYkSZIk9Y5hWJIkSZLUO4ZhSZIkSVLvGIYlSZIkSb1jGJYkSZIk9Y5hWJIkSZLUO4ZhSZIkSVLvGIYlSZLAfaMkAAAexElEQVQkSb1jGJYkSZIk9Y5hWJIkSZLUO4ZhSZIkSVLvGIYlSZIkSb1jGJYkSZIk9Y5hWJIkSZLUO4ZhSZIkSVLvGIYlSZIkSb1jGJYkSZIk9Y5hWJIkSZLUO4ZhSZIkSVLvGIYlSZIkSb1jGJYkSZIk9Y5hWJIkSZLUO4ZhSZIkSVLvGIYlSZIkSb1jGJYkSZIk9Y5hWJIkSZLUO4ZhSZIkSVLvGIYlSZIkSb1jGJYkSZIk9Y5hWJIkSZLUO4ZhSZIkSVLvGIYlSZIkSb1jGJYkSZIk9Y5hWJIkSZLUO4ZhSZIkSVLvjCwMJzkuyU1JLh0o2yzJOUl+3P5u2sqT5Mgky5JcnOQxA/Mc0Kb/cZIDBsr/OMklbZ4jk2RUbZEkSZIkrVtG2TN8PLBkXNkbgS9V1Y7Al9pzgL2AHdvjYOCD0IVn4DBgV2AX4LCxAN2mednAfOPXJUmSJEnSUCMLw1X1dWDFuOJ9gBPa8AnAvgPlJ1bnXGBBkq2ApwLnVNWKqroFOAdY0sZtUlXnVlUBJw4sS5IkSZKkSc32b4a3rKob2vBPgS3b8NbAtQPTXdfKJiu/bkj5UEkOTrI0ydLly5evXgskSZIkSWu9ObuBVuvRrVla19FVtbiqFi9cuHA2VilJkiRJWoPNdhi+sV3iTPt7Uyu/Hth2YLptWtlk5dsMKZckSZIkaUqzHYbPAMbuCH0AcPpA+YvbXaV3A37eLqc+G9gzyabtxll7Ame3cbcl2a3dRfrFA8uSJEmSJGlS80e14CQnAXsAWyS5ju6u0O8APpnkIOAa4Llt8rOAvYFlwK+AlwBU1YokbwMuaNO9tarGbsr1Cro7Vm8EfL49JEmSJEma0sjCcFU9f4JRTx4ybQGHTLCc44DjhpQvBR65OnWUJEmSJPXTnN1AS5IkSZKkuWIYliRJkiT1jmFYkiRJktQ7hmFJkiRJUu8YhiVJkiRJvWMYliRJkiT1jmFYkiRJktQ7hmFJkiRJUu8YhiVJkiRJvWMYliRJkiT1jmFYkiRJktQ7hmFJkiRJUu8YhiVJkiRJvWMYliRJkiT1jmFYkiRJktQ7hmFJkiRJUu8YhiVJkiRJvWMYliRJkiT1jmFYkiRJktQ78+e6ApIkSdLa5NRTfjbXVVgtf7HfFnNdBWmNYM+wJEmSJKl3DMOSJEmSpN4xDEuSJEmSescwLEmSJEnqHcOwJEmSJKl3DMOSJEmSpN7xXytJkiRJmtD3jrlprquwWh790gfNdRW0hrJnWJIkSZLUO4ZhSZIkSVLvGIYlSZIkSb1jGJYkSZIk9Y5hWJIkSZLUO4ZhSZIkSVLvGIYlSZIkSb1jGJYkSZIk9Y5hWJIkSZLUO4ZhSZIkSVLvGIYlSZIkSb0zJ2E4ydVJLklyUZKlrWyzJOck+XH7u2krT5IjkyxLcnGSxwws54A2/Y+THDAXbZEkSZIkrX3msmf4iVW1c1Utbs/fCHypqnYEvtSeA+wF7NgeBwMfhC48A4cBuwK7AIeNBWhJkiRJkiazJl0mvQ9wQhs+Adh3oPzE6pwLLEiyFfBU4JyqWlFVtwDnAEtmu9KSJEmSpLXPXIXhAv4zyYVJDm5lW1bVDW34p8CWbXhr4NqBea9rZROV30eSg5MsTbJ0+fLlM9UGSZIkSdJaav4crXf3qro+yYOAc5L8YHBkVVWSmqmVVdXRwNEAixcvnrHlSpIkqfPK066deqI11JHP2nauqyBpDsxJz3BVXd/+3gScRveb3xvb5c+0vze1ya8HBs9Q27SyicolSZIkSZrUrIfhJL+TZOOxYWBP4FLgDGDsjtAHAKe34TOAF7e7Su8G/LxdTn02sGeSTduNs/ZsZZIkSZIkTWouLpPeEjgtydj6P15VX0hyAfDJJAcB1wDPbdOfBewNLAN+BbwEoKpWJHkbcEGb7q1VtWL2miFJkiRJWlvNehiuqiuBRw0pvxl48pDyAg6ZYFnHAcfNdB0lSZIkSeu2ubqBliRJ0jrtWZ/+5lxXYbWc9uzd57oKkjRSa9L/GZYkSZIkaVYYhiVJkiRJvWMYliRJkiT1jmFYkiRJktQ7hmFJkiRJUu94N2lJkiRJAn76rmVzXYXV8ruv3WGuq7BWsWdYkiRJktQ7hmFJkiRJUu8YhiVJkiRJvWMYliRJkiT1jmFYkiRJktQ7hmFJkiRJUu8YhiVJkiRJvWMYliRJkiT1jmFYkiRJktQ7hmFJkiRJUu8YhiVJkiRJvWMYliRJkiT1jmFYkiRJktQ7hmFJkiRJUu8YhiVJkiRJvWMYliRJkiT1jmFYkiRJktQ7hmFJkiRJUu8YhiVJkiRJvWMYliRJkiT1jmFYkiRJktQ78+e6ApIkSZKk2XfjkV+d6yqsli1fucdqzW/PsCRJkiSpdwzDkiRJkqTeMQxLkiRJknrHMCxJkiRJ6h3DsCRJkiSpdwzDkiRJkqTeMQxLkiRJknrHMCxJkiRJ6h3DsCRJkiSpd9b6MJxkSZIfJlmW5I1zXR9JkiRJ0ppvrQ7DSeYBRwF7ATsBz0+y09zWSpIkSZK0plurwzCwC7Csqq6sqt8CJwP7zHGdJEmSJElruFTVXNdhlSXZD1hSVS9tz18E7FpVh46b7mDg4Pb094EfzmpFYQvgZ7O8zrlke9ddfWor9Ku9fWor9Ku9fWor9Ku9fWor9Ku9fWor9Ku9fWorzF17H1JVC6eaaP5s1GSuVdXRwNFztf4kS6tq8Vytf7bZ3nVXn9oK/Wpvn9oK/Wpvn9oK/Wpvn9oK/Wpvn9oK/Wpvn9oKa3571/bLpK8Hth14vk0rkyRJkiRpQmt7GL4A2DHJ9knWB/YHzpjjOkmSJEmS1nBr9WXSVXVnkkOBs4F5wHFVddkcV2uYObtEe47Y3nVXn9oK/Wpvn9oK/Wpvn9oK/Wpvn9oK/Wpvn9oK/Wpvn9oKa3h71+obaEmSJEmStCrW9sukJUmSJElaaYZhSZIkSVLv9CoMJ7kryUUDj0VJFic5so0/MMn7V2P5SXJ0ksuTXJLkcRNMd1iSfx5XtnOSK9rwWUkWDJnv8CSvXdX6rYokleSjA8/nJ1me5MxVXN4xSXaauRpOub6xfX5pkk8luf8U0381yX1u/z54bCR5eZIXT7KMCfdTkm+vbBtmUpItk3w8yZVJLkzynSTPmss6TVeSzQdeuz9Ncv3A8/VXYjlHJHnVDNXpo0n2nYllra6BY/2yJN9P8pokq3SOn+gctDZKsm87jz18gvELkrxituu1uoa9n83AMoee/2bDTLQnye0TlE96zp5tk5zLbk1y+QytY48kj5+JZc2UJL+b5OQkP2nvP2cl+b3VXOZI9+2wzxDts+OlK7mcA5M8eFT1nGYdhr4+RrCe1drPSZ6T5IokX2mfjfceZX2HrH9WttOQ9d59XGUgm6wpZvIzxppmrb6B1ir4dVXtPK7samDpDC1/d2BH4BHAhsAmE0x3EvAF4E0DZfu3cqpqVl/4U/gl8MgkG1XVr4GnsBr/vqqqXjpjNZueu/d5ko8BLwfeszoLrKr/WI155+zDSZIAnwFOqKoXtLKHAM+cqzqtjKq6GRjbl4cDt1fVu+a0UmuWwWP9QcDH6c5Bh63sgtawc9Dqej7wzfb3XtsiyXxgAfAK4AOzX7XVMuz9bG02svaszjl7FCY6l7UvAFbpi+Yh9gBuB6b9BWyS+VV15wytf/yyA5xG9/6zfyt7FLAl8KNVXe4s7NthnyFOXYXlHAhcCvz36lZolPtpdU1nP0+j/gcBL6uqbyY5EFgMnDXSis+wJPOq6q5Vnb+qljJz2WSmzNhnjDXNOpHoV0f79vQ+bz5Jjk/ywSTntl60PZIc176tOn6Cxf2W7gW/XlX9uqpuHDZRVf0IuCXJrgPFz6WF4SRXJ9miDb8lyY+SfBP4/YH6PSzJF9q3bt8Y6/Vo3yx9OcnFSb6UZLuV3yr3cRbwtDb8/LF6tvXdqxe0fXu6KMnvJPlc+/bo0iTPa+Pv7nlIsiTJd9s0X5qBek7lG8AO47/VTfLa9oFkzIsGvgneZfxCBtuc5JXprgS4OMnJA5Pt1Np6ZZJXDsx7e/u7Rxt/SpIfJPlYexMhyd6t7MIkRw47PlfRk4DfDn54qKprqurf2zb5Rtsf303rUWj1/FqS01tb3pHkhUnOT3f1w8PadAuTfDrJBe3xhFb+Z7mnB+R7STaeobbcS5IDWp0uSvKBsW8rkzxt4Bj7z4FZ/rC168okh7Rpd2j7/Nh033x+PsmGbdxjkpzX9vOnkzxwSB32bOu/JMmH0nqrkzwzyQ/b/vz3JJ9Jcr8ky5Js1qaZ1+qy2Uxsj6q6CTgYODSdeUn+te2bi5P8dVvvVkm+PnC8/0krHzwH/d9W/28mOWng2P9qkne27f6jsXnXJEkeQPcl5UF0XziOHdPfSHIGcDnwDuBhbRv869zVdvUl2TDJh9sx+L0kT5yifKN0PThXJDkN2GhOGzDOJOelocdtG/f29no/N8mWrWzwnL1zG3dxktOSbNrK15TjeV47f1yW5D+TbNTqN9F7/jPauel7Sb6Y7uqfRXSh7f+0bfQnmfgcfXiSjyT5FvCREbbricD/jHv/+T7wzXZuurQdn2OfFab73jO4b3do2+D77Xh52GTHyir4BrBDG55oP93n+EqyH12g+1irx0ZJntz22SXpPltu0OYf+v4/fj9N8trYo7X3c+nO2/+Rgd678a+PJBsnuSrJem38JoPPV8FE+3le7n3eJd174YVtGx7cyv6B7px9bJL3Am8Fnte22/Mye58pMslxeebAdO9PF9jH3jffmeS7wHMywTllon03bv13r2dYm6f7+hiVIZ8xJjseJ/qs+9gk327H4/mtXUM/q4xcVfXmAdwFXNQep7WyPYAz2/CBwPvb8PHAyUCAfYDbgD+k+wLhQmDnIctfBFxL921JpqjLa4H3tuHdgKUD464GtgD+GLgEuD/dty/LgNe2ab4E7NiGdwW+3IY/CxzQhv8K+MxqbrPbgT8CTqHr7b5o3DY7fKxO7fmlbTs8G/jQQPkD29+v0r0pLGzbavtWvtmI9vnt7e984HTgb1r9Lh23Lw4fqN+H2vCfjk037ti4u8103/Ju0IYXDIz/NrBB2483031BMlifPYCfA9u0Y+o7dG8AG47bLieNbesZ2BavHDvmhoy7P7BhG95x7Hhs9bwV2Kq153rgH9u4vwPe14Y/DuzehrcDrhg4Hp/Qhh8AzJ+htgzug0fS9XjPb8+PBl4A/C7wX8BDBo8x4Ai6DzXrAw9q+2ce3Yec/wH+sE13KrB/G758oB3/BLyrDX8U2Ldtv+uAh7XyjwGHDpQ/hO5c8inaaxJ4G3BoG94b+MRMHOvjym6l+4LuYODvW9kGdN84bw+8BnhLK58HbNyGr27H7mPpXvMbAhsDPx7Y7l8F3j1Q/y+O4jW8mtvkhcCxbfjbdOfUPeiueBl7jS1i4HywtjwY/n72Grp/MQjw8Hb8bzhJ+asHyv8IuBNYvAa1Z6Lz0kTHbQHPaMP/MnDMHz5w3F4M/Fkbfiv3nMPm5HgeV7dFbR/s3J5/EvjLNjzRe/6mcPd/BnnpQBvuXm57PtE5+nC6zzQbjbidQ99/6D4rnNP245bt2NyK6b/3DG6/84BnteEN2/Ez9FhZiXpP9Bliov002fG1eKBu1wK/156fCLyKSd7/x+8nJn/P/g3w0Nbec4D9pnh9fBjYtw0fPHYMzfB+3oOB824rG3tP3ojus+PmQ7bVgbTPXu35SD5TjN/nUxyXZw5M+37gwDZ8NfD6gXFfZcg5ZZJ9t4h7PnPevZ5hbWaar4+Z3i5DysY+Y0x2PA77rLs+cCXw2DbdJq1dQz+rzHRbxj+8THpyn62qSnIJcGNVXQKQ5DK6g/aicdOfQheg3gK8F3hVkqOAz1fV+N69TwDfTvIaBi6RHudP6D4U/Kqt94z29wHA44FPtS9YoDtoAB4H/EUb/gjdCW+1VNXF6b5pfj7Tv1TlEuDdSd5J94L+xrjxuwFfr6qr2jpWrG49J7BRkrH99A3gWGCq3+2MXa7+9fYt6WS/nbyY7tvez9AFsjGfq6o7gDuS3ER3srhu3LznV9V1AK2Oi+hOwleObZdWl4OnqO8qacfm7nRXNPw58P4kO9N9KB38fc8FVXVDm+cnwFgP6yV03wLT5t9p4HjcpB2n3wLek+7yslPH2jvD/pwutC1t69+I7gPFr4GvVNU1cJ9j7Myq+i1wU5IVdF/OACwbe53TffBYlGRzupP8t1r5Cdy3B+UPgB9V1U/a8xPpeiPPBX44VockJwFjv287li4cv5/ui6tjVn0TTGlP4I/S9VAAPJDuDesC4LjWC/CZqhp/TnsCcHpV/Qb4TZLPjhs/drnghXTH75rm+cC/teGT2/Mz6V57V00419ph2PvZ7sC/A1TVD5JcQ/danqj8T4EjW/nFSS6ercoPMaw96zH8vDTRcftb7rnM+EK6n/XcLd0VHQuq6mut6AS61+CYNeF4vmqgPWPnoMne87cBPpFkK7oPmBMd1xOdowHOqO5nUHNhd+Ck6i4pvTHJ1+jO57cxvfce2viNga2r6jSAds4iyVTnuKlM9Bli2H6a6vga8/tt/h8NTHcIXXia7P1/cD9N9NqA7vx2Jdz9nrM73efTiV4fxwCvp/sM8xLgZZNukVU3/rz7ytxzz5Jt6d6Tbp5iGbPxmQImPy4n84lxz4edUybbd8Pcp83tdTzt18csmOp4HP9Z9+fADVV1AUBV3dbGT/RZZaTv130Lwyvrjvb3fweGx57fa9ulu35+i6q6qnXrfzrJYXQvntePX3BVXZvkKuDP6L6BGnqzrQncD7h1JYP96joDeBfdtzybD5Tfyb0vt98QukvBkzyG7tuwI5J8qareOkt1HXSfD1hJhtZ5QE3xfNDT6D5QPgN4S5I/bOWDx8tdDH+tTWeamXQZ3bEGQFUdku5S2KXA/wFuBB5Ft21+M0E9B18Lg6+D+wG7jX0AGfCOJJ+jOw6+leSpVfWDGWrPmND1bv3fexVOfmOwibb9rO2Tqro6yS3pLll9NPe8kc2IJA+la8NNdNvob6vq7CHT/SndcXx8kvdU1YkrsZqx7TUbx+9KSXfJ+ZPoLokvum/4C/gcXQ+F1nxDz0vti8phx+3/VOtSYNWOyTXheB5/DtqIyd/z/x14T1WdkWQPuh7EYYaeo9uH6tl4Pfz/9u4+Rq6qjOP490cxpWZJIwiIsYBioSICQuVFRIpgjEEDFbSQZVMBJTaRaowJoCigURBMMFZjS7EUaQOm4VVBLFAKpBVaoO32BREEDCURqrwEKDYNPP7xnGGH2ZnZnd2d7dL9ff7Z2Tv3beaee+859zzPmfXAqX3O9U79ufc01aSs9Fe9OkTtvlWOU7tVH6dm9+xGdZi650dELCthrlOAMRHR0uBgNZod57f3v2zrBOCoiNgsaSm962K9RMRw1Cma6av+WHsu1bumNDt2vdT7zDXrhgGeH4NRU8e4iP7VIfu6tjasq7TTqM8ZHkKbyDSD48qTpHPIUIVHI6LRjeZ6sgf5qQZPt+4HTlbml+xMNrgqT1CelvRVeDu34eCyzHJKbhwZIljbIztQ88gQjLU1058BDi37cSgZeoly1MTNEbEAuKIyT5UHgc9Kqsw/JLmS/fQ8sLtyRM+xwJdq3q/khnwGeCUiXqm3EmUezoSIuBc4j3yC1VFv3hY8DnxEPSOpThvk+qotAXaSNKNqWmV07fHkU7q3gC6y4dCKxcC5lX/K00Ek7RsRayPiF2RvTt0RfQfpbuBr6slx3VWZK78cOE45SNiAy1jkYDdvqCevpwu4r2a2x4CJ5eYAcEaZZwOwv6QJyhpU7fH8PRlSfUP57oeEpN2A2WR4WQB/BWaoJy9sP2Ve/95k1Mtcsneg9jxdBnxZmXPaQe9zZSQ7FbguIvaOiH0iYgL5dLk2Z/BVMgR8e/AAed1HOXrrXuQ1pdH0+8mUAiQdSIZKjyR1r0v9KLd1lWv5S+rJG613Lo84fdzzx9MzqOX0qsVqy3Xda/QwWgKMVckNLftwEBlmOU2ZK7gb+XB5Rasrj4hXgY0qo/tLGqsc+XlAZWUg+ihf1cfjcbIn+aM187Vy/292zz5c0odLHWUaOYBgX/5AhtJf0495m2l0nGuvu+OBl0pDeBIZLVjPO8rxMNUpIK+Z9crlv8gIi7HKqMHjB7Dulupbw/iZ+61OHaPVOuTjwJ6SPlXWt7NyQMu6dZV2fY4KN4aHSCkMpwA/K2EAt5A5g0dWdffXWkSOPF0vRJqIeJQMuVgD/IU8CSo6gbMlrSGfxJ1Upp8LnKkMd+siG+SDFhEbI6LeMO83ArsoQ8e/Tc+okJ8AVpTv4iIyT7N6fZvIBwY3lc9QG1rSNhGxlczlWUHmhNQ+VfyfpFXkiX52k1WNARYow+hXAb+OiJcHuW9vkCPb3inpEfJGULcxPoB1B5nfeqxygIwVZHjWeeRIutPLsZhE6z0FM4HJygEPNpCDt0CmCqwr5XErWY6HVHlAcwlwd9nOYmCPyAHsZgC3ls+1cBCb6QKuLOs/gN7leTNZVm4q5WELmXu+mTwv7iZ74F/mncfzZvImMn8Q+1YxTuVnD8r2FpPfC2QlcAPwqHLwuDn05B2tKeV9Gj0hxZXPtZKMCukmj91ahqg8DoPTye+32o1l+tvKw45lpZy+qwfQIs/jHUoZ/COZy7alyfTfAR3Kn/X7CRnKN5I0ui5NoUm57cN04IpyLh9Cfu53g0b3/IvJ8OlHgP9Uzf8nYGq5JhxD42v0sCj3n6nACcqf3FkPXEo2wLrJes4SMufy3wPcTBcZettNPgz9AIMrKwPRqHzNB2aXOpHIcORF5Zx8C5jd4v2/2T17JZl+8xj5ALD2OljPQjL/vG59tL+aHOfaY3onsGO59lxGdpDUcy/Z+FytHMSqrXWK0iDbQn5nvcplRDxL5oivK39XDWAzrda32l6P6qdmdYyWPlNkmto0YFZZ5i6yl71RXaWtKoMumNkIIakjIl4rPYm/BZ6IiCu39X7ZwNQczznA2oiYVd47Erg0IoY7v6ffqvb/vWRP4jnlQZ2ZmQ2hwd7/leHH34+IlqJ4SqfNSRHR1dIOb2dKxMXciOj1SyK2/RpROV5mBsA3JU0nB0NZRTag7N1rhqROekZGnAsg6YdkdMRpTZYdCa6SdAD51PZaN4TNzNpm2O//kmYBXyRzUkctSd8iIyi+u633xYaXe4bNzMzMzMxs1HHOsJmZmZmZmY06bgybmZmZmZnZqOPGsJmZmZmZmY06bgybmZkNMUkhaUHV/ztK2iTpz30sN1lSvZ+xa7ZMh6Q55adMHpG0VNIRfSzzg1a2YWZmtj1yY9jMzGzovQ4cKGlc+f/zwHN9LRQRD0fEzBa3dTXwIjAxIg4jf8P0/X0s0/bGcPnNTjMzsxHLjWEzM7P2uAM4sbw+Hbi+8oakwyX9TdIqScsl7V+mT6n0Hku6WNK80tP7lKRejWRJ+wJHABdGxFsAEfF0RNxe3r+l9Bavl3ROmXYZME7SakkLy7QzJK0o0+ZIGlOmny3pH+W9uZJ+U6bvI2mJpG5J90jaq0yfL2m2pIeAyyU9IWm38t4Okp6s/G9mZratuTFsZmbWHjcAp0naCTgIeKjqvb8Dx0TEJ4EfAz9vsI5JwBeAw4GLJL2n5v2PA6sj4s0Gy59VeosnAzMl7RoR5wNvRMQhEdEp6WPANODoiDgEeBPolPRB4EfAkcDRZV8qZpG/O30QsBCoDu3+EPDpiPgesADoLNNPANZExKYG+2pmZjasHMJkZmbWBhHRLWkfslf4jpq3xwPXSpoIBFDbyK24PSK2AFskvQDsAWxsYTdmSppaXk8AJgL/rZnneOAwYKUkgHHAC2QD/L6IeBFA0iJgv7LMUcBXyuvrgMur1reoqnE+D7gV+BVwFnBNC/tuZmbWVm4Mm5mZtc9twC+BKcCuVdN/CtwbEVNLg3lpg+W3VL1+k9737fXAwZLG1PYOS5pC9sYeFRGbJS0FdqqzDZG9vBfULH9yow/Vh9crLyLiWUnPS/oc2bjubLyYmZnZ8HKYtJmZWfvMAy6JiLU108fTM6DW1we68oj4J/AwcIlKt27J5z2xbOOl0hCeRIY7V2ytCrm+BzhV0u5l+V0k7Q2sBI6V9L4yGNYpVcsvB04rrzuBB5rs5tVkuPSiJuHcZmZmw86NYTMzszaJiI0RUe+nki4HLpW0isFHaX2DDJ9+UtI6YD4Z5nwnsKOkx4DLgAerlrkK6Ja0MCI2ABcCiyV1A3cBe0bEc2Qu8wpgGfAM8EpZ/lzgzDJ/F/CdJvt3G9CBQ6TNzGyEUURs630wMzOzEUhSR0S8VnqGbwbmRcTNLa5jMnBlRBzTlp00MzMbIPcMm5mZWSMXS1oNrAOeBm5pZWFJ5wM3Ahf0Na+Zmdlwc8+wmZmZmZmZjTruGTYzMzMzM7NRx41hMzMzMzMzG3XcGDYzMzMzM7NRx41hMzMzMzMzG3XcGDYzMzMzM7NR5/8d3ypfeZdOMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,7))\n",
    "sns.countplot(dfsuccess['main_category'],order=df['main_category'].value_counts().index)\n",
    "plt.xlabel('Main Category')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Count plot of the successful kickstarter projects')\n",
    "\n",
    "plt.figure(figsize=(16,7))\n",
    "sns.countplot(dffail['main_category'],order=df['main_category'].value_counts().index)\n",
    "plt.xlabel('Main Category')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Count plot of the failed kickstarter projects')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state             failed  successful\n",
      "main_category                       \n",
      "Dance          37.419700   62.580300\n",
      "Theater        39.778802   60.221198\n",
      "Comics         45.503731   54.496269\n",
      "Music          50.873026   49.126974\n",
      "Art            58.690737   41.309263\n",
      "Film & Video   62.070903   37.929097\n",
      "Games          63.948968   36.051032\n",
      "Design         64.256674   35.743326\n",
      "Publishing     68.499501   31.500499\n",
      "Photography    69.039813   30.960187\n",
      "Food           74.922728   25.077272\n",
      "Fashion        75.059086   24.940914\n",
      "Crafts         75.580187   24.419813\n",
      "Journalism     78.339041   21.660959\n",
      "Technology     79.745002   20.254998\n"
     ]
    }
   ],
   "source": [
    "df1=df.groupby(('main_category','state')).size().unstack().fillna(0)\n",
    "df1=df1.divide(df1.iloc[:,:].sum(axis=1),axis=0)*100\n",
    "print(df1.sort_values(by='successful',ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10816d898>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SJEmqlEFUkiRJklQpV82VhotjRg/i2rmtq0OSJEkaJHtEJUmSJEmVskdUkrTU3GtSkiQNhj2ikiRJkqRKGUQlSZIkSZUyiEqSJEmSKmUQlSRJkiRVyiAqSZIkSaqUQVSSJEmSVKkBBdGIeF1EnBsRf4iIOyNi64hYPSKuiIi7y++rtbtYSZIkSdLwN9Ae0e8Al2XmRsBE4E7gSOCqzHwrcFV5LEmSJEnSEo3o7wURMRrYHvgXgMx8AXghIvYAdihfdgZwNXBEO4qUJEmSJC2FY0YP4tq5ratjMQbSIzoemAP8JCJujohTI2JlYK3M/Ev5mr8Ca/V1cURMjYjuiOieM2dOa6qWJEmSJA1bAwmiI4DJwEmZOQl4lkWG4WZmAtnXxZl5SmZ2ZWbXmDFjBluvJEmSJGmYG0gQnQ3Mzswby+NzKYLpIxHxRoDy+6PtKVGSJEmS1CT9BtHM/Cvw54jYsDy1MzALuAiYUp6bAlzYlgolSZIkSY3S72JFpX8FzoyIFYB7gY9RhNhzIuIA4AFgn/aUKEmSJElqkgEF0cy8Bejq46mdW1uOJEmSJKnpBtojWrtxR168zNfef/xuLaxEkiRJkjQYA1msSJIkSZKkljGISpIkSZIqZRCVJEmSJFXKICpJkiRJqpRBVJIkSZJUKYOoJEmSJKlSBlFJkiRJUqUMopIkSZKkShlEJUmSJEmVMohKkiRJkiplEJUkSZIkVcogKkmSJEmqlEFUkiRJklQpg6gkSZIkqVIGUUmSJElSpQyikiRJkqRKGUQlSZIkSZUyiEqSJEmSKjWi7gLUYseMHsS1c1tXhyRJkiQthj2ikiRJkqRKGUQlSZIkSZUyiEqSJEmSKmUQlSRJkiRVyiAqSZIkSaqUQVSSJEmSVCmDqCRJkiSpUgZRSZIkSVKlDKKSJEmSpEoZRCVJkiRJlTKISpIkSZIqZRCVJEmSJFXKICpJkiRJqpRBVJIkSZJUKYOoJEmSJKlSI+ouQJIkSe037siLl/na+4/frYWVSNIAg2hE3A88DbwMvJSZXRGxOvALYBxwP7BPZj7RnjIlSZIkSU2xNENzd8zMzTOzqzw+ErgqM98KXFUeS5IkSZK0RIOZI7oHcEb5+Axgz8GXI0mSJElquoEG0QQuj4jpETG1PLdWZv6lfPxXYK2+LoyIqRHRHRHdc+bMGWS5kiRJkqThbqCLFW2XmQ9FxOuBKyLiD72fzMyMiOzrwsw8BTgFoKurq8/XSJIkSZI6x4B6RDPzofL7o8AFwJbAIxHxRoDy+6PtKlKSJEmS1Bz9BtGIWDkiVul5DLwbuAO4CJhSvmwKcGG7ipQkSZIkNcdAhuauBVwQET2vPyszL4uIm4BzIuIA4AFgn/aVKUmSpNocM3oQ185tXR2SGqPfIJqZ9wIT+zj/OLBzO4qSJEmSJDXXYLZvkSRJkiRpqRlEJUmSJEmVMohKkiRJkiplEJUkSZIkVcogKkmSJEmqlEFUkiRJklQpg6gkSZIkqVIGUUmSJElSpQyikiRJkqRKGUQlSZIkSZUyiEqSJEmSKmUQlSRJkiRVyiAqSZIkSaqUQVSSJEmSVCmDqCRJkiSpUiPqLkCvNu7Ii5f52vtHtbAQSZIkSWoDe0QlSZIkSZUyiEqSJEmSKmUQlSRJkiRVyiAqSZIkSaqUQVSSJEmSVCmDqCRJkiSpUgZRSZIkSVKlDKKSJEmSpEoZRCVJkiRJlTKISpIkSZIqZRCVJEmSJFXKICpJkiRJqpRBVJIkSZJUKYOoJEmSJKlSBlFJkiRJUqUMopIkSZKkShlEJUmSJEmVMohKkiRJkio1ou4CKnHM6GW8bm5r65AkSZIk2SMqSZIkSarWgINoRCwfETdHxG/K4/ERcWNE3BMRv4iIFdpXpiRJkiSpKZamR/QQ4M5exycA38rMtwBPAAe0sjBJkiRJUjMNKIhGxFhgN+DU8jiAnYBzy5ecAezZjgIlSZIkSc0y0B7RbwOfB+aXx2sAT2bmS+XxbGCdFtcmSZIkSWqgfoNoROwOPJqZ05flD4iIqRHRHRHdc+bMWZb/hCRJkiSpQQayfcu2wPsjYldgFLAq8B3gdRExouwVHQs81NfFmXkKcApAV1dXtqRqSZIkSWq4cUdevMzX3j+qhYW0Qb89opn5hcwcm5njgI8A/y8z9wOmAXuVL5sCXNi2KiVJkiRJjTGYfUSPAD4XEfdQzBk9rTUlSZIkSZKabCBDcxfIzKuBq8vH9wJbtr4kSZIkSVKTDaZHVJIkSZKkpWYQlSRJkiRVyiAqSZIkSaqUQVSSJEmSVKmlWqxIkqRBO2b0IK6d27o6JElSbewRlSRJkiRVyh5RqULjjrx4ma+9f1QLC5EkSZJqZI+oJEmSJKlSBlFJkiRJUqUMopIkSZKkShlEJUmSJEmVMohKkiRJkiplEJUkSZIkVcrtWyRJUmscM3oQ185tXR2SpCHPICppaPIDrSRJUmM5NFeSJEmSVCmDqCRJkiSpUgZRSZIkSVKlDKKSJEmSpEoZRCVJkiRJlTKISpIkSZIqZRCVJEmSJFXKICpJkiRJqpRBVJIkSZJUKYOoJEmSJKlSBlFJkiRJUqUMopIkSZKkShlEJUmSJEmVMohKkiRJkiplEJUkSZIkVcogKkmSJEmqlEFUkiRJklQpg6gkSZIkqVIj6i5AkiRJUk2OGT2Ia+e2rg51HHtEJUmSJEmVMohKkiRJkirl0FxJkiSph0NVpUr02yMaEaMi4vcRcWtEzIyIY8vz4yPixoi4JyJ+ERErtL9cSZIkSdJwN5Chuc8DO2XmRGBz4L0RsRVwAvCtzHwL8ARwQPvKlCRJkiQ1Rb9BNAvPlIcjy68EdgLOLc+fAezZlgolSZIkSY0yoMWKImL5iLgFeBS4AvgT8GRmvlS+ZDawzmKunRoR3RHRPWfOnFbULEmSJEkaxgYURDPz5czcHBgLbAlsNNA/IDNPycyuzOwaM2bMMpYpSZIkSWqKpdq+JTOfBKYBWwOvi4ieVXfHAg+1uDZJkiRJUgMNZNXcMRHxuvLxisAuwJ0UgXSv8mVTgAvbVaQkSZIkqTkGso/oG4EzImJ5iuB6Tmb+JiJmAT+PiOOAm4HT2linJEmSJKkh+g2imXkbMKmP8/dSzBeVJEmSJGnAlmqOqCRJkiRJg2UQlSRJkiRVyiAqSZIkSaqUQVSSJEmSVCmDqCRJkiSpUgZRSZIkSVKlDKKSJEmSpEoZRCVJkiRJlTKISpIkSZIqZRCVJEmSJFXKICpJkiRJqpRBVJIkSZJUKYOoJEmSJKlSBlFJkiRJUqUMopIkSZKkShlEJUmSJEmVGlF3AZIkNdYxowdx7dzW1SFJ0hBjEJWkuhlWJElSh3ForiRJkiSpUvaIanizJ0mSJEkaduwRlSRJkiRVyiAqSZIkSaqUQVSSJEmSVCmDqCRJkiSpUgZRSZIkSVKlDKKSJEmSpEoZRCVJkiRJlTKISpIkSZIqZRCVJEmSJFXKICpJkiRJqpRBVJIkSZJUKYOoJEmSJKlSI+ouQJIkadg5ZvQgrp3bujokaZiyR1SSJEmSVCmDqCRJkiSpUgZRSZIkSVKl+g2iEbFuREyLiFkRMTMiDinPrx4RV0TE3eX31dpfriRJkiRpuBtIj+hLwL9l5tuArYCDIuJtwJHAVZn5VuCq8liSJEmSpCXqd9XczPwL8Jfy8dMRcSewDrAHsEP5sjOAq4Ej2lKlJEmSJA2Gq10PKUs1RzQixgGTgBuBtcqQCvBXYK2WViZJkiRJaqQBB9GIeC1wHnBoZj7V+7nMTCAXc93UiOiOiO45c+YMqlhJkiRJ0vA3oCAaESMpQuiZmXl+efqRiHhj+fwbgUf7ujYzT8nMrszsGjNmTCtqliRJkiQNYwNZNTeA04A7M/ObvZ66CJhSPp4CXNj68iRJkiRJTdPvYkXAtsBHgdsj4pby3BeB44FzIuIA4AFgn/aUKEmSJElqkoGsmns9EIt5eufWliNJkiRJarqlWjVXkiRJkqTBMohKkiRJkiplEJUkSZIkVcogKkmSJEmqlEFUkiRJklQpg6gkSZIkqVIGUUmSJElSpQyikiRJkqRKGUQlSZIkSZUyiEqSJEmSKmUQlSRJkiRVyiAqSZIkSaqUQVSSJEmSVCmDqCRJkiSpUgZRSZIkSVKlDKKSJEmSpEoZRCVJkiRJlTKISpIkSZIqZRCVJEmSJFXKICpJkiRJqpRBVJIkSZJUKYOoJEmSJKlSBlFJkiRJUqUMopIkSZKkShlEJUmSJEmVMohKkiRJkiplEJUkSZIkVcogKkmSJEmqlEFUkiRJklQpg6gkSZIkqVIGUUmSJElSpQyikiRJkqRKGUQlSZIkSZUyiEqSJEmSKmUQlSRJkiRVyiAqSZIkSaqUQVSSJEmSVKl+g2hE/DgiHo2IO3qdWz0iroiIu8vvq7W3TEmSJElSUwykR/R04L2LnDsSuCoz3wpcVR5LkiRJktSvfoNoZl4L/G2R03sAZ5SPzwD2bHFdkiRJkqSGWtY5omtl5l/Kx38F1lrcCyNiakR0R0T3nDlzlvGPkyRJkiQ1xaAXK8rMBHIJz5+SmV2Z2TVmzJjB/nGSJEmSpGFuWYPoIxHxRoDy+6OtK0mSJEmS1GTLGkQvAqaUj6cAF7amHEmSJElS0w1k+5azgd8BG0bE7Ig4ADge2CUi7gbeVR5LkiRJktSvEf29IDP3XcxTO7e4FkmSJElSBxj0YkWSJEmSJC0Ng6gkSZIkqVIGUUmSJElSpQyikiRJkqRKGUQlSZIkSZUyiEqSJEmSKmUQlSRJkiRVyiAqSZIkSaqUQVSSJEmSVCmDqCRJkiSpUgZRSZIkSVKlDKKSJEmSpEoZRCVJkiRJlTKISpIkSZIqZRCVJEmSJFXKICpJkiRJqpRBVJIkSZJUKYOoJEmSJKlSBlFJkiRJUqUMopIkSZKkShlEJUmSJEmVMohKkiRJkiplEJUkSZIkVcogKkmSJEmqlEFUkiRJklQpg6gkSZIkqVIGUUmSJElSpQyikiRJkqRKGUQlSZIkSZUyiEqSJEmSKmUQlSRJkiRVyiAqSZIkSaqUQVSSJEmSVCmDqCRJkiSpUgZRSZIkSVKlDKKSJEmSpEoNKohGxHsj4q6IuCcijmxVUZIkSZKk5lrmIBoRywPfB94HvA3YNyLe1qrCJEmSJEnNNJge0S2BezLz3sx8Afg5sEdrypIkSZIkNVVk5rJdGLEX8N7MPLA8/ijwjsz87CKvmwpMLQ83BO5a9nKX2ZrAYzX8uXXopHuFzrrfTrpX6Kz77aR7hc663066V+is++2ke4XOut9OulforPvtpHuF+u53/cwc09+LRrS7isw8BTil3X/OkkREd2Z21VlDVTrpXqGz7reT7hU663476V6hs+63k+4VOut+O+leobPut5PuFTrrfjvpXmHo3+9ghuY+BKzb63hseU6SJEmSpMUaTBC9CXhrRIyPiBWAjwAXtaYsSZIkSVJTLfPQ3Mx8KSI+C/wPsDzw48yc2bLKWqvWocEV66R7hc663066V+is++2ke4XOut9OulforPvtpHuFzrrfTrpX6Kz77aR7hSF+v8u8WJEkSZIkSctiMENzJUmSJElaagZRSZIkSVKlDKKSJEmSpEoZRCVJkiQ1TkS8ru4aqhIRy0fEmXXXsTQaGUQj4oSBnGuKiNg2IlYuH+8fEd+MiPXrrqsdImLliFiufLxBRLw/IkbWXVc7RcT6EfGu8vGKEbFK3TVJSyMixkbEBRExJyIejYjzImJs3XW1WkRMXtJX3fW1Qyf9Ti63q/tmRJwfERf1fNVdl1ojIpavuwa1xfSIODsi3l13Ie2WmS8D65fbag4LjVw1NyJmZObkRc7dlpkT6qqpnSLiNmAiMAE4HTgV2Ccz/6HOutohIqYD7wRWA35LsZ/tC5m5X62FtUlEfAKYCqyemW+OiLcCJ2fmzjWX1hYRsS1wDLA+xfZSAWRmvqnOutqhw+71CuAs4Gflqf2B/TJzl/qqar2ImFY+HAV0AbdS/LtOALozc+u6amuXTvqdHBG3AqcBtwPze85n5jW1FdVm5c/u3pn5ZHm8GvDzzHxPvZW1XkTcC5wH/CQzZ9VdT7tFxK/6b/5jAAAgAElEQVSBRUPAXKAb+GFmzqu+qtYrG8reA3wc2Bw4GzgjM/9Ua2FtEhE/BTYGLgKe7Tmfmd+sraglWOZ9RIeiiPg08BngzWU467EKxRtkU72UmRkRewDfy8zTIuKAuotqk8jMv5f394PM/I+IuKXuotroIGBL4EaAzLw7Il5fb0ltdRpwGDAdeLnmWtqtk+51TGb+pNfx6RFxaG3VtElm7ggQEecDkzPz9vJ4U4pGhybqpN/J8zLzxLqLqNiaPSEUIDOfaPB70ETgI8CpZXj5MUXofqrestrmXmAMRTAD+DDwNLAB8CPgozXV1VKZOR+4FLg0InYAzgQOi4jfA1/IzN/XWV8b/Kn8Wo4i/wxpjQqiFC3ulwL/Fziy1/mnM/Nv9ZRUiacj4gsUvQzbl79AGzk0CoiI2BrYD+gJ200eTvN8Zr4QEQBExAhe3YLZJHMz89K6i6hIJ93r4xGxP6984NkXeLzGetptw54QCpCZd0TExnUW1Ead9Dv5OxFxNHA58HzPycycUV9JbTc/ItbLzAehmCpCQ9+DMvNpigD2o4j4B4rPlN+KiHOBr2bmPbUW2HrbZOYWvY5/HRE3ZeYWETGztqparJwjuh/wz8ATFA3AFwBvB34BjK+vutbLzGMBImKlzPx73fX0p1FBNDPnRsQzwKTMfKDueir0YeCfgAMy868RsR7wnzXX1C6HAF8ALsjMmRHxJmBaP9cMZ9dExBeBFSNiF4oe/1/XXFM7TYuI/wTOp/kf9DrpXj8OfBf4FsWH2P8FPlZrRe11W0ScCvx3ebwfcNsSXj+cHUrn/E7ejKKXaCdeGZqb5XFT/TtwfURcQzHM/J0U00Uap5wjuhvF76ZxwH9R9J69E7iEoqewSV67SCPDesBry+deqK+slruJolFhn0WywQ0R8aOaamqbsmHwNIp/y/UiYiLwycz8TL2V9a2pc0QvBP6154er6SJiPPDXzHyuPF4RWCsz76+1sDaIiL0z85f9nWuKsnf7AODdFB8C/gc4NZv4g8tCc+x6y8xs3Ae9TrrXThMRo4BPA9uXp64FTmrKnKu+DJfW98GIiHuAt2Vmkz6k9ysi1gS2Kg9vyMzH6qynXco5otOA0zLzfxd57sTMPLieytojInYFTqYYxhkUPYOfAa4GPpGZ366vutaJiGjqZ6a+RMSNwF7ARZk5qTx3R2ZuWm9lfWtqEL0WmAT8nlcm6mZm7lFfVe0TEd0UQyxeKI9XAH67yJCLRljMQlSvOtcUUayGPK9cCa2nxfY1Tf/Ap2aJiA2AkygayDaNiAnA+zPzuJpLa5vy9/CGFD1md2XmizWX1Ba9W98zc8i3vg9GRPwKmJqZj9ZdS7tFxEaZ+YfFrfbcxJEbEfHazHym7jqqFBGvATYqD+9qYmNZOWd/UT2LMv2oaQ1LEXFjZr4jIm7uFURvzcyJddfWl0YNze3lqF6Pe4aSfKSmWqowovcPUjmncNgs3TwQEfE+YFdgnYjovVjEqsBL9VRViauAdwE9b44rUsxP2qa2itooIj7Xx+m5wPTMbNQCKBExGjiaV3rNrgG+kplz66uqbX4EHA78ECAzb4uIs4BGBtFyQYwzgPsp3oPWjYgpmXltnXW1ybcpVqS8CCAzb42I7Zd8ybD1OuAPEXETCw+nf399JbXN5yiG4P5XH881dTjyihFxMMWw3AWfjzPz47VV1H5v55X7nRgRZOZP6y2p5WYDb2DhRZnmUaxm/iNgSk11tcufI2IbIKPYSusQ4M6aa1qsRgbRzLwmIiZRzJvcG7iPYvhBU82JiPdn5kUA5eq5TRs68xBF69X7KVYZ7fE0xcTzphrVu4U2M5+JiJXqLKjNusqvnnmwu1PMrftURPwyM/+jtspa78fAHcA+5fFHgZ8AH6ytovZZKTN/37PoVqnJDUj/Bbw7M++CBT3CZ1N86GuczPzzIv+2TV0F+ui6C6hKZk4tv+9Ydy0VuhC4DriS5v4/vEBE/Ax4M3ALr9xvAk0Lolv3HiFYjmz4fbkoUxO36fkU8B1gHYrPzpdTDLkekhoVRMs3+33Lr8coVsOKDvhF+ingzIj4HkXr+58pVgdrkm9l5s4RMTEzz6i7mAo9GxGTe4ZBRcTbgedqrqmdxlJse/EMQLlC5cUUvYbTgSYF0Tdn5od6HR/b4G0vHouIN1OuthkRewF/qbekthrZE0IBMvOPZct0Ew2r1vdB6gaey8z55eeNjShW6m+siNgbuCwzn46ILwGTKVaQvbnm0tphpcw8ou4iKtRFMee5eXP0FrZKRIzNzNnl8dq8sq3J84u5ZjjbMBfZxzmKfcuH5DaWjQqiwB8oWrN271lmOyKa3FsGQBab8m4VEa8tj5s4x+GN5Yed95WteAs1vzdxvkrpUOCXEfEwxT2/gWJYSVO9noXfGF6kmFf4XEQ07Q3juYjYLjOvhwVvFE1tZDgIOAXYKCIeohilsn+9JbVVdx+r5nbXWE879dX6flCtFbXPtcA7I2I1ivu8ieL38X5LvGp4OyozfxkR21FME/lPihFm76i3rLb4TUTsmpmX1F1IRe6g+EzR5EZBgM8Dv4uIP1B8jtoA+Gy5BseZtVbWHt+laDDq79yQ0KjFiiJiT4q5oNsClwE/p1hhtFF7BPWIiP0z878XM6+OzPxm1TW1S9mDcgCwHa/+QNfolUbLXoYNy8PGLnoCEBFHAR+gGCIF8I8Uc8/+Czhl0Va+4SwiNqeYRzia4s3xb8C/ZOattRbWRuUb/3Llfn2NVS4AchDF7ysoGkh/kJlNa0zpKD0L40XEvwIrZuZ/DOVFQFqhZ8GTiPi/wO2ZeVbvRVCaICKephitEcDKFFuX9LzPZmauWldt7VSu3L45xcKejZ7zXO4m8bbycFbPLhNNUi4ctw1FB8a3ej21KvCBofp7qlE9opn5K+BX5YedPSj+MV4fESdR7HF2ea0Ftt7K5fdVlviqBsjMc4FzI+KozPxq3fVUbAteWUxgckMXEwAgM78aEZdSNCYBfCozexoeGhNCAcrFlyZGxKrl8VM1l9Ryi2sk65lP2KTGst4y8/lyqsQVNH/V3PHAv/LqBV4a92GWYieIrSl+Fx1Qnluuxnqq8FBE/BDYBTihbGRp1D1nZuM/Qy3GMXUXUIWIGEGxN2zPImpXR8Spmdm0dQpWoNg7dAQL54KnKLZzGZIa1SPal3IIzd7AhzNz57rr0eCV/6ZvBUb1nGvoapSLXUyggfuZrZqZT0XE6n09n5l/q7qmdumwkQw9i7tsSNGgclF5/I8Ui0U0cnhuX6vmAo1cNTcibqXYvuV2YH7P+cy8prai2iQi/gH4N4rt0U6IiDcBhzbt93Fv5eJ476XoDb07It4IbNbAhn0AIuKDFCMZEriu7ODQMFY2pKzMK4sw7U+xLd7U+qpqn4hYPzMfqLuOgWp8EO0EndQiHREHUiyGMZYinG0F/K6pQ3Mj4k46YDGBiPhNZu4eEfdRLmjT8xRF8H5TTaW1XER8MjN/2CukLSQzj626pnaLYm/n3XqG5EbEKsDFmdnIbT4iYjrwT4uumpuZjVs1t2fPurrrUHtExHp9nc/MB6uupd0i4gfAW1h4m48/ZWaj5jxHxPWZuV2vIckLnqKBQ5H7Gj7f5CH1ETGGYl7sJizcYTMkPyc3amhuB/sVRYv0r+nVIt1Qh1D0rNyQmTtGxEbA12uuqZ06YjGBzNy9/N7I+dy9ZWbPXpqNC5xLsBbFvKseL5TnmqqTVs39TtmocjkLzzNrzAJyEfHtzDw0In7Nwh/cgWY2+vZyMa/MnxwFjAfuoviQ2zQ7ARv3NPxGxBnAzHpLar3M3K783ilDkudHxLjMvB8gIsbR7M/KZ1LsGrI7xWJyU4A5tVa0BAbRZpiXmSfWXURF5mXmvIggIl6TmX+IiA37v2zYWhOYFRGNX0wAFqwce0tmPhsR+1Os8vbthra+jwE+QWdsnv5T4PcRcUF5vCdwen3ltF0nrZq7GcUeuDvxyoe7LI+b4mfl92/UWkUNMnOz3scRMZkhvCfhIN0DrAf0DGtctzzXWBGxPEWjYO/3oKa93x4BXBcRd1E0qLyFV+Z4N9EamXlaRBxSTpG4JiJuqruoxXFobgNExD9RzJlsbIt0j/KD7McoFqLaCXiCovdh11oLa5NyTtKrNHH+FUBE3AZMBCZQBJVTgX0ys8+/h+EsIv6XYjXV6fTaPD0zz6utqDYqP8C+szy8tqH7EAKdtWpuRNxDMX3ghX5frEaIiNsXDahNEBHXUIy4+j1FY8qWFA1Ic6F5DcDl6s9HA4/QqxEpMyfUV1V7lKvmblwe3tnEVXN7RMQNmblVRPwPcCLwMHBuZr655tL6ZBBtgHJZ9Y8Cf2LhXyZNapF+lTKkjabYbNsPQQ3Qa3uELwMPla16MzJzSO5/NRgRcUtmbl53He3USYtQQTGfroG9CUsUEb8Cpmbmo3XX0i4RcTt9DMnt0cQP7j0WWVRtOYpRKmtk5ntqKqltFtfw26NpDcBlI9I7MvPxumtph4hYYsNBZl60pOeHq4jYnaLxc12K/UNXBY4dqvfr0Nxm2Bt4U9PDWDmEZGZmbgTNe1PoS0RsRfGLZGOKpbmXB55t2mICvTwdEV+gaFh5Z0QsBzR1bl0nbJ5+FsU8len0sSgG0JhFqEq/otw0PCLOy8wP1VxPFV4H/KEc+tXU6QO7111AjXrPI3yJYs5oI0dtdMJnikX8mbK3t6H2XsJzySuruDdKZv6mfDgX2LHOWgbCHtEG6IQW6R4RcSHwr53S6xAR3cBHgF8CXcA/Axtk5hdqLaxNIuINwD8BN2XmdeWKjTtkg/ZN7WPz9OcpNk9v5IqFnSQibs7MSYs+brJOmz6g5uqUht9evdybUGytdTELNyI1ZguxTlSu0n4SsFZmbhoRE4D3Z+ZxNZfWJ3tEm6ETWqR7rAbMLBfvebbnZEPvFYDMvCcils/Ml4GfRMTNQCODaGb+NSLOo5jzDPAYcMESLhl2OmilQiJiFkWv6FmZeW/d9VQgF/O4sTopcHZKUAGIiCX2FjX0Pfd79NHwW2tF7dHzHvRg+bVC+dVI5XZhRwE924VdAxzXs51YA/0IOBzoWaH/tog4CzCIqm363I+wSSLiLRQrux21yFPvpNlbm/w9IlYAbomI/6C41+VqrqltIuITwFRgdeDNwDrAycDOddbVDh2yQvC+FB/sroiIxyn25/tFZj5cb1ltMzEinqLo3V6xfAwN7u3upHBG5wQVgK0phm6eDdxI8f9w43VCw29fW4eV02Bem5lP9XHJcPdj4I8UP69QTP35CbBXbRW110qZ+fuIhX5kX6qrmP409gNtJylbpP9A0cq1CsWKYE1rpf428FRmXtP7C7iQYiuIpvooxc/pZyl6gNcFmjzv7CBgW+ApgMy8G3h9rRW1z0kUDQ0TgX+jWGzsZ0u+ZHjJzFsz8wvlan0HU2yNcENETCsbHRolM5fPzFUzc5XMHFE+7jluYjCDIpztC9wNrAgcCHy/1oraKDPvAZbPzJcz8yfAe+uuqU3eAHwR2BT4DrAL8Fiv994mWqjhNyIOo8GfkyPirIhYNSJWptizfFZEHF53XW3w1sz898z8Y/l1FMUWLk31WES8mXJUTkTsxRDusGnsD1gniYh9KJYb3xvYB7ix/B+vSdbKzNsXPVmeG1d9OdXIzAcoGhdek5nHZubnyg9CTfV870W3ImIEzR3i+FIWk/T3AL6Xmd9n4YVBGiUzb8jMwyhapV9HEWDUAB0UzjomqJT/lpdl5hRgK4r9NK+OiM/WXFo7dVrD79vKHtA9gUuB8RR/B00zrxy5ASwYxTGvxnra7SCKYbkbRcRDFNsdfrrekhbPobnN8O/AFj2LFUXEGOBK4Nxaq2qt1y3huRUrq6IiUYypOJriDXG58tRLwHcz8yu1Ftde10TEFymGNe5CsXH6r2uuqV16VgjeH9i+ySsER8QWFL1mHwLuo3iT/GWtRalVesLZrR0wfaB3UDmMhgeVcj/c3Sh+dsdR7EnYqDn7PcpV+b+emftRhJRXDV9toJERMZIiiH4vM1+MiCY2/H4G+Fn5/3MAf+eVYbqNU67H8K6yp3u5oT4X1iDaDMstsmLu4zTvg0B3RHwiM3/U+2REHEixNUTTHEYxRHWLzLwPICLeBJwUEYdl5rdqra59jgQOAG4HPglcApxaa0Xt82GKFYIPKBdpWg/4z5praqmI+DrFff4N+DmwbWbOrrcqtVhPODuI4vfWWBoazsoRKtABQSUifkoxLPcSij0I76i5pLbKzJcjYv2IWKHpW+H18kPgfuBW4NqIWJ9yWkyTZOYMYJOe/aybtn/1osrA/SGKxqMRPXNFh2onhtu3NEBE/CcwgWJRASg++N2WmUfUV1VrRcRaFC2xL/BK8OyiWBzjA5n517pqa4dygYRdMvOxRc6PAS5v8rYQ5T2SmXPqrkWDExFfBs4u5/qqQSJiD2BsOaSciLiRYj53Ap/PzCaNyAEWLDB2DLA+vRryM7Np++ESEfN5ZWX6V+0B3MQ5z2X43phif8neq/J3zHYmETEiM4fswjbLohyxsSdlMOs5n5lfr6umdoqIyyj2EJ0OvNxzPjP/q7ailsAe0WGsZyXZzDw8Ij4IbFc+9TvgzPoqa73MfATYJiJ2pGilBbg4M/9fjWW108hFQygU4awcStMofQxFJiJepsFDkRez2ugzmTm61sJaqKn/dgLg8xQryPZ4DfB24LUUK1I2LogCp1H0+i70Aa+JMrNpo6oG4k/l13I0eL5+ROyfmf/daz/RRTUteF9AMYqh8T+3pbGZOWzm6RtEh7dvUy4rnpnnA+cDRMRm5XP/WF9p7ZGZ04BpdddRgSUNDWrisKFOHIrcSVtBqHlWyMw/9zq+vhzy9rdyblITzc3MS+suQu3R17YmDdXz89nYsL2I9TNz0/5f1hj/GxGb9bXA51Dk0NxhLCJuyswtFvPc7Zm5WdU1qTXK3sBn+3oKGJWZjeoV7cShyBHRnZldEXFbZk4oz93cxHtV80TEPZnZ5xYIEfGncsueRomI4ylGLpwPPN9zvpyDpmEuIn7Nq1dpnwt0Az/MzCavtNpYEXEq8M3MnFV3Le0UEXcA8yk6Gd8K3Evxe6pnOP2EGstbLHtEh7eOWkm2k2Tm8nXXULGOGopcWmgrCJq92igAEbErMC0zn4uID5YjOTQ83biYBeQ+SbGdWBO9o/ze1etcAjvVUIta715gDAuvt/E0xUiVH9GQrU0i4sQlPZ+ZB1dVS0XeAdwcEfewcDCbXG9ZLbcOsHndRSwtg+jw1mkryaq5Om0oMnTYVhClXYEvR8QMir0JDaLD12HAryLin4CeHsG3U8wV3bO2qtqk3F7ppMw8p+5a1DbbLDLK7Nc9I88iYmZtVbVep30+bNzvo8W4r9fK3sOGQ3OHsU5bSVbN1UlDkSNivcx8sO46qhAR7wDu7b0CcrmS7iHAQZn589qKU0tExE7AJuXhzAYvILdgOH3ddag9IuJO4D09v5/LLbX+JzM3bvK0iYhYlaKHcEjvNzkY5YKIY1h41dyH66uo9SJiNktYaGqorv5sj+gw1oEryaqhOmwo8q+AyQARcV5mNrkX9BRgy56DiPgmxRL6G1E0ohlEh7ny/aZT3nOujIj/A/yChbf3aPS+hB3k34DrI+JPFI2g44HPlItvnVFrZW0QEV0UK1yvUhzGk8DHM7NRPaYR8RngK8DjFHMooRhS/7baimqP5SlWLY+6C1ka9ohKUoV6t6w3uZUdICJmZuYmETECOB14DvhkZs6PiBkNnKOjBouI+/o4nU3cR7RTRcRrKBrKAO5q8gJFEXEbxciU68rj7YAfDNVFbZZVOTd066bvTT5c31PtEZWkauViHjfR9RFxFfAGipba7csQ+g8UoVQaNjJzfN01qH3KhfE+CWxfnro6In6YmS/WWFY7vdwTQgEy8/qIeKnOgtpkNtAJoxaGVU9oD3tEJalCvebDBsXq1n/veYqid2XVumprh7KV/QXgEeBcYM3yqQ+57YWGk4j4577OZ+ZPq65FrVdu8zGSV4bhfpQirB1YX1WtFxE9vWb/TPEedDZFo+iHgXmZ+bm6amuliOhZ/XcCxXYmv2HhbZeWuHrwcBMRqw/HaQIGUUlSZSJiTNOHSOn/t3enMXaVdRzHv78CWlCLoBXcgKBGqayKRlm0KhKIuFYIMS64IPhCXHllNIAhJi5oBBUTKos7ihsaQVkkQBWk1toWxQWDgQQVi6yKLfx9cc4wF5yZ2uk998zy/SQ3Oc8599zzuzOZZP7nOc/zzE1JTh9oLgReBvyqql7fUyQNUZLVVbXPpvbNdkkun+JwVdWcWI4oyUenOl5VHx5VFk3OQlSSJGkzJXks8I2qOqzvLNpy7bJSR1bVn9r27sC3Z+O4O2m2cIyoJEnS5ruHZmZVzQ0nApcnuZFmqMSuwNv6jdSddimt/1FVp4w6S5eSXAQcXVX/bNs7AF+pqlf0m0xgISpJkrRJSS5kfIKxrYA9gPP7S6Qhu4pmLOEz2/YNPWYZhcG1uxcCRwC/7SlLl3YeK0IBqur2JE/qM5DG+WiuJKlzSfamWUN0cEHx7/QWSNpM7WzPYzYCN1XVzX3l0XBNtPzFbF0SYzrapWsurqqlfWcZpiQrgVeP/a0m2QX4/lxeOm02sUdUktSpJF+imblwHQ9dUNxCVLNGVV2RZCfgee2uP/SZR8ORZGfgycC2SfZjfBmMRcB2vQUbve2Ap/QdogMfAa5OchnN73Yp8K5eE+lB9ohKkjqV5PqqWtJ3DmlLJDkK+ATwM5p/aA8GTqyqb/eZS1smyVuAY4D9gesGDt0FnDNXn9xIsoaHPmq+GDilqs7oL1U32htIL2ybK6rqb33m0TgLUUlSp5IsBz5VVdf3nUWariSrgZeP/RObZDFwyVxb3mO+SrKsqi7oO8eoJNl1oLkR+GtVbewrT5eSHA08rapOTfJU4AlVtbLvXPLRXElS984Dfp7kVpoFxUOzXt3e/caSNsuCh/Wk/ANY0FcYDd2lSU4DXtS2r6DpIbyjx0xDl2QhcDzwdGANsHyuFqAASc4AtqH5vZ5KM0nTmYw/Yq8eWYhKkrq2HHgTzT89D2zivdJMdVGSi4Gvt+2jgR/3mEfDtRxYCxzVtt8EnA28rrdE3TgX2ABcCRwOLAHe02uibh1QVc9JsgqgqtYneUTfodSwEJUkde3vVfWDvkNIW6KqTkzyOuDAdteZVfW9PjNpqJ5WVcsG2icn+XVvabqzpKr2ggeHTVzbc56ubUiygHY8bJLH4Q3RGcNCVJLUtVVJvgZcSPNoLuDyLZodktzF+KQuGTj0ziT/Bv4EfKiqLh15OA3Tv5IcVFVXASQ5EPhXz5m6sGFso6o2JpnqvbNWkq3bR44/B1wALE5yMk2P98m9htODnKxIktSpJGdPsLuq6m0jDyMNUZKtgD2Br1bVnn3n0fQl2YdmPPv2NDcc1gPHVNXqXoMNWZL7acZJQvM9twXuZXzs/qK+sg3T4BqwSZ4NHELzHS+pqrW9htODLEQlSZK2QJLjquqLfefQlkuyCKCq7uw7i6Yvyaqq2q/vHJqahagkqRNJTmf8kcb/UVUnjDCOJE0qySOBZcBuDAxdq6pT+sqk6UtyM3DaZMeratJjGh3HiEqSunLdpt8iSTPC94E7gJUMjGXXrLUV8GgeOq5bM4w9opIkSZrXkqx1nO/cMThGVDOXPaKSpE4k+UxVvTfJhUzwiG5VvaqHWJI0kRVJ9qqqNX0H0VDYEzoL2CMqSepEkudW1cokL57oeFVdMepMkjQoyRqaG2VbA88AbqR5NHdsFtm9e4ynaUqyY1Wt7zuHpmYhKknqRJJdquovfeeQpMkk2XWq41V106iySPONhagkqRMPW8ftgqpa1ncmSRqUZCFwPPB0YA2wvKo29ptKmh8W9B1AkjRnDY7R2b23FJI0uXOB/WmK0MOBT/UbR5o/nKxIktSVmmRbkmaKJVW1F0CS5cC1PeeR5g0LUUlSV/ZJcidNz+i27TaMTwKyqL9okgTAhrGNqtqYONmqNCqOEZUkSdK8lOR+4J6xJrAtcC/eMJM6ZyEqSZIkSRopJyuSJEmSJI2UhagkSZIkaaQsRCVJkiRJI2UhKkkSkGT/JJ8dwXWWJjmg6+tIkjSTuXyLJElAVV0HXDeCSy0F7gZWdHWBNGtQpKoe6OoakiRtCXtEJUlzRpLdkvwuyTlJfp/kq0kOSXJ1kj8keX77+nmSVUlWJHlme+7SJD9st09K8qUkP0tyY5ITNnHdNyf5TZLVSb7c7ntlkmva61ySZKckuwHHA+9L8uskBydZnOSCJL9sXwe25y9O8tMk65KcleSmJI9vj70/ydr29d6B735DkvOAtcCHk3xmIOOxST497J+5JEnT4fItkqQ5oy30/gjsB6wDfgmsBt4OvAp4K/Bm4N528fpDgHdV1bIkS4EPVtURSU4CDgVeAjwGuAHYuao2THDNZwPfBQ6oqtuS7FhV65PsAPyzqirJO4A9quoD7WffXVWfbM//GvD5qroqyS7AxVW1R5IzgFuq6mNJDgN+DCwGdgXOAV5As9bhNcAbgduBG9scv0jy6Pa7P6uqNiRZARxXVWuG8sOWJGkL+GiuJGmu+fNYsZVkHXBpWwyuAXYDtgfOTfIMoIBtJvmcH1XVfcB9Sf4G7ATcPMH7Xgp8q6puA6iq9e3+pwDfTPJE4BHAnye5ziHAkuZpWgAWtUXkQcBr28+8KMnt7fGDgO9W1T3td/wOcDDwA+CmqvpFe87dSS4DjkjyW2Abi1BJ0kzho7mSpLnmvoHtBwbaD9DcgP0ocHlV7Qm8Elj4f3zO/Wz+zdvTgTOqai/guCmuswB4QVXt276eXFV3b+a1xtzzsPZZwDE0PcFnT/MzJUkaOgtRSdJ8sz1wS7t9zBA+77Hkmb0AAAE1SURBVDLgyCSPA0iy4wTXecvA+++iedx3zE+Ad481kuzbbl4NHNXuOxTYod1/JfCaJNsleRRNr+mVEwWrqmuApwJvAL4+nS8nSVIXLEQlSfPNx4GPJVnFEIaoVNU64FTgiiSrgdPaQycB30qyErht4JQLgdeOTVYEnADs3052dD3NZEYAJwOHJlkLHAncCtxVVb+iGSN6Lc340LOqatUUEc8Hrq6q26d4jyRJI+VkRZIkzUBJHgnc306q9ELgC1W176bOm+Bzfgh8uqouHXpISZKmycmKJEmamXYBzk+yAPgPcOzmnJzksTS9pqstQiVJM409opIk/R/aMaATFXQvq6p/jDqPJEmzmYWoJEmSJGmknKxIkiRJkjRSFqKSJEmSpJGyEJUkSZIkjZSFqCRJkiRppP4Lx1LuM6uW1SgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot(kind=\"bar\",figsize=(16,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "main_category\n",
       "Games           11304754\n",
       "Design           7130876\n",
       "Technology       5201257\n",
       "Film & Video     4190762\n",
       "Music            2700566\n",
       "Publishing       2217571\n",
       "Comics           1453945\n",
       "Fashion          1388135\n",
       "Food             1320109\n",
       "Art              1180804\n",
       "Theater           512364\n",
       "Photography       426071\n",
       "Crafts            239297\n",
       "Journalism        181103\n",
       "Dance             161012\n",
       "Name: backers, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(('main_category'))['backers'].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116bc65f8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pled = df.groupby('main_category')['usd_pledged_real'].sum().sort_values(ascending=False)\n",
    "pled.plot(kind=\"bar\",figsize=(16,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116c54358>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfcountry=df.groupby(('country','state')).size().unstack()\n",
    "dfcountry.plot(kind=\"bar\",figsize=(16,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116c54748>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfcountry_exceptUS = dfcountry[dfcountry.index != \"US\"]\n",
    "dfcountry_exceptUS.plot(kind=\"bar\",figsize=(16,7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Test 1: Country and state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Null Hypothesis: Success rate changes depend on the country\n",
    "#### Alternative Hypothesis: Success rate changes does not depend on the country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AT</th>\n",
       "      <td>0.185764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AU</th>\n",
       "      <td>0.261685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>0.254606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>0.284946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>0.254076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>0.230959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DK</th>\n",
       "      <td>0.332410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES</th>\n",
       "      <td>0.223230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR</th>\n",
       "      <td>0.316046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GB</th>\n",
       "      <td>0.363300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HK</th>\n",
       "      <td>0.382979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IE</th>\n",
       "      <td>0.261034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>0.158198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP</th>\n",
       "      <td>0.225806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LU</th>\n",
       "      <td>0.311475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MX</th>\n",
       "      <td>0.242647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N,0\"</th>\n",
       "      <td>0.452586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL</th>\n",
       "      <td>0.219808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>0.233429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NZ</th>\n",
       "      <td>0.314386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>0.296275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG</th>\n",
       "      <td>0.341651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>0.377321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            state\n",
       "country          \n",
       "AT       0.185764\n",
       "AU       0.261685\n",
       "BE       0.254606\n",
       "CA       0.284946\n",
       "CH       0.254076\n",
       "DE       0.230959\n",
       "DK       0.332410\n",
       "ES       0.223230\n",
       "FR       0.316046\n",
       "GB       0.363300\n",
       "HK       0.382979\n",
       "IE       0.261034\n",
       "IT       0.158198\n",
       "JP       0.225806\n",
       "LU       0.311475\n",
       "MX       0.242647\n",
       "N,0\"     0.452586\n",
       "NL       0.219808\n",
       "NO       0.233429\n",
       "NZ       0.314386\n",
       "SE       0.296275\n",
       "SG       0.341651\n",
       "US       0.377321"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"state\"] = np.where((df[\"state\"] == \"successful\"),1,0)\n",
    "countryGrouped = pd.DataFrame(df[\"state\"].groupby(df[\"country\"]).mean())\n",
    "countryGrouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.36159955082142453"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.state.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "countryGrouped[\"Expected\"] = np.where((countryGrouped[\"state\"] < df.state.mean()),0,1)\n",
    "expectedCountry = (countryGrouped[\"Expected\"]).append(countryGrouped[\"Expected\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "countrytest = df[[\"country\",\"state\"]]\n",
    "y = pd.DataFrame(countrytest.groupby([countrytest.state,countrytest.country]).size())\n",
    "y[\"Expected\"] = expectedCountry.values\n",
    "\n",
    "y = y.reset_index(level=[\"state\",\"country\"])\n",
    "y = y.drop(\"country\",axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### State and Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th>Expected</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>34503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>0</th>\n",
       "      <td>12269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>121687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     0\n",
       "state Expected        \n",
       "0     0          34503\n",
       "      1         201995\n",
       "1     0          12269\n",
       "      1         121687"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupbycountry = pd.DataFrame(y.groupby([y.state,y.Expected]).sum())\n",
    "groupbycountry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 12269,  34503],\n",
       "       [121687, 201995]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([[groupbycountry[0][1][0],groupbycountry[0][0][0]],[groupbycountry[0][1][1],groupbycountry[0][0][1]]])\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test t=2285.335205 p-value=0.000000\n"
     ]
    }
   ],
   "source": [
    "chi_stats = stats.chi2_contingency(arr)\n",
    "\n",
    "#(t,p)= stats.chisquare([chitest[\"state\"],chitest[\"goalClf\"]],ddof=5)\n",
    "print(\"Test t=%f p-value=%f\" % (chi_stats[0],chi_stats[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alpha = 0.05  # significance level "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.841458820694124"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "critical_value = crit = stats.chi2.ppf(q = 1 - alpha, # Find the critical value for 95% confidence*\n",
    "                      df = chi_stats[2]) #degree of freedom\n",
    "critical_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x117b51828>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aLQZK3X0s8ChwR7ILDcp/nT+a3XsbuefZt4MuRUQkYYn03CcCFe6+2t3rgVnAlPgG7v68u7fc9TMfGJzcMoMzsiiXqROH8sdX17GqqibockREEpJIuA8C4h+VWBlbdzBfBJ46mqK6mhvPHklWJMTtc3Rjk4h0D0m9oGpmlwOlwI8Psv1qMys3s/KqqqpkHrpDFeZm8e+fOI5nV2zmlVWaSFtEur5Ewn0DMCRueXBs3X7M7Gzg68Bkd9/b1o7cfaa7l7p7aWFh4ZHUG5gvnj6cgfnZ3DZnOc3NurFJRLq2RMJ9ATDCzIabWSYwFSiLb2BmJwO/JBrsKfm83OyMMF+dNIo3N1Tz+OIDfreJiHQp7Ya7uzcC1wJzgRXAbHdfZma3mtnkWLMfA72AR8xsiZmVHWR33dqUcYMYOzifH89dSXVdQ9DliIgclAX17JTS0lIvLy8P5NhHY9G67Vzy369wzugi7r/8FMws6JJEJI2Y2UJ3L22vne5QPUynDC1gxqTjeXrZJv7npXeDLkdEpE0K9yNw1RnD+eSYIm5/6i0WrNFjgUWk61G4HwEz48eXjGNIQQ7XPriI92vaHBwkIhIYhfsRysvO4BefG8+OPQ185aHFNGl4pIh0IQr3o1AyMI/vf+oEXl61lbuf0bNnRKTrULgfpUtKh3BZ6RDufb6C597aHHQ5IiKAwj0pvjtlDCUD8rjx4ddZv21P+x8QEelgCvckyM4Ic//lp9DszjUPLmJvo2ZuEpFgKdyTZNgxPbnrknG8UbmTW/+yPOhyRCTNKdyT6Nwx/fm3j3+IP766jp888zZB3f0rIhIJuoBU87VPHs+O3Q387G/v0Nzs3HzuSD2iQEQ6ncI9yUIh4/bPnEgoZNz7fAWNzc7XJo1SwItIp1K4d4BQyLjtUycQDsF/v7CKpuZm/uv80Qp4Eek0CvcOEgoZ35tyApFQiF+9+C6Nzc63LixRwItIp1C4dyAz49v/XELIjAf+8S5Nzc53J49RwItIh1O4dzAz45sXjiYSNmb+fTVNzc73ppxAKKSAF5GOo3DvBGbGLecdTzhk3D9vFY1Nzm2fPoFIWCNRRaRjKNw7iZnxn58cRSRk/Py5Ct7dupt7p51Mv7zsoEsTkRSkrmMnMjNuPncUd182jqWVOzn/Zy/x8qr3gy5LRFKQwj0Anz55ME9cexr5OREu//Wr3Pd8Bc16HryIJFFC4W5mk8xspZlVmNmMNrZ/zMwWmVmjmV2c/DJTz8iiXMquPZ0Lxg7kx3NXctXvytmxpz7oskQkRbQb7mYWBu4DzgNKgGlmVtKq2TrgSuDBZBeYynpmRfjZ1JO4dcoYXnynigt+9hJvVO4IuiwRSQGJ9NwnAhXuvtrd64FZwJT4Bu6+xt3fAJo7oMaUZmb8y0eLeeRLpwJw8f2v8PtX1uihYyJyVBIJ90HA+rjlyti6w2ZmV5tZuZmVV1VVHckuUtZJQ3rz1+tO59TjjuGbTyzjspnzWblpV9BliUg31akXVN19pruXuntpYWFhZx66WyjomckDV0zgh585kbc37+L8n73ID+asYPfexqBLE5FuJpFw3wAMiVseHFsnHSAUMqZOHMpzN5/JJeMHM/Pvqznrrhd48o2NOlUjIglLJNwXACPMbLiZZQJTgbKOLUv69MzkhxeN5U//fip9emZyzYOL+JcHXuPd93cHXZqIdAPthru7NwLXAnOBFcBsd19mZrea2WQAM5tgZpXAJcAvzWxZRxadTk4ZWkDZtafxnX8uYcm6HXzy7r9z59yVVNc1BF2aiHRhFtQ/9UtLS728vDyQY3dXW3bV8YMnV/DnJe+Rmx1h+qnFfOH04fTukRl0aSLSScxsobuXtttO4d79vLlhJz9/7h3mLttMz8wwn/9oMVedMZy+vbKCLk1EOpjCPQ28tamae5+r4MmlG8mKhPjch4fxbx/7kB5GJpLCFO5ppGJLDb94voInXn+PcMi4tHQw//LRYkYW5QZdmogkmcI9Da3dupv7563isUWVNDQ544cVMG3iUC44cQA5meGgyxORJFC4p7GtNXt5bFEls15bz+r3d5ObHeHTJw9i6oShlAzM65Qatm3bxpe//GXq6uoS/sxZZ53FV77ylQ6sSqT7SzTcNVlHCjqmVxZXf+xY/vWMD/Hqu9t46LV1zFqwnt+9spZxQ3ozdcIQzjuhf4eOssnKymLOnDnU1NQk1D4cDjNq1KgOq0ck3ajnnia2767nT4s38NBr66jYUkMkZHz02GM474QBnDumqENG2vzgBz/gtttuY8+ePe227dGjB+vWreOYY45Jeh0iqUSnZaRN7s4blTt56s1NPP3mRtZs3UPIYEJxH84/cQCTTuhPUZJG29TU1DBw4EB27Tr0A9Cys7O57rrruOOOO5JyXJFUpnCXdrk7Kzbu4uk3N/LUm5t4Z0v0FMopQ3vzsZGFnDGiL+MG9z6qibwT6b2r1y6SOIW7HLaKLbt4aukmnlmxmaUbduIOuVkRPnLsMZwxoi+nH9eX4X17YmYJ77O93rt67SKHR+EuR2X77npeXrWVlyqqePGd96ncXgvAoN45nHbcMZQO68Mpwwr4UN+ehEKHDvtD9d7Vaxc5PAp3SRp3Z+3WPbxY8T4vvVPF/NXb2FkbfXBZfk4GJw/tzSlDCxg/rIBxQ3rTK2v/QVgH672r1y5y+BIN906drEO6JzOjuG9PPv+RYfzy86Us/uY5PHvTx7jjorGcd0J/Nmyv5SfPvM3nfv0qY78zl0k//Ts3z36d/3npXV5ZtZWmUBYzZsygR48e++23oaGBRx55hPHjx3P++efz9ttvt3n8U0+NTkG4Zs0aHnzwg2l6y8vLj3hc/JlnnknrzsV3v/tdbrnllv3WLVmyhNGjRx/2vjrDF7/4RcaNG8fYsWO5+OKL2xx22tDQwBVXXMGJJ57I6NGjuf322/fb3tTUxMknn8yFF17YWWVLJ9E4dzlsoZBxXL9cjuuXy6UTovO47KxtYMn6HSxau50l63fw93eqeGxR5b7P9M8ZR0PcDLtmxic+8QmeeeYZAF5//XU2b97MyJEj97VpbGwkEonw8ssvAx+E+2c/+1kASktLKS1ttwOTsGnTpjFp0qT9AnDWrFlMmzYtacdIprvvvpu8vOhNaTfddBP33nsvM2bM2K/NI488wt69e1m6dCl79uyhpKSEadOmUVxcDMA999zD6NGjqa6u7uzypYOp5y5JkZ+TwcdHFnLjOSP57RcmsuDrZ/Pa18/it1+YyNcmHc/EkQMZ8onPYpHoeHp3qBjzBf7pR3/jX39XztMbs9jcYzi/evgvnHra6UyePJmSkhIAevXqBcCMGTN48cUXOemkk7j77ruZN2/evh5nTU0N06dP58QTT2Ts2LE89thjAHz5y1+mtLSUMWPG8O1vf/uQ32HkyJEUFBTw6quv7ls3e/bsfeGeyL5aagV49NFHufLKKwGoqqrioosuYsKECUyYMIF//OMfh/tHfICWYHd3amtr27zQbWbs3r2bxsZGamtryczM3Pe5yspKnnzySa666qqjrkW6HvXcpcP0y82mX242Hx8ZnS/3B/98NwMHPszumnqKs3rypUVPseqYwVT0K+b5/P40hsLUrXuTLQvKOf5L9zP67GO55sFFNDQ5D722js9ddwt//t1/M3fOk0TCIebNm7fvWN/73vfIz89n6dKlAGzfvh2A2267jT59+tDU1MRZZ53FG2+8wdixYw9a87Rp05g1axYf/vCHmT9/Pn369GHEiBFHtK94119/PTfeeCOnn34669at45Of/CQrVqzYr83KlSu57LLL2vz8vHnz6N279wHrp0+fzpw5cygpKeGuu+46YPvFF1/ME088wYABA9izZw933303ffr0AeCGG27gjjvuaPc+BOmeFO7SaXr16sWMGTP4zje+wTl1NXz1xd/v29YQCrM+v4g/5eTxq+w8Pr2ynHWbK1l2zCAampq55U9LqVu3nOpVWxn1jafo1yNC+P23qdywk+//dTmz/vwk37jrlyxet50B+Tn0zcsHoj3vmTNn0tjYyMaNG1m+fPkhA/myyy7j1FNP5a677jrglMzh7ives88+y/Lly/ctV1dXU1NTs19Pf9SoUSxZsiThP0+A3/zmNzQ1NXHdddfx8MMPM3369P22v/baa4TDYd577z22b9/OGWecwdlnn83y5cvp168f48eP3++XpKQOhbt0qptvvpnCZ57h960CJaO5iQ9tf4/S7e8xD7jt/36xb1sv4B+/mM6fs3vxQN0uPvuPWbyX25fXa6up2VbNH15YyaaddXynbBkZL+0AwICetVtY/cfvceE3/5fB/Qt54VffZe4b68g5fgM7axtYVVXDoJ219OmZSVYk+tTMIUOGMHz4cF544QUee+wxXnnlFQDeffdd7rzzThYsWEBBQQFXXnllmw9Fiz81Er+9ubmZ+fPnk5198Lt/j6TnDtHn8kydOpU77rjjgHB/8MEHmTRpEhkZGfTr14/TTjuN8vJyFi9eTFlZGXPmzKGuro7q6mouv/xy/vCHPxy0PuleFO7SqbKysrhqzBh+PW8eM4GrY+vfAHYe4nODdlXxkV1VzAZufjEaQPOAO4G/3PkZbgxnsPUPX+XKgaPYmNuXtVk9WR0Ks7Ghkb0L3+DFjCxef+V51mYV83zzEjZtrOaGWUvIeiE64XiviFHQI4M+udk0FH+US6d/iZ75/XlkxR56r32X7evfwSNZVOxoomnDKp6c8xQfPf0MWg8lLioqYsWKFYwaNYrHH3+c3NzoM/XPPfdcfv7zn/PVr34ViI7COemkk/b77OH03N2dVatWcdxxx+HulJWVcfzxxx/QbujQoTz33HN8/vOfZ/fu3cyfP58bbriBSy+9dN+F43nz5nHnnXcq2FNMQuFuZpOAe4Aw8Gt3/2Gr7VnA74DxwFbgMndfk9xSJVWYGY8DNwA/ArKBYuCnwIZDfG4s0b+A44ArgZNb9gd8v6mBa2obuH7VAsLAt4GvA03Ay7//D4YAk4Gz/+8XnPvqo1y2axtXzHuAYwoGsb1HHlt75LM9J/q6J9ybretXkfmJL3DP397Zd/z3MwZw6vhxRPL6YgXH8vXH3+THq+awac02rnpgPgNfrKHfP03nwx8/hx75BQwacQK+cyf3PPsOE6bdxOyffZf7fvUbvLmRUyaeyrd/9FN6ZIbJyQjTIzNMj8wI2RmhhO4AdneuuOIKqqurcXfGjRvH/fffD0BZWRnl5eXceuutXHPNNUyfPp0xY8bg7kyfPj3hU0nSvbV7E5OZhYG3gXOASmABMM3dl8e1+XdgrLt/ycymAp9297b/fRmjm5jS2HXXwb33Bl1FQhotxK6snuzIyWVndi92ZvdiR3YvdmX1ZFdWj1av0ffVObnsyerB7swcaiJZ7A1nJHw8A3LCRnbEyMkIk5URIjszQk5WBtmZYbIjYbIzw2RFQmRFYq8Zce8jIbIywmSFQ2REjMxwmMxIKPoTjr5mRUJkhENkhI2M2Lr9lsOhdu86luAk83nuE4EKd18d2/EsYAqwPK7NFOA7sfePAveamXlQt7+KJEnEmymo20VB3ZGPKGm0ELszc/b72ZORRW1GNnsysmOvWdTGrauLZFKbkUVdJJO9kei2uowsdmRG2++NZLI3ksHecAb1ochh/QJJRAiIhCAjZHGvISKxXwDhkBEJh4iEQ4TD0V8O+15DRiRkhON+osshwiH2vUZCIUJm+60L2wftzGKfNyMUMkIG4ZARMtv3ObOW7exbH/354PMhi24z44DtIWPfvuGDti3tzcCI7t9o+dwHbc0Mg/3bx9a1fDZ+P9FX6JkVITujY2dHSyTcBwHr45YrgQ8frI27N5rZTuAY4P1kFCkpplcv6Ns36Co6TQTIj/1ALTTUQkNyj+GwL+TrIpnUhyPUhyI0xF7rwxnsDUeoD2VQH47QGArTEIpQH47QEHvfEHtfH4rQGIq+b4z9NOx7ja5vCoVptNC+7U0WoiEUpjYcoTEcoSm2rSkUpikUirWPvm+0EM0WosmMZgvRSNx7S49bb77/qRO4/CPDOvQYnXpB1cyuJnYNbejQoZ15aOlKbr89+iNJY0BW7KdzJlLsOM3NTpPlXlk7AAAESUlEQVQ7ze40N0OTO03N/sH6ZqfZoTm23lveu+PuNDWDs/+2lvb7tsfWedy2Znc8dvyWzznRNtHl6H5bPgct+6TN9tH30c947DMt6yYU9+nwP8dEwn0DMCRueTAHXvdqaVNpZi0dla2td+TuM4GZED3nfiQFi0hqC4WMEDrnf7QS+TfQAmCEmQ03s0xgKlDWqk0ZcEXs/cXAczrfLiISnHZ77rFz6NcCc4mORHvA3ZeZ2a1AubuXAf8D/N7MKoBtRH8BiIhIQBI65+7uc4A5rdZ9K+59HXBJcksTEZEjlR6XpkVE0ozCXUQkBSncRURSkMJdRCQFKdxFRFJQuw8O67ADm1UBaw+yuS/p++iCdP3u6fq9IX2/e7p+bzi67z7M3QvbaxRYuB+KmZUn8tSzVJSu3z1dvzek73dP1+8NnfPddVpGRCQFKdxFRFJQVw33mUEXEKB0/e7p+r0hfb97un5v6ITv3iXPuYuIyNHpqj13ERE5Cl0u3M1skpmtNLMKM5sRdD2dwcyGmNnzZrbczJaZ2fVB19TZzCxsZovN7K9B19JZzKy3mT1qZm+Z2Qoz+2jQNXUWM7sx9nf9TTN7yMyyg66pI5jZA2a2xczejFvXx8yeMbN3Yq8FHXHsLhXuscm47wPOA0qAaWZWEmxVnaIRuNndS4CPANekyfeOdz2wIugiOtk9wNPufjwwjjT5/mY2CPgKUOruJxB9lHiqPib8f4FJrdbNAP7m7iOAv8WWk65LhTtxk3G7ez3QMhl3SnP3je6+KPZ+F9H/yQcFW1XnMbPBwAXAr4OupbOYWT7wMaJzIeDu9e6+I9iqOlUEyInN3NYDeC/gejqEu/+d6BwX8aYAv429/y3wqY44dlcL97Ym406bkAMws2LgZODVYCvpVD8F/hNoDrqQTjQcqAJ+Ezsd9Wsz6xl0UZ3B3TcAdwLrgI3ATnf/v2Cr6lRF7r4x9n4TUNQRB+lq4Z7WzKwX8Bhwg7tXB11PZzCzC4Et7r4w6Fo6WQQ4Bbjf3U8GdtNB/zzvamLnmKcQ/QU3EOhpZpcHW1UwYtORdsiQxa4W7olMxp2SzCyDaLD/0d3/FHQ9neg0YLKZrSF6Gu6fzOwPwZbUKSqBSndv+Rfao0TDPh2cDbzr7lXu3gD8CTg14Jo602YzGwAQe93SEQfpauGeyGTcKcfMjOi51xXu/pOg6+lM7n6Luw9292Ki/72fc/eU78W5+yZgvZmNiq06C1geYEmdaR3wETPrEfu7fxZpcjE5pgy4Ivb+CuCJjjhIQnOodpaDTcYdcFmd4TTg88BSM1sSW/dfsblrJXVdB/wx1pFZDUwPuJ5O4e6vmtmjwCKiI8UWk6J3q5rZQ8CZQF8zqwS+DfwQmG1mXyT6ZNxLO+TYukNVRCT1dLXTMiIikgQKdxGRFKRwFxFJQQp3EZEUpHAXEUlBCncRkRSkcBcRSUEKdxGRFPT/X3+BuzG0AyEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 40)\n",
    "plt.plot(x, stats.chi2.pdf(x, chi_stats[2]))\n",
    "plt.axvline(x=critical_value, ymin=0.05,ymax=0.05, c='r')\n",
    "plt.annotate('Critical Value = {0:.2f}'.format(critical_value), xy=(critical_value, 0.02), xytext=(critical_value, 0.04), \n",
    "            arrowprops=dict(facecolor='black', shrink=0.5), verticalalignment='top' )\n",
    "plt.fill_between(x, stats.chi2.pdf(x, chi_stats[2]), where= x > critical_value, facecolor='red', interpolate=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### According to shown attributes we can suggest that country does not have a strong affect on state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#     \n",
    "#   \n",
    "# Test 2: Category and state\n",
    "#   \n",
    "#### Null Hypothesis: Success rate changes depend on the category\n",
    "#### Alternative Hypothesis: Success rate changes does not depend on the category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117d34908>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "category = df1.sort_values(by='successful',ascending=False)\n",
    "category.plot(kind=\"bar\",figsize=(16,7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Even though the number of projects in Dance category is lower than others, we can suggest that it is more successful in ratio\n",
    "#   \n",
    "#   \n",
    "## Result\n",
    "##   \n",
    "### As we can see in the above graph, category has a affect on success rate\n",
    "### So, as our null hypthosesis stated category has correlation with success rate and the graph above corrects\n",
    "### the statement.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#  \n",
    "#  \n",
    "# Future Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 378661 entries, 0 to 378660\n",
      "Data columns (total 15 columns):\n",
      "ID                  378661 non-null int64\n",
      "name                378657 non-null object\n",
      "category            378661 non-null object\n",
      "main_category       378661 non-null object\n",
      "currency            378661 non-null object\n",
      "deadline            378661 non-null object\n",
      "goal                378661 non-null float64\n",
      "launched            378661 non-null object\n",
      "pledged             378661 non-null float64\n",
      "state               378661 non-null object\n",
      "backers             378661 non-null int64\n",
      "country             378661 non-null object\n",
      "usd pledged         374864 non-null float64\n",
      "usd_pledged_real    378661 non-null float64\n",
      "usd_goal_real       378661 non-null float64\n",
      "dtypes: float64(5), int64(2), object(8)\n",
      "memory usage: 43.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data=pd.read_csv('ks-projects-201801.csv')\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Undefined, Live and Suspended state is dropped and canceled added to failed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 370454 entries, 0 to 378660\n",
      "Data columns (total 15 columns):\n",
      "ID                  370454 non-null int64\n",
      "name                370451 non-null object\n",
      "category            370454 non-null object\n",
      "main_category       370454 non-null object\n",
      "currency            370454 non-null object\n",
      "deadline            370454 non-null object\n",
      "goal                370454 non-null float64\n",
      "launched            370454 non-null object\n",
      "pledged             370454 non-null float64\n",
      "state               370454 non-null int64\n",
      "backers             370454 non-null int64\n",
      "country             370454 non-null object\n",
      "usd pledged         370222 non-null float64\n",
      "usd_pledged_real    370454 non-null float64\n",
      "usd_goal_real       370454 non-null float64\n",
      "dtypes: float64(5), int64(3), object(7)\n",
      "memory usage: 45.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data = data.loc[(data['state'] != 'undefined') & (data['state'] != 'live') & (data['state'] != 'suspended')]\n",
    "data['state'] = data['state'].replace('canceled','failed')\n",
    "data['state'] = data['state'].replace(['failed','successful'],[0,1])\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping main_category, goal, pledged, backers, usd_pledged and usd_pledged_real columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 370454 entries, 0 to 378660\n",
      "Data columns (total 9 columns):\n",
      "ID               370454 non-null int64\n",
      "state            370454 non-null int64\n",
      "name             370451 non-null object\n",
      "category         370454 non-null object\n",
      "currency         370454 non-null object\n",
      "deadline         370454 non-null object\n",
      "launched         370454 non-null object\n",
      "country          370454 non-null object\n",
      "usd_goal_real    370454 non-null float64\n",
      "dtypes: float64(1), int64(2), object(6)\n",
      "memory usage: 28.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data = data[[\"ID\",\"state\",\"name\",\"category\",\"currency\",\"deadline\",\"launched\",\"country\",\"usd_goal_real\"]]\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Name Length and most used common words analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 370451 entries, 0 to 378660\n",
      "Data columns (total 9 columns):\n",
      "ID               370451 non-null int64\n",
      "state            370451 non-null int64\n",
      "name             370451 non-null object\n",
      "category         370451 non-null object\n",
      "currency         370451 non-null object\n",
      "deadline         370451 non-null object\n",
      "launched         370451 non-null object\n",
      "country          370451 non-null object\n",
      "usd_goal_real    370451 non-null float64\n",
      "dtypes: float64(1), int64(2), object(6)\n",
      "memory usage: 28.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data = data[data['name'].notnull()]\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 370451 entries, 0 to 378660\n",
      "Data columns (total 9 columns):\n",
      "ID               370451 non-null int64\n",
      "state            370451 non-null int64\n",
      "category         370451 non-null object\n",
      "currency         370451 non-null object\n",
      "deadline         370451 non-null object\n",
      "launched         370451 non-null object\n",
      "country          370451 non-null object\n",
      "usd_goal_real    370451 non-null float64\n",
      "nameLen          370451 non-null int64\n",
      "dtypes: float64(1), int64(3), object(5)\n",
      "memory usage: 28.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data['nameLen'] = data['name'].map(lambda n: len(n))\n",
    "data = data.dropna()\n",
    "data.drop(['name'], axis=1, inplace=True)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Category and Currency Categorized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['category', 'currency'], dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['category'] = data['category'].astype('category')\n",
    "data['currency'] = data['currency'].astype('category')\n",
    "\n",
    "cat_columns = data.select_dtypes(['category']).columns\n",
    "cat_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>ID</th>\n",
       "      <th>state</th>\n",
       "      <th>category</th>\n",
       "      <th>currency</th>\n",
       "      <th>deadline</th>\n",
       "      <th>launched</th>\n",
       "      <th>country</th>\n",
       "      <th>usd_goal_real</th>\n",
       "      <th>nameLen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000002330</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-10-09</td>\n",
       "      <td>2015-08-11 12:12:28</td>\n",
       "      <td>GB</td>\n",
       "      <td>1533.95</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000003930</td>\n",
       "      <td>0</td>\n",
       "      <td>93</td>\n",
       "      <td>13</td>\n",
       "      <td>2017-11-01</td>\n",
       "      <td>2017-09-02 04:43:57</td>\n",
       "      <td>US</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000004038</td>\n",
       "      <td>0</td>\n",
       "      <td>93</td>\n",
       "      <td>13</td>\n",
       "      <td>2013-02-26</td>\n",
       "      <td>2013-01-12 00:20:50</td>\n",
       "      <td>US</td>\n",
       "      <td>45000.00</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000007540</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>13</td>\n",
       "      <td>2012-04-16</td>\n",
       "      <td>2012-03-17 03:24:11</td>\n",
       "      <td>US</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000011046</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>13</td>\n",
       "      <td>2015-08-29</td>\n",
       "      <td>2015-07-04 08:35:03</td>\n",
       "      <td>US</td>\n",
       "      <td>19500.00</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID  state  category  currency    deadline             launched  \\\n",
       "0  1000002330      0       108         5  2015-10-09  2015-08-11 12:12:28   \n",
       "1  1000003930      0        93        13  2017-11-01  2017-09-02 04:43:57   \n",
       "2  1000004038      0        93        13  2013-02-26  2013-01-12 00:20:50   \n",
       "3  1000007540      0        90        13  2012-04-16  2012-03-17 03:24:11   \n",
       "4  1000011046      0        55        13  2015-08-29  2015-07-04 08:35:03   \n",
       "\n",
       "  country  usd_goal_real  nameLen  \n",
       "0      GB        1533.95       31  \n",
       "1      US       30000.00       45  \n",
       "2      US       45000.00       14  \n",
       "3      US        5000.00       49  \n",
       "4      US       19500.00       58  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[cat_columns] = data[cat_columns].apply(lambda x: x.cat.codes)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time Interval added instead of deadline and launch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>state</th>\n",
       "      <th>category</th>\n",
       "      <th>currency</th>\n",
       "      <th>country</th>\n",
       "      <th>usd_goal_real</th>\n",
       "      <th>nameLen</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000002330</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>5</td>\n",
       "      <td>GB</td>\n",
       "      <td>1533.95</td>\n",
       "      <td>31</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000003930</td>\n",
       "      <td>0</td>\n",
       "      <td>93</td>\n",
       "      <td>13</td>\n",
       "      <td>US</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>45</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000004038</td>\n",
       "      <td>0</td>\n",
       "      <td>93</td>\n",
       "      <td>13</td>\n",
       "      <td>US</td>\n",
       "      <td>45000.00</td>\n",
       "      <td>14</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000007540</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>13</td>\n",
       "      <td>US</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>49</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000011046</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>13</td>\n",
       "      <td>US</td>\n",
       "      <td>19500.00</td>\n",
       "      <td>58</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID  state  category  currency country  usd_goal_real  nameLen  time\n",
       "0  1000002330      0       108         5      GB        1533.95       31    58\n",
       "1  1000003930      0        93        13      US       30000.00       45    59\n",
       "2  1000004038      0        93        13      US       45000.00       14    44\n",
       "3  1000007540      0        90        13      US        5000.00       49    29\n",
       "4  1000011046      0        55        13      US       19500.00       58    55"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['deadline'] = pd.to_datetime(data['deadline'], format='%Y-%m-%d')\n",
    "data['launched'] = pd.to_datetime(data['launched'], format='%Y-%m-%d')\n",
    "data['time'] = (data['deadline'] - data['launched']).dt.days\n",
    "data.drop(['deadline','launched'], axis=1, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Country categorized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>ID</th>\n",
       "      <th>state</th>\n",
       "      <th>category</th>\n",
       "      <th>currency</th>\n",
       "      <th>country</th>\n",
       "      <th>usd_goal_real</th>\n",
       "      <th>nameLen</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000002330</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>1533.95</td>\n",
       "      <td>31</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000003930</td>\n",
       "      <td>0</td>\n",
       "      <td>93</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>45</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000004038</td>\n",
       "      <td>0</td>\n",
       "      <td>93</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>45000.00</td>\n",
       "      <td>14</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000007540</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>49</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000011046</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>19500.00</td>\n",
       "      <td>58</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID  state  category  currency  country  usd_goal_real  nameLen  \\\n",
       "0  1000002330      0       108         5        9        1533.95       31   \n",
       "1  1000003930      0        93        13       22       30000.00       45   \n",
       "2  1000004038      0        93        13       22       45000.00       14   \n",
       "3  1000007540      0        90        13       22        5000.00       49   \n",
       "4  1000011046      0        55        13       22       19500.00       58   \n",
       "\n",
       "   time  \n",
       "0    58  \n",
       "1    59  \n",
       "2    44  \n",
       "3    29  \n",
       "4    55  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['country'] = data['country'].astype('category')\n",
    "cat_columns = data.select_dtypes(['category']).columns\n",
    "data[cat_columns] = data[cat_columns].apply(lambda x: x.cat.codes)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting ID as index and drop ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>state</th>\n",
       "      <th>category</th>\n",
       "      <th>currency</th>\n",
       "      <th>country</th>\n",
       "      <th>usd_goal_real</th>\n",
       "      <th>nameLen</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</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>1000002330</th>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>1533.95</td>\n",
       "      <td>31</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000003930</th>\n",
       "      <td>0</td>\n",
       "      <td>93</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>45</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000004038</th>\n",
       "      <td>0</td>\n",
       "      <td>93</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>45000.00</td>\n",
       "      <td>14</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000007540</th>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>49</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000011046</th>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>19500.00</td>\n",
       "      <td>58</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            state  category  currency  country  usd_goal_real  nameLen  time\n",
       "ID                                                                          \n",
       "1000002330      0       108         5        9        1533.95       31    58\n",
       "1000003930      0        93        13       22       30000.00       45    59\n",
       "1000004038      0        93        13       22       45000.00       14    44\n",
       "1000007540      0        90        13       22        5000.00       49    29\n",
       "1000011046      0        55        13       22       19500.00       58    55"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.set_index(data['ID'],inplace=True)\n",
    "data.drop('ID', axis=1, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  \n",
    "#   \n",
    "# Machine Learning\n",
    "#  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_dataset = data[[\"category\",\"currency\",\"country\",\"usd_goal_real\",\"nameLen\",\"time\"]]\n",
    "y_dataset = data[\"state\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_dataset, y_dataset, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "\n",
    "rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction_scores = pd.DataFrame(X_test.index)\n",
    "prediction_scores.set_index(prediction_scores['ID'],inplace=True)\n",
    "prediction_scores.drop('ID', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error: 0.35163515136791246\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ebd42e8>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6IAAAGtCAYAAAAMMcl7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAEvxJREFUeJzt3X/I7nddx/HX2x2HYuVc3h3GJp6VSxmCs26GYgS5jJnR9ocMR8UhBodAS0mw5V8KBUqUCYlwUPP8YeqwZMtEG0uRQJb32rLNo2wOxTO2ndt0pSWNrXd/nGtxtLPd1zn3fb/vnes8HnBzfX98rvN9n79unnyv73VXdwcAAACmPGOvBwAAAODcIkQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYtW/yYs9//vP7wIEDk5cEAABgyB133PHt7l7bat1oiB44cCAbGxuTlwQAAGBIVX1zmXU+mgsAAMAoIQoAAMAoIQoAAMAoIQoAAMAoIQoAAMAoIQoAAMAoIQoAAMAoIQoAAMAoIQoAAMAoIQoAAMAoIQoAAMAoIQoAAMAoIQoAAMAoIQoAAMAoIQoAAMAoIQoAAMCofXs9AADA09I7nrvXE8C56R3/vtcTMMAdUQAAAEYJUQAAAEYJUQAAAEYJUQAAAEYJUQAAAEYJUQAAAEYJUQAAAEYJUQAAAEYJUQAAAEYJUQAAAEYJUQAAAEYJUQAAAEYJUQAAAEYJUQAAAEYJUQAAAEYJUQAAAEYJUQAAAEYJUQAAAEYJUQAAAEYJUQAAAEYJUQAAAEYJUQAAAEYJUQAAAEYtFaJVdUFVfaKqvlpVR6vqlVV1YVXdWlX3Ll6ft9vDAgAAcPZb9o7oe5N8prtfkuRlSY4muTHJbd19WZLbFvsAAADwlLYM0ap6bpJfTPLBJOnuR7v7kSTXJDmyWHYkybW7NSQAAACrY5k7opcm2Uzyl1V1Z1V9oKqek2R/dz+4WPNQkv2nenNVHaqqjara2Nzc3JmpAQAAOGstE6L7kvxckvd398uT/Gd+5GO43d1J+lRv7u7D3b3e3etra2vbnRcAAICz3DIheizJse6+fbH/iZwI04er6qIkWbwe350RAQAAWCVbhmh3P5TkW1X14sWhq5J8JcktSQ4ujh1McvOuTAgAAMBK2bfkut9N8pGqOj/J/Ul+Oyci9qaquiHJN5NctzsjAgAAsEqWCtHuvivJ+ilOXbWz4wAAALDqlv07ogAAALAjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACj9i2zqKq+keR7SR5P8lh3r1fVhUk+nuRAkm8kua67v7s7YwIAALAqTueO6C919xXdvb7YvzHJbd19WZLbFvsAAADwlLbz0dxrkhxZbB9Jcu32xwEAAGDVLRuineTvq+qOqjq0OLa/ux9cbD+UZP+p3lhVh6pqo6o2Njc3tzkuAAAAZ7ulnhFN8gvd/UBV/VSSW6vqqyef7O6uqj7VG7v7cJLDSbK+vn7KNQAAAJw7lroj2t0PLF6PJ/lkkiuTPFxVFyXJ4vX4bg0JAADA6tgyRKvqOVX1409sJ/mVJHcnuSXJwcWyg0lu3q0hAQAAWB3LfDR3f5JPVtUT6/+quz9TVV9KclNV3ZDkm0mu270xAQAAWBVbhmh335/kZac4/m9JrtqNoQAAAFhd2/nzLQAAAHDahCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjhCgAAACjlg7Rqjqvqu6sqk8t9i+tqtur6r6q+nhVnb97YwIAALAqTueO6JuTHD1p/91J3tPdL0ry3SQ37ORgAAAArKalQrSqLknyuiQfWOxXklcn+cRiyZEk1+7GgAAAAKyWZe+I/nmStyX5n8X+TyZ5pLsfW+wfS3Lxqd5YVYeqaqOqNjY3N7c1LAAAAGe/LUO0qn4tyfHuvuNMLtDdh7t7vbvX19bWzuSfAAAAYIXsW2LNq5L8elX9apJnJfmJJO9NckFV7VvcFb0kyQO7NyYAAACrYss7ot39h919SXcfSPKGJP/Q3b+R5HNJXr9YdjDJzbs2JQAAACtjO39H9A+S/H5V3ZcTz4x+cGdGAgAAYJUt89Hc/9Pdn0/y+cX2/Umu3PmRAAAAWGXbuSMKAAAAp02IAgAAMEqIAgAAMEqIAgAAMEqIAgAAMEqIAgAAMEqIAgAAMEqIAgAAMEqIAgAAMEqIAgAAMEqIAgAAMEqIAgAAMEqIAgAAMEqIAgAAMEqIAgAAMEqIAgAAMEqIAgAAMEqIAgAAMEqIAgAAMEqIAgAAMEqIAgAAMGrfXg/A2eHAjX+31yPAOesb73rdXo8AALCj3BEFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABg1JYhWlXPqqp/qqp/qap7quqdi+OXVtXtVXVfVX28qs7f/XEBAAA42y1zR/S/k7y6u1+W5IokV1fVK5K8O8l7uvtFSb6b5IbdGxMAAIBVsWWI9gnfX+w+c/HTSV6d5BOL40eSXLsrEwIAALBSlnpGtKrOq6q7khxPcmuSryd5pLsfWyw5luTiJ3nvoaraqKqNzc3NnZgZAACAs9hSIdrdj3f3FUkuSXJlkpcse4HuPtzd6929vra2doZjAgAAsCpO61tzu/uRJJ9L8sokF1TVvsWpS5I8sMOzAQAAsIKW+dbctaq6YLH97CSvSXI0J4L09YtlB5PcvFtDAgAAsDr2bb0kFyU5UlXn5US43tTdn6qqryT5WFX9UZI7k3xwF+cEAABgRWwZot395SQvP8Xx+3PieVEAAABY2mk9IwoAAADbJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYtWWIVtULqupzVfWVqrqnqt68OH5hVd1aVfcuXp+3++MCAABwtlvmjuhjSd7a3ZcneUWSN1bV5UluTHJbd1+W5LbFPgAAADylLUO0ux/s7n9ebH8vydEkFye5JsmRxbIjSa7drSEBAABYHaf1jGhVHUjy8iS3J9nf3Q8uTj2UZP+OTgYAAMBKWjpEq+rHkvx1krd093+cfK67O0k/yfsOVdVGVW1sbm5ua1gAAADOfkuFaFU9Myci9CPd/TeLww9X1UWL8xclOX6q93b34e5e7+71tbW1nZgZAACAs9gy35pbST6Y5Gh3/9lJp25JcnCxfTDJzTs/HgAAAKtm3xJrXpXkt5L8a1XdtTj29iTvSnJTVd2Q5JtJrtudEQEAAFglW4Zod/9jknqS01ft7DgAAACsutP61lwAAADYLiEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAKCEKAADAqC1DtKo+VFXHq+ruk45dWFW3VtW9i9fn7e6YAAAArIpl7oh+OMnVP3LsxiS3dfdlSW5b7AMAAMCWtgzR7v5Cku/8yOFrkhxZbB9Jcu0OzwUAAMCKOtNnRPd394OL7YeS7N+heQAAAFhx2/6you7uJP1k56vqUFVtVNXG5ubmdi8HAADAWe5MQ/ThqrooSRavx59sYXcf7u717l5fW1s7w8sBAACwKs40RG9JcnCxfTDJzTszDgAAAKtumT/f8tEkX0zy4qo6VlU3JHlXktdU1b1JfnmxDwAAAFvat9WC7r7+SU5dtcOzAAAAcA7Y9pcVAQAAwOkQogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIzaVohW1dVV9bWquq+qbtypoQAAAFhdZxyiVXVekvcleW2Sy5NcX1WX79RgAAAArKbt3BG9Msl93X1/dz+a5GNJrtmZsQAAAFhV2wnRi5N866T9Y4tjAAAA8KT27fYFqupQkkOL3e9X1dd2+5rA//P8JN/e6yE4M/XuvZ4A4Kzj997Z7J211xOwPS9cZtF2QvSBJC84af+SxbEf0t2HkxzexnWAbaqqje5e3+s5AGCC33vw9Ledj+Z+KcllVXVpVZ2f5A1JbtmZsQAAAFhVZ3xHtLsfq6o3JflskvOSfKi779mxyQAAAFhJ23pGtLs/neTTOzQLsHt8PB6Ac4nfe/A0V9291zMAAABwDtnOM6IAAABw2oQorLiqurqqvlZV91XVjXs9DwDslqr6UFUdr6q793oW4KkJUVhhVXVekvcleW2Sy5NcX1WX7+1UALBrPpzk6r0eAtiaEIXVdmWS+7r7/u5+NMnHklyzxzMBwK7o7i8k+c5ezwFsTYjCars4ybdO2j+2OAYAAHtGiAIAADBKiMJqeyDJC07av2RxDAAA9owQhdX2pSSXVdWlVXV+kjckuWWPZwIA4BwnRGGFdfdjSd6U5LNJjia5qbvv2dupAGB3VNVHk3wxyYur6lhV3bDXMwGnVt291zMAAABwDnFHFAAAgFFCFAAAgFFCFAAAgFFCFAAAgFFCFAAAgFFCFADOQFU9XlV3VdXdVfW3VXXB4viBqvrB4twTP+cvzr22qjaq6itVdWdV/ene/i8AYG8IUQA4Mz/o7iu6+6VJvpPkjSed+/ri3BM/j1bVS5P8RZLf7O7Lk6wnuW8P5gaAPSdEAWD7vpjk4i3WvC3JH3f3V5Okux/v7vfv+mQA8DQkRAFgG6rqvCRXJbnlpMM/c9LHct+3OPbSJHeMDwgAT0P79noAADhLPbuq7sqJO6FHk9x60rmvd/cVezMWADz9uSMKAGfmB4vYfGGSyg8/I3oq9yT5+V2fCgDOAkIUALahu/8rye8leWtVPdUnjf4kydur6meTpKqeUVW/MzEjADzdCFEA2KbuvjPJl5Nc/xRrvpzkLUk+WlVHk9yd5KdnJgSAp5fq7r2eAQAAgHOIO6IAAACMEqIAAACMEqIAAACMEqIAAACMEqIAAACMEqIAAACMEqIAAACMEqIAAACM+l9SQbEH0V5q3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted = rf.predict(X_test)\n",
    "\n",
    "print(\"Error: \" + str(np.mean(y_test.values != predicted)))\n",
    "\n",
    "prediction_scores['RFC'] = np.equal(predicted,y_test.values)\n",
    "prediction_scores['RFC'].astype(str)\n",
    "prediction_scores['RFC'].replace([False,True],[0,1],inplace=True)\n",
    "countRFC = prediction_scores.copy()\n",
    "countRFC = countRFC.groupby(\"RFC\").size()\n",
    "countRFC = (countRFC*100)/74091\n",
    "countRFC.plot(kind=\"bar\",figsize=(16,7),rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c35a048>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_cols = X_train.columns.values\n",
    "features = data[feature_cols]\n",
    "\n",
    "importances = rf.feature_importances_\n",
    "indices = np.argsort(importances)\n",
    "feature_cols = feature_cols[indices]\n",
    "\n",
    "feat_imp_rfc = pd.DataFrame(feature_cols)\n",
    "feat_imp_rfc.sort_index(ascending = False,inplace=True)\n",
    "\n",
    "feat_imp_rfc['importanceRFC'] = feat_imp_rfc.index\n",
    "feat_imp_rfc.set_index(feat_imp_rfc[0],inplace=True)\n",
    "feat_imp_rfc.drop([0], axis=1, inplace=True)\n",
    "feat_imp_rfc.plot(kind=\"bar\",figsize=(16,7),rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.70      0.72      0.71     47227\n",
      "          1       0.47      0.45      0.46     26864\n",
      "\n",
      "avg / total       0.62      0.62      0.62     74091\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classifier = DecisionTreeClassifier()  \n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "predicted = classifier.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, predicted))\n",
    "\n",
    "prediction_scores['DTC'] = np.equal(predicted,y_test.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c3c6ba8>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction_scores['DTC'] = np.equal(predicted,y_test.values)\n",
    "prediction_scores['DTC'].astype(str)\n",
    "prediction_scores['DTC'].replace([False,True],[0,1],inplace=True)\n",
    "\n",
    "countDTC = prediction_scores.copy()\n",
    "countDTC = countDTC.groupby(\"DTC\").size()\n",
    "countDTC = (countDTC*100)/74091\n",
    "countDTC.plot(kind=\"bar\",figsize=(16,7),rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f1c66a0>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = classifier.feature_importances_\n",
    "indices = np.argsort(importances)\n",
    "feature_cols = feature_cols[indices]\n",
    "\n",
    "feat_imp_dtc = pd.DataFrame(feature_cols)\n",
    "feat_imp_dtc.sort_index(ascending = False,inplace=True)\n",
    "\n",
    "feat_imp_dtc['importanceDTC'] = feat_imp_dtc.index\n",
    "feat_imp_dtc.set_index(feat_imp_dtc[0],inplace=True)\n",
    "feat_imp_dtc.drop([0], axis=1, inplace=True)\n",
    "feat_imp_dtc.plot(kind=\"bar\",figsize=(16,7),rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11dff8470>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_imp = feat_imp_dtc.copy()\n",
    "feat_imp['Sum'] = feat_imp_rfc['importanceRFC'] + feat_imp_dtc['importanceDTC']\n",
    "\n",
    "feat_imp.Sum.plot(kind=\"bar\",figsize=(16,7),rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "comparison = pd.DataFrame({\"Failed\": [countRFC[0],countDTC[0]],\"Predicted\": [countRFC[1],countDTC[1]]},index=[\"Random Forest Classifier\",\"Decision Tree Classifier\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x128a32978>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparison.plot(kind=\"bar\",figsize=(16,7),rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importances Compared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12be36908>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importances = pd.DataFrame({\"nameLen\": [feat_imp_rfc.loc[\"nameLen\":\"nameLen\"][\"importanceRFC\"].nameLen,feat_imp_dtc.loc[\"nameLen\":\"nameLen\"][\"importanceDTC\"].nameLen],\n",
    "                                    \"category\": [feat_imp_rfc.loc[\"category\":\"category\"][\"importanceRFC\"].category,feat_imp_dtc.loc[\"category\":\"category\"][\"importanceDTC\"].category],\n",
    "                                    \"currency\": [feat_imp_rfc.loc[\"currency\":\"currency\"][\"importanceRFC\"].currency,feat_imp_dtc.loc[\"currency\":\"currency\"][\"importanceDTC\"].currency],\n",
    "                                    \"usd_goal_real\": [feat_imp_rfc.loc[\"usd_goal_real\":\"usd_goal_real\"][\"importanceRFC\"].usd_goal_real,feat_imp_dtc.loc[\"usd_goal_real\":\"usd_goal_real\"][\"importanceDTC\"].usd_goal_real],\n",
    "                                    \"time\": [feat_imp_rfc.loc[\"time\":\"time\"][\"importanceRFC\"].time,feat_imp_dtc.loc[\"time\":\"time\"][\"importanceDTC\"].time],\n",
    "                                    \"country\": [feat_imp_rfc.loc[\"country\":\"country\"][\"importanceRFC\"].country,feat_imp_dtc.loc[\"country\":\"country\"][\"importanceDTC\"].country],\n",
    "                                   },index=[\"Random Forest Classifier\",\"Decision Tree Classifier\"])\n",
    "\n",
    "\n",
    "feature_importances.plot(kind=\"bar\",figsize=(16,7),rot=0)"
   ]
  }
 ],
 "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": "3.6.3"
  }
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