AngelList_ML_Python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from urllib import urlencode\n",
    "import urllib2\n",
    "import json\n",
    "import requests\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# #The individual frame; first page of startups on angellist site"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#loop through pages(here two) and extract the startups/individ fields info and add to DF;\n",
    "#does not matter what page it comes from\n",
    "urls = 'https://api.angel.co/1/startups?filter=raising&page='\n",
    "fields = ['id','name','created_at','high_concept','quality','updated_at','company_url',\n",
    "          'video_url','twitter_url','linkedin_url','facebook_url','thumb_url','blog_url',\n",
    "          'community_profile','product_desc','launch_date','hidden','follower_count',\n",
    "          'angellist_url',\n",
    "          'logo_url','company_size']\n",
    "l=list()\n",
    "for i in range(1,2):\n",
    "    r = requests.get(urls+str(i))\n",
    "    t = r.json()\n",
    "    startups = t['startups']\n",
    "    for j in range(0,len(startups)):\n",
    "        comp = startups[j]\n",
    "        l.append(comp)\n",
    "indiv = pd.DataFrame(l,columns = fields)\n",
    "         \n",
    "#this works perfectly\n",
    "#same thing for the composed fields dataframes,\n",
    "#uniquely identified by company field and name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Now get the fundraising data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "urls = 'https://api.angel.co/1/startups?filter=raising&page='\n",
    "l=list()\n",
    "for i in range(1,9):\n",
    "    r = requests.get(urls+str(i))\n",
    "    t = r.json()\n",
    "    startups = t['startups']\n",
    "    for j in range(0,len(startups)):\n",
    "        comp = startups[j]\n",
    "        d = comp['fundraising']\n",
    "        d['name']=comp['name']\n",
    "        d['id']=comp['id']\n",
    "        l.append(d)\n",
    "fund = pd.DataFrame(l)\n",
    "#this works very well in a minute or so to extract the fundraising dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 10)"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fund.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'id', u'name', u'created_at', u'high_concept', u'quality', u'updated_at', u'company_url', u'video_url', u'twitter_url', u'linkedin_url', u'facebook_url', u'thumb_url', u'blog_url', u'community_profile', u'product_desc', u'launch_date', u'hidden', u'follower_count', u'angellist_url', u'logo_url', u'company_size'], dtype='object')"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indiv.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 21)"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indiv.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>created_at</th>\n",
       "      <th>high_concept</th>\n",
       "      <th>quality</th>\n",
       "      <th>updated_at</th>\n",
       "      <th>company_url</th>\n",
       "      <th>video_url</th>\n",
       "      <th>twitter_url</th>\n",
       "      <th>linkedin_url</th>\n",
       "      <th>...</th>\n",
       "      <th>thumb_url</th>\n",
       "      <th>blog_url</th>\n",
       "      <th>community_profile</th>\n",
       "      <th>product_desc</th>\n",
       "      <th>launch_date</th>\n",
       "      <th>hidden</th>\n",
       "      <th>follower_count</th>\n",
       "      <th>angellist_url</th>\n",
       "      <th>logo_url</th>\n",
       "      <th>company_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 389582</td>\n",
       "      <td>     CoffeetheApp</td>\n",
       "      <td> 2014-04-29T20:07:33Z</td>\n",
       "      <td>                  Professional Networking for Jobs</td>\n",
       "      <td> 4</td>\n",
       "      <td> 2014-12-16T17:08:52Z</td>\n",
       "      <td>            http://coffeetheapp.com</td>\n",
       "      <td>       https://www.youtube.com/watch?v=4ShUlSzxwWg</td>\n",
       "      <td> https://twitter.com/coffeetheapp</td>\n",
       "      <td> https://www.linkedin.com/company/5078073?trk=p...</td>\n",
       "      <td>...</td>\n",
       "      <td> https://d1qb2nb5cznatu.cloudfront.net/startups...</td>\n",
       "      <td>                               </td>\n",
       "      <td> False</td>\n",
       "      <td> Coffeetheapp is a mobile community of hiring m...</td>\n",
       "      <td> 2014-07-01</td>\n",
       "      <td> False</td>\n",
       "      <td> 48</td>\n",
       "      <td>     https://angel.co/coffeetheapp</td>\n",
       "      <td> https://d1qb2nb5cznatu.cloudfront.net/startups...</td>\n",
       "      <td> 1-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 293071</td>\n",
       "      <td>     Wear My Tags</td>\n",
       "      <td> 2013-11-11T23:09:59Z</td>\n",
       "      <td>                       Fashion Sourcing Made Easy!</td>\n",
       "      <td> 6</td>\n",
       "      <td> 2014-12-17T05:38:42Z</td>\n",
       "      <td> http://wearmytags.com/development/</td>\n",
       "      <td> https://www.youtube.com/watch?v=44Xj8bRnoWA&amp;fe...</td>\n",
       "      <td>   https://twitter.com/WearMyTags</td>\n",
       "      <td>                                                  </td>\n",
       "      <td>...</td>\n",
       "      <td> https://d1qb2nb5cznatu.cloudfront.net/startups...</td>\n",
       "      <td>                               </td>\n",
       "      <td> False</td>\n",
       "      <td> WearMyTags.com solves the fashion/apparel indu...</td>\n",
       "      <td> 2014-07-01</td>\n",
       "      <td> False</td>\n",
       "      <td> 97</td>\n",
       "      <td>     https://angel.co/wear-my-tags</td>\n",
       "      <td> https://d1qb2nb5cznatu.cloudfront.net/startups...</td>\n",
       "      <td> 1-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 235684</td>\n",
       "      <td> The Cherry Share</td>\n",
       "      <td> 2013-07-17T11:31:00Z</td>\n",
       "      <td> your way to find the healthiest and best price...</td>\n",
       "      <td> 5</td>\n",
       "      <td> 2014-12-17T01:51:15Z</td>\n",
       "      <td>     http://www.thecherryshare.com/</td>\n",
       "      <td> http://www.youtube.com/watch?v=8QJQ9F8atOQ&amp;fea...</td>\n",
       "      <td>       http://twitter.com/foodlve</td>\n",
       "      <td>                                                  </td>\n",
       "      <td>...</td>\n",
       "      <td> https://d1qb2nb5cznatu.cloudfront.net/startups...</td>\n",
       "      <td> http://www.thecherryshare.com/</td>\n",
       "      <td> False</td>\n",
       "      <td> The Cherry Share is a mobile and web applicati...</td>\n",
       "      <td> 2012-06-01</td>\n",
       "      <td> False</td>\n",
       "      <td> 36</td>\n",
       "      <td> https://angel.co/the-cherry-share</td>\n",
       "      <td> https://d1qb2nb5cznatu.cloudfront.net/startups...</td>\n",
       "      <td> 1-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 144128</td>\n",
       "      <td>      Vidinterest</td>\n",
       "      <td> 2012-12-05T11:47:08Z</td>\n",
       "      <td>      Online Video Discovery &amp; Promotion Platform </td>\n",
       "      <td> 4</td>\n",
       "      <td> 2014-12-12T19:54:29Z</td>\n",
       "      <td>              http://vidinterest.tv</td>\n",
       "      <td>                                                  </td>\n",
       "      <td>   http://twitter.com/vidinterest</td>\n",
       "      <td>                                                  </td>\n",
       "      <td>...</td>\n",
       "      <td> https://d1qb2nb5cznatu.cloudfront.net/startups...</td>\n",
       "      <td>    http://blog.vidinterest.com</td>\n",
       "      <td> False</td>\n",
       "      <td> Vidinterest lets you discover and create priva...</td>\n",
       "      <td> 2012-06-01</td>\n",
       "      <td> False</td>\n",
       "      <td> 19</td>\n",
       "      <td>      https://angel.co/vidinterest</td>\n",
       "      <td> https://d1qb2nb5cznatu.cloudfront.net/startups...</td>\n",
       "      <td> 1-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 355204</td>\n",
       "      <td>   UberOnTime.com</td>\n",
       "      <td> 2014-03-05T18:58:10Z</td>\n",
       "      <td>                                 On Demand Postman</td>\n",
       "      <td> 4</td>\n",
       "      <td> 2014-12-11T16:16:46Z</td>\n",
       "      <td>              http://uberontime.com</td>\n",
       "      <td>                                                  </td>\n",
       "      <td>    http://twitter.com/uberontime</td>\n",
       "      <td>                                                  </td>\n",
       "      <td>...</td>\n",
       "      <td> https://d1qb2nb5cznatu.cloudfront.net/startups...</td>\n",
       "      <td>     http://uberontime.com/blog</td>\n",
       "      <td> False</td>\n",
       "      <td> On-demand postman delivers packages locally.  ...</td>\n",
       "      <td> 2014-06-01</td>\n",
       "      <td> False</td>\n",
       "      <td>  5</td>\n",
       "      <td>   https://angel.co/uberontime-com</td>\n",
       "      <td> https://d1qb2nb5cznatu.cloudfront.net/startups...</td>\n",
       "      <td> 1-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id              name            created_at  \\\n",
       "0  389582      CoffeetheApp  2014-04-29T20:07:33Z   \n",
       "1  293071      Wear My Tags  2013-11-11T23:09:59Z   \n",
       "2  235684  The Cherry Share  2013-07-17T11:31:00Z   \n",
       "3  144128       Vidinterest  2012-12-05T11:47:08Z   \n",
       "4  355204    UberOnTime.com  2014-03-05T18:58:10Z   \n",
       "\n",
       "                                        high_concept  quality  \\\n",
       "0                   Professional Networking for Jobs        4   \n",
       "1                        Fashion Sourcing Made Easy!        6   \n",
       "2  your way to find the healthiest and best price...        5   \n",
       "3       Online Video Discovery & Promotion Platform         4   \n",
       "4                                  On Demand Postman        4   \n",
       "\n",
       "             updated_at                         company_url  \\\n",
       "0  2014-12-16T17:08:52Z             http://coffeetheapp.com   \n",
       "1  2014-12-17T05:38:42Z  http://wearmytags.com/development/   \n",
       "2  2014-12-17T01:51:15Z      http://www.thecherryshare.com/   \n",
       "3  2014-12-12T19:54:29Z               http://vidinterest.tv   \n",
       "4  2014-12-11T16:16:46Z               http://uberontime.com   \n",
       "\n",
       "                                           video_url  \\\n",
       "0        https://www.youtube.com/watch?v=4ShUlSzxwWg   \n",
       "1  https://www.youtube.com/watch?v=44Xj8bRnoWA&fe...   \n",
       "2  http://www.youtube.com/watch?v=8QJQ9F8atOQ&fea...   \n",
       "3                                                      \n",
       "4                                                      \n",
       "\n",
       "                        twitter_url  \\\n",
       "0  https://twitter.com/coffeetheapp   \n",
       "1    https://twitter.com/WearMyTags   \n",
       "2        http://twitter.com/foodlve   \n",
       "3    http://twitter.com/vidinterest   \n",
       "4     http://twitter.com/uberontime   \n",
       "\n",
       "                                        linkedin_url  \\\n",
       "0  https://www.linkedin.com/company/5078073?trk=p...   \n",
       "1                                                      \n",
       "2                                                      \n",
       "3                                                      \n",
       "4                                                      \n",
       "\n",
       "                         ...                          \\\n",
       "0                        ...                           \n",
       "1                        ...                           \n",
       "2                        ...                           \n",
       "3                        ...                           \n",
       "4                        ...                           \n",
       "\n",
       "                                           thumb_url  \\\n",
       "0  https://d1qb2nb5cznatu.cloudfront.net/startups...   \n",
       "1  https://d1qb2nb5cznatu.cloudfront.net/startups...   \n",
       "2  https://d1qb2nb5cznatu.cloudfront.net/startups...   \n",
       "3  https://d1qb2nb5cznatu.cloudfront.net/startups...   \n",
       "4  https://d1qb2nb5cznatu.cloudfront.net/startups...   \n",
       "\n",
       "                         blog_url community_profile  \\\n",
       "0                                             False   \n",
       "1                                             False   \n",
       "2  http://www.thecherryshare.com/             False   \n",
       "3     http://blog.vidinterest.com             False   \n",
       "4      http://uberontime.com/blog             False   \n",
       "\n",
       "                                        product_desc launch_date hidden  \\\n",
       "0  Coffeetheapp is a mobile community of hiring m...  2014-07-01  False   \n",
       "1  WearMyTags.com solves the fashion/apparel indu...  2014-07-01  False   \n",
       "2  The Cherry Share is a mobile and web applicati...  2012-06-01  False   \n",
       "3  Vidinterest lets you discover and create priva...  2012-06-01  False   \n",
       "4  On-demand postman delivers packages locally.  ...  2014-06-01  False   \n",
       "\n",
       "  follower_count                      angellist_url  \\\n",
       "0             48      https://angel.co/coffeetheapp   \n",
       "1             97      https://angel.co/wear-my-tags   \n",
       "2             36  https://angel.co/the-cherry-share   \n",
       "3             19       https://angel.co/vidinterest   \n",
       "4              5    https://angel.co/uberontime-com   \n",
       "\n",
       "                                            logo_url company_size  \n",
       "0  https://d1qb2nb5cznatu.cloudfront.net/startups...         1-10  \n",
       "1  https://d1qb2nb5cznatu.cloudfront.net/startups...         1-10  \n",
       "2  https://d1qb2nb5cznatu.cloudfront.net/startups...         1-10  \n",
       "3  https://d1qb2nb5cznatu.cloudfront.net/startups...         1-10  \n",
       "4  https://d1qb2nb5cznatu.cloudfront.net/startups...         1-10  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indiv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#create new variables in indiv df based on presence or absence of links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "indiv['website'] = (indiv['company_url'].fillna('')) != '' \n",
    "#this checks for Nan and none and blanks but not for incomplete or incorrect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#same check for facebook_url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "indiv['facebook']= (indiv['facebook_url'].fillna('')) != ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#check for linkedin link"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "indiv['linkedin'] = (indiv['linkedin_url'].fillna('')) != ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#check for twitter link"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "indiv['twitter'] = (indiv['twitter_url'].fillna('')) != ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "indiv['blog'] = (indiv['blog_url'].fillna('')) != ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#create new data frame with a combination of columns from indiv and fund; \n",
    "#first merge the two by company id;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_merged = pd.merge(indiv,fund, on = 'id', how = 'outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 35)"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name_x</th>\n",
       "      <th>created_at</th>\n",
       "      <th>high_concept</th>\n",
       "      <th>quality</th>\n",
       "      <th>updated_at_x</th>\n",
       "      <th>company_url</th>\n",
       "      <th>video_url</th>\n",
       "      <th>twitter_url</th>\n",
       "      <th>linkedin_url</th>\n",
       "      <th>...</th>\n",
       "      <th>blog</th>\n",
       "      <th>discount</th>\n",
       "      <th>equity_basis</th>\n",
       "      <th>name_y</th>\n",
       "      <th>pre_money_valuation</th>\n",
       "      <th>public</th>\n",
       "      <th>raised_amount</th>\n",
       "      <th>raising_amount</th>\n",
       "      <th>round_opened_at</th>\n",
       "      <th>updated_at_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 389582</td>\n",
       "      <td>     CoffeetheApp</td>\n",
       "      <td> 2014-04-29T20:07:33Z</td>\n",
       "      <td>                  Professional Networking for Jobs</td>\n",
       "      <td> 4</td>\n",
       "      <td> 2014-12-16T17:08:52Z</td>\n",
       "      <td>            http://coffeetheapp.com</td>\n",
       "      <td>       https://www.youtube.com/watch?v=4ShUlSzxwWg</td>\n",
       "      <td> https://twitter.com/coffeetheapp</td>\n",
       "      <td> https://www.linkedin.com/company/5078073?trk=p...</td>\n",
       "      <td>...</td>\n",
       "      <td> False</td>\n",
       "      <td>NaN</td>\n",
       "      <td> equity</td>\n",
       "      <td>     CoffeetheApp</td>\n",
       "      <td> 3000000</td>\n",
       "      <td> True</td>\n",
       "      <td>     0</td>\n",
       "      <td> 250000</td>\n",
       "      <td> 2014-12-15</td>\n",
       "      <td> 2014-12-15T19:55:19Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 293071</td>\n",
       "      <td>     Wear My Tags</td>\n",
       "      <td> 2013-11-11T23:09:59Z</td>\n",
       "      <td>                       Fashion Sourcing Made Easy!</td>\n",
       "      <td> 6</td>\n",
       "      <td> 2014-12-17T05:38:42Z</td>\n",
       "      <td> http://wearmytags.com/development/</td>\n",
       "      <td> https://www.youtube.com/watch?v=44Xj8bRnoWA&amp;fe...</td>\n",
       "      <td>   https://twitter.com/WearMyTags</td>\n",
       "      <td>                                                  </td>\n",
       "      <td>...</td>\n",
       "      <td> False</td>\n",
       "      <td>NaN</td>\n",
       "      <td> equity</td>\n",
       "      <td>     Wear My Tags</td>\n",
       "      <td>     NaN</td>\n",
       "      <td> True</td>\n",
       "      <td>     0</td>\n",
       "      <td> 750000</td>\n",
       "      <td> 2014-12-14</td>\n",
       "      <td> 2014-12-17T01:58:36Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 235684</td>\n",
       "      <td> The Cherry Share</td>\n",
       "      <td> 2013-07-17T11:31:00Z</td>\n",
       "      <td> your way to find the healthiest and best price...</td>\n",
       "      <td> 5</td>\n",
       "      <td> 2014-12-17T01:51:15Z</td>\n",
       "      <td>     http://www.thecherryshare.com/</td>\n",
       "      <td> http://www.youtube.com/watch?v=8QJQ9F8atOQ&amp;fea...</td>\n",
       "      <td>       http://twitter.com/foodlve</td>\n",
       "      <td>                                                  </td>\n",
       "      <td>...</td>\n",
       "      <td>  True</td>\n",
       "      <td>NaN</td>\n",
       "      <td> equity</td>\n",
       "      <td> The Cherry Share</td>\n",
       "      <td> 3000000</td>\n",
       "      <td> True</td>\n",
       "      <td> 91000</td>\n",
       "      <td> 400000</td>\n",
       "      <td> 2014-12-14</td>\n",
       "      <td> 2014-12-14T06:20:03Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 144128</td>\n",
       "      <td>      Vidinterest</td>\n",
       "      <td> 2012-12-05T11:47:08Z</td>\n",
       "      <td>      Online Video Discovery &amp; Promotion Platform </td>\n",
       "      <td> 4</td>\n",
       "      <td> 2014-12-12T19:54:29Z</td>\n",
       "      <td>              http://vidinterest.tv</td>\n",
       "      <td>                                                  </td>\n",
       "      <td>   http://twitter.com/vidinterest</td>\n",
       "      <td>                                                  </td>\n",
       "      <td>...</td>\n",
       "      <td>  True</td>\n",
       "      <td>NaN</td>\n",
       "      <td> equity</td>\n",
       "      <td>      Vidinterest</td>\n",
       "      <td> 1000000</td>\n",
       "      <td> True</td>\n",
       "      <td>     0</td>\n",
       "      <td> 100000</td>\n",
       "      <td> 2014-12-12</td>\n",
       "      <td> 2014-12-12T19:58:06Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 355204</td>\n",
       "      <td>   UberOnTime.com</td>\n",
       "      <td> 2014-03-05T18:58:10Z</td>\n",
       "      <td>                                 On Demand Postman</td>\n",
       "      <td> 4</td>\n",
       "      <td> 2014-12-11T16:16:46Z</td>\n",
       "      <td>              http://uberontime.com</td>\n",
       "      <td>                                                  </td>\n",
       "      <td>    http://twitter.com/uberontime</td>\n",
       "      <td>                                                  </td>\n",
       "      <td>...</td>\n",
       "      <td>  True</td>\n",
       "      <td>NaN</td>\n",
       "      <td> equity</td>\n",
       "      <td>   UberOnTime.com</td>\n",
       "      <td> 1000000</td>\n",
       "      <td> True</td>\n",
       "      <td>     0</td>\n",
       "      <td> 100000</td>\n",
       "      <td> 2014-12-11</td>\n",
       "      <td> 2014-12-11T16:12:13Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id            name_x            created_at  \\\n",
       "0  389582      CoffeetheApp  2014-04-29T20:07:33Z   \n",
       "1  293071      Wear My Tags  2013-11-11T23:09:59Z   \n",
       "2  235684  The Cherry Share  2013-07-17T11:31:00Z   \n",
       "3  144128       Vidinterest  2012-12-05T11:47:08Z   \n",
       "4  355204    UberOnTime.com  2014-03-05T18:58:10Z   \n",
       "\n",
       "                                        high_concept  quality  \\\n",
       "0                   Professional Networking for Jobs        4   \n",
       "1                        Fashion Sourcing Made Easy!        6   \n",
       "2  your way to find the healthiest and best price...        5   \n",
       "3       Online Video Discovery & Promotion Platform         4   \n",
       "4                                  On Demand Postman        4   \n",
       "\n",
       "           updated_at_x                         company_url  \\\n",
       "0  2014-12-16T17:08:52Z             http://coffeetheapp.com   \n",
       "1  2014-12-17T05:38:42Z  http://wearmytags.com/development/   \n",
       "2  2014-12-17T01:51:15Z      http://www.thecherryshare.com/   \n",
       "3  2014-12-12T19:54:29Z               http://vidinterest.tv   \n",
       "4  2014-12-11T16:16:46Z               http://uberontime.com   \n",
       "\n",
       "                                           video_url  \\\n",
       "0        https://www.youtube.com/watch?v=4ShUlSzxwWg   \n",
       "1  https://www.youtube.com/watch?v=44Xj8bRnoWA&fe...   \n",
       "2  http://www.youtube.com/watch?v=8QJQ9F8atOQ&fea...   \n",
       "3                                                      \n",
       "4                                                      \n",
       "\n",
       "                        twitter_url  \\\n",
       "0  https://twitter.com/coffeetheapp   \n",
       "1    https://twitter.com/WearMyTags   \n",
       "2        http://twitter.com/foodlve   \n",
       "3    http://twitter.com/vidinterest   \n",
       "4     http://twitter.com/uberontime   \n",
       "\n",
       "                                        linkedin_url  \\\n",
       "0  https://www.linkedin.com/company/5078073?trk=p...   \n",
       "1                                                      \n",
       "2                                                      \n",
       "3                                                      \n",
       "4                                                      \n",
       "\n",
       "                         ...                           blog discount  \\\n",
       "0                        ...                          False      NaN   \n",
       "1                        ...                          False      NaN   \n",
       "2                        ...                           True      NaN   \n",
       "3                        ...                           True      NaN   \n",
       "4                        ...                           True      NaN   \n",
       "\n",
       "  equity_basis            name_y pre_money_valuation public raised_amount  \\\n",
       "0       equity      CoffeetheApp             3000000   True             0   \n",
       "1       equity      Wear My Tags                 NaN   True             0   \n",
       "2       equity  The Cherry Share             3000000   True         91000   \n",
       "3       equity       Vidinterest             1000000   True             0   \n",
       "4       equity    UberOnTime.com             1000000   True             0   \n",
       "\n",
       "   raising_amount round_opened_at          updated_at_y  \n",
       "0          250000      2014-12-15  2014-12-15T19:55:19Z  \n",
       "1          750000      2014-12-14  2014-12-17T01:58:36Z  \n",
       "2          400000      2014-12-14  2014-12-14T06:20:03Z  \n",
       "3          100000      2014-12-12  2014-12-12T19:58:06Z  \n",
       "4          100000      2014-12-11  2014-12-11T16:12:13Z  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'id', u'name_x', u'created_at', u'high_concept', u'quality', u'updated_at_x', u'company_url', u'video_url', u'twitter_url', u'linkedin_url', u'facebook_url', u'thumb_url', u'blog_url', u'community_profile', u'product_desc', u'launch_date', u'hidden', u'follower_count', u'angellist_url', u'logo_url', u'company_size', u'website', u'facebook', u'linkedin', u'twitter', u'blog', u'discount', u'equity_basis', u'name_y', u'pre_money_valuation', u'public', u'raised_amount', u'raising_amount', u'round_opened_at', u'updated_at_y'], dtype='object')"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#get the dataframe to be used for meetup analytics, visualiz and scikit learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ana = df_merged[['id','name_x','high_concept','product_desc','launch_date','created_at',\n",
    "                 'follower_count','quality','company_size','twitter','linkedin','facebook',\n",
    "                 'website','blog','discount','equity_basis','pre_money_valuation',\n",
    "                 'raised_amount',\n",
    "                 'round_opened_at','raising_amount']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del indiv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del fund"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name_x</th>\n",
       "      <th>high_concept</th>\n",
       "      <th>product_desc</th>\n",
       "      <th>launch_date</th>\n",
       "      <th>created_at</th>\n",
       "      <th>follower_count</th>\n",
       "      <th>quality</th>\n",
       "      <th>company_size</th>\n",
       "      <th>twitter</th>\n",
       "      <th>linkedin</th>\n",
       "      <th>facebook</th>\n",
       "      <th>website</th>\n",
       "      <th>blog</th>\n",
       "      <th>discount</th>\n",
       "      <th>equity_basis</th>\n",
       "      <th>pre_money_valuation</th>\n",
       "      <th>raised_amount</th>\n",
       "      <th>round_opened_at</th>\n",
       "      <th>raising_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 389582</td>\n",
       "      <td>     CoffeetheApp</td>\n",
       "      <td>                  Professional Networking for Jobs</td>\n",
       "      <td> Coffeetheapp is a mobile community of hiring m...</td>\n",
       "      <td> 2014-07-01</td>\n",
       "      <td> 2014-04-29T20:07:33Z</td>\n",
       "      <td> 48</td>\n",
       "      <td> 4</td>\n",
       "      <td> 1-10</td>\n",
       "      <td> True</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "      <td> True</td>\n",
       "      <td> False</td>\n",
       "      <td>NaN</td>\n",
       "      <td> equity</td>\n",
       "      <td> 3000000</td>\n",
       "      <td>     0</td>\n",
       "      <td> 2014-12-15</td>\n",
       "      <td> 250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 293071</td>\n",
       "      <td>     Wear My Tags</td>\n",
       "      <td>                       Fashion Sourcing Made Easy!</td>\n",
       "      <td> WearMyTags.com solves the fashion/apparel indu...</td>\n",
       "      <td> 2014-07-01</td>\n",
       "      <td> 2013-11-11T23:09:59Z</td>\n",
       "      <td> 97</td>\n",
       "      <td> 6</td>\n",
       "      <td> 1-10</td>\n",
       "      <td> True</td>\n",
       "      <td> False</td>\n",
       "      <td> False</td>\n",
       "      <td> True</td>\n",
       "      <td> False</td>\n",
       "      <td>NaN</td>\n",
       "      <td> equity</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     0</td>\n",
       "      <td> 2014-12-14</td>\n",
       "      <td> 750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 235684</td>\n",
       "      <td> The Cherry Share</td>\n",
       "      <td> your way to find the healthiest and best price...</td>\n",
       "      <td> The Cherry Share is a mobile and web applicati...</td>\n",
       "      <td> 2012-06-01</td>\n",
       "      <td> 2013-07-17T11:31:00Z</td>\n",
       "      <td> 36</td>\n",
       "      <td> 5</td>\n",
       "      <td> 1-10</td>\n",
       "      <td> True</td>\n",
       "      <td> False</td>\n",
       "      <td>  True</td>\n",
       "      <td> True</td>\n",
       "      <td>  True</td>\n",
       "      <td>NaN</td>\n",
       "      <td> equity</td>\n",
       "      <td> 3000000</td>\n",
       "      <td> 91000</td>\n",
       "      <td> 2014-12-14</td>\n",
       "      <td> 400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 144128</td>\n",
       "      <td>      Vidinterest</td>\n",
       "      <td>      Online Video Discovery &amp; Promotion Platform </td>\n",
       "      <td> Vidinterest lets you discover and create priva...</td>\n",
       "      <td> 2012-06-01</td>\n",
       "      <td> 2012-12-05T11:47:08Z</td>\n",
       "      <td> 19</td>\n",
       "      <td> 4</td>\n",
       "      <td> 1-10</td>\n",
       "      <td> True</td>\n",
       "      <td> False</td>\n",
       "      <td>  True</td>\n",
       "      <td> True</td>\n",
       "      <td>  True</td>\n",
       "      <td>NaN</td>\n",
       "      <td> equity</td>\n",
       "      <td> 1000000</td>\n",
       "      <td>     0</td>\n",
       "      <td> 2014-12-12</td>\n",
       "      <td> 100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 355204</td>\n",
       "      <td>   UberOnTime.com</td>\n",
       "      <td>                                 On Demand Postman</td>\n",
       "      <td> On-demand postman delivers packages locally.  ...</td>\n",
       "      <td> 2014-06-01</td>\n",
       "      <td> 2014-03-05T18:58:10Z</td>\n",
       "      <td>  5</td>\n",
       "      <td> 4</td>\n",
       "      <td> 1-10</td>\n",
       "      <td> True</td>\n",
       "      <td> False</td>\n",
       "      <td> False</td>\n",
       "      <td> True</td>\n",
       "      <td>  True</td>\n",
       "      <td>NaN</td>\n",
       "      <td> equity</td>\n",
       "      <td> 1000000</td>\n",
       "      <td>     0</td>\n",
       "      <td> 2014-12-11</td>\n",
       "      <td> 100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id            name_x  \\\n",
       "0  389582      CoffeetheApp   \n",
       "1  293071      Wear My Tags   \n",
       "2  235684  The Cherry Share   \n",
       "3  144128       Vidinterest   \n",
       "4  355204    UberOnTime.com   \n",
       "\n",
       "                                        high_concept  \\\n",
       "0                   Professional Networking for Jobs   \n",
       "1                        Fashion Sourcing Made Easy!   \n",
       "2  your way to find the healthiest and best price...   \n",
       "3       Online Video Discovery & Promotion Platform    \n",
       "4                                  On Demand Postman   \n",
       "\n",
       "                                        product_desc launch_date  \\\n",
       "0  Coffeetheapp is a mobile community of hiring m...  2014-07-01   \n",
       "1  WearMyTags.com solves the fashion/apparel indu...  2014-07-01   \n",
       "2  The Cherry Share is a mobile and web applicati...  2012-06-01   \n",
       "3  Vidinterest lets you discover and create priva...  2012-06-01   \n",
       "4  On-demand postman delivers packages locally.  ...  2014-06-01   \n",
       "\n",
       "             created_at  follower_count  quality company_size twitter  \\\n",
       "0  2014-04-29T20:07:33Z              48        4         1-10    True   \n",
       "1  2013-11-11T23:09:59Z              97        6         1-10    True   \n",
       "2  2013-07-17T11:31:00Z              36        5         1-10    True   \n",
       "3  2012-12-05T11:47:08Z              19        4         1-10    True   \n",
       "4  2014-03-05T18:58:10Z               5        4         1-10    True   \n",
       "\n",
       "  linkedin facebook website   blog  discount equity_basis  \\\n",
       "0     True     True    True  False       NaN       equity   \n",
       "1    False    False    True  False       NaN       equity   \n",
       "2    False     True    True   True       NaN       equity   \n",
       "3    False     True    True   True       NaN       equity   \n",
       "4    False    False    True   True       NaN       equity   \n",
       "\n",
       "   pre_money_valuation  raised_amount round_opened_at  raising_amount  \n",
       "0              3000000              0      2014-12-15          250000  \n",
       "1                  NaN              0      2014-12-14          750000  \n",
       "2              3000000          91000      2014-12-14          400000  \n",
       "3              1000000              0      2014-12-12          100000  \n",
       "4              1000000              0      2014-12-11          100000  "
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ana.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'id', u'name_x', u'high_concept', u'product_desc', u'launch_date', u'created_at', u'follower_count', u'quality', u'company_size', u'twitter', u'linkedin', u'facebook', u'website', u'blog', u'discount', u'equity_basis', u'pre_money_valuation', u'raised_amount', u'round_opened_at', u'raising_amount'], dtype='object')"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ana.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#create precentage_raised column = raised_amount/raising_amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ana['percentage_raised'] = 100*ana['raised_amount']/ana['raising_amount']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#response variable can be = percentage_raised or, for classifier, percentage_raised > 0; \n",
    "# =0; > 100 (if we want three categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#now get column \"days since round open\" and column \"days since launched\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#First need to parse strings into datetime objects and then apply the delta operator for temporal difference between two datetime objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#use the pandas to_datetime; NaT is (not a time) for missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "launch_d=pd.to_datetime(ana['launch_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "created = pd.to_datetime(ana['created_at'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d = pd.to_datetime(ana['round_opened_at'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2014-12-15\n",
       "1   2014-12-14\n",
       "2   2014-12-14\n",
       "3   2014-12-12\n",
       "4   2014-12-11\n",
       "Name: round_opened_at, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#today's date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014-12-17 13:26:35.653000\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "tod = datetime.now()\n",
    "print tod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#number of days between now and launch date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "days_since_launch = tod - launch_d\n",
    "days_since_launch.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#number of days since raising round opened"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2 days, 13:26:35.653000\n",
       "1   3 days, 13:26:35.653000\n",
       "2   3 days, 13:26:35.653000\n",
       "3   5 days, 13:26:35.653000\n",
       "4   6 days, 13:26:35.653000\n",
       "Name: round_opened_at, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days_since_round = tod - d\n",
    "days_since_round.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "days_since_created = tod - created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#get the number of days since launch, created and round opened as numbers not timedelta objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ana['days_launch'] = days_since_launch.astype('timedelta64[D]')\n",
    "ana['days_r'] = days_since_round.astype('timedelta64[D]')\n",
    "ana['days_created'] = days_since_created.astype('timedelta64[D]')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Create a temporary local copy of the data for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ana.to_pickle('ana_pickled')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del ana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#load pickle for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ana = pd.read_pickle('ana_pickled')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#ANALYTICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>follower_count</th>\n",
       "      <th>quality</th>\n",
       "      <th>twitter</th>\n",
       "      <th>linkedin</th>\n",
       "      <th>facebook</th>\n",
       "      <th>website</th>\n",
       "      <th>blog</th>\n",
       "      <th>discount</th>\n",
       "      <th>pre_money_valuation</th>\n",
       "      <th>raised_amount</th>\n",
       "      <th>raising_amount</th>\n",
       "      <th>percentage_raised</th>\n",
       "      <th>days_launch</th>\n",
       "      <th>days_r</th>\n",
       "      <th>days_created</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>    400.000000</td>\n",
       "      <td> 400.000000</td>\n",
       "      <td> 400.000000</td>\n",
       "      <td>       400</td>\n",
       "      <td>       400</td>\n",
       "      <td>       400</td>\n",
       "      <td>    400</td>\n",
       "      <td>       400</td>\n",
       "      <td> 53.000000</td>\n",
       "      <td> 2.450000e+02</td>\n",
       "      <td>     400.000000</td>\n",
       "      <td> 4.000000e+02</td>\n",
       "      <td> 400.000000</td>\n",
       "      <td>  128.000000</td>\n",
       "      <td> 400.000000</td>\n",
       "      <td>  200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td> 361130.905000</td>\n",
       "      <td>  23.810000</td>\n",
       "      <td>   3.025000</td>\n",
       "      <td>      0.46</td>\n",
       "      <td>    0.1525</td>\n",
       "      <td>    0.2475</td>\n",
       "      <td> 0.9975</td>\n",
       "      <td>    0.2325</td>\n",
       "      <td> 17.849057</td>\n",
       "      <td> 1.668349e+07</td>\n",
       "      <td>   35829.377500</td>\n",
       "      <td> 1.572787e+06</td>\n",
       "      <td>   3.542742</td>\n",
       "      <td>  482.140625</td>\n",
       "      <td> 165.277500</td>\n",
       "      <td>  375.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>  99434.201323</td>\n",
       "      <td>  73.550923</td>\n",
       "      <td>   1.693086</td>\n",
       "      <td> 0.4990216</td>\n",
       "      <td> 0.3599551</td>\n",
       "      <td> 0.4321001</td>\n",
       "      <td>   0.05</td>\n",
       "      <td> 0.4229551</td>\n",
       "      <td>  8.244803</td>\n",
       "      <td> 2.038386e+08</td>\n",
       "      <td>  228628.002403</td>\n",
       "      <td> 1.126272e+07</td>\n",
       "      <td>  13.780317</td>\n",
       "      <td>  490.235613</td>\n",
       "      <td>  55.494772</td>\n",
       "      <td>  287.841207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>   1447.000000</td>\n",
       "      <td>   0.000000</td>\n",
       "      <td>   1.000000</td>\n",
       "      <td>     False</td>\n",
       "      <td>     False</td>\n",
       "      <td>     False</td>\n",
       "      <td>  False</td>\n",
       "      <td>     False</td>\n",
       "      <td>  0.000000</td>\n",
       "      <td> 0.000000e+00</td>\n",
       "      <td>       0.000000</td>\n",
       "      <td> 1.000000e+01</td>\n",
       "      <td>   0.000000</td>\n",
       "      <td> -196.000000</td>\n",
       "      <td>   2.000000</td>\n",
       "      <td>  154.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td> 346012.500000</td>\n",
       "      <td>   3.000000</td>\n",
       "      <td>   2.000000</td>\n",
       "      <td>         0</td>\n",
       "      <td>         0</td>\n",
       "      <td>         0</td>\n",
       "      <td>      1</td>\n",
       "      <td>         0</td>\n",
       "      <td> 15.000000</td>\n",
       "      <td> 2.500000e+05</td>\n",
       "      <td>       0.000000</td>\n",
       "      <td> 8.000000e+04</td>\n",
       "      <td>   0.000000</td>\n",
       "      <td>  222.250000</td>\n",
       "      <td> 166.750000</td>\n",
       "      <td>  178.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td> 406998.000000</td>\n",
       "      <td>   5.000000</td>\n",
       "      <td>   3.000000</td>\n",
       "      <td>         0</td>\n",
       "      <td>         0</td>\n",
       "      <td>         0</td>\n",
       "      <td>      1</td>\n",
       "      <td>         0</td>\n",
       "      <td> 20.000000</td>\n",
       "      <td> 1.700000e+06</td>\n",
       "      <td>       0.000000</td>\n",
       "      <td> 2.500000e+05</td>\n",
       "      <td>   0.000000</td>\n",
       "      <td>  350.000000</td>\n",
       "      <td> 187.000000</td>\n",
       "      <td>  231.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td> 417614.750000</td>\n",
       "      <td>  15.000000</td>\n",
       "      <td>   4.000000</td>\n",
       "      <td>         1</td>\n",
       "      <td>         0</td>\n",
       "      <td>         0</td>\n",
       "      <td>      1</td>\n",
       "      <td>         0</td>\n",
       "      <td> 20.000000</td>\n",
       "      <td> 4.500000e+06</td>\n",
       "      <td>       0.000000</td>\n",
       "      <td> 6.000000e+05</td>\n",
       "      <td>   0.000000</td>\n",
       "      <td>  541.500000</td>\n",
       "      <td> 202.000000</td>\n",
       "      <td>  451.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td> 438675.000000</td>\n",
       "      <td> 863.000000</td>\n",
       "      <td>   9.000000</td>\n",
       "      <td>      True</td>\n",
       "      <td>      True</td>\n",
       "      <td>      True</td>\n",
       "      <td>   True</td>\n",
       "      <td>      True</td>\n",
       "      <td> 40.000000</td>\n",
       "      <td> 3.192118e+09</td>\n",
       "      <td> 3760250.000000</td>\n",
       "      <td> 1.600000e+08</td>\n",
       "      <td> 110.595588</td>\n",
       "      <td> 3272.000000</td>\n",
       "      <td> 215.000000</td>\n",
       "      <td> 1468.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id  follower_count     quality    twitter   linkedin  \\\n",
       "count     400.000000      400.000000  400.000000        400        400   \n",
       "mean   361130.905000       23.810000    3.025000       0.46     0.1525   \n",
       "std     99434.201323       73.550923    1.693086  0.4990216  0.3599551   \n",
       "min      1447.000000        0.000000    1.000000      False      False   \n",
       "25%    346012.500000        3.000000    2.000000          0          0   \n",
       "50%    406998.000000        5.000000    3.000000          0          0   \n",
       "75%    417614.750000       15.000000    4.000000          1          0   \n",
       "max    438675.000000      863.000000    9.000000       True       True   \n",
       "\n",
       "        facebook website       blog   discount  pre_money_valuation  \\\n",
       "count        400     400        400  53.000000         2.450000e+02   \n",
       "mean      0.2475  0.9975     0.2325  17.849057         1.668349e+07   \n",
       "std    0.4321001    0.05  0.4229551   8.244803         2.038386e+08   \n",
       "min        False   False      False   0.000000         0.000000e+00   \n",
       "25%            0       1          0  15.000000         2.500000e+05   \n",
       "50%            0       1          0  20.000000         1.700000e+06   \n",
       "75%            0       1          0  20.000000         4.500000e+06   \n",
       "max         True    True       True  40.000000         3.192118e+09   \n",
       "\n",
       "        raised_amount  raising_amount  percentage_raised  days_launch  \\\n",
       "count      400.000000    4.000000e+02         400.000000   128.000000   \n",
       "mean     35829.377500    1.572787e+06           3.542742   482.140625   \n",
       "std     228628.002403    1.126272e+07          13.780317   490.235613   \n",
       "min          0.000000    1.000000e+01           0.000000  -196.000000   \n",
       "25%          0.000000    8.000000e+04           0.000000   222.250000   \n",
       "50%          0.000000    2.500000e+05           0.000000   350.000000   \n",
       "75%          0.000000    6.000000e+05           0.000000   541.500000   \n",
       "max    3760250.000000    1.600000e+08         110.595588  3272.000000   \n",
       "\n",
       "           days_r  days_created  \n",
       "count  400.000000    200.000000  \n",
       "mean   165.277500    375.850000  \n",
       "std     55.494772    287.841207  \n",
       "min      2.000000    154.000000  \n",
       "25%    166.750000    178.000000  \n",
       "50%    187.000000    231.000000  \n",
       "75%    202.000000    451.750000  \n",
       "max    215.000000   1468.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ana.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    400.000000\n",
       "mean       3.542742\n",
       "std       13.780317\n",
       "min        0.000000\n",
       "50%        0.000000\n",
       "60%        0.000000\n",
       "65%        0.000000\n",
       "70%        0.000000\n",
       "75%        0.000000\n",
       "80%        0.000000\n",
       "85%        0.000000\n",
       "90%        6.066667\n",
       "95%       22.812500\n",
       "99%       79.207000\n",
       "100%     110.595588\n",
       "max      110.595588\n",
       "dtype: float64"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ana['percentage_raised'].describe(percentiles=[0.5,0.6,0.65,0.7,0.75,0.80,0.85,0.9,0.95,0.99,1]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#it seems that majority did not raise anything, but some raised more than \"raising_amount\";\n",
    "#interesting to see characteristics\n",
    "#for those ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     399\n",
       "False      1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(ana['website'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x14a2f4e0>"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AVgCPAZdQ5vQAd4bz/cA24OCk+zOjlxIwo89db99Hvwe4KVy+CfhWZfuNwFJgNXAx8OQc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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ced198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ana['website'].hist(bins = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    139\n",
       "True      61\n",
       "dtype: int64"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(ana['blog'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x10cdc390>"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DOXRmL9pSdAEzz5CXpB4z5ENV0QUUwzl0Zi/aqugCZp4hL0k9ZsiHStEFFMM5dGYv2lJ0\nATPPkJekHjPkQ1XRBRTDOXRmL9qq6AJmniEvST1myIdK0QUUwzl0Zi/aUnQBM8+Ql6QeM+RDVdEF\nFMM5dGYv2qroAmaeIS9JPWbIh0rRBRTDOXRmL9pSdAEzz5CXpB4z5ENV0QUUwzl0Zi/aqugCZp4h\nL0k9ZsiHStEFFMM5dGYv2lJ0ATPPkJekHjPkQ1XRBRTDOXRmL9qq6AJmniEvST1myIdK0QUUwzl0\nZi/aUnQBM2/ckD8VuAO4D/gG8J5m/2bgNuAB4FZgU9cCJUnjGzfknwJ+F3gJcB7w28CLgSuoQ/50\n4PZmrRVV0QUUwzl0Zi/aqugCZt64If8IMGy2/xf4JnAycDGwt9m/F7i0U3WSpE4mMZOfA14O3AVs\nAQ41+w81a60oRRdQDOfQmb1oS9EFzLwNHZ//HOBTwHuBJ5Y8ttDclrGd+m8D1GP7c8hvy1Jz77rE\n9fz8AVJKR0YKi4HUdb1oUueb5fVwOFy389cSpfw8rb4ervJ4n9cJ2NOs5xjXYOxnwvHAZ4DPAdc0\n+/ZRV/cIcBL1h7NnLnneworZr8LtYseOg+zevSu6EI1pMBjg79+sGhz5z1qMO64ZAH8L3E8OeICb\ngG3N9jbgxjHPL0magHFD/jXAW4Hzgbub21bgauAi6ksoL2jWWlGKLqAYzqEze9GWoguYeePO5P+F\nlf9AXDjmOSVJE+Y3XkNV0QUUw2vDM3vRVkUXMPMMeUnqMUM+VIouoBjOoTN70ZaiC5h5hrwk9Zgh\nH6qKLqAYzqEze9FWRRcw8wx5SeoxQz5Uii6gGM6hM3vRlqILmHmGvCT1mCEfqoouoBjOoTN70VZF\nFzDzDHlJ6jFDPlSKLqAYzqEze9GWoguYeYa8JPWYIR+qii6gGM6hM3vRVkUXMPMMeUnqMUM+VIou\noBjOoTN70ZaiC5h5hrwk9ZghH6qKLqAYzqEze9FWRRcw8wx5SeoxQz5Uii6gGM6hM3vRlqILmHmG\nvCT1mCEfqoouoBjOoTN70VZFFzDzDHlJ6rH1CPmtwD7gQeAP1+H8PZKiCyiGc+jMXrSl6AJm3qRD\n/jjgI9RBfxZwOfDiCf8bPTKMLqAYw6G9WGQv2uxFV5MO+XOBh4D9wFPAPwCXTPjf6JHHowsoxuOP\n24tF9qLNXnQ16ZA/GTjQWs83+yRJATZM+HwLoxy0ceObJ/zPzqYf/OBuTjjha9FljOzw4Yc57rg3\nrcu59+/fvy7nnUX2om1/dAEzbzDh850H7KSeyQNcCTwD/FnrmIeAF0z435WkvnsYeGF0ERuaQuaA\nZ1F/auIHr5LUI28AvkX9iv3K4FokSZIkrdUoX4r6q+bxe4CXT6muCKv14tepe/B14F+Bl06vtKkb\n9ctyrwKeBn5lGkUFGaUXFXA38A36/c2g1XpxInAL9Qj4G8D2qVU2XdcBh4B7j3JMEbl5HPW4Zg44\nnuVn828EPttsvxr48rSKm7JRevHzwHOb7a0c271YPO4LwGeAX51WcVM2Si82AfcBpzTrE6dV3JSN\n0oudwJ822ycC/8Pkrw4swWupg3ulkF9zbq7X/3fNKF+KuhjY22zfRf0DvWWd6ok0Si++BHyv2b6L\n/EvdN6N+We7dwCeB706tsukbpRe/BnyK+vsmAP89reKmbJRe/CewsdneSB3yT0+pvmm6E3jsKI+v\nOTfXK+RH+VLUcsf0MdzW+gWx3yT/pe6bUX8uLgH+ulmP9N2LGTRKL14EbAbuAL4KvG06pU3dKL24\nFngJcJB6TPHe6ZRWnDXn5nq93Rn1F3Ppdfp9/IVey/+m84G3A69Zp1qijdKLa4ArmmMHTP67HKUY\npRfHA68AXgecQP2O78vU89g+GaUX76ce41TU37O5DXgZ8MT6lVWsNeXmeoX8d4BTW+tTyW85Vzrm\nlGZf34zSC6g/bL2WeiZ/tLdrs2yUXryS+u061LPXN1C/hb9p3aubrlF6cYB6RPN/ze2fqYOtbyE/\nSi9+Afhgs/0w8G3gDOp3OMeSYnJzlC9FtT9AOI/+ftg4Si9+jnomed5UK5u+tX5Z7nr6e3XNKL04\nE/g89QeTJ1B/GHfW9EqcmlF68WHgA832Fuo/ApunVN+0zTHaB6/hubncl6Le2dwWfaR5/B7qt6V9\ntVovPkb9QdLdze0r0y5wikb5uVjU55CH0Xrx+9RX2NwLvGeq1U3Xar04EbiZOivupf5Quo9uoP7c\n4TD1O7m3c+zmpiRJkiRJkiRJkiRJkiRJkiRJkqRjyf8D+LlvdcwPMxUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xabc8668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ana['blog'].hist(bins = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    156\n",
       "True      44\n",
       "dtype: int64"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(ana['linkedin'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x1118b208>"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9C7Ch4fElSQ00Dfl3AXuIf81cSRnypwC3VWutqEhdQDacvUb2ol+RuoCJ1yTkNwNvBD4B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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11092710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ana['linkedin'].hist(bins = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    126\n",
       "True      74\n",
       "dtype: int64"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(ana['facebook'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x128b2048>"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kQ6XoAMVwDp1Zi7YUHaD3bPKSNGA2+VBVdIBiOIfOrEVbFR2g92zykjRgNvlQKTpAMZxD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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x128b8860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ana['facebook'].hist(bins = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     116\n",
       "False     84\n",
       "dtype: int64"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(ana['twitter'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x129a29b0>"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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41okfuoP6CQDUr81uI//GTs3HWVgvb3clz9rrw4cPkVI6fra5PD8edb28b1zHK3m9sLDA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\nSZI0S/4f0C+WBqfrQogAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11092b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ana['twitter'].hist(bins = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Visualizations (more)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Distribution of percentage raised"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x14f364e0>"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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19QFQqVQootXupwLsYbSzHyRM2uPAIuAx4HJCbw+wLZ7vA7YCB8c8np29JE1SJ46z3w1s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\nZZMkSZIkSZIkSZIkSZIkSZIkSZKk6fX/lB+oTYM9gCUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x149f9b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ana['percentage_raised'].hist(bins=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#percentage raised vs number of days since round open"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15394dd8>]"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x14c03550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ana['days_r'],ana['percentage_raised'],'bo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Plot percentage raised( y axis) vs number of days since company launch (x axis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x153b9860>]"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UBZ+jGevtp00uN0Ff3w1ks8P09d1ALjfR6pCkprKpJyKVRt6sXftGHnmkF7gR6ARmgM2s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\ncesmWEg9vLcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14f3e400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ana['days_launch'],ana['percentage_raised'],'bo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x156eb748>]"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FIeimnunpGe69dx/wLmAl8DLxUENXVjxsPOttnx4Cr0b5GVKztHuLKYqiKLr77n3R6tXv\ni+CKss3aj0arV78vuvvufQvmHxzcVjLv4OC2BfO1U+Uat2aqRmWbn6HsIsWmnmCP3B0e3s6ePY8A\ndyy47+STf5fnnvtShfk/XmE5O9i9+8ZmlVmzpRwCn/UtF7WXwyhkU5ZOvdix4rbMFRXvW7bs5Crz\nL5S1ts/FRga114YW4+iy3S/YnbtxW+bhivf19c1WmX+hTmv79OQXkoIN/tHRIVavfhn4w5Lpvb0f\n5aqrFq7tdMuZtTply0XKsunpGYaHt5PLTTA8vJ3p6Zl2l1STYJt6Nm++kC98AUZHd/H44+8iilay\ncuVRrrnmd5iYuLLi/ND5J9DotC0X90coa2wurU+7d44HrZN6bXRCTyqFp10HuZFir55g1/hD1Ulb\nLtX3R+zIZL0KQzc0lxr8AeqUXhvd8AVT9+m05tJKgt252006fUdTNd3wBVP36YaOHq7xd7hu3tE0\nOjpEoTC24GTcIyOb2liVQtdJzaXVBHvkbrfolCOK6+VRpFLMI3c7TNpdEouX9/3vPwrMAKXL65Z2\n8E7ZHyF1kuCDv5FQXspj026KqbQ8mGtvnF+e7eCqhcdLqNma2te1FuPjn4pWrLiirn7iS+1jnnaf\n32rLg+2Z75evbPJ4ic6AJ2Jp3PT0DDt37uPw4f9aMn2p49YsdcybtLskVlveaac9zoYNEwwP72DX\nrs7a0aT2cvym8DSjqWcT8EmgF/gMcEsTnqNhk5N7OHz4TRXvW0ooLzXQ0+6SWG15F1zwGnbvnqhr\nmQqbx0uEJ+01/l7gL4nD/3zgg0DldG2z+MNePZTz+fwJH7/UQK/U53dg4A958smf1NXffuHy8h3R\nh3ix1zMrQqyzmcdLdMLr2Qk1pi3tNf4LgEeBx5LbfwO8B3gw5edp2L59+4AbgF9JppwLrAJe4J57\n9nPPPV8CXs+yZbOsX386k5N/xLe//QM+8Ym7OXx4OceO/QLYDLwhedxZLFu2l298Y4BTTnkvy5Y9\nz4svruKVV3qBX7B8+UZefrkX6Gd2diX33fdTLr54R/LYAVavPsqaNX08/fRyoqifKDrAc89FRNGr\ngOXAi8BLwAp6e09i2bJ3EveKfZQo+lVgExMTn2bnzr/n8OFngdOP/z/wM3p7Xw88zxlnHOW8836L\n/fvv56mnIo4dW8lLLz3LypXP8YtfnMJJJ53KihUvc9VVG5iYuLKmnX5z8z7xxFMcPPgM69at46yz\nVjE6OsQXv3gr11zzBR588ACzs33Ep7lcRn//yaxa1XP8+aoprmP//gd56qkjHDu2gpdeeoGzzz6F\nNWtW8Oyzz/P00z1EUT8DA4d54YUX6O39Jfr6Dpcs/0T/Uz6fJ5fL1fx5Kq/zxz9+gZ/85CesXbua\ns89+9fHnONFzT0/PsGPH3/CjH71IT88R1q9fxY03/qeKHQZGR/+Ec8+96ISdCqq9F+Xz1nK8RK07\ngRt9PVuhE2rMuvcDf1V0+1Jgqmyedu8jieDNZZePlO0o/UgEr06ub4vg8mhg4P0RbEpu70v+zs2/\nL4I/KFvGFcn0udtXR3Bp0fyXly0jiuCy5L5PRfAfKjzHR8vmvzqCD0WwLVq5cmME/z6CjVX+n2uL\nrr+3wiknP5I8b3y7r++K6IMfvHbJO/3mdxCWvzZRtHbt1VF//3lF//PCefr6rojGxz9V8f0q3fm4\n8LHxci+tMP3q4+/B3PIX25E5Pj5e9+eq0rLn/t/BwW3R+Pinqj733Xfvi9auvXrBY9euvbzk9Z5/\njvGq78mJ3osTvX/Dw9ujDRvGo+Hh7Yu8x0vfCdzI69kqnVBjFKW7czdtv0tHBP+mskulXjKDRde3\nJ5dLktvlPWsW72lTentskcdcUsNzvC35e3Eyz8VV5iueXu1//kBZGFdeVqUeSfO9jarVeX7RfZXn\nOf30D1R8v0p7MlV67GKv5/zyF+tl1UgILNbj6vTTL6n63Cd6bPHrPT/feMX6l/JepN2j7ETL64RQ\n7YQaoyjd4E/7yN23AhPEbfwAW4FXKN3B+ygwmPLzSlK3KwC/3O4iKukjLm49cBLwXTK6c1eSlJ5/\nB/xf4jX7rW2uRZIkSVIrbQIeAh4BrmtjHecCXwd+CPwAGE2mrwH2Ag8De4DVRY/ZSlz3Q8BQyyqN\n9QL3AV9NbmexztXA3xF33X0A+NcZrXMr8ft+P3Ab0J+ROj8LHErqmlNPXf8qWcYjwK4W1flnxO/7\n94AvA6dmtM45/5l43+OaNtdZrcYR4tfzB5TuH23Xa9mQXuLmn/XEHdPb2f6/FviXyfVVxE1TbwJ2\nAtcm068Dbk6un09c73Li+h+ltcNdXAN8EfhKcjuLdd4KXJ5c7yP+8metzvXAPxOHPcAdwGUZqfNt\nwK9RGgK11DXXUeNbxMfTAHyN+Y4WzaxzI/Ovy80ZrhPilb7dwI+YD/521VmpxrcT/9gvT26/us01\nNuy3iF/wOdcnlyy4C3gH8S/pmcm0tcltiH9pi7dQdhP3YGqFc4B/IP5AzK3xZ63OU4kDtVzW6lxD\n/CN/GvGP01eJQysrda6nNARqrWsdpQdL/h5QOhhVc+os9j7gC8n1LNb5t8C/oDT421lneY13AhdV\nmC/VGlu51no2sL/o9oFkWrutJ/7VvZf4S3YomX6I+S/dWcT1zmll7X8BfIx403RO1up8HfAU8NfA\nd4iP5XhVBuv8OfAJ4HHgx8AzxGtXWatzTq11lU9/gtZ/xy4nXuukQj3trvM9yfN+v2x6lup8A/H4\n6v8I5IHfaEaNrQz+1A4+SNEq4EvAFuD5svsWO2CiFf/PxcCTxO371Y65yEKdfcCvA59O/r7Iwq25\nLNQ5CPwx8Y/9WcTv/6UV6mh3ndWeN4vfoWJjxOOK3NbuQipYCWwDxoumteMMhIvpI94ifSvxCt+d\nzXiSVgb/E8Tta3POpfSXqtWWE4f+54mbeiBeq1qbXF9HHLqwsPZzkmnN9m+AdxNvlt5OvAn4+QzW\neSC5fDu5/XfEPwAHyVadvwH8b+BnxCP0fZm4CTJrdc6p5X0+kEw/p2x6q+r9EPBO4D8WTctSnYPE\nP/jfI/4+nQP8E/FWVJbqPED8uYT4+/QKcEbGaqxJlg7u6gE+R9yMUmwn8+1o17NwJ9VJxM0aBVq/\ntrCB+Tb+LNY5A7wxuT6R1Ji1Ot9C3FNiRfJ8twJ/lKE617Nw526tdd1L3KOqh+bt6CuvcxNxT6kz\nyubLWp3FKu3cbUed5TVeQTx6JMTfp8czUGPDsnJw128T/5J+l7gZ5T7iF2sN8Y7USt3nthHX/RAw\n3MpiExuY79WTxTrfQryGUtylL4t1Xst8d85bibf8slDn7cT7HV4i3hf24Trrmuva9ygw2YI6Lyfu\nRvj/mP8ufTpDdR5h/vUs9s+UdudsR52ValxOvFV/P/EWSa7NNUqSJEmSJEmSJEmSJEmSJEmSJEmS\navH/Aex1Ggs9ncMBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14be0400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ana['days_created'],ana['percentage_raised'],'bo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#percentage raised vs quality of the company (variable specific to Angellist site)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x158a3630>]"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wCvBMrTsODw8fvJzL5cjlci2U0T5uHOtM3/ve/9Zpr71HbpLcxtMeWRheLRaLFIvFeXmu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\ncCnatAYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14bf0e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ana['quality'],ana['percentage_raised'],'bo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#percentage raised vs discount of the company (variable specific to Angellist site)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x158ce518>]"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x158a9198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ana['discount'],ana['percentage_raised'],'bo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15c6e898>]"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Hx49/ZvJ5LjfAtm35mgHvJCxJWZD0pKplwN8BG4HnKn4WUTlVtkytYZbHj1867flso2+y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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15c4a908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ana['follower_count'],ana['percentage_raised'],'bo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pre_m = ana['pre_money_valuation']/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        245.000000\n",
       "mean       16683.489216\n",
       "std       203838.602312\n",
       "min            0.000000\n",
       "25%          250.000000\n",
       "50%         1700.000000\n",
       "75%         4500.000000\n",
       "max      3192118.227000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_m.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p1 = ana['pre_money_valuation'] < 3000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pre_m_lower = ana[p1]['pre_money_valuation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        160.00000\n",
       "mean      855041.44375\n",
       "std       847131.43358\n",
       "min            0.00000\n",
       "25%        50000.00000\n",
       "50%       600000.00000\n",
       "75%      1500000.00000\n",
       "max      2800000.00000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_m_lower.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x15f867b8>"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gST1nj6CWHsGZhN+Mq1NHeaSuGrR17Wr0uUcwMzNX6Zlg18qL9giyMCyPVOfw4WSvCalx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\nSZIkSZIkSZIkSVJO/h+jqfkhBeO8KQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15f89470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pre_m_lower.hist(bins=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x163a86d8>]"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x15c75438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ana[p1]['pre_money_valuation'],ana[p1]['percentage_raised'],'bo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16696518>]"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LfI4sE5QN9uiaqcC2PuvbgbP779TZuZSenmSAf2vrnEGugiTFoVzeBswG7gUagsoY7DP5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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x163b6a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ana['website'],ana['percentage_raised'],'bo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x166bdf28>]"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x166a29b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ana['facebook'],ana['percentage_raised'],'bo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x169b4978>]"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16999da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ana['linkedin'],ana['percentage_raised'],'bo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16c7fda0>]"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gSc1vyZIrRy0z3FJj8huBbezePcEilZIkhodfkbuMQCG/ASgxNPRsmOolqQW0t+8jWYFy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16c63ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ana['blog'],ana['percentage_raised'],'bo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16f827f0>]"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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USVKdam6+mP37PwVspKEmQ7l2jSRN7MCBJqKATy6T9eSLxQ2Uy+sA7LKRpDFUKnNSl+GD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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16f65f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ana['twitter'],ana['percentage_raised'],'bo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Create the boolean variable raise = 0 if the company raised 0% and raise = 1 if the company raised more than 0%; "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "target = ana['percentage_raised'] > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Implement a logistic regression classification algorithm to see how well we can use \n",
    "#the following features to predict if the company will raise something or nothing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Use Scikit-Learn Python libraries for model implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#We know the true classification for our data; will first create another dataset with the \n",
    "#features of interest \n",
    "#and then take a train and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "features = ['website', 'blog' , 'linkedin', 'facebook', 'twitter','days_launch','days_created',\n",
    "            'days_r','follower_count','quality']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = ana[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del ana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Create test and train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.25, \n",
    "                                                    random_state=33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((300L, 10L), (300L,))"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((100L, 10L), (100L,))"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#fit classification model with training data; first deal with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "imp = Imputer(missing_values='NaN', strategy='mean', axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Imputer(axis=0, copy=True, missing_values='NaN', strategy='mean', verbose=0)"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train_imp = imp.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = LogisticRegression()\n",
    "m = model.fit(X_train_imp, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -1.18947911e+00,   1.71814396e-01,   5.61057452e-01,\n",
       "          1.02474909e-01,   3.86547994e-01,   1.83987118e-04,\n",
       "         -1.65509435e-03,  -6.27958267e-03,   1.07225575e-02,\n",
       "          3.36200353e-01]])"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.30437977,  1.18745742,  1.75252473,  1.1079095 ,  1.47189104,\n",
       "         1.000184  ,  0.99834627,  0.99374009,  1.01078025,  1.39961942]])"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp(m.coef_) #exponential of coefficients is odds of increase in probability\n",
    "# of raising due to increase in one of the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Can look at the relative importance of features through their odds ratios (can eyeball)\n",
    "#features = ['website', 'blog' , 'linkedin', 'facebook', 'twitter','days_launch','days_created',\n",
    "# 'days_r','follower_count','quality']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# make predictions using train data\n",
    "predicted = m.predict(X_train_imp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "      False       0.90      0.98      0.94       261\n",
      "       True       0.62      0.26      0.36        39\n",
      "\n",
      "avg / total       0.86      0.88      0.86       300\n",
      "\n",
      "[[255   6]\n",
      " [ 29  10]]\n"
     ]
    }
   ],
   "source": [
    "# summarize the fit of the model using train data\n",
    "print(metrics.classification_report(y_train, predicted))\n",
    "print(metrics.confusion_matrix(y_train, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# imput test data; using imputation means from train data\n",
    "X_test_imp = imp.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# make predictions using train data\n",
    "predicted = m.predict(X_test_imp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "      False       0.92      0.98      0.95        89\n",
      "       True       0.60      0.27      0.37        11\n",
      "\n",
      "avg / total       0.88      0.90      0.88       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# summarize the fit of the model using test data\n",
    "print(metrics.classification_report(y_test, predicted))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[87  2]\n",
      " [ 8  3]]\n"
     ]
    }
   ],
   "source": [
    "print(metrics.confusion_matrix(y_test, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90000000000000002"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(y_test, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
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