Introduction_and_Data_Cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Kickstarter project success\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
      ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "Kickstarter, founded in 2009, is a crowdfunding platform where project creators can raise money from the public,  circumventing traditional avenues of investment. It has an all-or-nothing funding model, whereby a project is only funded if it meets its goal amount; otherwise no funds are collected.\n",
    "\n",
    "A huge variety of factors contribute to the success or failure of a project on Kickstarter. Some of these factors are able to be quantified or categorized, which allows for the construction of a model to attempt to predict whether a project will succeed or not. \n",
    "\n",
    "The goal of this project is to predict if a Kickstart project will succeed or fail through using Exploratory Data Analysis and supervised Machine Learning models.\n",
    "\n",
    "More generally, the aim is to help potential project creators as well as potential investors assess what their chances of success on Kickstarter will be."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About the data\n",
    "\n",
    "The dataset contains data on all projects hosted on Kickstarter between the company’s launch in April 2009 until the date of the webscrape on 14 March 14, 2019. The raw data consists of 56 separate .csv files. This notebook combines all the .csv files into a single file called ```\"Kickstarter.csv\"```. \n",
    "\n",
    "## Column names and descriptions \n",
    "\n",
    "- **backers_count** - number of backers of the project\n",
    "- **blurb** - short description about the project\n",
    "- **category** - contains category and subcategory information for the project\n",
    "- **converted_pledged_amount** - amount of money pledged, converted to the currency in the 'current_currency' column\n",
    "- **country** - country of project creator \n",
    "- **created_at** - date and time in Unix time of when the project was initially created\n",
    "- **creator** - contains name of the project creator\n",
    "- **currency** - original currency of project goal\n",
    "- **currency_symbol** symbol of the original currency the project goal was denominated in\n",
    "- **currency_trailing_code** - code of the original currency the project goal was denominated in\n",
    "- **current_currency** - currency the project goal was converted to\n",
    "- **deadline** - date and time of when the project will close for donations\n",
    "- **disable_communication** - whether or not a project owner disabled communication with their backers\n",
    "- **friends** - unclear (null or empty list)\n",
    "- **fx_rate** - foreign exchange rate between the original currency and the current_currency\n",
    "- **goal** - funding goal in the currency denominated by 'currency'\n",
    "- **id** - id number of the project\n",
    "- **is_backing** - ? (null or false)\n",
    "- **is_starrable** - whether or not a project can be starred by users\n",
    "- **is_starred** - whether or not a project has been starred by users\n",
    "- **launched_at** - date and time of when the project was launched\n",
    "- **location** - contains city or state information of the project creator\n",
    "- **name** - name of the project\n",
    "- **permissions** - ? (null or empty)\n",
    "- **photo** - link and information to the project photo\n",
    "- **pledged** - amount pledged in 'current_currency'\n",
    "- **profile** - contains information about the project's profile and some visualization parameters\n",
    "- **slug** - name of the project in lowercase with dashes instead of spaces\n",
    "- **source_url** - url for the project's category\n",
    "- **spotlight** - after a project has been successful, it was spotlighted on the Kickstarter website\n",
    "- **staff_pick** - whether a project was highlighted as a staff pick when it was launched/live\n",
    "- **state** - target variable, has 5 unique values: [successful, failed, live, canceled, suspended]\n",
    "- **state_changed_at** - date and time of when a project's status was changed (same as the deadline for successful and failed projects)\n",
    "- **static_usd_rate** - conversion rate between the original currency and USD\n",
    "- **urls** - url link to the project's page\n",
    "- **usd_pledged** - amount pledged in USD\n",
    "- **usd_type** - unclear (domestic or international)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combining the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The raw data consists of 56 separate .csv files. The .csv files are combined into a single file called ```\"Kickstarter.csv\"```. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading: data/Kickstarter000.csv\n",
      "Reading: data/Kickstarter001.csv\n",
      "Reading: data/Kickstarter002.csv\n",
      "Reading: data/Kickstarter003.csv\n",
      "Reading: data/Kickstarter004.csv\n",
      "Reading: data/Kickstarter005.csv\n",
      "Reading: data/Kickstarter006.csv\n",
      "Reading: data/Kickstarter007.csv\n",
      "Reading: data/Kickstarter008.csv\n",
      "Reading: data/Kickstarter009.csv\n",
      "Reading: data/Kickstarter010.csv\n",
      "Reading: data/Kickstarter011.csv\n",
      "Reading: data/Kickstarter012.csv\n",
      "Reading: data/Kickstarter013.csv\n",
      "Reading: data/Kickstarter014.csv\n",
      "Reading: data/Kickstarter015.csv\n",
      "Reading: data/Kickstarter016.csv\n",
      "Reading: data/Kickstarter017.csv\n",
      "Reading: data/Kickstarter018.csv\n",
      "Reading: data/Kickstarter019.csv\n",
      "Reading: data/Kickstarter020.csv\n",
      "Reading: data/Kickstarter021.csv\n",
      "Reading: data/Kickstarter022.csv\n",
      "Reading: data/Kickstarter023.csv\n",
      "Reading: data/Kickstarter024.csv\n",
      "Reading: data/Kickstarter025.csv\n",
      "Reading: data/Kickstarter026.csv\n",
      "Reading: data/Kickstarter027.csv\n",
      "Reading: data/Kickstarter028.csv\n",
      "Reading: data/Kickstarter029.csv\n",
      "Reading: data/Kickstarter030.csv\n",
      "Reading: data/Kickstarter031.csv\n",
      "Reading: data/Kickstarter032.csv\n",
      "Reading: data/Kickstarter033.csv\n",
      "Reading: data/Kickstarter034.csv\n",
      "Reading: data/Kickstarter035.csv\n",
      "Reading: data/Kickstarter036.csv\n",
      "Reading: data/Kickstarter037.csv\n",
      "Reading: data/Kickstarter038.csv\n",
      "Reading: data/Kickstarter039.csv\n",
      "Reading: data/Kickstarter040.csv\n",
      "Reading: data/Kickstarter041.csv\n",
      "Reading: data/Kickstarter042.csv\n",
      "Reading: data/Kickstarter043.csv\n",
      "Reading: data/Kickstarter044.csv\n",
      "Reading: data/Kickstarter045.csv\n",
      "Reading: data/Kickstarter046.csv\n",
      "Reading: data/Kickstarter047.csv\n",
      "Reading: data/Kickstarter048.csv\n",
      "Reading: data/Kickstarter049.csv\n",
      "Reading: data/Kickstarter050.csv\n",
      "Reading: data/Kickstarter051.csv\n",
      "Reading: data/Kickstarter052.csv\n",
      "Reading: data/Kickstarter053.csv\n",
      "Reading: data/Kickstarter054.csv\n",
      "Reading: data/Kickstarter055.csv\n"
     ]
    }
   ],
   "source": [
    "# Initialize empty DataFrame\n",
    "df = pd.DataFrame()\n",
    "\n",
    "# Loop over every .csv file\n",
    "for i in range(0,56):\n",
    "    filename = f'data/Kickstarter{i:#03}.csv'\n",
    "    print(\"Reading:\", filename)\n",
    "    df_i = pd.read_csv(filename, sep = ',')\n",
    "\n",
    "    # Concatenate the DataFrames\n",
    "    df = pd.concat([df, df_i], axis=0, ignore_index=True)\n",
    "\n",
    "# Write to a single .csv file\n",
    "df.to_csv('data/Kickstarter.csv', sep=',', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clear df variable\n",
    "del df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reload data file and check if it was combined correctly \n",
    "df = pd.read_csv(\"data/Kickstarter.csv\", sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>backers_count</th>\n",
       "      <th>blurb</th>\n",
       "      <th>category</th>\n",
       "      <th>converted_pledged_amount</th>\n",
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       "      <th>currency_trailing_code</th>\n",
       "      <th>...</th>\n",
       "      <th>slug</th>\n",
       "      <th>source_url</th>\n",
       "      <th>spotlight</th>\n",
       "      <th>staff_pick</th>\n",
       "      <th>state</th>\n",
       "      <th>state_changed_at</th>\n",
       "      <th>static_usd_rate</th>\n",
       "      <th>urls</th>\n",
       "      <th>usd_pledged</th>\n",
       "      <th>usd_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>2006 was almost 7 years ago.... Can you believ...</td>\n",
       "      <td>{\"id\":43,\"name\":\"Rock\",\"slug\":\"music/rock\",\"po...</td>\n",
       "      <td>802</td>\n",
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       "      <td>1387659690</td>\n",
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       "      <td>USD</td>\n",
       "      <td>$</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>new-final-round-album</td>\n",
       "      <td>https://www.kickstarter.com/discover/categorie...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>successful</td>\n",
       "      <td>1391899046</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>{\"web\":{\"project\":\"https://www.kickstarter.com...</td>\n",
       "      <td>802.00000</td>\n",
       "      <td>international</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>An adorable fantasy enamel pin series of princ...</td>\n",
       "      <td>{\"id\":54,\"name\":\"Mixed Media\",\"slug\":\"art/mixe...</td>\n",
       "      <td>2259</td>\n",
       "      <td>US</td>\n",
       "      <td>1549659768</td>\n",
       "      <td>{\"id\":1175589980,\"name\":\"Katherine\",\"slug\":\"fr...</td>\n",
       "      <td>USD</td>\n",
       "      <td>$</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>princess-pals-enamel-pin-series</td>\n",
       "      <td>https://www.kickstarter.com/discover/categorie...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>successful</td>\n",
       "      <td>1551801611</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>{\"web\":{\"project\":\"https://www.kickstarter.com...</td>\n",
       "      <td>2259.00000</td>\n",
       "      <td>international</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>88</td>\n",
       "      <td>Helping a community come together to set the s...</td>\n",
       "      <td>{\"id\":280,\"name\":\"Photobooks\",\"slug\":\"photogra...</td>\n",
       "      <td>29638</td>\n",
       "      <td>US</td>\n",
       "      <td>1477242384</td>\n",
       "      <td>{\"id\":1196856269,\"name\":\"MelissaThomas\",\"is_re...</td>\n",
       "      <td>USD</td>\n",
       "      <td>$</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>their-life-through-their-lens-the-amish-and-me...</td>\n",
       "      <td>https://www.kickstarter.com/discover/categorie...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>successful</td>\n",
       "      <td>1480607932</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>{\"web\":{\"project\":\"https://www.kickstarter.com...</td>\n",
       "      <td>29638.00000</td>\n",
       "      <td>international</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>193</td>\n",
       "      <td>Every revolution starts from the bottom and we...</td>\n",
       "      <td>{\"id\":266,\"name\":\"Footwear\",\"slug\":\"fashion/fo...</td>\n",
       "      <td>49158</td>\n",
       "      <td>IT</td>\n",
       "      <td>1540369920</td>\n",
       "      <td>{\"id\":1569700626,\"name\":\"WAO\",\"slug\":\"wearewao...</td>\n",
       "      <td>EUR</td>\n",
       "      <td>€</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>wao-the-eco-effect-shoes</td>\n",
       "      <td>https://www.kickstarter.com/discover/categorie...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>successful</td>\n",
       "      <td>1544309940</td>\n",
       "      <td>1.136525</td>\n",
       "      <td>{\"web\":{\"project\":\"https://www.kickstarter.com...</td>\n",
       "      <td>49075.15252</td>\n",
       "      <td>international</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>Learn to build 10+ Applications in this comple...</td>\n",
       "      <td>{\"id\":51,\"name\":\"Software\",\"slug\":\"technology/...</td>\n",
       "      <td>549</td>\n",
       "      <td>US</td>\n",
       "      <td>1425706517</td>\n",
       "      <td>{\"id\":1870845385,\"name\":\"Kalpit Jain\",\"is_regi...</td>\n",
       "      <td>USD</td>\n",
       "      <td>$</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>apple-watch-development-course</td>\n",
       "      <td>https://www.kickstarter.com/discover/categorie...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>failed</td>\n",
       "      <td>1428511019</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>{\"web\":{\"project\":\"https://www.kickstarter.com...</td>\n",
       "      <td>549.00000</td>\n",
       "      <td>domestic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   backers_count                                              blurb  \\\n",
       "0             21  2006 was almost 7 years ago.... Can you believ...   \n",
       "1             97  An adorable fantasy enamel pin series of princ...   \n",
       "2             88  Helping a community come together to set the s...   \n",
       "3            193  Every revolution starts from the bottom and we...   \n",
       "4             20  Learn to build 10+ Applications in this comple...   \n",
       "\n",
       "                                            category  \\\n",
       "0  {\"id\":43,\"name\":\"Rock\",\"slug\":\"music/rock\",\"po...   \n",
       "1  {\"id\":54,\"name\":\"Mixed Media\",\"slug\":\"art/mixe...   \n",
       "2  {\"id\":280,\"name\":\"Photobooks\",\"slug\":\"photogra...   \n",
       "3  {\"id\":266,\"name\":\"Footwear\",\"slug\":\"fashion/fo...   \n",
       "4  {\"id\":51,\"name\":\"Software\",\"slug\":\"technology/...   \n",
       "\n",
       "   converted_pledged_amount country  created_at  \\\n",
       "0                       802      US  1387659690   \n",
       "1                      2259      US  1549659768   \n",
       "2                     29638      US  1477242384   \n",
       "3                     49158      IT  1540369920   \n",
       "4                       549      US  1425706517   \n",
       "\n",
       "                                             creator currency currency_symbol  \\\n",
       "0  {\"id\":1495925645,\"name\":\"Daniel\",\"is_registere...      USD               $   \n",
       "1  {\"id\":1175589980,\"name\":\"Katherine\",\"slug\":\"fr...      USD               $   \n",
       "2  {\"id\":1196856269,\"name\":\"MelissaThomas\",\"is_re...      USD               $   \n",
       "3  {\"id\":1569700626,\"name\":\"WAO\",\"slug\":\"wearewao...      EUR               €   \n",
       "4  {\"id\":1870845385,\"name\":\"Kalpit Jain\",\"is_regi...      USD               $   \n",
       "\n",
       "   currency_trailing_code  ...  \\\n",
       "0                    True  ...   \n",
       "1                    True  ...   \n",
       "2                    True  ...   \n",
       "3                   False  ...   \n",
       "4                    True  ...   \n",
       "\n",
       "                                                slug  \\\n",
       "0                              new-final-round-album   \n",
       "1                    princess-pals-enamel-pin-series   \n",
       "2  their-life-through-their-lens-the-amish-and-me...   \n",
       "3                           wao-the-eco-effect-shoes   \n",
       "4                     apple-watch-development-course   \n",
       "\n",
       "                                          source_url  spotlight staff_pick  \\\n",
       "0  https://www.kickstarter.com/discover/categorie...       True      False   \n",
       "1  https://www.kickstarter.com/discover/categorie...       True      False   \n",
       "2  https://www.kickstarter.com/discover/categorie...       True       True   \n",
       "3  https://www.kickstarter.com/discover/categorie...       True      False   \n",
       "4  https://www.kickstarter.com/discover/categorie...      False      False   \n",
       "\n",
       "        state  state_changed_at  static_usd_rate  \\\n",
       "0  successful        1391899046         1.000000   \n",
       "1  successful        1551801611         1.000000   \n",
       "2  successful        1480607932         1.000000   \n",
       "3  successful        1544309940         1.136525   \n",
       "4      failed        1428511019         1.000000   \n",
       "\n",
       "                                                urls  usd_pledged  \\\n",
       "0  {\"web\":{\"project\":\"https://www.kickstarter.com...    802.00000   \n",
       "1  {\"web\":{\"project\":\"https://www.kickstarter.com...   2259.00000   \n",
       "2  {\"web\":{\"project\":\"https://www.kickstarter.com...  29638.00000   \n",
       "3  {\"web\":{\"project\":\"https://www.kickstarter.com...  49075.15252   \n",
       "4  {\"web\":{\"project\":\"https://www.kickstarter.com...    549.00000   \n",
       "\n",
       "        usd_type  \n",
       "0  international  \n",
       "1  international  \n",
       "2  international  \n",
       "3  international  \n",
       "4       domestic  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine the first 5 rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>...</th>\n",
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       "      <th>spotlight</th>\n",
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       "      <th>state</th>\n",
       "      <th>state_changed_at</th>\n",
       "      <th>static_usd_rate</th>\n",
       "      <th>urls</th>\n",
       "      <th>usd_pledged</th>\n",
       "      <th>usd_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>209217</th>\n",
       "      <td>57</td>\n",
       "      <td>Steam Hollow is a Veteran owned Craft Brewery ...</td>\n",
       "      <td>{\"id\":307,\"name\":\"Drinks\",\"slug\":\"food/drinks\"...</td>\n",
       "      <td>10320</td>\n",
       "      <td>US</td>\n",
       "      <td>1487697908</td>\n",
       "      <td>{\"id\":86849824,\"name\":\"Blane White\",\"slug\":\"st...</td>\n",
       "      <td>USD</td>\n",
       "      <td>$</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>steam-hollow-brewing-co</td>\n",
       "      <td>https://www.kickstarter.com/discover/categorie...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>successful</td>\n",
       "      <td>1492171945</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>{\"web\":{\"project\":\"https://www.kickstarter.com...</td>\n",
       "      <td>10320.000000</td>\n",
       "      <td>international</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209218</th>\n",
       "      <td>11</td>\n",
       "      <td>Over 250 healthy recipes free from ANY added S...</td>\n",
       "      <td>{\"id\":306,\"name\":\"Cookbooks\",\"slug\":\"food/cook...</td>\n",
       "      <td>305</td>\n",
       "      <td>AU</td>\n",
       "      <td>1450753265</td>\n",
       "      <td>{\"id\":1899017630,\"name\":\"Alan Wichert\",\"is_reg...</td>\n",
       "      <td>AUD</td>\n",
       "      <td>$</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>fusion-detox-chef-alan-wichert-creates-the-health</td>\n",
       "      <td>https://www.kickstarter.com/discover/categorie...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>failed</td>\n",
       "      <td>1453713310</td>\n",
       "      <td>0.727575</td>\n",
       "      <td>{\"web\":{\"project\":\"https://www.kickstarter.com...</td>\n",
       "      <td>316.495068</td>\n",
       "      <td>domestic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209219</th>\n",
       "      <td>0</td>\n",
       "      <td>Give your baby style and flair with \"Gorgeous ...</td>\n",
       "      <td>{\"id\":264,\"name\":\"Childrenswear\",\"slug\":\"fashi...</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>1470991682</td>\n",
       "      <td>{\"id\":1589905505,\"name\":\"T. Simms\",\"slug\":\"1pr...</td>\n",
       "      <td>USD</td>\n",
       "      <td>$</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>gorgeous-princess-cheetah-collection</td>\n",
       "      <td>https://www.kickstarter.com/discover/categorie...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>failed</td>\n",
       "      <td>1474837755</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>{\"web\":{\"project\":\"https://www.kickstarter.com...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>international</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209220</th>\n",
       "      <td>11</td>\n",
       "      <td>\"The Lion &amp; the Lyceum\" is a children's book a...</td>\n",
       "      <td>{\"id\":46,\"name\":\"Children's Books\",\"slug\":\"pub...</td>\n",
       "      <td>1400</td>\n",
       "      <td>US</td>\n",
       "      <td>1424101898</td>\n",
       "      <td>{\"id\":1468696154,\"name\":\"Alex Beene\",\"is_regis...</td>\n",
       "      <td>USD</td>\n",
       "      <td>$</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>the-lion-and-the-lyceum-childrens-book-based-o...</td>\n",
       "      <td>https://www.kickstarter.com/discover/categorie...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>successful</td>\n",
       "      <td>1426741214</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>{\"web\":{\"project\":\"https://www.kickstarter.com...</td>\n",
       "      <td>1400.000000</td>\n",
       "      <td>domestic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209221</th>\n",
       "      <td>49</td>\n",
       "      <td>Award-winning aviation artist Philip E West's ...</td>\n",
       "      <td>{\"id\":23,\"name\":\"Painting\",\"slug\":\"art/paintin...</td>\n",
       "      <td>4867</td>\n",
       "      <td>GB</td>\n",
       "      <td>1519817658</td>\n",
       "      <td>{\"id\":230724301,\"name\":\"CPS Books / G2 Enterta...</td>\n",
       "      <td>GBP</td>\n",
       "      <td>£</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>philip-e-west-aviation-masterworks</td>\n",
       "      <td>https://www.kickstarter.com/discover/categorie...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>successful</td>\n",
       "      <td>1529851297</td>\n",
       "      <td>1.394768</td>\n",
       "      <td>{\"web\":{\"project\":\"https://www.kickstarter.com...</td>\n",
       "      <td>5118.799367</td>\n",
       "      <td>international</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        backers_count                                              blurb  \\\n",
       "209217             57  Steam Hollow is a Veteran owned Craft Brewery ...   \n",
       "209218             11  Over 250 healthy recipes free from ANY added S...   \n",
       "209219              0  Give your baby style and flair with \"Gorgeous ...   \n",
       "209220             11  \"The Lion & the Lyceum\" is a children's book a...   \n",
       "209221             49  Award-winning aviation artist Philip E West's ...   \n",
       "\n",
       "                                                 category  \\\n",
       "209217  {\"id\":307,\"name\":\"Drinks\",\"slug\":\"food/drinks\"...   \n",
       "209218  {\"id\":306,\"name\":\"Cookbooks\",\"slug\":\"food/cook...   \n",
       "209219  {\"id\":264,\"name\":\"Childrenswear\",\"slug\":\"fashi...   \n",
       "209220  {\"id\":46,\"name\":\"Children's Books\",\"slug\":\"pub...   \n",
       "209221  {\"id\":23,\"name\":\"Painting\",\"slug\":\"art/paintin...   \n",
       "\n",
       "        converted_pledged_amount country  created_at  \\\n",
       "209217                     10320      US  1487697908   \n",
       "209218                       305      AU  1450753265   \n",
       "209219                         0      US  1470991682   \n",
       "209220                      1400      US  1424101898   \n",
       "209221                      4867      GB  1519817658   \n",
       "\n",
       "                                                  creator currency  \\\n",
       "209217  {\"id\":86849824,\"name\":\"Blane White\",\"slug\":\"st...      USD   \n",
       "209218  {\"id\":1899017630,\"name\":\"Alan Wichert\",\"is_reg...      AUD   \n",
       "209219  {\"id\":1589905505,\"name\":\"T. Simms\",\"slug\":\"1pr...      USD   \n",
       "209220  {\"id\":1468696154,\"name\":\"Alex Beene\",\"is_regis...      USD   \n",
       "209221  {\"id\":230724301,\"name\":\"CPS Books / G2 Enterta...      GBP   \n",
       "\n",
       "       currency_symbol  currency_trailing_code  ...  \\\n",
       "209217               $                    True  ...   \n",
       "209218               $                    True  ...   \n",
       "209219               $                    True  ...   \n",
       "209220               $                    True  ...   \n",
       "209221               £                   False  ...   \n",
       "\n",
       "                                                     slug  \\\n",
       "209217                            steam-hollow-brewing-co   \n",
       "209218  fusion-detox-chef-alan-wichert-creates-the-health   \n",
       "209219               gorgeous-princess-cheetah-collection   \n",
       "209220  the-lion-and-the-lyceum-childrens-book-based-o...   \n",
       "209221                 philip-e-west-aviation-masterworks   \n",
       "\n",
       "                                               source_url  spotlight  \\\n",
       "209217  https://www.kickstarter.com/discover/categorie...       True   \n",
       "209218  https://www.kickstarter.com/discover/categorie...      False   \n",
       "209219  https://www.kickstarter.com/discover/categorie...      False   \n",
       "209220  https://www.kickstarter.com/discover/categorie...       True   \n",
       "209221  https://www.kickstarter.com/discover/categorie...       True   \n",
       "\n",
       "       staff_pick       state  state_changed_at  static_usd_rate  \\\n",
       "209217      False  successful        1492171945         1.000000   \n",
       "209218      False      failed        1453713310         0.727575   \n",
       "209219      False      failed        1474837755         1.000000   \n",
       "209220      False  successful        1426741214         1.000000   \n",
       "209221      False  successful        1529851297         1.394768   \n",
       "\n",
       "                                                     urls   usd_pledged  \\\n",
       "209217  {\"web\":{\"project\":\"https://www.kickstarter.com...  10320.000000   \n",
       "209218  {\"web\":{\"project\":\"https://www.kickstarter.com...    316.495068   \n",
       "209219  {\"web\":{\"project\":\"https://www.kickstarter.com...      0.000000   \n",
       "209220  {\"web\":{\"project\":\"https://www.kickstarter.com...   1400.000000   \n",
       "209221  {\"web\":{\"project\":\"https://www.kickstarter.com...   5118.799367   \n",
       "\n",
       "             usd_type  \n",
       "209217  international  \n",
       "209218       domestic  \n",
       "209219  international  \n",
       "209220       domestic  \n",
       "209221  international  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine last 5 rows\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(209222, 37)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of rows and columns\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>backers_count</th>\n",
       "      <th>converted_pledged_amount</th>\n",
       "      <th>created_at</th>\n",
       "      <th>deadline</th>\n",
       "      <th>fx_rate</th>\n",
       "      <th>goal</th>\n",
       "      <th>id</th>\n",
       "      <th>launched_at</th>\n",
       "      <th>pledged</th>\n",
       "      <th>state_changed_at</th>\n",
       "      <th>static_usd_rate</th>\n",
       "      <th>usd_pledged</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>209222.000000</td>\n",
       "      <td>2.092220e+05</td>\n",
       "      <td>2.092220e+05</td>\n",
       "      <td>2.092220e+05</td>\n",
       "      <td>209222.000000</td>\n",
       "      <td>2.092220e+05</td>\n",
       "      <td>2.092220e+05</td>\n",
       "      <td>2.092220e+05</td>\n",
       "      <td>2.092220e+05</td>\n",
       "      <td>2.092220e+05</td>\n",
       "      <td>209222.000000</td>\n",
       "      <td>2.092220e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>145.419057</td>\n",
       "      <td>1.289290e+04</td>\n",
       "      <td>1.456089e+09</td>\n",
       "      <td>1.463033e+09</td>\n",
       "      <td>0.994857</td>\n",
       "      <td>4.917604e+04</td>\n",
       "      <td>1.073222e+09</td>\n",
       "      <td>1.460206e+09</td>\n",
       "      <td>1.881403e+04</td>\n",
       "      <td>1.462838e+09</td>\n",
       "      <td>1.010757</td>\n",
       "      <td>1.289213e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>885.967976</td>\n",
       "      <td>8.889414e+04</td>\n",
       "      <td>6.339711e+07</td>\n",
       "      <td>6.305618e+07</td>\n",
       "      <td>0.211654</td>\n",
       "      <td>1.179427e+06</td>\n",
       "      <td>6.198051e+08</td>\n",
       "      <td>6.309029e+07</td>\n",
       "      <td>3.229596e+05</td>\n",
       "      <td>6.290421e+07</td>\n",
       "      <td>0.231893</td>\n",
       "      <td>8.890124e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.240366e+09</td>\n",
       "      <td>1.241334e+09</td>\n",
       "      <td>0.008966</td>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>8.624000e+03</td>\n",
       "      <td>1.240603e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.241334e+09</td>\n",
       "      <td>0.008771</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.060000e+02</td>\n",
       "      <td>1.413317e+09</td>\n",
       "      <td>1.420607e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000e+03</td>\n",
       "      <td>5.351054e+08</td>\n",
       "      <td>1.417639e+09</td>\n",
       "      <td>1.100000e+02</td>\n",
       "      <td>1.420485e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.060014e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>1.537000e+03</td>\n",
       "      <td>1.457895e+09</td>\n",
       "      <td>1.464754e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000e+03</td>\n",
       "      <td>1.074579e+09</td>\n",
       "      <td>1.461924e+09</td>\n",
       "      <td>1.556000e+03</td>\n",
       "      <td>1.464709e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.537358e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>89.000000</td>\n",
       "      <td>6.548000e+03</td>\n",
       "      <td>1.511595e+09</td>\n",
       "      <td>1.519437e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000e+04</td>\n",
       "      <td>1.609369e+09</td>\n",
       "      <td>1.516694e+09</td>\n",
       "      <td>6.887200e+03</td>\n",
       "      <td>1.519366e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.550000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>105857.000000</td>\n",
       "      <td>8.596474e+06</td>\n",
       "      <td>1.552527e+09</td>\n",
       "      <td>1.557721e+09</td>\n",
       "      <td>1.876033</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>2.147476e+09</td>\n",
       "      <td>1.552537e+09</td>\n",
       "      <td>8.103074e+07</td>\n",
       "      <td>1.552537e+09</td>\n",
       "      <td>1.716408</td>\n",
       "      <td>8.596475e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       backers_count  converted_pledged_amount    created_at      deadline  \\\n",
       "count  209222.000000              2.092220e+05  2.092220e+05  2.092220e+05   \n",
       "mean      145.419057              1.289290e+04  1.456089e+09  1.463033e+09   \n",
       "std       885.967976              8.889414e+04  6.339711e+07  6.305618e+07   \n",
       "min         0.000000              0.000000e+00  1.240366e+09  1.241334e+09   \n",
       "25%         4.000000              1.060000e+02  1.413317e+09  1.420607e+09   \n",
       "50%        27.000000              1.537000e+03  1.457895e+09  1.464754e+09   \n",
       "75%        89.000000              6.548000e+03  1.511595e+09  1.519437e+09   \n",
       "max    105857.000000              8.596474e+06  1.552527e+09  1.557721e+09   \n",
       "\n",
       "             fx_rate          goal            id   launched_at       pledged  \\\n",
       "count  209222.000000  2.092220e+05  2.092220e+05  2.092220e+05  2.092220e+05   \n",
       "mean        0.994857  4.917604e+04  1.073222e+09  1.460206e+09  1.881403e+04   \n",
       "std         0.211654  1.179427e+06  6.198051e+08  6.309029e+07  3.229596e+05   \n",
       "min         0.008966  1.000000e-02  8.624000e+03  1.240603e+09  0.000000e+00   \n",
       "25%         1.000000  1.500000e+03  5.351054e+08  1.417639e+09  1.100000e+02   \n",
       "50%         1.000000  5.000000e+03  1.074579e+09  1.461924e+09  1.556000e+03   \n",
       "75%         1.000000  1.500000e+04  1.609369e+09  1.516694e+09  6.887200e+03   \n",
       "max         1.876033  1.000000e+08  2.147476e+09  1.552537e+09  8.103074e+07   \n",
       "\n",
       "       state_changed_at  static_usd_rate   usd_pledged  \n",
       "count      2.092220e+05    209222.000000  2.092220e+05  \n",
       "mean       1.462838e+09         1.010757  1.289213e+04  \n",
       "std        6.290421e+07         0.231893  8.890124e+04  \n",
       "min        1.241334e+09         0.008771  0.000000e+00  \n",
       "25%        1.420485e+09         1.000000  1.060014e+02  \n",
       "50%        1.464709e+09         1.000000  1.537358e+03  \n",
       "75%        1.519366e+09         1.000000  6.550000e+03  \n",
       "max        1.552537e+09         1.716408  8.596475e+06  "
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary statistics\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 209222 entries, 0 to 209221\n",
      "Data columns (total 37 columns):\n",
      " #   Column                    Non-Null Count   Dtype  \n",
      "---  ------                    --------------   -----  \n",
      " 0   backers_count             209222 non-null  int64  \n",
      " 1   blurb                     209214 non-null  object \n",
      " 2   category                  209222 non-null  object \n",
      " 3   converted_pledged_amount  209222 non-null  int64  \n",
      " 4   country                   209222 non-null  object \n",
      " 5   created_at                209222 non-null  int64  \n",
      " 6   creator                   209222 non-null  object \n",
      " 7   currency                  209222 non-null  object \n",
      " 8   currency_symbol           209222 non-null  object \n",
      " 9   currency_trailing_code    209222 non-null  bool   \n",
      " 10  current_currency          209222 non-null  object \n",
      " 11  deadline                  209222 non-null  int64  \n",
      " 12  disable_communication     209222 non-null  bool   \n",
      " 13  friends                   300 non-null     object \n",
      " 14  fx_rate                   209222 non-null  float64\n",
      " 15  goal                      209222 non-null  float64\n",
      " 16  id                        209222 non-null  int64  \n",
      " 17  is_backing                300 non-null     object \n",
      " 18  is_starrable              209222 non-null  bool   \n",
      " 19  is_starred                300 non-null     object \n",
      " 20  launched_at               209222 non-null  int64  \n",
      " 21  location                  208996 non-null  object \n",
      " 22  name                      209222 non-null  object \n",
      " 23  permissions               300 non-null     object \n",
      " 24  photo                     209222 non-null  object \n",
      " 25  pledged                   209222 non-null  float64\n",
      " 26  profile                   209222 non-null  object \n",
      " 27  slug                      209222 non-null  object \n",
      " 28  source_url                209222 non-null  object \n",
      " 29  spotlight                 209222 non-null  bool   \n",
      " 30  staff_pick                209222 non-null  bool   \n",
      " 31  state                     209222 non-null  object \n",
      " 32  state_changed_at          209222 non-null  int64  \n",
      " 33  static_usd_rate           209222 non-null  float64\n",
      " 34  urls                      209222 non-null  object \n",
      " 35  usd_pledged               209222 non-null  float64\n",
      " 36  usd_type                  208742 non-null  object \n",
      "dtypes: bool(5), float64(5), int64(7), object(20)\n",
      "memory usage: 52.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# Checking for null values\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>backers_count</th>\n",
       "      <th>converted_pledged_amount</th>\n",
       "      <th>created_at</th>\n",
       "      <th>deadline</th>\n",
       "      <th>fx_rate</th>\n",
       "      <th>goal</th>\n",
       "      <th>id</th>\n",
       "      <th>launched_at</th>\n",
       "      <th>pledged</th>\n",
       "      <th>state_changed_at</th>\n",
       "      <th>static_usd_rate</th>\n",
       "      <th>usd_pledged</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>209222.000000</td>\n",
       "      <td>2.092220e+05</td>\n",
       "      <td>2.092220e+05</td>\n",
       "      <td>2.092220e+05</td>\n",
       "      <td>209222.000000</td>\n",
       "      <td>2.092220e+05</td>\n",
       "      <td>2.092220e+05</td>\n",
       "      <td>2.092220e+05</td>\n",
       "      <td>2.092220e+05</td>\n",
       "      <td>2.092220e+05</td>\n",
       "      <td>209222.000000</td>\n",
       "      <td>2.092220e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>145.419057</td>\n",
       "      <td>1.289290e+04</td>\n",
       "      <td>1.456089e+09</td>\n",
       "      <td>1.463033e+09</td>\n",
       "      <td>0.994857</td>\n",
       "      <td>4.917604e+04</td>\n",
       "      <td>1.073222e+09</td>\n",
       "      <td>1.460206e+09</td>\n",
       "      <td>1.881403e+04</td>\n",
       "      <td>1.462838e+09</td>\n",
       "      <td>1.010757</td>\n",
       "      <td>1.289213e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>885.967976</td>\n",
       "      <td>8.889414e+04</td>\n",
       "      <td>6.339711e+07</td>\n",
       "      <td>6.305618e+07</td>\n",
       "      <td>0.211654</td>\n",
       "      <td>1.179427e+06</td>\n",
       "      <td>6.198051e+08</td>\n",
       "      <td>6.309029e+07</td>\n",
       "      <td>3.229596e+05</td>\n",
       "      <td>6.290421e+07</td>\n",
       "      <td>0.231893</td>\n",
       "      <td>8.890124e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.240366e+09</td>\n",
       "      <td>1.241334e+09</td>\n",
       "      <td>0.008966</td>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>8.624000e+03</td>\n",
       "      <td>1.240603e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.241334e+09</td>\n",
       "      <td>0.008771</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.060000e+02</td>\n",
       "      <td>1.413317e+09</td>\n",
       "      <td>1.420607e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000e+03</td>\n",
       "      <td>5.351054e+08</td>\n",
       "      <td>1.417639e+09</td>\n",
       "      <td>1.100000e+02</td>\n",
       "      <td>1.420485e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.060014e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>1.537000e+03</td>\n",
       "      <td>1.457895e+09</td>\n",
       "      <td>1.464754e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000e+03</td>\n",
       "      <td>1.074579e+09</td>\n",
       "      <td>1.461924e+09</td>\n",
       "      <td>1.556000e+03</td>\n",
       "      <td>1.464709e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.537358e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>89.000000</td>\n",
       "      <td>6.548000e+03</td>\n",
       "      <td>1.511595e+09</td>\n",
       "      <td>1.519437e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000e+04</td>\n",
       "      <td>1.609369e+09</td>\n",
       "      <td>1.516694e+09</td>\n",
       "      <td>6.887200e+03</td>\n",
       "      <td>1.519366e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.550000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>105857.000000</td>\n",
       "      <td>8.596474e+06</td>\n",
       "      <td>1.552527e+09</td>\n",
       "      <td>1.557721e+09</td>\n",
       "      <td>1.876033</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>2.147476e+09</td>\n",
       "      <td>1.552537e+09</td>\n",
       "      <td>8.103074e+07</td>\n",
       "      <td>1.552537e+09</td>\n",
       "      <td>1.716408</td>\n",
       "      <td>8.596475e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       backers_count  converted_pledged_amount    created_at      deadline  \\\n",
       "count  209222.000000              2.092220e+05  2.092220e+05  2.092220e+05   \n",
       "mean      145.419057              1.289290e+04  1.456089e+09  1.463033e+09   \n",
       "std       885.967976              8.889414e+04  6.339711e+07  6.305618e+07   \n",
       "min         0.000000              0.000000e+00  1.240366e+09  1.241334e+09   \n",
       "25%         4.000000              1.060000e+02  1.413317e+09  1.420607e+09   \n",
       "50%        27.000000              1.537000e+03  1.457895e+09  1.464754e+09   \n",
       "75%        89.000000              6.548000e+03  1.511595e+09  1.519437e+09   \n",
       "max    105857.000000              8.596474e+06  1.552527e+09  1.557721e+09   \n",
       "\n",
       "             fx_rate          goal            id   launched_at       pledged  \\\n",
       "count  209222.000000  2.092220e+05  2.092220e+05  2.092220e+05  2.092220e+05   \n",
       "mean        0.994857  4.917604e+04  1.073222e+09  1.460206e+09  1.881403e+04   \n",
       "std         0.211654  1.179427e+06  6.198051e+08  6.309029e+07  3.229596e+05   \n",
       "min         0.008966  1.000000e-02  8.624000e+03  1.240603e+09  0.000000e+00   \n",
       "25%         1.000000  1.500000e+03  5.351054e+08  1.417639e+09  1.100000e+02   \n",
       "50%         1.000000  5.000000e+03  1.074579e+09  1.461924e+09  1.556000e+03   \n",
       "75%         1.000000  1.500000e+04  1.609369e+09  1.516694e+09  6.887200e+03   \n",
       "max         1.876033  1.000000e+08  2.147476e+09  1.552537e+09  8.103074e+07   \n",
       "\n",
       "       state_changed_at  static_usd_rate   usd_pledged  \n",
       "count      2.092220e+05    209222.000000  2.092220e+05  \n",
       "mean       1.462838e+09         1.010757  1.289213e+04  \n",
       "std        6.290421e+07         0.231893  8.890124e+04  \n",
       "min        1.241334e+09         0.008771  0.000000e+00  \n",
       "25%        1.420485e+09         1.000000  1.060014e+02  \n",
       "50%        1.464709e+09         1.000000  1.537358e+03  \n",
       "75%        1.519366e+09         1.000000  6.550000e+03  \n",
       "max        1.552537e+09         1.716408  8.596475e+06  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examining the summary statistics\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting columns of interest\n",
    "\n",
    "Examining the column names and descriptions, we conclude that the target variable is ```state```. There are five different outcomes for ```state```, namely: successful, failed, live, canceled and suspended."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['successful', 'failed', 'live', 'canceled', 'suspended'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['state'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "successful    117465\n",
       "failed         75199\n",
       "canceled        8624\n",
       "live            7311\n",
       "suspended        623\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count of each value in 'state'\n",
    "df['state'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After analyzing the column names and descriptions, the list of columns we are interested in are as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_names = ['id', 'backers_count', 'blurb', 'category', 'country', 'deadline', \\\n",
    "                'goal', 'launched_at', 'name', 'staff_pick', 'state', \\\n",
    "                'static_usd_rate', 'usd_pledged']\n",
    "df = df[col_names]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering for successful and failed projects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select rows only where state is successful or failed\n",
    "df = df.query(\"state in ['successful', 'failed']\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This leaves us with 192664 rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(192664, 13)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting ```state``` to 1 for successful and 0 for failed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target'] = [1 if i == 'successful' else 0 for i in df['state']]\n",
    "df.drop('state', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing duplicate rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identifying duplicate rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "duplicate_id = df[df.duplicated('id')].sort_values('id')\n",
    "duplicate_id['duplicate'] = True\n",
    "duplicate_id = duplicate_id[['id', 'duplicate']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23685, 2)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicate_id.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There are 23685  duplicated ids.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop duplicate rows\n",
    "df = df.drop_duplicates('id', keep='first').reset_index()\n",
    "df = df.drop('index', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop id column\n",
    "if 'id' in df.columns:\n",
    "    df.drop('id', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(168979, 12)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After filtering for successful/failed project and removing duplicate rows, we end up with **168979** unique rows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Blurb length and name length"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After selecting only the columns of interest, there are only 8 null values in the blurb column, which is a short description of the project and holds no predictive value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of null values in the blurb column\n",
    "df['blurb'].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ```blurb``` column is a short description of the project and ```name``` is the name of the project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate length of the blurb and fill the null value with 0\n",
    "df['blurb_length'] = df['blurb'].str.split().str.len()\n",
    "df['blurb_length'].fillna(0, inplace=True)\n",
    "\n",
    "# Calculate name length\n",
    "df['name_length'] = df['name'].str.split().str.len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert blurb length to integer\n",
    "df['blurb_length'] = df['blurb_length'].astype(int)\n",
    "df['name_length'] = df['name_length'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop blurb column\n",
    "if 'blurb' in df.columns:\n",
    "    df.drop('blurb', axis=1, inplace=True)\n",
    "\n",
    "# Drop name column\n",
    "if 'name' in df.columns:\n",
    "    df.drop('name', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting the category and subcategory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting the relevant subcategory section from the string\n",
    "f = lambda x: x['category'].split('/')[1].split('\",\"position')[0]\n",
    "df['subcategory'] = df.apply(f, axis=1)\n",
    "\n",
    "# Extracting the relevant category section from the string, and replacing the original category variable\n",
    "f = lambda x: x['category'].split('\"slug\":\"')[1].split('/')[0]\n",
    "df['category'] = df.apply(f, axis=1)\n",
    "\n",
    "# Some categories do not have a sub-category, so do not have a '/' to split with\n",
    "f = lambda x: x['category'].split('\",\"position\"')[0] \n",
    "df['category'] = df.apply(f, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>backers_count</th>\n",
       "      <th>category</th>\n",
       "      <th>country</th>\n",
       "      <th>deadline</th>\n",
       "      <th>goal</th>\n",
       "      <th>launched_at</th>\n",
       "      <th>staff_pick</th>\n",
       "      <th>static_usd_rate</th>\n",
       "      <th>usd_pledged</th>\n",
       "      <th>target</th>\n",
       "      <th>blurb_length</th>\n",
       "      <th>name_length</th>\n",
       "      <th>subcategory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>music</td>\n",
       "      <td>US</td>\n",
       "      <td>1391899046</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1388011046</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>802.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>art</td>\n",
       "      <td>US</td>\n",
       "      <td>1551801611</td>\n",
       "      <td>400.0</td>\n",
       "      <td>1550073611</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2259.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>mixed media</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>88</td>\n",
       "      <td>photography</td>\n",
       "      <td>US</td>\n",
       "      <td>1480607930</td>\n",
       "      <td>27224.0</td>\n",
       "      <td>1478012330</td>\n",
       "      <td>True</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29638.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>photobooks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>193</td>\n",
       "      <td>fashion</td>\n",
       "      <td>IT</td>\n",
       "      <td>1544309940</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>1540684582</td>\n",
       "      <td>False</td>\n",
       "      <td>1.136525</td>\n",
       "      <td>49075.15252</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>footwear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>technology</td>\n",
       "      <td>US</td>\n",
       "      <td>1428511017</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1425919017</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>549.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>software</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   backers_count     category country    deadline     goal  launched_at  \\\n",
       "0             21        music      US  1391899046    200.0   1388011046   \n",
       "1             97          art      US  1551801611    400.0   1550073611   \n",
       "2             88  photography      US  1480607930  27224.0   1478012330   \n",
       "3            193      fashion      IT  1544309940  40000.0   1540684582   \n",
       "4             20   technology      US  1428511017   1000.0   1425919017   \n",
       "\n",
       "   staff_pick  static_usd_rate  usd_pledged  target  blurb_length  \\\n",
       "0       False         1.000000    802.00000       1            26   \n",
       "1       False         1.000000   2259.00000       1             9   \n",
       "2        True         1.000000  29638.00000       1            25   \n",
       "3       False         1.136525  49075.15252       1            13   \n",
       "4       False         1.000000    549.00000       0            22   \n",
       "\n",
       "   name_length  subcategory  \n",
       "0            4         rock  \n",
       "1            5  mixed media  \n",
       "2            9   photobooks  \n",
       "3            5     footwear  \n",
       "4            4     software  "
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert columns containing time information to datetime format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to datetime\n",
    "df['launched_at'] = pd.to_datetime(df['launched_at'], origin='unix', unit='s')\n",
    "df['deadline'] = pd.to_datetime(df['deadline'], origin='unix', unit='s')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate duration of kickstarter campaign in days\n",
    "df['duration'] = df['deadline'] - df['launched_at']\n",
    "df['duration_days'] = df['duration'].dt.days\n",
    "df['year'] = pd.DatetimeIndex(df['launched_at']).year\n",
    "df['month'] = pd.DatetimeIndex(df['launched_at']).month\n",
    "df['year'] = df['year'].astype('category')\n",
    "df['month'] = df['month'].astype('category')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 168979 entries, 0 to 168978\n",
      "Data columns (total 14 columns):\n",
      " #   Column           Non-Null Count   Dtype   \n",
      "---  ------           --------------   -----   \n",
      " 0   backers_count    168979 non-null  int64   \n",
      " 1   category         168979 non-null  object  \n",
      " 2   country          168979 non-null  object  \n",
      " 3   goal             168979 non-null  float64 \n",
      " 4   staff_pick       168979 non-null  bool    \n",
      " 5   static_usd_rate  168979 non-null  float64 \n",
      " 6   usd_pledged      168979 non-null  float64 \n",
      " 7   target           168979 non-null  int64   \n",
      " 8   blurb_length     168979 non-null  int64   \n",
      " 9   name_length      168979 non-null  int64   \n",
      " 10  subcategory      168979 non-null  object  \n",
      " 11  duration_days    168979 non-null  int64   \n",
      " 12  year             168979 non-null  category\n",
      " 13  month            168979 non-null  category\n",
      "dtypes: bool(1), category(2), float64(3), int64(5), object(3)\n",
      "memory usage: 14.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# Drop unneeded columns\n",
    "drop_cols = ['launched_at', 'deadline', 'duration']\n",
    "for col in drop_cols:\n",
    "    if col in df.columns:\n",
    "        df.drop(col, axis=1, inplace=True)\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting 'goal' to USD\n",
    "\n",
    "Comparing the ```converted_pledged_amount``` and ```usd_pledged``` columns, the values are similar but not exactly the same in some cases. When the currency is not USD, ```usd_pledged = pledged * static_usd_rate```. Therefore we can drop ```converted_pledged_amount``` and ```pledged``` and just use ```usd_pledged```, which has already been converted to USD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal amount on the website is specified by the project owner in their country's currency, therefore we will convert ```goal``` to USD by also multiplying by the ```static_usd_rate```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['goal'] = (df['goal'] * df['static_usd_rate']).round(2)\n",
    "df = df.drop(['static_usd_rate'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>backers_count</th>\n",
       "      <th>category</th>\n",
       "      <th>country</th>\n",
       "      <th>goal</th>\n",
       "      <th>staff_pick</th>\n",
       "      <th>usd_pledged</th>\n",
       "      <th>target</th>\n",
       "      <th>blurb_length</th>\n",
       "      <th>name_length</th>\n",
       "      <th>subcategory</th>\n",
       "      <th>duration_days</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>music</td>\n",
       "      <td>US</td>\n",
       "      <td>200.0</td>\n",
       "      <td>False</td>\n",
       "      <td>802.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>rock</td>\n",
       "      <td>45</td>\n",
       "      <td>2013</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>art</td>\n",
       "      <td>US</td>\n",
       "      <td>400.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2259.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>mixed media</td>\n",
       "      <td>20</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>88</td>\n",
       "      <td>photography</td>\n",
       "      <td>US</td>\n",
       "      <td>27224.0</td>\n",
       "      <td>True</td>\n",
       "      <td>29638.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>photobooks</td>\n",
       "      <td>30</td>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>193</td>\n",
       "      <td>fashion</td>\n",
       "      <td>IT</td>\n",
       "      <td>45461.0</td>\n",
       "      <td>False</td>\n",
       "      <td>49075.15252</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>footwear</td>\n",
       "      <td>41</td>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>technology</td>\n",
       "      <td>US</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>False</td>\n",
       "      <td>549.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>software</td>\n",
       "      <td>30</td>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   backers_count     category country     goal  staff_pick  usd_pledged  \\\n",
       "0             21        music      US    200.0       False    802.00000   \n",
       "1             97          art      US    400.0       False   2259.00000   \n",
       "2             88  photography      US  27224.0        True  29638.00000   \n",
       "3            193      fashion      IT  45461.0       False  49075.15252   \n",
       "4             20   technology      US   1000.0       False    549.00000   \n",
       "\n",
       "   target  blurb_length  name_length  subcategory  duration_days  year month  \n",
       "0       1            26            4         rock             45  2013    12  \n",
       "1       1             9            5  mixed media             20  2019     2  \n",
       "2       1            25            9   photobooks             30  2016    11  \n",
       "3       1            13            5     footwear             41  2018    10  \n",
       "4       0            22            4     software             30  2015     3  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting 'staff_pick' to 0 or 1 values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['staff_pick'] = [1 if i == True else 0 for i in df['staff_pick']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>backers_count</th>\n",
       "      <th>category</th>\n",
       "      <th>country</th>\n",
       "      <th>goal</th>\n",
       "      <th>staff_pick</th>\n",
       "      <th>usd_pledged</th>\n",
       "      <th>target</th>\n",
       "      <th>blurb_length</th>\n",
       "      <th>name_length</th>\n",
       "      <th>subcategory</th>\n",
       "      <th>duration_days</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>music</td>\n",
       "      <td>US</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0</td>\n",
       "      <td>802.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>rock</td>\n",
       "      <td>45</td>\n",
       "      <td>2013</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>art</td>\n",
       "      <td>US</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2259.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>mixed media</td>\n",
       "      <td>20</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>88</td>\n",
       "      <td>photography</td>\n",
       "      <td>US</td>\n",
       "      <td>27224.0</td>\n",
       "      <td>1</td>\n",
       "      <td>29638.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>photobooks</td>\n",
       "      <td>30</td>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>193</td>\n",
       "      <td>fashion</td>\n",
       "      <td>IT</td>\n",
       "      <td>45461.0</td>\n",
       "      <td>0</td>\n",
       "      <td>49075.15252</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>footwear</td>\n",
       "      <td>41</td>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>technology</td>\n",
       "      <td>US</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>549.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>software</td>\n",
       "      <td>30</td>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   backers_count     category country     goal  staff_pick  usd_pledged  \\\n",
       "0             21        music      US    200.0           0    802.00000   \n",
       "1             97          art      US    400.0           0   2259.00000   \n",
       "2             88  photography      US  27224.0           1  29638.00000   \n",
       "3            193      fashion      IT  45461.0           0  49075.15252   \n",
       "4             20   technology      US   1000.0           0    549.00000   \n",
       "\n",
       "   target  blurb_length  name_length  subcategory  duration_days  year month  \n",
       "0       1            26            4         rock             45  2013    12  \n",
       "1       1             9            5  mixed media             20  2019     2  \n",
       "2       1            25            9   photobooks             30  2016    11  \n",
       "3       1            13            5     footwear             41  2018    10  \n",
       "4       0            22            4     software             30  2015     3  "
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine first 5 rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 168979 entries, 0 to 168978\n",
      "Data columns (total 13 columns):\n",
      " #   Column         Non-Null Count   Dtype   \n",
      "---  ------         --------------   -----   \n",
      " 0   backers_count  168979 non-null  int64   \n",
      " 1   category       168979 non-null  object  \n",
      " 2   country        168979 non-null  object  \n",
      " 3   goal           168979 non-null  float64 \n",
      " 4   staff_pick     168979 non-null  int64   \n",
      " 5   usd_pledged    168979 non-null  float64 \n",
      " 6   target         168979 non-null  int64   \n",
      " 7   blurb_length   168979 non-null  int64   \n",
      " 8   name_length    168979 non-null  int64   \n",
      " 9   subcategory    168979 non-null  object  \n",
      " 10  duration_days  168979 non-null  int64   \n",
      " 11  year           168979 non-null  category\n",
      " 12  month          168979 non-null  category\n",
      "dtypes: category(2), float64(2), int64(6), object(3)\n",
      "memory usage: 14.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# Check to see if the data types are correct\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write clean data to .csv file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the original data has a lot of unnecessary features, we save the cleaned DataFrame to ```Kickstarter_clean.csv``` that can be loaded later for Exploratory Data Analysis and training the Machine Learning models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('data/Kickstarter_clean.csv', sep=',', index=False)"
   ]
  }
 ],
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