FinalProject_group137.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Final Project: What Makes a Kickstarter Successful? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our project, we looked at Kickstarters and whether or not they reached their initial monetary goal. By observing the six characteristics (variables): “Goal”, “Duration”, “Friends”, “Projects”, “Images”, and “Words”, and the strength of their influence on Kickstarters’ success, we were able to determine which variable was the most important in a Kickstarter’s success."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Jessyca Beksa\n",
    "- David Yang\n",
    "- Atul Antil\n",
    "- Elizabeth Chang\n",
    "- Camron Chermak\n",
    "- Temesgen Teklemariam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Out of the six variables: “Goal”, “Duration”, “Friends”, “Projects”, “Images”, and “Words”, which variable contributes most to a Kickstarter’s success? In other words, If we removed one of these variables from the Kickstarter, which one would cause the biggest loss of support? Analysis of successful and failed Kickstarters and their characteristics may show us this information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In today’s society, the Internet and social media have become a popular past time fulfilling a large portion of most individuals days. Whether it’s portrayed in movies or through news reports, it’s safe to estimate that the internet is the largest available socio-technological system, connecting millions of individuals around the world.  USA Today reports that individuals spend an estimated 23.6 hours a week on the internet, whether it’s through a laptop, mobile phone, or tablet. One advantage to our societies over saturated media use is its ability to potentiate new young entrepreneurs into the workforce via a new trend known as crowdfunding. Websites such as: GoFundMe, Crowd Supply, and Kickstarter, allow individuals or organizations to create a fundraising page complete with a monetary goal and pitch to garner donations. Crowdfunding allows individuals to bypass traditional fundraising models and source their startup income straight from the general public. While people’s intent for creating a crowdfunding page vary inevitably, the goal of gaining monetary support for their idea is fundamentally the same. One adult may be raising money to support his/her non-profit of choice, or a group of people may be raising money to cover medical treatment costs. Amongst the variety of reasons why people create crowdfunding pages, there are two perceived outcomes. Where some individuals reach their initial proposed goal, receiving well beyond the monetary value they asked for, others fall short. With a plethora of options to choose from, Kickstarter has been one of the more favored crowdfunding websites as well as one of the longest standing, having launched in 2009. Due to its popularity, it has also become one of the most heavily analyzed crowdfunding sites (Alvaris, 8). Through analysis of the components that make up a successful Kickstarter, researchers can gain knowledge and insight into how to create successful Kickstarters, making the site more effective for future users.\n",
    "\n",
    "References (include links):\n",
    "- 1) https://thehustle.co/archive/02102019d/\n",
    "- 2) https://towardsdatascience.com/predicting-the-success-of-kickstarter-campaigns-3f4a976419b9\n",
    "\n",
    "One analysis done by “The Hustle” noted that the most successful kickstarters are those proposed in regards to a technological idea or company. \n",
    "\n",
    "Another individual examined classification models which predicted a Kickstarter’s success or failure using three different programs, K-Nearest Neighbors algorithm, Random Forest, and Linear Regression, to which he concluded that the Random Forest was the most accurate predictor. While both of these highlight aspects of successful Kickstarters and their components, our analysis will focus solely on the variables stated above to narrow down one specific characteristic that is most useful in our prediction model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We hypothesize that the most important characteristic in a Kickstarter’s success is “Friends”. In other words, if we take away “Friends” from a Kickstarter, it will lose more support compared to taking away any of the other characteristics: “Goal”, “Duration”, “Projects”, “Images”, or “Words”. We chose this hypothesis because we are in a world that is the most connected to the Internet than ever before and the presence of social media is so prevalent in this day and age. As such, we feel that the more friends on Facebook people have, the more connections people have to help raise these funds, leading to successful kickstarters that will reach their goal from the donations and additionals shares it will receive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Dataset Name: Kickstarter\n",
    "- Link to the dataset: https://www.kaggle.com/tayoaki/kickstarter-dataset\n",
    "- Number of observations: 18,142\n",
    "\n",
    "We used data from kaggle, using the “Kickstarter Dataset” which contains 18065 observations. It  uses a variety of different factors such as duration of movies, facebook friends, words and images used in descriptions, and initial goals set for the kickstarter to predict if the company was a success. The dataset consists of 18,142 rows and 35 columns, but we reduced it to 9978 rows and 8 columns for a more meaningful use. “State” is defined as whether the Kickstarter was successful or not, “Currency” is defined as the type of currency required, “Goal” is defined as the number amount of money a Kickstarter needs to reach, “Duration” is defined as the number of days the Kickstarter is open for donations, “Friends” is defined as the number of Facebook friends the Kickstarter’s founder has, “Projects” is defined as the number of previous projects by the founder, “Images” is defined as the number of images included in the Kickstarter description, and “Words” is defined as the number of words included in the Kickstarter description."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To set up for our project, we have the following imports for our code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Needed imports for the program\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "from sklearn.ensemble import RandomForestRegressor # For using Random Forests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we can configure our libraries to prepare the data and graphs, such as making them look nicer or rounding the numbers in the data to be read:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure libraries\n",
    "# The seaborn library makes plots look nicer\n",
    "sns.set()\n",
    "sns.set_context('talk')\n",
    "\n",
    "# Don't display too many rows/cols of DataFrames\n",
    "pd.options.display.max_rows = 7\n",
    "pd.options.display.max_columns = 8\n",
    "\n",
    "# Round decimals when displaying DataFrames\n",
    "pd.set_option('precision', 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can read our data from a file into a dataframe and use it for our data cleaning, visualization, and analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading in the original dataset\n",
    "data_initial = pd.read_csv('18k_Projects.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'Name', 'Url', 'State', 'Currency', 'Top Category', 'Category',\n",
       "       'Creator', 'Location', 'Updates', 'Comments', 'Rewards', 'Goal',\n",
       "       'Pledged', 'Backers', 'Start', 'End', 'Duration in Days',\n",
       "       'Facebook Connected', 'Facebook Friends', 'Facebook Shares',\n",
       "       'Has Video', 'Latitude', 'Longitude', 'Start Timestamp (UTC)',\n",
       "       'End Timestamp (UTC)', 'Creator Bio', 'Creator Website',\n",
       "       'Creator - # Projects Created', 'Creator - # Projects Backed',\n",
       "       '# Videos', '# Images', '# Words (Description)',\n",
       "       '# Words (Risks and Challenges)', '# FAQs'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing out the initial columns\n",
    "data_initial.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<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>Url</th>\n",
       "      <th>State</th>\n",
       "      <th>...</th>\n",
       "      <th># Images</th>\n",
       "      <th># Words (Description)</th>\n",
       "      <th># Words (Risks and Challenges)</th>\n",
       "      <th># FAQs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1007121454</td>\n",
       "      <td>Nail Art and Photos Printed on your Nails w/ E...</td>\n",
       "      <td>https://www.kickstarter.com/projects/137019948...</td>\n",
       "      <td>failed</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>297</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2032015036</td>\n",
       "      <td>Cold Again</td>\n",
       "      <td>https://www.kickstarter.com/projects/737783165...</td>\n",
       "      <td>failed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>276</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>733782855</td>\n",
       "      <td>Uchu Bijin Jewelry</td>\n",
       "      <td>https://www.kickstarter.com/projects/uchubijin...</td>\n",
       "      <td>failed</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>1049</td>\n",
       "      <td>66.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18139</th>\n",
       "      <td>1856281681</td>\n",
       "      <td>The Awkward Yeti's Great Big Project - 3 Books...</td>\n",
       "      <td>https://www.kickstarter.com/projects/106395834...</td>\n",
       "      <td>successful</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>962</td>\n",
       "      <td>238.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18140</th>\n",
       "      <td>1845568888</td>\n",
       "      <td>King James Version</td>\n",
       "      <td>https://www.kickstarter.com/projects/193669267...</td>\n",
       "      <td>failed</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>654</td>\n",
       "      <td>131.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18141</th>\n",
       "      <td>1693087893</td>\n",
       "      <td>BBC Gardeners' World Live - RHS Show Garden co...</td>\n",
       "      <td>https://www.kickstarter.com/projects/jasonloh/...</td>\n",
       "      <td>failed</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>372</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18142 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id                                               Name  \\\n",
       "0      1007121454  Nail Art and Photos Printed on your Nails w/ E...   \n",
       "1      2032015036                                         Cold Again   \n",
       "2       733782855                                 Uchu Bijin Jewelry   \n",
       "...           ...                                                ...   \n",
       "18139  1856281681  The Awkward Yeti's Great Big Project - 3 Books...   \n",
       "18140  1845568888                                 King James Version   \n",
       "18141  1693087893  BBC Gardeners' World Live - RHS Show Garden co...   \n",
       "\n",
       "                                                     Url       State  ...    \\\n",
       "0      https://www.kickstarter.com/projects/137019948...      failed  ...     \n",
       "1      https://www.kickstarter.com/projects/737783165...      failed  ...     \n",
       "2      https://www.kickstarter.com/projects/uchubijin...      failed  ...     \n",
       "...                                                  ...         ...  ...     \n",
       "18139  https://www.kickstarter.com/projects/106395834...  successful  ...     \n",
       "18140  https://www.kickstarter.com/projects/193669267...      failed  ...     \n",
       "18141  https://www.kickstarter.com/projects/jasonloh/...      failed  ...     \n",
       "\n",
       "      # Images # Words (Description) # Words (Risks and Challenges) # FAQs  \n",
       "0            2                   297                          153.0      0  \n",
       "1            0                   276                           98.0      0  \n",
       "2           12                  1049                           66.0      2  \n",
       "...        ...                   ...                            ...    ...  \n",
       "18139        6                   962                          238.0      1  \n",
       "18140        1                   654                          131.0      0  \n",
       "18141        6                   372                          156.0      0  \n",
       "\n",
       "[18142 rows x 35 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing out the initial data\n",
    "data_initial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because our dataset was very large, we cleaned our data using multiple steps and methods. First, we dropped all columns that we did not choose to analyze from our dataset. This was so that we could focus on the columns we wanted to analyze. Next, we cleaned the data by setting the success or failed column, “State”, with values of 1 and 0, respectively. We did this so that we could read our dataset more efficiently. Afterwards, we went through the data and made sure all monetary values, or the “Currency” column, were all in one currency (USD). We dropped all rows with “Currency” that did not have “USD” to make our dataset more relevant and easy to compare. Following this, we dropped all rows that contained a null value because we cannot use them in our analysis. And lastly, we renamed some columns to shorter phrases for readability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing all of the extra columns\n",
    "data_initial.drop(labels=['Id', 'Name', 'Url', 'Category', \\\n",
    "    'Creator', 'Location'], axis=1, inplace=True)\n",
    "data_initial.drop(labels=['Updates', 'Start', 'End',  \\\n",
    "    '# FAQs', 'Start Timestamp (UTC)'], axis=1, inplace=True)\n",
    "data_initial.drop(labels=['End Timestamp (UTC)', 'Latitude', \\\n",
    "    'Longitude'], axis=1, inplace=True)\n",
    "data_initial.drop(labels=['Comments', 'Rewards', 'Pledged', \\\n",
    "    'Backers' ], axis=1, inplace=True)\n",
    "data_initial.drop(labels=['Creator Bio', 'Creator Website', \\\n",
    "    'Creator - # Projects Backed'], axis=1, inplace=True)\n",
    "data_initial.drop(labels=['# Videos', 'Facebook Connected', \\\n",
    "    'Facebook Shares', 'Has Video'],  axis=1, inplace=True)\n",
    "data_initial.drop(labels=['# Words (Risks and Challenges)', \\\n",
    "    'Top Category'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['State', 'Currency', 'Goal', 'Duration in Days', 'Facebook Friends',\n",
       "       'Creator - # Projects Created', '# Images', '# Words (Description)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing out the remaining columns\n",
    "data_initial.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing successful and failed with 1 and 0, respectively\n",
    "data_initial.loc[data_initial['State'] == 'successful', 'State'] = 1 \n",
    "data_initial.loc[data_initial['State'] == 'failed', 'State'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making sure they all have the same currency in USD and then removing column\n",
    "data_initial = data_initial[data_initial['Currency'] == 'USD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing all of the null data\n",
    "data = data_initial.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming the columns\n",
    "data.columns = ['State', 'Currency', 'Goal', 'Duration', 'Friends', \\\n",
    "                'Projects', 'Images', 'Words']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9978 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Currency    Goal  Duration  Friends  Projects  Images  Words\n",
       "0          0      USD   20000        30    272.0         1       2    297\n",
       "1          0      USD    3000        33    573.0         1       0    276\n",
       "3          0      USD    5000        60   1285.0         1       0    309\n",
       "...      ...      ...     ...       ...      ...       ...     ...    ...\n",
       "18138      1      USD    7000        30   2346.0         2       0    123\n",
       "18139      1      USD   18000        30    316.0         4       6    962\n",
       "18140      0      USD  100000        30   1103.0         2       1    654\n",
       "\n",
       "[9978 rows x 8 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing out the final data\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After cleaning the data, we created scatterplots and bar graphs that show the correlation between the different variables we are comparing. Below are said scatterplots and bar graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting out the correlation matrix of the variables we are comparing\n",
    "f1 = pd.plotting.scatter_matrix(data[['Duration', 'Friends', 'Goal' , \\\n",
    "                    'Words', 'Images', 'Projects']], figsize=[10,10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a matrix of correlations between all of the characteristics we chose to analyze. Notice that the “Goal” column and rows correlation behaviors seems weird, where it is close to either a vertical or horizontal line. We will examine these more closely with a smaller axis to see the actual correlation without any outliers affecting the indexing on the graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of projects that failed and succeeded')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting total number of successes and failures in dataset\n",
    "f2 = data['State'].value_counts().plot('bar')\n",
    "plt.xlabel(\"Successful or Failed\")\n",
    "plt.ylabel(\"Number of projects\")\n",
    "plt.title(\"Number of projects that failed and succeeded\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This bar graph shows the number of “Successful” and “Failed” Kickstarter projects and their label as either a “1” or a “0”.\n",
    "Next we printed the correlation between “Words” and “Images”, “Goal” and “Words”, and “Words” and “State”. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation of Words and Images')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Expanding plot that looked most correlated, Words and Images\n",
    "plt.scatter(data['Words'], data['Images'], s=.4)\n",
    "plt.xlabel('# Words')\n",
    "plt.ylabel('# Images')\n",
    "plt.xlim(0, 3000)\n",
    "plt.ylim(0,75)\n",
    "plt.title('Correlation of Words and Images')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plotted “Words” against “Images” because in our scatterplot above, all the goal columns and rows seem to be uncorrelated, where most of the data seemingly represents a horizontal line. Upon looking more closely at this graph and setting the axis to smaller maximum values, we can see that “Words” and “Images” are actually correlated. When examined more closely, the correlation does appear to be low between words and images, meaning both will most likely be needed to capture the data, instead of being able to leave one or the other out with minimal effects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation of Goal and Words')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Looking at one of the goal plots which appear the strangest\n",
    "plt.scatter(data['Words'], data['Goal'], s=.8)\n",
    "plt.xlabel('# Words')\n",
    "plt.ylabel('Goal ($)')\n",
    "plt.ylim(0, 200000)\n",
    "plt.title('Correlation of Goal and Words')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plotted “Goal” against “Words” because this looked the most strange. The goal plot also appears to look strange with all the values appearing to be stuck at 0 for all the plots, but this is actually due to the fact that there were a few very high asking amounts for the goal so autofitting the plot to include all points makes the scaling look weird, but when a more appropriate scaling that captures most of the data is used, the plot becomes more similar to the others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Whether Succeeded')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting State vs Words\n",
    "plt.scatter(data['Words'], data['State'], s=.5)\n",
    "plt.title('Correlation of Words and State')\n",
    "plt.xlabel('# Words')\n",
    "plt.ylabel('Whether Succeeded')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plotted “Words” against “State” to see if we can predict “State” using just the category “Words”. As can be seen, there is not a strong correlation between Words and State, meaning there is not a simple way to predict State using just the number of words. This means we need a more complex model to take into account the different attributes we have in order to predict whether or not a startup will be funded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting array of each of attributes of data\n",
    "State_Data = data['State'].values\n",
    "Goal_Data = data['Goal'].values\n",
    "Duration_Data = data['Duration'].values\n",
    "Friends_Data = data['Friends'].values\n",
    "Projects_Data = data['Projects'].values\n",
    "Images_Data = data['Images'].values\n",
    "Words_Data = data['Words'].values\n",
    "\n",
    "# Creating dictionary to hold accuracy values\n",
    "accuracy_rf = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The project analyzed the factors for failure and success of kickstarter. We used a data set of 9978 projects and 6 features to differentiate successful and failed projects. When we started, the data set had more than 20 categories or columns, but for simplicity in our project, we are only measuring six of the attributes to predict the failure and success of the projects, dropping all the other extra columns. Also, we make sure our data contained the same currency which is USD. We started with the data set contained more successful projects which is about 5200 projects compared with failed projects, about 4900 projects. The prediction is based on the 6 features we have Goal, Duration, Friends, projects, images, and words. As we can see on our “Accuracy for each leave one out Analysis” histogram, we explored the importance of each attribute by leaving out each attribute to see which one is most important. To do this we first train with 0.75 of data and use 0.25 to test by leaving out each attribute and comparing it with actual data to see which one is the most important and least important predictor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with all attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "##############################\n",
    "#  Getting the training set  #\n",
    "##############################\n",
    "\n",
    "# Getting the X or predictor variable set\n",
    "Train_X = Goal_Data[0:int(len(Goal_Data) * .75)]\n",
    "Train_X = np.expand_dims(Train_X, axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Friends_Data[0:int(len(Friends_Data) \\\n",
    "                                            * .75)], axis=1), axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Duration_Data[0:int(len(Duration_Data) \\\n",
    "                                            * .75)], axis=1), axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Projects_Data[0:int(len(Projects_Data) \\\n",
    "                                            * .75)], axis=1), axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Images_Data[0:int(len(Images_Data) \\\n",
    "                                            * .75)], axis=1), axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Words_Data[0:int(len(Words_Data) \\\n",
    "                                            * .75)], axis=1), axis=1)\n",
    "\n",
    "# Getting the Y or prediction variable set\n",
    "Train_Y = State_Data[0:int(len(State_Data) * .75)]\n",
    "\n",
    "#############################\n",
    "#  Getting the testing set  #\n",
    "#############################\n",
    "\n",
    "# Getting the X or predictor variable set\n",
    "Test_X = Goal_Data[int(len(Goal_Data) * .75):]\n",
    "Test_X = np.expand_dims(Test_X, axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Friends_Data[int(len(Friends_Data) \\\n",
    "                                                * .75):], axis=1), axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Duration_Data[int(len(Duration_Data) \\\n",
    "                                                * .75):], axis=1), axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Projects_Data[int(len(Projects_Data) \\\n",
    "                                                * .75):], axis=1), axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Images_Data[int(len(Images_Data) \\\n",
    "                                                * .75):], axis=1), axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Words_Data[int(len(Words_Data) \\\n",
    "                                                * .75):], axis=1), axis=1)\n",
    "\n",
    "# Getting the Y or prediction variable set\n",
    "Test_Y = State_Data[int(len(State_Data) * .75):]\n",
    "\n",
    "\n",
    "#################\n",
    "# Random Forest #\n",
    "#################\n",
    "\n",
    "clf = RandomForestRegressor(random_state=0)\n",
    "clf.fit(Train_X, Train_Y)\n",
    "\n",
    "# Predicting using the random forest\n",
    "predictions = clf.predict(Test_X)\n",
    "\n",
    "predictions = predictions >= 0.5\n",
    "Test_Y = (Test_Y == 1)\n",
    "\n",
    "# Checking correct or not\n",
    "correct = (predictions == Test_Y)\n",
    "\n",
    "# Getting and printing accuracy on valid set\n",
    "Accuracy = sum(correct) / len(Test_Y)\n",
    "accuracy_rf['All'] = Accuracy*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Leaving out Goal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "##############################\n",
    "#  Getting the training set  #\n",
    "##############################\n",
    "\n",
    "# Getting the X or predictor variable set\n",
    "Train_X = Friends_Data[0:int(len(Friends_Data) * .75)]\n",
    "Train_X = np.expand_dims(Train_X, axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Duration_Data[0:int(len(Duration_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Projects_Data[0:int(len(Projects_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Images_Data[0:int(len(Images_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Words_Data[0:int(len(Words_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "\n",
    "# Getting the Y or prediction variable set\n",
    "Train_Y = State_Data[0:int(len(State_Data) * .75)]\n",
    "\n",
    "#############################\n",
    "#  Getting the testing set  #\n",
    "#############################\n",
    "\n",
    "# Getting the X or predictor variable set\n",
    "Test_X = Friends_Data[int(len(Friends_Data) * .75):]\n",
    "Test_X = np.expand_dims(Test_X, axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Duration_Data[int(len(Duration_Data) \\\n",
    "                                                * .75):], axis=1), axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Projects_Data[int(len(Projects_Data) \\\n",
    "                                                * .75):], axis=1), axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Images_Data[int(len(Images_Data) \\\n",
    "                                                * .75):], axis=1), axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Words_Data[int(len(Words_Data) \\\n",
    "                                                * .75):], axis=1), axis=1)\n",
    "\n",
    "# Getting the Y or prediction variable set\n",
    "Test_Y = State_Data[int(len(State_Data) * .75):]\n",
    "\n",
    "\n",
    "#################\n",
    "# Random Forest #\n",
    "#################\n",
    "\n",
    "clf = RandomForestRegressor(random_state=0)\n",
    "clf.fit(Train_X, Train_Y)\n",
    "\n",
    "# Predicting using the random forest\n",
    "predictions = clf.predict(Test_X)\n",
    "\n",
    "predictions = predictions >= 0.5\n",
    "Test_Y = (Test_Y == 1)\n",
    "\n",
    "# Checking correct or not\n",
    "correct = (predictions == Test_Y)\n",
    "\n",
    "# Getting and printing accuracy on valid set\n",
    "Accuracy = sum(correct) / len(Test_Y)\n",
    "accuracy_rf['W/O Goal'] = Accuracy*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Leaving out duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "##############################\n",
    "#  Getting the training set  #\n",
    "##############################\n",
    "\n",
    "# Getting the X or predictor variable set\n",
    "Train_X = Goal_Data[0:int(len(Goal_Data) * .75)]\n",
    "Train_X = np.expand_dims(Train_X, axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Friends_Data[0:int(len(Friends_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Projects_Data[0:int(len(Projects_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Images_Data[0:int(len(Images_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Words_Data[0:int(len(Words_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "\n",
    "# Getting the Y or prediction variable set\n",
    "Train_Y = State_Data[0:int(len(State_Data) * .75)]\n",
    "\n",
    "#############################\n",
    "#  Getting the testing set  #\n",
    "#############################\n",
    "\n",
    "# Getting the X or predictor variable set\n",
    "Test_X = Goal_Data[int(len(Goal_Data) * .75):]\n",
    "Test_X = np.expand_dims(Test_X, axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Friends_Data[int(len(Friends_Data) \\\n",
    "                                                    * .75):], axis=1), axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Projects_Data[int(len(Projects_Data) \\\n",
    "                                                    * .75):], axis=1), axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Images_Data[int(len(Images_Data) \\\n",
    "                                                    * .75):], axis=1), axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Words_Data[int(len(Words_Data) \\\n",
    "                                                    * .75):], axis=1), axis=1)\n",
    "\n",
    "# Getting the Y or prediction variable set\n",
    "Test_Y = State_Data[int(len(State_Data) * .75):]\n",
    "\n",
    "#################\n",
    "# Random Forest #\n",
    "#################\n",
    "\n",
    "clf = RandomForestRegressor(random_state=0)\n",
    "clf.fit(Train_X, Train_Y)\n",
    "\n",
    "# Predicting using the random forest\n",
    "predictions = clf.predict(Test_X)\n",
    "\n",
    "predictions = predictions >= 0.5\n",
    "Test_Y = (Test_Y == 1)\n",
    "\n",
    "# Checking correct or not\n",
    "correct = (predictions == Test_Y)\n",
    "\n",
    "# Getting and printing accuracy on valid set\n",
    "Accuracy = sum(correct) / len(Test_Y)\n",
    "accuracy_rf['W/O Duration'] = Accuracy*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Leaving out friends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "##############################\n",
    "#  Getting the training set  #\n",
    "##############################\n",
    "\n",
    "# Getting the X or predictor variable set\n",
    "Train_X = Goal_Data[0:int(len(Goal_Data) * .75)]\n",
    "Train_X = np.expand_dims(Train_X, axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Duration_Data[0:int(len(Duration_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Projects_Data[0:int(len(Projects_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Images_Data[0:int(len(Images_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Words_Data[0:int(len(Words_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "\n",
    "# Getting the Y or prediction variable set\n",
    "Train_Y = State_Data[0:int(len(State_Data) * .75)]\n",
    "\n",
    "#############################\n",
    "#  Getting the testing set  #\n",
    "#############################\n",
    "\n",
    "# Getting the X or predictor variable set\n",
    "Test_X = Goal_Data[int(len(Goal_Data) * .75):]\n",
    "Test_X = np.expand_dims(Test_X, axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Duration_Data[int(len(Duration_Data) \\\n",
    "                                                    * .75):], axis=1), axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Projects_Data[int(len(Projects_Data) \\\n",
    "                                                    * .75):], axis=1), axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Images_Data[int(len(Images_Data) \\\n",
    "                                                    * .75):], axis=1), axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Words_Data[int(len(Words_Data) \\\n",
    "                                                    * .75):], axis=1), axis=1)\n",
    "\n",
    "# Getting the Y or prediction variable set\n",
    "Test_Y = State_Data[int(len(State_Data) * .75):]\n",
    "\n",
    "#################\n",
    "# Random Forest #\n",
    "#################\n",
    "\n",
    "clf = RandomForestRegressor(random_state=0)\n",
    "clf.fit(Train_X, Train_Y)\n",
    "\n",
    "# Predicting using the random forest\n",
    "predictions = clf.predict(Test_X)\n",
    "\n",
    "predictions = predictions >= 0.5\n",
    "Test_Y = (Test_Y == 1)\n",
    "\n",
    "# Checking correct or not\n",
    "correct = (predictions == Test_Y)\n",
    "\n",
    "# Getting and printing accuracy on valid set\n",
    "Accuracy = sum(correct) / len(Test_Y)\n",
    "accuracy_rf['W/O Friends'] = Accuracy*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Leaving out projects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "##############################\n",
    "#  Getting the training set  #\n",
    "##############################\n",
    "\n",
    "# Getting the X or predictor variable set\n",
    "Train_X = Goal_Data[0:int(len(Goal_Data) * .75)]\n",
    "Train_X = np.expand_dims(Train_X, axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Duration_Data[0:int(len(Duration_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Friends_Data[0:int(len(Friends_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Images_Data[0:int(len(Images_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Words_Data[0:int(len(Words_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "\n",
    "# Getting the Y or prediction variable set\n",
    "Train_Y = State_Data[0:int(len(State_Data) * .75)]\n",
    "\n",
    "#############################\n",
    "#  Getting the testing set  #\n",
    "#############################\n",
    "\n",
    "# Getting the X or predictor variable set\n",
    "Test_X = Goal_Data[int(len(Goal_Data) * .75):]\n",
    "Test_X = np.expand_dims(Test_X, axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Duration_Data[int(len(Duration_Data) \\\n",
    "                                                    * .75):], axis=1), axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Friends_Data[int(len(Friends_Data) \\\n",
    "                                                    * .75):], axis=1), axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Images_Data[int(len(Images_Data) \\\n",
    "                                                    * .75):], axis=1), axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Words_Data[int(len(Words_Data) \\\n",
    "                                                    * .75):], axis=1), axis=1)\n",
    "\n",
    "# Getting the Y or prediction variable set\n",
    "Test_Y = State_Data[int(len(State_Data) * .75):]\n",
    "\n",
    "#################\n",
    "# Random Forest #\n",
    "#################\n",
    "\n",
    "clf = RandomForestRegressor(random_state=0)\n",
    "clf.fit(Train_X, Train_Y)\n",
    "\n",
    "# Predicting using the random forest\n",
    "predictions = clf.predict(Test_X)\n",
    "\n",
    "predictions = predictions >= 0.5\n",
    "Test_Y = (Test_Y == 1)\n",
    "\n",
    "# Checking correct or not\n",
    "correct = (predictions == Test_Y)\n",
    "\n",
    "# Getting and printing accuracy on valid set\n",
    "Accuracy = sum(correct) / len(Test_Y)\n",
    "accuracy_rf['W/O Projects'] = Accuracy*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Leaving out images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "##############################\n",
    "#  Getting the training set  #\n",
    "##############################\n",
    "\n",
    "# Getting the X or predictor variable set\n",
    "Train_X = Goal_Data[0:int(len(Goal_Data) * .75)]\n",
    "Train_X = np.expand_dims(Train_X, axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Duration_Data[0:int(len(Duration_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Friends_Data[0:int(len(Friends_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Projects_Data[0:int(len(Projects_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Words_Data[0:int(len(Words_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "\n",
    "# Getting the Y or prediction variable set\n",
    "Train_Y = State_Data[0:int(len(State_Data) * .75)]\n",
    "\n",
    "#############################\n",
    "#  Getting the testing set  #\n",
    "#############################\n",
    "\n",
    "# Getting the X or predictor variable set\n",
    "Test_X = Goal_Data[int(len(Goal_Data) * .75):]\n",
    "Test_X = np.expand_dims(Test_X, axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Duration_Data[int(len(Duration_Data) \\\n",
    "                                                    * .75):], axis=1), axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Friends_Data[int(len(Friends_Data) \\\n",
    "                                                    * .75):], axis=1), axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Projects_Data[int(len(Projects_Data) \\\n",
    "                                                    * .75):], axis=1), axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Words_Data[int(len(Words_Data) \\\n",
    "                                                    * .75):], axis=1), axis=1)\n",
    "\n",
    "# Getting the Y or prediction variable set\n",
    "Test_Y = State_Data[int(len(State_Data) * .75):]\n",
    "\n",
    "#################\n",
    "# Random Forest #\n",
    "#################\n",
    "\n",
    "clf = RandomForestRegressor(random_state=0)\n",
    "clf.fit(Train_X, Train_Y)\n",
    "\n",
    "# Predicting using the random forest\n",
    "predictions = clf.predict(Test_X)\n",
    "\n",
    "predictions = predictions >= 0.5\n",
    "Test_Y = (Test_Y == 1)\n",
    "\n",
    "# Checking correct or not\n",
    "correct = (predictions == Test_Y)\n",
    "\n",
    "# Getting and printing accuracy on valid set\n",
    "Accuracy = sum(correct) / len(Test_Y)\n",
    "accuracy_rf['W/O Images'] = Accuracy*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Leaving out words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "##############################\n",
    "#  Getting the training set  #\n",
    "##############################\n",
    "\n",
    "# Getting the X or predictor variable set\n",
    "Train_X = Goal_Data[0:int(len(Goal_Data) * .75)]\n",
    "Train_X = np.expand_dims(Train_X, axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Duration_Data[0:int(len(Duration_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Friends_Data[0:int(len(Friends_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Projects_Data[0:int(len(Projects_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "Train_X = np.append(Train_X, np.expand_dims(Images_Data[0:int(len(Images_Data) \\\n",
    "                                                    * .75)], axis=1), axis=1)\n",
    "\n",
    "# Getting the Y or prediction variable set\n",
    "Train_Y = State_Data[0:int(len(State_Data) * .75)]\n",
    "\n",
    "#############################\n",
    "#  Getting the testing set  #\n",
    "#############################\n",
    "\n",
    "# Getting the X or predictor variable set\n",
    "Test_X = Goal_Data[int(len(Goal_Data) * .75):]\n",
    "Test_X = np.expand_dims(Test_X, axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Duration_Data[int(len(Duration_Data) \\\n",
    "                                                    * .75):], axis=1), axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Friends_Data[int(len(Friends_Data) \\\n",
    "                                                    * .75):], axis=1), axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Projects_Data[int(len(Projects_Data) \\\n",
    "                                                    * .75):], axis=1), axis=1)\n",
    "Test_X = np.append(Test_X, np.expand_dims(Images_Data[int(len(Images_Data) \\\n",
    "                                                    * .75):], axis=1), axis=1)\n",
    "\n",
    "# Getting the Y or prediction variable set\n",
    "Test_Y = State_Data[int(len(State_Data) * .75):]\n",
    "\n",
    "#################\n",
    "# Random Forest #\n",
    "#################\n",
    "\n",
    "clf = RandomForestRegressor(random_state=0)\n",
    "clf.fit(Train_X, Train_Y)\n",
    "\n",
    "# Predicting using the random forest\n",
    "predictions = clf.predict(Test_X)\n",
    "\n",
    "predictions = predictions >= 0.5\n",
    "Test_Y = (Test_Y == 1)\n",
    "\n",
    "# Checking correct or not\n",
    "correct = (predictions == Test_Y)\n",
    "\n",
    "# Getting and printing accuracy on valid set\n",
    "Accuracy = sum(correct) / len(Test_Y)\n",
    "accuracy_rf['W/O Words'] = Accuracy*100\n",
    "              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'All': 65.93186372745491,\n",
       " 'W/O Duration': 66.733466933867732,\n",
       " 'W/O Friends': 61.643286573146291,\n",
       " 'W/O Goal': 59.839679358717433,\n",
       " 'W/O Images': 65.290581162324642,\n",
       " 'W/O Projects': 65.250501002004015,\n",
       " 'W/O Words': 63.366733466933866}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing out the accuracies for rf\n",
    "accuracy_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting out the accuracies\n",
    "f, ax = plt.subplots(figsize=(18,7))\n",
    "plt.bar(*zip(*accuracy_rf.items()))\n",
    "plt.ylabel('Accuracy %')\n",
    "plt.title('Accuracy for each of Leave One Out Analysis')\n",
    "plt.ylim(50,70)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the final result of our analysis that shows us the accuracy of our model after training it with our train data. We tested our models with the test data and compared it with the actual result of whether or not a kickstarter was successful. As we can see, without the “Goal” column, the accuracy of predicting whether a kickstarter is successful or not decrease, so we can conclude that “Goal” is most correlated to predicting a kickstarter’s success."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Most important attribute: Goal\n",
    "\n",
    "After running multiple models, each leaving out an attribute to measure the different accuracies of each model, our results can be found on the bar graph. The most important attribute: “Goal”, which is the column representing the amount of the kickstarter’s initial monetary goal. Looking at the results we can see that “Goal” is the most important factor of the attributes we examined for determining whether or not a kickstarter will be funded since the accuracy went down the furthest when this attribute was removed from training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Least important attribute: Duration\n",
    "\n",
    "Looking at the results we can also see that “Duration” is the least important factor of the attributes we examined for determining whether or not a kickstarter will be funded since the accuracy actually improved when this attribute was removed from training. This means that duration did not help improve the training and even caused problems that slightly threw off the training, leading to worse predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are using data on Kaggle. This data is publically available and anyone can access it. When looking at our dataset, there are some privacy concerns because Kickstarters can contain personal information provided by the creators of them for the purpose of telling a more complete story that would generate more shares and support in donations. However, this is a concern that can be dismissed because all information that is provided on Kickstarters was knowingly provided and also published onto the public Internet to be viewed and shared. Our dataset, however, is not biased. This is because it was collected by an individual that has no apparent affiliation or involvement with the company Kickstarter or any of the Kickstarters listed in the dataset. We checked this by viewing the public Kaggle profile of the user that created and posted this dataset. Upon viewing the user’s public profile, we found that the user does not not have any biography or datasets under “favorites”. The user only has one public dataset that was posted one year ago, which is the dataset we are using for this project. Additionally, in looking at the “Categories” column of the dataset, we found that there are 146 unique categories, or topics of Kickstarters that the dataset contains. This shows that the dataset is not biased towards one or a few specific topics, instead, it contains data on Kickstarters across a variety of topics. One thing we have considered is that our data can be used for ill. It is possible that someone could use our dataset to slander efforts that have not been successful. For example, if someone disliked certain films, he/she could look through our dataset and find Kickstarters that were unsuccessful in reaching their “Goal” and slander their lack of support. However, we do not foresee anyone doing this because it would not benefit them in any way. Now, we will discuss how there could be unintended consequences from our findings. Someone who reads our projectand is interested in creating a Kickstarter could miscontrue our findings and try to choose a “Goal” that is unachievable or out of scope of the actual cause the Kickstarter was created for.\n",
    "Someone could profit from our findings if they followed our conclusion and created Kickstarter(s) that focused on the “Goal”. We can guard against this by putting a disclaimer that our result will not necessarily result in profit. We can also guard against someone profiting from our findings by keeping our project private and unshared with the public. There are most certainly things to consider when choosing and analyzing our dataset. Ultimately, however, there was little to none significant disturbance or harm caused by our usage of our chosen dataset to those that were contained in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our hypothesis was that the most important characteristic in a Kickstarter’s success is “Friends”. In other words, we thought that if we took away “Friends” from a Kickstarter, it would lose more support compared to taking away any of the other characteristics: “Goal”, “Duration”, “Projects”, “Images”, or “Words”. Our data analysis, however, proved otherwise. To answer our research question, “Out of the six variables: ‘Goal’, ‘Duration’, ‘Friends’, ‘Projects’, ‘Images’, and ‘Words’, which variable contributes most to a Kickstarter’s success?”, the most important characteristic is “Goal”. We came to this conclusion because of what our final graphs show after running multiple simulation models, leaving out a certain attribute. The final graphs show that the least accurate model, at roughly 60%, comes when the characteristic “Goal” is left out. On the other hand, “Duration” was a variable that should have been left out since our graph indicated that without it as an attribute, our accuracy of predicting a kickstarter’s success actually increased! Our model may have relied on “Duration” as a predictor too much, which led to these results, which shows that our analysis is limited in that we did not include all 35 columns of variables that were present in the dataset. If we were to do this data analysis again, we could include more or even all of the columns in the dataset to produce a more complete analysis of what characteristic makes Kickstarters the most successful. It would also be interesting if we compared the effectiveness of Kickstarters in comparison to other crowdfunding websites mentioned earlier like GoFundme and Crowd Supply."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Works Cited"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.) Falcon, Alvaris. “10 Crowdfunding Sites To Fuel Your Dream Project.” Hongkiat, 27 May 2019, https://hongkiat.com/blog/crowdfunding-sites/.\n",
    "\n",
    "2.) Crockett, Zachary. “Kickstarter: A Data Analysis.” The Hustle, 10 Feb. 2019, https://thehustle.co/archive/02102019d/.\n",
    "\n",
    "3.) Lamidi, Adebola, and Adebola Lamidi. “Predicting the Success of Kickstarter Campaigns.” Towards Data Science, Towards Data Science, 20 Sept. 2017, https://towardsdatascience.com/predicting-the-success-of-kickstarter-campaigns-3f4a976419b9."
   ]
  }
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