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"# Predicting The Outcome of Kickstarter Projects\n",
"Author: Matthew Huh\n",
"\n",
"\n",
"## What is Kickstarter\n",
"\n",
"To put it in their own words “Kickstarter helps artists, musicians, filmmakers, designers, and other creators find the resources and support they need to make their ideas a reality. To date, tens of thousands of creative projects — big and small — have come to life with the support of the Kickstarter community.”\n",
"\n",
"It is a platform that allows creators to transform ideas through the use of crowdfunding from backers in order to create tangible products. Creators set deadlines with funding goals that need to be met in order for them to create their projects, as well as stretch goals that may be created if that goal is surpassed. There is no guarantee that the creator will successfully create their project, or even meet expectations after the funding has been received. In addition, pledges are between backers and the creator, so refunds are left up to the creators. There is a lot of risk involved, but that is the nature of undertaking a kickstarter project. \n",
"\n",
"## About the Data\n",
"\n",
"In this dataset lies a collection of 378,661 projects from April 2009 to January 2018 that contain a wide variety of project types and goals. The columns provided in the dataset are the\n",
"1. Project ID\n",
"2. Project name\n",
"3. Category\n",
"4. Main category\n",
"5. Currency type (USD, EUR, GBP, etc.)\n",
"6. Deadline\n",
"7. Goal\n",
"8. Launch date\n",
"9. Pledged amount\n",
"10. State of the project (Successful, failed, cancelled, live, suspended)\n",
"11. Number of backers\n",
"12. Country of origin\n",
"13. USD pledged\n",
"14. USD pledged real\n",
"15. USD goal real\n",
"\n",
"## Research Question\n",
"\n",
"Now, the goal of this project is to determine what factors ultimately determine the success (or failure) of a kickstarter project, given the data and tools at our disposal. So the columns that we want to focus on out of these are variables that the project designer is in control of\n",
"1. Main category (15 categories)\n",
"2. Duration (deadline - launch date)\n",
"3. Goal (USD goal real specifically)\n",
"4. What month to launch\n",
"5. What day of the month to launch\n",
"\n",
"A few extra variables that we will consider are \n",
"4. Year\n",
"5. Currency\n",
"6. Country\n",
"\n",
"The objective is to see if we can predict the state of the project given these predictors.\n",
"\n",
"## Packages"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
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"text": [
"C:\\Users\\mhuh22\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning:\n",
"\n",
"numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
"\n"
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"source": [
"# Basic import statements\n",
"import pandas as pd\n",
"import numpy as np\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"from IPython.core.display import HTML\n",
"import datetime\n",
"import timeit\n",
"\n",
"# Visualization packages\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from IPython.display import Image\n",
"%matplotlib inline\n",
"\n",
"# (Optional) Plotly packages\n",
"import plotly as py\n",
"import plotly.graph_objs as go\n",
"from plotly import tools\n",
"import cufflinks as cf\n",
"import ipywidgets as widgets\n",
"from scipy import special\n",
"py.offline.init_notebook_mode(connected=True)\n",
"\n",
"# Machine learning packages\n",
"from sklearn import ensemble, linear_model, preprocessing, tree\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.feature_selection import f_classif, RFECV, SelectKBest\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.metrics import classification_report, confusion_matrix\n",
"from sklearn.model_selection import cross_val_score, GridSearchCV, train_test_split\n",
"from sklearn.utils import resample"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Preview\n",
"\n",
"Let's begin by taking a took at our dataset. It contains a unique identifier for every project on the platform since it was created, and a few features that we can make use of in order to model the outcome."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ID</th>\n",
" <th>name</th>\n",
" <th>category</th>\n",
" <th>main_category</th>\n",
" <th>currency</th>\n",
" <th>deadline</th>\n",
" <th>goal</th>\n",
" <th>launched</th>\n",
" <th>pledged</th>\n",
" <th>state</th>\n",
" <th>backers</th>\n",
" <th>country</th>\n",
" <th>usd pledged</th>\n",
" <th>usd_pledged_real</th>\n",
" <th>usd_goal_real</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>138572</th>\n",
" <td>1703704063</td>\n",
" <td>drawing for dollars</td>\n",
" <td>Illustration</td>\n",
" <td>Art</td>\n",
" <td>USD</td>\n",
" <td>2009-05-03</td>\n",
" <td>20.0</td>\n",
" <td>2009-04-24 21:52:03</td>\n",
" <td>35.0</td>\n",
" <td>successful</td>\n",
" <td>3</td>\n",
" <td>US</td>\n",
" <td>35.0</td>\n",
" <td>35.0</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>213711</th>\n",
" <td>2089078683</td>\n",
" <td>New York Makes a Book!!</td>\n",
" <td>Journalism</td>\n",
" <td>Journalism</td>\n",
" <td>USD</td>\n",
" <td>2009-05-16</td>\n",
" <td>3000.0</td>\n",
" <td>2009-04-28 13:55:41</td>\n",
" <td>3329.0</td>\n",
" <td>successful</td>\n",
" <td>110</td>\n",
" <td>US</td>\n",
" <td>3329.0</td>\n",
" <td>3329.0</td>\n",
" <td>3000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>342226</th>\n",
" <td>813230527</td>\n",
" <td>Sponsor Dereck Blackburn (Lostwars) Artist in ...</td>\n",
" <td>Rock</td>\n",
" <td>Music</td>\n",
" <td>USD</td>\n",
" <td>2009-05-16</td>\n",
" <td>300.0</td>\n",
" <td>2009-04-29 05:26:32</td>\n",
" <td>15.0</td>\n",
" <td>failed</td>\n",
" <td>2</td>\n",
" <td>US</td>\n",
" <td>15.0</td>\n",
" <td>15.0</td>\n",
" <td>300.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28960</th>\n",
" <td>1147015301</td>\n",
" <td>\"All We Had\" Gets Into Cannes -- $10 or More G...</td>\n",
" <td>Documentary</td>\n",
" <td>Film & Video</td>\n",
" <td>USD</td>\n",
" <td>2009-05-20</td>\n",
" <td>300.0</td>\n",
" <td>2009-04-30 22:10:30</td>\n",
" <td>40.0</td>\n",
" <td>failed</td>\n",
" <td>4</td>\n",
" <td>US</td>\n",
" <td>40.0</td>\n",
" <td>40.0</td>\n",
" <td>300.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>196244</th>\n",
" <td>199916122</td>\n",
" <td>Mr. Squiggles</td>\n",
" <td>Illustration</td>\n",
" <td>Art</td>\n",
" <td>USD</td>\n",
" <td>2009-05-22</td>\n",
" <td>30.0</td>\n",
" <td>2009-05-12 23:39:58</td>\n",
" <td>0.0</td>\n",
" <td>failed</td>\n",
" <td>0</td>\n",
" <td>US</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>30.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ID name \\\n",
"138572 1703704063 drawing for dollars \n",
"213711 2089078683 New York Makes a Book!! \n",
"342226 813230527 Sponsor Dereck Blackburn (Lostwars) Artist in ... \n",
"28960 1147015301 \"All We Had\" Gets Into Cannes -- $10 or More G... \n",
"196244 199916122 Mr. Squiggles \n",
"\n",
" category main_category currency deadline goal \\\n",
"138572 Illustration Art USD 2009-05-03 20.0 \n",
"213711 Journalism Journalism USD 2009-05-16 3000.0 \n",
"342226 Rock Music USD 2009-05-16 300.0 \n",
"28960 Documentary Film & Video USD 2009-05-20 300.0 \n",
"196244 Illustration Art USD 2009-05-22 30.0 \n",
"\n",
" launched pledged state backers country \\\n",
"138572 2009-04-24 21:52:03 35.0 successful 3 US \n",
"213711 2009-04-28 13:55:41 3329.0 successful 110 US \n",
"342226 2009-04-29 05:26:32 15.0 failed 2 US \n",
"28960 2009-04-30 22:10:30 40.0 failed 4 US \n",
"196244 2009-05-12 23:39:58 0.0 failed 0 US \n",
"\n",
" usd pledged usd_pledged_real usd_goal_real \n",
"138572 35.0 35.0 20.0 \n",
"213711 3329.0 3329.0 3000.0 \n",
"342226 15.0 15.0 300.0 \n",
"28960 40.0 40.0 300.0 \n",
"196244 0.0 0.0 30.0 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Import data and sort by deadline date\n",
"kickstart_data = pd.read_csv(\"ks-projects-201801.csv\")\n",
"\n",
"# Drop all empty rows\n",
"kickstart_data.dropna()\n",
"\n",
"# Preview the data, ordered by deadline\n",
"kickstart_data.sort_values('deadline').head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(378661, 15)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check the size of our dataset\n",
"kickstart_data.shape"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"name 375764\n",
"category 159\n",
"main_category 15\n",
"currency 14\n",
"deadline 3164\n",
"launched 378089\n",
"state 6\n",
"country 23\n",
"dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Print out column names and # of unique values in each categorical variable\n",
"kickstart_data.select_dtypes(include=['object']).nunique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Cleaning"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Convert 'launched' and 'deadline' columns into dates\n",
"kickstart_data['launched'] = kickstart_data['launched'].astype('datetime64[ns]')\n",
"kickstart_data['deadline'] = pd.to_datetime(kickstart_data['deadline'])\n",
"kickstart_data['launched'] = pd.DatetimeIndex(kickstart_data.launched).normalize()\n",
"\n",
"# Project duration is more useful and less data\n",
"# than the launch date and deadline\n",
"kickstart_data['duration'] = kickstart_data['deadline'] - kickstart_data['launched']\n",
"kickstart_data['duration'] = (kickstart_data['duration'] / np.timedelta64(1, 'D')).astype(int)\n",
"\n",
"# Create year variable from the deadline\n",
"kickstart_data['year'] = kickstart_data['launched'].dt.year\n",
"kickstart_data['launch_month'] = kickstart_data['launched'].dt.month\n",
"kickstart_data['deadline_month'] = kickstart_data['deadline'].dt.month\n",
"kickstart_data['day'] = kickstart_data['launched'].dt.day\n",
"\n",
"# Drop the launch and duration now that we don't need them\n",
"kickstart_data.drop(['launched', 'deadline'], 1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Drop variables that have no impact on the outcome, and\n",
"# variables that have over 15 unique values\n",
"kickstart_data.drop(['ID', 'category', 'country', 'goal', 'pledged', 'usd pledged'], 1, inplace=True)\n",
"\n",
"# Rename usd_pledged_real and usd_goal_real to \n",
"# pledged and goal\n",
"kickstart_data = kickstart_data.rename(index=str, columns = {'usd_pledged_real': 'pledged', \n",
" 'usd_goal_real': 'goal', \n",
" 'main_category': 'category'})"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Clean the name column\n",
"kickstart_data['name'] = kickstart_data['name'].str.lower()\n",
"kickstart_data['name'] = kickstart_data['name'].str.replace('[^\\w\\s]','')\n",
"kickstart_data['name'] = kickstart_data['name'].str.strip()\n",
"\n",
"# Save name length as a new variable\n",
"kickstart_data['name_length'] = kickstart_data['name'].str.split(\" \").str.len()\n",
"\n",
"# Drop name from the dataset\n",
"kickstart_data.drop(['name'], 1, inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exploratory Data Analysis"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>backers</th>\n",
" <th>pledged</th>\n",
" <th>goal</th>\n",
" <th>duration</th>\n",
" <th>year</th>\n",
" <th>launch_month</th>\n",
" <th>deadline_month</th>\n",
" <th>day</th>\n",
" <th>name_length</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>378661.000000</td>\n",
" <td>3.786610e+05</td>\n",
" <td>3.786610e+05</td>\n",
" <td>378661.000000</td>\n",
" <td>378661.000000</td>\n",
" <td>378661.000000</td>\n",
" <td>378661.000000</td>\n",
" <td>378661.000000</td>\n",
" <td>378657.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>105.617476</td>\n",
" <td>9.058924e+03</td>\n",
" <td>4.545440e+04</td>\n",
" <td>34.481095</td>\n",
" <td>2014.247829</td>\n",
" <td>6.461550</td>\n",
" <td>6.720726</td>\n",
" <td>15.294142</td>\n",
" <td>5.69296</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>907.185035</td>\n",
" <td>9.097334e+04</td>\n",
" <td>1.152950e+06</td>\n",
" <td>65.909173</td>\n",
" <td>1.933293</td>\n",
" <td>3.330133</td>\n",
" <td>3.342550</td>\n",
" <td>8.808409</td>\n",
" <td>2.77218</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>1.000000e-02</td>\n",
" <td>1.000000</td>\n",
" <td>1970.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.000000</td>\n",
" <td>3.100000e+01</td>\n",
" <td>2.000000e+03</td>\n",
" <td>30.000000</td>\n",
" <td>2013.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>8.000000</td>\n",
" <td>3.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>12.000000</td>\n",
" <td>6.243300e+02</td>\n",
" <td>5.500000e+03</td>\n",
" <td>30.000000</td>\n",
" <td>2014.000000</td>\n",
" <td>7.000000</td>\n",
" <td>7.000000</td>\n",
" <td>15.000000</td>\n",
" <td>5.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>56.000000</td>\n",
" <td>4.050000e+03</td>\n",
" <td>1.550000e+04</td>\n",
" <td>37.000000</td>\n",
" <td>2016.000000</td>\n",
" <td>9.000000</td>\n",
" <td>10.000000</td>\n",
" <td>23.000000</td>\n",
" <td>8.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>219382.000000</td>\n",
" <td>2.033899e+07</td>\n",
" <td>1.663614e+08</td>\n",
" <td>16739.000000</td>\n",
" <td>2018.000000</td>\n",
" <td>12.000000</td>\n",
" <td>12.000000</td>\n",
" <td>31.000000</td>\n",
" <td>41.00000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" backers pledged goal duration \\\n",
"count 378661.000000 3.786610e+05 3.786610e+05 378661.000000 \n",
"mean 105.617476 9.058924e+03 4.545440e+04 34.481095 \n",
"std 907.185035 9.097334e+04 1.152950e+06 65.909173 \n",
"min 0.000000 0.000000e+00 1.000000e-02 1.000000 \n",
"25% 2.000000 3.100000e+01 2.000000e+03 30.000000 \n",
"50% 12.000000 6.243300e+02 5.500000e+03 30.000000 \n",
"75% 56.000000 4.050000e+03 1.550000e+04 37.000000 \n",
"max 219382.000000 2.033899e+07 1.663614e+08 16739.000000 \n",
"\n",
" year launch_month deadline_month day \\\n",
"count 378661.000000 378661.000000 378661.000000 378661.000000 \n",
"mean 2014.247829 6.461550 6.720726 15.294142 \n",
"std 1.933293 3.330133 3.342550 8.808409 \n",
"min 1970.000000 1.000000 1.000000 1.000000 \n",
"25% 2013.000000 4.000000 4.000000 8.000000 \n",
"50% 2014.000000 7.000000 7.000000 15.000000 \n",
"75% 2016.000000 9.000000 10.000000 23.000000 \n",
"max 2018.000000 12.000000 12.000000 31.000000 \n",
"\n",
" name_length \n",
"count 378657.00000 \n",
"mean 5.69296 \n",
"std 2.77218 \n",
"min 1.00000 \n",
"25% 3.00000 \n",
"50% 5.00000 \n",
"75% 8.00000 \n",
"max 41.00000 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Summary of the dataset\n",
"kickstart_data.describe()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x360 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Plot all of variables, and try to identify outliers# Plot \n",
"plt.rcParams['figure.figsize'] = [20,5]\n",
"\n",
"plt.subplot(1,4,1)\n",
"plt.title('Backers')\n",
"plt.xticks(rotation=60)\n",
"ax = sns.boxplot(x=\"category\", y=\"backers\", data=kickstart_data)\n",
"\n",
"plt.subplot(1,4,2)\n",
"plt.title('Pledged')\n",
"plt.xticks(rotation=60)\n",
"ax = sns.boxplot(x=\"category\", y=\"pledged\", data=kickstart_data)\n",
"\n",
"plt.subplot(1,4,3)\n",
"plt.title('Goal')\n",
"plt.xticks(rotation=60)\n",
"ax = sns.boxplot(x=\"category\", y=\"goal\", data=kickstart_data)\n",
"\n",
"plt.subplot(1,4,4)\n",
"plt.title('Duration')\n",
"plt.xticks(rotation=60)\n",
"ax = sns.boxplot(x=\"category\", y=\"duration\", data=kickstart_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Well, one of the great things about Kickstarter is that there are technically no limits to the scale of the projects that can emerge from the platform. Feel free to look at Star Citizen if you were wondering what project generated over 2 million dollars in pledges.\n",
"\n",
"<a href=\"https://www.kickstarter.com/projects/cig/star-citizen\">Star Citizen</a>\n",
"\n",
"\n",
"\n",
"So, the problem is that there are quite a lot of ambitious projects that drew in a substantial amount of backers with goals far above 75% of other projects, resulting in the messy distribution above. The duration variable looks like there are few enough genuine outliers for us to examine. As for the rest, it seems like we'll need to transform them before proceeding because there are simply too many outliers for us to ignore."
]
},
{
"cell_type": "code",
"execution_count": 10,
"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>category</th>\n",
" <th>currency</th>\n",
" <th>state</th>\n",
" <th>backers</th>\n",
" <th>pledged</th>\n",
" <th>goal</th>\n",
" <th>duration</th>\n",
" <th>year</th>\n",
" <th>launch_month</th>\n",
" <th>deadline_month</th>\n",
" <th>day</th>\n",
" <th>name_length</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>319002</th>\n",
" <td>Publishing</td>\n",
" <td>CHF</td>\n",
" <td>suspended</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1905.97</td>\n",
" <td>16739</td>\n",
" <td>1970</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2842</th>\n",
" <td>Film & Video</td>\n",
" <td>USD</td>\n",
" <td>canceled</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>5000.00</td>\n",
" <td>14867</td>\n",
" <td>1970</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>9.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48147</th>\n",
" <td>Art</td>\n",
" <td>USD</td>\n",
" <td>canceled</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>15000.00</td>\n",
" <td>14835</td>\n",
" <td>1970</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94579</th>\n",
" <td>Theater</td>\n",
" <td>USD</td>\n",
" <td>canceled</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>4000.00</td>\n",
" <td>14761</td>\n",
" <td>1970</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>9.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75397</th>\n",
" <td>Film & Video</td>\n",
" <td>USD</td>\n",
" <td>canceled</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>700.00</td>\n",
" <td>14750</td>\n",
" <td>1970</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>247913</th>\n",
" <td>Music</td>\n",
" <td>USD</td>\n",
" <td>canceled</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>10000.00</td>\n",
" <td>14733</td>\n",
" <td>1970</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>16.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>273779</th>\n",
" <td>Design</td>\n",
" <td>USD</td>\n",
" <td>canceled</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>500.00</td>\n",
" <td>14709</td>\n",
" <td>1970</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>200310</th>\n",
" <td>Film & Video</td>\n",
" <td>USD</td>\n",
" <td>failed</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>250000.00</td>\n",
" <td>92</td>\n",
" <td>2011</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38476</th>\n",
" <td>Technology</td>\n",
" <td>USD</td>\n",
" <td>failed</td>\n",
" <td>1</td>\n",
" <td>100.0</td>\n",
" <td>65000.00</td>\n",
" <td>92</td>\n",
" <td>2011</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>15</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>305126</th>\n",
" <td>Film & Video</td>\n",
" <td>USD</td>\n",
" <td>failed</td>\n",
" <td>4</td>\n",
" <td>1530.0</td>\n",
" <td>20000.00</td>\n",
" <td>92</td>\n",
" <td>2011</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" category currency state backers pledged goal \\\n",
"319002 Publishing CHF suspended 0 0.0 1905.97 \n",
"2842 Film & Video USD canceled 0 0.0 5000.00 \n",
"48147 Art USD canceled 0 0.0 15000.00 \n",
"94579 Theater USD canceled 0 0.0 4000.00 \n",
"75397 Film & Video USD canceled 0 0.0 700.00 \n",
"247913 Music USD canceled 0 0.0 10000.00 \n",
"273779 Design USD canceled 0 0.0 500.00 \n",
"200310 Film & Video USD failed 0 0.0 250000.00 \n",
"38476 Technology USD failed 1 100.0 65000.00 \n",
"305126 Film & Video USD failed 4 1530.0 20000.00 \n",
"\n",
" duration year launch_month deadline_month day name_length \n",
"319002 16739 1970 1 10 1 4.0 \n",
"2842 14867 1970 1 9 1 9.0 \n",
"48147 14835 1970 1 8 1 8.0 \n",
"94579 14761 1970 1 6 1 9.0 \n",
"75397 14750 1970 1 5 1 3.0 \n",
"247913 14733 1970 1 5 1 16.0 \n",
"273779 14709 1970 1 4 1 4.0 \n",
"200310 92 2011 1 4 2 3.0 \n",
"38476 92 2011 3 6 15 4.0 \n",
"305126 92 2011 1 4 2 4.0 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Order by duration\n",
"kickstart_data.sort_values('duration', ascending = False).head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we had expected earlier, it seems as though out of 370,000+ projects, only 7 of them seem to have a suspiciously long duration at well over 10,000 days. The rest of the projects in this dataset last no more than 92 days. It seems as though the reason for this difference is because they have a start date with the year, 1970. Kickstarter was launched in 2009, so this is clearly an error. Since this is an extremely small subset of rows from our dataframe, we can safely remove these rows from the dataset before proceeding."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# Drop our 7 outliers from the dataset based on duration\n",
"kickstart_data.drop(kickstart_data.sort_values('duration', ascending = False).head(7).index, axis=0, inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# Applying log transformations to 'goal' column\n",
"kickstart_data['goal'] = np.log(kickstart_data['goal'] + 1)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Plot all of variables, and try to identify outliers\n",
"plt.rcParams['figure.figsize'] = [15,5]\n",
"\n",
"plt.subplot(1,2,1)\n",
"plt.title('Goal')\n",
"plt.xticks(rotation=70)\n",
"ax = sns.boxplot(x=\"category\", y=\"goal\", data=kickstart_data)\n",
"\n",
"plt.subplot(1,2,2)\n",
"plt.title('Duration')\n",
"plt.xticks(rotation=70)\n",
"ax = sns.boxplot(x=\"category\", y=\"duration\", data=kickstart_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So after removing our outliers from the 'duration' column, and applying a log transformation to the 'backers', 'pledged', and 'goal' columns, we can see a somewhat normal distribution."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Creates a correlation matrix among the predictor variables\n",
"plt.rcParams['figure.figsize'] = [5,5]\n",
"\n",
"correlation_martix = kickstart_data.corr()\n",
"sns.heatmap(correlation_martix, vmax = 1, square = True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There appears to be a very high degree of similarity between the number of backers, and total monetary amount contributed. This makes sense if we assume that each backer donates roughly the same amount, leading to two columns that have quite a lot in common. We can try removing one or the other later if we need to determine which of these factors is more important than the other. For now, we'll use both predictors in our model."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# Drop backers and pledged from the dataset since they are dependent variables\n",
"kickstart_data.drop(['backers', 'pledged'], 1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(-1.2248786255360546,\n",
" 1.2131965717027007,\n",
" -1.212274366954329,\n",
" 1.2109555139336867)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.rcParams['figure.figsize'] = [10,10]\n",
"\n",
"# Creates a pie chart that represents the different categories in our dataset\n",
"plt.pie(kickstart_data['category'].value_counts(), explode = np.ones(15)*0.1, labels=kickstart_data['category'].unique(),\n",
" labeldistance = 1.1, autopct='%1.1f%%', shadow=True, startangle=140)\n",
"plt.axis('equal')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Kickstarter divides their main categories into 14 distinct groups of unequal size. Publishing, at 16.8% (63,000 projects), is the largest category in the dataset, while journalism is a little under 1% (3768 projects). While the distribution is unfortunately unbalanced, that is the data that we have, and we still have plenty of observations for each category to work with."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"data": [
{
"labels": [
"Publishing",
"Film & Video",
"Music",
"Food",
"Design",
"Crafts",
"Games",
"Comics",
"Fashion",
"Theater",
"Art",
"Photography",
"Technology",
"Dance",
"Journalism"
],
"type": "pie",
"uid": "510b3274-7d1a-4de0-84ea-cfef09382773",
"values": [
63583,
51917,
39873,
35231,
32569,
30069,
28152,
24602,
22816,
10912,
10819,
10779,
8809,
4755,
3768
]
}
],
"layout": {
"autosize": false,
"height": 600,
"title": "Kickstarter Projects by Category",
"width": 800
}
},
"text/html": [
"<div id=\"89be15bb-b66b-4c7e-9d46-a0fd9201d3c3\" style=\"height: 600px; width: 800px;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"89be15bb-b66b-4c7e-9d46-a0fd9201d3c3\", [{\"labels\": [\"Publishing\", \"Film & Video\", \"Music\", \"Food\", \"Design\", \"Crafts\", \"Games\", \"Comics\", \"Fashion\", \"Theater\", \"Art\", \"Photography\", \"Technology\", \"Dance\", \"Journalism\"], \"values\": [63583, 51917, 39873, 35231, 32569, 30069, 28152, 24602, 22816, 10912, 10819, 10779, 8809, 4755, 3768], \"type\": \"pie\", \"uid\": \"510b3274-7d1a-4de0-84ea-cfef09382773\"}], {\"autosize\": false, \"height\": 600, \"title\": \"Kickstarter Projects by Category\", \"width\": 800}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
],
"text/vnd.plotly.v1+html": [
"<div id=\"89be15bb-b66b-4c7e-9d46-a0fd9201d3c3\" style=\"height: 600px; width: 800px;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"89be15bb-b66b-4c7e-9d46-a0fd9201d3c3\", [{\"labels\": [\"Publishing\", \"Film & Video\", \"Music\", \"Food\", \"Design\", \"Crafts\", \"Games\", \"Comics\", \"Fashion\", \"Theater\", \"Art\", \"Photography\", \"Technology\", \"Dance\", \"Journalism\"], \"values\": [63583, 51917, 39873, 35231, 32569, 30069, 28152, 24602, 22816, 10912, 10819, 10779, 8809, 4755, 3768], \"type\": \"pie\", \"uid\": \"510b3274-7d1a-4de0-84ea-cfef09382773\"}], {\"autosize\": false, \"height\": 600, \"title\": \"Kickstarter Projects by Category\", \"width\": 800}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# This pie chart looks a lot better, but it does not play well with github due to its interactivity\n",
"# To view plotly images, please copy/paste this url to https://nbviewer.jupyter.org/\n",
"\n",
"# Pass in values for our pie chart\n",
"trace = go.Pie(labels=kickstart_data['category'].unique(), values = kickstart_data['category'].value_counts())\n",
"\n",
"# Create the layout\n",
"layout = go.Layout(\n",
" title = 'Kickstarter Projects by Category',\n",
" height = 600,\n",
" width = 800,\n",
" autosize = False\n",
")\n",
"\n",
"# Construct the chart\n",
"fig = go.Figure(data = [trace], layout = layout)\n",
"py.offline.iplot(fig, filename ='cufflinks/simple')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1a8f17ca860>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Plot kickstarter states\n",
"plt.rcParams['figure.figsize'] = [12,6]\n",
"plt.title('Kickstarter States')\n",
"plt.xlabel('State count')\n",
"kickstart_data['state'].value_counts().plot(kind='barh')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So in this dataset, the six possible states for a project are suspended, live, undefined, canceled, successful, and failed. The first three states have very few values, relative to what we really want to know - if a project succeeds or fails. In addition, if we do want to utilize these three columns, we would need to look at the following factors.\n",
"1. A follow up to investigate the reason for a project to be suspended\n",
"2. Live projects can fall into either category - success or failure\n",
"3. Undefined is a result that we can't interpret or use.\n",
"\n",
"Success and failure are easy to define, and can be recoded as 1 and 0 so that we can predict the outcome as an easily modelable binary variable. Cancellation is generally caused by the inability to meet a project's demands for a multitude of reasons leading to lack of funding, planning, or budgeting.. It would simplify the following models to treat cancellation the same as failure, 0."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# Remove 'suspended', 'live', and 'undefined' states from the dataset\n",
"kickstart_data = kickstart_data[kickstart_data.state != ('suspended', 'live', 'undefined')]\n",
"\n",
"# Consolidate 'canceled' and 'failed' into one category\n",
"kickstart_data['state'] = np.where(kickstart_data.state == 'successful', 1, 0)\n",
"success_rate = kickstart_data['state']"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(-1.1154681955525665,\n",
" 1.1143348565040996,\n",
" -1.1132377615610414,\n",
" 1.117099503783597)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Constructs a pie chart displaying our final successful and unsuccessful values\n",
"plt.pie(np.bincount(success_rate), labels=['unsuccessful', 'successful'],\n",
" autopct='%1.1f%%', shadow=True, startangle=140)\n",
"plt.axis('equal')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After removing 3 of the project states, and combining cancelled and failed into 1 state, we end up with 35.4% projects succeeding, and 67.6% failing. The outcome is a bit unbalanced, so it will be necessary to keep in mind that there will most likely be some bias in the predictive variables towards unsuccessful projects."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"plt.rcParams['figure.figsize'] = [15,5]\n",
"\n",
"def percent_extraction (feature):\n",
" # Finds percentages of all values in a category\n",
" container = kickstart_data.groupby([feature, 'state'])[feature].count().unstack('state')\n",
" totals = kickstart_data.groupby([feature])['state'].count()\n",
" container = container.div(totals, axis = 0)\n",
" \n",
" # Plots the bar chart\n",
" container.columns = ['unsuccessful', 'successful']\n",
" container.plot(kind='bar', stacked=True)\n",
" plt.title('Kickstarter success by {}'.format(feature))\n",
" plt.axhline(y=0.646, color='r', ls='--')\n",
" plt.legend(bbox_to_anchor=(1, 1), loc=2, borderaxespad=0.)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# View success rate bu year, launch month, deadline month, and day of month\n",
"percent_extraction('year')\n",
"percent_extraction('launch_month')\n",
"percent_extraction('deadline_month')\n",
"percent_extraction('day')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-block alert-warning\">\n",
"The dotted red line above represents the line between success/ failure in the entire dataset.\n",
"</div>\n",
"\n",
"Based on the observations from kickstarter success rates with respect to year, month, and day, we can observe some trends. \n",
"\n",
"By examining the success rates with respect to year the first thing that should scream out is that the success rate in 2018 is 0. This should not be very surprising given that the year column has been extracted from the deadline date, and that this dataset terminates in January 2018. It would make sense to remove projects with an end date in 2018 given the limitations of this dataset.\n",
"\n",
"What we can see in our month columns is that month doesn't quite have too much of an impact, but it seems as though the worst month to launch is December and worst to end on is January. This is most likely due to the holiday season. If you want every edge you can as a creator, make sure to avoid the season.\n",
"\n",
"Once again, not too much variance with the day, but the best time to try appears to be on the 1st."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# Drop rows where the deadline is in 2018\n",
"kickstart_data[kickstart_data.year != 2018];"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"# Normalize year column for modelling by changing from 2009 - 2018 to 0 - 9\n",
"kickstart_data['year'] = kickstart_data['year'] - 2009"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"percent_extraction('category')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Earlier in the page, we investigated the size of each category in the dataset, and noticed that the groups are not equal, meaning some groups have a higher impact on the average outcome than others. Above, we see that the categories do not mirror the average results with some categories being much more successful than not, such as dance and theater, while certain others don't fare quite as well such as journalism and technology."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"currency\n",
"AUD 7950\n",
"CAD 14962\n",
"CHF 767\n",
"DKK 1129\n",
"EUR 17405\n",
"GBP 34132\n",
"HKD 618\n",
"JPY 40\n",
"MXN 1752\n",
"NOK 722\n",
"NZD 1475\n",
"SEK 1788\n",
"SGD 555\n",
"USD 295359\n",
"dtype: int64"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kickstart_data.groupby(['currency']).size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As an American company and one of the largest countries in the list, it's not too surprising to see that the vast majority of projects are backed by the US dollar. Given the vast difference in population size of the projects, this may not be the most useful category for prediction, but we'll keep it for now and see if it makes it past the feature selection process."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"percent_extraction('currency')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Well isn't that ... not all that interesting? It seems like only 2 of the currencies have a success rate greater than average, one of which is the US, which heavily skews the result. The success rate is quite a bit lower in the majority of other available currencies, but it hardly has an impact on the average."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"# Create separate dataframe for numerical values\n",
"numerical = kickstart_data.select_dtypes(exclude=['object'])\n",
"\n",
"# Create separate dataframe for categorical values\n",
"categorical = pd.get_dummies(kickstart_data.select_dtypes(include='object'))\n",
"\n",
"# Create the combined dataframe\n",
"kickstart_data = pd.concat([numerical, categorical], axis=1, sort = False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to make use of the categorical variables in the dataset, category and country, they'll first need to be recoded to 1s and 0s so that they can be interpretted."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x1080 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.rcParams['figure.figsize'] = [15,15]\n",
"\n",
"# Creates a correlation matrix among the predictor variables\n",
"correlation_martix = kickstart_data.corr()\n",
"sns.heatmap(correlation_martix, vmax = 1, square = True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By re-examining our correlation matrix after recoding the categorical variables (category and currency), the results are the same as before, except we can also confirm that the different values in category and country have no correlation to each other, or any other variable.\n",
"\n",
"Now that we've examined and prepared our dataset, it's time to get to the fun part, modeling."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Modelling"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Feature Selection\n",
"\n",
"In order to expedite the modelling process, remove any potential overfitting, and decrease runtimes, we will be reducing the size of the dataset and apply feature selection. To maintain a sufficient sample size, we will still be keeping ⅓ of the dataset, and as for feature selection, we will be limiting it to the 10 (of 34) best features using the SelectKBest algorithm."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"# Separate majority and minority outcomes\n",
"df_majority = kickstart_data[kickstart_data.state==0]\n",
"df_minority = kickstart_data[kickstart_data.state==1]\n",
"\n",
"# Upsample minority class\n",
"df_minority_upsampled = resample(df_minority, \n",
" replace=True,\n",
" n_samples=kickstart_data.state.value_counts().max(),\n",
" random_state=123)\n",
"\n",
"# Combine majority class with upsampled minority class\n",
"df_upsampled = pd.concat([df_majority, df_minority_upsampled])"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"# Drop any remaining issues before the modelling phase\n",
"df_upsampled.dropna(inplace=True)\n",
"\n",
"# Samples data\n",
"df_upsampled = df_upsampled.sample(frac=0.33, replace=True)\n",
"\n",
"# Create a series with the target data\n",
"y = df_upsampled['state']\n",
"\n",
"# Remove the 'state' column from our dataset\n",
"# since it's not a predictor, it's the outcome\n",
"df_upsampled.drop(['state'], axis = 1, inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"min_max_scaler = preprocessing.MinMaxScaler()\n",
"np_scaled = min_max_scaler.fit_transform(df_upsampled)\n",
"df_normalized = pd.DataFrame(np_scaled)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"# Select 10 best features\n",
"kclassifier = SelectKBest(f_classif, k=10)\n",
"X = kclassifier.fit_transform(df_upsampled, y)\n",
"\n",
"# Retrieve feature names\n",
"mask = kclassifier.get_support()\n",
"column_names = df_upsampled.columns[mask]\n",
"\n",
"# Create new dataframe with k best features\n",
"X = pd.DataFrame(data=X, columns=column_names)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"# Divide the dataset into training and testing datasets\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify = y, random_state=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Logistic Regression Model\n",
"\n",
"Let's start out with one of the simplest and quickest models for predicting outcomes, logistic regression. This model will determine the correlation between each of our predictor variables and outcome, then weigh the correlations depending on the strength of the relationships in order to construct the line of best fit. \n",
"\n",
"We'll start with a standard logistic regression model with L2 (Ridge) penalty, then test if using an L1 (Lasso) penalty and modifying the cost penalty will have any improvement in our model. Lastly, we'll examine if implementing feature selection will improve the accuracy by removing any overfitting from an excess number of variables."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'C': 1, 'penalty': 'l1'}\n",
"0.6250563914762625\n"
]
}
],
"source": [
"lr = LogisticRegression(random_state=0)\n",
"\n",
"# Parameters for logistic regression model\n",
"param_grid = {'penalty': ['l1', 'l2'],\n",
" 'C': [1, 10, 100, 1000]}\n",
"\n",
"# Run grid search to find ideal parameters\n",
"start_time = timeit.default_timer()\n",
"lr_grid = GridSearchCV(lr, param_grid, cv=5, n_jobs=-2)\n",
"elapsed_lr = timeit.default_timer() - start_time\n",
"\n",
"# Fitting the parameters to the data\n",
"lr_grid.fit(X_train, y_train)\n",
"\n",
"# Reports best model parameters and accuracy\n",
"print(lr_grid.best_params_)\n",
"print(lr_grid.best_score_)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"# Store the predicted values in a dataframe\n",
"y_pred = lr_grid.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[15164, 9028],\n",
" [ 9070, 15188]], dtype=int64)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a confusion matrix\n",
"confusion_matrix(y_test, y_pred)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.63 0.63 0.63 24192\n",
" 1 0.63 0.63 0.63 24258\n",
"\n",
"avg / total 0.63 0.63 0.63 48450\n",
"\n"
]
}
],
"source": [
"# Print model statisitcs\n",
"print(classification_report(y_test, y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cross Validation Accuracy Scores - Training Set: 0.62560(+/- 0.01)\n"
]
}
],
"source": [
"# Checks the accuracy of the model using cross validation\n",
"lr_cross_val = cross_val_score(lr_grid, X, y, cv=5, n_jobs=-2) \n",
"print('Cross Validation Accuracy Scores - Training Set: {:.5f}(+/- {:.2f})'.format(lr_cross_val.mean(), lr_cross_val.std()*2))"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimal number of features: 10\n",
"Selected features: ['goal', 'duration', 'year', 'name_length', 'category_Comics', 'category_Music', 'category_Technology', 'category_Theater', 'currency_EUR', 'currency_USD']\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Create the RFE object and compute a cross-validated score.\n",
"# The \"accuracy\" scoring is proportional to the number of correct classifications\n",
"rfecv = RFECV(estimator=LogisticRegression(), step=1, cv=5, scoring='accuracy', n_jobs=-2)\n",
"rfecv.fit(X, y)\n",
"\n",
"print(\"Optimal number of features: %d\" % rfecv.n_features_)\n",
"print('Selected features: %s' % list(X.columns[rfecv.support_]))\n",
"\n",
"# Plot number of features VS. cross-validation scores\n",
"plt.figure(figsize=(10,6))\n",
"plt.xlabel(\"Number of features selected\")\n",
"plt.ylabel(\"Cross validation score (nb of correct classifications)\")\n",
"plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Analysis\n",
"\n",
"From our results, it seems as though the strongest predictors for the outcome based on logistic regression is the goal, followed by the duration, year, name length. It is able to predict the outcome 66.8% of the time in the testing dataset. Not bad, but let’s see if our ensemble methods can fare any better."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Random Forest Classifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The second model that I will utilize is the random forest model, aka \"The Black Box\". This is a far more complex and opaque model than logistic regression, and many other models. The reason that we want to try using a random forest classifier is because this model constructs a multitude of decision trees that randomly classifies subsets of the data into bins. This ensemble method runs these in parallel and vote to determine the single best model. "
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
" max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=-2,\n",
" oob_score=False, random_state=None, verbose=0,\n",
" warm_start=False)"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Constructing the shape of our decision tree\n",
"rfc = RandomForestClassifier(n_jobs=-2)\n",
"\n",
"# Fit model to data\n",
"rfc.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'max_depth': 12, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'n_estimators': 50}\n",
"0.6584224539801325\n"
]
}
],
"source": [
"# Setting up parameters for the random forest\n",
"param_grid = {'max_features': ['sqrt'],\n",
" 'n_estimators' : [20,30,40,50],\n",
" 'max_depth' : [4, 8, 12],\n",
" 'min_samples_leaf' : [4, 8, 12]}\n",
"\n",
"# Run grid search to find ideal parameters\n",
"rfc_grid = GridSearchCV(rfc, param_grid, cv=5, n_jobs=-2)\n",
"\n",
"# Fit the best parameters to our model\n",
"start_time = timeit.default_timer()\n",
"rfc_grid.fit(X_train, y_train)\n",
"elapsed_rfc = timeit.default_timer() - start_time\n",
"\n",
"# Return model scores\n",
"print(rfc_grid.best_params_)\n",
"print(rfc_grid.best_score_)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"# Predict outcome using random forest model\n",
"y_pred = rfc_grid.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[15269, 8923],\n",
" [ 7732, 16526]], dtype=int64)"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a confusion matrix\n",
"confusion_matrix(y_test, y_pred)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.66 0.63 0.65 24192\n",
" 1 0.65 0.68 0.66 24258\n",
"\n",
"avg / total 0.66 0.66 0.66 48450\n",
"\n"
]
}
],
"source": [
"# Print model statisitcs\n",
"print(classification_report(y_test, y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cross Validation Accuracy Scores - Training Set: 0.65850(+/- 0.01)\n"
]
}
],
"source": [
"# Cross validation test with 10 samples\n",
"rfc_cross_val = cross_val_score(rfc_grid, X_train, y_train, cv=5, n_jobs=-2)\n",
"print('Cross Validation Accuracy Scores - Training Set: {:.5f}(+/- {:.2f})'.format(rfc_cross_val.mean(), rfc_cross_val.std()*2))"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.rcParams['figure.figsize'] = [10,5]\n",
"\n",
"# Plot feature importances for random forest model\n",
"rfc_features = pd.Series(rfc.feature_importances_, X_train.columns).sort_values(ascending=False).head(15)\n",
"rfc_features.plot(kind='bar', title = 'Feature importance')\n",
"plt.ylabel('Feature Importance Score')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Analysis\n",
"\n",
"With our random forest classifier, we are able to see that once again, the goal is the most important feature for determining the success of a project, followed by the duration, name length, and year. Our model did take a lot longer to run as the best model demanded a large number of estimators, 50, and a fairly deep length at up to 12 splits."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Gradient Boosting Classifier\n",
"\n",
"Now for the last model in this project, gradient boosting. Compared to the previous random forest mode, the gradient boosting model instead runs weaker models in succession and build upon the last. What we hope to see is that our relatively weak predictors can be fixed to allow for a much stronger predictive model in the end."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
" learning_rate=0.1, loss='deviance', max_depth=3,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=100,\n",
" presort='auto', random_state=None, subsample=1.0, verbose=0,\n",
" warm_start=False)"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Creating the gradient boosting classifer\n",
"gbc = ensemble.GradientBoostingClassifier()\n",
"\n",
"gbc.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best parameters:\n",
"{'loss': 'deviance', 'max_depth': 12, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'n_estimators': 400}\n",
"Best Score:\n",
"0.6782987907898345\n"
]
}
],
"source": [
"# Parameters for gradient boosting classifier\n",
"param_grid = {'loss':['deviance'],\n",
" 'max_features': ['sqrt'],\n",
" 'n_estimators': [100,200,400],\n",
" 'max_depth': [4, 8, 12],\n",
" \"min_samples_leaf\" : [4, 8, 12]}\n",
"\n",
"# Run grid search to find ideal parameters\n",
"gbc_grid = GridSearchCV(gbc, param_grid = param_grid, n_jobs=-2)\n",
"\n",
"# Initialize and fit the model.\n",
"start_time = timeit.default_timer()\n",
"gbc_grid.fit(X_train, y_train)\n",
"elapsed_gbc = timeit.default_timer() - start_time\n",
"\n",
"# Return best parameters and best score\n",
"print('Best parameters:')\n",
"print(gbc_grid.best_params_)\n",
"print('Best Score:')\n",
"print(gbc_grid.best_score_)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"# Predict outcome using gradient boosting classifier\n",
"y_pred = rfc_grid.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[15269, 8923],\n",
" [ 7732, 16526]], dtype=int64)"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a confusion matrix\n",
"confusion_matrix(y_test, y_pred)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.66 0.63 0.65 24192\n",
" 1 0.65 0.68 0.66 24258\n",
"\n",
"avg / total 0.66 0.66 0.66 48450\n",
"\n"
]
}
],
"source": [
"# Print model statisitcs\n",
"print(classification_report(y_test, y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cross Validation Accuracy Scores - Training Set: 0.68672(+/- 0.01)\n"
]
}
],
"source": [
"# Cross validation test\n",
"gbc_cross_val = cross_val_score(gbc_grid, X_train, y_train, cv=5, n_jobs=-2)\n",
"print('Cross Validation Accuracy Scores - Training Set: {:.5f}(+/- {:.2f})'.format(gbc_cross_val.mean(), gbc_cross_val.std()*2))"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Plot feature importances for gradient boosting classifier\n",
"gbc_features = pd.Series(gbc.feature_importances_, X_train.columns).sort_values(ascending=False).head(15)\n",
"gbc_features.plot(kind='bar', title = 'Feature importance')\n",
"plt.ylabel('Feature Importance Score')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Analysis\n",
"\n",
"So, it seems as though the longer the model runs, the more accurate it becomes. In our gradient boosting classifier, we have the highest accuracy at 69.2%, but also the longest runtime. The order of feature importance is a little different here as our gradient boosting classifier values duration more than goal, then year and name length again. Unlike the other two models though, gradient boosting classifier has more emphasis on our weaker predictors like category and currency.\n",
"\n",
"# Model Comparison"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Logistic regression accuracy: 0.62560(+/- 0.01)\n",
"Logistic regression runtime: 0.0001336549999990666 \n",
"\n",
"Random forest accuracy: 0.65850(+/- 0.01)\n",
"Random forest runtime: 166.19763523700001 \n",
"\n",
"Gradient boosting classifier accuracy: 0.68672(+/- 0.01)\n",
"Gradient boosting classifier runtime: 1286.8462718819997 \n"
]
}
],
"source": [
"print('Logistic regression accuracy: {:.5f}(+/- {:.2f})'.format(lr_cross_val.mean(), lr_cross_val.std()*2))\n",
"print('Logistic regression runtime: {} \\n'.format(elapsed_lr))\n",
"print('Random forest accuracy: {:.5f}(+/- {:.2f})'.format(rfc_cross_val.mean(), rfc_cross_val.std()*2))\n",
"print('Random forest runtime: {} \\n'.format(elapsed_rfc))\n",
"print('Gradient boosting classifier accuracy: {:.5f}(+/- {:.2f})'.format(gbc_cross_val.mean(), gbc_cross_val.std()*2))\n",
"print('Gradient boosting classifier runtime: {} '.format(elapsed_gbc))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conclusion\n",
"\n",
"All 3 of the models in this project agree that the goal amount and duration are the most important factors to determine the success of a project. Now, all of these variables do play a part in determining the success of a project, so it is still in the creator's best interest to acknowledge the outcome in relation to these variables.\n",
"\n",
"# Source:\n",
"Kickstarter Data Page - \n",
"https://www.kaggle.com/kemical/kickstarter-projects/data"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
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