0 Preprocessing_encoding.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "source": [
    "import pandas as pd\r\n",
    "import seaborn as sns\r\n",
    "import numpy as np"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "source": [
    "df = pd.read_csv('data\\Kickstarter_projects_Feb19.csv')\r\n",
    "\r\n",
    "# remove duplicates \r\n",
    "df.drop_duplicates(keep='first', inplace = True)\r\n",
    "df = df.reset_index(drop=True)\r\n",
    "df.info()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 170730 entries, 0 to 170729\n",
      "Data columns (total 20 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   id             170730 non-null  int64  \n",
      " 1   name           170730 non-null  object \n",
      " 2   currency       170730 non-null  object \n",
      " 3   main_category  170730 non-null  object \n",
      " 4   sub_category   170730 non-null  object \n",
      " 5   launched_at    170730 non-null  object \n",
      " 6   deadline       170730 non-null  object \n",
      " 7   duration       170730 non-null  float64\n",
      " 8   goal_usd       170730 non-null  float64\n",
      " 9   city           170730 non-null  object \n",
      " 10  state          170730 non-null  object \n",
      " 11  country        170730 non-null  object \n",
      " 12  blurb_length   170730 non-null  int64  \n",
      " 13  name_length    170730 non-null  int64  \n",
      " 14  status         170730 non-null  object \n",
      " 15  start_month    170730 non-null  int64  \n",
      " 16  end_month      170730 non-null  int64  \n",
      " 17  start_Q        170730 non-null  object \n",
      " 18  end_Q          170730 non-null  object \n",
      " 19  usd_pledged    170730 non-null  float64\n",
      "dtypes: float64(3), int64(5), object(12)\n",
      "memory usage: 26.1+ MB\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "source": [
    "df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "           id                                              name currency  \\\n",
       "0  1687733153             Socks of Speed and Socks of Elvenkind      USD   \n",
       "1   227936657  Power Punch Boot Camp: An All-Ages Graphic Novel      GBP   \n",
       "2   454186436        Live Printing with SX8: \"Squeegee Pulp Up\"      USD   \n",
       "3   629469071                 Lost Dog Street Band's Next Album      USD   \n",
       "4   183973060                             Qto-X, a Tiny Lantern      USD   \n",
       "\n",
       "  main_category    sub_category          launched_at             deadline  \\\n",
       "0         games  Tabletop Games  2018-10-30 20:00:02  2018-11-15 17:59:00   \n",
       "1        comics     Comic Books  2018-08-06 10:00:43  2018-09-05 10:00:43   \n",
       "2       fashion         Apparel  2017-06-09 15:41:03  2017-07-09 15:41:03   \n",
       "3         music  Country & Folk  2014-09-25 18:46:01  2014-11-10 06:00:00   \n",
       "4    technology         Gadgets  2016-11-28 16:35:11  2017-01-27 16:35:11   \n",
       "\n",
       "   duration     goal_usd        city    state country  blurb_length  \\\n",
       "0      16.0   2000.00000     Menasha       WI      US            14   \n",
       "1      30.0   3870.99771  Shepperton  England      GB            24   \n",
       "2      30.0   1100.00000   Manhattan       NY      US            21   \n",
       "3      45.0   3500.00000   Nashville       TN      US            15   \n",
       "4      60.0  30000.00000        Troy       MI      US            15   \n",
       "\n",
       "   name_length      status  start_month  end_month start_Q end_Q  usd_pledged  \n",
       "0            7  successful           10         11      Q4    Q4   6061.00000  \n",
       "1            8  successful            8          9      Q3    Q3   3914.50512  \n",
       "2            7  successful            6          7      Q2    Q3   1110.00000  \n",
       "3            6  successful            9         11      Q3    Q4   4807.00000  \n",
       "4            4  successful           11          1      Q4    Q1  40368.00000  "
      ],
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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>currency</th>\n",
       "      <th>main_category</th>\n",
       "      <th>sub_category</th>\n",
       "      <th>launched_at</th>\n",
       "      <th>deadline</th>\n",
       "      <th>duration</th>\n",
       "      <th>goal_usd</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>blurb_length</th>\n",
       "      <th>name_length</th>\n",
       "      <th>status</th>\n",
       "      <th>start_month</th>\n",
       "      <th>end_month</th>\n",
       "      <th>start_Q</th>\n",
       "      <th>end_Q</th>\n",
       "      <th>usd_pledged</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1687733153</td>\n",
       "      <td>Socks of Speed and Socks of Elvenkind</td>\n",
       "      <td>USD</td>\n",
       "      <td>games</td>\n",
       "      <td>Tabletop Games</td>\n",
       "      <td>2018-10-30 20:00:02</td>\n",
       "      <td>2018-11-15 17:59:00</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2000.00000</td>\n",
       "      <td>Menasha</td>\n",
       "      <td>WI</td>\n",
       "      <td>US</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>successful</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>Q4</td>\n",
       "      <td>Q4</td>\n",
       "      <td>6061.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>227936657</td>\n",
       "      <td>Power Punch Boot Camp: An All-Ages Graphic Novel</td>\n",
       "      <td>GBP</td>\n",
       "      <td>comics</td>\n",
       "      <td>Comic Books</td>\n",
       "      <td>2018-08-06 10:00:43</td>\n",
       "      <td>2018-09-05 10:00:43</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3870.99771</td>\n",
       "      <td>Shepperton</td>\n",
       "      <td>England</td>\n",
       "      <td>GB</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>successful</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>Q3</td>\n",
       "      <td>Q3</td>\n",
       "      <td>3914.50512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>454186436</td>\n",
       "      <td>Live Printing with SX8: \"Squeegee Pulp Up\"</td>\n",
       "      <td>USD</td>\n",
       "      <td>fashion</td>\n",
       "      <td>Apparel</td>\n",
       "      <td>2017-06-09 15:41:03</td>\n",
       "      <td>2017-07-09 15:41:03</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1100.00000</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>NY</td>\n",
       "      <td>US</td>\n",
       "      <td>21</td>\n",
       "      <td>7</td>\n",
       "      <td>successful</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>Q2</td>\n",
       "      <td>Q3</td>\n",
       "      <td>1110.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>629469071</td>\n",
       "      <td>Lost Dog Street Band's Next Album</td>\n",
       "      <td>USD</td>\n",
       "      <td>music</td>\n",
       "      <td>Country &amp; Folk</td>\n",
       "      <td>2014-09-25 18:46:01</td>\n",
       "      <td>2014-11-10 06:00:00</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3500.00000</td>\n",
       "      <td>Nashville</td>\n",
       "      <td>TN</td>\n",
       "      <td>US</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>successful</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>Q3</td>\n",
       "      <td>Q4</td>\n",
       "      <td>4807.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>183973060</td>\n",
       "      <td>Qto-X, a Tiny Lantern</td>\n",
       "      <td>USD</td>\n",
       "      <td>technology</td>\n",
       "      <td>Gadgets</td>\n",
       "      <td>2016-11-28 16:35:11</td>\n",
       "      <td>2017-01-27 16:35:11</td>\n",
       "      <td>60.0</td>\n",
       "      <td>30000.00000</td>\n",
       "      <td>Troy</td>\n",
       "      <td>MI</td>\n",
       "      <td>US</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>successful</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>Q4</td>\n",
       "      <td>Q1</td>\n",
       "      <td>40368.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 69
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Model 1: Classification model \r\n",
    "- predict whether the project can be successfully funded\r\n",
    "- Dependent variable: status\r\n",
    "- Independent variable: main_category, duration, goal_usd, country, start_month\r\n",
    "\r\n",
    "## Model 2: Regression model \r\n",
    "- predict how much the project can be funded\r\n",
    "- Dependent variable: usd_pledged\r\n",
    "- Independent variable: main_category, duration, goal_usd, country, start_month\r\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "source": [
    "## Drop columns\r\n",
    "df.drop(['id','name','currency', 'sub_category', 'launched_at','deadline','city', 'state', 'blurb_length', \r\n",
    "'name_length', 'end_month', 'start_Q', 'end_Q'], axis=1, inplace = True)\r\n",
    "df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "  main_category  duration     goal_usd country      status  start_month  \\\n",
       "0         games      16.0   2000.00000      US  successful           10   \n",
       "1        comics      30.0   3870.99771      GB  successful            8   \n",
       "2       fashion      30.0   1100.00000      US  successful            6   \n",
       "3         music      45.0   3500.00000      US  successful            9   \n",
       "4    technology      60.0  30000.00000      US  successful           11   \n",
       "\n",
       "   usd_pledged  \n",
       "0   6061.00000  \n",
       "1   3914.50512  \n",
       "2   1110.00000  \n",
       "3   4807.00000  \n",
       "4  40368.00000  "
      ],
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>main_category</th>\n",
       "      <th>duration</th>\n",
       "      <th>goal_usd</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>start_month</th>\n",
       "      <th>usd_pledged</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>games</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2000.00000</td>\n",
       "      <td>US</td>\n",
       "      <td>successful</td>\n",
       "      <td>10</td>\n",
       "      <td>6061.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>comics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3870.99771</td>\n",
       "      <td>GB</td>\n",
       "      <td>successful</td>\n",
       "      <td>8</td>\n",
       "      <td>3914.50512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fashion</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1100.00000</td>\n",
       "      <td>US</td>\n",
       "      <td>successful</td>\n",
       "      <td>6</td>\n",
       "      <td>1110.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>music</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3500.00000</td>\n",
       "      <td>US</td>\n",
       "      <td>successful</td>\n",
       "      <td>9</td>\n",
       "      <td>4807.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>technology</td>\n",
       "      <td>60.0</td>\n",
       "      <td>30000.00000</td>\n",
       "      <td>US</td>\n",
       "      <td>successful</td>\n",
       "      <td>11</td>\n",
       "      <td>40368.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 79
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Label Encode for target variable"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "source": [
    "from sklearn.preprocessing import LabelEncoder\r\n",
    "\r\n",
    "lbl_encode = LabelEncoder()\r\n",
    "df['status'] = lbl_encode.fit_transform(df['status'])\r\n",
    "\r\n",
    "# Print out keys\r\n",
    "keys = lbl_encode.classes_\r\n",
    "values = lbl_encode.transform (lbl_encode.classes_)\r\n",
    "dictionary = dict (zip(keys, values))\r\n",
    "print(dictionary)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "{'failed': 0, 'successful': 1}\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Encode for independent categorical variable \r\n",
    " - Location label: We noticed that majority (two-third) of the data is in USA. The rest is scattered in different countries \r\n",
    "   which is a imbalance dataset if we sample for all countries. Therefore, we categorize to column 'US', 1-US and 0-Non-US\r\n",
    " - main_category & start_month: One-Hot Encode\r\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "source": [
    "# Country\r\n",
    "df['US_based'] = df['country'].apply(lambda x: 1 if x == 'US' else 0)\r\n",
    "df.drop(['country'], axis = 1, inplace= True)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "source": [
    "# main_category, start_month\r\n",
    "from sklearn.preprocessing import OneHotEncoder\r\n",
    "\r\n",
    "cat_col = df[['main_category', 'start_month']]\r\n",
    "enc=OneHotEncoder(drop='first')\r\n",
    "cat_col = enc.fit_transform(cat_col).toarray()\r\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "source": [
    "column_name = []\r\n",
    "for col in enc.get_feature_names():\r\n",
    "    column_name += [col[3:]]\r\n",
    "\r\n",
    "df2 = pd.DataFrame(cat_col,columns = column_name)\r\n",
    "df2.shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(170730, 25)"
      ]
     },
     "metadata": {},
     "execution_count": 83
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "source": [
    "final_df = pd.concat([df,df2],axis = 1)\r\n",
    "final_df"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "       main_category  duration      goal_usd  status  start_month  \\\n",
       "0              games      16.0    2000.00000       1           10   \n",
       "1             comics      30.0    3870.99771       1            8   \n",
       "2            fashion      30.0    1100.00000       1            6   \n",
       "3              music      45.0    3500.00000       1            9   \n",
       "4         technology      60.0   30000.00000       1           11   \n",
       "...              ...       ...           ...     ...          ...   \n",
       "170725          food      60.0   57858.66500       0           10   \n",
       "170726    technology      47.0  115717.33000       0           11   \n",
       "170727          food      30.0   30000.00000       0            2   \n",
       "170728           art      60.0    1200.00000       0            8   \n",
       "170729        crafts      60.0    7600.00000       0           12   \n",
       "\n",
       "         usd_pledged  US_based  comics  crafts  dance  ...    3    4    5  \\\n",
       "0        6061.000000         1     0.0     0.0    0.0  ...  0.0  0.0  0.0   \n",
       "1        3914.505120         0     1.0     0.0    0.0  ...  0.0  0.0  0.0   \n",
       "2        1110.000000         1     0.0     0.0    0.0  ...  0.0  0.0  0.0   \n",
       "3        4807.000000         1     0.0     0.0    0.0  ...  0.0  0.0  0.0   \n",
       "4       40368.000000         1     0.0     0.0    0.0  ...  0.0  0.0  0.0   \n",
       "...              ...       ...     ...     ...    ...  ...  ...  ...  ...   \n",
       "170725      1.224260         0     0.0     0.0    0.0  ...  0.0  0.0  0.0   \n",
       "170726    157.273939         0     0.0     0.0    0.0  ...  0.0  0.0  0.0   \n",
       "170727      0.000000         1     0.0     0.0    0.0  ...  0.0  0.0  0.0   \n",
       "170728    143.000000         1     0.0     0.0    0.0  ...  0.0  0.0  0.0   \n",
       "170729    311.000000         1     0.0     1.0    0.0  ...  0.0  0.0  0.0   \n",
       "\n",
       "          6    7    8    9   10   11   12  \n",
       "0       0.0  0.0  0.0  0.0  1.0  0.0  0.0  \n",
       "1       0.0  0.0  1.0  0.0  0.0  0.0  0.0  \n",
       "2       1.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "3       0.0  0.0  0.0  1.0  0.0  0.0  0.0  \n",
       "4       0.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "...     ...  ...  ...  ...  ...  ...  ...  \n",
       "170725  0.0  0.0  0.0  0.0  1.0  0.0  0.0  \n",
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     },
     "metadata": {},
     "execution_count": 84
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "source": [
    "final_df.drop (['main_category', 'start_month'],axis =1,  inplace = True)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "source": [
    "final_df.to_csv('processed_data.csv', index = False)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
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