Random_Forest_Hybrid_Method.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7238d700-f596-4774-9ffc-569779b832fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "\n",
    "# Load your dataset\n",
    "df = pd.read_csv('startups_data_cleaned_step1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4e9b57ad-891f-4728-ac8f-848510e39ec0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12306 entries, 0 to 12305\n",
      "Data columns (total 39 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   name                  12306 non-null  object \n",
      " 1   market                12306 non-null  object \n",
      " 2   funding_total_usd     12306 non-null  float64\n",
      " 3   country_code          12306 non-null  object \n",
      " 4   state_code            12301 non-null  object \n",
      " 5   region                12306 non-null  object \n",
      " 6   city                  12291 non-null  object \n",
      " 7   funding_rounds        12306 non-null  int64  \n",
      " 8   founded_at            12306 non-null  object \n",
      " 9   founded_month         12306 non-null  int64  \n",
      " 10  founded_quarter       12306 non-null  int64  \n",
      " 11  founded_year          12306 non-null  int64  \n",
      " 12  first_funding_at      12306 non-null  object \n",
      " 13  last_funding_at       12306 non-null  object \n",
      " 14  seed                  12306 non-null  int64  \n",
      " 15  venture               12306 non-null  int64  \n",
      " 16  equity_crowdfunding   12306 non-null  int64  \n",
      " 17  undisclosed           12306 non-null  int64  \n",
      " 18  convertible_note      12306 non-null  int64  \n",
      " 19  debt_financing        12306 non-null  int64  \n",
      " 20  angel                 12306 non-null  int64  \n",
      " 21  grant                 12306 non-null  int64  \n",
      " 22  private_equity        12306 non-null  int64  \n",
      " 23  post_ipo_equity       12306 non-null  int64  \n",
      " 24  post_ipo_debt         12306 non-null  int64  \n",
      " 25  secondary_market      12306 non-null  int64  \n",
      " 26  product_crowdfunding  12306 non-null  int64  \n",
      " 27  round_A               12306 non-null  int64  \n",
      " 28  round_B               12306 non-null  int64  \n",
      " 29  round_C               12306 non-null  int64  \n",
      " 30  round_D               12306 non-null  int64  \n",
      " 31  round_E               12306 non-null  int64  \n",
      " 32  round_F               12306 non-null  int64  \n",
      " 33  round_G               12306 non-null  int64  \n",
      " 34  round_H               12306 non-null  int64  \n",
      " 35  category_count        12306 non-null  int64  \n",
      " 36  clean_name            12306 non-null  object \n",
      " 37  is_operating          12306 non-null  int64  \n",
      " 38  category_updated      12306 non-null  object \n",
      "dtypes: float64(1), int64(27), object(11)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e259f7be-b1fa-467e-89c3-97e5f0b779f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "462a9ccb-b4b9-49e9-ba9c-3bf70f368d80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create copy of dataframe\n",
    "df_processed = df.copy()\n",
    "\n",
    "# Convert date columns to datetime\n",
    "date_columns = ['founded_at', 'first_funding_at', 'last_funding_at']\n",
    "for col in date_columns:\n",
    "    df_processed[col] = pd.to_datetime(df_processed[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0a763568-12b0-440e-8972-6e7845da0403",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create temporal features\n",
    "df_processed['days_to_first_funding'] = (df_processed['first_funding_at'] - df_processed['founded_at']).dt.days\n",
    "df_processed['funding_duration'] = (df_processed['last_funding_at'] - df_processed['first_funding_at']).dt.days\n",
    "df_processed['company_age'] = (pd.Timestamp('2024-01-01') - df_processed['founded_at']).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a610e588-017e-4419-8792-b681c025d553",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill NaN values\n",
    "df_processed['days_to_first_funding'] = df_processed['days_to_first_funding'].fillna(-1)\n",
    "df_processed['funding_duration'] = df_processed['funding_duration'].fillna(0)\n",
    "df_processed['company_age'] = df_processed['company_age'].fillna(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ba70edbc-df07-4f00-a88f-0a71585f66cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now check for negative values in 'days_to_first_funding'\n",
    "negative_days_to_funding = df_processed[df_processed['days_to_first_funding'] < 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "defb279f-c9d9-42eb-9d7b-3a8473a94492",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>founded_at</th>\n",
       "      <th>first_funding_at</th>\n",
       "      <th>days_to_first_funding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-10-10</td>\n",
       "      <td>2014-10-07</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>2014-09-14</td>\n",
       "      <td>-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-09-18</td>\n",
       "      <td>2014-09-14</td>\n",
       "      <td>-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2013-08-06</td>\n",
       "      <td>-391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2013-01-16</td>\n",
       "      <td>-593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12253</th>\n",
       "      <td>1999-01-01</td>\n",
       "      <td>1996-12-31</td>\n",
       "      <td>-731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12275</th>\n",
       "      <td>1998-11-28</td>\n",
       "      <td>1998-01-01</td>\n",
       "      <td>-331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12280</th>\n",
       "      <td>1998-01-01</td>\n",
       "      <td>1997-09-30</td>\n",
       "      <td>-93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12299</th>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>-365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12305</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>-1096</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>920 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      founded_at first_funding_at  days_to_first_funding\n",
       "6     2014-10-10       2014-10-07                     -3\n",
       "7     2014-10-01       2014-09-14                    -17\n",
       "12    2014-09-18       2014-09-14                     -4\n",
       "23    2014-09-01       2013-08-06                   -391\n",
       "24    2014-09-01       2013-01-16                   -593\n",
       "...          ...              ...                    ...\n",
       "12253 1999-01-01       1996-12-31                   -731\n",
       "12275 1998-11-28       1998-01-01                   -331\n",
       "12280 1998-01-01       1997-09-30                    -93\n",
       "12299 1996-01-01       1995-01-01                   -365\n",
       "12305 2010-01-01       2007-01-01                  -1096\n",
       "\n",
       "[920 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_days_to_funding.loc[:,['founded_at','first_funding_at','days_to_first_funding']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "29486273-c22d-4a45-b0af-785eef4faa28",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop rows where 'days_to_first_funding' is negative\n",
    "df_processed = df_processed[df_processed['days_to_first_funding'] >= 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c296ac66-f992-4b65-8353-7e87612e67dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'market', 'funding_total_usd', 'country_code', 'state_code',\n",
       "       'region', 'city', 'funding_rounds', 'founded_at', 'founded_month',\n",
       "       'founded_quarter', 'founded_year', 'first_funding_at',\n",
       "       'last_funding_at', 'seed', 'venture', 'equity_crowdfunding',\n",
       "       'undisclosed', 'convertible_note', 'debt_financing', 'angel', 'grant',\n",
       "       'private_equity', 'post_ipo_equity', 'post_ipo_debt',\n",
       "       'secondary_market', 'product_crowdfunding', 'round_A', 'round_B',\n",
       "       'round_C', 'round_D', 'round_E', 'round_F', 'round_G', 'round_H',\n",
       "       'category_count', 'clean_name', 'is_operating', 'category_updated',\n",
       "       'days_to_first_funding', 'funding_duration', 'company_age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_processed.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9e84adca-e84f-4e75-bb04-0db61e88742f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>market</th>\n",
       "      <th>funding_total_usd</th>\n",
       "      <th>country_code</th>\n",
       "      <th>state_code</th>\n",
       "      <th>region</th>\n",
       "      <th>city</th>\n",
       "      <th>funding_rounds</th>\n",
       "      <th>founded_at</th>\n",
       "      <th>founded_month</th>\n",
       "      <th>...</th>\n",
       "      <th>round_F</th>\n",
       "      <th>round_G</th>\n",
       "      <th>round_H</th>\n",
       "      <th>category_count</th>\n",
       "      <th>clean_name</th>\n",
       "      <th>is_operating</th>\n",
       "      <th>category_updated</th>\n",
       "      <th>days_to_first_funding</th>\n",
       "      <th>funding_duration</th>\n",
       "      <th>company_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Le HerbÈ</td>\n",
       "      <td>Medical Marijuana Patients</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>WA</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>le herbè</td>\n",
       "      <td>1</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kandid</td>\n",
       "      <td>Apps</td>\n",
       "      <td>1400000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>CA</td>\n",
       "      <td>SF Bay Area</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-11-19</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>kandid</td>\n",
       "      <td>1</td>\n",
       "      <td>Technology</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Manly Man Can</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>30.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>MI</td>\n",
       "      <td>Detroit</td>\n",
       "      <td>Pinckney</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-10-26</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>the manly man can</td>\n",
       "      <td>1</td>\n",
       "      <td>Fashion &amp; Lifestyle</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MyKidsCalendar</td>\n",
       "      <td>Software</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Newark</td>\n",
       "      <td>Long Valley</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-10-22</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>mykidscalendar</td>\n",
       "      <td>1</td>\n",
       "      <td>Technology</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Peach</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>CA</td>\n",
       "      <td>SF Bay Area</td>\n",
       "      <td>Berkeley</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-10-19</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>peach</td>\n",
       "      <td>1</td>\n",
       "      <td>Fashion &amp; Lifestyle</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3361</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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12300</th>\n",
       "      <td>XO Group</td>\n",
       "      <td>Media</td>\n",
       "      <td>19600000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>NY</td>\n",
       "      <td>New York City</td>\n",
       "      <td>New York</td>\n",
       "      <td>3</td>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>xo group</td>\n",
       "      <td>1</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>1200</td>\n",
       "      <td>10227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12301</th>\n",
       "      <td>eBay</td>\n",
       "      <td>Business Services</td>\n",
       "      <td>6700000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>CA</td>\n",
       "      <td>SF Bay Area</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>1</td>\n",
       "      <td>1995-09-01</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>ebay</td>\n",
       "      <td>1</td>\n",
       "      <td>Business Services</td>\n",
       "      <td>639</td>\n",
       "      <td>0</td>\n",
       "      <td>10349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12302</th>\n",
       "      <td>Arkansas Children's Hospital</td>\n",
       "      <td>Biotechnology</td>\n",
       "      <td>3500000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>AR</td>\n",
       "      <td>Little Rock</td>\n",
       "      <td>Little Rock</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>arkansas children's hospital</td>\n",
       "      <td>1</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>628</td>\n",
       "      <td>0</td>\n",
       "      <td>4383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12303</th>\n",
       "      <td>Northcentral Technical College</td>\n",
       "      <td>Education</td>\n",
       "      <td>6200000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>WI</td>\n",
       "      <td>WI - Other</td>\n",
       "      <td>Antigo</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>northcentral technical college</td>\n",
       "      <td>1</td>\n",
       "      <td>Education</td>\n",
       "      <td>627</td>\n",
       "      <td>0</td>\n",
       "      <td>4383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12304</th>\n",
       "      <td>EATON</td>\n",
       "      <td>Automotive</td>\n",
       "      <td>2400000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>OH</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>eaton</td>\n",
       "      <td>0</td>\n",
       "      <td>Transport &amp; Automotive</td>\n",
       "      <td>941</td>\n",
       "      <td>0</td>\n",
       "      <td>4748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11386 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 name                        market  \\\n",
       "0                            Le HerbÈ   Medical Marijuana Patients    \n",
       "1                              Kandid                         Apps    \n",
       "2                   The Manly Man Can                      Fashion    \n",
       "3                      MyKidsCalendar                     Software    \n",
       "4                               Peach                      Fashion    \n",
       "...                               ...                           ...   \n",
       "12300                        XO Group                        Media    \n",
       "12301                            eBay            Business Services    \n",
       "12302    Arkansas Children's Hospital                Biotechnology    \n",
       "12303  Northcentral Technical College                    Education    \n",
       "12304                           EATON                   Automotive    \n",
       "\n",
       "       funding_total_usd country_code state_code         region  \\\n",
       "0               100000.0          USA         WA        Seattle   \n",
       "1              1400000.0          USA         CA    SF Bay Area   \n",
       "2                   30.0          USA         MI        Detroit   \n",
       "3               110000.0          USA         NJ         Newark   \n",
       "4               500000.0          USA         CA    SF Bay Area   \n",
       "...                  ...          ...        ...            ...   \n",
       "12300         19600000.0          USA         NY  New York City   \n",
       "12301          6700000.0          USA         CA    SF Bay Area   \n",
       "12302          3500000.0          USA         AR    Little Rock   \n",
       "12303          6200000.0          USA         WI     WI - Other   \n",
       "12304          2400000.0          USA         OH      Cleveland   \n",
       "\n",
       "                city  funding_rounds founded_at  founded_month  ...  round_F  \\\n",
       "0            Seattle               1 2014-12-01             12  ...        0   \n",
       "1      San Francisco               1 2014-11-19             11  ...        0   \n",
       "2           Pinckney               1 2014-10-26             10  ...        0   \n",
       "3        Long Valley               1 2014-10-22             10  ...        0   \n",
       "4           Berkeley               1 2014-10-19             10  ...        0   \n",
       "...              ...             ...        ...            ...  ...      ...   \n",
       "12300       New York               3 1996-01-01              1  ...        0   \n",
       "12301       San Jose               1 1995-09-01              9  ...        0   \n",
       "12302    Little Rock               1 2012-01-01              1  ...        0   \n",
       "12303         Antigo               1 2012-01-01              1  ...        0   \n",
       "12304      Cleveland               1 2011-01-01              1  ...        0   \n",
       "\n",
       "       round_G round_H category_count                      clean_name  \\\n",
       "0            0       0              3                        le herbè   \n",
       "1            0       0              1                          kandid   \n",
       "2            0       0              1               the manly man can   \n",
       "3            0       0              1                  mykidscalendar   \n",
       "4            0       0              3                           peach   \n",
       "...        ...     ...            ...                             ...   \n",
       "12300        0       0              3                        xo group   \n",
       "12301        0       0              6                            ebay   \n",
       "12302        0       0              1    arkansas children's hospital   \n",
       "12303        0       0              1  northcentral technical college   \n",
       "12304        0       0              3                           eaton   \n",
       "\n",
       "       is_operating        category_updated  days_to_first_funding  \\\n",
       "0                 1              Healthcare                      0   \n",
       "1                 1              Technology                     13   \n",
       "2                 1     Fashion & Lifestyle                      1   \n",
       "3                 1              Technology                      0   \n",
       "4                 1     Fashion & Lifestyle                      4   \n",
       "...             ...                     ...                    ...   \n",
       "12300             1                   other                      0   \n",
       "12301             1       Business Services                    639   \n",
       "12302             1              Healthcare                    628   \n",
       "12303             1               Education                    627   \n",
       "12304             0  Transport & Automotive                    941   \n",
       "\n",
       "       funding_duration  company_age  \n",
       "0                     0         3318  \n",
       "1                     0         3330  \n",
       "2                     0         3354  \n",
       "3                     0         3358  \n",
       "4                     0         3361  \n",
       "...                 ...          ...  \n",
       "12300              1200        10227  \n",
       "12301                 0        10349  \n",
       "12302                 0         4383  \n",
       "12303                 0         4383  \n",
       "12304                 0         4748  \n",
       "\n",
       "[11386 rows x 42 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_processed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "40f8e613-6e76-48f5-b93b-afde20e38f4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selected features (reduced set)\n",
    "selected_features = [\n",
    "    'funding_total_usd',\n",
    "    'funding_rounds',\n",
    "    'days_to_first_funding',\n",
    "    'funding_duration',\n",
    "    'category_count',\n",
    "    'company_age'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c2a98b4b-ba04-45e9-9eed-9969aa5b5d5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode categorical variables (reduced set)\n",
    "categorical_features = ['category_updated', 'state_code']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9164e59a-4901-4ee3-9fe9-02f6a2103419",
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-Hot Encoding with pandas\n",
    "df_processed = pd.get_dummies(df_processed, columns=categorical_features, drop_first=False)\n",
    "\n",
    "# Identify dummy columns\n",
    "dummy_columns = [col for col in df_processed.columns if any(prefix in col for prefix in categorical_features)]\n",
    "\n",
    "# Update feature_columns\n",
    "feature_columns = selected_features + dummy_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bb13cd52-9521-43e2-bcdc-46ddeee945b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create feature matrix\n",
    "X = df_processed[feature_columns]\n",
    "y = df_processed['is_operating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0f2a5203-a35a-48f9-8cc3-2ad992962756",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 11386 entries, 0 to 12304\n",
      "Data columns (total 68 columns):\n",
      " #   Column                                   Non-Null Count  Dtype  \n",
      "---  ------                                   --------------  -----  \n",
      " 0   funding_total_usd                        11386 non-null  float64\n",
      " 1   funding_rounds                           11386 non-null  int64  \n",
      " 2   days_to_first_funding                    11386 non-null  int64  \n",
      " 3   funding_duration                         11386 non-null  int64  \n",
      " 4   category_count                           11386 non-null  int64  \n",
      " 5   company_age                              11386 non-null  int64  \n",
      " 6   category_updated_Business Services       11386 non-null  bool   \n",
      " 7   category_updated_Education               11386 non-null  bool   \n",
      " 8   category_updated_Energy & Environment    11386 non-null  bool   \n",
      " 9   category_updated_Fashion & Lifestyle     11386 non-null  bool   \n",
      " 10  category_updated_Finance                 11386 non-null  bool   \n",
      " 11  category_updated_Healthcare              11386 non-null  bool   \n",
      " 12  category_updated_Media & Entertainment   11386 non-null  bool   \n",
      " 13  category_updated_Retail & E-Commerce     11386 non-null  bool   \n",
      " 14  category_updated_Technology              11386 non-null  bool   \n",
      " 15  category_updated_Transport & Automotive  11386 non-null  bool   \n",
      " 16  category_updated_other                   11386 non-null  bool   \n",
      " 17  state_code_AK                            11386 non-null  bool   \n",
      " 18  state_code_AL                            11386 non-null  bool   \n",
      " 19  state_code_AR                            11386 non-null  bool   \n",
      " 20  state_code_AZ                            11386 non-null  bool   \n",
      " 21  state_code_CA                            11386 non-null  bool   \n",
      " 22  state_code_CO                            11386 non-null  bool   \n",
      " 23  state_code_CT                            11386 non-null  bool   \n",
      " 24  state_code_DC                            11386 non-null  bool   \n",
      " 25  state_code_DE                            11386 non-null  bool   \n",
      " 26  state_code_FL                            11386 non-null  bool   \n",
      " 27  state_code_GA                            11386 non-null  bool   \n",
      " 28  state_code_HI                            11386 non-null  bool   \n",
      " 29  state_code_IA                            11386 non-null  bool   \n",
      " 30  state_code_ID                            11386 non-null  bool   \n",
      " 31  state_code_IL                            11386 non-null  bool   \n",
      " 32  state_code_IN                            11386 non-null  bool   \n",
      " 33  state_code_KS                            11386 non-null  bool   \n",
      " 34  state_code_KY                            11386 non-null  bool   \n",
      " 35  state_code_LA                            11386 non-null  bool   \n",
      " 36  state_code_MA                            11386 non-null  bool   \n",
      " 37  state_code_MD                            11386 non-null  bool   \n",
      " 38  state_code_ME                            11386 non-null  bool   \n",
      " 39  state_code_MI                            11386 non-null  bool   \n",
      " 40  state_code_MN                            11386 non-null  bool   \n",
      " 41  state_code_MO                            11386 non-null  bool   \n",
      " 42  state_code_MS                            11386 non-null  bool   \n",
      " 43  state_code_MT                            11386 non-null  bool   \n",
      " 44  state_code_NC                            11386 non-null  bool   \n",
      " 45  state_code_ND                            11386 non-null  bool   \n",
      " 46  state_code_NE                            11386 non-null  bool   \n",
      " 47  state_code_NH                            11386 non-null  bool   \n",
      " 48  state_code_NJ                            11386 non-null  bool   \n",
      " 49  state_code_NM                            11386 non-null  bool   \n",
      " 50  state_code_NV                            11386 non-null  bool   \n",
      " 51  state_code_NY                            11386 non-null  bool   \n",
      " 52  state_code_OH                            11386 non-null  bool   \n",
      " 53  state_code_OK                            11386 non-null  bool   \n",
      " 54  state_code_OR                            11386 non-null  bool   \n",
      " 55  state_code_PA                            11386 non-null  bool   \n",
      " 56  state_code_RI                            11386 non-null  bool   \n",
      " 57  state_code_SC                            11386 non-null  bool   \n",
      " 58  state_code_SD                            11386 non-null  bool   \n",
      " 59  state_code_TN                            11386 non-null  bool   \n",
      " 60  state_code_TX                            11386 non-null  bool   \n",
      " 61  state_code_UT                            11386 non-null  bool   \n",
      " 62  state_code_VA                            11386 non-null  bool   \n",
      " 63  state_code_VT                            11386 non-null  bool   \n",
      " 64  state_code_WA                            11386 non-null  bool   \n",
      " 65  state_code_WI                            11386 non-null  bool   \n",
      " 66  state_code_WV                            11386 non-null  bool   \n",
      " 67  state_code_WY                            11386 non-null  bool   \n",
      "dtypes: bool(62), float64(1), int64(5)\n",
      "memory usage: 1.3 MB\n"
     ]
    }
   ],
   "source": [
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5259a122-631c-4885-92e9-3797b9e0dcd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8475\n",
      "Precision: 0.8640\n",
      "True Positive Rate (TPR/Recall): 0.9714\n",
      "False Positive Rate (FPR): 0.8040\n",
      "F1 Score: 0.9145\n",
      "ROC-AUC Score: 0.8050\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import (\n",
    "    classification_report, accuracy_score, confusion_matrix, roc_auc_score, roc_curve, precision_score, recall_score, f1_score\n",
    ")\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "# Assuming feature_columns and 'is_operating' are already defined\n",
    "# Create feature matrix (X) and target vector (y)\n",
    "#X = df_processed[feature_columns]\n",
    "#y = df_processed['is_operating']\n",
    "\n",
    "# Train-test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.3, random_state=42)\n",
    "\n",
    "# Feature scaling\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Initialize Random Forest model\n",
    "model = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# Train the model\n",
    "model.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Make predictions\n",
    "y_pred = model.predict(X_test_scaled)\n",
    "y_pred_prob = model.predict_proba(X_test_scaled)[:, 1]  # Probabilities for the positive class\n",
    "\n",
    "# Confusion Matrix\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"Not Operating\", \"Operating\"], yticklabels=[\"Not Operating\", \"Operating\"])\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.show()\n",
    "\n",
    "# Metrics Calculation\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)  # This is the True Positive Rate (TPR)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "fpr = conf_matrix[0, 1] / (conf_matrix[0, 0] + conf_matrix[0, 1])  # False Positive Rate (FPR)\n",
    "roc_auc = roc_auc_score(y_test, y_pred_prob)\n",
    "\n",
    "# Print Metrics\n",
    "print(f\"Accuracy: {accuracy:.4f}\")\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"True Positive Rate (TPR/Recall): {recall:.4f}\")\n",
    "print(f\"False Positive Rate (FPR): {fpr:.4f}\")\n",
    "print(f\"F1 Score: {f1:.4f}\")\n",
    "print(f\"ROC-AUC Score: {roc_auc:.4f}\")\n",
    "\n",
    "# Plot ROC-AUC Curve\n",
    "fpr_values, tpr_values, thresholds = roc_curve(y_test, y_pred_prob)\n",
    "plt.figure()\n",
    "plt.plot(fpr_values, tpr_values, label=f\"ROC Curve (AUC = {roc_auc:.4f})\", color=\"blue\")\n",
    "plt.plot([0, 1], [0, 1], \"k--\", label=\"Random Chance\")\n",
    "plt.title(\"ROC-AUC Curve\")\n",
    "plt.xlabel(\"False Positive Rate (FPR)\")\n",
    "plt.ylabel(\"True Positive Rate (TPR)\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cc1bc04c-435a-4f29-91c8-08c22fc2fa2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 107  439]\n",
      " [  80 2790]]\n",
      "Accuracy: 0.8481\n",
      "Precision: 0.8640\n",
      "True Positive Rate (TPR/Recall): 0.9721\n",
      "False Positive Rate (FPR): 0.8040\n",
      "F1 Score: 0.9149\n",
      "ROC-AUC Score: 0.8041\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize Random Forest model\n",
    "model = RandomForestClassifier(class_weight='balanced',random_state=42)\n",
    "\n",
    "# Train the model\n",
    "model.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Make predictions\n",
    "y_pred = model.predict(X_test_scaled)\n",
    "y_pred_prob = model.predict_proba(X_test_scaled)[:, 1]  # Probabilities for the positive class\n",
    "\n",
    "# Confusion Matrix\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(conf_matrix)\n",
    "#sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"Not Operating\", \"Operating\"], yticklabels=[\"Not Operating\", \"Operating\"])\n",
    "#plt.title(\"Confusion Matrix\")\n",
    "#plt.xlabel(\"Predicted\")\n",
    "#plt.ylabel(\"Actual\")\n",
    "#plt.show()\n",
    "\n",
    "# Metrics Calculation\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)  # This is the True Positive Rate (TPR)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "fpr = conf_matrix[0, 1] / (conf_matrix[0, 0] + conf_matrix[0, 1])  # False Positive Rate (FPR)\n",
    "roc_auc = roc_auc_score(y_test, y_pred_prob)\n",
    "\n",
    "# Print Metrics\n",
    "print(f\"Accuracy: {accuracy:.4f}\")\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"True Positive Rate (TPR/Recall): {recall:.4f}\")\n",
    "print(f\"False Positive Rate (FPR): {fpr:.4f}\")\n",
    "print(f\"F1 Score: {f1:.4f}\")\n",
    "print(f\"ROC-AUC Score: {roc_auc:.4f}\")\n",
    "\n",
    "# Plot ROC-AUC Curve\n",
    "fpr_values, tpr_values, thresholds = roc_curve(y_test, y_pred_prob)\n",
    "plt.figure()\n",
    "plt.plot(fpr_values, tpr_values, label=f\"ROC Curve (AUC = {roc_auc:.4f})\", color=\"blue\")\n",
    "plt.plot([0, 1], [0, 1], \"k--\", label=\"Random Chance\")\n",
    "plt.title(\"ROC-AUC Curve\")\n",
    "plt.xlabel(\"False Positive Rate (FPR)\")\n",
    "plt.ylabel(\"True Positive Rate (TPR)\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "de882f61-52aa-435f-bf3a-ae843979c05b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: imbalanced-learn in /opt/anaconda3/lib/python3.12/site-packages (0.12.3)\n",
      "Requirement already satisfied: numpy>=1.17.3 in /opt/anaconda3/lib/python3.12/site-packages (from imbalanced-learn) (1.26.4)\n",
      "Requirement already satisfied: scipy>=1.5.0 in /opt/anaconda3/lib/python3.12/site-packages (from imbalanced-learn) (1.13.1)\n",
      "Requirement already satisfied: scikit-learn>=1.0.2 in /opt/anaconda3/lib/python3.12/site-packages (from imbalanced-learn) (1.4.2)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /opt/anaconda3/lib/python3.12/site-packages (from imbalanced-learn) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/anaconda3/lib/python3.12/site-packages (from imbalanced-learn) (2.2.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install imbalanced-learn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "be90edd0-ae11-4621-ac3f-97be0d7f48be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original class distribution: is_operating\n",
      "1    6695\n",
      "0    1275\n",
      "Name: count, dtype: int64\n",
      "Resampled class distribution: is_operating\n",
      "1    6695\n",
      "0    6695\n",
      "Name: count, dtype: int64\n",
      "[[ 194  352]\n",
      " [ 215 2655]]\n",
      "Accuracy: 0.8340\n",
      "Precision: 0.8829\n",
      "True Positive Rate (TPR/Recall): 0.9251\n",
      "False Positive Rate (FPR): 0.6447\n",
      "F1 Score: 0.9035\n",
      "ROC-AUC Score: 0.8007\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, roc_curve\n",
    "\n",
    "# Train-test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.3, random_state=42)\n",
    "\n",
    "# Apply SMOTE\n",
    "smote = SMOTE(random_state=42)\n",
    "X_train_sm, y_train_sm = smote.fit_resample(X_train, y_train)\n",
    "\n",
    "# Check the balance\n",
    "print(\"Original class distribution:\", y_train.value_counts())\n",
    "print(\"Resampled class distribution:\", y_train_sm.value_counts())\n",
    "\n",
    "# Train Random Forest\n",
    "rf = RandomForestClassifier(random_state=42)\n",
    "rf.fit(X_train_sm, y_train_sm)\n",
    "\n",
    "# Predict\n",
    "y_pred = rf.predict(X_test)\n",
    "y_pred_prob = rf.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Evaluation\n",
    "#print(\"Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred))\n",
    "#print(\"Classification Report:\\n\", classification_report(y_test, y_pred))\n",
    "#print(\"ROC-AUC Score:\", roc_auc_score(y_test, y_pred_prob))\n",
    "\n",
    "# Confusion Matrix\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(conf_matrix)\n",
    "#sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"Not Operating\", \"Operating\"], yticklabels=[\"Not Operating\", \"Operating\"])\n",
    "#plt.title(\"Confusion Matrix\")\n",
    "#plt.xlabel(\"Predicted\")\n",
    "#plt.ylabel(\"Actual\")\n",
    "#plt.show()\n",
    "\n",
    "# Metrics Calculation\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)  # This is the True Positive Rate (TPR)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "fpr = conf_matrix[0, 1] / (conf_matrix[0, 0] + conf_matrix[0, 1])  # False Positive Rate (FPR)\n",
    "roc_auc = roc_auc_score(y_test, y_pred_prob)\n",
    "\n",
    "# Print Metrics\n",
    "print(f\"Accuracy: {accuracy:.4f}\")\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"True Positive Rate (TPR/Recall): {recall:.4f}\")\n",
    "print(f\"False Positive Rate (FPR): {fpr:.4f}\")\n",
    "print(f\"F1 Score: {f1:.4f}\")\n",
    "print(f\"ROC-AUC Score: {roc_auc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c23d6421-c59a-41dd-82c2-3f97481d342f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original class distribution: is_operating\n",
      "1    6695\n",
      "0    1275\n",
      "Name: count, dtype: int64\n",
      "Resampled class distribution: is_operating\n",
      "0    1275\n",
      "1    1275\n",
      "Name: count, dtype: int64\n",
      "[[ 431  115]\n",
      " [ 889 1981]]\n",
      "Accuracy: 0.7061\n",
      "Precision: 0.9451\n",
      "True Positive Rate (TPR/Recall): 0.6902\n",
      "False Positive Rate (FPR): 0.2106\n",
      "F1 Score: 0.7978\n",
      "ROC-AUC Score: 0.8015\n"
     ]
    }
   ],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "# Apply undersampling\n",
    "rus = RandomUnderSampler(random_state=42)\n",
    "X_train_us, y_train_us = rus.fit_resample(X_train, y_train)\n",
    "\n",
    "# Check the balance\n",
    "print(\"Original class distribution:\", y_train.value_counts())\n",
    "print(\"Resampled class distribution:\", y_train_us.value_counts())\n",
    "\n",
    "# Train Random Forest\n",
    "rf = RandomForestClassifier(random_state=42)\n",
    "rf.fit(X_train_us, y_train_us)\n",
    "\n",
    "# Predict\n",
    "y_pred = rf.predict(X_test)\n",
    "y_pred_prob = rf.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Evaluation\n",
    "#print(\"Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred))\n",
    "#print(\"Classification Report:\\n\", classification_report(y_test, y_pred))\n",
    "#print(\"ROC-AUC Score:\", roc_auc_score(y_test, y_pred_prob))\n",
    "\n",
    "# Confusion Matrix\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(conf_matrix)\n",
    "#sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"Not Operating\", \"Operating\"], yticklabels=[\"Not Operating\", \"Operating\"])\n",
    "#plt.title(\"Confusion Matrix\")\n",
    "#plt.xlabel(\"Predicted\")\n",
    "#plt.ylabel(\"Actual\")\n",
    "#plt.show()\n",
    "\n",
    "# Metrics Calculation\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)  # This is the True Positive Rate (TPR)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "fpr = conf_matrix[0, 1] / (conf_matrix[0, 0] + conf_matrix[0, 1])  # False Positive Rate (FPR)\n",
    "roc_auc = roc_auc_score(y_test, y_pred_prob)\n",
    "\n",
    "# Print Metrics\n",
    "print(f\"Accuracy: {accuracy:.4f}\")\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"True Positive Rate (TPR/Recall): {recall:.4f}\")\n",
    "print(f\"False Positive Rate (FPR): {fpr:.4f}\")\n",
    "print(f\"F1 Score: {f1:.4f}\")\n",
    "print(f\"ROC-AUC Score: {roc_auc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "01d26ada-6dc0-4d2d-950b-558981303f9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      " [[ 155  209]\n",
      " [ 227 1687]]\n",
      "Accuracy: 0.8086\n",
      "Precision: 0.8898\n",
      "True Positive Rate (Recall): 0.8814\n",
      "False Positive Rate (FPR): 0.5742\n",
      "F1 Score: 0.8856\n",
      "ROC-AUC Score: 0.8098\n"
     ]
    }
   ],
   "source": [
    "# Importing required libraries\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix, classification_report, roc_auc_score\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Create feature matrix and target variable\n",
    "X = df_processed[feature_columns]\n",
    "y = df_processed['is_operating']\n",
    "\n",
    "# Split data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
    "\n",
    "# Scale the features (Standardization)\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Define SMOTE and Random Under-sampler\n",
    "smote = SMOTE(sampling_strategy='auto', random_state=42)\n",
    "undersample = RandomUnderSampler(sampling_strategy='auto', random_state=42)\n",
    "\n",
    "# Combine SMOTE and undersampling into a pipeline\n",
    "pipeline = Pipeline(steps=[('smote', smote), \n",
    "                           ('undersample', undersample), \n",
    "                           ('classifier', RandomForestClassifier(random_state=42))])\n",
    "\n",
    "# Fit the model\n",
    "pipeline.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Predict on test data\n",
    "y_pred = pipeline.predict(X_test_scaled)\n",
    "\n",
    "# Evaluation Metrics\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "accuracy = (conf_matrix[0,0] + conf_matrix[1,1]) / conf_matrix.sum()\n",
    "precision = conf_matrix[1,1] / (conf_matrix[1,1] + conf_matrix[0,1])\n",
    "recall = conf_matrix[1,1] / (conf_matrix[1,1] + conf_matrix[1,0])\n",
    "fpr = conf_matrix[0,1] / (conf_matrix[0,1] + conf_matrix[0,0])\n",
    "f1 = 2 * (precision * recall) / (precision + recall)\n",
    "roc_auc = roc_auc_score(y_test, pipeline.predict_proba(X_test_scaled)[:,1])\n",
    "\n",
    "# Print the evaluation metrics\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "print(f\"Accuracy: {accuracy:.4f}\")\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"True Positive Rate (Recall): {recall:.4f}\")\n",
    "print(f\"False Positive Rate (FPR): {fpr:.4f}\")\n",
    "print(f\"F1 Score: {f1:.4f}\")\n",
    "print(f\"ROC-AUC Score: {roc_auc:.4f}\")\n",
    "\n",
    "# Optionally, also print the classification report for detailed performance\n",
    "#print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4ccfc4b5-6015-4f22-9210-2f0349e6632b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Fit the Random Forest model (using the previous pipeline or model)\n",
    "rf_model = RandomForestClassifier(random_state=42)\n",
    "rf_model.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Get feature importance\n",
    "importances = rf_model.feature_importances_\n",
    "\n",
    "# Create a list of feature names (the same order as in your feature matrix)\n",
    "features = X.columns\n",
    "\n",
    "# Sort the feature importances in descending order\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Print the feature importances\n",
    "#print(\"Feature Importance:\")\n",
    "#for i in range(X_train_scaled.shape[1]):\n",
    "    #print(f\"{features[indices[i]]}: {importances[indices[i]]:.4f}\")\n",
    "\n",
    "# Plot the feature importances as a vertical bar chart (point graph)\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"Feature Importance (Random Forest)\")\n",
    "plt.bar(range(X_train_scaled.shape[1]), importances[indices], align=\"center\")\n",
    "plt.xticks(range(X_train_scaled.shape[1]), features[indices], rotation=90)\n",
    "plt.xlabel(\"Features\")\n",
    "plt.ylabel(\"Importance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "189aee92-7809-478a-ba48-ffd0c03961ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top N Selected Features:\n",
      "Index(['company_age', 'days_to_first_funding', 'funding_total_usd',\n",
      "       'funding_duration', 'category_count', 'funding_rounds', 'state_code_CA',\n",
      "       'category_updated_Technology', 'category_updated_other',\n",
      "       'state_code_NY'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Number of top features you want to select\n",
    "top_n = 10\n",
    "\n",
    "# Get the indices of the top N important features\n",
    "top_n_indices = np.argsort(importances)[::-1][:top_n]\n",
    "\n",
    "# Get the feature names corresponding to the top N indices\n",
    "selected_features_top_n = features[top_n_indices]\n",
    "\n",
    "# Select the top N features from the original feature matrix X\n",
    "X_selected_top_n = X[selected_features_top_n]\n",
    "\n",
    "# Print selected top N features\n",
    "print(\"Top N Selected Features:\")\n",
    "print(selected_features_top_n)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5b2b12c2-2ed5-491d-bdbd-729a98a113f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected Features Based on Threshold:\n",
      "Index(['funding_total_usd', 'days_to_first_funding', 'funding_duration',\n",
      "       'category_count', 'company_age'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Set a threshold value for feature importance\n",
    "threshold = 0.05  # Select features with importance greater than this threshold\n",
    "\n",
    "# Get the indices of features whose importance exceeds the threshold\n",
    "selected_features_threshold = features[importances > threshold]\n",
    "\n",
    "# Select the features from the original feature matrix X\n",
    "X_selected_threshold = X[selected_features_threshold]\n",
    "\n",
    "# Print selected features based on the threshold\n",
    "print(\"Selected Features Based on Threshold:\")\n",
    "print(selected_features_threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "fcc02292-fa57-4337-8a45-186b56b2d4d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resampled class distribution: is_operating\n",
      "1    7638\n",
      "0    3819\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Assume that X and y are your features and target variable\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create the pipeline for SMOTE and undersampling\n",
    "smote = SMOTE(sampling_strategy=0.5, random_state=42)  # Oversampling the minority class (adjust ratio as needed)\n",
    "undersample = RandomUnderSampler(sampling_strategy=0.5, random_state=42)  # Undersampling the majority class\n",
    "pipeline = Pipeline([('smote', smote), ('undersample', undersample)])\n",
    "\n",
    "# Apply the resampling\n",
    "X_res, y_res = pipeline.fit_resample(X_train, y_train)\n",
    "\n",
    "# Print the class distribution after resampling\n",
    "print(\"Resampled class distribution:\", pd.Series(y_res).value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "91c3178a-6568-46ee-bac6-70050e7d2c57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Fit the Random Forest model (using the previous pipeline or model)\n",
    "rf_model = RandomForestClassifier(random_state=42)\n",
    "rf_model.fit(X_res, y_res)\n",
    "\n",
    "# Get feature importance\n",
    "importances = rf_model.feature_importances_\n",
    "\n",
    "# Create a list of feature names (the same order as in your feature matrix)\n",
    "features = X.columns\n",
    "\n",
    "# Sort the feature importances in descending order\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Print the feature importances\n",
    "#print(\"Feature Importance:\")\n",
    "#for i in range(X_train_scaled.shape[1]):\n",
    "    #print(f\"{features[indices[i]]}: {importances[indices[i]]:.4f}\")\n",
    "\n",
    "# Plot the feature importances as a vertical bar chart (point graph)\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"Feature Importance (Random Forest)\")\n",
    "plt.bar(range(X_train_scaled.shape[1]), importances[indices], align=\"center\")\n",
    "plt.xticks(range(X_train_scaled.shape[1]), features[indices], rotation=90)\n",
    "plt.xlabel(\"Features\")\n",
    "plt.ylabel(\"Importance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "05b34319-26f7-451d-9238-a8d1fee80490",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top N Selected Features:\n",
      "Index(['company_age', 'days_to_first_funding', 'funding_total_usd',\n",
      "       'funding_duration', 'state_code_CA', 'category_updated_other',\n",
      "       'category_count', 'funding_rounds', 'category_updated_Technology',\n",
      "       'category_updated_Media & Entertainment'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Number of top features you want to select\n",
    "top_n = 10\n",
    "\n",
    "# Get the indices of the top N important features\n",
    "top_n_indices = np.argsort(importances)[::-1][:top_n]\n",
    "\n",
    "# Get the feature names corresponding to the top N indices\n",
    "selected_features_top_n = features[top_n_indices]\n",
    "\n",
    "# Select the top N features from the original feature matrix X\n",
    "#X_selected_top_n = X[selected_features_top_n]\n",
    "\n",
    "# Print selected top N features\n",
    "print(\"Top N Selected Features:\")\n",
    "print(selected_features_top_n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "5e95a502-d4b8-4291-aebb-92ff0cdb84ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the model using the resampled data and selected features\n",
    "X_res_selected_top_n = X_res[selected_features_top_n]\n",
    "rf_model_selected = RandomForestClassifier(random_state=42)\n",
    "rf_model_selected.fit(X_res_selected_top_n, y_res)\n",
    "\n",
    "# Make predictions on the test set (using the selected features)\n",
    "X_test_selected_top_n = X_test[selected_features_top_n]\n",
    "y_pred = rf_model_selected.predict(X_test_selected_top_n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "52c5d3ee-e4a3-46be-bd90-16efb7c0c708",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      " [[ 147  204]\n",
      " [ 171 1756]]\n",
      "Accuracy: 0.8354\n",
      "Precision: 0.8959\n",
      "True Positive Rate (Recall): 0.9113\n",
      "False Positive Rate (FPR): 0.5812\n",
      "F1 Score: 0.9035\n",
      "ROC-AUC Score: 0.6650\n"
     ]
    }
   ],
   "source": [
    "# Evaluation Metrics\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "accuracy = (conf_matrix[0,0] + conf_matrix[1,1]) / conf_matrix.sum()\n",
    "precision = conf_matrix[1,1] / (conf_matrix[1,1] + conf_matrix[0,1])\n",
    "recall = conf_matrix[1,1] / (conf_matrix[1,1] + conf_matrix[1,0])\n",
    "fpr = conf_matrix[0,1] / (conf_matrix[0,1] + conf_matrix[0,0])\n",
    "f1 = 2 * (precision * recall) / (precision + recall)\n",
    "roc_auc = roc_auc_score(y_test, y_pred)\n",
    "\n",
    "# Print the evaluation metrics\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "print(f\"Accuracy: {accuracy:.4f}\")\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"True Positive Rate (Recall): {recall:.4f}\")\n",
    "print(f\"False Positive Rate (FPR): {fpr:.4f}\")\n",
    "print(f\"F1 Score: {f1:.4f}\")\n",
    "print(f\"ROC-AUC Score: {roc_auc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "4a5bef21-88c7-4f46-814a-3123c73fa444",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected Features Based on Threshold:\n",
      "Index(['funding_total_usd', 'days_to_first_funding', 'funding_duration',\n",
      "       'company_age'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Set a threshold value for feature importance\n",
    "threshold = 0.05  # Select features with importance greater than this threshold\n",
    "\n",
    "# Get the indices of features whose importance exceeds the threshold\n",
    "selected_features_threshold = features[importances > threshold]\n",
    "\n",
    "# Select the features from the original feature matrix X\n",
    "X_selected_threshold = X[selected_features_threshold]\n",
    "\n",
    "# Print selected features based on the threshold\n",
    "print(\"Selected Features Based on Threshold:\")\n",
    "print(selected_features_threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "9658621f-87f1-4af4-a838-846a2bd57a75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the model using the resampled data and selected features\n",
    "X_res_selected = X_res[selected_features_threshold]\n",
    "rf_model_selected = RandomForestClassifier(random_state=42)\n",
    "rf_model_selected.fit(X_res_selected, y_res)\n",
    "\n",
    "# Make predictions on the test set (using the selected features)\n",
    "X_test_selected = X_test[selected_features_threshold]\n",
    "y_pred = rf_model_selected.predict(X_test_selected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "811bb161-e4f3-4191-afd0-045f3a034920",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      " [[ 177  174]\n",
      " [ 229 1698]]\n",
      "Accuracy: 0.8231\n",
      "Precision: 0.9071\n",
      "True Positive Rate (Recall): 0.8812\n",
      "False Positive Rate (FPR): 0.4957\n",
      "F1 Score: 0.8939\n",
      "ROC-AUC Score: 0.6927\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluation Metrics\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "accuracy = (conf_matrix[0,0] + conf_matrix[1,1]) / conf_matrix.sum()\n",
    "precision = conf_matrix[1,1] / (conf_matrix[1,1] + conf_matrix[0,1])\n",
    "recall = conf_matrix[1,1] / (conf_matrix[1,1] + conf_matrix[1,0])\n",
    "fpr = conf_matrix[0,1] / (conf_matrix[0,1] + conf_matrix[0,0])\n",
    "f1 = 2 * (precision * recall) / (precision + recall)\n",
    "roc_auc = roc_auc_score(y_test, y_pred)\n",
    "\n",
    "# Print the evaluation metrics\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "print(f\"Accuracy: {accuracy:.4f}\")\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"True Positive Rate (Recall): {recall:.4f}\")\n",
    "print(f\"False Positive Rate (FPR): {fpr:.4f}\")\n",
    "print(f\"F1 Score: {f1:.4f}\")\n",
    "print(f\"ROC-AUC Score: {roc_auc:.4f}\")\n",
    "\n",
    "y_pred_prob = rf_model_selected.predict_proba(X_test_selected)[:, 1]  # Probabilities for the positive class\n",
    "\n",
    "# Plot ROC-AUC Curve\n",
    "fpr_values, tpr_values, thresholds = roc_curve(y_test, y_pred_prob)\n",
    "plt.figure()\n",
    "plt.plot(fpr_values, tpr_values, label=f\"ROC Curve (AUC = {roc_auc:.4f})\", color=\"blue\")\n",
    "plt.plot([0, 1], [0, 1], \"k--\", label=\"Random Chance\")\n",
    "plt.title(\"ROC-AUC Curve\")\n",
    "plt.xlabel(\"False Positive Rate (FPR)\")\n",
    "plt.ylabel(\"True Positive Rate (TPR)\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31161288-0c65-4b96-b51b-3738d88f2915",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}