SK_Companies.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b3fca835",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "88711888",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>CORPORATE_IDENTIFICATION_NUMBER</th>\n",
       "      <th>Company_Name</th>\n",
       "      <th>Company_status</th>\n",
       "      <th>Company_class</th>\n",
       "      <th>Company_Category</th>\n",
       "      <th>Company_sub_category</th>\n",
       "      <th>DATE_OF_REGISTRATION</th>\n",
       "      <th>REGISTERED_STATE</th>\n",
       "      <th>AUTHORIZED_CAP</th>\n",
       "      <th>PAIDUP_CAPITAL</th>\n",
       "      <th>Industrial_Class</th>\n",
       "      <th>PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN</th>\n",
       "      <th>Registered_Office_Address</th>\n",
       "      <th>REGISTRAR_OF_COMPANIES</th>\n",
       "      <th>EMAIL_ADDR</th>\n",
       "      <th>Latest_Year_AR</th>\n",
       "      <th>Latest_Year_BS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U63030SK2016PTC013231</td>\n",
       "      <td>TRIP OF LIFETIME TRAVELTREK PRIVATELIMITED</td>\n",
       "      <td>Active</td>\n",
       "      <td>Private</td>\n",
       "      <td>Company limited by Shares</td>\n",
       "      <td>Non-govt company</td>\n",
       "      <td>13-06-16</td>\n",
       "      <td>Sikkim</td>\n",
       "      <td>100000</td>\n",
       "      <td>100000</td>\n",
       "      <td>63030</td>\n",
       "      <td>Transport, storage and communications</td>\n",
       "      <td>Tadong, Daragoan, Near Greendale School, Below...</td>\n",
       "      <td>RoC-Shillong</td>\n",
       "      <td>pnupam@yahoo.com</td>\n",
       "      <td>31-03-18</td>\n",
       "      <td>31-03-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U74999SK2016PTC013232</td>\n",
       "      <td>PANDIM ONLINE SOLUTIONS PRIVATE LIMITED</td>\n",
       "      <td>Active</td>\n",
       "      <td>Private</td>\n",
       "      <td>Company limited by Shares</td>\n",
       "      <td>Non-govt company</td>\n",
       "      <td>06-10-16</td>\n",
       "      <td>Sikkim</td>\n",
       "      <td>100000</td>\n",
       "      <td>100000</td>\n",
       "      <td>74999</td>\n",
       "      <td>Other business activities</td>\n",
       "      <td>6th Mile,Tadong, Near Sikkim University, Gangt...</td>\n",
       "      <td>RoC-Shillong</td>\n",
       "      <td>vrnkhrl1@gmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CORPORATE_IDENTIFICATION_NUMBER                                Company_Name  \\\n",
       "0           U63030SK2016PTC013231  TRIP OF LIFETIME TRAVELTREK PRIVATELIMITED   \n",
       "1           U74999SK2016PTC013232     PANDIM ONLINE SOLUTIONS PRIVATE LIMITED   \n",
       "\n",
       "  Company_status Company_class           Company_Category  \\\n",
       "0         Active       Private  Company limited by Shares   \n",
       "1         Active       Private  Company limited by Shares   \n",
       "\n",
       "  Company_sub_category DATE_OF_REGISTRATION REGISTERED_STATE  AUTHORIZED_CAP  \\\n",
       "0     Non-govt company             13-06-16           Sikkim          100000   \n",
       "1     Non-govt company             06-10-16           Sikkim          100000   \n",
       "\n",
       "   PAIDUP_CAPITAL  Industrial_Class PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN  \\\n",
       "0          100000             63030  Transport, storage and communications   \n",
       "1          100000             74999              Other business activities   \n",
       "\n",
       "                           Registered_Office_Address REGISTRAR_OF_COMPANIES  \\\n",
       "0  Tadong, Daragoan, Near Greendale School, Below...           RoC-Shillong   \n",
       "1  6th Mile,Tadong, Near Sikkim University, Gangt...           RoC-Shillong   \n",
       "\n",
       "           EMAIL_ADDR Latest_Year_AR Latest_Year_BS  \n",
       "0    pnupam@yahoo.com       31-03-18       31-03-18  \n",
       "1  vrnkhrl1@gmail.com            NaN            NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data\\Sikkim.csv\", encoding='ISO-8859-1',low_memory=False)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6dee4ff1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Assuming df is your DataFrame\n",
    "\n",
    "# Filter out rows with unknown or invalid values in 'DATE_OF_REGISTRATION'\n",
    "df = df[pd.to_datetime(df['DATE_OF_REGISTRATION'], errors='coerce', dayfirst=True).notna()]\n",
    "\n",
    "# Convert 'DATE_OF_REGISTRATION' to datetime format\n",
    "df['DATE_OF_REGISTRATION'] = pd.to_datetime(df['DATE_OF_REGISTRATION'], errors='coerce', dayfirst=True)\n",
    "\n",
    "# Extract year from the registration date\n",
    "df['Registration_Year'] = df['DATE_OF_REGISTRATION'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5038868a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CORPORATE_IDENTIFICATION_NUMBER           0\n",
       "Company_Name                              0\n",
       "Company_status                            0\n",
       "Company_class                             0\n",
       "Company_Category                          0\n",
       "Company_sub_category                      0\n",
       "DATE_OF_REGISTRATION                      0\n",
       "REGISTERED_STATE                          0\n",
       "AUTHORIZED_CAP                            0\n",
       "PAIDUP_CAPITAL                            0\n",
       "Industrial_Class                          0\n",
       "PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN    0\n",
       "Registered_Office_Address                 0\n",
       "REGISTRAR_OF_COMPANIES                    0\n",
       "EMAIL_ADDR                                0\n",
       "Latest_Year_AR                            1\n",
       "Latest_Year_BS                            1\n",
       "Registration_Year                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2fdfc540",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CORPORATE_IDENTIFICATION_NUMBER</th>\n",
       "      <th>Company_Name</th>\n",
       "      <th>Company_status</th>\n",
       "      <th>Company_class</th>\n",
       "      <th>Company_Category</th>\n",
       "      <th>Company_sub_category</th>\n",
       "      <th>REGISTERED_STATE</th>\n",
       "      <th>AUTHORIZED_CAP</th>\n",
       "      <th>PAIDUP_CAPITAL</th>\n",
       "      <th>Industrial_Class</th>\n",
       "      <th>PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN</th>\n",
       "      <th>Registered_Office_Address</th>\n",
       "      <th>REGISTRAR_OF_COMPANIES</th>\n",
       "      <th>Latest_Year_AR</th>\n",
       "      <th>Latest_Year_BS</th>\n",
       "      <th>Registration_Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U63030SK2016PTC013231</td>\n",
       "      <td>TRIP OF LIFETIME TRAVELTREK PRIVATELIMITED</td>\n",
       "      <td>Active</td>\n",
       "      <td>Private</td>\n",
       "      <td>Company limited by Shares</td>\n",
       "      <td>Non-govt company</td>\n",
       "      <td>Sikkim</td>\n",
       "      <td>100000</td>\n",
       "      <td>100000</td>\n",
       "      <td>63030</td>\n",
       "      <td>Transport, storage and communications</td>\n",
       "      <td>Tadong, Daragoan, Near Greendale School, Below...</td>\n",
       "      <td>RoC-Shillong</td>\n",
       "      <td>31-03-18</td>\n",
       "      <td>31-03-18</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U74999SK2016PTC013232</td>\n",
       "      <td>PANDIM ONLINE SOLUTIONS PRIVATE LIMITED</td>\n",
       "      <td>Active</td>\n",
       "      <td>Private</td>\n",
       "      <td>Company limited by Shares</td>\n",
       "      <td>Non-govt company</td>\n",
       "      <td>Sikkim</td>\n",
       "      <td>100000</td>\n",
       "      <td>100000</td>\n",
       "      <td>74999</td>\n",
       "      <td>Other business activities</td>\n",
       "      <td>6th Mile,Tadong, Near Sikkim University, Gangt...</td>\n",
       "      <td>RoC-Shillong</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CORPORATE_IDENTIFICATION_NUMBER                                Company_Name  \\\n",
       "0           U63030SK2016PTC013231  TRIP OF LIFETIME TRAVELTREK PRIVATELIMITED   \n",
       "1           U74999SK2016PTC013232     PANDIM ONLINE SOLUTIONS PRIVATE LIMITED   \n",
       "\n",
       "  Company_status Company_class           Company_Category  \\\n",
       "0         Active       Private  Company limited by Shares   \n",
       "1         Active       Private  Company limited by Shares   \n",
       "\n",
       "  Company_sub_category REGISTERED_STATE  AUTHORIZED_CAP  PAIDUP_CAPITAL  \\\n",
       "0     Non-govt company           Sikkim          100000          100000   \n",
       "1     Non-govt company           Sikkim          100000          100000   \n",
       "\n",
       "   Industrial_Class PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN  \\\n",
       "0             63030  Transport, storage and communications   \n",
       "1             74999              Other business activities   \n",
       "\n",
       "                           Registered_Office_Address REGISTRAR_OF_COMPANIES  \\\n",
       "0  Tadong, Daragoan, Near Greendale School, Below...           RoC-Shillong   \n",
       "1  6th Mile,Tadong, Near Sikkim University, Gangt...           RoC-Shillong   \n",
       "\n",
       "  Latest_Year_AR Latest_Year_BS  Registration_Year  \n",
       "0       31-03-18       31-03-18               2016  \n",
       "1            NaN            NaN               2016  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.drop(['DATE_OF_REGISTRATION','EMAIL_ADDR'],axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3d70e98a",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2100x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# EDA - Trends over time\n",
    "plt.figure(figsize=(21, 9))\n",
    "sns.countplot(x='Registration_Year', data=df[(df['Registration_Year']<=2020) & (df['Registration_Year']>=1990)])\n",
    "plt.title('Company Registrations Over Time')\n",
    "plt.xlabel('Registration Year')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b6a291e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# EDA - Company Status\n",
    "plt.figure(figsize=(12, 5))\n",
    "sns.countplot(x='Company_status', data=df[(df['Registration_Year']<=2020) & (df['Registration_Year']>=1990)])\n",
    "plt.title('Distribution of Company Status')\n",
    "plt.xlabel('Company Status')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=45,ha='right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b2471ccc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WrZsbb7xxhV/2rI7vfve7Oeuss/LTn/40r776ao499thsscUWmT9/fv785z/nyiuvzOGHH/65H8N2wAEH5I477sigQYNy6KGH5u23385Pf/rTtGzZMq+//nrVemeffXbeeeed7L333mndunU+/PDD/PznP692Tf2BBx6YbbfdNt26dcumm26aadOmZfTo0WnXrl223HLLJMnPf/7zfOtb30rPnj1zwgknpH379pk3b17++te/5u677666W/yqvBYAX1EJAFbi2muvLSWp+qpXr15ps802K+2+++6l8847rzRr1qwVthk+fHjpH/9pmTx5cumggw4qtWvXrlReXl5q2rRpaffddy/ddddd1bZ78MEHSzvuuGOpvLy8lKR09NFHV3u9v//971+6r1KpVGrXrl1p//33L91+++2lzp07l+rVq1dq37596eKLL15h+7/85S+lPn36lBo1alTadNNNSyeffHLpnnvuKSUpPfzww1XrzZkzp3TooYeWGjduXCorK6u2zySl4cOHV3vdF198sXTggQeWKisrS/Xq1Sttv/32pWuvvbbaOg8//HApSem2226rtvzNN98sJVlh/ZWZOHFiaa+99io1bNiwVL9+/dIuu+xSuvvuu1f6ehdeeOGXvt7K1l3+M/DUU0+tdP5//D4tXLiwNGzYsNJmm21WqqioKO2yyy6lyZMnl9q1a1f197nck08+WerRo0epYcOGpc0337w0fPjw0lVXXVVKUnrzzTdLpdL6/9mZNGlSqXv37qUGDRqUNt1009LAgQNLzzzzzAp/Hyvb9os88sgjpUMPPbTUsmXLUt26dUuNGjUqde/evXThhReW5s6dW7Xeyr5P//u//1tq3759qby8vNSpU6fSr371qxX2/8c//rHUt2/f0uabb171/9H99tuvNHHixKp1LrroolKPHj1KzZo1K9WrV6/Utm3b0rHHHlt66623qu3vzTffLB1zzDGlzTffvFS3bt3SpptuWurRo0fp3HPPXe3XAmDNlZVKX3JrXgAAAGC9cE06AAAAFIRIBwAAgIIQ6QAAAFAQIh0AAAAKQqQDAABAQWxwn5O+dOnSvPfee9l4443X++fIAgAAsOEplUqZN29eWrVqlVq1vvhY+QYX6e+9917atGlT02MAAACwgXn77bfTunXrL1xng4v0jTfeOMmyb06jRo1qeBoAAAD+1c2dOzdt2rSp6tEvssFF+vJT3Bs1aiTSAQAAWG9W5ZJrN44DAACAghDpAAAAUBAiHQAAAApCpAMAAEBBiHQAAAAoCJEOAAAABSHSAQAAoCBEOgAAABSESAcAAICCEOkAAABQECIdAAAACkKkAwAAQEGIdAAAACgIkQ4AAAAFIdIBAACgIEQ6AAAAFESNRvqjjz6aAw88MK1atUpZWVl+//vff+k2jzzySLp27ZqKiop07NgxV1xxxbofFAAAANaDGo30Tz75JNtvv31+8YtfrNL6b775Zvbbb7/07Nkzzz77bH784x9n8ODB+d3vfreOJwUAAIB1r05N7rxv377p27fvKq9/xRVXpG3bthk9enSSpFOnTnn66afzs5/9LIcccshKt1m4cGEWLlxY9Xju3LlfaWYAAABYV2o00lfX5MmT06dPn2rL9tlnn1x99dX57LPPUrdu3RW2GTVqVM4555z1NeI61/W039T0CACsBVMu7F/TI2xwpo/crqZHAGAtaHv2izU9wjr1tbpx3MyZM9O8efNqy5o3b57Fixdn9uzZK93mjDPOyEcffVT19fbbb6+PUQEAAGC1fa2OpCdJWVlZtcelUmmly5crLy9PeXn5Op8LAAAAvqqv1ZH0Fi1aZObMmdWWzZo1K3Xq1EnTpk1raCoAAABYO75Wkd69e/eMGzeu2rIHHngg3bp1W+n16AAAAPB1UqOR/vHHH+e5557Lc889l2TZR6w999xzmT59epJl15P37///b6xz/PHHZ9q0aRk6dGheeeWVXHPNNbn66qtz6qmn1sT4AAAAsFbV6DXpTz/9dPbcc8+qx0OHDk2SHH300bnuuusyY8aMqmBPkg4dOuTee+/NkCFDcvnll6dVq1a59NJLP/fj1wAAAODrpEYjfY899qi68dvKXHfddSss23333fPMM8+sw6kAAACgZnytrkkHAACAf2UiHQAAAApCpAMAAEBBiHQAAAAoCJEOAAAABSHSAQAAoCBEOgAAABSESAcAAICCEOkAAABQECIdAAAACkKkAwAAQEGIdAAAACgIkQ4AAAAFIdIBAACgIEQ6AAAAFIRIBwAAgIIQ6QAAAFAQIh0AAAAKQqQDAABAQYh0AAAAKAiRDgAAAAUh0gEAAKAgRDoAAAAUhEgHAACAghDpAAAAUBAiHQAAAApCpAMAAEBBiHQAAAAoCJEOAAAABSHSAQAAoCBEOgAAABSESAcAAICCEOkAAABQECIdAAAACkKkAwAAQEGIdAAAACgIkQ4AAAAFIdIBAACgIEQ6AAAAFIRIBwAAgIIQ6QAAAFAQIh0AAAAKQqQDAABAQYh0AAAAKAiRDgAAAAUh0gEAAKAgRDoAAAAUhEgHAACAghDpAAAAUBAiHQAAAApCpAMAAEBBiHQAAAAoCJEOAAAABSHSAQAAoCBEOgAAABSESAcAAICCEOkAAABQECIdAAAACkKkAwAAQEGIdAAAACgIkQ4AAAAFIdIBAACgIEQ6AAAAFIRIBwAAgIIQ6QAAAFAQIh0AAAAKQqQDAABAQYh0AAAAKAiRDgAAAAUh0gEAAKAgRDoAAAAUhEgHAACAghDpAAAAUBAiHQAAAApCpAMAAEBBiHQAAAAoCJEOAAAABVHjkT5mzJh06NAhFRUV6dq1ayZOnPiF6994443Zfvvt06BBg7Rs2TL/9V//lffff389TQsAAADrTo1G+q233ppTTjklZ555Zp599tn07Nkzffv2zfTp01e6/mOPPZb+/fvn2GOPzUsvvZTbbrstTz31VAYOHLieJwcAAIC1r0Yj/eKLL86xxx6bgQMHplOnThk9enTatGmTsWPHrnT9J554Iu3bt8/gwYPToUOHfOtb38pxxx2Xp59++nP3sXDhwsydO7faFwAAABRRjUX6okWLMmXKlPTp06fa8j59+mTSpEkr3aZHjx555513cu+996ZUKuVvf/tbbr/99uy///6fu59Ro0alsrKy6qtNmzZr9X0AAADA2lJjkT579uwsWbIkzZs3r7a8efPmmTlz5kq36dGjR2688cYcfvjhqVevXlq0aJHGjRvnsssu+9z9nHHGGfnoo4+qvt5+++21+j4AAABgbanxG8eVlZVVe1wqlVZYttzLL7+cwYMH5+yzz86UKVNy33335c0338zxxx//ua9fXl6eRo0aVfsCAACAIqpTUztu1qxZateuvcJR81mzZq1wdH25UaNGZdddd81pp52WJOnSpUsaNmyYnj175txzz03Lli3X+dwAAACwrtTYkfR69eqla9euGTduXLXl48aNS48ePVa6zfz581OrVvWRa9eunWTZEXgAAAD4OqvR092HDh2aq666Ktdcc01eeeWVDBkyJNOnT686ff2MM85I//79q9Y/8MADc8cdd2Ts2LF544038vjjj2fw4MH55je/mVatWtXU2wAAAIC1osZOd0+Sww8/PO+//35GjhyZGTNmZNttt829996bdu3aJUlmzJhR7TPTBwwYkHnz5uUXv/hFhg0blsaNG2evvfbK+eefX1NvAQAAANaastIGdp743LlzU1lZmY8++uhreRO5rqf9pqZHAGAtmHJh/y9fibVq+sjtanoEANaCtme/WNMjrLbV6dAav7s7AAAAsIxIBwAAgIIQ6QAAAFAQIh0AAAAKQqQDAABAQYh0AAAAKAiRDgAAAAUh0gEAAKAgRDoAAAAUhEgHAACAghDpAAAAUBAiHQAAAApCpAMAAEBBiHQAAAAoCJEOAAAABSHSAQAAoCBEOgAAABSESAcAAICCEOkAAABQECIdAAAACkKkAwAAQEGIdAAAACgIkQ4AAAAFIdIBAACgIEQ6AAAAFIRIBwAAgIIQ6QAAAFAQIh0AAAAKQqQDAABAQYh0AAAAKAiRDgAAAAUh0gEAAKAgRDoAAAAUhEgHAACAghDpAAAAUBAiHQAAAApCpAMAAEBBiHQAAAAoCJEOAAAABSHSAQAAoCBEOgAAABSESAcAAICCEOkAAABQECIdAAAACkKkAwAAQEGIdAAAACgIkQ4AAAAFIdIBAACgIEQ6AAAAFIRIBwAAgIIQ6QAAAFAQIh0AAAAKQqQDAABAQYh0AAAAKAiRDgAAAAUh0gEAAKAgRDoAAAAUhEgHAACAghDpAAAAUBAiHQAAAApCpAMAAEBBiHQAAAAoCJEOAAAABSHSAQAAoCBEOgAAABSESAcAAICCEOkAAABQECIdAAAACkKkAwAAQEGIdAAAACgIkQ4AAAAFIdIBAACgIEQ6AAAAFIRIBwAAgIIQ6QAAAFAQIh0AAAAKQqQDAABAQdR4pI8ZMyYdOnRIRUVFunbtmokTJ37h+gsXLsyZZ56Zdu3apby8PFtssUWuueaa9TQtAAAArDt1anLnt956a0455ZSMGTMmu+66a6688sr07ds3L7/8ctq2bbvSbQ477LD87W9/y9VXX51vfOMbmTVrVhYvXryeJwcAAIC1r0Yj/eKLL86xxx6bgQMHJklGjx6d+++/P2PHjs2oUaNWWP++++7LI488kjfeeCObbLJJkqR9+/ZfuI+FCxdm4cKFVY/nzp279t4AAAAArEU1drr7okWLMmXKlPTp06fa8j59+mTSpEkr3eauu+5Kt27dcsEFF2TzzTfPVlttlVNPPTWffvrp5+5n1KhRqaysrPpq06bNWn0fAAAAsLbU2JH02bNnZ8mSJWnevHm15c2bN8/MmTNXus0bb7yRxx57LBUVFbnzzjsze/bsDBo0KHPmzPnc69LPOOOMDB06tOrx3LlzhToAAACFVKOnuydJWVlZtcelUmmFZcstXbo0ZWVlufHGG1NZWZlk2Snzhx56aC6//PLUr19/hW3Ky8tTXl6+9gcHAACAtazGTndv1qxZateuvcJR81mzZq1wdH25li1bZvPNN68K9CTp1KlTSqVS3nnnnXU6LwAAAKxrNRbp9erVS9euXTNu3Lhqy8eNG5cePXqsdJtdd9017733Xj7++OOqZX/5y19Sq1attG7dep3OCwAAAOtajX5O+tChQ3PVVVflmmuuySuvvJIhQ4Zk+vTpOf7445Msu568f//+Vev369cvTZs2zX/913/l5ZdfzqOPPprTTjstxxxzzEpPdQcAAICvkxq9Jv3www/P+++/n5EjR2bGjBnZdtttc++996Zdu3ZJkhkzZmT69OlV62+00UYZN25cTj755HTr1i1NmzbNYYcdlnPPPbem3gIAAACsNTV+47hBgwZl0KBBK33uuuuuW2HZv/3bv61wijwAAAD8K6jR090BAACA/0+kAwAAQEGIdAAAACgIkQ4AAAAFIdIBAACgIEQ6AAAAFIRIBwAAgIIQ6QAAAFAQaxTpHTt2zPvvv7/C8g8//DAdO3b8ykMBAADAhmiNIv2tt97KkiVLVli+cOHCvPvuu195KAAAANgQ1Vmdle+6666qP99///2prKyserxkyZI89NBDad++/VobDgAAADYkqxXp//Ef/5EkKSsry9FHH13tubp166Z9+/a56KKL1tpwAAAAsCFZrUhfunRpkqRDhw556qmn0qxZs3UyFAAAAGyIVivSl3vzzTfX9hwAAACwwVujSE+Shx56KA899FBmzZpVdYR9uWuuueYrDwYAAAAbmjWK9HPOOScjR45Mt27d0rJly5SVla3tuQAAAGCDs0aRfsUVV+S6667LUUcdtbbnAQAAgA3WGn1O+qJFi9KjR4+1PQsAAABs0NYo0gcOHJibbrppbc8CAAAAG7Q1Ot19wYIF+eUvf5kHH3wwXbp0Sd26das9f/HFF6+V4QAAAGBDskaR/sILL2SHHXZIkkydOrXac24iBwAAAGtmjSL94YcfXttzAAAAwAZvja5JBwAAANa+NTqSvueee37hae3jx49f44EAAABgQ7VGkb78evTlPvvsszz33HOZOnVqjj766LUxFwAAAGxw1ijSL7nkkpUuHzFiRD7++OOvNBAAAABsqNbqNelHHnlkrrnmmrX5kgAAALDBWKuRPnny5FRUVKzNlwQAAIANxhqd7n7wwQdXe1wqlTJjxow8/fTTOeuss9bKYAAAALChWaNIr6ysrPa4Vq1a2XrrrTNy5Mj06dNnrQwGAAAAG5o1ivRrr712bc8BAAAAG7w1ivTlpkyZkldeeSVlZWXZZpttsuOOO66tuQAAAGCDs0aRPmvWrBxxxBGZMGFCGjdunFKplI8++ih77rlnbrnllmy66aZre04AAAD4l7dGd3c/+eSTM3fu3Lz00kuZM2dOPvjgg0ydOjVz587N4MGD1/aMAAAAsEFYoyPp9913Xx588MF06tSpatk222yTyy+/3I3jAAAAYA2t0ZH0pUuXpm7duissr1u3bpYuXfqVhwIAAIAN0RpF+l577ZUf/OAHee+996qWvfvuuxkyZEj23nvvtTYcAAAAbEjWKNJ/8YtfZN68eWnfvn222GKLfOMb30iHDh0yb968XHbZZWt7RgAAANggrNE16W3atMkzzzyTcePG5dVXX02pVMo222yTXr16re35AAAAYIOxWkfSx48fn2222SZz585NkvTu3Tsnn3xyBg8enJ122imdO3fOxIkT18mgAAAA8K9utSJ99OjR+f73v59GjRqt8FxlZWWOO+64XHzxxWttOAAAANiQrFakP//889l3330/9/k+ffpkypQpX3koAAAA2BCtVqT/7W9/W+lHry1Xp06d/P3vf//KQwEAAMCGaLUiffPNN8+LL774uc+/8MILadmy5VceCgAAADZEqxXp++23X84+++wsWLBghec+/fTTDB8+PAcccMBaGw4AAAA2JKv1EWw/+clPcscdd2SrrbbKSSedlK233jplZWV55ZVXcvnll2fJkiU588wz19WsAAAA8C9ttSK9efPmmTRpUk444YScccYZKZVKSZKysrLss88+GTNmTJo3b75OBgUAAIB/dasV6UnSrl273Hvvvfnggw/y17/+NaVSKVtuuWWaNGmyLuYDAACADcZqR/pyTZo0yU477bQ2ZwEAAIAN2mrdOA4AAABYd0Q6AAAAFIRIBwAAgIIQ6QAAAFAQIh0AAAAKQqQDAABAQYh0AAAAKAiRDgAAAAUh0gEAAKAgRDoAAAAUhEgHAACAghDpAAAAUBAiHQAAAApCpAMAAEBBiHQAAAAoCJEOAAAABSHSAQAAoCBEOgAAABSESAcAAICCEOkAAABQECIdAAAACkKkAwAAQEGIdAAAACgIkQ4AAAAFIdIBAACgIEQ6AAAAFIRIBwAAgIIQ6QAAAFAQNR7pY8aMSYcOHVJRUZGuXbtm4sSJq7Td448/njp16mSHHXZYtwMCAADAelKjkX7rrbfmlFNOyZlnnplnn302PXv2TN++fTN9+vQv3O6jjz5K//79s/fee6+nSQEAAGDdq9FIv/jii3Psscdm4MCB6dSpU0aPHp02bdpk7NixX7jdcccdl379+qV79+5fuo+FCxdm7ty51b4AAACgiGos0hctWpQpU6akT58+1Zb36dMnkyZN+tztrr322vzf//1fhg8fvkr7GTVqVCorK6u+2rRp85XmBgAAgHWlxiJ99uzZWbJkSZo3b15tefPmzTNz5syVbvP666/nRz/6UW688cbUqVNnlfZzxhln5KOPPqr6evvtt7/y7AAAALAurFrprkNlZWXVHpdKpRWWJcmSJUvSr1+/nHPOOdlqq61W+fXLy8tTXl7+lecEAACAda3GIr1Zs2apXbv2CkfNZ82atcLR9SSZN29enn766Tz77LM56aSTkiRLly5NqVRKnTp18sADD2SvvfZaL7MDAADAulBjp7vXq1cvXbt2zbhx46otHzduXHr06LHC+o0aNcqLL76Y5557rurr+OOPz9Zbb53nnnsuO++88/oaHQAAANaJGj3dfejQoTnqqKPSrVu3dO/ePb/85S8zffr0HH/88UmWXU/+7rvv5je/+U1q1aqVbbfdttr2m222WSoqKlZYDgAAAF9HNRrphx9+eN5///2MHDkyM2bMyLbbbpt777037dq1S5LMmDHjSz8zHQAAAP5VlJVKpVJND7E+zZ07N5WVlfnoo4/SqFGjmh5ntXU97Tc1PQIAa8GUC/vX9AgbnOkjt6vpEQBYC9qe/WJNj7DaVqdDa+yadAAAAKA6kQ4AAAAFIdIBAACgIEQ6AAAAFIRIBwAAgIIQ6QAAAFAQIh0AAAAKQqQDAABAQYh0AAAAKAiRDgAAAAUh0gEAAKAgRDoAAAAUhEgHAACAghDpAAAAUBAiHQAAAApCpAMAAEBBiHQAAAAoCJEOAAAABSHSAQAAoCBEOgAAABSESAcAAICCEOkAAABQECIdAAAACkKkAwAAQEGIdAAAACgIkQ4AAAAFIdIBAACgIEQ6AAAAFIRIBwAAgIIQ6QAAAFAQIh0AAAAKQqQDAABAQYh0AAAAKAiRDgAAAAUh0gEAAKAgRDoAAAAUhEgHAACAghDpAAAAUBAiHQAAAApCpAMAAEBBiHQAAAAoCJEOAAAABSHSAQAAoCBEOgAAABSESAcAAICCEOkAAABQECIdAAAACkKkAwAAQEGIdAAAACgIkQ4AAAAFIdIBAACgIEQ6AAAAFIRIBwAAgIIQ6QAAAFAQIh0AAAAKQqQDAABAQYh0AAAAKAiRDgAAAAUh0gEAAKAgRDoAAAAUhEgHAACAghDpAAAAUBAiHQAAAApCpAMAAEBBiHQAAAAoCJEOAAAABSHSAQAAoCBEOgAAABSESAcAAICCEOkAAABQECIdAAAACkKkAwAAQEGIdAAAACgIkQ4AAAAFIdIBAACgIEQ6AAAAFIRIBwAAgIKo8UgfM2ZMOnTokIqKinTt2jUTJ0783HXvuOOO9O7dO5tuumkaNWqU7t275/7771+P0wIAAMC6U6ORfuutt+aUU07JmWeemWeffTY9e/ZM3759M3369JWu/+ijj6Z379659957M2XKlOy555458MAD8+yzz67nyQEAAGDtKyuVSqWa2vnOO++cf//3f8/YsWOrlnXq1Cn/8R//kVGjRq3Sa3Tu3DmHH354zj777JU+v3DhwixcuLDq8dy5c9OmTZt89NFHadSo0Vd7AzWg62m/qekRAFgLplzYv6ZH2OBMH7ldTY8AwFrQ9uwXa3qE1TZ37txUVlauUofW2JH0RYsWZcqUKenTp0+15X369MmkSZNW6TWWLl2aefPmZZNNNvncdUaNGpXKysqqrzZt2nyluQEAAGBdqbFInz17dpYsWZLmzZtXW968efPMnDlzlV7joosuyieffJLDDjvsc9c544wz8tFHH1V9vf32219pbgAAAFhX6tT0AGVlZdUel0qlFZatzM0335wRI0bkD3/4QzbbbLPPXa+8vDzl5eVfeU4AAABY12os0ps1a5batWuvcNR81qxZKxxd/2e33nprjj322Nx2223p1avXuhwTAAAA1psaO929Xr166dq1a8aNG1dt+bhx49KjR4/P3e7mm2/OgAEDctNNN2X//fdf12MCAADAelOjp7sPHTo0Rx11VLp165bu3bvnl7/8ZaZPn57jjz8+ybLryd9999385jfL7mh+8803p3///vn5z3+eXXbZpeoofP369VNZWVlj7wMAAADWhhqN9MMPPzzvv/9+Ro4cmRkzZmTbbbfNvffem3bt2iVJZsyYUe0z06+88sosXrw4J554Yk488cSq5UcffXSuu+669T0+AAAArFU1fuO4QYMGZdCgQSt97p/De8KECet+IAAAAKghNXZNOgAAAFCdSAcAAICCEOkAAABQECIdAAAACkKkAwAAQEGIdAAAACgIkQ4AAAAFIdIBAACgIEQ6AAAAFIRIBwAAgIIQ6QAAAFAQIh0AAAAKQqQDAABAQYh0AAAAKAiRDgAAAAUh0gEAAKAgRDoAAAAUhEgHAACAghDpAAAAUBAiHQAAAApCpAMAAEBBiHQAAAAoCJEOAAAABSHSAQAAoCBEOgAAABSESAcAAICCEOkAAABQECIdAAAACkKkAwAAQEGIdAAAACgIkQ4AAAAFIdIBAACgIEQ6AAAAFIRIBwAAgIIQ6QAAAFAQIh0AAAAKQqQDAABAQYh0AAAAKAiRDgAAAAUh0gEAAKAgRDoAAAAUhEgHAACAghDpAAAAUBAiHQAAAApCpAMAAEBBiHQAAAAoCJEOAAAABSHSAQAAoCBEOgAAABSESAcAAICCEOkAAABQECIdAAAACkKkAwAAQEGIdAAAACgIkQ4AAAAFIdIBAACgIEQ6AAAAFIRIBwAAgIIQ6QAAAFAQIh0AAAAKQqQDAABAQYh0AAAAKAiRDgAAAAUh0gEAAKAgRDoAAAAUhEgHAACAghDpAAAAUBAiHQAAAApCpAMAAEBBiHQAAAAoCJEOAAAABSHSAQAAoCBEOgAAABSESAcAAICCEOkAAABQECIdAAAACkKkAwAAQEHUeKSPGTMmHTp0SEVFRbp27ZqJEyd+4fqPPPJIunbtmoqKinTs2DFXXHHFepoUAAAA1q0ajfRbb701p5xySs4888w8++yz6dmzZ/r27Zvp06evdP0333wz++23X3r27Jlnn302P/7xjzN48OD87ne/W8+TAwAAwNpXpyZ3fvHFF+fYY4/NwIEDkySjR4/O/fffn7Fjx2bUqFErrH/FFVekbdu2GT16dJKkU6dOefrpp/Ozn/0shxxyyEr3sXDhwixcuLDq8UcffZQkmTt37lp+N+vHkoWf1vQIAKwFX9d/h77O5i1YUtMjALAWfB3/DV0+c6lU+tJ1ayzSFy1alClTpuRHP/pRteV9+vTJpEmTVrrN5MmT06dPn2rL9tlnn1x99dX57LPPUrdu3RW2GTVqVM4555wVlrdp0+YrTA8AX03lZcfX9AgA8PU0qrKmJ1hj8+bNS2XlF89fY5E+e/bsLFmyJM2bN6+2vHnz5pk5c+ZKt5k5c+ZK11+8eHFmz56dli1brrDNGWeckaFDh1Y9Xrp0aebMmZOmTZumrKxsLbwTYG2aO3du2rRpk7fffjuNGjWq6XEA4GvDv6FQXKVSKfPmzUurVq2+dN0aPd09yQqhXCqVvjCeV7b+ypYvV15envLy8mrLGjduvAaTAutTo0aN/AcGAKwB/4ZCMX3ZEfTlauzGcc2aNUvt2rVXOGo+a9asFY6WL9eiRYuVrl+nTp00bdp0nc0KAAAA60ONRXq9evXStWvXjBs3rtrycePGpUePHivdpnv37ius/8ADD6Rbt24rvR4dAAAAvk5q9CPYhg4dmquuuirXXHNNXnnllQwZMiTTp0/P8ccvu5nOGWeckf79+1etf/zxx2fatGkZOnRoXnnllVxzzTW5+uqrc+qpp9bUWwDWsvLy8gwfPnyFy1QAgC/m31D411BWWpV7wK9DY8aMyQUXXJAZM2Zk2223zSWXXJLddtstSTJgwIC89dZbmTBhQtX6jzzySIYMGZKXXnoprVq1yg9/+MOqqAcAAICvsxqPdAAAAGCZGj3dHQAAAPj/RDoAAAAUhEgHAACAghDpAAAAUBAiHQAAAApCpAPrlQ+UAACAz1enpgcA/rXNmjUrb7/9dpYuXZrtttsuFRUVNT0SAHxtlUqllJWV1fQYwDok0oF15oUXXsghhxySxYsX57PPPkvDhg1zxRVXZJdddkn9+vVrejwAKLQ333wz9913X0qlUjbbbLMceuihAh02AE53B9aJmTNn5jvf+U6++93v5k9/+lPuvPPO7Ljjjvn2t7+d3/zmN5k3b15NjwgAhTV16tTssssu+f3vf59bbrklxxxzTA455JC8+OKLLh2Df3GOpAPrxIwZM1JeXp4BAwZkq622SpLccsstGTZsWIYNG5YGDRrkqKOOctoeAPyTOXPm5Mgjj8yAAQNy/vnn59NPP82jjz6aAw44IAsXLszw4cOz00471fSYwDoi0oF14v3338+0adOy0UYbJUkWLFiQioqKXHTRRVm4cGFOOumk7LnnnmndunUNTwoAxTJnzpzUrl07xxxzTJKkXr166dSpU/7t3/4tEyZMyKJFi3L33XenvLzcL7vhX5DT3YF1Yu+9906nTp1y0kknZenSpamoqMiiRYuSJL/4xS+yzTbb5Lzzzkviju8A8I8WL16cV155Jc8//3ySpHbt2lm8eHFatmyZ22+/PU888URGjx6dJAId/gWJdGCt+Pjjj/Pxxx9n1qxZSZIlS5bkBz/4QaZNm5Yf/vCHKZVKqVevXhYvXpwk6dChQz788MMk/gMDAP5Ry5Ytc8QRR+TnP/95zjvvvNx5553ZaaedssUWW2TffffNwIED89JLL9X0mMA6ItKBr+zll1/OIYcckr322itdu3bN/fffnzp16uSggw7KvvvumwkTJuTkk09OktSpU6fqfxs0aJAlS5Y4kg7ABm3WrFmZOnVqHn/88ZRKpVRWVmbQoEHZaaed8vOf/zzDhw/PCSeckLFjxyZJ5s+fn3feece/n/Avqqzk/93AVzB16tT07NkzAwYMSJcuXTJ58uT86U9/ynPPPZemTZvmgw8+yNixY3PTTTelVCpl3333zXvvvZe77747f/7zn9O5c+eafgsAUGNeeOGFHHbYYamoqMiHH36Y+vXr5+KLL06vXr1St27dzJ49OwsWLKi6h8uSJUty9NFHp1WrVrngggtqeHpgXXDjOGCNTZ8+PUcccUROOOGEquvL27Ztm7///e8pKyvL22+/nTZt2uTUU0/Nvvvum8suuyyvv/56GjdunCeeeEKgA7BBmz59eg488MAcc8wx+d73vpfy8vIcc8wx6d+/f370ox+lf//+2XTTTavWf+mll3LrrbfmnnvuyaRJk2pwcmBdEunAGps5c2Y6d+6c73//+1XLJkyYkEceeSS777573nvvvZxwwgk566yz8u///u+59tprkyRLly5NrVqutgFgw/b000+nY8eOGTZsWOrXr5/atWtn0KBB6devX371q1+lcePGOfbYY7N06dIsXLgwl156ae6///48/PDD6dSpU02PD6wjTncHvpJ33nmn6hS8q666KieddFKuvPLKbLvttnnttddy5JFH5ne/+10OOuigqm18XAwAJBdeeGFGjx6dd999t2rZuHHjcsstt2T27Nl58skn89e//jUNGzZMsuyj2ebPn+/jS+FfnENZwFey/D8Ult+1ffz48Tn66KPTtWvX9OvXLzvuuGMmTpxYbRuBDsCGbPkxsgMOOCB169bN0KFDM2vWrDz99NM56KCD0rlz5/zhD39IgwYNcuONNyZZdi36JptsItBhA+B0d2CV/eUvf8mVV16ZDz74IB06dMiRRx6ZDh06JFl2t/aBAwdWW/+DDz5I48aNs8MOO9TAtABQLPPnz09FRUUWLVqUioqKtG/fPqeeemouuOCC3HTTTfn000/z/e9/P0OHDs2SJUtSXl6eOXPmJFn2WenAhkGkA6vk5ZdfTvfu3dOzZ880atQol156aR544IEcddRRGThwYGrVqpXFixdXfcRaklx88cV5++23s/vuu9fg5ABQ86ZOnZohQ4Zk8eLFee+99/KDH/wghx56aE466aQccsghefHFF1NZWZmdd945ybKg33zzzdO+ffskLhWDDYlIB77UokWL8tOf/jTf/e53c9VVVyVJZs+enUGDBuXaa6/Np59+mpNPPrkq0B977LHccsstuemmm/LQQw+lXbt2NTk+ANSo119/PXvttVe+973vpUePHpk6dWpOOumkPPjggznttNPSvXv3tGzZsmr9BQsWZNSoUVW/IE9cKgYbEpEOfKl69erlww8/zCabbJJk2XVxzZo1yxVXXJEhQ4bkt7/9bbbYYosccMAB+dvf/papU6fmtddey6OPPpptt922hqcHgJp1+eWXZ++9984ll1ySJPnud7+badOm5be//W3q1KmTevXqpWvXrkmSKVOm5JJLLslDDz2Ue++91y+6YQPkxnHAF1q6dGk+++yzNGjQoOrus7Vr185nn32WTTbZJBdffHGSZOzYsUmS5s2bp1+/frn99tsFOgAkeffdd9O0adMkybx585IkW265ZXr27JkXXnghd955Z9W6Xbt2Ta9evTJx4sTsuOOONTIvULN8BBuwSp544on06NEjF110UYYMGZJk2Wnw9erVy3PPPZdddtkljz/+eNWRAABgmWHDhuXmm2/O66+/noYNG2bmzJnZZptt8tvf/jZ//etfM2zYsLz22mvu3A4kcSQdWInp06fn9ttvz8UXX5x33nknS5YsyS677JJRo0blhz/8YS6//PIky06DT5YdbW/fvn0aN25cg1MDQDHMmTMnf//736seDxo0KN/4xjfStGnT9O3bN1tssUUOPfTQ9OrVK9/97nfTpEmTTJs2rQYnBorENelANS+++GK+/e1vp1WrVnn11Vdz2WWXZfLkyWnRokUGDBiQ+fPn55RTTsm7776bY445Jo0aNcodd9yRJUuWZOONN67p8QGgRr3xxhvp3bt3vve97+X4449Pq1atssUWW+SOO+7Ir3/96yxatChHHnlkvve97yVZ9ovxBg0apLKysoYnB4pCpANVXnvttfTq1SvHHXdchgwZksaNG6d169Z54IEH0r9//zRv3jynn356OnTokFNOOSXXX399GjRokE8++SR33XVXNttss5p+CwBQox544IG8+eab+eMf/5iKiooMGDAgrVq1SrNmzTJs2LAsXbo0tWr9/5NZb7nlljRs2DAtWrSowamBInFNOpAk+fjjjzNo0KA0btw4l1xySWrVqpWysrJ85zvfSffu3TNnzpzsu+++2X777dO0adO88847mTp1amrVqpVtttnGdXQAkOSFF17IxRdfnC233DJjxozJCSeckBNPPDFNmjSptt7EiRNz22235brrrsujjz6aHXbYoWYGBgrHkXQgSbLRRhtl//33T4cOHVK7du0kyU9/+tPce++9qVu3bt55553cfvvtVUfZW7duLcwB4B+USqWUSqVMmjQp1157bZYsWZJf/vKX2XjjjfPII4+kU6dO+Z//+Z8kSa1atbJ48eJMnjw5nTt3ruHJgSJxJB02cPPnz0+tWrVSUVFRbfkLL7yQI444IhdccEH23Xff1KlTJ4MGDcr48eMzZcqUNGzYsIYmBoDieOedd/L3v/+92sel7bPPPrnyyivTvn37XHjhhTnnnHNSp06d/Pa3v02fPn2q1luwYMEK//4CuLs7bMCmTp2a//zP/8xTTz2VTz/9tNpz7du3z4MPPpgDDjggy3+Xt8suu6SioiJLly6tiXEBoFBeeuml9OjRIzfccEOSZMmSJUmWfUTpo48+mmTZ/V5q166d+vXr5/nnn897771Xtb1AB1ZGpMMG6qWXXspuu+2W1q1bp2PHjqlfv3615xs1apSWLVsmSerWrZskefLJJ7PVVltVPQaADdXzzz+fb37zm6lTp05uuummzJo1q+pysZ133jm1atXK4MGD86c//SnPPfdcBg8enBEjRuSWW26pinmAlXFNOmyAPvnkkwwdOjRHHHFE1Weev/rqq1m4cGE22WSTtGnTJklSVlaWJPn0009z3nnn5dZbb80jjzziN/8AbNCef/75dO/ePUOGDMmQIUOyxx575Fe/+lXOOOOM1KpVK1tvvXX69++fFi1a5K677kqHDh1yxhlnpHbt2jnwwAOrYh5gZVyTDhughQsXplevXrn00kvTpUuX7L///pkzZ05effXVdO7cOQMHDsyxxx6bJLnvvvvyi1/8IlOnTs2dd95Z7Zo7ANjQvPDCC/nmN7+ZYcOG5X/+53+ydOnSHH744Zk2bVqefPLJJMlf/vKXXH/99TnkkEOyww47rPCxawBfRKTDBuhvf/tbtttuu9x4443505/+lJdffjnnn39+ZsyYkfHjx+f666/PZZddlkMPPTSffvppxo4dmwMPPDBbbrllTY8OADXqqaeeyt13352RI0dWxfdrr72WnXfeOeedd14GDRqUxE3hgDUn0mEDVCqV0q9fvzRr1ixvvfVWTjrppOyzzz5Jlt2l9owzzkjDhg1z2WWXuf4cAL5AqVTK3LlzM2DAgNSrVy833HBD6tSpU3XJGMDqct4NbIDKysoybNiwXHvttbnnnnuyaNGiqudat26d5s2b55VXXkmdOm5bAQBfpKysLJWVlTnqqKNy22235cknnxTowFci0mED1a1bt/zpT39Kkvzyl7/MSy+9VPXcZ599lq222iqLFy+uqfEA4GvlgAMOSO/evTN27NgVPtYUYHU43R02cI8++mj+8z//M61bt852222XRYsW5a677spjjz2WbbfdtqbHA4Cvjf/93//NqFGj8tprr6VFixY1PQ7wNeVIOmzgdtttt4wfPz59+vTJtGnTstFGGwl0AFgNy495HXfccdlyyy2zYMGCGp4I+DpzJB2osnTp0iTxMTEAsAZKpVLmz5+fhg0b1vQowNeYSAcAAICCcLgMAAAACkKkAwAAQEGIdAAAACgIkQ4AAAAFIdIBAACgIEQ6AAAAFIRIBwAAgIIQ6QDwNVBWVpbf//73X4t9jxgxIjvssMNX3u/aeh0A+DoR6QCwDg0YMCD/8R//UdNjVDNhwoSUlZXlww8/XKX1Z8yYkb59+671OX73u99ljz32SGVlZTbaaKN06dIlI0eOzJw5c9b6vgDg60KkAwArtWjRoiRJixYtUl5evlZf+8wzz8zhhx+enXbaKX/6058yderUXHTRRXn++edz/fXXr9V9AcDXiUgHgPVojz32yODBg3P66adnk002SYsWLTJixIhq67z++uvZbbfdUlFRkW222Sbjxo2r9vzKjoQ/99xzKSsry1tvvZUkmTZtWg488MA0adIkDRs2TOfOnXPvvffmrbfeyp577pkkadKkScrKyjJgwICq2U466aQMHTo0zZo1S+/evZOseLr7D3/4w2y11VZp0KBBOnbsmLPOOiufffbZKn8PnnzyyZx33nm56KKLcuGFF6ZHjx5p3759evfund/97nc5+uijV7rdU089ld69e6dZs2aprKzM7rvvnmeeeabaOiNGjEjbtm1TXl6eVq1aZfDgwVXPjRkzJltuuWUqKirSvHnzHHrooas8MwCsL3VqegAA2ND8+te/ztChQ/PnP/85kydPzoABA7Lrrrumd+/eWbp0aQ4++OA0a9YsTzzxRObOnZtTTjlltfdx4oknZtGiRXn00UfTsGHDvPzyy9loo43Spk2b/O53v8shhxyS1157LY0aNUr9+vWrzXbCCSfk8ccfT6lUWulrb7zxxrnuuuvSqlWrvPjii/n+97+fjTfeOKeffvoqzXbjjTdmo402yqBBg1b6fOPGjVe6fN68eTn66KNz6aWXJkkuuuii7Lfffnn99dez8cYb5/bbb88ll1ySW265JZ07d87MmTPz/PPPJ0mefvrpDB48ONdff3169OiROXPmZOLEias0LwCsTyIdANazLl26ZPjw4UmSLbfcMr/4xS/y0EMPpXfv3nnwwQfzyiuv5K233krr1q2TJOedd95qXxM+ffr0HHLIIdluu+2SJB07dqx6bpNNNkmSbLbZZisE8Te+8Y1ccMEFX/jaP/nJT6r+3L59+wwbNiy33nrrKkf666+/no4dO6Zu3bqrtP5ye+21V7XHV155ZZo0aZJHHnkkBxxwQKZPn54WLVqkV69eqVu3btq2bZtvfvObSZZ9Pxo2bJgDDjggG2+8cdq1a5cdd9xxtfYPAOuD090BYD3r0qVLtcctW7bMrFmzkiSvvPJK2rZtWxXoSdK9e/fV3sfgwYNz7rnnZtddd83w4cPzwgsvrNJ23bp1+9J1br/99nzrW99KixYtstFGG+Wss87K9OnTV3m2UqmUsrKyVV5/uVmzZuX444/PVlttlcrKylRWVubjjz+u2vd3v/vdfPrpp+nYsWO+//3v584778zixYuTJL179067du3SsWPHHHXUUbnxxhszf/781Z4BANY1kQ4A69k/H0EuKyvL0qVLk2Slp5j/c9DWqlVrhXX/+ZrwgQMH5o033shRRx2VF198Md26dctll132pbM1bNjwC59/4okncsQRR6Rv37754x//mGeffTZnnnlm1U3mVsVWW22V//u//1ut69iTZXfKnzJlSkaPHp1JkyblueeeS9OmTav23aZNm7z22mu5/PLLU79+/QwaNCi77bZbPvvss2y88cZ55plncvPNN6dly5Y5++yzs/3226/yHe4BYH0R6QBQINtss02mT5+e9957r2rZ5MmTq62z6aabJln20WjLPffccyu8Vps2bXL88cfnjjvuyLBhw/KrX/0qSVKvXr0kyZIlS1Z7vscffzzt2rXLmWeemW7dumXLLbfMtGnTVus1+vXrl48//jhjxoxZ6fOfF84TJ07M4MGDs99++6Vz584pLy/P7Nmzq61Tv379fPvb386ll16aCRMmZPLkyXnxxReTJHXq1EmvXr1ywQUX5IUXXshbb72V8ePHr9bsALCuuSYdAAqkV69e2XrrrdO/f/9cdNFFmTt3bs4888xq63zjG99ImzZtMmLEiJx77rl5/fXXc9FFF1Vb55RTTknfvn2z1VZb5YMPPsj48ePTqVOnJEm7du1SVlaWP/7xj9lvv/1Sv379bLTRRqs03ze+8Y1Mnz49t9xyS3baaafcc889ufPOO1frPe688845/fTTM2zYsLz77rs56KCD0qpVq/z1r3/NFVdckW9961v5wQ9+sNJ9X3/99enWrVvmzp2b0047rdpN76677rosWbIkO++8cxo0aJDrr78+9evXT7t27fLHP/4xb7zxRnbbbbc0adIk9957b5YuXZqtt956tWYHgHXNkXQAKJBatWrlzjvvzMKFC/PNb34zAwcOzP/8z/9UW6du3bq5+eab8+qrr2b77bfP+eefn3PPPbfaOkuWLMmJJ56YTp06Zd99983WW29ddeR68803zznnnJMf/ehHad68eU466aRVnu873/lOhgwZkpNOOik77LBDJk2alLPOOmu13+f555+fm266KX/+85+zzz77pHPnzhk6dGi6dOnyuR/Bds011+SDDz7IjjvumKOOOiqDBw/OZpttVvV848aN86tf/Sq77rprunTpkoceeih33313mjZtmsaNG+eOO+7IXnvtlU6dOuWKK67IzTffnM6dO6/27ACwLpWVPu/zVQAAAID1ypF0AAAAKAiRDgAAAAUh0gEAAKAgRDoAAAAUhEgHAACAghDpAAAAUBAiHQAAAApCpAMAAEBBiHQAAAAoCJEOAAAABSHSAQAAoCD+H9QyKp0cNx6hAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# EDA - Industrial Class Distribution\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.countplot(x='Industrial_Class', data=df[(df['Registration_Year']<=2020) & (df['Registration_Year']>=1990)].head(10))\n",
    "plt.title('Distribution of Industrial Classes')\n",
    "plt.xlabel('Industrial Class')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=45, ha='right')  # Rotate x-axis labels for better readability\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2b429180",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# EDA - Relationship between Authorized Capital and Paid-up Capital\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='AUTHORIZED_CAP', y='PAIDUP_CAPITAL', data=df[(df['Registration_Year']<=2020) & (df['Registration_Year']>=1990)])\n",
    "plt.title('Authorized Capital vs Paid-up Capital')\n",
    "plt.xlabel('Authorized Capital')\n",
    "plt.ylabel('Paid-up Capital')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ca165418",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Company limited by Shares']\n"
     ]
    }
   ],
   "source": [
    "# Print the unique values in the 'Company_Category' column\n",
    "print(df['Company_Category'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e3e13691",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Company_Category'] =df['Company_Category'].replace(' ',np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f5074284",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# EDA - Distribution of Company Categories\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(x='Company_Category', data=df[(df['Registration_Year']<=2020) & (df['Registration_Year']>=1990)])\n",
    "plt.title('Distribution of Company Categories')\n",
    "plt.xlabel('Company Category')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b016e779",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# EDA - Distribution of Principal Business Activities\n",
    "plt.figure(figsize=(14, 6))\n",
    "sns.countplot(x='PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN', data=df[(df['Registration_Year']<=2020) & (df['Registration_Year']>=1990)], order=df['PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN'].value_counts().index)\n",
    "plt.title('Distribution of Principal Business Activities')\n",
    "plt.xlabel('Principal Business Activity')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1ddc1124",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4800x2400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find the top 5 business activities for each year\n",
    "top_activities = df.loc[(df['Registration_Year']>=1990) & (df['Registration_Year'] <= 2020)].groupby('Registration_Year')['PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN'].apply(lambda x: x.value_counts().nlargest(5).index.tolist())\n",
    "\n",
    "# Filter the dataframe to include only the top activities\n",
    "df_top_activities = df[df.apply(lambda x: x['Registration_Year'] in top_activities.index and x['PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN'] in top_activities.loc[x['Registration_Year']], axis=1)]\n",
    "\n",
    "plt.figure(figsize=(16, 8),dpi=300)\n",
    "sns.countplot(x='Registration_Year', hue='PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN', data=df_top_activities, palette='Set1')\n",
    "plt.title('Top 5 Principal Business Activities Over Time')\n",
    "plt.xlabel('Registration Year')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=0)\n",
    "plt.legend(title='Principal Business Activity', fontsize='small')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f9788d9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN from 2013 to 2020:\n",
      "Transport, storage and communications    1\n",
      "Other business activities                1\n",
      "Name: PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Filter companies registered between 2013 and 2020\n",
    "filtered_df = df[(df['Registration_Year'] >= 2013) & (df['Registration_Year'] <= 2020)]\n",
    "\n",
    "# Get the top 5 business activities\n",
    "top_business_activities = filtered_df['PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN'].value_counts().head(5)\n",
    "\n",
    "# Print the result\n",
    "print(\"Top 5 PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN from 2013 to 2020:\")\n",
    "print(top_business_activities)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa1708cf",
   "metadata": {},
   "source": [
    "### Authorized capital"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e73755a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kings\\AppData\\Local\\Temp\\ipykernel_11768\\2524205423.py:3: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, 'AUTHORIZED_CAP'] = df['AUTHORIZED_CAP'].astype(float)\n",
      "C:\\Users\\kings\\AppData\\Local\\Temp\\ipykernel_11768\\2524205423.py:4: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, 'PAIDUP_CAPITAL'] = df['PAIDUP_CAPITAL'].astype(float)\n"
     ]
    }
   ],
   "source": [
    "# Convert columns to appropriate data types\n",
    "# Use .loc to ensure you're modifying the original DataFrame\n",
    "df.loc[:, 'AUTHORIZED_CAP'] = df['AUTHORIZED_CAP'].astype(float)\n",
    "df.loc[:, 'PAIDUP_CAPITAL'] = df['PAIDUP_CAPITAL'].astype(float)\n",
    "# Group by 'PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN' and calculate the mean of 'AUTHORIZED_CAP' and 'PAIDUP_CAPITAL'\n",
    "grouped_df = df.groupby('PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN')[['AUTHORIZED_CAP', 'PAIDUP_CAPITAL']].mean().reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a22f537d",
   "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>PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN</th>\n",
       "      <th>Registration_Year</th>\n",
       "      <th>CAPITAL_CHANGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Other business activities</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Transport, storage and communications</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN  Registration_Year  CAPITAL_CHANGE\n",
       "0              Other business activities               2016             0.0\n",
       "1  Transport, storage and communications               2016             0.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Trend analysis of Authorized capital\n",
    "# Ensure the DataFrame is sorted by the time column\n",
    "df = df.sort_values(by='Registration_Year')\n",
    "\n",
    "# Calculate the percentage change in authorized capital for each activity over time\n",
    "df['CAPITAL_CHANGE'] = df.groupby(['PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN', 'Registration_Year'])['AUTHORIZED_CAP'].pct_change().fillna(0)\n",
    "\n",
    "# Group by 'PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN' and calculate the mean of 'CAPITAL_CHANGE'\n",
    "activity_trend_df = df.groupby(['PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN','Registration_Year'])['CAPITAL_CHANGE'].mean().reset_index()\n",
    "\n",
    "# Print the new DataFrame with the trend analysis\n",
    "activity_trend_df2= activity_trend_df.interpolate()\n",
    "activity_trend_df2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1d13748",
   "metadata": {},
   "source": [
    "### Comparison Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eee6cfa1",
   "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>Activity_1</th>\n",
       "      <th>Activity_2</th>\n",
       "      <th>Comparison</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Other business activities</td>\n",
       "      <td>Transport, storage and communications</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Activity_1                             Activity_2  \\\n",
       "0  Other business activities  Transport, storage and communications   \n",
       "\n",
       "   Comparison  \n",
       "0         1.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an empty DataFrame to store the results\n",
    "results_df = pd.DataFrame(columns=['Activity_1', 'Activity_2', 'Comparison'])\n",
    "\n",
    "# Comparative Analysis: This compares the mean paid-up capital of all business activities.\n",
    "# It helps in understanding their relative performance and market standing.\n",
    "for i in range(len(grouped_df) - 1):\n",
    "    for j in range(i + 1, len(grouped_df)):\n",
    "        activity1_capital = grouped_df.loc[i, 'PAIDUP_CAPITAL']\n",
    "        activity2_capital = grouped_df.loc[j, 'PAIDUP_CAPITAL']\n",
    "        comparison = activity1_capital / activity2_capital\n",
    "        new_row = pd.DataFrame({'Activity_1': [grouped_df.loc[i, 'PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN']],\n",
    "                                'Activity_2': [grouped_df.loc[j, 'PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN']],\n",
    "                                'Comparison': [comparison]})\n",
    "        results_df = pd.concat([results_df, new_row], ignore_index=True)\n",
    "\n",
    "# Print the results DataFrame\n",
    "results_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42e3ed19",
   "metadata": {},
   "source": [
    "### Liquidity Ratios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a7b71b8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Smaller DataFrame showcasing Liquidity Ratios:\n"
     ]
    },
    {
     "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>Company_Name</th>\n",
       "      <th>AUTHORIZED_CAP</th>\n",
       "      <th>PAIDUP_CAPITAL</th>\n",
       "      <th>LIQUIDITY_RATIO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TRIP OF LIFETIME TRAVELTREK PRIVATELIMITED</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PANDIM ONLINE SOLUTIONS PRIVATE LIMITED</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Company_Name  AUTHORIZED_CAP  PAIDUP_CAPITAL  \\\n",
       "0  TRIP OF LIFETIME TRAVELTREK PRIVATELIMITED        100000.0        100000.0   \n",
       "1     PANDIM ONLINE SOLUTIONS PRIVATE LIMITED        100000.0        100000.0   \n",
       "\n",
       "   LIQUIDITY_RATIO  \n",
       "0              1.0  \n",
       "1              1.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Liquidity Ratios: This calculates the liquidity ratio (authorized capital to paid-up capital) for each company.\n",
    "# It is crucial for assessing the financial health of a company.\n",
    "# Make sure 'df' is the original DataFrame or explicitly create a copy if it's a subset\n",
    "df = df.copy()\n",
    "\n",
    "# Calculate the liquidity ratio without warnings\n",
    "df.loc[:, 'LIQUIDITY_RATIO'] = df['AUTHORIZED_CAP'] / df['PAIDUP_CAPITAL']\n",
    "\n",
    "# Now, let's create a smaller DataFrame to showcase the liquidity ratios\n",
    "# Selecting relevant columns for the smaller DataFrame\n",
    "liquidity_df = df[['Company_Name', 'AUTHORIZED_CAP', 'PAIDUP_CAPITAL', 'LIQUIDITY_RATIO']].copy()\n",
    "\n",
    "# Displaying the smaller DataFrame\n",
    "print(\"Smaller DataFrame showcasing Liquidity Ratios:\")\n",
    "liquidity_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "192b0d19",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6baaf1f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kings\\anaconda3\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\kings\\anaconda3\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\kings\\anaconda3\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\kings\\anaconda3\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:866: UserWarning: Too few observations to estimate starting parameters for ARMA and trend. All parameters except for variances will be set to zeros.\n",
      "  warn('Too few observations to estimate starting parameters%s.'\n",
      "C:\\Users\\kings\\anaconda3\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\mlemodel.py:1234: RuntimeWarning: invalid value encountered in divide\n",
      "  np.inner(score_obs, score_obs) /\n",
      "C:\\Users\\kings\\anaconda3\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
      "  return get_prediction_index(\n",
      "C:\\Users\\kings\\anaconda3\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:836: FutureWarning: No supported index is available. In the next version, calling this method in a model without a supported index will result in an exception.\n",
      "  return get_prediction_index(\n"
     ]
    },
    {
     "data": {
      "image/png": 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huS+v9+uvv6pOnTq274ysHDhwQIcOHVLHjh1tbWn/NqhVq1a6/rVq1dLatWuzXGZycrLWrFljNxU+J6W9Jteub8eOHSpRooQCAwPt+qZt07X//snI6tWrlZKSkuE2/PHHH/Lz89Ply5dVqVIl9e7dW6+++mq60+gEBgaqatWq+vXXX7MVUAPAHSNPjycFANyUtKnrGd2SkpJMSkqKCQoKMjVr1rSbynXu3DlTsmRJExYWZmsbMWKEkWSGDx9ut47169cbSeaDDz6waz9y5Ijx9vY2b7zxhm2Z/v7+5r777jOpqanZ3obk5GRz/vx5U6hQIfPRRx/Z2mvUqGE6duyY5XMdmbp+9uxZ4+3tbVq3bm3XfvjwYePp6Wm6du1qa7vZUwJkNHX9wQcfNJLM8uXLs3xuamqqSUpKMqtXrzaSzLZt2+yWK8n85z//sXtO69atTZUqVWz3+/XrZwoXLpzletK2rVOnTnbtaVMF33nnHWOMY69XWn0zZsxItz5nTCn39fU1r776apZ9MlvPtGnTMnztxo8fbySZpUuX2tokGavVas6cOZPlulJSUkxSUpIZPXq0KVasmO39vnPnTiPJDBo0yK7/d999ZyTZTU197rnnjK+vrzl06JBd3/fff99IMjt37syyhms5470zdepUI8n8/PPPdv369u2branrzzzzjHF3dze7du3KtM/gwYMzPCXE888/bywWi9m7d68x5v+my9euXdvuc2vSpElGkmnfvr3d81999VUjycTHx9vaypUrZ7y9vc3Ro0dtbZGRkUaSKVWqlN0UzwULFhhJZuHChba2Bx980Dz44IPptqFnz55277O0WmvWrGmSk5Nt7Rs3bjSSzHfffWdrS/uMTXPgwAHj5uZmunXrlulrdr20sT506FC68cpq6vr12+PofhEQEGASEhJsbTExMaZAgQJm3Lhxtrbs7KcZudXvnjNnzhhPT0/zxBNPZPj8a7c7o1Mx3MrnS9ryKlSoYK5cuZLlMpKTk01SUpLp3bu3qVOnjt1jmU1dX7lypd2065v5Tp8wYYLdMl944QXj5eVl+9xK+8yJi4vLsv6M+Pj4mIiIiBv2S0pKMk2aNDH+/v7m8OHDtvZvvvnGSDLr169P95xnn33WeHh4ZLncoUOHGklmwYIFmfa5manrGdm2bZvx9vZO993ZrFkzu8/Sa3l4eJhnn30202UmJCSYatWqmeDgYHPu3Dm7x1544QUzY8YMs3r1arNgwQLTrVs3I8k89dRTGS6rW7duJiAgwMGtAoDbG1PXAcCFzZkzR5s2bbK7FSxYUHv37tXx48fVvXt3u6lcvr6+euSRR7RhwwZdvHjRblmPPPKI3f1FixbJYrHoqaeeUnJysu0WGBio2rVr26bUrVu3TgkJCXrhhReynJ58/vx5DRo0SBUrVlTBggVVsGBB+fr66sKFC3ZTohs0aKD//ve/Gjx4sFatWqVLly7d0mu0fv16Xbp0Kd304eDgYD300ENavnz5LS0/K0WKFNFDDz2Urv3AgQPq2rWrAgMD5ebmJnd3dz344IOSlG56uMViUbt27ezaatWqZXfkV4MGDRQXF6cuXbro559/tpvaer1u3brZ3Q8LC1O5cuW0cuVKSTf3el3/3nGWBg0aaNasWXrnnXe0YcMGJSUlZfu5K1asUKFChfToo4/atadt1/Xb8dBDD6lIkSIZLqdp06ayWq22sRo+fLhiY2Nt06ZXr14tSXr88cftnvvoo4/aTcuUru5X4eHhCgoKstuvWrVqZbeszDj7vbNy5Ur5+fmpffv2dv26du2aZR1p/vvf/yo8PFzVqlXLtM+KFStUvXp12/TPNL169ZIxJt0RZK1bt7b73Epbdps2bez6pbUfPnzYrv3uu+9W6dKl0/Vr0qSJfHx80rVff/oMR7Rp08buKKu0o7myWuayZcuUkpKiF198Mctlnzx5UhEREQoODlbBggXl7u6ucuXKSUo/1tnl6H4RHh4uPz8/2/2AgACVLFky3efPze6n0s1/92zYsEGJiYnp9rt777033UWZMnKrdUtS+/bt5e7unq79+++/V+PGjeXr62sbu+nTp9/0uN3Md/r1+3StWrV0+fJl2+dW/fr1JV393PrPf/6jY8eOZauWuLg4Xbx4McPp2Ncyxqh3795as2aN5syZo+Dg4HR9Mvs3Q1b/lvjyyy81ZswYDRw4UB06dMhWzddLmw2Tdrv2FBrXOnjwoNq2bavg4GB9+eWXDtWZ2WOXL19W586ddejQIX3//ffy9fW1e/zTTz/V008/rQceeEAdOnTQ119/rX79+unrr7/W1q1b0y2vZMmSOnnyZLZOfQIAdwqCTgBwYdWqVVO9evXsbpJsU+8yms4dFBSk1NRUnT171q79+r4nTpyQMUYBAQFyd3e3u23YsMEWpp06dUqSVKZMmSxr7dq1qyZPnqw+ffrot99+08aNG7Vp0yaVKFHCLsz8+OOPNWjQIC1YsEDh4eEqWrSoOnbsqH/++cfBV+eqG70W2Z1eeTMyWuf58+d1//3366+//tI777yjVatWadOmTZo/f74kpQt2fXx85OXlZdfm6empy5cv2+53797dNp33kUceUcmSJdWwYUMtW7Ys3fqvn2aX1pb2Ojj6evn4+OTYVYHnzZunnj176ssvv1SjRo1UtGhR9ejRI1vnj4yNjVVgYGC6H5slS5ZUwYIF021HRtu7ceNGNW/eXJL0xRdfaO3atdq0aZOGDh0q6f/GKm1Z1145V7p6GolixYrZtZ04cUK//PJLun0qbfpiViF1Trx3YmNj09UtZfw+ycipU6duuO/HxsZm+n5Ke/xaRYsWtbvv4eGRZfu12+OM5zvi+vFNu1J0Vn+gyc5nZmpqqpo3b6758+frjTfe0PLly7Vx40bb+T9v9g9Aju4X12+fdHUbr13/reyn0s1/92S232XWdr1brTuj2iVp/vz5evzxx1W6dGl9/fXXWr9+vTZt2qRnnnnmpt9rN/OdfqP35gMPPKAFCxYoOTlZPXr0UJkyZVSjRg3b+bAzk/b86z9brmWMUZ8+ffT1119r1qxZ6QLJtNoy+v49c+ZMun01zcyZM/Xcc8/p2Wef1XvvvZdlnVl5+OGH7d5XGU37PnTokMLDw1WwYEEtX748XU3FihXLsP4LFy7oypUrGW5DYmKiOnXqpD///FMLFy5Uw4YNs1Vv2vnDrz//r3R1HIwxt/Q5BgC3G87RCQC3obQfEdHR0ekeO378uAoUKJDu6LXrf/gWL15cFotFa9assf1AulZaW9p5LI8ePZppPfHx8Vq0aJFGjBihwYMH29rTzmV2rUKFCmnUqFEaNWqUTpw4YTu6s127dtqzZ09Wm52hG70WaecGzQkZHdGxYsUKHT9+XKtWrbIdiSddPUrmVjz99NN6+umndeHCBf3xxx8aMWKE2rZtq3379tmOApOU4Y/4mJgYVaxYUZLjr9fNXGQou4oXL65JkyZp0qRJOnz4sBYuXKjBgwfr5MmTdhd6yEixYsX0119/yRhjV2PakS/Z2Y65c+fK3d1dixYtsvtRv2DBgnTrkq4GNNceSZicnJzuh3Dx4sVVq1YtjRkzJsO608K/jOTEe6dYsWLauHFjuvbshj0lSpTIct9PW0dm7ydJOboPOsrLyyvdBVukrANoR137mZnRUW7S1fP7bdu2TbNmzVLPnj1t7f/+++8trdvR/SI7bmU/lW7+u+fa/e56MTExNzyq81brzqh2Sfr6668VGhqqefPm2T1+/cWeHHEz3+nZ0aFDB3Xo0EGJiYnasGGDxo0bp65duyokJESNGjXKspbrv7vTpIWcM2fO1PTp020h3bVq1KghSdq+fbtat25t99j27dttj19r5syZ6tOnj3r27Klp06bd0nfPZ599pnPnztnuX/++P3TokJo0aSJjjFatWpXhHyVq1qypuXPnKiYmxu4PQ9u3b7fbxjSJiYnq2LGjVq5cqZ9//lkPP/xwtus1///idxldbOnMmTPy9PRMd2QoANzJOKITAG5DVapUUenSpfXtt9/aXR36woUL+vHHH21Xbc1K27ZtZYzRsWPH0h01Wq9ePdWsWVPS1anPVqtV06ZNS3cl6jQWi0XGmHQ/Wr/88kulpKRkWkNAQIB69eqlLl26aO/evbapedk5aipNo0aN5O3tra+//tqu/ejRo1qxYoVDPzacIe3H2fWvxWeffeaU5RcqVEitWrXS0KFDdeXKFe3cudPu8W+++cbu/rp162w/6iTnvV7XH/V1q8qWLat+/fqpWbNm+vvvv2+4nocffljnz59PF0rOmTPH9viNWCwWFSxY0G5q8qVLl/TVV1/Z9XvggQckXT1C7Fo//PBDuumEbdu21Y4dO1ShQoUM96usgs6ceO+Eh4fr3Llz6S7o8+2332br+a1atdLKlSu1d+/eTPs8/PDD2rVrl924SVfHwmKxKDw83PHCc0hISIj27dtnF0rFxsZq3bp1TltH8+bN5ebmpqlTp2bax5GxduTz0Bn7RVYy208dkd3vnoYNG8rT0zPdfrdhwwaHT0fg6OdLViwWizw8POyCuJiYmHRXXXdk+c74Ts+Kp6enHnzwQY0fP16SMpwincbDw0Ply5fX/v370z1mjFHfvn01c+ZMffbZZ+muSJ6mdOnSatCggb7++mu7fwNs2LBBe/fuVefOne36z5o1S3369NFTTz2lL7/88pb/wFalShW799S1ofjhw4fVpEkTpaSkaMWKFXZ/KLxWhw4dZLFYNHv27HS1ent7q2XLlra2tCM5V6xYoR9//FEtWrRwqN60/fPee+9N99iBAwcyvfgYANypOKITAG5DBQoU0IQJE9StWze1bdtWzz33nBITE/Xee+8pLi5O77777g2X0bhxYz377LN6+umntXnzZj3wwAMqVKiQoqOj9eeff6pmzZp6/vnn5evrqw8++EB9+vRR06ZN1bdvXwUEBOjff//Vtm3bNHnyZPn7++uBBx7Qe++9p+L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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forecasted registrations for Transport, storage and communications for 2021-2025:\n",
      "1    1.0\n",
      "2    1.0\n",
      "3    1.0\n",
      "4    1.0\n",
      "5    1.0\n",
      "Name: predicted_mean, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kings\\anaconda3\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\kings\\anaconda3\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\kings\\anaconda3\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\kings\\anaconda3\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:866: UserWarning: Too few observations to estimate starting parameters for ARMA and trend. All parameters except for variances will be set to zeros.\n",
      "  warn('Too few observations to estimate starting parameters%s.'\n",
      "C:\\Users\\kings\\anaconda3\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\mlemodel.py:1234: RuntimeWarning: invalid value encountered in divide\n",
      "  np.inner(score_obs, score_obs) /\n",
      "C:\\Users\\kings\\anaconda3\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
      "  return get_prediction_index(\n",
      "C:\\Users\\kings\\anaconda3\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:836: FutureWarning: No supported index is available. In the next version, calling this method in a model without a supported index will result in an exception.\n",
      "  return get_prediction_index(\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forecasted registrations for Other business activities for 2021-2025:\n",
      "1    1.0\n",
      "2    1.0\n",
      "3    1.0\n",
      "4    1.0\n",
      "5    1.0\n",
      "Name: predicted_mean, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "\n",
    "# Assuming 'df' is your DataFrame\n",
    "# Convert 'Latest_Year_AR' to datetime format\n",
    "df['Latest_Year_AR'] = pd.to_datetime(df['Latest_Year_AR'], errors='coerce')\n",
    "\n",
    "# Define the top 5 categories for forecasting\n",
    "top_categories = [\n",
    "    'Transport, storage and communications',\n",
    "    'Other business activities',\n",
    "    \n",
    "]\n",
    "\n",
    "# Loop through each category for forecasting\n",
    "for category_to_forecast in top_categories:\n",
    "    # Filter data for the specific category and the specified time range\n",
    "    category_df = df[(df['PRINCIPAL_BUSINESS_ACTIVITY_AS_PER_CIN'] == category_to_forecast) & \n",
    "                     (df['Registration_Year'] >= 2013) & (df['Registration_Year'] <= 2020)]\n",
    "\n",
    "    # Extract relevant features and target variable\n",
    "    X = category_df[['Registration_Year']]\n",
    "    y = category_df.groupby('Registration_Year').size()\n",
    "\n",
    "    # Convert 'Registration_Year' to DateTime format\n",
    "    y.index = pd.to_datetime(y.index, format='%Y')\n",
    "\n",
    "    # Fit ARIMA model\n",
    "    model = ARIMA(y, order=(1, 1, 1))  # Adjust order as needed\n",
    "    fit_model = model.fit()\n",
    "\n",
    "    # Forecast registrations for the next 5 years (2021-2025)\n",
    "    forecast_steps = 5\n",
    "    forecast_values = fit_model.forecast(steps=forecast_steps)\n",
    "\n",
    "    # Plot the forecast\n",
    "    plt.figure(figsize=(16, 6))\n",
    "    plt.plot(y.index.year, y.values, marker='o', label='Historical Data')\n",
    "    plt.plot(range(y.index.year.max() + 1, y.index.year.max() + 1 + forecast_steps), forecast_values, marker='o', label='Forecast')\n",
    "    plt.title(f'Forecast of {category_to_forecast} registrations (2021-2025)')\n",
    "    plt.xticks(np.arange(2013, 2026, 1))\n",
    "    plt.xlabel('Year')\n",
    "    plt.ylabel('Count of Registrations')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "    # Display the forecasted values\n",
    "    print(f\"Forecasted registrations for {category_to_forecast} for 2021-2025:\")\n",
    "    print(forecast_values)"
   ]
  }
 ],
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  "kernelspec": {
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   "language": "python",
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   "file_extension": ".py",
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