GeoID-Reg Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt # we only need pyplot\n",
    "sb.set() # set the default Seaborn style for graphics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "EarthquakeDamage = pd.read_csv('train_labels.csv')\n",
    "Earthquake = pd.read_csv('train_values.csv')\n",
    "\n",
    "MainData = EarthquakeDamage.merge(Earthquake, on='building_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>building_id</th>\n",
       "      <th>damage_grade</th>\n",
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       "      <th>geo_level_2_id</th>\n",
       "      <th>geo_level_3_id</th>\n",
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       "      <th>land_surface_condition</th>\n",
       "      <th>...</th>\n",
       "      <th>has_secondary_use_agriculture</th>\n",
       "      <th>has_secondary_use_hotel</th>\n",
       "      <th>has_secondary_use_rental</th>\n",
       "      <th>has_secondary_use_institution</th>\n",
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       "      <th>has_secondary_use_gov_office</th>\n",
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      ],
      "text/plain": [
       "   building_id  damage_grade  geo_level_1_id  geo_level_2_id  geo_level_3_id  \\\n",
       "0       802906             3               6             487           12198   \n",
       "1        28830             2               8             900            2812   \n",
       "2        94947             3              21             363            8973   \n",
       "3       590882             2              22             418           10694   \n",
       "4       201944             3              11             131            1488   \n",
       "\n",
       "   count_floors_pre_eq  age  area_percentage  height_percentage  \\\n",
       "0                    2   30                6                  5   \n",
       "1                    2   10                8                  7   \n",
       "2                    2   10                5                  5   \n",
       "3                    2   10                6                  5   \n",
       "4                    3   30                8                  9   \n",
       "\n",
       "  land_surface_condition  ... has_secondary_use_agriculture  \\\n",
       "0                      t  ...                             0   \n",
       "1                      o  ...                             0   \n",
       "2                      t  ...                             0   \n",
       "3                      t  ...                             0   \n",
       "4                      t  ...                             0   \n",
       "\n",
       "  has_secondary_use_hotel has_secondary_use_rental  \\\n",
       "0                       0                        0   \n",
       "1                       0                        0   \n",
       "2                       0                        0   \n",
       "3                       0                        0   \n",
       "4                       0                        0   \n",
       "\n",
       "  has_secondary_use_institution has_secondary_use_school  \\\n",
       "0                             0                        0   \n",
       "1                             0                        0   \n",
       "2                             0                        0   \n",
       "3                             0                        0   \n",
       "4                             0                        0   \n",
       "\n",
       "  has_secondary_use_industry  has_secondary_use_health_post  \\\n",
       "0                          0                              0   \n",
       "1                          0                              0   \n",
       "2                          0                              0   \n",
       "3                          0                              0   \n",
       "4                          0                              0   \n",
       "\n",
       "   has_secondary_use_gov_office  has_secondary_use_use_police  \\\n",
       "0                             0                             0   \n",
       "1                             0                             0   \n",
       "2                             0                             0   \n",
       "3                             0                             0   \n",
       "4                             0                             0   \n",
       "\n",
       "   has_secondary_use_other  \n",
       "0                        0  \n",
       "1                        0  \n",
       "2                        0  \n",
       "3                        0  \n",
       "4                        0  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MainData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col_name in MainData.columns:\n",
    "    if(MainData[col_name].dtype == 'object'):\n",
    "        MainData[col_name]= MainData[col_name].astype('category')\n",
    "        MainData[col_name] = MainData[col_name].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   building_id  damage_grade  geo_level_1_id  geo_level_2_id  geo_level_3_id  \\\n",
       "0       802906             3               6             487           12198   \n",
       "1        28830             2               8             900            2812   \n",
       "2        94947             3              21             363            8973   \n",
       "3       590882             2              22             418           10694   \n",
       "4       201944             3              11             131            1488   \n",
       "\n",
       "   count_floors_pre_eq  age  area_percentage  height_percentage  \\\n",
       "0                    2   30                6                  5   \n",
       "1                    2   10                8                  7   \n",
       "2                    2   10                5                  5   \n",
       "3                    2   10                6                  5   \n",
       "4                    3   30                8                  9   \n",
       "\n",
       "   land_surface_condition  ...  has_secondary_use_agriculture  \\\n",
       "0                       2  ...                              0   \n",
       "1                       1  ...                              0   \n",
       "2                       2  ...                              0   \n",
       "3                       2  ...                              0   \n",
       "4                       2  ...                              0   \n",
       "\n",
       "   has_secondary_use_hotel  has_secondary_use_rental  \\\n",
       "0                        0                         0   \n",
       "1                        0                         0   \n",
       "2                        0                         0   \n",
       "3                        0                         0   \n",
       "4                        0                         0   \n",
       "\n",
       "   has_secondary_use_institution  has_secondary_use_school  \\\n",
       "0                              0                         0   \n",
       "1                              0                         0   \n",
       "2                              0                         0   \n",
       "3                              0                         0   \n",
       "4                              0                         0   \n",
       "\n",
       "   has_secondary_use_industry  has_secondary_use_health_post  \\\n",
       "0                           0                              0   \n",
       "1                           0                              0   \n",
       "2                           0                              0   \n",
       "3                           0                              0   \n",
       "4                           0                              0   \n",
       "\n",
       "   has_secondary_use_gov_office  has_secondary_use_use_police  \\\n",
       "0                             0                             0   \n",
       "1                             0                             0   \n",
       "2                             0                             0   \n",
       "3                             0                             0   \n",
       "4                             0                             0   \n",
       "\n",
       "   has_secondary_use_other  \n",
       "0                        0  \n",
       "1                        0  \n",
       "2                        0  \n",
       "3                        0  \n",
       "4                        0  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MainData.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Taking GeoLevel 1 , 2 , 3 as predictors for Damage Grade**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geo Level 1: \n",
      "Data type :  <class 'pandas.core.frame.DataFrame'>\n",
      "Data dimensions :  260601\n",
      "Geo Level 3: \n",
      "Data type :  <class 'pandas.core.frame.DataFrame'>\n",
      "Data dimensions :  260601\n",
      "Geo Level 3: \n",
      "Data type :  <class 'pandas.core.frame.DataFrame'>\n",
      "Data dimensions :  260601\n"
     ]
    }
   ],
   "source": [
    "gl_1=pd.DataFrame(MainData['geo_level_1_id'])\n",
    "print(\"Geo Level 1: \")\n",
    "print(\"Data type : \", type(gl_1))\n",
    "print(\"Data dimensions : \", gl_1.size)\n",
    "\n",
    "gl_2=pd.DataFrame(MainData['geo_level_2_id'])\n",
    "print(\"Geo Level 3: \")\n",
    "print(\"Data type : \", type(gl_2))\n",
    "print(\"Data dimensions : \", gl_2.size)\n",
    "\n",
    "gl_3=pd.DataFrame(MainData['geo_level_3_id'])\n",
    "print(\"Geo Level 3: \")\n",
    "print(\"Data type : \", type(gl_3))\n",
    "print(\"Data dimensions : \", gl_3.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>260601.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>13.900353</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.033617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       geo_level_1_id\n",
       "count   260601.000000\n",
       "mean        13.900353\n",
       "std          8.033617\n",
       "min          0.000000\n",
       "25%          7.000000\n",
       "50%         12.000000\n",
       "75%         21.000000\n",
       "max         30.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gl_1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geo_level_2_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>260601.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>701.074685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>412.710734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>350.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>702.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1050.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1427.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       geo_level_2_id\n",
       "count   260601.000000\n",
       "mean       701.074685\n",
       "std        412.710734\n",
       "min          0.000000\n",
       "25%        350.000000\n",
       "50%        702.000000\n",
       "75%       1050.000000\n",
       "max       1427.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gl_2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>geo_level_3_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>260601.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6257.876148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3646.369645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3073.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6270.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9412.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12567.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       geo_level_3_id\n",
       "count   260601.000000\n",
       "mean      6257.876148\n",
       "std       3646.369645\n",
       "min          0.000000\n",
       "25%       3073.000000\n",
       "50%       6270.000000\n",
       "75%       9412.000000\n",
       "max      12567.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gl_3.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a6d20b5dc8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1728x864 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(3, 3, figsize=(24, 12))\n",
    "\n",
    "# Plot the basic uni-variate figures for Geo Level 1\n",
    "sb.boxplot(gl_1, orient = \"h\", ax = axes[0,0])\n",
    "sb.distplot(gl_1, color = \"red\", ax = axes[0,1])\n",
    "sb.violinplot(gl_1, ax = axes[0,2])\n",
    "\n",
    "# Plot the basic uni-variate figures for Geo Level 2\n",
    "sb.boxplot(gl_2, orient = \"h\", ax = axes[1,0])\n",
    "sb.distplot(gl_2, color = \"red\", ax = axes[1,1])\n",
    "sb.violinplot(gl_2, ax = axes[1,2])\n",
    "\n",
    "# Plot the basic uni-variate figures for Geo Level 3\n",
    "sb.boxplot(gl_3, orient = \"h\", ax = axes[2,0])\n",
    "sb.distplot(gl_3, color = \"red\", ax = axes[2,1])\n",
    "sb.violinplot(gl_3, ax = axes[2,2])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking no of outliers for each level in box plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers is: \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "geo_level_1_id    0\n",
       "geo_level_2_id    0\n",
       "geo_level_3_id    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_df=pd.DataFrame(MainData[['geo_level_1_id','geo_level_2_id','geo_level_3_id']])\n",
    "\n",
    "Q1 = out_df.quantile(0.25) #Quartile 1\n",
    "Q3 = out_df.quantile(0.75) #Quartile 3\n",
    "DQR = Q3 - Q1 #difference between Quartile 1 and Quartile 3\n",
    "print(\"Number of outliers is: \")\n",
    "((out_df < (Q1 - 1.5 * DQR)) | (out_df > (Q3 + 1.5 * DQR))).sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Damage Grade: \n",
      "Data type :  <class 'pandas.core.frame.DataFrame'>\n",
      "Data dimensions :  521202\n"
     ]
    }
   ],
   "source": [
    "print(\"Damage Grade: \")\n",
    "print(\"Data type : \", type(EarthquakeDamage))\n",
    "print(\"Data dimensions : \", EarthquakeDamage.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}