Damage_to_a_building.ipynb

{
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   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "source": [
    "# Predict Damage to a building after an Earthquake "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Determining the degree of damage that is done to buildings post an earthquake can help identify safe and unsafe buildings, thus avoiding death and injuries resulting from aftershocks.  Leveraging the power of machine learning is one viable option that can potentially prevent massive loss of lives while simultaneously making rescue efforts easy and efficient.\n",
    "\n",
    "In this challenge we are provided with the before and after details of nearly one million buildings after an earthquake. The damage to a building is categorized in __five grades__. Each grade depicts the extent of damage done to a building post an earthquake.\n",
    "\n",
    "Given building details,__ our task is to build a model that can predict the extent of damage that has been done to a building after an earthquake.__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Variables Description:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "__area_assesed__ : Indicates the nature of the damage assessment in terms of the areas of the building that were assessed\n",
    "\n",
    "__building_id__: A unique ID that identifies every individual building\n",
    "\n",
    "__damage_grade__: Damage grade assigned to the building after assessment (Target Variable)\n",
    "\n",
    "__district_id__: District where the building is located\n",
    "\n",
    "__has_geotechnical_risk__: Indicates if building has geotechnical risks\n",
    "\n",
    "__has_geotechnical_risk_fault_crack__: Indicates if building has geotechnical risks related to fault cracking\n",
    "\n",
    "__has_geotechnical_risk_flood__: Indicates if building has geotechnical risks related to flood\n",
    "\n",
    "__ has_geotechnical_risk_land_settlement__: Indicates if building has geotechnical risks related to land settlement\n",
    "\n",
    "__has_geotechnical_risk_landslide__: Indicates if building has geotechnical risks related to landslide\n",
    "\n",
    "__has_geotechnical_risk_liquefaction__: Indicates if building has geotechnical risks related to liquefaction\n",
    "\n",
    "__has_geotechnical_risk_other__: Indicates if building has any other  geotechnical risks\n",
    "\n",
    "__has_geotechnical_risk_rock_fall__: Indicates if building has geotechnical risks related to rock fall\n",
    "\n",
    "__has_repair_started__: Indicates if the repair work had started\n",
    "\n",
    "__vdcmun_id__: Municipality where the building is located"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*__code by__* : [Brungi Vishwa Sourab](https://www.brungivishwasourab.com)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.layers import Dropout, Flatten, Dense\n",
    "from keras.models import Sequential\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#importing the datasets\n",
    "train_df_first=pd.read_csv('data/train.csv')\n",
    "test_df=pd.read_csv('data/test.csv')\n",
    "building_ownership_df_first=pd.read_csv('data/Building_Ownership_Use.csv')\n",
    "building_structure_df_first=pd.read_csv('data/Building_Structure.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_uuid": "c2ac0bb8d8005b4c1e076e27db63815a97171ac2"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area_assesed</th>\n",
       "      <th>building_id</th>\n",
       "      <th>damage_grade</th>\n",
       "      <th>district_id</th>\n",
       "      <th>has_geotechnical_risk</th>\n",
       "      <th>has_geotechnical_risk_fault_crack</th>\n",
       "      <th>has_geotechnical_risk_flood</th>\n",
       "      <th>has_geotechnical_risk_land_settlement</th>\n",
       "      <th>has_geotechnical_risk_landslide</th>\n",
       "      <th>has_geotechnical_risk_liquefaction</th>\n",
       "      <th>has_geotechnical_risk_other</th>\n",
       "      <th>has_geotechnical_risk_rock_fall</th>\n",
       "      <th>has_repair_started</th>\n",
       "      <th>vdcmun_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Both</td>\n",
       "      <td>24385bfd2a2</td>\n",
       "      <td>Grade 4</td>\n",
       "      <td>24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Both</td>\n",
       "      <td>405d1bbebbf</td>\n",
       "      <td>Grade 2</td>\n",
       "      <td>44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Both</td>\n",
       "      <td>351d9bc71f6</td>\n",
       "      <td>Grade 1</td>\n",
       "      <td>36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Building removed</td>\n",
       "      <td>2be3a971166</td>\n",
       "      <td>Grade 5</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Both</td>\n",
       "      <td>34c7d073ea6</td>\n",
       "      <td>Grade 3</td>\n",
       "      <td>36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3627</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       area_assesed  building_id damage_grade  district_id  \\\n",
       "0              Both  24385bfd2a2      Grade 4           24   \n",
       "1              Both  405d1bbebbf      Grade 2           44   \n",
       "2              Both  351d9bc71f6      Grade 1           36   \n",
       "3  Building removed  2be3a971166      Grade 5           30   \n",
       "4              Both  34c7d073ea6      Grade 3           36   \n",
       "\n",
       "   has_geotechnical_risk  has_geotechnical_risk_fault_crack  \\\n",
       "0                    0.0                                  0   \n",
       "1                    0.0                                  0   \n",
       "2                    0.0                                  0   \n",
       "3                    0.0                                  0   \n",
       "4                    0.0                                  0   \n",
       "\n",
       "   has_geotechnical_risk_flood  has_geotechnical_risk_land_settlement  \\\n",
       "0                            0                                      0   \n",
       "1                            0                                      0   \n",
       "2                            0                                      0   \n",
       "3                            0                                      0   \n",
       "4                            0                                      0   \n",
       "\n",
       "   has_geotechnical_risk_landslide  has_geotechnical_risk_liquefaction  \\\n",
       "0                                0                                   0   \n",
       "1                                0                                   0   \n",
       "2                                0                                   0   \n",
       "3                                0                                   0   \n",
       "4                                0                                   0   \n",
       "\n",
       "   has_geotechnical_risk_other  has_geotechnical_risk_rock_fall  \\\n",
       "0                            0                                0   \n",
       "1                            0                                0   \n",
       "2                            0                                0   \n",
       "3                            0                                0   \n",
       "4                            0                                0   \n",
       "\n",
       "   has_repair_started  vdcmun_id  \n",
       "0                 0.0       2489  \n",
       "1                 1.0       4423  \n",
       "2                 0.0       3650  \n",
       "3                 0.0       3016  \n",
       "4                 0.0       3627  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_first.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess the datasets - (Remove/Fill Null Values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_uuid": "ee43fac4c29a66e701dba11e7c3ecb95a28a938a",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_df_first['has_repair_started']=train_df_first['has_repair_started'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "89b2bca1ed9f7374bd13dbc3362b30da143f1f10"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>building_id</th>\n",
       "      <th>district_id</th>\n",
       "      <th>vdcmun_id</th>\n",
       "      <th>ward_id</th>\n",
       "      <th>legal_ownership_status</th>\n",
       "      <th>count_families</th>\n",
       "      <th>has_secondary_use</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",
       "      <th>has_secondary_use_school</th>\n",
       "      <th>has_secondary_use_industry</th>\n",
       "      <th>has_secondary_use_health_post</th>\n",
       "      <th>has_secondary_use_gov_office</th>\n",
       "      <th>has_secondary_use_use_police</th>\n",
       "      <th>has_secondary_use_other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a3380c4f75</td>\n",
       "      <td>7</td>\n",
       "      <td>701</td>\n",
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       "      <td>Private</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>a3380c4fd9</td>\n",
       "      <td>7</td>\n",
       "      <td>701</td>\n",
       "      <td>70102</td>\n",
       "      <td>Private</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a3380c503e</td>\n",
       "      <td>7</td>\n",
       "      <td>701</td>\n",
       "      <td>70102</td>\n",
       "      <td>Private</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a338a4e5f2</td>\n",
       "      <td>7</td>\n",
       "      <td>701</td>\n",
       "      <td>70103</td>\n",
       "      <td>Private</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a338a4e653</td>\n",
       "      <td>7</td>\n",
       "      <td>701</td>\n",
       "      <td>70103</td>\n",
       "      <td>Private</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "  building_id  district_id  vdcmun_id  ward_id legal_ownership_status  \\\n",
       "0  a3380c4f75            7        701    70102                Private   \n",
       "1  a3380c4fd9            7        701    70102                Private   \n",
       "2  a3380c503e            7        701    70102                Private   \n",
       "3  a338a4e5f2            7        701    70103                Private   \n",
       "4  a338a4e653            7        701    70103                Private   \n",
       "\n",
       "   count_families  has_secondary_use  has_secondary_use_agriculture  \\\n",
       "0             1.0                0.0                              0   \n",
       "1             1.0                0.0                              0   \n",
       "2             1.0                0.0                              0   \n",
       "3             1.0                0.0                              0   \n",
       "4             1.0                0.0                              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  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "building_ownership_df_first['count_families']=building_ownership_df_first['count_families'].fillna(1)\n",
    "#fill the null values with mode i.e 0\n",
    "building_ownership_df_first['has_secondary_use']=building_ownership_df_first['has_secondary_use'].fillna(0)\n",
    "building_ownership_df_first.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "8a63262827fcf4c6e9c4b1e5af6b0038750e88b9",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "building_structure_df_first=building_structure_df_first.replace({'condition_post_eq':{'Damaged-Repaired and used':'Damaged Repaired and used','Damaged-Rubble unclear':'Damaged Rubble unclear','Damaged-Rubble Clear-New building built':'Damaged Rubble Clear New building built','Damaged-Not used':'Damaged Not used','Damaged-Rubble clear':'Damaged Rubble clear','Damaged-Used in risk':'Damaged Used in risk'}})\n",
    "\n",
    "building_structure_df_first=building_structure_df_first.replace({'foundation_type':{'Mud mortar-Stone/Brick':'Mud mortar Stone or Brick','Cement-Stone/Brick':'Cement Stone or Brick','Bamboo/Timber':'Bamboo or Timber'}})\n",
    "\n",
    "building_structure_df_first=building_structure_df_first.replace({'roof_type':{'Bamboo/Timber-Light roof':'Bamboo or Timber Light roof','RCC/RB/RBC':'RCC or RB or RBC','Bamboo/Timber-Heavy roof':'Bamboo or Timber Heavy roof'}})\n",
    "\n",
    "building_structure_df_first=building_structure_df_first.replace({'ground_floor_type':{'Brick/Stone':'Brick or Stone'}})\n",
    "\n",
    "building_structure_df_first=building_structure_df_first.replace({'other_floor_type':{'TImber/Bamboo-Mud':'TImber or Bamboo Mud','Timber-Planck':'Timber Planck','RCC/RB/RBC':'RCC or RB or RBC'}})\n",
    "\n",
    "building_structure_df_first=building_structure_df_first.replace({'position':{'Attached-1 side':'Attached 1 side','Attached-2 side':'Attached 2 side','Attached-3 side':'Attached 3 side'}})\n",
    "\n",
    "building_structure_df_first=building_structure_df_first.replace({'plan_configuration':{'L-shape':'L shape','T-shape':'T shape','U-shape':'U shape','Multi-projected':'Multi projected','E-shape':'E shape','H-shape':'H shape'}})\n",
    "\n",
    "#fill null values with most occuring values\n",
    "building_structure_df_first['position']=building_structure_df_first['position'].fillna('Not attached')\n",
    "building_structure_df_first['plan_configuration']=building_structure_df_first['plan_configuration'].fillna('Rectangular')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "419a827d9e0b2319245657740aebcf64f1de2066"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>building_id</th>\n",
       "      <th>district_id</th>\n",
       "      <th>vdcmun_id</th>\n",
       "      <th>ward_id</th>\n",
       "      <th>count_floors_pre_eq</th>\n",
       "      <th>count_floors_post_eq</th>\n",
       "      <th>age_building</th>\n",
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       "      <th>...</th>\n",
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       "      <th>has_superstructure_bamboo</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a3380c503e</td>\n",
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       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Damaged Repaired and used</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a338a4e5f2</td>\n",
       "      <td>7</td>\n",
       "      <td>701</td>\n",
       "      <td>70103</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>452</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
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       "      <td>1</td>\n",
       "      <td>Damaged Repaired and used</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a338a4e653</td>\n",
       "      <td>7</td>\n",
       "      <td>701</td>\n",
       "      <td>70103</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>542</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Damaged Rubble unclear</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  building_id  district_id  vdcmun_id  ward_id  count_floors_pre_eq  \\\n",
       "0  a3380c4f75            7        701    70102                    1   \n",
       "1  a3380c4fd9            7        701    70102                    1   \n",
       "2  a3380c503e            7        701    70102                    2   \n",
       "3  a338a4e5f2            7        701    70103                    2   \n",
       "4  a338a4e653            7        701    70103                    1   \n",
       "\n",
       "   count_floors_post_eq  age_building  plinth_area_sq_ft  height_ft_pre_eq  \\\n",
       "0                     1            28                454                 9   \n",
       "1                     1            32                324                 9   \n",
       "2                     2            34                456                18   \n",
       "3                     2            20                452                18   \n",
       "4                     0            25                542                 9   \n",
       "\n",
       "   height_ft_post_eq            ...             has_superstructure_stone_flag  \\\n",
       "0                  9            ...                                         0   \n",
       "1                  9            ...                                         0   \n",
       "2                 18            ...                                         0   \n",
       "3                 18            ...                                         0   \n",
       "4                  0            ...                                         0   \n",
       "\n",
       "  has_superstructure_cement_mortar_stone has_superstructure_mud_mortar_brick  \\\n",
       "0                                      0                                   0   \n",
       "1                                      0                                   0   \n",
       "2                                      0                                   0   \n",
       "3                                      0                                   0   \n",
       "4                                      0                                   0   \n",
       "\n",
       "  has_superstructure_cement_mortar_brick has_superstructure_timber  \\\n",
       "0                                      0                         1   \n",
       "1                                      0                         1   \n",
       "2                                      0                         1   \n",
       "3                                      0                         1   \n",
       "4                                      0                         1   \n",
       "\n",
       "  has_superstructure_bamboo has_superstructure_rc_non_engineered  \\\n",
       "0                         1                                    0   \n",
       "1                         1                                    0   \n",
       "2                         1                                    0   \n",
       "3                         1                                    0   \n",
       "4                         1                                    0   \n",
       "\n",
       "   has_superstructure_rc_engineered  has_superstructure_other  \\\n",
       "0                                 0                         1   \n",
       "1                                 0                         1   \n",
       "2                                 0                         1   \n",
       "3                                 0                         1   \n",
       "4                                 0                         1   \n",
       "\n",
       "           condition_post_eq  \n",
       "0  Damaged Repaired and used  \n",
       "1  Damaged Repaired and used  \n",
       "2  Damaged Repaired and used  \n",
       "3  Damaged Repaired and used  \n",
       "4     Damaged Rubble unclear  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "building_structure_df_first.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "68ac339bc2e2e2d752b0707865f7c8cceb6b365d",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_df['has_repair_started']=test_df['has_repair_started'].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge all other datasets with train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "16bb194706a0c80ddd386414058fb97e2241407b",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#merge two building datasets on common column i.e building_id\n",
    "buildings_other_df=pd.merge(building_ownership_df_first,building_structure_df_first,on=\"building_id\")\n",
    "final_test_df=pd.merge(test_df,buildings_other_df,how=\"left\",on=\"building_id\")\n",
    "building_id=final_test_df['building_id']\n",
    "final_test_df=final_test_df.drop('building_id',axis=1)\n",
    "final_train_df=pd.merge(train_df_first,buildings_other_df,how=\"left\",on=\"building_id\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "  damage_grade\n",
       "0      Grade 4\n",
       "1      Grade 2\n",
       "2      Grade 1\n",
       "3      Grade 5\n",
       "4      Grade 3"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "damage_grade=pd.DataFrame(final_train_df.damage_grade)\n",
    "damage_grade.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "   1  2  3  4  5\n",
       "0  0  0  0  1  0\n",
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       "2  1  0  0  0  0\n",
       "3  0  0  0  0  1\n",
       "4  0  0  1  0  0"
      ]
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "damage_grade_one_hot=pd.get_dummies(damage_grade)\n",
    "damage_grade_one_hot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "a82f526e4b0213e48ec5d51bd6807a9264713285"
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   "outputs": [
    {
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Damaged Rubble clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Both</td>\n",
       "      <td>36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Damaged Used in risk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       area_assesed  district_id  has_geotechnical_risk  \\\n",
       "0              Both           24                    0.0   \n",
       "1              Both           44                    0.0   \n",
       "2              Both           36                    0.0   \n",
       "3  Building removed           30                    0.0   \n",
       "4              Both           36                    0.0   \n",
       "\n",
       "   has_geotechnical_risk_fault_crack  has_geotechnical_risk_flood  \\\n",
       "0                                  0                            0   \n",
       "1                                  0                            0   \n",
       "2                                  0                            0   \n",
       "3                                  0                            0   \n",
       "4                                  0                            0   \n",
       "\n",
       "   has_geotechnical_risk_land_settlement  has_geotechnical_risk_landslide  \\\n",
       "0                                      0                                0   \n",
       "1                                      0                                0   \n",
       "2                                      0                                0   \n",
       "3                                      0                                0   \n",
       "4                                      0                                0   \n",
       "\n",
       "   has_geotechnical_risk_liquefaction  has_geotechnical_risk_other  \\\n",
       "0                                   0                            0   \n",
       "1                                   0                            0   \n",
       "2                                   0                            0   \n",
       "3                                   0                            0   \n",
       "4                                   0                            0   \n",
       "\n",
       "   has_geotechnical_risk_rock_fall            ...              \\\n",
       "0                                0            ...               \n",
       "1                                0            ...               \n",
       "2                                0            ...               \n",
       "3                                0            ...               \n",
       "4                                0            ...               \n",
       "\n",
       "   has_superstructure_stone_flag  has_superstructure_cement_mortar_stone  \\\n",
       "0                              0                                       0   \n",
       "1                              0                                       0   \n",
       "2                              0                                       0   \n",
       "3                              0                                       0   \n",
       "4                              0                                       0   \n",
       "\n",
       "   has_superstructure_mud_mortar_brick  \\\n",
       "0                                    0   \n",
       "1                                    1   \n",
       "2                                    0   \n",
       "3                                    0   \n",
       "4                                    0   \n",
       "\n",
       "   has_superstructure_cement_mortar_brick  has_superstructure_timber  \\\n",
       "0                                       0                          0   \n",
       "1                                       0                          0   \n",
       "2                                       0                          0   \n",
       "3                                       0                          0   \n",
       "4                                       0                          0   \n",
       "\n",
       "  has_superstructure_bamboo  has_superstructure_rc_non_engineered  \\\n",
       "0                         0                                     0   \n",
       "1                         0                                     0   \n",
       "2                         0                                     0   \n",
       "3                         0                                     0   \n",
       "4                         0                                     0   \n",
       "\n",
       "   has_superstructure_rc_engineered  has_superstructure_other  \\\n",
       "0                                 0                         0   \n",
       "1                                 0                         0   \n",
       "2                                 1                         0   \n",
       "3                                 0                         0   \n",
       "4                                 0                         0   \n",
       "\n",
       "           condition_post_eq  \n",
       "0           Damaged Not used  \n",
       "1  Damaged Repaired and used  \n",
       "2                Not damaged  \n",
       "3       Damaged Rubble clear  \n",
       "4       Damaged Used in risk  \n",
       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop building id and damage grade from the train data\n",
    "final_train_df=final_train_df.drop('building_id',axis=1)\n",
    "final_train_df=final_train_df.drop('damage_grade',axis=1)\n",
    "final_train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One-Hot Encode the train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "4a432c02620bf54fd883fb7b8ede31792e7781ed",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final_train_encoded_features=pd.get_dummies(final_train_df)\n",
    "final_test_encoded_features=pd.get_dummies(final_test_df)\n",
    "final_train, final_test = final_train_encoded_features.align(final_test_encoded_features,\n",
    "                                                                    join='left', \n",
    "                                                                    axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_uuid": "d8055772d3e0d49b65d07ae2c6aece83b067575d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97 No of columns in train after one hot encoding\n"
     ]
    }
   ],
   "source": [
    "encoded=list(final_train.columns)\n",
    "print('{} No of columns in train after one hot encoding'. format(len(encoded)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "5946c749c070eab2822e5f9d119691a1cb1f13c0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97 No of columns in test after one hot encoding\n"
     ]
    }
   ],
   "source": [
    "encoded_2=list(final_test.columns)\n",
    "print('{} No of columns in test after one hot encoding'. format(len(encoded_2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "6654588dc0b5775b1d0f677571c5d8b8dcd7a1d3",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#final_train.columns\n",
    "final_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalize Numerical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_uuid": "662a54eb6ea994970e6518de080f62c00600e5c7"
   },
   "outputs": [
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       "      <th>has_repair_started</th>\n",
       "      <th>...</th>\n",
       "      <th>plan_configuration_T shape</th>\n",
       "      <th>plan_configuration_U shape</th>\n",
       "      <th>condition_post_eq_Covered by landslide</th>\n",
       "      <th>condition_post_eq_Damaged Not used</th>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   district_id  has_geotechnical_risk  has_geotechnical_risk_fault_crack  \\\n",
       "0     0.386364                    0.0                                0.0   \n",
       "1     0.840909                    0.0                                0.0   \n",
       "2     0.659091                    0.0                                0.0   \n",
       "3     0.522727                    0.0                                0.0   \n",
       "4     0.659091                    0.0                                0.0   \n",
       "\n",
       "   has_geotechnical_risk_flood  has_geotechnical_risk_land_settlement  \\\n",
       "0                          0.0                                    0.0   \n",
       "1                          0.0                                    0.0   \n",
       "2                          0.0                                    0.0   \n",
       "3                          0.0                                    0.0   \n",
       "4                          0.0                                    0.0   \n",
       "\n",
       "   has_geotechnical_risk_landslide  has_geotechnical_risk_liquefaction  \\\n",
       "0                              0.0                                 0.0   \n",
       "1                              0.0                                 0.0   \n",
       "2                              0.0                                 0.0   \n",
       "3                              0.0                                 0.0   \n",
       "4                              0.0                                 0.0   \n",
       "\n",
       "   has_geotechnical_risk_other  has_geotechnical_risk_rock_fall  \\\n",
       "0                          0.0                              0.0   \n",
       "1                          0.0                              0.0   \n",
       "2                          0.0                              0.0   \n",
       "3                          0.0                              0.0   \n",
       "4                          0.0                              0.0   \n",
       "\n",
       "   has_repair_started              ...                \\\n",
       "0                 0.0              ...                 \n",
       "1                 1.0              ...                 \n",
       "2                 0.0              ...                 \n",
       "3                 0.0              ...                 \n",
       "4                 0.0              ...                 \n",
       "\n",
       "   plan_configuration_T shape  plan_configuration_U shape  \\\n",
       "0                         0.0                         0.0   \n",
       "1                         0.0                         0.0   \n",
       "2                         0.0                         0.0   \n",
       "3                         0.0                         0.0   \n",
       "4                         0.0                         0.0   \n",
       "\n",
       "   condition_post_eq_Covered by landslide  condition_post_eq_Damaged Not used  \\\n",
       "0                                     0.0                                 1.0   \n",
       "1                                     0.0                                 0.0   \n",
       "2                                     0.0                                 0.0   \n",
       "3                                     0.0                                 0.0   \n",
       "4                                     0.0                                 0.0   \n",
       "\n",
       "   condition_post_eq_Damaged Repaired and used  \\\n",
       "0                                          0.0   \n",
       "1                                          1.0   \n",
       "2                                          0.0   \n",
       "3                                          0.0   \n",
       "4                                          0.0   \n",
       "\n",
       "   condition_post_eq_Damaged Rubble Clear New building built  \\\n",
       "0                                                0.0           \n",
       "1                                                0.0           \n",
       "2                                                0.0           \n",
       "3                                                0.0           \n",
       "4                                                0.0           \n",
       "\n",
       "   condition_post_eq_Damaged Rubble clear  \\\n",
       "0                                     0.0   \n",
       "1                                     0.0   \n",
       "2                                     0.0   \n",
       "3                                     1.0   \n",
       "4                                     0.0   \n",
       "\n",
       "   condition_post_eq_Damaged Rubble unclear  \\\n",
       "0                                       0.0   \n",
       "1                                       0.0   \n",
       "2                                       0.0   \n",
       "3                                       0.0   \n",
       "4                                       0.0   \n",
       "\n",
       "   condition_post_eq_Damaged Used in risk  condition_post_eq_Not damaged  \n",
       "0                                     0.0                            0.0  \n",
       "1                                     0.0                            0.0  \n",
       "2                                     0.0                            1.0  \n",
       "3                                     0.0                            0.0  \n",
       "4                                     1.0                            0.0  \n",
       "\n",
       "[5 rows x 97 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "scaler=MinMaxScaler()\n",
    "final_scaled_train=pd.DataFrame(scaler.fit_transform(final_train))\n",
    "final_scaled_train.columns=final_train.columns\n",
    "final_scaled_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_uuid": "a803248c03e643a743ebe2e68d3d22be68e29e1b"
   },
   "outputs": [
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   district_id  has_geotechnical_risk  has_geotechnical_risk_fault_crack  \\\n",
       "0          0.0                    0.0                                0.0   \n",
       "1          0.0                    0.0                                0.0   \n",
       "2          0.0                    0.0                                0.0   \n",
       "3          0.0                    0.0                                0.0   \n",
       "4          0.0                    0.0                                0.0   \n",
       "\n",
       "   has_geotechnical_risk_flood  has_geotechnical_risk_land_settlement  \\\n",
       "0                          0.0                                    0.0   \n",
       "1                          0.0                                    0.0   \n",
       "2                          0.0                                    0.0   \n",
       "3                          0.0                                    0.0   \n",
       "4                          0.0                                    0.0   \n",
       "\n",
       "   has_geotechnical_risk_landslide  has_geotechnical_risk_liquefaction  \\\n",
       "0                              0.0                                 0.0   \n",
       "1                              0.0                                 0.0   \n",
       "2                              0.0                                 0.0   \n",
       "3                              0.0                                 0.0   \n",
       "4                              0.0                                 0.0   \n",
       "\n",
       "   has_geotechnical_risk_other  has_geotechnical_risk_rock_fall  \\\n",
       "0                          0.0                              0.0   \n",
       "1                          0.0                              0.0   \n",
       "2                          0.0                              0.0   \n",
       "3                          0.0                              0.0   \n",
       "4                          0.0                              0.0   \n",
       "\n",
       "   has_repair_started              ...                \\\n",
       "0                 1.0              ...                 \n",
       "1                 1.0              ...                 \n",
       "2                 1.0              ...                 \n",
       "3                 1.0              ...                 \n",
       "4                 1.0              ...                 \n",
       "\n",
       "   plan_configuration_T shape  plan_configuration_U shape  \\\n",
       "0                         0.0                         0.0   \n",
       "1                         0.0                         0.0   \n",
       "2                         0.0                         0.0   \n",
       "3                         0.0                         0.0   \n",
       "4                         0.0                         0.0   \n",
       "\n",
       "   condition_post_eq_Covered by landslide  condition_post_eq_Damaged Not used  \\\n",
       "0                                     0.0                                 0.0   \n",
       "1                                     0.0                                 0.0   \n",
       "2                                     0.0                                 0.0   \n",
       "3                                     0.0                                 0.0   \n",
       "4                                     0.0                                 0.0   \n",
       "\n",
       "   condition_post_eq_Damaged Repaired and used  \\\n",
       "0                                          1.0   \n",
       "1                                          0.0   \n",
       "2                                          0.0   \n",
       "3                                          1.0   \n",
       "4                                          0.0   \n",
       "\n",
       "   condition_post_eq_Damaged Rubble Clear New building built  \\\n",
       "0                                                0.0           \n",
       "1                                                0.0           \n",
       "2                                                1.0           \n",
       "3                                                0.0           \n",
       "4                                                1.0           \n",
       "\n",
       "   condition_post_eq_Damaged Rubble clear  \\\n",
       "0                                     0.0   \n",
       "1                                     0.0   \n",
       "2                                     0.0   \n",
       "3                                     0.0   \n",
       "4                                     0.0   \n",
       "\n",
       "   condition_post_eq_Damaged Rubble unclear  \\\n",
       "0                                       0.0   \n",
       "1                                       1.0   \n",
       "2                                       0.0   \n",
       "3                                       0.0   \n",
       "4                                       0.0   \n",
       "\n",
       "   condition_post_eq_Damaged Used in risk  condition_post_eq_Not damaged  \n",
       "0                                     0.0                            0.0  \n",
       "1                                     0.0                            0.0  \n",
       "2                                     0.0                            0.0  \n",
       "3                                     0.0                            0.0  \n",
       "4                                     0.0                            0.0  \n",
       "\n",
       "[5 rows x 97 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_scaled_test=pd.DataFrame(scaler.fit_transform(final_test))\n",
    "final_scaled_test.columns=final_test.columns\n",
    "final_scaled_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_uuid": "64838c27fc7ca3476b0e831f25b235466cb04ecd",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final_scaled_train.to_csv('processed_data/final_scaled_train.csv',index=False)\n",
    "final_scaled_test.to_csv('processed_data/final_scaled_test.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final_scaled_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(final_scaled_train,damage_grade_one_hot,test_size=0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network/ MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 256)               25088     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 5)                 85        \n",
      "=================================================================\n",
      "Total params: 68,933\n",
      "Trainable params: 68,933\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "keras_model= Sequential()\n",
    "\n",
    "keras_model.add(Dense(256,input_shape=X_train.shape[1:],activation='relu'))\n",
    "keras_model.add(Dense(128,activation='relu'))\n",
    "keras_model.add(Dense(64,activation='relu'))\n",
    "keras_model.add(Dense(32,activation='relu'))\n",
    "keras_model.add(Dense(16,activation='relu'))\n",
    "keras_model.add(Dense(5,activation='softmax'))\n",
    "\n",
    "keras_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "keras_model.compile(optimizer='sgd',loss='categorical_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Keras Model:\n",
      "Train on 473820 samples, validate on 157941 samples\n",
      "Epoch 1/10\n",
      "473820/473820 [==============================] - 39s 82us/step - loss: 0.6857 - acc: 0.6879 - val_loss: 0.6414 - val_acc: 0.7073\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.64144, saving model to saved_models/keras_model_best_weights.hdf5\n",
      "Epoch 2/10\n",
      "473820/473820 [==============================] - 37s 77us/step - loss: 0.6400 - acc: 0.7072 - val_loss: 0.6321 - val_acc: 0.7115\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.64144 to 0.63206, saving model to saved_models/keras_model_best_weights.hdf5\n",
      "Epoch 3/10\n",
      "473820/473820 [==============================] - 37s 78us/step - loss: 0.6319 - acc: 0.7107 - val_loss: 0.6543 - val_acc: 0.7027\n",
      "\n",
      "Epoch 00003: val_loss did not improve from 0.63206\n",
      "Epoch 4/10\n",
      "473820/473820 [==============================] - 37s 77us/step - loss: 0.6273 - acc: 0.7135 - val_loss: 0.6250 - val_acc: 0.7152\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.63206 to 0.62497, saving model to saved_models/keras_model_best_weights.hdf5\n",
      "Epoch 5/10\n",
      "473820/473820 [==============================] - 37s 78us/step - loss: 0.6239 - acc: 0.7151 - val_loss: 0.6320 - val_acc: 0.7101\n",
      "\n",
      "Epoch 00005: val_loss did not improve from 0.62497\n",
      "Epoch 6/10\n",
      "473820/473820 [==============================] - 37s 78us/step - loss: 0.6211 - acc: 0.7166 - val_loss: 0.6231 - val_acc: 0.7160\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.62497 to 0.62308, saving model to saved_models/keras_model_best_weights.hdf5\n",
      "Epoch 7/10\n",
      "473820/473820 [==============================] - 37s 78us/step - loss: 0.6186 - acc: 0.7180 - val_loss: 0.6190 - val_acc: 0.7180\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.62308 to 0.61903, saving model to saved_models/keras_model_best_weights.hdf5\n",
      "Epoch 8/10\n",
      "473820/473820 [==============================] - 37s 78us/step - loss: 0.6166 - acc: 0.7187 - val_loss: 0.6231 - val_acc: 0.7166\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 0.61903\n",
      "Epoch 9/10\n",
      "473820/473820 [==============================] - 38s 80us/step - loss: 0.6148 - acc: 0.7197 - val_loss: 0.6188 - val_acc: 0.7190\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.61903 to 0.61883, saving model to saved_models/keras_model_best_weights.hdf5\n",
      "Epoch 10/10\n",
      "473820/473820 [==============================] - 37s 78us/step - loss: 0.6131 - acc: 0.7206 - val_loss: 0.6156 - val_acc: 0.7189\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.61883 to 0.61558, saving model to saved_models/keras_model_best_weights.hdf5\n",
      "Time Taken(in Minutes) to Train the Model: 6.204842785994212\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "epochs=10\n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath='saved_models/keras_model_best_weights.hdf5', \n",
    "                               verbose=1, save_best_only=True)\n",
    "start=time.time()\n",
    "\n",
    "print(\"Training Keras Model:\")\n",
    "\n",
    "history= keras_model.fit(X_train,y_train,validation_data=(X_test, y_test),epochs=epochs,\n",
    "                         callbacks=[checkpointer], verbose=1)\n",
    "\n",
    "end=time.time()\n",
    "\n",
    "total_time=end-start\n",
    "print(\"Time Taken(in Minutes) to Train the Model:\", total_time/60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157941/157941 [==============================] - 4s 24us/step\n",
      "Accuracy of Keras model :  0.7188950304238849\n"
     ]
    }
   ],
   "source": [
    "keras_model.load_weights('saved_models/keras_model_best_weights.hdf5')\n",
    "\n",
    "accuracy_1=keras_model.evaluate(x=X_test,y=y_test,batch_size=32)\n",
    "print(\"Accuracy of Keras model : \",accuracy_1[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions_keras=keras_model.predict_classes(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4, 1, ..., 4, 4, 0])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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b4ZsZcMb/wrFX+xerMWEi/OtKWqK8rfDMWS6JjLgO7vwaUgfBe7+AkoJQRwfA\n/tJyHp+zgZP+nM6fPlzLMd2TePuWsUy9dtThJRGAdR/Cno1uPfZwmD49qQf85CNIHQDTLoOvXwvu\nOFX48F5Y8RKM+zUc/3N/4zQmTFiJJNysnenW666scHXrR1/stp/3T3j6NPjsQTjrzyEL70BpBS8u\n3sLjczeQW1jKyf1TufPUNI7tUY9eVoumQNvuMOj8hgu0vtqkwDXvwrTL4c0bYP+eujsBpD8EXzwO\nY26BU+5pnDiNCQOWSMJFeSl8+gdXxdP5GLj4mYMHrXUf5dbv/uIJOPoS97oRlVVU8sKiLfx79gZy\nC0s4KS2FO09NY0TP9vU78fblsGU+nP5Q+E2hHpsIV0yHN65364Xs3w3jf1NzqWnBozD3/1xV1hkP\nhUfJyphGEmZ/uS3Unk0w/Tr3pTr6Jjj9wZqnFJ94H6x939XB3zgHomIaJbwlm/fw27dWsm5XISf0\nTeY/Vx7LqF71TCBVFk2BmAQ49qqGOV9Di46FS56D9+6EuX+G/blw1l8gIvL7fZZOdWutD/kRnPN3\nSyKmxfG1jUREJolIhohkisi9Nbz/iIis8B7rRCQv4L0PRSRPRN6rdkxvEflcRNaLyKsi0jjfpn5Z\n/TY8frJrI/jxi67aqrZ1KVolwNl/g+w1sPBR30PbU1TKr6Z/xSWPLaKopIInrx7JyzeMabgkkp8F\nq99yv+L9GFHeUCKjXNXi2Dtd0njjeleCBPj6ddd2lXaGm8MrMMEY00L4ViIRkUhgCnAakAUsEZEZ\nqrqmah9VvStg/9uA4QGn+D8gDrip2qn/BDyiqtNE5DHgeuA//tyFj8qK4ePfulUAu45wVVntetZ9\n3IBJbjLDOX+GwRdASlqDh1ZZqUz/Mov/nfkNBcXl3HxKX26f2I+4mAb+5/LFE6CVjT8A8UiIwGn/\nDXHJ8Mnv3fxcw6+Et25yAw0vfa7RSojGhBs/SySjgUxV3aiqpcA04FCtqZcBr1S9UNXPgIO6KImI\nABOA6d6m54ALGjLoRpGbCU+d6pLI8be6sQvBJJEqk/7kJhh89w7X3bQBrdtVwOQnFvOr6V/Tr0M8\n799+EveeObDhk0hJISx9Fgaf73pJNRVjb4fz/w2b5rqSSedj3NT0fk74aEyY87ONpCuwNeB1FlDj\nIAER6Qn0BmbVcc5kIE9VywPOWeNc3CJyI3AjQI8eYfRF9fXrrr49Mhoue9WVMA5XQke3jsaM22D5\n8w2y2t/+0nL+8VkmT83bSHxsFH++aCgXj+hGRIRP9f3LX4SS/NAOQDxSw69wJZNV0+HMP7sqR2Na\nMD8TSU3fQFrLvpOB6apa0VDtqiDqAAAeE0lEQVTnVNUngCcARo4cWdt1G0/pfvjgV7D8Beg+Bi5+\n2q2Qd6SGX+XGN3x8H/Sf5JaTPUKffbOL+95Zzba8A1w6shv3njmI9m18rKaprHC907ofB91G+ncd\nPw2YdGQ/Aoxphvys2soCuge87gZsr2XfyQRUax1CLpAkIlUJ8FDnDB/Za+HJCS6JnPgLuPb9+iUR\ncHX25z4K5cUw8+4jOsX2vAPc9MJSrn9uKXExkbx20/H8+eJj/E0i4Hqe5W1pmqURY8wP+FkiWQKk\niUhvYBsuWVxefScRGQC0AxbVdUJVVRFJBy7GtblcA7zTkEE3uOUvwcxfQnScm0a836kNd+7kvjDu\nHrds7Nr3YeDZQR1WVlHJsws288in66hU5Z5JA7n+xN7ERDXSRAeLpkBSz6DjNcaEN98SiaqWi8it\nwEdAJDBVVVeLyAPAUlWd4e16GTBNVQ+qfhKRecBAIF5EsoDrVfUj4B5gmoj8EVgOPO3XPdRLSaFL\nIF+9Ar1Ogoueqlf1U61OuB1WvekWZ+p1khtEdwjLtuzlt2+tZO3OAiYO7MD95w2he/u4ho+rNllL\nYeti12HAusoa0yxIte/vZmnkyJG6dOnSxrvgzlXw+rWwOxPG3Qsn3+3vl2bWMnj6VLcg1Nl/rXGX\nvP2l/OnDDF754ls6t43l/vOGcPrgjkhjD557/VrInAW/WG2N1MaEORFZpqp1NmTayPaGpArLnoEP\n7oXWSW5Z1d4n+3/dbiPguJtdA/bRlxw09bmq8tbybTz0/jfkHSjjhpN6c+ep/WnTKgT/6/dugTXv\nwAm3WRIxphmxRNJQive5cR2r34S+E+DCJyA+tfGuP/638M17MON2uHkeRLUiM7uQ3729ksUb9zC8\nRxIvXHA0g7scuurLV188ARLhpoExxjQblkgawvYVrsom71s3H9bYuxp/NcNW8XDO3+Cliymb81ce\nLb+Ix+duIC4miv+58Ggmj+ru35iQYBTnw7Ln3Kj8tjUO/THGNFGWSOpD1f3K/vh3EJfiuvX2PD50\n8aSdxq6e59J+3l/5sCSZc4cfx2/OGkRKfC1zdzWmL1+A0gK35ogxplmxRHKkDuyFd26Fte+5Cfsu\n+A+0SQ5ZODvzi3nwvTUszjiLWbGzebPbNBIvviE81nmvKIfPH4OeJ0KX4XXvb4xpUiyRHImspW7a\n933b3VQlY24J2Rd2eUUlLyzewl8/XkdZRSW3nT6KuLYPE/3uLbBsKoz6aUjiOsg3MyB/q5tOxBjT\n7FgiORyVlbB4Cnx6PyR0ccuxhnCKjxVb8/jtWytZvX0fp/RP5YHzh9AzuQ1oP1j9OnxyPww4CxK7\nhCxGVGHRv6B9HzeVizGm2QmDeo8mYv8eeGWyaw/pPwlunhuyJJJ/oIzfv72KC/+9gNzCEqZcfizP\nXjfKJRFw06ec8whUlruBiqEcK7T1c9i2DMb8PDyq2YwxDc5KJMHYsshNGV6UA2f+n1vyNgSr4Kkq\nM77azoPvfcOeohKuPaEXvzitPwmx0T/cuX0fGP9rt3LfNzPcdO2hsOhf0LodDPvB7DjGmGYiqEQi\nIm8AU4EPVLVhF8AIZ5WVsOARmPWQWzPj+o9D1li8KbeI37+9ivmZuRzTrS3PXjeKo7rWsargmFtg\n5XQ3qWPvU9wgyca0Z6Mb23LSLyCmTeNe2xjTaIKta/gPbsLF9SLysIgM9DGm8KAKr13lJkQcfD7c\nNDdkSeSZBZs445G5fLU1jwfPH8KbPx9bdxIBb4nYf7iS1Kd/8D/Q6hY/BhFRMOqGxr+2MabRBFUi\nUdVPgU9FpC1uksVPRGQr8CTwoqqW+RhjaIjAoHOh30QYcV1IqrIAikrK+Z+Z33Bc72T+9uNj6JAQ\ne3gn6DLcjd1Y+E84+lLoNdafQKs7sNctXnX0xZDYuXGuaYwJiaBbP0UkGbgW+Clu1t1HgWOBT3yJ\nLBwcM9lNhBiiJAKwIDOXsgrllvH9Dj+JVBn3Gzdt+7u3u7XiG8Oy56CsyAYgGtMCBJVIRORNYB4Q\nB5yrquep6quqehsQ72eALV16Rg7xraIY2avdkZ8kJg7O/bubjXjeXxouuNpUlMHnj7t2mU5H+389\nY0xIBVsi+ZeqDlbV/1XVHYFvBDPFsDkyqsrsjGxOSkshOrKeXWf7ToChk2H+I7BrTcMEWJvVb0HB\ndlsB0ZgWIthvp0Ei8l2XHxFpJyI/9ykm41m7s4Ad+cWMH9ChYU54xv9AbFuYcZtbN90PVQMQU/o3\n7GqQxpiwFWwiuUFV86peqOpewLri+Cw9IxuAUwY00HT0bZJh0sOwbSkseaphzlndlgWw4yvXNmID\nEI1pEYL9S4+QgKX0RCQSiPEnJFNl9tochnRJpGPiETay1+ToS6DvRNetOT+r4c5bZdEUiEuGoT9u\n+HMbY8JSsInkI+A1EZkoIhOAV4AP/QvL5O8vY9m3exuuWqtK1fQpWgnv/1fDTp+SmwkZH7iJIqNb\nN9x5jTFhLdhEcg8wC/gZcAvwGfArv4IyMC8zh4pKZfzABk4kAO16woTfwboPXcN4Q1n8b4iMCY8Z\nh40xjSbYAYmVuNHt//E3HFMlfW0OSXHRDOvu07Qmx90MK1+HD34FfcZBXPv6nW//HljxMgy9FOJ9\nSH7GmLAV7DiSNBGZLiJrRGRj1SOI4yaJSIaIZIrIvTW8/4iIrPAe60QkL+C9a0Rkvfe4JmD7bO+c\nVcc1u2+tykplzrpsTumfSqRfy+NGRMJ5/3QJ4JPf1/98S6dC+QEbgGhMCxTs7L/PAH8AHgHGA9cB\nh/yG8xrkpwCnAVnAEhGZoarfDWJQ1bsC9r8NGO49b+9dbySgwDLv2L3e7leo6tIgY29yVm7LJ7ew\ntOHbR6rrdDSccBss+LtrHO998pGdp7zELTncdyJ0GNSwMRpjwl6wbSStVfUzQFR1i6reD0yo45jR\nQKaqblTVUmAacKi5zC/DNeIDnAF8oqp7vOTxCdBiVkVKz8hGBE7u30Ddfg9l3L3Qrje8eweUHTiy\nc6x6Awp3WWnEmBYq2ERSLCIRuNl/bxWRC4G6fi53BbYGvM7ytv2AiPQEeuMa9IM59hmvWuv3gd2S\nm4v0jByGdU+ifZtG6GEd3RrOfdRN+T7nT4d/vKrr8tthsBs9b4xpcYJNJHfi5tm6HRgBXAlcc8gj\naq76qq2v6WRguqpWDbc+1LFXqOrRwEne46oaLy5yo4gsFZGlOTk5dYQaPnILS/g6K48JfldrBepz\nCgy7Ehb8A3auPLxjN82BXatcaaT55XRjTBDqTCReW8elqlqoqlmqep2qXqSqi+s4NAvoHvC6G7C9\nln0n83211iGPVdVt3n8LgJdxVWg/oKpPqOpIVR2ZmtoIVUQNZO66HFTxp9vvoZz+oOu5NeP2w5s+\nZdEUaNPBDXQ0xrRIdSYSr5Qw4giqkJYAaSLSW0RicMliRvWdRGQA0A5YFLD5I+B0b06vdsDpwEci\nEiUiKd5x0cA5wKrDjCuszVqbTWpCKwZ3TmzcC8e1d9OnbP/SzdwbjJwMWP+xW3o4qpW/8Rljwlaw\nvbaWA++IyOtAUdVGVX2ztgNUtVxEbsUlhUhgqqquFpEHgKWqWpVULgOmqX4/xFpV94jIg7hkBPCA\nt60NLqFEe+f8FLe4VrNQXlHJ3HU5nDGkExF+dfs9lKMugq9fg1kPwsCz3cDFQ1k0BaJiYeT1jROf\nMSYsBZtI2gO7ObinlgK1JhIAVZ0JzKy27b5qr++v5dipuHXiA7cV4dpomqXlW/PYV1ze+NVaVUTg\n7L/Cv8fA+7+AK6bX3u5RlAtfTYNhl7vJII0xLVawI9uv8zsQA+lrs4mMEE5MSwldEEndYcLv4cN7\nYOV0GFpL28eSp6GiBMbYagLGtHRBJRIReYYaelyp6k8aPKIWLD0jh5E925EYGx3aQEbf4KZP+fAe\n16W3eomjrBiWPAlpZ0Bq/9DEaIwJG8F2/30PeN97fAYkAoV+BdUS7cwv5psd+5gQqmqtQBGRcN4/\noDgfPv7tD99f+RoU5cAJtgKiMSb4qq03Al+LyCu4hm7TQGZ7i1iFrH2kuo5DYOydbo33oZd+P9iw\nagBip6Oh10mhjdEYExaOdAm7NKBHQwbS0s1am03XpNakdYgPdSjfO/luSO4H790Fpfvdtg2fQc5a\ntx67DUA0xhD87L8FIrKv6gG8i1ujxDSAkvIKFmTmMm5AKmE140t0rJs+Ze9mmP2/btvCf0FCZxjy\no5CGZowJH8FWbSX4HUhLtnTzXopKK/yf7fdI9DoRjr0GFv3Lzey7MR0m/gGibKVlY4wTbInkQhFp\nG/A6SUQu8C+sliV9bTYxkRGc0C9Mx2Oc9gC0SYW3fwbRcTDi2lBHZIwJI8G2kfxBVfOrXqhqHm69\nENMA0jOyOa5Pe+Jigh0f2shaJ8GZf3bPh11e/9UUjTHNSrDfXDUlnDD91mtavt29nw05RVw5po7p\nSEJt8Plw1dvQvcY5Mo0xLViwJZKlIvI3EekrIn1E5BFgmZ+BtRTpVd1+w7F9JJAI9B0PMW1CHYkx\nJswEm0huA0qBV4HXgAOALYfXANIzsumd0oZeKfYFbYxpmoLttVUE3OtzLC3OgdIKFm3YzeXH2ZAc\nY0zTFWyvrU9EJCngdTsR+ci/sFqGxRt3U1JeGf7VWsYYcwjBVm2leD21AFDVvdS9ZrupQ3pGNq2j\nIxnd23pBGWOarmATSaWIfFf/IiK9qH39dRMEVWXW2mzG9kshNjoy1OEYY8wRC7YL72+B+SIyx3t9\nMnCjPyG1DBtyisjae4Cfjesb6lCMMaZegm1s/1BERuKSxwrgHVzPLXOE0te6br/jrH3EGNPEBbuw\n1U+BO4BuuEQyBljEwUvvmsOQnpHNgI4JdE1qHepQjDGmXoJtI7kDGAVsUdXxwHAgx7eomrmC4jKW\nbN7DuIGpoQ7FGGPqLdhEUqyqxQAi0kpV1wID/AureVuQuZuyCrVuv8aYZiHYxvYsbxzJ28AnIrIX\n2O5fWM3b7IxsEmKjGNGzXahDMcaYeguqRKKqF6pqnqreD/weeBqocxp5EZkkIhkikikiPxgZLyKP\niMgK77FORPIC3rtGRNZ7j2sCto8QkZXeOf8hYbUSVN1UlfSMbE5OSyU68kgXqDTGmPBx2DP4quqc\nuvcCEYkEpgCnAVnAEhGZoaprAs51V8D+t+HaXhCR9rhp6kfixqss847dC/wH13tsMTATmAR8cLj3\nESrf7Chg174Sxg2w9hFjTPPg50/i0UCmqm5U1VJgGnD+Ifa/DHjFe34G8Imq7vGSxyfAJBHpDCSq\n6iJVVeB5gigZhZOq2X5PsURijGkm/EwkXYGtAa+zvG0/ICI9gd7ArDqO7eo9D+acN4rIUhFZmpMT\nPh3M0tdmc3TXtnRIiA11KMYY0yD8TCQ1tV3UNq3KZGC6qlbUcWzQ51TVJ1R1pKqOTE0Nj1//eftL\n+fLbvYy30ogxphnxM5FkAd0DXnej9p5ek/m+WutQx2Z5z4M5Z9iZuz6XSoVxA63brzGm+fAzkSwB\n0kSkt4jE4JLFjOo7icgAoB1upHyVj4DTvenq2wGnAx+p6g6gQETGeL21rsZN19IkzF6bTfs2MRzT\nLanunY0xponwbd11VS0XkVtxSSESmKqqq0XkAWCpqlYllcuAaV7jedWxe0TkQVwyAnhAVfd4z38G\nPAu0xvXWahI9tiorldnrcjilfyqREU2qx7IxxhySb4kEQFVn4rroBm67r9rr+2s5diowtYbtS4Gj\nGi7KxvFVVh57ikqt268xptmxEXGNJD0jhwiBk9MskRhjmhdLJI1kdkY2w3u0o12bmFCHYowxDcoS\nSSPIKSjh66x86/ZrjGmWLJE0gjnr3IBIW8TKGNMcWSJpBOkZ2XRIaMWQLomhDsUYYxqcJRKflVdU\nMnddDuMHdKCJTVRsjDFBsUTis2Vb9lJQXM54Ww3RGNNMWSLxWXpGDlERwth+KaEOxRhjfGGJxGez\nM7IZ1as9CbHRoQ7FGGN8YYnER9vzDrB2Z4FVaxljmjVLJD6aneG6/Y63br/GmGbMEomP0jOy6dau\nNf06xIc6FGOM8Y0lEp+UlFewIDPXuv0aY5o9SyQ++WLTHvaXVlj7iDGm2bNE4pP0tTnEREVwfB/r\n9muMad4skfhkdkY2x/dJpnVMZKhDMcYYX1ki8cHm3CI25hbZbL/GmBbBEokPZmdkAzB+oHX7NcY0\nf5ZIfJCekUOf1Db0TG4T6lCMMcZ3lkga2P7SchZt3G2DEI0xLYYlkga2aMNuSssrLZEYY1oMXxOJ\niEwSkQwRyRSRe2vZ51IRWSMiq0Xk5YDtfxKRVd7jxwHbnxWRTSKywnsM8/MeDld6RjZxMZGM6t0u\n1KEYY0yjiPLrxCISCUwBTgOygCUiMkNV1wTskwb8GhirqntFpIO3/WzgWGAY0AqYIyIfqOo+79C7\nVXW6X7EfKVUlfW0OY/ul0CrKuv0aY1oGP0sko4FMVd2oqqXANOD8avvcAExR1b0AqprtbR8MzFHV\nclUtAr4CJvkYa4PIzC5kW94Bq9YyxrQofiaSrsDWgNdZ3rZA/YH+IrJARBaLSFWy+Ao4U0TiRCQF\nGA90DzjuIRH5WkQeEZFWNV1cRG4UkaUisjQnJ6dh7qgO6V6333E2fsQY04L4mUhqmqlQq72OAtKA\nccBlwFMikqSqHwMzgYXAK8AioNw75tfAQGAU0B64p6aLq+oTqjpSVUempjbOF3v62hwGdkqgS1Lr\nRrmeMcaEAz8TSRYHlyK6Adtr2OcdVS1T1U1ABi6xoKoPqeowVT0Nl5TWe9t3qFMCPIOrQgu5fcVl\nLNm8xwYhGmNaHD8TyRIgTUR6i0gMMBmYUW2ft3HVVnhVWP2BjSISKSLJ3vahwFDgY+91Z++/AlwA\nrPLxHoK2YH0u5ZVq7SPGmBbHt15bqlouIrcCHwGRwFRVXS0iDwBLVXWG997pIrIGqMD1xtotIrHA\nPG8dj33AlapaVbX1koik4kopK4Cb/bqHw5GekU1CbBTH9kgKdSjGGNOofEskAKo6E9fWEbjtvoDn\nCvzCewTuU4zruVXTOSc0fKT1o6qkZ+Rwcv9UoiJtjKcxpmWxb70GsHr7PnIKSqxayxjTIlkiaQCz\nrduvMaYFs0TSAGatzeaYbm1Jia9xSIsxxjRrlkjqaU9RKcu35jHOqrWMMS2UJZJ6mrc+B1VbxMoY\n03JZIqmn9LXZJLeJYWjXtqEOxRhjQsISST1UVCpz1uVwSv9UIiJqmhHGGGOaP0sk9fBVVh5795cx\nzqq1jDEtmCWSepi9NpsIgVPSrNuvMablskRSD7MyshnRsx1t46JDHYoxxoSMJZIjlL2vmFXb9lm3\nX2NMi2eJ5AjNXucWy7JpUYwxLZ0lkiM0OyObjomtGNQ5IdShGGNMSFkiOQJlFZXMW5fL+AEd8Ka6\nN8aYFssSyRFYtmUvBSXl1j5ijDFYIjki6RnZREcKJ6alhDoUY4wJOUskRyB9bTaje7cnvpWv64IZ\nY0yTYInkMGXt3c+6XYXWW8sYYzyWSA7T7AzX7dfaR4wxxrFEcphmZ2TTvX1r+qa2CXUoxhgTFiyR\nHIbisgoWZO62br/GGBPA10QiIpNEJENEMkXk3lr2uVRE1ojIahF5OWD7n0Rklff4ccD23iLyuYis\nF5FXRSTGz3sI9MWmPRwoq7BFrIwxJoBviUREIoEpwJnAYOAyERlcbZ804NfAWFUdAtzpbT8bOBYY\nBhwH3C0iid5hfwIeUdU0YC9wvV/3UN2stdm0iorg+D7JjXVJY4wJe36WSEYDmaq6UVVLgWnA+dX2\nuQGYoqp7AVQ129s+GJijquWqWgR8BUwSV580AZju7fcccIGP93CQ2RnZnNA3mdjoyMa6pDHGhD0/\nE0lXYGvA6yxvW6D+QH8RWSAii0Vkkrf9K+BMEYkTkRRgPNAdSAbyVLX8EOcEQERuFJGlIrI0Jyen\n3jezKbeIzbv3W7WWMcZU4+eIuppao7WG66cB44BuwDwROUpVPxaRUcBCIAdYBJQHeU63UfUJ4AmA\nkSNH1rjP4Uhf6wpL4/pbIjHGmEB+lkiycKWIKt2A7TXs846qlqnqJiADl1hQ1YdUdZiqnoZLIOuB\nXCBJRKIOcU5fpGdk0ze1DT2S4xrjcsYY02T4mUiWAGleL6sYYDIwo9o+b+OqrfCqsPoDG0UkUkSS\nve1DgaHAx6qqQDpwsXf8NcA7Pt4DAPtLy/l84x4bzW6MMTXwrWpLVctF5FbgIyASmKqqq0XkAWCp\nqs7w3jtdRNYAFcDdqrpbRGJx1VwA+4ArA9pF7gGmicgfgeXA037dQ5WFmbsprahkgrWPGGPMD/g6\n66CqzgRmVtt2X8BzBX7hPQL3Kcb13KrpnBtxPcIazayMbNrERDKyV/vGvKwxxjQJNrK9DqrK7LXZ\nnJiWQkyUfVzGGFOdfTPWYd2uQrbnF1v7iDHG1MISSR3SM7xuv5ZIjDGmRpZI6pC+NptBnRPp1DY2\n1KEYY0xYskRyCPuKy1i6ZS/jB6SGOhRjjAlblkgOYf76XCoq1br9GmPMIVgiOYRZa7Np2zqaYd2T\nQh2KMcaELUskh9AntQ2XH9eDqEj7mIwxpja+Dkhs6n4+rl+oQzDGmLBnP7WNMcbUiyUSY4wx9WKJ\nxBhjTL1YIjHGGFMvlkiMMcbUiyUSY4wx9WKJxBhjTL1YIjHGGFMv4hYpbN5EJAfYcoSHpwC5DRhO\nU2efx/fssziYfR4Haw6fR09VrXPW2haRSOpDRJaq6shQxxEu7PP4nn0WB7PP42At6fOwqi1jjDH1\nYonEGGNMvVgiqdsToQ4gzNjn8T37LA5mn8fBWsznYW0kxhhj6sVKJMYYY+rFEokxxph6sURyCCIy\nSUQyRCRTRO4NdTyhIiLdRSRdRL4RkdUickeoYwoHIhIpIstF5L1QxxJqIpIkItNFZK337+T4UMcU\nKiJyl/d3skpEXhGR2FDH5DdLJLUQkUhgCnAmMBi4TEQGhzaqkCkH/ktVBwFjgFta8GcR6A7gm1AH\nESYeBT5U1YHAMbTQz0VEugK3AyNV9SggEpgc2qj8Z4mkdqOBTFXdqKqlwDTg/BDHFBKqukNVv/Se\nF+C+JLqGNqrQEpFuwNnAU6GOJdREJBE4GXgaQFVLVTUvtFGFVBTQWkSigDhge4jj8Z0lktp1BbYG\nvM6ihX95AohIL2A48HloIwm5vwO/AipDHUgY6APkAM94VX1PiUibUAcVCqq6DfgL8C2wA8hX1Y9D\nG5X/LJHUTmrY1qL7SotIPPAGcKeq7gt1PKEiIucA2aq6LNSxhIko4FjgP6o6HCgCWmSbooi0w9Vc\n9Aa6AG1E5MrQRuU/SyS1ywK6B7zuRgsootZGRKJxSeQlVX0z1PGE2FjgPBHZjKvynCAiL4Y2pJDK\nArJUtaqUOh2XWFqiU4FNqpqjqmXAm8AJIY7Jd5ZIarcESBOR3iISg2swmxHimEJCRARX//2Nqv4t\n1PGEmqr+WlW7qWov3L+LWara7H911kZVdwJbRWSAt2kisCaEIYXSt8AYEYnz/m4m0gI6HkSFOoBw\nparlInIr8BGu58VUVV0d4rBCZSxwFbBSRFZ4236jqjNDGJMJL7cBL3k/ujYC14U4npBQ1c9FZDrw\nJa6343JawFQpNkWKMcaYerGqLWOMMfViicQYY0y9WCIxxhhTL5ZIjDHG1IslEmOMMfViicSYMCci\n42yGYRPOLJEYY4ypF0skxjQQEblSRL4QkRUi8ri3XkmhiPxVRL4Ukc9EJNXbd5iILBaRr0XkLW+O\nJkSkn4h8KiJfecf09U4fH7Dex0veqGljwoIlEmMagIgMAn4MjFXVYUAFcAXQBvhSVY8F5gB/8A55\nHrhHVYcCKwO2vwRMUdVjcHM07fC2DwfuxK2N0wc324AxYcGmSDGmYUwERgBLvMJCayAbN838q94+\nLwJvikhbIElV53jbnwNeF5EEoKuqvgWgqsUA3vm+UNUs7/UKoBcw3//bMqZulkiMaRgCPKeqvz5o\no8jvq+13qDmJDlVdVRLwvAL72zVhxKq2jGkYnwEXi0gHABFpLyI9cX9jF3v7XA7MV9V8YK+InORt\nvwqY463xkiUiF3jnaCUicY16F8YcAftVY0wDUNU1IvI74GMRiQDKgFtwizwNEZFlQD6uHQXgGuAx\nL1EEzpZ7FfC4iDzgneOSRrwNY46Izf5rjI9EpFBV40MdhzF+sqotY4wx9WIlEmOMMfViJRJjjDH1\nYonEGGNMvVgiMcYYUy+WSIwxxtSLJRJjjDH18v9Na5vtjw8hQwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd86f6e3d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Xwj4vqmq9qu4EtuLC42zgThHZBfwS+HcRecjDWluWPgXuWA5Zl8DS++AfN0N1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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd86c13cc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# list all data in history\n",
    "print(history.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7535596808882193"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "random_forest_model=RandomForestClassifier(n_estimators=400,min_samples_split=3,random_state=120)\n",
    "random_forest_model.fit(X_train,y_train)\n",
    "predictions_random_forest_model=random_forest_model.predict(X_test)\n",
    "random_model_f1_score=f1_score(y_test,predictions_random_forest_model,average='weighted')\n",
    "random_model_f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature_importances = pd.DataFrame(random_forest_model.feature_importances_,\n",
    "                                   index = X_train.columns,\n",
    "                                    columns=['importance']).sort_values('importance',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>height_ft_post_eq</th>\n",
       "      <td>0.120426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count_floors_post_eq</th>\n",
       "      <td>0.111310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition_post_eq_Not damaged</th>\n",
       "      <td>0.054922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_building</th>\n",
       "      <td>0.050770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plinth_area_sq_ft</th>\n",
       "      <td>0.049867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ward_id_x</th>\n",
       "      <td>0.047228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ward_id_y</th>\n",
       "      <td>0.046001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_assesed_Both</th>\n",
       "      <td>0.044097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition_post_eq_Damaged Not used</th>\n",
       "      <td>0.037066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_assesed_Building removed</th>\n",
       "      <td>0.035681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    importance\n",
       "height_ft_post_eq                     0.120426\n",
       "count_floors_post_eq                  0.111310\n",
       "condition_post_eq_Not damaged         0.054922\n",
       "age_building                          0.050770\n",
       "plinth_area_sq_ft                     0.049867\n",
       "ward_id_x                             0.047228\n",
       "ward_id_y                             0.046001\n",
       "area_assesed_Both                     0.044097\n",
       "condition_post_eq_Damaged Not used    0.037066\n",
       "area_assesed_Building removed         0.035681"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 97 artists>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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o441XLkmSNCs5czcLVNUdST5MtzwvwPlVdTbAWOWTsBg4oi2r/FhVncGjM2on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3c34GlgtqTrbdeXou4H7FzqCbhW0tttT6/3bXsyMBmqVyG0ii8iIiJisEpyF6vL3sBU208BSLoa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Tgq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5N79eXyfVog6djad+QBSd8H5gDvaPG5rg+8ACyr3cNTVb3LcOcSX2c59+HynbtP0mveqyjp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\nzMysBv4Fq+uNNHOlpaUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd8496415c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(10,30))\n",
    "plt.barh(feature_importances.index,feature_importances.importance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Thank You for your time :) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a20e5be91b92a25ce59e9e06a6ec213f0bc65158",
    "collapsed": true
   },
   "source": [
    "#### Contact Me through my [Website](https://brungivishwasourab.com) "
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}