main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.kaggle.com/competitions/playground-series-s4e5/overview\n",
    "# Made by Hüseyin Battal.\n",
    "# GitHub: https://github.com/huseyinbattal3469\n",
    "# LinkedIn: https://www.linkedin.com/in/huseyin-battal/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "\n",
    "pd.set_option('display.width', 500)\n",
    "\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')\n",
    "sample_submission = pd.read_csv('sample_submission.csv')\n",
    "\n",
    "df = pd.concat([train, test], axis=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ratio1'] = ((df['Deforestation'] + df['Urbanization']) * df['PoliticalFactors']) - (df['InadequatePlanning'] + 0.99)\n",
    "df['Ratio2'] = ((df['Deforestation'] + df['Urbanization'] + df['ClimateChange']) - ((df['DamsQuality'] + df['DrainageSystems'])) + 0.99) - df['ClimateChange']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>MonsoonIntensity</th>\n",
       "      <th>TopographyDrainage</th>\n",
       "      <th>RiverManagement</th>\n",
       "      <th>Deforestation</th>\n",
       "      <th>Urbanization</th>\n",
       "      <th>ClimateChange</th>\n",
       "      <th>DamsQuality</th>\n",
       "      <th>Siltation</th>\n",
       "      <th>AgriculturalPractices</th>\n",
       "      <th>Encroachments</th>\n",
       "      <th>IneffectiveDisasterPreparedness</th>\n",
       "      <th>DrainageSystems</th>\n",
       "      <th>CoastalVulnerability</th>\n",
       "      <th>Landslides</th>\n",
       "      <th>Watersheds</th>\n",
       "      <th>DeterioratingInfrastructure</th>\n",
       "      <th>PopulationScore</th>\n",
       "      <th>WetlandLoss</th>\n",
       "      <th>InadequatePlanning</th>\n",
       "      <th>PoliticalFactors</th>\n",
       "      <th>FloodProbability</th>\n",
       "      <th>Ratio1</th>\n",
       "      <th>Ratio2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>5.990</td>\n",
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       "      <td>31.010</td>\n",
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       "    </tr>\n",
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       "      <th>2</th>\n",
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       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
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       "      <td>0.530</td>\n",
       "      <td>26.010</td>\n",
       "      <td>2.990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
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       "      <td>0.535</td>\n",
       "      <td>37.010</td>\n",
       "      <td>3.990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
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       "      <td>41.010</td>\n",
       "      <td>-1.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745301</th>\n",
       "      <td>1863258</td>\n",
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       "      <td>-7.010</td>\n",
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       "      <td>-0.010</td>\n",
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       "    <tr>\n",
       "      <th>745304</th>\n",
       "      <td>1863261</td>\n",
       "      <td>4</td>\n",
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       "      <td>5</td>\n",
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       "      <td>8</td>\n",
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       "      <td>6</td>\n",
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       "      <td>10</td>\n",
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       "      <td>6</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>26.010</td>\n",
       "      <td>4.990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1863262 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id  MonsoonIntensity  TopographyDrainage  RiverManagement  Deforestation  Urbanization  ClimateChange  DamsQuality  Siltation  AgriculturalPractices  Encroachments  IneffectiveDisasterPreparedness  DrainageSystems  CoastalVulnerability  Landslides  Watersheds  DeterioratingInfrastructure  PopulationScore  WetlandLoss  InadequatePlanning  PoliticalFactors  FloodProbability  Ratio1  Ratio2\n",
       "0             0                 5                   8                5              8             6              4            4          3                      3              4                                2                5                     3           3           5                            4                7            5                   7                 3             0.445  34.010   5.990\n",
       "1             1                 6                   7                4              4             8              8            3          5                      4              6                                9                7                     2           0           3                            5                3            3                   4                 3             0.450  31.010   2.990\n",
       "2             2                 6                   5                6              7             3              7            1          5                      4              5                                6                7                     3           7           5                            6                8            2                   3                 3             0.530  26.010   2.990\n",
       "3             3                 3                   4                6              5             4              8            4          7                      6              8                                5                2                     4           7           4                            4                6            5                   7                 5             0.535  37.010   3.990\n",
       "4             4                 5                   3                2              6             4              4            3          3                      3              3                                5                2                     2           6           6                            4                1            2                   3                 5             0.415  46.010   5.990\n",
       "...         ...               ...                 ...              ...            ...           ...            ...          ...        ...                    ...            ...                              ...              ...                   ...         ...         ...                          ...              ...          ...                 ...               ...               ...     ...     ...\n",
       "745300  1863257                 5                   4                8              3             5              4            4          5                      5              5                                5                6                     1           3           5                            6                4            4                   6                 6               NaN  41.010  -1.010\n",
       "745301  1863258                 4                   4                2             12             4              3            4          3                      5              5                                3                7                     4           4           3                            5                5            3                   5                 4               NaN  58.010   5.990\n",
       "745302  1863259                 5                   7                9              5             5              6            7          5                      5              3                                6               11                     3          11           4                            5                9            5                   5                 4               NaN  34.010  -7.010\n",
       "745303  1863260                 4                   7                6              3             5              2            3          8                      6              7                                6                6                     8           6           2                            3                8            7                   5                 5               NaN  34.010  -0.010\n",
       "745304  1863261                 4                   2                5              3             8              4            5          3                      5              6                                4                2                     6          10           4                            3                9            8                   6                 3               NaN  26.010   4.990\n",
       "\n",
       "[1863262 rows x 24 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'for col in train.columns[1:-1]:\\n    sns.boxplot(x=df[col])\\n    plt.show(block=True)'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"for col in train.columns[1:-1]:\n",
    "    sns.boxplot(x=df[col])\n",
    "    plt.show(block=True)\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'for col in train.columns[1:-1]:\\n    sns.histplot(x=df[col], kde=True)\\n    plt.show(block=True)'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"for col in train.columns[1:-1]:\n",
    "    sns.histplot(x=df[col], kde=True)\n",
    "    plt.show(block=True)\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>1863262.000</td>\n",
       "      <td>931630.500</td>\n",
       "      <td>537877.553</td>\n",
       "      <td>0.000</td>\n",
       "      <td>465815.250</td>\n",
       "      <td>931630.500</td>\n",
       "      <td>1397445.750</td>\n",
       "      <td>1863261.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonsoonIntensity</th>\n",
       "      <td>1863262.000</td>\n",
       "      <td>4.919</td>\n",
       "      <td>2.056</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>16.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TopographyDrainage</th>\n",
       "      <td>1863262.000</td>\n",
       "      <td>4.928</td>\n",
       "      <td>2.094</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>18.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RiverManagement</th>\n",
       "      <td>1863262.000</td>\n",
       "      <td>4.957</td>\n",
       "      <td>2.072</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>16.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deforestation</th>\n",
       "      <td>1863262.000</td>\n",
       "      <td>4.944</td>\n",
       "      <td>2.052</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>17.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urbanization</th>\n",
       "      <td>1863262.000</td>\n",
       "      <td>4.941</td>\n",
       "      <td>2.083</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>17.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ClimateChange</th>\n",
       "      <td>1863262.000</td>\n",
       "      <td>4.934</td>\n",
       "      <td>2.058</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>17.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DamsQuality</th>\n",
       "      <td>1863262.000</td>\n",
       "      <td>4.957</td>\n",
       "      <td>2.086</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>16.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Siltation</th>\n",
       "      <td>1863262.000</td>\n",
       "      <td>4.928</td>\n",
       "      <td>2.067</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>16.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AgriculturalPractices</th>\n",
       "      <td>1863262.000</td>\n",
       "      <td>4.944</td>\n",
       "      <td>2.070</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>16.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Encroachments</th>\n",
       "      <td>1863262.000</td>\n",
       "      <td>4.950</td>\n",
       "      <td>2.083</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>18.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IneffectiveDisasterPreparedness</th>\n",
       "      <td>1863262.000</td>\n",
       "      <td>4.946</td>\n",
       "      <td>2.079</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>16.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DrainageSystems</th>\n",
       "      <td>1863262.000</td>\n",
       "      <td>4.946</td>\n",
       "      <td>2.072</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>17.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CoastalVulnerability</th>\n",
       "      <td>1863262.000</td>\n",
       "      <td>4.955</td>\n",
       "      <td>2.089</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>17.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Landslides</th>\n",
       "      <td>1863262.000</td>\n",
       "      <td>4.930</td>\n",
       "      <td>2.079</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>16.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Watersheds</th>\n",
       "      <td>1863262.000</td>\n",
       "      <td>4.930</td>\n",
       "      <td>2.083</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>16.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeterioratingInfrastructure</th>\n",
       "      <td>1863262.000</td>\n",
       "      <td>4.926</td>\n",
       "      <td>2.065</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>17.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PopulationScore</th>\n",
       "      <td>1863262.000</td>\n",
       "      <td>4.927</td>\n",
       "      <td>2.074</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>19.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WetlandLoss</th>\n",
       "      <td>1863262.000</td>\n",
       "      <td>4.950</td>\n",
       "      <td>2.068</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>22.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InadequatePlanning</th>\n",
       "      <td>1863262.000</td>\n",
       "      <td>4.940</td>\n",
       "      <td>2.080</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>16.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoliticalFactors</th>\n",
       "      <td>1863262.000</td>\n",
       "      <td>4.941</td>\n",
       "      <td>2.089</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>16.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FloodProbability</th>\n",
       "      <td>1117957.000</td>\n",
       "      <td>0.504</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.470</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.540</td>\n",
       "      <td>0.725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ratio1</th>\n",
       "      <td>1863262.000</td>\n",
       "      <td>42.811</td>\n",
       "      <td>25.714</td>\n",
       "      <td>-16.990</td>\n",
       "      <td>24.010</td>\n",
       "      <td>39.010</td>\n",
       "      <td>57.010</td>\n",
       "      <td>281.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ratio2</th>\n",
       "      <td>1863262.000</td>\n",
       "      <td>0.972</td>\n",
       "      <td>4.169</td>\n",
       "      <td>-22.010</td>\n",
       "      <td>-2.010</td>\n",
       "      <td>0.990</td>\n",
       "      <td>3.990</td>\n",
       "      <td>22.990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      count       mean        std     min        25%        50%         75%         max\n",
       "id                              1863262.000 931630.500 537877.553   0.000 465815.250 931630.500 1397445.750 1863261.000\n",
       "MonsoonIntensity                1863262.000      4.919      2.056   0.000      3.000      5.000       6.000      16.000\n",
       "TopographyDrainage              1863262.000      4.928      2.094   0.000      3.000      5.000       6.000      18.000\n",
       "RiverManagement                 1863262.000      4.957      2.072   0.000      4.000      5.000       6.000      16.000\n",
       "Deforestation                   1863262.000      4.944      2.052   0.000      4.000      5.000       6.000      17.000\n",
       "Urbanization                    1863262.000      4.941      2.083   0.000      3.000      5.000       6.000      17.000\n",
       "ClimateChange                   1863262.000      4.934      2.058   0.000      3.000      5.000       6.000      17.000\n",
       "DamsQuality                     1863262.000      4.957      2.086   0.000      4.000      5.000       6.000      16.000\n",
       "Siltation                       1863262.000      4.928      2.067   0.000      3.000      5.000       6.000      16.000\n",
       "AgriculturalPractices           1863262.000      4.944      2.070   0.000      3.000      5.000       6.000      16.000\n",
       "Encroachments                   1863262.000      4.950      2.083   0.000      4.000      5.000       6.000      18.000\n",
       "IneffectiveDisasterPreparedness 1863262.000      4.946      2.079   0.000      3.000      5.000       6.000      16.000\n",
       "DrainageSystems                 1863262.000      4.946      2.072   0.000      4.000      5.000       6.000      17.000\n",
       "CoastalVulnerability            1863262.000      4.955      2.089   0.000      3.000      5.000       6.000      17.000\n",
       "Landslides                      1863262.000      4.930      2.079   0.000      3.000      5.000       6.000      16.000\n",
       "Watersheds                      1863262.000      4.930      2.083   0.000      3.000      5.000       6.000      16.000\n",
       "DeterioratingInfrastructure     1863262.000      4.926      2.065   0.000      3.000      5.000       6.000      17.000\n",
       "PopulationScore                 1863262.000      4.927      2.074   0.000      3.000      5.000       6.000      19.000\n",
       "WetlandLoss                     1863262.000      4.950      2.068   0.000      4.000      5.000       6.000      22.000\n",
       "InadequatePlanning              1863262.000      4.940      2.080   0.000      3.000      5.000       6.000      16.000\n",
       "PoliticalFactors                1863262.000      4.941      2.089   0.000      3.000      5.000       6.000      16.000\n",
       "FloodProbability                1117957.000      0.504      0.051   0.285      0.470      0.505       0.540       0.725\n",
       "Ratio1                          1863262.000     42.811     25.714 -16.990     24.010     39.010      57.010     281.010\n",
       "Ratio2                          1863262.000      0.972      4.169 -22.010     -2.010      0.990       3.990      22.990"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "scaler = RobustScaler()\n",
    "for col in [col for col in df.columns if col not in [\"id\",\"FloodProbability\"]]:\n",
    "    # print(col)\n",
    "    df[col] = scaler.fit_transform(df[col].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>id</th>\n",
       "      <th>MonsoonIntensity</th>\n",
       "      <th>TopographyDrainage</th>\n",
       "      <th>RiverManagement</th>\n",
       "      <th>Deforestation</th>\n",
       "      <th>Urbanization</th>\n",
       "      <th>ClimateChange</th>\n",
       "      <th>DamsQuality</th>\n",
       "      <th>Siltation</th>\n",
       "      <th>AgriculturalPractices</th>\n",
       "      <th>Encroachments</th>\n",
       "      <th>IneffectiveDisasterPreparedness</th>\n",
       "      <th>DrainageSystems</th>\n",
       "      <th>CoastalVulnerability</th>\n",
       "      <th>Landslides</th>\n",
       "      <th>Watersheds</th>\n",
       "      <th>DeterioratingInfrastructure</th>\n",
       "      <th>PopulationScore</th>\n",
       "      <th>WetlandLoss</th>\n",
       "      <th>InadequatePlanning</th>\n",
       "      <th>PoliticalFactors</th>\n",
       "      <th>FloodProbability</th>\n",
       "      <th>Ratio1</th>\n",
       "      <th>Ratio2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.500</td>\n",
       "      <td>0.333</td>\n",
       "      <td>-0.333</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>-0.667</td>\n",
       "      <td>-0.667</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.667</td>\n",
       "      <td>-0.667</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.333</td>\n",
       "      <td>0.667</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.667</td>\n",
       "      <td>-0.667</td>\n",
       "      <td>0.445</td>\n",
       "      <td>-0.152</td>\n",
       "      <td>0.833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.667</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.333</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.333</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.667</td>\n",
       "      <td>-0.667</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.667</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-0.333</td>\n",
       "      <td>-0.667</td>\n",
       "      <td>0.450</td>\n",
       "      <td>-0.242</td>\n",
       "      <td>0.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.667</td>\n",
       "      <td>0.667</td>\n",
       "      <td>-2.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.333</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.333</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.667</td>\n",
       "      <td>0.667</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.333</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-1.500</td>\n",
       "      <td>-0.667</td>\n",
       "      <td>-0.667</td>\n",
       "      <td>0.530</td>\n",
       "      <td>-0.394</td>\n",
       "      <td>0.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.667</td>\n",
       "      <td>-0.333</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.333</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>0.667</td>\n",
       "      <td>0.333</td>\n",
       "      <td>1.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.500</td>\n",
       "      <td>-0.333</td>\n",
       "      <td>0.667</td>\n",
       "      <td>-0.333</td>\n",
       "      <td>-0.333</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.667</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.535</td>\n",
       "      <td>-0.061</td>\n",
       "      <td>0.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.667</td>\n",
       "      <td>-1.500</td>\n",
       "      <td>0.500</td>\n",
       "      <td>-0.333</td>\n",
       "      <td>-0.333</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-0.667</td>\n",
       "      <td>-0.667</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.500</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.333</td>\n",
       "      <td>-0.333</td>\n",
       "      <td>-1.333</td>\n",
       "      <td>-1.500</td>\n",
       "      <td>-0.667</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.415</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  MonsoonIntensity  TopographyDrainage  RiverManagement  Deforestation  Urbanization  ClimateChange  DamsQuality  Siltation  AgriculturalPractices  Encroachments  IneffectiveDisasterPreparedness  DrainageSystems  CoastalVulnerability  Landslides  Watersheds  DeterioratingInfrastructure  PopulationScore  WetlandLoss  InadequatePlanning  PoliticalFactors  FloodProbability  Ratio1  Ratio2\n",
       "0   0             0.000               1.000            0.000          1.500         0.333         -0.333       -0.500     -0.667                 -0.667         -0.500                           -1.000            0.000                -0.667      -0.667       0.000                       -0.333            0.667        0.000               0.667            -0.667             0.445  -0.152   0.833\n",
       "1   1             0.333               0.667           -0.500         -0.500         1.000          1.000       -1.000      0.000                 -0.333          0.500                            1.333            1.000                -1.000      -1.667      -0.667                        0.000           -0.667       -1.000              -0.333            -0.667             0.450  -0.242   0.333\n",
       "2   2             0.333               0.000            0.500          1.000        -0.667          0.667       -2.000      0.000                 -0.333          0.000                            0.333            1.000                -0.667       0.667       0.000                        0.333            1.000       -1.500              -0.667            -0.667             0.530  -0.394   0.333\n",
       "3   3            -0.667              -0.333            0.500          0.000        -0.333          1.000       -0.500      0.667                  0.333          1.500                            0.000           -1.500                -0.333       0.667      -0.333                       -0.333            0.333        0.000               0.667             0.000             0.535  -0.061   0.500\n",
       "4   4             0.000              -0.667           -1.500          0.500        -0.333         -0.333       -1.000     -0.667                 -0.667         -1.000                            0.000           -1.500                -1.000       0.333       0.333                       -0.333           -1.333       -1.500              -0.667             0.000             0.415   0.212   0.833"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id  MonsoonIntensity  TopographyDrainage  RiverManagement  Deforestation  Urbanization  ClimateChange  DamsQuality  Siltation  AgriculturalPractices  Encroachments  IneffectiveDisasterPreparedness  DrainageSystems  CoastalVulnerability  Landslides  Watersheds  DeterioratingInfrastructure  PopulationScore  WetlandLoss  InadequatePlanning  PoliticalFactors  FloodProbability  Ratio1  Ratio2\n",
      "0   0             0.000               1.000            0.000          1.500         0.333         -0.333       -0.500     -0.667                 -0.667         -0.500                           -1.000            0.000                -0.667      -0.667       0.000                       -0.333            0.667        0.000               0.667            -0.667             0.445  -0.152   0.833\n",
      "1   1             0.333               0.667           -0.500         -0.500         1.000          1.000       -1.000      0.000                 -0.333          0.500                            1.333            1.000                -1.000      -1.667      -0.667                        0.000           -0.667       -1.000              -0.333            -0.667             0.450  -0.242   0.333\n",
      "2   2             0.333               0.000            0.500          1.000        -0.667          0.667       -2.000      0.000                 -0.333          0.000                            0.333            1.000                -0.667       0.667       0.000                        0.333            1.000       -1.500              -0.667            -0.667             0.530  -0.394   0.333\n",
      "3   3            -0.667              -0.333            0.500          0.000        -0.333          1.000       -0.500      0.667                  0.333          1.500                            0.000           -1.500                -0.333       0.667      -0.333                       -0.333            0.333        0.000               0.667             0.000             0.535  -0.061   0.500\n",
      "4   4             0.000              -0.667           -1.500          0.500        -0.333         -0.333       -1.000     -0.667                 -0.667         -1.000                            0.000           -1.500                -1.000       0.333       0.333                       -0.333           -1.333       -1.500              -0.667             0.000             0.415   0.212   0.833\n"
     ]
    }
   ],
   "source": [
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                      0\n",
       "MonsoonIntensity                        0\n",
       "TopographyDrainage                      0\n",
       "RiverManagement                         0\n",
       "Deforestation                           0\n",
       "Urbanization                            0\n",
       "ClimateChange                           0\n",
       "DamsQuality                             0\n",
       "Siltation                               0\n",
       "AgriculturalPractices                   0\n",
       "Encroachments                           0\n",
       "IneffectiveDisasterPreparedness         0\n",
       "DrainageSystems                         0\n",
       "CoastalVulnerability                    0\n",
       "Landslides                              0\n",
       "Watersheds                              0\n",
       "DeterioratingInfrastructure             0\n",
       "PopulationScore                         0\n",
       "WetlandLoss                             0\n",
       "InadequatePlanning                      0\n",
       "PoliticalFactors                        0\n",
       "FloodProbability                   745305\n",
       "Ratio1                                  0\n",
       "Ratio2                                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df[df['FloodProbability'].notnull()]\n",
    "test = df[df['FloodProbability'].isnull()]\n",
    "\n",
    "X_train = train.drop([\"id\",'FloodProbability'], axis=1)\n",
    "y_train = train['FloodProbability']\n",
    "\n",
    "X_test = test.drop([\"id\",'FloodProbability'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow version: 2.10.0\n",
      "GPU device available: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\HBattal\\anaconda3\\envs\\tensorflowgpu\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from keras import backend as K\n",
    "import optuna as opt\n",
    "\n",
    "from keras import models, layers, optimizers, callbacks, regularizers\n",
    "\n",
    "def r2_score(y_true, y_pred):\n",
    "    ss_res = K.sum(K.square(y_true - y_pred))\n",
    "    ss_tot = K.sum(K.square(y_true - K.mean(y_true)))\n",
    "    return (1 - ss_res / (ss_tot + K.epsilon()))\n",
    "\n",
    "\n",
    "# TensorFlow'un GPU'yu görüp görmediğini kontrol edin\n",
    "print(\"TensorFlow version:\", tf.__version__)\n",
    "print(\"GPU device available:\", tf.config.list_physical_devices('GPU'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "if gpus:\n",
    "    try:\n",
    "        for gpu in gpus:\n",
    "            tf.config.experimental.set_memory_growth(gpu, True)\n",
    "    except RuntimeError as e:\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Not: CPU daha hızlı, ama HER ZAMAN DEĞİL. Bu yüzden GPU kullanılacak tuning sırasında.\n",
    "\n",
    "# early_stopping = callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
    "batch_size = 1024\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(layers.Dense(64, input_shape=(X_train.shape[1],), activation='relu'))\n",
    "model.add(layers.Dense(32, activation='relu'))\n",
    "model.add(layers.Dense(1, activation='linear'))\n",
    "\n",
    "# Modeli derleme\n",
    "model.compile(optimizer='adam', loss='mean_squared_error', metrics=[r2_score])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 0.0085 - r2_score: -2.2603\n",
      "Epoch 2/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 4.6307e-04 - r2_score: 0.8217\n",
      "Epoch 3/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 4.0426e-04 - r2_score: 0.8443\n",
      "Epoch 4/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.9673e-04 - r2_score: 0.8472\n",
      "Epoch 5/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.9646e-04 - r2_score: 0.8473\n",
      "Epoch 6/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.9608e-04 - r2_score: 0.8475\n",
      "Epoch 7/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.9551e-04 - r2_score: 0.8476\n",
      "Epoch 8/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.9406e-04 - r2_score: 0.8482\n",
      "Epoch 9/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.9433e-04 - r2_score: 0.8481\n",
      "Epoch 10/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.9300e-04 - r2_score: 0.8487\n",
      "Epoch 11/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.9220e-04 - r2_score: 0.8489\n",
      "Epoch 12/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.9125e-04 - r2_score: 0.8493\n",
      "Epoch 13/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.9016e-04 - r2_score: 0.8497\n",
      "Epoch 14/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.8438e-04 - r2_score: 0.8520\n",
      "Epoch 15/100\n",
      "1092/1092 [==============================] - 2s 2ms/step - loss: 3.6482e-04 - r2_score: 0.8595\n",
      "Epoch 16/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.6228e-04 - r2_score: 0.8605\n",
      "Epoch 17/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.6134e-04 - r2_score: 0.8608\n",
      "Epoch 18/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.6128e-04 - r2_score: 0.8609\n",
      "Epoch 19/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.6104e-04 - r2_score: 0.8609\n",
      "Epoch 20/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.6100e-04 - r2_score: 0.8610\n",
      "Epoch 21/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.6061e-04 - r2_score: 0.8611\n",
      "Epoch 22/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.6044e-04 - r2_score: 0.8612\n",
      "Epoch 23/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.6016e-04 - r2_score: 0.8613\n",
      "Epoch 24/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5986e-04 - r2_score: 0.8614\n",
      "Epoch 25/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5989e-04 - r2_score: 0.8614\n",
      "Epoch 26/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5973e-04 - r2_score: 0.8614\n",
      "Epoch 27/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5952e-04 - r2_score: 0.8615\n",
      "Epoch 28/100\n",
      "1092/1092 [==============================] - 4s 3ms/step - loss: 3.5868e-04 - r2_score: 0.8619\n",
      "Epoch 29/100\n",
      "1092/1092 [==============================] - 4s 3ms/step - loss: 3.5790e-04 - r2_score: 0.8621\n",
      "Epoch 30/100\n",
      "1092/1092 [==============================] - 4s 3ms/step - loss: 3.5736e-04 - r2_score: 0.8624\n",
      "Epoch 31/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5677e-04 - r2_score: 0.8626\n",
      "Epoch 32/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5638e-04 - r2_score: 0.8627\n",
      "Epoch 33/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5621e-04 - r2_score: 0.8628\n",
      "Epoch 34/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5574e-04 - r2_score: 0.8630\n",
      "Epoch 35/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5533e-04 - r2_score: 0.8632\n",
      "Epoch 36/100\n",
      "1092/1092 [==============================] - 4s 3ms/step - loss: 3.5525e-04 - r2_score: 0.8632\n",
      "Epoch 37/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5474e-04 - r2_score: 0.8633\n",
      "Epoch 38/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5400e-04 - r2_score: 0.8637\n",
      "Epoch 39/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5386e-04 - r2_score: 0.8637\n",
      "Epoch 40/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5412e-04 - r2_score: 0.8636\n",
      "Epoch 41/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5357e-04 - r2_score: 0.8638\n",
      "Epoch 42/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5361e-04 - r2_score: 0.8638\n",
      "Epoch 43/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5331e-04 - r2_score: 0.8639\n",
      "Epoch 44/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5307e-04 - r2_score: 0.8640\n",
      "Epoch 45/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5320e-04 - r2_score: 0.8640\n",
      "Epoch 46/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5296e-04 - r2_score: 0.8640\n",
      "Epoch 47/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5282e-04 - r2_score: 0.8642\n",
      "Epoch 48/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5275e-04 - r2_score: 0.8641\n",
      "Epoch 49/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5253e-04 - r2_score: 0.8642\n",
      "Epoch 50/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5231e-04 - r2_score: 0.8643\n",
      "Epoch 51/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5243e-04 - r2_score: 0.8643\n",
      "Epoch 52/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5235e-04 - r2_score: 0.8643\n",
      "Epoch 53/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5229e-04 - r2_score: 0.8643\n",
      "Epoch 54/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5229e-04 - r2_score: 0.8643\n",
      "Epoch 55/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5197e-04 - r2_score: 0.8645\n",
      "Epoch 56/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5214e-04 - r2_score: 0.8644\n",
      "Epoch 57/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5170e-04 - r2_score: 0.8645\n",
      "Epoch 58/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5187e-04 - r2_score: 0.8645\n",
      "Epoch 59/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5168e-04 - r2_score: 0.8646\n",
      "Epoch 60/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5186e-04 - r2_score: 0.8645\n",
      "Epoch 61/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5158e-04 - r2_score: 0.8646\n",
      "Epoch 62/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5160e-04 - r2_score: 0.8646\n",
      "Epoch 63/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5180e-04 - r2_score: 0.8645\n",
      "Epoch 64/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5155e-04 - r2_score: 0.8646\n",
      "Epoch 65/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5152e-04 - r2_score: 0.8646\n",
      "Epoch 66/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5159e-04 - r2_score: 0.8646\n",
      "Epoch 67/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5139e-04 - r2_score: 0.8647\n",
      "Epoch 68/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5153e-04 - r2_score: 0.8646\n",
      "Epoch 69/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5127e-04 - r2_score: 0.8647\n",
      "Epoch 70/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5132e-04 - r2_score: 0.8647\n",
      "Epoch 71/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5134e-04 - r2_score: 0.8647\n",
      "Epoch 72/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5140e-04 - r2_score: 0.8646\n",
      "Epoch 73/100\n",
      "1092/1092 [==============================] - 2s 2ms/step - loss: 3.5120e-04 - r2_score: 0.8647\n",
      "Epoch 74/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5120e-04 - r2_score: 0.8648\n",
      "Epoch 75/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5116e-04 - r2_score: 0.8647\n",
      "Epoch 76/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5130e-04 - r2_score: 0.8647\n",
      "Epoch 77/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5099e-04 - r2_score: 0.8648\n",
      "Epoch 78/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5112e-04 - r2_score: 0.8648\n",
      "Epoch 79/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5095e-04 - r2_score: 0.8649\n",
      "Epoch 80/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5117e-04 - r2_score: 0.8647\n",
      "Epoch 81/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5107e-04 - r2_score: 0.8648\n",
      "Epoch 82/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5095e-04 - r2_score: 0.8648\n",
      "Epoch 83/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5101e-04 - r2_score: 0.8648\n",
      "Epoch 84/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5096e-04 - r2_score: 0.8648\n",
      "Epoch 85/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5103e-04 - r2_score: 0.8648\n",
      "Epoch 86/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5096e-04 - r2_score: 0.8648\n",
      "Epoch 87/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5091e-04 - r2_score: 0.8649\n",
      "Epoch 88/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5073e-04 - r2_score: 0.8649\n",
      "Epoch 89/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5074e-04 - r2_score: 0.8649\n",
      "Epoch 90/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5087e-04 - r2_score: 0.8649\n",
      "Epoch 91/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5080e-04 - r2_score: 0.8649\n",
      "Epoch 92/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5058e-04 - r2_score: 0.8650\n",
      "Epoch 93/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5077e-04 - r2_score: 0.8649\n",
      "Epoch 94/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5071e-04 - r2_score: 0.8649\n",
      "Epoch 95/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5058e-04 - r2_score: 0.8649\n",
      "Epoch 96/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5052e-04 - r2_score: 0.8650\n",
      "Epoch 97/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5068e-04 - r2_score: 0.8649\n",
      "Epoch 98/100\n",
      "1092/1092 [==============================] - 3s 2ms/step - loss: 3.5053e-04 - r2_score: 0.8650\n",
      "Epoch 99/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5055e-04 - r2_score: 0.8650\n",
      "Epoch 100/100\n",
      "1092/1092 [==============================] - 3s 3ms/step - loss: 3.5063e-04 - r2_score: 0.8650\n"
     ]
    }
   ],
   "source": [
    "# Modeli eğitme\n",
    "history = model.fit(X_train, y_train, epochs=100, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"plt.figure(figsize=(10, 6))\\nsns.lineplot(y=history.history['r2_score'], x=range(len(history.history['r2_score'])), label='train')\\nsns.lineplot(y=history.history['val_r2_score'], x=range(len(history.history['val_r2_score'])), label='validation')\\nplt.show(block=True)\""
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"plt.figure(figsize=(10, 6))\n",
    "sns.lineplot(y=history.history['r2_score'], x=range(len(history.history['r2_score'])), label='train')\n",
    "sns.lineplot(y=history.history['val_r2_score'], x=range(len(history.history['val_r2_score'])), label='validation')\n",
    "plt.show(block=True)\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#max(history.history['r2_score'])\n",
    "# batch_size=128, epochs=100: 0.8616292476654053\n",
    "# batch_size=256, epochs=100: 0.8640527129173279\n",
    "# batch_size=512, epochs=100: 0.8648195862770081\n",
    "# batch_size=1024, epochs=100: 0.8651111721992493\n",
    "# batch_size=2048, epochs=100: 0.8649401664733887"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(None, 1)\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_3 (Dense)             (None, 64)                1472      \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 3,585\n",
      "Trainable params: 3,585\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "{'name': 'sequential_1', 'layers': [{'class_name': 'InputLayer', 'config': {'batch_input_shape': (None, 22), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'dense_3_input'}}, {'class_name': 'Dense', 'config': {'name': 'dense_3', 'trainable': True, 'batch_input_shape': (None, 22), 'dtype': 'float32', 'units': 64, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}}, {'class_name': 'Dense', 'config': {'name': 'dense_4', 'trainable': True, 'dtype': 'float32', 'units': 32, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}}, {'class_name': 'Dense', 'config': {'name': 'dense_5', 'trainable': True, 'dtype': 'float32', 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}}]}\n",
      "1092/1092 [==============================] - 2s 2ms/step - loss: 3.4850e-04 - r2_score: 0.8658\n",
      "Test loss: 0.0003485011402517557\n",
      "Test R^2 score: 0.8657658100128174\n"
     ]
    }
   ],
   "source": [
    "print(model.output_shape)\n",
    "print(model.summary())\n",
    "print(model.get_config())\n",
    "# print(model.get_weights())\n",
    "\n",
    "loss, r2 = model.evaluate(X_train, y_train, batch_size=batch_size)\n",
    "print(f'Test loss: {loss}')\n",
    "print(f'Test R^2 score: {r2}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "728/728 [==============================] - 1s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "pred = model.predict(X_test, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_file = pd.DataFrame(data=pred, index=test['id'], columns=[\"FloodProbability\"])\n",
    "\n",
    "submission_file.to_csv(\"submission.csv\")"
   ]
  }
 ],
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