Logistic Regression/LogisticRegression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1f3bc30b",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "01ff1145",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>...</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>Albury</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>44.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>71.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1007.7</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12-02</td>\n",
       "      <td>Albury</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WNW</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12-03</td>\n",
       "      <td>Albury</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WSW</td>\n",
       "      <td>46.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12-04</td>\n",
       "      <td>Albury</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NE</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12-05</td>\n",
       "      <td>Albury</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>...</td>\n",
       "      <td>82.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2008-12-06</td>\n",
       "      <td>Albury</td>\n",
       "      <td>14.6</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WNW</td>\n",
       "      <td>56.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>55.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1009.2</td>\n",
       "      <td>1005.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.6</td>\n",
       "      <td>28.9</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2008-12-07</td>\n",
       "      <td>Albury</td>\n",
       "      <td>14.3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>50.0</td>\n",
       "      <td>SW</td>\n",
       "      <td>...</td>\n",
       "      <td>49.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1009.6</td>\n",
       "      <td>1008.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.1</td>\n",
       "      <td>24.6</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2008-12-08</td>\n",
       "      <td>Albury</td>\n",
       "      <td>7.7</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>35.0</td>\n",
       "      <td>SSE</td>\n",
       "      <td>...</td>\n",
       "      <td>48.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1013.4</td>\n",
       "      <td>1010.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.3</td>\n",
       "      <td>25.5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2008-12-09</td>\n",
       "      <td>Albury</td>\n",
       "      <td>9.7</td>\n",
       "      <td>31.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NNW</td>\n",
       "      <td>80.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>42.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1008.9</td>\n",
       "      <td>1003.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.3</td>\n",
       "      <td>30.2</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2008-12-10</td>\n",
       "      <td>Albury</td>\n",
       "      <td>13.1</td>\n",
       "      <td>30.1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>28.0</td>\n",
       "      <td>S</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>1005.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.1</td>\n",
       "      <td>28.2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
       "0  2008-12-01   Albury     13.4     22.9       0.6          NaN       NaN   \n",
       "1  2008-12-02   Albury      7.4     25.1       0.0          NaN       NaN   \n",
       "2  2008-12-03   Albury     12.9     25.7       0.0          NaN       NaN   \n",
       "3  2008-12-04   Albury      9.2     28.0       0.0          NaN       NaN   \n",
       "4  2008-12-05   Albury     17.5     32.3       1.0          NaN       NaN   \n",
       "5  2008-12-06   Albury     14.6     29.7       0.2          NaN       NaN   \n",
       "6  2008-12-07   Albury     14.3     25.0       0.0          NaN       NaN   \n",
       "7  2008-12-08   Albury      7.7     26.7       0.0          NaN       NaN   \n",
       "8  2008-12-09   Albury      9.7     31.9       0.0          NaN       NaN   \n",
       "9  2008-12-10   Albury     13.1     30.1       1.4          NaN       NaN   \n",
       "\n",
       "  WindGustDir  WindGustSpeed WindDir9am  ... Humidity9am  Humidity3pm  \\\n",
       "0           W           44.0          W  ...        71.0         22.0   \n",
       "1         WNW           44.0        NNW  ...        44.0         25.0   \n",
       "2         WSW           46.0          W  ...        38.0         30.0   \n",
       "3          NE           24.0         SE  ...        45.0         16.0   \n",
       "4           W           41.0        ENE  ...        82.0         33.0   \n",
       "5         WNW           56.0          W  ...        55.0         23.0   \n",
       "6           W           50.0         SW  ...        49.0         19.0   \n",
       "7           W           35.0        SSE  ...        48.0         19.0   \n",
       "8         NNW           80.0         SE  ...        42.0          9.0   \n",
       "9           W           28.0          S  ...        58.0         27.0   \n",
       "\n",
       "   Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  \\\n",
       "0       1007.7       1007.1       8.0       NaN     16.9     21.8         No   \n",
       "1       1010.6       1007.8       NaN       NaN     17.2     24.3         No   \n",
       "2       1007.6       1008.7       NaN       2.0     21.0     23.2         No   \n",
       "3       1017.6       1012.8       NaN       NaN     18.1     26.5         No   \n",
       "4       1010.8       1006.0       7.0       8.0     17.8     29.7         No   \n",
       "5       1009.2       1005.4       NaN       NaN     20.6     28.9         No   \n",
       "6       1009.6       1008.2       1.0       NaN     18.1     24.6         No   \n",
       "7       1013.4       1010.1       NaN       NaN     16.3     25.5         No   \n",
       "8       1008.9       1003.6       NaN       NaN     18.3     30.2         No   \n",
       "9       1007.0       1005.7       NaN       NaN     20.1     28.2        Yes   \n",
       "\n",
       "   RainTomorrow  \n",
       "0            No  \n",
       "1            No  \n",
       "2            No  \n",
       "3            No  \n",
       "4            No  \n",
       "5            No  \n",
       "6            No  \n",
       "7            No  \n",
       "8           Yes  \n",
       "9            No  \n",
       "\n",
       "[10 rows x 23 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "df = pd.read_csv('../Datasets/weatherAUS.csv')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3718c5ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 145460 entries, 0 to 145459\n",
      "Data columns (total 23 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   Date           145460 non-null  object \n",
      " 1   Location       145460 non-null  object \n",
      " 2   MinTemp        143975 non-null  float64\n",
      " 3   MaxTemp        144199 non-null  float64\n",
      " 4   Rainfall       142199 non-null  float64\n",
      " 5   Evaporation    82670 non-null   float64\n",
      " 6   Sunshine       75625 non-null   float64\n",
      " 7   WindGustDir    135134 non-null  object \n",
      " 8   WindGustSpeed  135197 non-null  float64\n",
      " 9   WindDir9am     134894 non-null  object \n",
      " 10  WindDir3pm     141232 non-null  object \n",
      " 11  WindSpeed9am   143693 non-null  float64\n",
      " 12  WindSpeed3pm   142398 non-null  float64\n",
      " 13  Humidity9am    142806 non-null  float64\n",
      " 14  Humidity3pm    140953 non-null  float64\n",
      " 15  Pressure9am    130395 non-null  float64\n",
      " 16  Pressure3pm    130432 non-null  float64\n",
      " 17  Cloud9am       89572 non-null   float64\n",
      " 18  Cloud3pm       86102 non-null   float64\n",
      " 19  Temp9am        143693 non-null  float64\n",
      " 20  Temp3pm        141851 non-null  float64\n",
      " 21  RainToday      142199 non-null  object \n",
      " 22  RainTomorrow   142193 non-null  object \n",
      "dtypes: float64(16), object(7)\n",
      "memory usage: 25.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5a2b5083",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 140787 entries, 0 to 145458\n",
      "Data columns (total 23 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   Date           140787 non-null  object \n",
      " 1   Location       140787 non-null  object \n",
      " 2   MinTemp        140319 non-null  float64\n",
      " 3   MaxTemp        140480 non-null  float64\n",
      " 4   Rainfall       140787 non-null  float64\n",
      " 5   Evaporation    81093 non-null   float64\n",
      " 6   Sunshine       73982 non-null   float64\n",
      " 7   WindGustDir    131624 non-null  object \n",
      " 8   WindGustSpeed  131682 non-null  float64\n",
      " 9   WindDir9am     131127 non-null  object \n",
      " 10  WindDir3pm     137117 non-null  object \n",
      " 11  WindSpeed9am   139732 non-null  float64\n",
      " 12  WindSpeed3pm   138256 non-null  float64\n",
      " 13  Humidity9am    139270 non-null  float64\n",
      " 14  Humidity3pm    137286 non-null  float64\n",
      " 15  Pressure9am    127044 non-null  float64\n",
      " 16  Pressure3pm    127018 non-null  float64\n",
      " 17  Cloud9am       88162 non-null   float64\n",
      " 18  Cloud3pm       84693 non-null   float64\n",
      " 19  Temp9am        140131 non-null  float64\n",
      " 20  Temp3pm        138163 non-null  float64\n",
      " 21  RainToday      140787 non-null  object \n",
      " 22  RainTomorrow   140787 non-null  object \n",
      "dtypes: float64(16), object(7)\n",
      "memory usage: 25.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.dropna(subset=['RainToday', 'RainTomorrow'], inplace=True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3f7f80d5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>140319.000000</td>\n",
       "      <td>140480.00000</td>\n",
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       "      <td>81093.000000</td>\n",
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       "      <td>127044.000000</td>\n",
       "      <td>127018.000000</td>\n",
       "      <td>88162.000000</td>\n",
       "      <td>84693.000000</td>\n",
       "      <td>140131.000000</td>\n",
       "      <td>138163.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.184824</td>\n",
       "      <td>23.23512</td>\n",
       "      <td>2.349974</td>\n",
       "      <td>5.472516</td>\n",
       "      <td>7.630540</td>\n",
       "      <td>39.970520</td>\n",
       "      <td>13.990496</td>\n",
       "      <td>18.631141</td>\n",
       "      <td>68.826833</td>\n",
       "      <td>51.449288</td>\n",
       "      <td>1017.654577</td>\n",
       "      <td>1015.257963</td>\n",
       "      <td>4.431161</td>\n",
       "      <td>4.499250</td>\n",
       "      <td>16.987066</td>\n",
       "      <td>21.693183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.403879</td>\n",
       "      <td>7.11450</td>\n",
       "      <td>8.465173</td>\n",
       "      <td>4.189132</td>\n",
       "      <td>3.781729</td>\n",
       "      <td>13.578201</td>\n",
       "      <td>8.886210</td>\n",
       "      <td>8.798096</td>\n",
       "      <td>19.063650</td>\n",
       "      <td>20.807310</td>\n",
       "      <td>7.104867</td>\n",
       "      <td>7.035411</td>\n",
       "      <td>2.886594</td>\n",
       "      <td>2.719752</td>\n",
       "      <td>6.496012</td>\n",
       "      <td>6.937784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-8.500000</td>\n",
       "      <td>-4.80000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>980.500000</td>\n",
       "      <td>977.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.200000</td>\n",
       "      <td>-5.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.600000</td>\n",
       "      <td>17.90000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>1013.000000</td>\n",
       "      <td>1010.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.300000</td>\n",
       "      <td>16.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>22.60000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>1017.600000</td>\n",
       "      <td>1015.200000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>16.700000</td>\n",
       "      <td>21.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16.800000</td>\n",
       "      <td>28.30000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>10.700000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>1022.400000</td>\n",
       "      <td>1020.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>26.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>33.900000</td>\n",
       "      <td>48.10000</td>\n",
       "      <td>371.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1041.000000</td>\n",
       "      <td>1039.600000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>40.200000</td>\n",
       "      <td>46.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             MinTemp       MaxTemp       Rainfall   Evaporation      Sunshine  \\\n",
       "count  140319.000000  140480.00000  140787.000000  81093.000000  73982.000000   \n",
       "mean       12.184824      23.23512       2.349974      5.472516      7.630540   \n",
       "std         6.403879       7.11450       8.465173      4.189132      3.781729   \n",
       "min        -8.500000      -4.80000       0.000000      0.000000      0.000000   \n",
       "25%         7.600000      17.90000       0.000000      2.600000      4.900000   \n",
       "50%        12.000000      22.60000       0.000000      4.800000      8.500000   \n",
       "75%        16.800000      28.30000       0.800000      7.400000     10.700000   \n",
       "max        33.900000      48.10000     371.000000    145.000000     14.500000   \n",
       "\n",
       "       WindGustSpeed   WindSpeed9am   WindSpeed3pm    Humidity9am  \\\n",
       "count  131682.000000  139732.000000  138256.000000  139270.000000   \n",
       "mean       39.970520      13.990496      18.631141      68.826833   \n",
       "std        13.578201       8.886210       8.798096      19.063650   \n",
       "min         6.000000       0.000000       0.000000       0.000000   \n",
       "25%        31.000000       7.000000      13.000000      57.000000   \n",
       "50%        39.000000      13.000000      19.000000      70.000000   \n",
       "75%        48.000000      19.000000      24.000000      83.000000   \n",
       "max       135.000000     130.000000      87.000000     100.000000   \n",
       "\n",
       "         Humidity3pm    Pressure9am    Pressure3pm      Cloud9am  \\\n",
       "count  137286.000000  127044.000000  127018.000000  88162.000000   \n",
       "mean       51.449288    1017.654577    1015.257963      4.431161   \n",
       "std        20.807310       7.104867       7.035411      2.886594   \n",
       "min         0.000000     980.500000     977.100000      0.000000   \n",
       "25%        37.000000    1013.000000    1010.400000      1.000000   \n",
       "50%        52.000000    1017.600000    1015.200000      5.000000   \n",
       "75%        66.000000    1022.400000    1020.000000      7.000000   \n",
       "max       100.000000    1041.000000    1039.600000      9.000000   \n",
       "\n",
       "           Cloud3pm        Temp9am        Temp3pm  \n",
       "count  84693.000000  140131.000000  138163.000000  \n",
       "mean       4.499250      16.987066      21.693183  \n",
       "std        2.719752       6.496012       6.937784  \n",
       "min        0.000000      -7.200000      -5.400000  \n",
       "25%        2.000000      12.300000      16.600000  \n",
       "50%        5.000000      16.700000      21.100000  \n",
       "75%        7.000000      21.600000      26.400000  \n",
       "max        9.000000      40.200000      46.700000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "886c0a58",
   "metadata": {},
   "source": [
    "#  Train/Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d576a102",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "61d542a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = train_test_split(df, test_size=0.3, random_state=42)\n",
    "train, val = train_test_split(df, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "74f21d47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((112629, 23), (28158, 23), (42237, 23))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, val.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cf54885b",
   "metadata": {},
   "outputs": [],
   "source": [
    "year = pd.to_datetime(df.Date).dt.year\n",
    "\n",
    "train = df[year<2015]\n",
    "val = df[year==2015]\n",
    "test = df[year>2015]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4b90a19c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "copy = train\n",
    "train_Y = copy.pop('RainTomorrow')\n",
    "train_X = copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1beaeaf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "copy = val\n",
    "val_Y = copy.pop('RainTomorrow')\n",
    "val_X = copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "16f1fa17",
   "metadata": {},
   "outputs": [],
   "source": [
    "copy = test\n",
    "test_Y = copy.pop('RainTomorrow')\n",
    "test_X = copy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93b8e09e",
   "metadata": {},
   "source": [
    "# Data Cleaning & PreProcessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "439544e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "20301544",
   "metadata": {},
   "outputs": [],
   "source": [
    "for X in [train_X, val_X, test_X]:\n",
    "    X.pop('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ce6c051a",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_train = train_X.select_dtypes(include=np.number).columns.to_list()\n",
    "cat_train = train_X.select_dtypes('object').columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aa7b8437-947d-4c24-8f32-7f15b9af9a00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MinTemp',\n",
       " 'MaxTemp',\n",
       " 'Rainfall',\n",
       " 'Evaporation',\n",
       " 'Sunshine',\n",
       " 'WindGustSpeed',\n",
       " 'WindSpeed9am',\n",
       " 'WindSpeed3pm',\n",
       " 'Humidity9am',\n",
       " 'Humidity3pm',\n",
       " 'Pressure9am',\n",
       " 'Pressure3pm',\n",
       " 'Cloud9am',\n",
       " 'Cloud3pm',\n",
       " 'Temp9am',\n",
       " 'Temp3pm']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dc928e3a",
   "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>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>97674.000000</td>\n",
       "      <td>97801.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>61657.000000</td>\n",
       "      <td>57942.000000</td>\n",
       "      <td>91160.000000</td>\n",
       "      <td>97114.000000</td>\n",
       "      <td>96919.000000</td>\n",
       "      <td>96936.000000</td>\n",
       "      <td>96872.000000</td>\n",
       "      <td>88876.000000</td>\n",
       "      <td>88857.000000</td>\n",
       "      <td>63000.000000</td>\n",
       "      <td>61966.000000</td>\n",
       "      <td>97414.000000</td>\n",
       "      <td>97392.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.007831</td>\n",
       "      <td>23.022202</td>\n",
       "      <td>2.372935</td>\n",
       "      <td>5.289991</td>\n",
       "      <td>7.609004</td>\n",
       "      <td>40.215873</td>\n",
       "      <td>14.092263</td>\n",
       "      <td>18.764608</td>\n",
       "      <td>68.628745</td>\n",
       "      <td>51.469547</td>\n",
       "      <td>1017.513734</td>\n",
       "      <td>1015.132352</td>\n",
       "      <td>4.302952</td>\n",
       "      <td>4.410677</td>\n",
       "      <td>16.835126</td>\n",
       "      <td>21.540138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.347175</td>\n",
       "      <td>6.984397</td>\n",
       "      <td>8.518819</td>\n",
       "      <td>3.952010</td>\n",
       "      <td>3.788813</td>\n",
       "      <td>13.697967</td>\n",
       "      <td>8.984203</td>\n",
       "      <td>8.872398</td>\n",
       "      <td>19.003097</td>\n",
       "      <td>20.756113</td>\n",
       "      <td>7.072510</td>\n",
       "      <td>6.997072</td>\n",
       "      <td>2.866634</td>\n",
       "      <td>2.693370</td>\n",
       "      <td>6.404586</td>\n",
       "      <td>6.831612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-8.500000</td>\n",
       "      <td>-4.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>980.500000</td>\n",
       "      <td>979.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.900000</td>\n",
       "      <td>-5.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.500000</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>1012.800000</td>\n",
       "      <td>1010.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.200000</td>\n",
       "      <td>16.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11.800000</td>\n",
       "      <td>22.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>1017.500000</td>\n",
       "      <td>1015.100000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>16.600000</td>\n",
       "      <td>20.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16.600000</td>\n",
       "      <td>27.900000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>10.600000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>1022.300000</td>\n",
       "      <td>1019.900000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>21.400000</td>\n",
       "      <td>26.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>33.900000</td>\n",
       "      <td>48.100000</td>\n",
       "      <td>371.000000</td>\n",
       "      <td>82.400000</td>\n",
       "      <td>14.300000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1041.000000</td>\n",
       "      <td>1039.600000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>40.200000</td>\n",
       "      <td>46.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MinTemp       MaxTemp      Rainfall   Evaporation      Sunshine  \\\n",
       "count  97674.000000  97801.000000  97988.000000  61657.000000  57942.000000   \n",
       "mean      12.007831     23.022202      2.372935      5.289991      7.609004   \n",
       "std        6.347175      6.984397      8.518819      3.952010      3.788813   \n",
       "min       -8.500000     -4.100000      0.000000      0.000000      0.000000   \n",
       "25%        7.500000     17.900000      0.000000      2.600000      4.800000   \n",
       "50%       11.800000     22.400000      0.000000      4.600000      8.500000   \n",
       "75%       16.600000     27.900000      0.800000      7.200000     10.600000   \n",
       "max       33.900000     48.100000    371.000000     82.400000     14.300000   \n",
       "\n",
       "       WindGustSpeed  WindSpeed9am  WindSpeed3pm   Humidity9am   Humidity3pm  \\\n",
       "count   91160.000000  97114.000000  96919.000000  96936.000000  96872.000000   \n",
       "mean       40.215873     14.092263     18.764608     68.628745     51.469547   \n",
       "std        13.697967      8.984203      8.872398     19.003097     20.756113   \n",
       "min         6.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        31.000000      7.000000     13.000000     57.000000     37.000000   \n",
       "50%        39.000000     13.000000     19.000000     70.000000     52.000000   \n",
       "75%        48.000000     19.000000     24.000000     83.000000     66.000000   \n",
       "max       135.000000     87.000000     87.000000    100.000000    100.000000   \n",
       "\n",
       "        Pressure9am   Pressure3pm      Cloud9am      Cloud3pm       Temp9am  \\\n",
       "count  88876.000000  88857.000000  63000.000000  61966.000000  97414.000000   \n",
       "mean    1017.513734   1015.132352      4.302952      4.410677     16.835126   \n",
       "std        7.072510      6.997072      2.866634      2.693370      6.404586   \n",
       "min      980.500000    979.000000      0.000000      0.000000     -5.900000   \n",
       "25%     1012.800000   1010.400000      1.000000      2.000000     12.200000   \n",
       "50%     1017.500000   1015.100000      5.000000      5.000000     16.600000   \n",
       "75%     1022.300000   1019.900000      7.000000      7.000000     21.400000   \n",
       "max     1041.000000   1039.600000      9.000000      9.000000     40.200000   \n",
       "\n",
       "            Temp3pm  \n",
       "count  97392.000000  \n",
       "mean      21.540138  \n",
       "std        6.831612  \n",
       "min       -5.100000  \n",
       "25%       16.600000  \n",
       "50%       20.900000  \n",
       "75%       26.200000  \n",
       "max       46.100000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X[num_train].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "13a20074",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Location       49\n",
       " WindGustDir    16\n",
       " WindDir9am     16\n",
       " WindDir3pm     16\n",
       " RainToday       2\n",
       " dtype: int64,\n",
       " Location       49\n",
       " WindGustDir    16\n",
       " WindDir9am     16\n",
       " WindDir3pm     16\n",
       " RainToday       2\n",
       " dtype: int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[cat_train].nunique(), train_X[cat_train].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ba8d6514",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b55845ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = SimpleImputer(strategy='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "30967682",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinTemp            468\n",
       "MaxTemp            307\n",
       "Rainfall             0\n",
       "Evaporation      59694\n",
       "Sunshine         66805\n",
       "WindGustSpeed     9105\n",
       "WindSpeed9am      1055\n",
       "WindSpeed3pm      2531\n",
       "Humidity9am       1517\n",
       "Humidity3pm       3501\n",
       "Pressure9am      13743\n",
       "Pressure3pm      13769\n",
       "Cloud9am         52625\n",
       "Cloud3pm         56094\n",
       "Temp9am            656\n",
       "Temp3pm           2624\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[num_train].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dd63235e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.fit(df[num_train])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "77448a3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/2644920895.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[num_train] = imputer.transform(train_X[num_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/2644920895.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[num_train] = imputer.transform(val_X[num_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/2644920895.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[num_train] = imputer.transform(test_X[num_train])\n"
     ]
    }
   ],
   "source": [
    "train_X[num_train] = imputer.transform(train_X[num_train])\n",
    "val_X[num_train] = imputer.transform(val_X[num_train])\n",
    "test_X[num_train] = imputer.transform(test_X[num_train])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "54278731",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 97988 entries, 0 to 144552\n",
      "Data columns (total 16 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   MinTemp        97988 non-null  float64\n",
      " 1   MaxTemp        97988 non-null  float64\n",
      " 2   Rainfall       97988 non-null  float64\n",
      " 3   Evaporation    97988 non-null  float64\n",
      " 4   Sunshine       97988 non-null  float64\n",
      " 5   WindGustSpeed  97988 non-null  float64\n",
      " 6   WindSpeed9am   97988 non-null  float64\n",
      " 7   WindSpeed3pm   97988 non-null  float64\n",
      " 8   Humidity9am    97988 non-null  float64\n",
      " 9   Humidity3pm    97988 non-null  float64\n",
      " 10  Pressure9am    97988 non-null  float64\n",
      " 11  Pressure3pm    97988 non-null  float64\n",
      " 12  Cloud9am       97988 non-null  float64\n",
      " 13  Cloud3pm       97988 non-null  float64\n",
      " 14  Temp9am        97988 non-null  float64\n",
      " 15  Temp3pm        97988 non-null  float64\n",
      "dtypes: float64(16)\n",
      "memory usage: 12.7 MB\n"
     ]
    }
   ],
   "source": [
    "train_X[num_train].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3dbeafe4",
   "metadata": {
    "scrolled": true
   },
   "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>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.008398</td>\n",
       "      <td>23.022609</td>\n",
       "      <td>2.372935</td>\n",
       "      <td>5.357666</td>\n",
       "      <td>7.617805</td>\n",
       "      <td>40.198776</td>\n",
       "      <td>14.091355</td>\n",
       "      <td>18.763152</td>\n",
       "      <td>68.630871</td>\n",
       "      <td>51.469317</td>\n",
       "      <td>1017.526831</td>\n",
       "      <td>1015.144057</td>\n",
       "      <td>4.348731</td>\n",
       "      <td>4.443238</td>\n",
       "      <td>16.836016</td>\n",
       "      <td>21.541069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.337005</td>\n",
       "      <td>6.977735</td>\n",
       "      <td>8.518819</td>\n",
       "      <td>3.136123</td>\n",
       "      <td>2.913498</td>\n",
       "      <td>13.212242</td>\n",
       "      <td>8.944051</td>\n",
       "      <td>8.823879</td>\n",
       "      <td>18.900823</td>\n",
       "      <td>20.637576</td>\n",
       "      <td>6.735769</td>\n",
       "      <td>6.663187</td>\n",
       "      <td>2.299375</td>\n",
       "      <td>2.142256</td>\n",
       "      <td>6.385810</td>\n",
       "      <td>6.810814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-8.500000</td>\n",
       "      <td>-4.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>980.500000</td>\n",
       "      <td>979.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.900000</td>\n",
       "      <td>-5.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.500000</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>1013.400000</td>\n",
       "      <td>1010.900000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12.200000</td>\n",
       "      <td>16.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11.800000</td>\n",
       "      <td>22.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.472516</td>\n",
       "      <td>7.630540</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>1017.654577</td>\n",
       "      <td>1015.257963</td>\n",
       "      <td>4.431161</td>\n",
       "      <td>4.499250</td>\n",
       "      <td>16.600000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16.600000</td>\n",
       "      <td>27.900000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>1021.800000</td>\n",
       "      <td>1019.300000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>26.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>33.900000</td>\n",
       "      <td>48.100000</td>\n",
       "      <td>371.000000</td>\n",
       "      <td>82.400000</td>\n",
       "      <td>14.300000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1041.000000</td>\n",
       "      <td>1039.600000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>40.200000</td>\n",
       "      <td>46.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MinTemp       MaxTemp      Rainfall   Evaporation      Sunshine  \\\n",
       "count  97988.000000  97988.000000  97988.000000  97988.000000  97988.000000   \n",
       "mean      12.008398     23.022609      2.372935      5.357666      7.617805   \n",
       "std        6.337005      6.977735      8.518819      3.136123      2.913498   \n",
       "min       -8.500000     -4.100000      0.000000      0.000000      0.000000   \n",
       "25%        7.500000     17.900000      0.000000      3.800000      7.500000   \n",
       "50%       11.800000     22.400000      0.000000      5.472516      7.630540   \n",
       "75%       16.600000     27.900000      0.800000      5.600000      9.200000   \n",
       "max       33.900000     48.100000    371.000000     82.400000     14.300000   \n",
       "\n",
       "       WindGustSpeed  WindSpeed9am  WindSpeed3pm   Humidity9am   Humidity3pm  \\\n",
       "count   97988.000000  97988.000000  97988.000000  97988.000000  97988.000000   \n",
       "mean       40.198776     14.091355     18.763152     68.630871     51.469317   \n",
       "std        13.212242      8.944051      8.823879     18.900823     20.637576   \n",
       "min         6.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        31.000000      7.000000     13.000000     57.000000     37.000000   \n",
       "50%        39.000000     13.000000     19.000000     69.000000     52.000000   \n",
       "75%        46.000000     19.000000     24.000000     83.000000     65.000000   \n",
       "max       135.000000     87.000000     87.000000    100.000000    100.000000   \n",
       "\n",
       "        Pressure9am   Pressure3pm      Cloud9am      Cloud3pm       Temp9am  \\\n",
       "count  97988.000000  97988.000000  97988.000000  97988.000000  97988.000000   \n",
       "mean    1017.526831   1015.144057      4.348731      4.443238     16.836016   \n",
       "std        6.735769      6.663187      2.299375      2.142256      6.385810   \n",
       "min      980.500000    979.000000      0.000000      0.000000     -5.900000   \n",
       "25%     1013.400000   1010.900000      3.000000      3.000000     12.200000   \n",
       "50%     1017.654577   1015.257963      4.431161      4.499250     16.600000   \n",
       "75%     1021.800000   1019.300000      6.000000      6.000000     21.300000   \n",
       "max     1041.000000   1039.600000      9.000000      9.000000     40.200000   \n",
       "\n",
       "            Temp3pm  \n",
       "count  97988.000000  \n",
       "mean      21.541069  \n",
       "std        6.810814  \n",
       "min       -5.100000  \n",
       "25%       16.600000  \n",
       "50%       21.000000  \n",
       "75%       26.100000  \n",
       "max       46.100000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X[num_train].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "511aafe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d3668bee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinMaxScaler()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "scaler.fit(df[num_train])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "596709a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/2289213085.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[num_train] = scaler.transform(train_X[num_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/2289213085.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[num_train] = scaler.transform(val_X[num_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/2289213085.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[num_train] = scaler.transform(test_X[num_train])\n"
     ]
    }
   ],
   "source": [
    "train_X[num_train] = scaler.transform(train_X[num_train])\n",
    "val_X[num_train] = scaler.transform(val_X[num_train])\n",
    "test_X[num_train] = scaler.transform(test_X[num_train])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "76410cae",
   "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>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "      <td>97988.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.483689</td>\n",
       "      <td>0.525947</td>\n",
       "      <td>0.006396</td>\n",
       "      <td>0.036949</td>\n",
       "      <td>0.525366</td>\n",
       "      <td>0.265107</td>\n",
       "      <td>0.108395</td>\n",
       "      <td>0.215668</td>\n",
       "      <td>0.686309</td>\n",
       "      <td>0.514693</td>\n",
       "      <td>0.612014</td>\n",
       "      <td>0.608705</td>\n",
       "      <td>0.483192</td>\n",
       "      <td>0.493693</td>\n",
       "      <td>0.507089</td>\n",
       "      <td>0.517103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.149458</td>\n",
       "      <td>0.131904</td>\n",
       "      <td>0.022962</td>\n",
       "      <td>0.021628</td>\n",
       "      <td>0.200931</td>\n",
       "      <td>0.102420</td>\n",
       "      <td>0.068800</td>\n",
       "      <td>0.101424</td>\n",
       "      <td>0.189008</td>\n",
       "      <td>0.206376</td>\n",
       "      <td>0.111335</td>\n",
       "      <td>0.106611</td>\n",
       "      <td>0.255486</td>\n",
       "      <td>0.238028</td>\n",
       "      <td>0.134722</td>\n",
       "      <td>0.130726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013233</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027426</td>\n",
       "      <td>0.005758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.377358</td>\n",
       "      <td>0.429112</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026207</td>\n",
       "      <td>0.517241</td>\n",
       "      <td>0.193798</td>\n",
       "      <td>0.053846</td>\n",
       "      <td>0.149425</td>\n",
       "      <td>0.570000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.543802</td>\n",
       "      <td>0.540800</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.409283</td>\n",
       "      <td>0.422265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.478774</td>\n",
       "      <td>0.514178</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.037741</td>\n",
       "      <td>0.526244</td>\n",
       "      <td>0.255814</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.218391</td>\n",
       "      <td>0.690000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.614125</td>\n",
       "      <td>0.610527</td>\n",
       "      <td>0.492351</td>\n",
       "      <td>0.499917</td>\n",
       "      <td>0.502110</td>\n",
       "      <td>0.506718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.591981</td>\n",
       "      <td>0.618147</td>\n",
       "      <td>0.002156</td>\n",
       "      <td>0.038621</td>\n",
       "      <td>0.634483</td>\n",
       "      <td>0.310078</td>\n",
       "      <td>0.146154</td>\n",
       "      <td>0.275862</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.682645</td>\n",
       "      <td>0.675200</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.601266</td>\n",
       "      <td>0.604607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.568276</td>\n",
       "      <td>0.986207</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.669231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.988484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MinTemp       MaxTemp      Rainfall   Evaporation      Sunshine  \\\n",
       "count  97988.000000  97988.000000  97988.000000  97988.000000  97988.000000   \n",
       "mean       0.483689      0.525947      0.006396      0.036949      0.525366   \n",
       "std        0.149458      0.131904      0.022962      0.021628      0.200931   \n",
       "min        0.000000      0.013233      0.000000      0.000000      0.000000   \n",
       "25%        0.377358      0.429112      0.000000      0.026207      0.517241   \n",
       "50%        0.478774      0.514178      0.000000      0.037741      0.526244   \n",
       "75%        0.591981      0.618147      0.002156      0.038621      0.634483   \n",
       "max        1.000000      1.000000      1.000000      0.568276      0.986207   \n",
       "\n",
       "       WindGustSpeed  WindSpeed9am  WindSpeed3pm   Humidity9am   Humidity3pm  \\\n",
       "count   97988.000000  97988.000000  97988.000000  97988.000000  97988.000000   \n",
       "mean        0.265107      0.108395      0.215668      0.686309      0.514693   \n",
       "std         0.102420      0.068800      0.101424      0.189008      0.206376   \n",
       "min         0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%         0.193798      0.053846      0.149425      0.570000      0.370000   \n",
       "50%         0.255814      0.100000      0.218391      0.690000      0.520000   \n",
       "75%         0.310078      0.146154      0.275862      0.830000      0.650000   \n",
       "max         1.000000      0.669231      1.000000      1.000000      1.000000   \n",
       "\n",
       "        Pressure9am   Pressure3pm      Cloud9am      Cloud3pm       Temp9am  \\\n",
       "count  97988.000000  97988.000000  97988.000000  97988.000000  97988.000000   \n",
       "mean       0.612014      0.608705      0.483192      0.493693      0.507089   \n",
       "std        0.111335      0.106611      0.255486      0.238028      0.134722   \n",
       "min        0.000000      0.030400      0.000000      0.000000      0.027426   \n",
       "25%        0.543802      0.540800      0.333333      0.333333      0.409283   \n",
       "50%        0.614125      0.610527      0.492351      0.499917      0.502110   \n",
       "75%        0.682645      0.675200      0.666667      0.666667      0.601266   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "            Temp3pm  \n",
       "count  97988.000000  \n",
       "mean       0.517103  \n",
       "std        0.130726  \n",
       "min        0.005758  \n",
       "25%        0.422265  \n",
       "50%        0.506718  \n",
       "75%        0.604607  \n",
       "max        0.988484  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "53966651",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f80e4fdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneHotEncoder(handle_unknown='ignore', sparse=False)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder = OneHotEncoder(sparse=False, handle_unknown='ignore')\n",
    "encoder.fit(df[cat_train])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "189b5a35",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/anshumantekriwal/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['Location_Adelaide',\n",
       " 'Location_Albany',\n",
       " 'Location_Albury',\n",
       " 'Location_AliceSprings',\n",
       " 'Location_BadgerysCreek',\n",
       " 'Location_Ballarat',\n",
       " 'Location_Bendigo',\n",
       " 'Location_Brisbane',\n",
       " 'Location_Cairns',\n",
       " 'Location_Canberra',\n",
       " 'Location_Cobar',\n",
       " 'Location_CoffsHarbour',\n",
       " 'Location_Dartmoor',\n",
       " 'Location_Darwin',\n",
       " 'Location_GoldCoast',\n",
       " 'Location_Hobart',\n",
       " 'Location_Katherine',\n",
       " 'Location_Launceston',\n",
       " 'Location_Melbourne',\n",
       " 'Location_MelbourneAirport',\n",
       " 'Location_Mildura',\n",
       " 'Location_Moree',\n",
       " 'Location_MountGambier',\n",
       " 'Location_MountGinini',\n",
       " 'Location_Newcastle',\n",
       " 'Location_Nhil',\n",
       " 'Location_NorahHead',\n",
       " 'Location_NorfolkIsland',\n",
       " 'Location_Nuriootpa',\n",
       " 'Location_PearceRAAF',\n",
       " 'Location_Penrith',\n",
       " 'Location_Perth',\n",
       " 'Location_PerthAirport',\n",
       " 'Location_Portland',\n",
       " 'Location_Richmond',\n",
       " 'Location_Sale',\n",
       " 'Location_SalmonGums',\n",
       " 'Location_Sydney',\n",
       " 'Location_SydneyAirport',\n",
       " 'Location_Townsville',\n",
       " 'Location_Tuggeranong',\n",
       " 'Location_Uluru',\n",
       " 'Location_WaggaWagga',\n",
       " 'Location_Walpole',\n",
       " 'Location_Watsonia',\n",
       " 'Location_Williamtown',\n",
       " 'Location_Witchcliffe',\n",
       " 'Location_Wollongong',\n",
       " 'Location_Woomera',\n",
       " 'WindGustDir_E',\n",
       " 'WindGustDir_ENE',\n",
       " 'WindGustDir_ESE',\n",
       " 'WindGustDir_N',\n",
       " 'WindGustDir_NE',\n",
       " 'WindGustDir_NNE',\n",
       " 'WindGustDir_NNW',\n",
       " 'WindGustDir_NW',\n",
       " 'WindGustDir_S',\n",
       " 'WindGustDir_SE',\n",
       " 'WindGustDir_SSE',\n",
       " 'WindGustDir_SSW',\n",
       " 'WindGustDir_SW',\n",
       " 'WindGustDir_W',\n",
       " 'WindGustDir_WNW',\n",
       " 'WindGustDir_WSW',\n",
       " 'WindGustDir_nan',\n",
       " 'WindDir9am_E',\n",
       " 'WindDir9am_ENE',\n",
       " 'WindDir9am_ESE',\n",
       " 'WindDir9am_N',\n",
       " 'WindDir9am_NE',\n",
       " 'WindDir9am_NNE',\n",
       " 'WindDir9am_NNW',\n",
       " 'WindDir9am_NW',\n",
       " 'WindDir9am_S',\n",
       " 'WindDir9am_SE',\n",
       " 'WindDir9am_SSE',\n",
       " 'WindDir9am_SSW',\n",
       " 'WindDir9am_SW',\n",
       " 'WindDir9am_W',\n",
       " 'WindDir9am_WNW',\n",
       " 'WindDir9am_WSW',\n",
       " 'WindDir9am_nan',\n",
       " 'WindDir3pm_E',\n",
       " 'WindDir3pm_ENE',\n",
       " 'WindDir3pm_ESE',\n",
       " 'WindDir3pm_N',\n",
       " 'WindDir3pm_NE',\n",
       " 'WindDir3pm_NNE',\n",
       " 'WindDir3pm_NNW',\n",
       " 'WindDir3pm_NW',\n",
       " 'WindDir3pm_S',\n",
       " 'WindDir3pm_SE',\n",
       " 'WindDir3pm_SSE',\n",
       " 'WindDir3pm_SSW',\n",
       " 'WindDir3pm_SW',\n",
       " 'WindDir3pm_W',\n",
       " 'WindDir3pm_WNW',\n",
       " 'WindDir3pm_WSW',\n",
       " 'WindDir3pm_nan',\n",
       " 'RainToday_No',\n",
       " 'RainToday_Yes']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_cols = list(encoder.get_feature_names(cat_train))\n",
    "encoded_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3f225d50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Location_Adelaide', 'Location_Albany', 'Location_Albury',\n",
       "       'Location_AliceSprings', 'Location_BadgerysCreek',\n",
       "       'Location_Ballarat', 'Location_Bendigo', 'Location_Brisbane',\n",
       "       'Location_Cairns', 'Location_Canberra', 'Location_Cobar',\n",
       "       'Location_CoffsHarbour', 'Location_Dartmoor', 'Location_Darwin',\n",
       "       'Location_GoldCoast', 'Location_Hobart', 'Location_Katherine',\n",
       "       'Location_Launceston', 'Location_Melbourne',\n",
       "       'Location_MelbourneAirport', 'Location_Mildura', 'Location_Moree',\n",
       "       'Location_MountGambier', 'Location_MountGinini',\n",
       "       'Location_Newcastle', 'Location_Nhil', 'Location_NorahHead',\n",
       "       'Location_NorfolkIsland', 'Location_Nuriootpa',\n",
       "       'Location_PearceRAAF', 'Location_Penrith', 'Location_Perth',\n",
       "       'Location_PerthAirport', 'Location_Portland', 'Location_Richmond',\n",
       "       'Location_Sale', 'Location_SalmonGums', 'Location_Sydney',\n",
       "       'Location_SydneyAirport', 'Location_Townsville',\n",
       "       'Location_Tuggeranong', 'Location_Uluru', 'Location_WaggaWagga',\n",
       "       'Location_Walpole', 'Location_Watsonia', 'Location_Williamtown',\n",
       "       'Location_Witchcliffe', 'Location_Wollongong', 'Location_Woomera',\n",
       "       'WindGustDir_E', 'WindGustDir_ENE', 'WindGustDir_ESE',\n",
       "       'WindGustDir_N', 'WindGustDir_NE', 'WindGustDir_NNE',\n",
       "       'WindGustDir_NNW', 'WindGustDir_NW', 'WindGustDir_S',\n",
       "       'WindGustDir_SE', 'WindGustDir_SSE', 'WindGustDir_SSW',\n",
       "       'WindGustDir_SW', 'WindGustDir_W', 'WindGustDir_WNW',\n",
       "       'WindGustDir_WSW', 'WindGustDir_nan', 'WindDir9am_E',\n",
       "       'WindDir9am_ENE', 'WindDir9am_ESE', 'WindDir9am_N',\n",
       "       'WindDir9am_NE', 'WindDir9am_NNE', 'WindDir9am_NNW',\n",
       "       'WindDir9am_NW', 'WindDir9am_S', 'WindDir9am_SE', 'WindDir9am_SSE',\n",
       "       'WindDir9am_SSW', 'WindDir9am_SW', 'WindDir9am_W',\n",
       "       'WindDir9am_WNW', 'WindDir9am_WSW', 'WindDir9am_nan',\n",
       "       'WindDir3pm_E', 'WindDir3pm_ENE', 'WindDir3pm_ESE', 'WindDir3pm_N',\n",
       "       'WindDir3pm_NE', 'WindDir3pm_NNE', 'WindDir3pm_NNW',\n",
       "       'WindDir3pm_NW', 'WindDir3pm_S', 'WindDir3pm_SE', 'WindDir3pm_SSE',\n",
       "       'WindDir3pm_SSW', 'WindDir3pm_SW', 'WindDir3pm_W',\n",
       "       'WindDir3pm_WNW', 'WindDir3pm_WSW', 'WindDir3pm_nan',\n",
       "       'RainToday_No', 'RainToday_Yes'], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.get_feature_names(cat_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "781cef0b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n",
      "/var/folders/37/cr55t1yj4_q7mn_gwsg1qk640000gn/T/ipykernel_25849/3821004150.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_X[encoded_cols] = encoder.transform(test_X[cat_train])\n"
     ]
    }
   ],
   "source": [
    "train_X[encoded_cols] = encoder.transform(train_X[cat_train])\n",
    "val_X[encoded_cols] = encoder.transform(val_X[cat_train])\n",
    "test_X[encoded_cols] = encoder.transform(test_X[cat_train])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "41ac006e",
   "metadata": {},
   "outputs": [],
   "source": [
    "for X in [train_X, val_X, test_X]:\n",
    "    for feature in cat_train:\n",
    "        X.pop(feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a21669a9",
   "metadata": {
    "tags": []
   },
   "source": [
    " # Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0bc1b488",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(solver='liblinear')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression(solver='liblinear')\n",
    "model.fit(train_X, train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "926d5342",
   "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>Features</th>\n",
       "      <th>Weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MinTemp</td>\n",
       "      <td>0.898631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MaxTemp</td>\n",
       "      <td>-2.879914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rainfall</td>\n",
       "      <td>3.162778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Evaporation</td>\n",
       "      <td>0.854248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sunshine</td>\n",
       "      <td>-1.671394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>WindDir3pm_WNW</td>\n",
       "      <td>-0.033499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>WindDir3pm_WSW</td>\n",
       "      <td>-0.275975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>WindDir3pm_nan</td>\n",
       "      <td>0.074933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>RainToday_No</td>\n",
       "      <td>-1.473517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>RainToday_Yes</td>\n",
       "      <td>-0.976037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Features   Weights\n",
       "0           MinTemp  0.898631\n",
       "1           MaxTemp -2.879914\n",
       "2          Rainfall  3.162778\n",
       "3       Evaporation  0.854248\n",
       "4          Sunshine -1.671394\n",
       "..              ...       ...\n",
       "113  WindDir3pm_WNW -0.033499\n",
       "114  WindDir3pm_WSW -0.275975\n",
       "115  WindDir3pm_nan  0.074933\n",
       "116    RainToday_No -1.473517\n",
       "117   RainToday_Yes -0.976037\n",
       "\n",
       "[118 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights_df = pd.DataFrame({\n",
    "    'Features': (train_X.columns),\n",
    "    'Weights': model.coef_[0]\n",
    "})\n",
    "weights_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fa9b5992-1ee4-4e3d-b36a-60006a3eb46e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_Y.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d14a6f6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Weights', ylabel='Features'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x3600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,50))\n",
    "sns.barplot(data = weights_df, x='Weights', y='Features')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "26d2959f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "val_preds = model.predict(val_X)\n",
    "test_preds = model.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "43efbaca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracies:  0.8539996488969512 , 0.8420070011668611\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "val_acc = accuracy_score(val_preds, val_Y) \n",
    "test_acc = accuracy_score(test_preds, test_Y) \n",
    "\n",
    "print('Accuracies: ', val_acc, ',', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "717230b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix = confusion_matrix(test_Y, test_preds, normalize='true')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "95672cf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAD4CAYAAABPLjVeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAQyElEQVR4nO3df4xdaV3H8ffHgSYgyK/VRdqCFQprVUh0KSEBQXClSyQVJbG7RuK6ZFJjFUwwNP5BYvgHQmIkoWQyIXVDYmhI5MeII9WguESWMKuWhXYpTkpCZxvdLEskEOLuzP36x727HIaZ+2N758zt6fuVnOSeH/e5zx+TT799znPOk6pCktSOH9vtDkjS9cTQlaQWGbqS1CJDV5JaZOhKUouetNM/8OhDl5weoR/xlOe9ere7oBm0/sgDudo2JsmcJ9/ws1f9e5Pa8dCVpFb1Nna7B0MZupK6pXq73YOhDF1J3dIzdCWpNWWlK0kt2ljf7R4MZehK6hZvpElSixxekKQWzfiNNJ9Ik9QpVb2xt1GSHElyMclqkpNbnH9Wkk8kuS/Jl5L8wqg2DV1J3dLrjb8NkWQOOAXcChwCbktyaNNlfw6cq6qXAm8FPjCqe4aupG7ZeHT8bbjDwGpVXaqqR4AzwNFN1xwCPgtQVV8DfibJjcMaNXQldUv1xt6SzCe5t7HNN1raC1xu7K8NjjV9GfgtgCSHgRcA+4Z1zxtpkrplghtpVbUILG5zequX4Wx+mc57gQ8kOQd8BfhPYOhEYUNXUrdMb8rYGrC/sb8PuPJDP1X1HeAOgCQBvjHYtuXwgqRumdKNNGAFOJjkQJI9wDFgqXlBkmcOzgG8Dbh7EMTbstKV1CnVG3mDbLx2qtaTnADOAnPA6ao6n+T44PwC8HPAR5JsABeAO0e1a+hK6pYpPhxRVcvA8qZjC43P9wAHJ2nT0JXULT4GLEkt8oU3ktQiK11JatGMv/DG0JXULb7EXJJaZKUrSe2p8kaaJLXHSleSWuTsBUlqkZWuJLXI2QuS1CKHFySpRQ4vSFKLDF1JatGMDy+4coSkbtlYH38bIcmRJBeTrCY5ucX5ZyT5uyRfTnI+yR2j2rTSldQtUxpeSDIHnAJuob9e2kqSpaq60Ljsj4ALVfWmJD8JXEzyN4Ml27dkpSupWyZYgn2Ew8BqVV0ahOgZ4OjmXwOePliU8mnAw4xYDdjQldQtEyxMmWQ+yb2Nbb7R0l7gcmN/bXCs6YP010m7Qn8J9rdXDU9zhxckdcsEwwtVtQgsbnM6W31l0/4bgHPA64AXAv+U5PPDVgS20pXULVXjb8OtAfsb+/voV7RNdwAfr75V4BvATcMaNXQldcv6+vjbcCvAwSQHkuwBjgFLm675JvB6gCQ3Ai8BLg1r1OEFSd0ypXm6VbWe5ARwFpgDTlfV+STHB+cXgPcAdyX5Cv3hiHdV1UPD2jV0JXXLFJ9Iq6plYHnTsYXG5yvAr0/SpqErqVtGj9XuKkNXUrf47gVJapGhK0ntqQ0XppSk9ljpSlKLZvzVjoaupG7pOXtBktrj8IIktcgbaZLUIitdSWqRY7qS1KJrffZCkpvoL1Gxl/4LfK8AS1V1/w73TZImN+OV7tD36SZ5F/11gQJ8if77JQN8dKuVMRvfe3wJjA9/5KPT7K8kDVW93tjbbhhV6d4J/HxVPdo8mOQvgfPAe7f6UnMJjEcfujTb/+xI6pYZn70wauWIHvC8LY7/9OCcJM2WXo2/jZDkSJKLSVa3+t99kj9Lcm6wfTXJRpJnD2tzVKX7DuCzSf6LH6yK+XzgRcCJkT2WpLZNadggyRxwCriF/nppK0mWqurCY9dU1fuB9w+ufxPwp1X18LB2h4ZuVX0myYvpr/++l/547hqwUlWzXcNLuj5N70baYWC1qi4BJDlDf1LBhW2uvw0YeRNr5OyFwRruXxy/n5K0iyaYMpZkHphvHFoc3JOCfqF5uXFuDXjFNu08FTjCGCMAztOV1C0TVLrNm/5byFZf2ebaNwH/NmpoAQxdSR1T61Mb+VwD9jf299F/TmErxxhjaAFGz16QpGvL9GYvrAAHkxxIsod+sC5tvijJM4DXAJ8ap3tWupK6ZUqPAVfVepITwFlgDjhdVeeTHB+cf2wp9jcD/1hV3xunXUNXUrdM8THgqloGljcdW9i0fxdw17htGrqSOqVm/N0Lhq6kbpnejbQdYehK6hYrXUlqkaErSe2pMnQlqT1WupLUIkNXktpT67P9qm9DV1K3zHbmGrqSusWHIySpTYauJLXI4QVJao/DC5LUolo3dCWpPQ4vSFJ7pvQO8x3jcj2SuqU3wTZCkiNJLiZZTXJym2tem+RckvNJ/nVUm1a6kjplWpVukjngFHAL/UUqV5IsVdWFxjXPBD4EHKmqbyb5qVHtGrqSOqXWp9bUYWC1qi4BJDkDHAUuNK65Hfh4VX0ToKoeHNWowwuSOqV6429J5pPc29jmG03tBS439tcGx5peDDwryeeS/HuSt47qn5WupE6ZZHihqhaBxW1OZ6uvbNp/EvDLwOuBpwD3JPliVX19u980dCV1S22VlU/IGrC/sb8PuLLFNQ8Nll//XpK7gZcB24auwwuSOmWS4YURVoCDSQ4k2QMcA5Y2XfMp4NVJnpTkqcArgPuHNWqlK6lTqjedSreq1pOcAM4Cc8Dpqjqf5Pjg/EJV3Z/kM8B99Cehfbiqvjqs3ez0ekKPPnRptp/J0654yvNevdtd0Axaf+SBq07MB175urEzZ+89/zy1sYhxWelK6pRZfyLN0JXUKdMaXtgphq6kTpnxFdgNXUndYqUrSS3qbRi6ktQaK11JalFN74m0HWHoSuoUp4xJUot6VrqS1B6HFySpRc5ekKQWOXtBklrkmK4ktcgxXUlq0ay/e8GVIyR1Sq8y9jZKkiNJLiZZTXJyi/OvTfK/Sc4NtnePatNKV1Kn9KZ0Iy3JHHAKuIX+WmgrSZaq6sKmSz9fVb8xbruGrqROmeKNtMPAalVdAkhyBjgKbA7diex46H7t5W/f6Z/QNehfnv3K3e6COmqSG2lJ5oH5xqHFwbLsAHuBy41za/QXntzslUm+TH+l4HdW1flhv2mlK6lTJql0BwG7uM3prRrafJvuP4AXVNV3k7wR+CRwcNhveiNNUqfUBNsIa8D+xv4++tXsD36r6jtV9d3B52XgyUluGNaola6kTtnoTa2WXAEOJjkAPAAcA25vXpDkucD/VFUlOUy/kP3WsEYNXUmdMq03O1bVepITwFlgDjhdVeeTHB+cXwDeAvxhknXg+8CxquEzhQ1dSZ1SWw7FPsG2+kMGy5uOLTQ+fxD44CRtGrqSOqU340+kGbqSOqU3xUp3Jxi6kjplmsMLO8HQldQpG4auJLVnxtelNHQldYuhK0ktckxXklo040ukGbqSusUpY5LUoo3d7sAIhq6kTunFSleSWjPjTwEbupK6xSljktQiZy9IUotm/TFgl+uR1Cm9jL+NkuRIkotJVpOcHHLdy5NsJHnLqDYNXUmd0ptgGybJHHAKuBU4BNyW5NA2172P/goTIxm6kjpligtTHgZWq+pSVT0CnAGObnHdHwN/Czw4Tv8MXUmdMsnwQpL5JPc2tvlGU3uBy439tcGxxyXZC7wZWGBM3kiT1CmTTBmrqkVgcZvTW436bi6Q/wp4V1VtZMyHMgxdSZ2yMb3JC2vA/sb+PuDKpmtuBs4MAvcG4I1J1qvqk9s1auhK6pQpPhyxAhxMcgB4ADgG3N68oKoOPPY5yV3Ap4cFLhi6kjpmWqFbVetJTtCflTAHnK6q80mOD86PPY7bZOhK6pRpvnuhqpaB5U3Htgzbqvr9cdo0dCV1io8BS1KLfOGNJLXIl5hLUoscXpCkFjm8IEktcuUISWpRb8Zj19CV1CneSJOkFjmmK0ktcvaCJLXIMV1JatFsR66hK6ljHNOVpBZtzHita+hK6hQrXUlq0azfSHM1YEmdMsUl2ElyJMnFJKtJTm5x/miS+5KcG6wm/KpRbVrpSuqUaQ0vJJkDTgG30F+kciXJUlVdaFz2WWCpqirJS4GPATcNa9fQldQpU7yRdhhYrapLAEnOAEeBx0O3qr7buP7HGaOAdnhBUqf0qLG3JPODYYHHtvlGU3uBy439tcGxH5LkzUm+Bvw98Aej+melK6lTJqlzq2oRWNzm9FYPFP9I81X1CeATSX4FeA/wa8N+09CV1ClTnL2wBuxv7O8Drmx3cVXdneSFSW6oqoe2u87hBUmd0ptgG2EFOJjkQJI9wDFgqXlBkhclyeDzLwF7gG8Na9RKV1Kn1JQq3apaT3ICOAvMAaer6nyS44PzC8BvA29N8ijwfeB3qmpoB55w6Ca5o6r+eptz88A8wLuf84u85ekveKI/I0kTmeZjwFW1DCxvOrbQ+Pw+4H2TtHk1wwt/sd2Jqlqsqpur6mYDV1Kbpji8sCOGVrpJ7tvuFHDj9LsjSVenN/x/97tu1PDCjcAbgG9vOh7gCzvSI0m6CrMduaND99PA06rq3OYTST63Ex2SpKsx6y+8GRq6VXXnkHO3T787knR1pjV7Yac4ZUxSp6wbupLUHitdSWqRK0dIUotGPBC26wxdSZ1yTc9ekKRrjasBS1KLrHQlqUWO6UpSi5y9IEktmvV5uq4cIalTJlmYcpQkR5JcTLKa5OQW5383yX2D7QtJXjaqTStdSZ2yUdMZYEgyB5wCbqG/XtpKkqWqutC47BvAa6rq20lupb/I5SuGtWvoSuqUKQ4vHAZWq+oSQJIzwFHg8dCtquYrbr9If/HKoRxekNQpvaqxtyTzSe5tbPONpvYClxv7a4Nj27kT+IdR/bPSldQpk9S5VbVIf0hgKxm3+SS/Sj90XzXqNw1dSZ0yxYcj1oD9jf19wJXNFyV5KfBh4NaqGrr8Oji8IKljpjh7YQU4mORAkj3AMWCpeUGS5wMfB36vqr4+Tv+sdCV1yrRmL1TVepITwFlgDjhdVeeTHB+cXwDeDTwH+FASgPWqunlYu4aupE6Z5sMRVbUMLG86ttD4/DbgbZO0aehK6hTfvSBJLfItY5LUIitdSWrRxoy/Z8zQldQpPStdSWrPrL/a0dCV1ClWupLUIitdSWqRla4ktWhajwHvFENXUqc4vCBJLSorXUlqj48BS1KLfAxYklo065WuK0dI6pSNXm/sbZQkR5JcTLKa5OQW529Kck+S/0vyznH6Z6UrqVOmNXshyRxwCriF/nppK0mWqupC47KHgT8BfnPcdq10JXVKVY29jXAYWK2qS1X1CHAGOLrptx6sqhXg0XH7Z+hK6pRJFqZMMp/k3sY232hqL3C5sb82OHZVHF6Q1CmTzF6oqkVgcZvT2eorT6RPTYaupE4Z5wbZmNaA/Y39fcCVq23U4QVJnTLJ8MIIK8DBJAeS7AGOAUtX2z8rXUmdMq2HI6pqPckJ4CwwB5yuqvNJjg/OLyR5LnAv8BNAL8k7gENV9Z3t2jV0JXXKNF/tWFXLwPKmYwuNz/9Nf9hhbIaupE7xLWOS1CJfYi5JLer5akdJao9vGZOkFhm6ktSi2Y5cyKz/q9AlSeYHjx1Kj/Pv4vriE2ntmh99ia5D/l1cRwxdSWqRoStJLTJ02+W4nbbi38V1xBtpktQiK11JapGhK0ktMnRbMmopZ11/kpxO8mCSr+52X9QeQ7cFjaWcbwUOAbclObS7vdIMuAs4studULsM3XaMXMpZ15+quht4eLf7oXYZuu3YkaWcJV17DN127MhSzpKuPYZuO3ZkKWdJ1x5Dtx07spSzpGuPoduCqloHHlvK+X7gY1V1fnd7pd2W5KPAPcBLkqwluXO3+6Sd52PAktQiK11JapGhK0ktMnQlqUWGriS1yNCVpBYZupLUIkNXklr0/w/A5O7SENbQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70dd3307",
   "metadata": {},
   "source": [
    "# Save & Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1ad41ff5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1fffd7fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "aus_rain = {\n",
    "    'model': model,\n",
    "    'imputer': imputer,\n",
    "    'scaler': scaler,\n",
    "    'encoder': encoder,\n",
    "    'train_X': train_X,\n",
    "    'train_Y': train_Y,\n",
    "    'num_cols': num_train,\n",
    "    'cat_cols': cat_train,\n",
    "    'encoded_cols': encoded_cols\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "268758b5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../models/logistic_ausRain.joblib']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(aus_rain, '../models/logistic_ausRain.joblib')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}