notebooks/weather-forecast.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Problem Statement:\n",
    "\n",
    "\n",
    "A major agricultural company needs you to help them maximize growth efficiency, save resources and optimize their production. To achieve these things, the company needs to have an accurate weather prediction algorithm which will improve their decision making on typical farming activities such as planting and irrigating.\n",
    "\n",
    "Using historical weather information from their region, can you predict what the weather will be in the next few days? Please note that areas A through E and the target area where you need to predict the weather, are all neighboring regions. The location of each region is not available.\n",
    "\n",
    "This is a beginner-level practice competition and your goal is to predict the next day’s weather (N: No rain L: Light rain H: Heavy rain) using the prior day’s weather data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install lightgbm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# essentials\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "# plotting\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "matplotlib.rcParams['figure.dpi'] = 100\n",
    "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
    "sns.set(style=\"whitegrid\")\n",
    "%matplotlib inline\n",
    "\n",
    "# ml\n",
    "from sklearn.metrics import accuracy_score, recall_score, ConfusionMatrixDisplay, classification_report, auc, precision_score, roc_auc_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler, OneHotEncoder\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "import lightgbm as lgb\n",
    "from lightgbm import LGBMClassifier\n",
    "import joblib\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we import the necessary libraries we need."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def missing_vals(df, drop=False, threshold=70):\n",
    "    '''\n",
    "    return missing values and percentages in a data frame\n",
    "    also has option to drop columns with missing values\n",
    "    '''\n",
    "    missing_df = (\n",
    "        df.isnull()\n",
    "        .sum()\n",
    "        .to_frame(name=\"counts\")\n",
    "        .query(\"counts > 0\")\n",
    "        .assign(percentage=lambda x: round((x.counts / len(df)) * 100, 2))\n",
    "        .sort_values(\"counts\", ascending=False)\n",
    "    )\n",
    "\n",
    "    if drop == True:\n",
    "        to_drop = list(missing_df.query(f\"percentage > {threshold}\").index)\n",
    "        print(f\"dropped columns {to_drop}\")\n",
    "        df = df.drop(to_drop, axis=1)\n",
    "        return df\n",
    "\n",
    "    return missing_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_a = pd.read_csv(\"data/comp_datasets_train/region_A_train.csv\")\n",
    "train_b = pd.read_csv(\"data/comp_datasets_train/region_B_train.csv\")\n",
    "train_c = pd.read_csv(\"data/comp_datasets_train/region_C_train.csv\")\n",
    "train_d = pd.read_csv(\"data/comp_datasets_train/region_D_train.csv\")\n",
    "train_e = pd.read_csv(\"data/comp_datasets_train/region_E_train.csv\")\n",
    "\n",
    "test_a = pd.read_csv(\"data/comp_datasets_test/region_A_test.csv\")\n",
    "test_b = pd.read_csv(\"data/comp_datasets_test/region_B_test.csv\")\n",
    "test_c = pd.read_csv(\"data/comp_datasets_test/region_C_test.csv\")\n",
    "test_d = pd.read_csv(\"data/comp_datasets_test/region_D_test.csv\")\n",
    "test_e = pd.read_csv(\"data/comp_datasets_test/region_E_test.csv\")\n",
    "\n",
    "labels_df = pd.read_csv(\"data/comp_datasets_train/solution_train.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we load the train and test data using the `read_csv` function. We also load the labels data which is the forms the target for our training dataset.\n",
    "\n",
    "Since our training data is split into 5 regions, we have to combine them into one single dataset, where the region would become a feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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></th>\n",
       "      <th>date</th>\n",
       "      <th>avg.temp</th>\n",
       "      <th>max.temp</th>\n",
       "      <th>min.temp</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>avg.wind.speed</th>\n",
       "      <th>max.wind.speed</th>\n",
       "      <th>max.wind.speed.dir</th>\n",
       "      <th>max.inst.wind.speed</th>\n",
       "      <th>max.inst.wind.speed.dir</th>\n",
       "      <th>min.atmos.pressure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">A</th>\n",
       "      <th>0</th>\n",
       "      <td>229b70a3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>10.2</td>\n",
       "      <td>-2.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>9.3</td>\n",
       "      <td>W</td>\n",
       "      <td>14.3</td>\n",
       "      <td>W</td>\n",
       "      <td>1015.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3134f4ff</td>\n",
       "      <td>5.7</td>\n",
       "      <td>13.7</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>10.7</td>\n",
       "      <td>W</td>\n",
       "      <td>15.8</td>\n",
       "      <td>W</td>\n",
       "      <td>1011.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dbfaf910</td>\n",
       "      <td>13.8</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>9.4</td>\n",
       "      <td>SW</td>\n",
       "      <td>15.2</td>\n",
       "      <td>W</td>\n",
       "      <td>1004.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3aea0cf0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>19.3</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>10.1</td>\n",
       "      <td>SW</td>\n",
       "      <td>20.6</td>\n",
       "      <td>SW</td>\n",
       "      <td>1001.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f0227f56</td>\n",
       "      <td>2.4</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>43.5</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3.7</td>\n",
       "      <td>SW</td>\n",
       "      <td>5.7</td>\n",
       "      <td>SW</td>\n",
       "      <td>1003.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">E</th>\n",
       "      <th>561</th>\n",
       "      <td>91b2797d</td>\n",
       "      <td>6.3</td>\n",
       "      <td>13.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>S</td>\n",
       "      <td>4.3</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>b807fd87</td>\n",
       "      <td>6.2</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.3</td>\n",
       "      <td>SW</td>\n",
       "      <td>6.3</td>\n",
       "      <td>W</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>8e0a48e0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>NW</td>\n",
       "      <td>5.7</td>\n",
       "      <td>W</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>9df85983</td>\n",
       "      <td>5.3</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>S</td>\n",
       "      <td>6.9</td>\n",
       "      <td>NW</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>c9d4fe7c</td>\n",
       "      <td>6.4</td>\n",
       "      <td>15.3</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>NW</td>\n",
       "      <td>5.5</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2830 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  avg.temp  max.temp  min.temp  precipitation  avg.wind.speed  \\\n",
       "A 0    229b70a3       3.3      10.2      -2.4            0.0             2.9   \n",
       "  1    3134f4ff       5.7      13.7      -2.9            0.0             3.6   \n",
       "  2    dbfaf910      13.8      20.0       9.0            0.0             5.3   \n",
       "  3    3aea0cf0      11.4      19.3       5.8            0.0             4.2   \n",
       "  4    f0227f56       2.4       7.7       0.3           43.5             0.9   \n",
       "...         ...       ...       ...       ...            ...             ...   \n",
       "E 561  91b2797d       6.3      13.1       0.3            0.0             0.6   \n",
       "  562  b807fd87       6.2      13.5       0.3            0.0             0.8   \n",
       "  563  8e0a48e0       9.0      15.9       2.4            0.0             0.6   \n",
       "  564  9df85983       5.3      13.9       0.1            0.0             1.0   \n",
       "  565  c9d4fe7c       6.4      15.3      -0.2            0.0             0.7   \n",
       "\n",
       "       max.wind.speed max.wind.speed.dir  max.inst.wind.speed  \\\n",
       "A 0               9.3                  W                 14.3   \n",
       "  1              10.7                  W                 15.8   \n",
       "  2               9.4                 SW                 15.2   \n",
       "  3              10.1                 SW                 20.6   \n",
       "  4               3.7                 SW                  5.7   \n",
       "...               ...                ...                  ...   \n",
       "E 561             2.2                  S                  4.3   \n",
       "  562             2.3                 SW                  6.3   \n",
       "  563             2.4                 NW                  5.7   \n",
       "  564             3.0                  S                  6.9   \n",
       "  565             2.1                 NW                  5.5   \n",
       "\n",
       "      max.inst.wind.speed.dir  min.atmos.pressure  \n",
       "A 0                         W              1015.1  \n",
       "  1                         W              1011.3  \n",
       "  2                         W              1004.2  \n",
       "  3                        SW              1001.7  \n",
       "  4                        SW              1003.5  \n",
       "...                       ...                 ...  \n",
       "E 561                       S                 NaN  \n",
       "  562                       W                 NaN  \n",
       "  563                       W                 NaN  \n",
       "  564                      NW                 NaN  \n",
       "  565                       N                 NaN  \n",
       "\n",
       "[2830 rows x 11 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.concat([train_a ,train_b ,train_c ,train_d ,train_e], keys=[\"A\", \"B\", \"C\", \"D\", \"E\"])\n",
    "train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first concatenate all the train datasets with specific regions, and set the keys as the region names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>region</th>\n",
       "      <th>date</th>\n",
       "      <th>avg.temp</th>\n",
       "      <th>max.temp</th>\n",
       "      <th>min.temp</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>avg.wind.speed</th>\n",
       "      <th>max.wind.speed</th>\n",
       "      <th>max.wind.speed.dir</th>\n",
       "      <th>max.inst.wind.speed</th>\n",
       "      <th>max.inst.wind.speed.dir</th>\n",
       "      <th>min.atmos.pressure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>229b70a3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>10.2</td>\n",
       "      <td>-2.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>9.3</td>\n",
       "      <td>W</td>\n",
       "      <td>14.3</td>\n",
       "      <td>W</td>\n",
       "      <td>1015.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>3134f4ff</td>\n",
       "      <td>5.7</td>\n",
       "      <td>13.7</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>10.7</td>\n",
       "      <td>W</td>\n",
       "      <td>15.8</td>\n",
       "      <td>W</td>\n",
       "      <td>1011.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>dbfaf910</td>\n",
       "      <td>13.8</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>9.4</td>\n",
       "      <td>SW</td>\n",
       "      <td>15.2</td>\n",
       "      <td>W</td>\n",
       "      <td>1004.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>3aea0cf0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>19.3</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>10.1</td>\n",
       "      <td>SW</td>\n",
       "      <td>20.6</td>\n",
       "      <td>SW</td>\n",
       "      <td>1001.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>f0227f56</td>\n",
       "      <td>2.4</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>43.5</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3.7</td>\n",
       "      <td>SW</td>\n",
       "      <td>5.7</td>\n",
       "      <td>SW</td>\n",
       "      <td>1003.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  region      date  avg.temp  max.temp  min.temp  precipitation  \\\n",
       "0      A  229b70a3       3.3      10.2      -2.4            0.0   \n",
       "1      A  3134f4ff       5.7      13.7      -2.9            0.0   \n",
       "2      A  dbfaf910      13.8      20.0       9.0            0.0   \n",
       "3      A  3aea0cf0      11.4      19.3       5.8            0.0   \n",
       "4      A  f0227f56       2.4       7.7       0.3           43.5   \n",
       "\n",
       "   avg.wind.speed  max.wind.speed max.wind.speed.dir  max.inst.wind.speed  \\\n",
       "0             2.9             9.3                  W                 14.3   \n",
       "1             3.6            10.7                  W                 15.8   \n",
       "2             5.3             9.4                 SW                 15.2   \n",
       "3             4.2            10.1                 SW                 20.6   \n",
       "4             0.9             3.7                 SW                  5.7   \n",
       "\n",
       "  max.inst.wind.speed.dir  min.atmos.pressure  \n",
       "0                       W              1015.1  \n",
       "1                       W              1011.3  \n",
       "2                       W              1004.2  \n",
       "3                      SW              1001.7  \n",
       "4                      SW              1003.5  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = train.reset_index()\n",
    "train.rename(columns = {\"level_0\": \"region\"}, inplace=True)\n",
    "train.drop(columns=['level_1'], inplace=True)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then reset index at the first level (the region names), and then name the column as region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset contains 2830 samples and 12 columns\n"
     ]
    }
   ],
   "source": [
    "print(f\"The dataset contains {train.shape[0]} samples and \"\n",
    "      f\"{train.shape[1]} columns\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2830 entries, 0 to 2829\n",
      "Data columns (total 12 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   region                   2830 non-null   object \n",
      " 1   date                     2830 non-null   object \n",
      " 2   avg.temp                 2830 non-null   float64\n",
      " 3   max.temp                 2830 non-null   float64\n",
      " 4   min.temp                 2830 non-null   float64\n",
      " 5   precipitation            2830 non-null   float64\n",
      " 6   avg.wind.speed           2830 non-null   float64\n",
      " 7   max.wind.speed           2830 non-null   float64\n",
      " 8   max.wind.speed.dir       2830 non-null   object \n",
      " 9   max.inst.wind.speed      2830 non-null   float64\n",
      " 10  max.inst.wind.speed.dir  2830 non-null   object \n",
      " 11  min.atmos.pressure       566 non-null    float64\n",
      "dtypes: float64(8), object(4)\n",
      "memory usage: 265.4+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>counts</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min.atmos.pressure</th>\n",
       "      <td>2264</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    counts  percentage\n",
       "min.atmos.pressure    2264        80.0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_vals(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "min.atmos.pressure is the only variable with missing data (80% in fact), we'll deal with this later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['avg.temp', 'max.temp', 'min.temp', 'precipitation', 'avg.wind.speed',\n",
       "       'max.wind.speed', 'max.wind.speed.dir', 'max.inst.wind.speed',\n",
       "       'max.inst.wind.speed.dir', 'min.atmos.pressure'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns[2:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the index above grabs all the features we are using to predict the target variable, label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram of all numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1440x1008 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = train.hist(figsize=(20, 14))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`precipitation` is heavily right skewed, along with `avg.wind.speed` and `max.wind.speed` being right skewed as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### line plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x3000 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(5,2,figsize=(14, 30), dpi=100)\n",
    "\n",
    "for i, col_name in enumerate(train.columns[2:]):\n",
    "    if train[col_name].dtype == 'O':\n",
    "        train.groupby('region')[col_name].hist(ax=axes[i%5][i//5], alpha=0.6);\n",
    "        axes[i%5][i//5].legend([\"A\", \"B\", \"C\", \"D\", \"E\"]);\n",
    "    else:\n",
    "        train.groupby('region')[col_name].plot(ax=axes[i%5][i//5], alpha=0.7);\n",
    "        axes[i%5][i//5].legend();\n",
    "    axes[i%5][i//5].set_title(f'{col_name}', fontsize=13);\n",
    "    plt.subplots_adjust(hspace=0.45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plots, we see that the patterns in the data are very similar except for regions C, D, E for min.wind.speed and avg.wind.speed which are on the lower scale.\n",
    "\n",
    "Now that we’ve explored the data a little, we check for missing values in our data.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>counts</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min.atmos.pressure</th>\n",
       "      <td>2264</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    counts  percentage\n",
       "min.atmos.pressure    2264        80.0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_vals(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s also use a heatmap to visualize the missing data for that column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.heatmap(train.isnull(), yticklabels=False, cmap='viridis', cbar=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "There are multiple ways we can deal with missing data.\n",
    "\n",
    "Let’s first look at the distribution of the column with missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['min.atmos.pressure'].hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the distribution of the column is pretty normal, let’s impute the missing data with the mean.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_atmos = train['min.atmos.pressure'].mean()\n",
    "train.fillna(mean_atmos, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>counts</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [counts, percentage]\n",
       "Index: []"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_vals(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s first add the labels to our data.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.merge(labels_df, on=\"date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we take a look at the categorical columns for our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['region', 'date', 'max.wind.speed.dir', 'max.inst.wind.speed.dir',\n",
       "       'label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.select_dtypes('object').columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We’ll have to convert the categorical features, including the target variable to a numerical format.\n",
    "\n",
    "Let’s use scikit-learn’s Label Encoder to do that.\n",
    "\n",
    "Here’s an example of using LabelEncoder() on the label column\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'H': 0, 'L': 1, 'N': 2}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "le.fit(train['label'])\n",
    "le_name_map = dict(zip(le.classes_, le.transform(le.classes_)))\n",
    "le_name_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating new weather features\n",
    "Since we’re given some weather features, there are interesting new features we can engineer.\n",
    "\n",
    "One example is the Beaufort scale, which is “an empirical measure that relates wind speed to observed conditions at sea or on land.”\n",
    "\n",
    "Below is a function to do our feature engineering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "BEAUFORT = [\n",
    "    (0, 0, 0.3),\n",
    "    (1, 0.3, 1.6),\n",
    "    (2, 1.6, 3.4),\n",
    "    (3, 3.4, 5.5),\n",
    "    (4, 5.5, 8),\n",
    "    (5, 8, 10.8),\n",
    "    (6, 10.8, 13.9),\n",
    "    (7, 13.9, 17.2),\n",
    "    (8, 17.2, 20.8),\n",
    "    (9, 20.8, 24.5),\n",
    "    (10, 24.5, 28.5),\n",
    "    (11, 28.5, 33),\n",
    "    (12, 33, 200),\n",
    "]\n",
    "\n",
    "\n",
    "def feature_eng(df):\n",
    "    le = LabelEncoder()\n",
    "    \n",
    "    cat_cols = [c for c in df.select_dtypes('object').columns if c not in ['region', 'date']]\n",
    "\n",
    "    for c in cat_cols:\n",
    "        df[c] = le.fit_transform(df[c])\n",
    "\n",
    "    # wind speed is in meter/second\n",
    "    # convert to knots to obtain beaufort scale\n",
    "    for item in BEAUFORT:\n",
    "        df.loc[\n",
    "            (df[\"avg.wind.speed\"] * 1.944 >= item[1]) & (df[\"avg.wind.speed\"] * 1.944 < item[2]),\n",
    "            \"avg_beaufort_scale\",\n",
    "        ] = item[0]\n",
    "        df.loc[\n",
    "            (df[\"max.wind.speed\"] * 1.944 >= item[1]) & (df[\"max.wind.speed\"] * 1.944 < item[2]),\n",
    "            \"max_beaufort_scale\",\n",
    "        ] = item[0]\n",
    "\n",
    "    df['avg_beaufort_scale'] = df['avg_beaufort_scale'].astype(int)\n",
    "    df['max_beaufort_scale'] = df['max_beaufort_scale'].astype(int)\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we apply this function to our train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = feature_eng(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>region</th>\n",
       "      <th>date</th>\n",
       "      <th>avg.temp</th>\n",
       "      <th>max.temp</th>\n",
       "      <th>min.temp</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>avg.wind.speed</th>\n",
       "      <th>max.wind.speed</th>\n",
       "      <th>max.wind.speed.dir</th>\n",
       "      <th>max.inst.wind.speed</th>\n",
       "      <th>max.inst.wind.speed.dir</th>\n",
       "      <th>min.atmos.pressure</th>\n",
       "      <th>label</th>\n",
       "      <th>avg_beaufort_scale</th>\n",
       "      <th>max_beaufort_scale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>229b70a3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>10.2</td>\n",
       "      <td>-2.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>9.3</td>\n",
       "      <td>7</td>\n",
       "      <td>14.3</td>\n",
       "      <td>7</td>\n",
       "      <td>1015.100000</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>229b70a3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>9.3</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3</td>\n",
       "      <td>11.4</td>\n",
       "      <td>3</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>229b70a3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-4.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6</td>\n",
       "      <td>8.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>229b70a3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>11.7</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>6</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>229b70a3</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>5.2</td>\n",
       "      <td>-5.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3</td>\n",
       "      <td>9.8</td>\n",
       "      <td>7</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  region      date  avg.temp  max.temp  min.temp  precipitation  \\\n",
       "0      A  229b70a3       3.3      10.2      -2.4            0.0   \n",
       "1      B  229b70a3       2.6       9.3      -3.3            0.0   \n",
       "2      C  229b70a3       1.4       9.0      -4.3            0.0   \n",
       "3      D  229b70a3       3.7      11.7      -2.6            0.0   \n",
       "4      E  229b70a3      -0.6       5.2      -5.9            0.0   \n",
       "\n",
       "   avg.wind.speed  max.wind.speed  max.wind.speed.dir  max.inst.wind.speed  \\\n",
       "0             2.9             9.3                   7                 14.3   \n",
       "1             2.8             6.3                   3                 11.4   \n",
       "2             1.0             2.9                   6                  8.2   \n",
       "3             1.5             5.4                   6                  9.4   \n",
       "4             1.5             3.8                   3                  9.8   \n",
       "\n",
       "   max.inst.wind.speed.dir  min.atmos.pressure  label  avg_beaufort_scale  \\\n",
       "0                        7         1015.100000      2                   4   \n",
       "1                        3         1010.216254      2                   3   \n",
       "2                        1         1010.216254      2                   2   \n",
       "3                        4         1010.216254      2                   2   \n",
       "4                        7         1010.216254      2                   2   \n",
       "\n",
       "   max_beaufort_scale  \n",
       "0                   8  \n",
       "1                   6  \n",
       "2                   4  \n",
       "3                   5  \n",
       "4                   4  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['region', 'date'], dtype='object')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.select_dtypes('object').columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It’s time to pivot our data into the longer format, which means instead of the region being a column, each feature will have its own region, such as avg.temp_A, avg.temp_B, until avg.temp_E for the rest of the features.\n",
    "\n",
    "This way the data will be in the right shape to build the model.\n",
    "\n",
    "We can use the pivot function in pandas to achieve that.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>date</th>\n",
       "      <th>label</th>\n",
       "      <th>('avg.temp', 'A')</th>\n",
       "      <th>('avg.temp', 'B')</th>\n",
       "      <th>('avg.temp', 'C')</th>\n",
       "      <th>('avg.temp', 'D')</th>\n",
       "      <th>('avg.temp', 'E')</th>\n",
       "      <th>('max.temp', 'A')</th>\n",
       "      <th>('max.temp', 'B')</th>\n",
       "      <th>('max.temp', 'C')</th>\n",
       "      <th>('max.temp', 'D')</th>\n",
       "      <th>('max.temp', 'E')</th>\n",
       "      <th>('min.temp', 'A')</th>\n",
       "      <th>('min.temp', 'B')</th>\n",
       "      <th>('min.temp', 'C')</th>\n",
       "      <th>('min.temp', 'D')</th>\n",
       "      <th>('min.temp', 'E')</th>\n",
       "      <th>('precipitation', 'A')</th>\n",
       "      <th>('precipitation', 'B')</th>\n",
       "      <th>('precipitation', 'C')</th>\n",
       "      <th>('precipitation', 'D')</th>\n",
       "      <th>('precipitation', 'E')</th>\n",
       "      <th>('avg.wind.speed', 'A')</th>\n",
       "      <th>('avg.wind.speed', 'B')</th>\n",
       "      <th>('avg.wind.speed', 'C')</th>\n",
       "      <th>('avg.wind.speed', 'D')</th>\n",
       "      <th>('avg.wind.speed', 'E')</th>\n",
       "      <th>('max.wind.speed', 'A')</th>\n",
       "      <th>('max.wind.speed', 'B')</th>\n",
       "      <th>('max.wind.speed', 'C')</th>\n",
       "      <th>('max.wind.speed', 'D')</th>\n",
       "      <th>('max.wind.speed', 'E')</th>\n",
       "      <th>('max.wind.speed.dir', 'A')</th>\n",
       "      <th>('max.wind.speed.dir', 'B')</th>\n",
       "      <th>('max.wind.speed.dir', 'C')</th>\n",
       "      <th>('max.wind.speed.dir', 'D')</th>\n",
       "      <th>('max.wind.speed.dir', 'E')</th>\n",
       "      <th>('max.inst.wind.speed', 'A')</th>\n",
       "      <th>('max.inst.wind.speed', 'B')</th>\n",
       "      <th>('max.inst.wind.speed', 'C')</th>\n",
       "      <th>('max.inst.wind.speed', 'D')</th>\n",
       "      <th>('max.inst.wind.speed', 'E')</th>\n",
       "      <th>('max.inst.wind.speed.dir', 'A')</th>\n",
       "      <th>('max.inst.wind.speed.dir', 'B')</th>\n",
       "      <th>('max.inst.wind.speed.dir', 'C')</th>\n",
       "      <th>('max.inst.wind.speed.dir', 'D')</th>\n",
       "      <th>('max.inst.wind.speed.dir', 'E')</th>\n",
       "      <th>('min.atmos.pressure', 'A')</th>\n",
       "      <th>('min.atmos.pressure', 'B')</th>\n",
       "      <th>('min.atmos.pressure', 'C')</th>\n",
       "      <th>('min.atmos.pressure', 'D')</th>\n",
       "      <th>('min.atmos.pressure', 'E')</th>\n",
       "      <th>('avg_beaufort_scale', 'A')</th>\n",
       "      <th>('avg_beaufort_scale', 'B')</th>\n",
       "      <th>('avg_beaufort_scale', 'C')</th>\n",
       "      <th>('avg_beaufort_scale', 'D')</th>\n",
       "      <th>('avg_beaufort_scale', 'E')</th>\n",
       "      <th>('max_beaufort_scale', 'A')</th>\n",
       "      <th>('max_beaufort_scale', 'B')</th>\n",
       "      <th>('max_beaufort_scale', 'C')</th>\n",
       "      <th>('max_beaufort_scale', 'D')</th>\n",
       "      <th>('max_beaufort_scale', 'E')</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00173aec</td>\n",
       "      <td>2</td>\n",
       "      <td>18.7</td>\n",
       "      <td>17.6</td>\n",
       "      <td>16.9</td>\n",
       "      <td>19.5</td>\n",
       "      <td>14.3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>23.7</td>\n",
       "      <td>23.9</td>\n",
       "      <td>26.7</td>\n",
       "      <td>21.1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>12.5</td>\n",
       "      <td>12.9</td>\n",
       "      <td>16.4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7.5</td>\n",
       "      <td>11.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1006.7</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0083f291</td>\n",
       "      <td>1</td>\n",
       "      <td>13.1</td>\n",
       "      <td>12.6</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>15.8</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>14.9</td>\n",
       "      <td>13.9</td>\n",
       "      <td>11.3</td>\n",
       "      <td>11.1</td>\n",
       "      <td>10.6</td>\n",
       "      <td>12.1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>50.0</td>\n",
       "      <td>46.5</td>\n",
       "      <td>49.0</td>\n",
       "      <td>45.5</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1000.6</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>014cfe7b</td>\n",
       "      <td>2</td>\n",
       "      <td>19.9</td>\n",
       "      <td>19.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>19.9</td>\n",
       "      <td>16.2</td>\n",
       "      <td>24.2</td>\n",
       "      <td>23.9</td>\n",
       "      <td>21.0</td>\n",
       "      <td>25.8</td>\n",
       "      <td>19.1</td>\n",
       "      <td>16.1</td>\n",
       "      <td>13.5</td>\n",
       "      <td>15.8</td>\n",
       "      <td>16.3</td>\n",
       "      <td>12.4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>63.5</td>\n",
       "      <td>19.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>11.3</td>\n",
       "      <td>8.6</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>14.5</td>\n",
       "      <td>14.6</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1004.6</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01947c8e</td>\n",
       "      <td>2</td>\n",
       "      <td>21.6</td>\n",
       "      <td>20.2</td>\n",
       "      <td>20.5</td>\n",
       "      <td>21.3</td>\n",
       "      <td>17.6</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>27.7</td>\n",
       "      <td>29.3</td>\n",
       "      <td>25.2</td>\n",
       "      <td>15.8</td>\n",
       "      <td>14.2</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.9</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.4</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0258884d</td>\n",
       "      <td>2</td>\n",
       "      <td>15.2</td>\n",
       "      <td>13.9</td>\n",
       "      <td>13.9</td>\n",
       "      <td>15.8</td>\n",
       "      <td>11.1</td>\n",
       "      <td>21.9</td>\n",
       "      <td>22.4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>17.2</td>\n",
       "      <td>10.3</td>\n",
       "      <td>5.6</td>\n",
       "      <td>8.9</td>\n",
       "      <td>10.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>6.8</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1020.1</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date  label  ('avg.temp', 'A')  ('avg.temp', 'B')  ('avg.temp', 'C')  \\\n",
       "0  00173aec      2               18.7               17.6               16.9   \n",
       "1  0083f291      1               13.1               12.6               12.0   \n",
       "2  014cfe7b      2               19.9               19.0               17.5   \n",
       "3  01947c8e      2               21.6               20.2               20.5   \n",
       "4  0258884d      2               15.2               13.9               13.9   \n",
       "\n",
       "   ('avg.temp', 'D')  ('avg.temp', 'E')  ('max.temp', 'A')  ('max.temp', 'B')  \\\n",
       "0               19.5               14.3               25.0               23.7   \n",
       "1               13.0               10.7               15.8               15.0   \n",
       "2               19.9               16.2               24.2               23.9   \n",
       "3               21.3               17.6               29.0               29.0   \n",
       "4               15.8               11.1               21.9               22.4   \n",
       "\n",
       "   ('max.temp', 'C')  ('max.temp', 'D')  ('max.temp', 'E')  ('min.temp', 'A')  \\\n",
       "0               23.9               26.7               21.1               14.9   \n",
       "1               13.7               14.9               13.9               11.3   \n",
       "2               21.0               25.8               19.1               16.1   \n",
       "3               27.7               29.3               25.2               15.8   \n",
       "4               21.0               22.4               17.2               10.3   \n",
       "\n",
       "   ('min.temp', 'B')  ('min.temp', 'C')  ('min.temp', 'D')  ('min.temp', 'E')  \\\n",
       "0               12.5               12.9               16.4                9.0   \n",
       "1               11.1               10.6               12.1                8.5   \n",
       "2               13.5               15.8               16.3               12.4   \n",
       "3               14.2               15.5               15.9               11.5   \n",
       "4                5.6                8.9               10.4                5.3   \n",
       "\n",
       "   ('precipitation', 'A')  ('precipitation', 'B')  ('precipitation', 'C')  \\\n",
       "0                     1.0                     1.5                     1.5   \n",
       "1                    50.0                    46.5                    49.0   \n",
       "2                    18.0                    17.0                    63.5   \n",
       "3                     0.0                     1.0                     0.0   \n",
       "4                     0.0                     0.0                     0.0   \n",
       "\n",
       "   ('precipitation', 'D')  ('precipitation', 'E')  ('avg.wind.speed', 'A')  \\\n",
       "0                     0.0                     4.5                      1.6   \n",
       "1                    45.5                    63.0                      1.4   \n",
       "2                    19.0                    32.0                      3.7   \n",
       "3                     0.0                     0.0                      1.6   \n",
       "4                     0.0                     0.0                      2.5   \n",
       "\n",
       "   ('avg.wind.speed', 'B')  ('avg.wind.speed', 'C')  ('avg.wind.speed', 'D')  \\\n",
       "0                      1.8                      0.9                      1.2   \n",
       "1                      1.0                      0.7                      0.7   \n",
       "2                      3.6                      0.7                      1.3   \n",
       "3                      1.2                      1.1                      1.0   \n",
       "4                      2.3                      1.2                      1.8   \n",
       "\n",
       "   ('avg.wind.speed', 'E')  ('max.wind.speed', 'A')  ('max.wind.speed', 'B')  \\\n",
       "0                      0.5                      5.0                      5.8   \n",
       "1                      0.6                      4.2                      3.6   \n",
       "2                      1.1                     11.3                      8.6   \n",
       "3                      0.6                      4.9                      3.8   \n",
       "4                      0.7                      6.8                      5.4   \n",
       "\n",
       "   ('max.wind.speed', 'C')  ('max.wind.speed', 'D')  ('max.wind.speed', 'E')  \\\n",
       "0                      2.3                      3.5                      1.9   \n",
       "1                      1.8                      1.9                      2.9   \n",
       "2                      2.6                      4.6                      2.9   \n",
       "3                      2.1                      3.2                      2.0   \n",
       "4                      3.3                      4.5                      2.2   \n",
       "\n",
       "   ('max.wind.speed.dir', 'A')  ('max.wind.speed.dir', 'B')  \\\n",
       "0                            3                            2   \n",
       "1                            6                            6   \n",
       "2                            7                            5   \n",
       "3                            6                            4   \n",
       "4                            3                            6   \n",
       "\n",
       "   ('max.wind.speed.dir', 'C')  ('max.wind.speed.dir', 'D')  \\\n",
       "0                            6                            6   \n",
       "1                            1                            6   \n",
       "2                            7                            6   \n",
       "3                            6                            6   \n",
       "4                            6                            6   \n",
       "\n",
       "   ('max.wind.speed.dir', 'E')  ('max.inst.wind.speed', 'A')  \\\n",
       "0                            6                           7.5   \n",
       "1                            3                           6.7   \n",
       "2                            3                          14.5   \n",
       "3                            4                           7.3   \n",
       "4                            3                          10.0   \n",
       "\n",
       "   ('max.inst.wind.speed', 'B')  ('max.inst.wind.speed', 'C')  \\\n",
       "0                          11.1                           5.5   \n",
       "1                           6.7                           5.6   \n",
       "2                          14.6                          12.0   \n",
       "3                           7.4                           5.8   \n",
       "4                          10.3                           7.3   \n",
       "\n",
       "   ('max.inst.wind.speed', 'D')  ('max.inst.wind.speed', 'E')  \\\n",
       "0                           6.9                           3.6   \n",
       "1                           4.0                           8.1   \n",
       "2                           9.2                          11.2   \n",
       "3                           5.5                           4.9   \n",
       "4                           7.2                           5.0   \n",
       "\n",
       "   ('max.inst.wind.speed.dir', 'A')  ('max.inst.wind.speed.dir', 'B')  \\\n",
       "0                                 3                                 1   \n",
       "1                                 7                                 6   \n",
       "2                                 7                                 5   \n",
       "3                                 6                                 6   \n",
       "4                                 7                                 4   \n",
       "\n",
       "   ('max.inst.wind.speed.dir', 'C')  ('max.inst.wind.speed.dir', 'D')  \\\n",
       "0                                 6                                 6   \n",
       "1                                 1                                 6   \n",
       "2                                 7                                 3   \n",
       "3                                 6                                 6   \n",
       "4                                 6                                 6   \n",
       "\n",
       "   ('max.inst.wind.speed.dir', 'E')  ('min.atmos.pressure', 'A')  \\\n",
       "0                                 7                       1006.7   \n",
       "1                                 7                       1000.6   \n",
       "2                                 3                       1004.6   \n",
       "3                                 5                       1011.0   \n",
       "4                                 2                       1020.1   \n",
       "\n",
       "   ('min.atmos.pressure', 'B')  ('min.atmos.pressure', 'C')  \\\n",
       "0                  1010.216254                  1010.216254   \n",
       "1                  1010.216254                  1010.216254   \n",
       "2                  1010.216254                  1010.216254   \n",
       "3                  1010.216254                  1010.216254   \n",
       "4                  1010.216254                  1010.216254   \n",
       "\n",
       "   ('min.atmos.pressure', 'D')  ('min.atmos.pressure', 'E')  \\\n",
       "0                  1010.216254                  1010.216254   \n",
       "1                  1010.216254                  1010.216254   \n",
       "2                  1010.216254                  1010.216254   \n",
       "3                  1010.216254                  1010.216254   \n",
       "4                  1010.216254                  1010.216254   \n",
       "\n",
       "   ('avg_beaufort_scale', 'A')  ('avg_beaufort_scale', 'B')  \\\n",
       "0                            2                            3   \n",
       "1                            2                            2   \n",
       "2                            4                            4   \n",
       "3                            2                            2   \n",
       "4                            3                            3   \n",
       "\n",
       "   ('avg_beaufort_scale', 'C')  ('avg_beaufort_scale', 'D')  \\\n",
       "0                            2                            2   \n",
       "1                            1                            1   \n",
       "2                            1                            2   \n",
       "3                            2                            2   \n",
       "4                            2                            3   \n",
       "\n",
       "   ('avg_beaufort_scale', 'E')  ('max_beaufort_scale', 'A')  \\\n",
       "0                            1                            5   \n",
       "1                            1                            5   \n",
       "2                            2                            9   \n",
       "3                            1                            5   \n",
       "4                            1                            6   \n",
       "\n",
       "   ('max_beaufort_scale', 'B')  ('max_beaufort_scale', 'C')  \\\n",
       "0                            6                            3   \n",
       "1                            4                            3   \n",
       "2                            7                            3   \n",
       "3                            4                            3   \n",
       "4                            5                            4   \n",
       "\n",
       "   ('max_beaufort_scale', 'D')  ('max_beaufort_scale', 'E')  \n",
       "0                            4                            3  \n",
       "1                            3                            4  \n",
       "2                            5                            4  \n",
       "3                            4                            3  \n",
       "4                            5                            3  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = train.pivot(index=[\"date\", \"label\"], columns=\"region\")\n",
    "train = pd.DataFrame(train.to_records())\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The column names aren’t ideal, so I wrote a function to fix that.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'avg.temp_A'"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def replace_all(text):\n",
    "    d = { \"('\": \"\", \"', '\": \"_\", \"')\" : \"\",}\n",
    "    for i, j in d.items():\n",
    "        text = text.replace(i, j)\n",
    "    return text\n",
    "\n",
    "# ('avg.temp', 'A') -> avg.temp_A\n",
    "\n",
    "test_str = \"('avg.temp', 'A')\"\n",
    "replace_all(test_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.columns = list(map(replace_all, train.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'label', 'avg.temp_A', 'avg.temp_B', 'avg.temp_C', 'avg.temp_D',\n",
       "       'avg.temp_E', 'max.temp_A', 'max.temp_B', 'max.temp_C', 'max.temp_D',\n",
       "       'max.temp_E', 'min.temp_A', 'min.temp_B', 'min.temp_C', 'min.temp_D',\n",
       "       'min.temp_E', 'precipitation_A', 'precipitation_B', 'precipitation_C',\n",
       "       'precipitation_D', 'precipitation_E', 'avg.wind.speed_A',\n",
       "       'avg.wind.speed_B', 'avg.wind.speed_C', 'avg.wind.speed_D',\n",
       "       'avg.wind.speed_E', 'max.wind.speed_A', 'max.wind.speed_B',\n",
       "       'max.wind.speed_C', 'max.wind.speed_D', 'max.wind.speed_E',\n",
       "       'max.wind.speed.dir_A', 'max.wind.speed.dir_B', 'max.wind.speed.dir_C',\n",
       "       'max.wind.speed.dir_D', 'max.wind.speed.dir_E', 'max.inst.wind.speed_A',\n",
       "       'max.inst.wind.speed_B', 'max.inst.wind.speed_C',\n",
       "       'max.inst.wind.speed_D', 'max.inst.wind.speed_E',\n",
       "       'max.inst.wind.speed.dir_A', 'max.inst.wind.speed.dir_B',\n",
       "       'max.inst.wind.speed.dir_C', 'max.inst.wind.speed.dir_D',\n",
       "       'max.inst.wind.speed.dir_E', 'min.atmos.pressure_A',\n",
       "       'min.atmos.pressure_B', 'min.atmos.pressure_C', 'min.atmos.pressure_D',\n",
       "       'min.atmos.pressure_E', 'avg_beaufort_scale_A', 'avg_beaufort_scale_B',\n",
       "       'avg_beaufort_scale_C', 'avg_beaufort_scale_D', 'avg_beaufort_scale_E',\n",
       "       'max_beaufort_scale_A', 'max_beaufort_scale_B', 'max_beaufort_scale_C',\n",
       "       'max_beaufort_scale_D', 'max_beaufort_scale_E'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here’s what the train data looks like so far!\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>date</th>\n",
       "      <th>label</th>\n",
       "      <th>avg.temp_A</th>\n",
       "      <th>avg.temp_B</th>\n",
       "      <th>avg.temp_C</th>\n",
       "      <th>avg.temp_D</th>\n",
       "      <th>avg.temp_E</th>\n",
       "      <th>max.temp_A</th>\n",
       "      <th>max.temp_B</th>\n",
       "      <th>max.temp_C</th>\n",
       "      <th>max.temp_D</th>\n",
       "      <th>max.temp_E</th>\n",
       "      <th>min.temp_A</th>\n",
       "      <th>min.temp_B</th>\n",
       "      <th>min.temp_C</th>\n",
       "      <th>min.temp_D</th>\n",
       "      <th>min.temp_E</th>\n",
       "      <th>precipitation_A</th>\n",
       "      <th>precipitation_B</th>\n",
       "      <th>precipitation_C</th>\n",
       "      <th>precipitation_D</th>\n",
       "      <th>precipitation_E</th>\n",
       "      <th>avg.wind.speed_A</th>\n",
       "      <th>avg.wind.speed_B</th>\n",
       "      <th>avg.wind.speed_C</th>\n",
       "      <th>avg.wind.speed_D</th>\n",
       "      <th>avg.wind.speed_E</th>\n",
       "      <th>max.wind.speed_A</th>\n",
       "      <th>max.wind.speed_B</th>\n",
       "      <th>max.wind.speed_C</th>\n",
       "      <th>max.wind.speed_D</th>\n",
       "      <th>max.wind.speed_E</th>\n",
       "      <th>max.wind.speed.dir_A</th>\n",
       "      <th>max.wind.speed.dir_B</th>\n",
       "      <th>max.wind.speed.dir_C</th>\n",
       "      <th>max.wind.speed.dir_D</th>\n",
       "      <th>max.wind.speed.dir_E</th>\n",
       "      <th>max.inst.wind.speed_A</th>\n",
       "      <th>max.inst.wind.speed_B</th>\n",
       "      <th>max.inst.wind.speed_C</th>\n",
       "      <th>max.inst.wind.speed_D</th>\n",
       "      <th>max.inst.wind.speed_E</th>\n",
       "      <th>max.inst.wind.speed.dir_A</th>\n",
       "      <th>max.inst.wind.speed.dir_B</th>\n",
       "      <th>max.inst.wind.speed.dir_C</th>\n",
       "      <th>max.inst.wind.speed.dir_D</th>\n",
       "      <th>max.inst.wind.speed.dir_E</th>\n",
       "      <th>min.atmos.pressure_A</th>\n",
       "      <th>min.atmos.pressure_B</th>\n",
       "      <th>min.atmos.pressure_C</th>\n",
       "      <th>min.atmos.pressure_D</th>\n",
       "      <th>min.atmos.pressure_E</th>\n",
       "      <th>avg_beaufort_scale_A</th>\n",
       "      <th>avg_beaufort_scale_B</th>\n",
       "      <th>avg_beaufort_scale_C</th>\n",
       "      <th>avg_beaufort_scale_D</th>\n",
       "      <th>avg_beaufort_scale_E</th>\n",
       "      <th>max_beaufort_scale_A</th>\n",
       "      <th>max_beaufort_scale_B</th>\n",
       "      <th>max_beaufort_scale_C</th>\n",
       "      <th>max_beaufort_scale_D</th>\n",
       "      <th>max_beaufort_scale_E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00173aec</td>\n",
       "      <td>2</td>\n",
       "      <td>18.7</td>\n",
       "      <td>17.6</td>\n",
       "      <td>16.9</td>\n",
       "      <td>19.5</td>\n",
       "      <td>14.3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>23.7</td>\n",
       "      <td>23.9</td>\n",
       "      <td>26.7</td>\n",
       "      <td>21.1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>12.5</td>\n",
       "      <td>12.9</td>\n",
       "      <td>16.4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7.5</td>\n",
       "      <td>11.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1006.7</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0083f291</td>\n",
       "      <td>1</td>\n",
       "      <td>13.1</td>\n",
       "      <td>12.6</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>15.8</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>14.9</td>\n",
       "      <td>13.9</td>\n",
       "      <td>11.3</td>\n",
       "      <td>11.1</td>\n",
       "      <td>10.6</td>\n",
       "      <td>12.1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>50.0</td>\n",
       "      <td>46.5</td>\n",
       "      <td>49.0</td>\n",
       "      <td>45.5</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1000.6</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>014cfe7b</td>\n",
       "      <td>2</td>\n",
       "      <td>19.9</td>\n",
       "      <td>19.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>19.9</td>\n",
       "      <td>16.2</td>\n",
       "      <td>24.2</td>\n",
       "      <td>23.9</td>\n",
       "      <td>21.0</td>\n",
       "      <td>25.8</td>\n",
       "      <td>19.1</td>\n",
       "      <td>16.1</td>\n",
       "      <td>13.5</td>\n",
       "      <td>15.8</td>\n",
       "      <td>16.3</td>\n",
       "      <td>12.4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>63.5</td>\n",
       "      <td>19.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>11.3</td>\n",
       "      <td>8.6</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>14.5</td>\n",
       "      <td>14.6</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1004.6</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01947c8e</td>\n",
       "      <td>2</td>\n",
       "      <td>21.6</td>\n",
       "      <td>20.2</td>\n",
       "      <td>20.5</td>\n",
       "      <td>21.3</td>\n",
       "      <td>17.6</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>27.7</td>\n",
       "      <td>29.3</td>\n",
       "      <td>25.2</td>\n",
       "      <td>15.8</td>\n",
       "      <td>14.2</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.9</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.4</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0258884d</td>\n",
       "      <td>2</td>\n",
       "      <td>15.2</td>\n",
       "      <td>13.9</td>\n",
       "      <td>13.9</td>\n",
       "      <td>15.8</td>\n",
       "      <td>11.1</td>\n",
       "      <td>21.9</td>\n",
       "      <td>22.4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>17.2</td>\n",
       "      <td>10.3</td>\n",
       "      <td>5.6</td>\n",
       "      <td>8.9</td>\n",
       "      <td>10.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>6.8</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1020.1</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>1010.216254</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date  label  avg.temp_A  avg.temp_B  avg.temp_C  avg.temp_D  \\\n",
       "0  00173aec      2        18.7        17.6        16.9        19.5   \n",
       "1  0083f291      1        13.1        12.6        12.0        13.0   \n",
       "2  014cfe7b      2        19.9        19.0        17.5        19.9   \n",
       "3  01947c8e      2        21.6        20.2        20.5        21.3   \n",
       "4  0258884d      2        15.2        13.9        13.9        15.8   \n",
       "\n",
       "   avg.temp_E  max.temp_A  max.temp_B  max.temp_C  max.temp_D  max.temp_E  \\\n",
       "0        14.3        25.0        23.7        23.9        26.7        21.1   \n",
       "1        10.7        15.8        15.0        13.7        14.9        13.9   \n",
       "2        16.2        24.2        23.9        21.0        25.8        19.1   \n",
       "3        17.6        29.0        29.0        27.7        29.3        25.2   \n",
       "4        11.1        21.9        22.4        21.0        22.4        17.2   \n",
       "\n",
       "   min.temp_A  min.temp_B  min.temp_C  min.temp_D  min.temp_E  \\\n",
       "0        14.9        12.5        12.9        16.4         9.0   \n",
       "1        11.3        11.1        10.6        12.1         8.5   \n",
       "2        16.1        13.5        15.8        16.3        12.4   \n",
       "3        15.8        14.2        15.5        15.9        11.5   \n",
       "4        10.3         5.6         8.9        10.4         5.3   \n",
       "\n",
       "   precipitation_A  precipitation_B  precipitation_C  precipitation_D  \\\n",
       "0              1.0              1.5              1.5              0.0   \n",
       "1             50.0             46.5             49.0             45.5   \n",
       "2             18.0             17.0             63.5             19.0   \n",
       "3              0.0              1.0              0.0              0.0   \n",
       "4              0.0              0.0              0.0              0.0   \n",
       "\n",
       "   precipitation_E  avg.wind.speed_A  avg.wind.speed_B  avg.wind.speed_C  \\\n",
       "0              4.5               1.6               1.8               0.9   \n",
       "1             63.0               1.4               1.0               0.7   \n",
       "2             32.0               3.7               3.6               0.7   \n",
       "3              0.0               1.6               1.2               1.1   \n",
       "4              0.0               2.5               2.3               1.2   \n",
       "\n",
       "   avg.wind.speed_D  avg.wind.speed_E  max.wind.speed_A  max.wind.speed_B  \\\n",
       "0               1.2               0.5               5.0               5.8   \n",
       "1               0.7               0.6               4.2               3.6   \n",
       "2               1.3               1.1              11.3               8.6   \n",
       "3               1.0               0.6               4.9               3.8   \n",
       "4               1.8               0.7               6.8               5.4   \n",
       "\n",
       "   max.wind.speed_C  max.wind.speed_D  max.wind.speed_E  max.wind.speed.dir_A  \\\n",
       "0               2.3               3.5               1.9                     3   \n",
       "1               1.8               1.9               2.9                     6   \n",
       "2               2.6               4.6               2.9                     7   \n",
       "3               2.1               3.2               2.0                     6   \n",
       "4               3.3               4.5               2.2                     3   \n",
       "\n",
       "   max.wind.speed.dir_B  max.wind.speed.dir_C  max.wind.speed.dir_D  \\\n",
       "0                     2                     6                     6   \n",
       "1                     6                     1                     6   \n",
       "2                     5                     7                     6   \n",
       "3                     4                     6                     6   \n",
       "4                     6                     6                     6   \n",
       "\n",
       "   max.wind.speed.dir_E  max.inst.wind.speed_A  max.inst.wind.speed_B  \\\n",
       "0                     6                    7.5                   11.1   \n",
       "1                     3                    6.7                    6.7   \n",
       "2                     3                   14.5                   14.6   \n",
       "3                     4                    7.3                    7.4   \n",
       "4                     3                   10.0                   10.3   \n",
       "\n",
       "   max.inst.wind.speed_C  max.inst.wind.speed_D  max.inst.wind.speed_E  \\\n",
       "0                    5.5                    6.9                    3.6   \n",
       "1                    5.6                    4.0                    8.1   \n",
       "2                   12.0                    9.2                   11.2   \n",
       "3                    5.8                    5.5                    4.9   \n",
       "4                    7.3                    7.2                    5.0   \n",
       "\n",
       "   max.inst.wind.speed.dir_A  max.inst.wind.speed.dir_B  \\\n",
       "0                          3                          1   \n",
       "1                          7                          6   \n",
       "2                          7                          5   \n",
       "3                          6                          6   \n",
       "4                          7                          4   \n",
       "\n",
       "   max.inst.wind.speed.dir_C  max.inst.wind.speed.dir_D  \\\n",
       "0                          6                          6   \n",
       "1                          1                          6   \n",
       "2                          7                          3   \n",
       "3                          6                          6   \n",
       "4                          6                          6   \n",
       "\n",
       "   max.inst.wind.speed.dir_E  min.atmos.pressure_A  min.atmos.pressure_B  \\\n",
       "0                          7                1006.7           1010.216254   \n",
       "1                          7                1000.6           1010.216254   \n",
       "2                          3                1004.6           1010.216254   \n",
       "3                          5                1011.0           1010.216254   \n",
       "4                          2                1020.1           1010.216254   \n",
       "\n",
       "   min.atmos.pressure_C  min.atmos.pressure_D  min.atmos.pressure_E  \\\n",
       "0           1010.216254           1010.216254           1010.216254   \n",
       "1           1010.216254           1010.216254           1010.216254   \n",
       "2           1010.216254           1010.216254           1010.216254   \n",
       "3           1010.216254           1010.216254           1010.216254   \n",
       "4           1010.216254           1010.216254           1010.216254   \n",
       "\n",
       "   avg_beaufort_scale_A  avg_beaufort_scale_B  avg_beaufort_scale_C  \\\n",
       "0                     2                     3                     2   \n",
       "1                     2                     2                     1   \n",
       "2                     4                     4                     1   \n",
       "3                     2                     2                     2   \n",
       "4                     3                     3                     2   \n",
       "\n",
       "   avg_beaufort_scale_D  avg_beaufort_scale_E  max_beaufort_scale_A  \\\n",
       "0                     2                     1                     5   \n",
       "1                     1                     1                     5   \n",
       "2                     2                     2                     9   \n",
       "3                     2                     1                     5   \n",
       "4                     3                     1                     6   \n",
       "\n",
       "   max_beaufort_scale_B  max_beaufort_scale_C  max_beaufort_scale_D  \\\n",
       "0                     6                     3                     4   \n",
       "1                     4                     3                     3   \n",
       "2                     7                     3                     5   \n",
       "3                     4                     3                     4   \n",
       "4                     5                     4                     5   \n",
       "\n",
       "   max_beaufort_scale_E  \n",
       "0                     3  \n",
       "1                     4  \n",
       "2                     4  \n",
       "3                     3  \n",
       "4                     3  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the LightGBM model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "it’s time to split the train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = train.drop([\"label\", \"date\"], axis=1), train[[\"label\"]].values.flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LightGBM likes it when the categorical features are given the categorical data type, so let’s do that.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_cols = [c for c in X.columns if X[c].dtype == 'object']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert cat_features to pd.Categorical dtype\n",
    "for col in categorical_cols:\n",
    "    X[col] = pd.Categorical(X[col])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use train_test_split to split our data into the training and evaluation sets.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_eval, y_train, y_eval = train_test_split(\n",
    "    X, y, test_size=0.25, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s create a base lgb.LGBMClassifier for a simple prediction\n",
    "\n",
    "We can then fit the training data.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMClassifier()"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = lgb.LGBMClassifier()\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we call the predict function on the evaluation data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=clf.predict(X_eval.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s see how a base LightGBM classifier did.\n",
    "\n",
    "A 99% accuracy can be meaningless for an imbalanced dataset, so we need more suitable metrics like precision, recall, and a confusion matrix.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification Report\n",
    "\n",
    "A classification report measures the quality of predictions from a classification algorithm.\n",
    "\n",
    "It answers the question of how many predictions are True and how many are False.\n",
    "\n",
    "More specifically, it uses True Positives, False Positives, True Negatives, and False Negatives to compute the metrics of precision, recall, and f1-score\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.33      0.60      0.43         5\n",
      "           1       0.33      0.39      0.36        33\n",
      "           2       0.81      0.73      0.77       104\n",
      "\n",
      "    accuracy                           0.65       142\n",
      "   macro avg       0.49      0.57      0.52       142\n",
      "weighted avg       0.68      0.65      0.66       142\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_pred, y_eval))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You might notice that there are three values for the overall F1-score 0.63, 0.51, 0.64 . Which value should you focus on?\n",
    "\n",
    "In an imbalanced dataset where all classes are equally important, macro average is a good choice as it treats all classes equally.\n",
    "\n",
    "If however, you want to assign greater weight to classes with more samples in the data, then the weighted average is preferred.\n",
    "\n",
    "One more metric you can use is Precision-Recall, which is a useful measure of the success of prediction when the classes are very imbalanced.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion matrix\n",
    "\n",
    "Let’s create a confusion matrix for our model predictions.\n",
    "\n",
    "First, we need to get the class names and the labels that the label encoder gave so our plot can show the label names.\n",
    "\n",
    "We then plot a non-normalized and normalized confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = le_name_map.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[ 3  3  3]\n",
      " [ 1 13 25]\n",
      " [ 1 17 76]]\n",
      "Normalized confusion matrix\n",
      "[[0.33333333 0.33333333 0.33333333]\n",
      " [0.02564103 0.33333333 0.64102564]\n",
      " [0.0106383  0.18085106 0.80851064]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjkAAAItCAYAAADbrGvgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAtjUlEQVR4nO3debxdVX338c+5lxAiiAwFE4w00MhPnBgiigNlMFARDXWgllgrtKJUEXjAR20FJVatoKI4PGoRBEUFC2Ijg0KwKqhFjVBB5CdgUsUEZYoMgibhPn/sffFwzR0S7j77nr0/77zOy3v22WevdQa5637Xb6/dGRoaQpIkqWkG6u6AJElSFRzkSJKkRnKQI0mSGslBjiRJaqSN6u6AJEmqx9KlS7cCNu9Rc/fMmzfvrh61BUDHs6skSWqfpUuXbnXP/b+/c/NNp/eqybuBub0c6JjkSJLUTptvvul03vC+i/nN3b+rtKFtt3wM/+9tB21JkRo5yJEkSdX7zaoHuO2uagc5dDrVHn8UFh5LkqRGMsmRJKnNOp3qkxaTHEmSpMnjIEeSJDWS01WSJLVZZ6C4Vd1GDUxyJElSI5nkSJLUZhYeS5Ik9ReTHEmSWq0HNTk1ZSomOZIkqZFMciRJajNrciRJkvqLSY4kSW3W6fRgnRyTHEmSpEljkiNJUptZkyNJktRfTHIkSWozr10lSZLUX0xyJElqM2tyJEmS+ouDHEmS1EhOV0mS1GYWHkuSJPUXkxxJktqsQw8Kj6s9/GhMciRJUiOZ5EiS1Go9qMmpKVMxyZEkSY1kkiNJUpt5dpUkSVJ/McmRJKnNBjrFreo2amCSI0mSGskkR5KkNrMmR5Ikqb84yFEtImIwIo6LiB9GxLURcUNEnBwR0x/lMf8zIn4WEUdtwPOfGRHnb2j7ky0iHhcR3xjj8WsjYosK278kIp5S/nxZRPxZ+fPyiHjmJLbz2oh4w2Qdb7JFxD4RcX3587si4u838DiP+Dyr/vykCRte8bjSWz0vzekq1eUTwJbACzLztxGxKfB54NPAqzfwmE8A/grYNDPXru+TM/OHwCs2sO0qbAk8a7QHM3PXKhvPzBd13d2/wqaeD1xf4fEnTWa+41E8/RGfZ9WfnyQHOapBRMwBXgXMysx7ADLz/og4Enheuc/jgI8DuwJDwKXAv2Tmmoh4EHgfcAAwCzgFOAf4GjANWBoRLwduBrbJzDvKYw4B2wAPAp8BngQ8BCwFXg/8JfCxzHza+rafmZ9Yx+t8EDgVmA9sBpwEHAI8HVgBvKR83f9Qtr8xsBXwvvJ4nwFmRMS1wDzgd8B/AruU798PytfzRorB3V7l/R8Br8rM/xrl/d8a+F/g8WX7nwKenJl7l4/fBCwoX/MryuMD/FdEDA98Xh8RnwS2BT6XmW8vn/s64GhgLfBr4KjM/FlEnAVcn5kfKPc7i2Jgc0vZ1v4R8UBmfryrn3OAK4BLgGdTDBLekpkXRsS08r19QdnW1cD/ycx7I2J5ef8ZwL8AHwK+AOxXHuMUiu/ZPGA1sCAzV0TEi8v9Ny5f19mZeeKI926431cCn+p6aHvgJ5m513p8nmsov58RcSJwaLntZ+X7dltEfBP4Xtnf7YElwOsy86GRn6u0wazJkSbVPIpfCPd0b8zM2zLzgvLuR4A7KQYEz6T4xf7m8rHpwB2Z+VyKX8Ifovhl9SLggczcNTNvGaP9lwKPLf+S3qPctuOIfdar/YjYZB3tTAduy8xnAWdTpFTHAk8BHgccHBGbAUcAL8rM3YBXUvwSBji86/Wspfil+dXMjDJ1Gvbu8vX/X+BzFAO1dQ5wADLzTuD7wL7lpn2AiIjNyump1Zn50679Dy9/3Dczf1n+/GBmPpMimTg+Ip4YEfsBbyn324ViYPGViBg1qM7MC4HFwIe6BzhddgS+Xr6HbwM+XG4/AdiO4nPZheK/Ze/vet71mblzeXyATTJzT+AdwL8Dp5V9/CVwWNnH44HXlK9rT+Cfh6fo1tHvq8vPZVeKz+8+4Ij1/DwBiIjDgQOBPTLzGRSDqLO6mvsLis/oGeV+e6+rT5L+lIMc1eEhxv/uHUjxy3ooM38PfLLcNuw/y//9EcVgYtP1aP8q4KnlX8lvAz6cmTdX1P7woO0W4LrM/FX5V/gyYKvMvA94MXBQRPwr8HaK1Gc0V47cUP7CfBXwVoqZ738b4/nDLgQOjIidgV8B36H45XlwV5/H8oWy7dsoEpttgRcC52Xm7eVjZ1FMIc6ZwPFGs5oiyYHivd6q/PlA4JOZubp8Pz/KIz+fke9T9+dwW2b+T9f9rTJzCHgJMC8i3kmREnUY53sVEXOBLwOvzswbN+DzHH4tn8nM+8v7pwEviIiNy/tfzcyHyj8Kbu56DySNw+kq1eFqYOeIeGxm3ju8MSKeQPFX9isoBkFDXc8ZoJiKGvYAQGYORQSMXtbWKY89/AuDzFxW/nLah2IKY0k5zXJv1/Mmq/3fd/28euSDETGbYjri3ykGX+dT/JIczX2jbP/zsk9/QTElc9cYx4BikPNtiqmRy4G7KabfngUcOc5z4ZGvZYji9Q8CfxixX4fifRveZ9jGTMwfuqZmuo8xyNifz8j3abzPYVPgGor35UrgTOCvGaNcMiK2pZjSe1tmfqvctr6f52ivZaOuth/oemzk+yg9esPFwVW3MUER8Vqg++SRHShS6q9Q/AEyg+IPqhPGO5ZJjnouM1dQFBmfGRGbA5T/+/+AOzPzAeDrwFER0SnPuHodxS/j9XE7xVQTwMLhjRHxTxT1EZdl5lvLtnYf8dzJaH8inln2893AZZS/ECNikKI+Y3Cs6Z5y3y0o3s/DgC8CZ4zXaGbeCtxBMaC5jOL1vhzYuivl6LaWRw4i1uVrwN9GxDZlvw6nmPK7ma7PIiK245FTLmsmcOx1tfVPETEtIgYo6oYezefzJGBz4ITM/CrFAHg6xQDkT5TTUhcDZ2Tm57se2pDP82vAP5QDLShqmr5dJohS62Tmp7umg18F/AY4meKPj4OBnYE9IuLA0Y9ScJCjurwBuAH4blmIeXV5/7Xl40dTTIFcV94SeM96tnE08PGI+BHF/ylWlts/S/HL64aIWEpRH/ORdTz30bY/EZcBt5bH/ylFcentwNyyv98HflIWC4/mdOCizLyMorh5x+FTssvTlEc73ftCikLlazJzGUVicOEo+/4H8K2IeNponcjMyynqo74RET8BXgO8uGs6aVZEJMUAs/vU+EuBIyPin8d4jSO9G7gNuJbifZsGHLMezx/px8BFwI0R8VOKqasbKD6HdXkTRS3QSyPimvJ9vpYN+zzPoCgo/n7Z9u4U/2GXemTgj8XHVd3K4cbixYtnR8ScEbctxujcJyhOCNgRuCkzl2XmGoqTTQ4Z75V1hoaGxttHUp+KiPdQnP10Y919kTS1LF26dA6w7BUnf5fbVj1YaVszt9iE89/6XI4++mjuuOOOkQ8vysyTRm6MiPnAv2XmHhFxKHBQZv5d12NvycwDxmrXJEdqqHJaZLkDHEljqnwhwD/W/MyfP38vihqb7tuHR+nZ6ylqcOBP6yQ7FCexjMnCY6mhyjOGTq+7H5I0bMGCBbcuWrRo+Xj7lSeL7E1RawjFNPCsrl1mUqw3NiYHOZIktVmn04PFANf77K1nAD/rWlrhaor1vOZSLMGxkKIQeUxTdpCzdOnS6RQLta2kOLNDkqQmG6RIK34wb968tp9dtyNFegNAZj4YEYdRrHm1CcX6WeNea3DKDnIoBjh/svCZJEkNtxfFOku9McXWyQHIzC8BXxqx7QqKsxonbCoPclYCzJ4zl2nTJrpumCRJ/Wn16j9w6/Kb4Y/LXehRmsqDnLUA06ZtzLSNp9fdF0mSeqW3JRpeoFOSJKm/TOUkR5IkVW0K1uRMFpMcSZLUSCY5kiS1mTU5kiRJ/cVBjiRJaiSnqyRJarOpeVmHSWGSI0mSGskkR5KkVuvBKeSY5EiSJE0akxxJktrMU8glSZL6i0mOJElt5mUdJEmS+otJjiRJbWZNjiRJUn8xyZEkqc2syZEkSeovJjmSJLVYEeRUm7TUFOSY5EiSpGYyyZEkqcU6nU4PkhxrciRJkiaNgxxJktRITldJktRmnfJWdRs1MMmRJEmNZJIjSVKLdehB4XFNUY5JjiRJaiSTHEmSWsxTyCVJkvqMSY4kSW3WgyTHC3RKkiRNIpMcSZJazJocSZKkPmOSI0lSm7nisSRJUn8xyZEkqcWsyZEkSeozJjmSJLWYSY4kSVKfcZAjSZIayekqSZJarNOpfjqpptkqkxxJktRMJjmSJLVYhx4UHte0GqBJjiRJaiSTHEmS2szLOkiSJPUXkxxJklrMxQAlSZL6jEmOJEktZpIjSZLUZ0xyJElqsx4kOXUteWySI0mSGskkR5KkNnOdHEmSpP5ikiNJUot5dpUkSVKfMcmRJKnFTHIkSZL6jIMcSZLUSE5XSZLUYh16MF1V0znkJjmSJKmRTHIkSWqxTqf6wuD1PXxEvAR4J7ApcFlmHhMR84FTgRnAeZl5wnjHMcmRJElTRkTsCHwS+GvgGcDuEXEgcCZwMLAzsEe5bUwmOZIktVkPL+uwePHi2QsXLhz56KrMXNV1/6UUSc2tABHxSuBJwE2Zuazcdg5wCHDpWM06yJEkST2xZMmSK9exeRFwUtf9ucAfImIxsD1wEfATYGXXPiuB2eO15yBHkqQW6+VigPPnz9/r3HPPvXXEw6tG3N8I+EtgH+A+YDHwADDUfUjgofHadZAjSZJ6YsGCBbcuWrRo+Ti73QYsyczbASLiQoqpqbVd+8wEVozXnoMcSZJabApe1uEi4OyI2AK4FzgQOB94W0TMBZYBCykKkcdU2SAnIuYA38zMOSO2D2VmPasCNczatQ9x3L99kZt/8RsGBwc47e0L2WH2NnV3S33M75Qmk98nbYjMvDoiTgGuAqYBlwOfAG4ELgA2AS6hGPiMySSnj339qusBuPjf/w/f+dFNvPMjF/LZU15Xc6/Uz/xOaTL5feoPUzDJITPP5E+TmiuAXdbnOA5y+tiL9n4GBzzvqQD8cuVdbLPV5jX3SP3O75Qmk98n1a3qQc52EXFtxW202kYbDXLUu87hkm/9D2e89x/q7o4awO+UJpPfpz7Qw3Vyeq3qQc6KzNy1e0NEDI2yrzbQx97xd/z6zgUc+NoPcuUX/oVNZ0yvu0vqc36nNJn8PqkuXtahj33p0u9z2tmXAfCYTaYx0OkwOOBHqg3nd0qTye9Tnyhrcqq8rffFqyaJNTl97KB9duGYd3+eBf90GqvXrOVfj30Zm0yfVne31Mf8Tmky+X1S3Rzk9LFNZ0zn0+9xjluTx++UJpPfJ9WtskFOZi4H5qxju2vkSJI0RXTowSnkNVUeOzkqSZIayekqSZJabCouBjhZTHIkSVIjmeRIktRiJjmSJEl9xiRHkqQ2a/BlHUxyJElSI5nkSJLUYsVVF6quyan08KMyyZEkSY1kkiNJUot5dpUkSVKfMcmRJKnFipqc6tuog0mOJElqJJMcSZJazJocSZKkPuMgR5IkNZLTVZIktVxdhcFVM8mRJEmNZJIjSVKLWXgsSZLUZ0xyJElqMRcDlCRJ6jMmOZIktdjAQIeBgWqjlqqPP2q7tbQqSZJUMZMcSZJazJocSZKkPmOSI0lSi7lOjiRJUp8xyZEkqcWsyZEkSeozJjmSJLVa9TU5YE2OJEnSpHGQI0mSGsnpKkmSWsxTyCVJkvqMSY4kSS3mKeSSJEl9xiRHkqQWK5KcqmtyKj38qExyJElSI5nkSJLUYtbkSJIk9RmTHEmSWqxYJ6f6NupgkiNJkhrJJEeSpBazJkeSJKnPmORIktRi1uRIkiT1GZMcSZJarq6amaqZ5EiSpEZykCNJkhrJ6SpJklrMwmNJkqQ+Y5IjSVKLuRigJElSnzHJkSSpxazJkSRJ6jMmOZIktViTa3Ic5EiSpCklIv4L2BZYXW56PfBY4FRgBnBeZp4w3nEc5EiS1GJTrSYnIjrATsCfZ+aactsMIIG9gV8CF0fEgZl56VjHcpAjSZJ6YvHixbMXLlw4cvOqzFzVdT/K/70sIrYGTgeuA27KzGUAEXEOcAjgIEeSJK1bL2tylixZcuU6Hl4EnNR1f0vgCuBNwDTgm8DJwMqufVYCs8dr10GOJEnqifnz5+917rnn3jpi86ruO5n5PeB7w/cj4gzgXcBVXbt1gIfGa89BjiRJLdbLmpwFCxbcumjRouVj7RsRzwemZ+YVw08HlgOzunabCawYr10HOZIkaSrZAnhXRDyXYrrqNcCRwJciYi6wDFgInDnegVwMUJKkVuuUaU51tyKMmZjMvAi4GLgGWAqcWU5hHQZcANwA3AicP96xTHIkSdKUkpknAieO2HYFsMv6HMckR5IkNZJJjiRJLeZlHWq00UYDTNvIwEmTY/nt99fdBTVM3n5v3V1QQwwOrXnE6UN69Kb8IEeSJFWnSHKqjVrqSnKMSCRJUiOZ5EiS1GJNrskxyZEkSY1kkiNJUov15rIO1R5/NCY5kiSpkUxyJElqMWtyJEmS+oxJjiRJLTbQ6TBQcdJS9fFHbbeeZiVJkqplkiNJUpv1oCYHkxxJkqTJY5IjSVKLdejBOjnVHn5UJjmSJKmRHORIkqRGcrpKkqQWG+hUf4q3p5BLkiRNIpMcSZJazAt0SpIk9RmTHEmSWswLdEqSJPUZkxxJklqsQ6fyxfpcDFCSJGkSmeRIktRinR6sk2NNjiRJ0iQyyZEkqcVcJ0eSJKnPmORIktRirpMjSZLUZ0xyJElqsYFOx6uQS5Ik9RMHOZIkqZGcrpIkqcU69KDwuNrDj8okR5IkNZJJjiRJLVacQl5t1tLpDFV6/NGY5EiSpEYyyZEkqcVcDFCSJKnPmORIktRinU6HgcprcgB6X5djkiNJkhrJJEeSpBbrUP06Nq6TI0mSNIlMciRJarFOp9ODdXLAmhxJkqRJYpIjSVKLDXSKW9Vt1MEkR5IkNZJJjiRJLda7mpzeM8mRJEmN5CBHkiQ10qjTVRGx1VhPzMy7Jr87kiSpl5p8gc6xanLuoDipfV1dGwIGK+mRJEnSJBh1kJOZTmVJktR41Rce12Xcs6siYgA4Dnga8CbgKOCUzFxbcd8kSZI22EROIX8/sA2wB0Wh8guBWcDRFfZLkiT1QNsXA3wBcBjwYGb+FjgA2L/KTkmSJD1aExnkrM7Mh4bvZObvgTXVdUmSJPXK8GKAVd/qMJHpqusj4o3AYEQERX3OtZX2SpIk6VGaSJJzDLA78HjgO8BmwLEV9kmSJPVIp0e3Ooyb5GTmPcA/9qAvkiRJk2Yip5BvC5xGUWy8GrgEOD4zV1XbNUmSVLWBToeBimtmpvLZVacDPweeBfwlcDfwqSo7JUmSFBEfiIizyp/nR8SPI+KmiHj3RJ4/kcLjOZl5cNf9N0fEdRvQV0mSNMVM1WtXRcQLgNcAF0fEDOBMYG/gl+W2AzPz0rGOMZFBzoqI2CEzl5WNzgZWrn93JUlSmy1evHj2woULR25eNbIEprxI+HuA9wK7UMwm3dQ1FjkHOATYsEFORHyV4kKc2wDXRsQSYC2wL/Djib8kSZI0ZXWofh2bzhAAS5YsuXIdjy4CThqx7VPA24Enlve345EBy0pg9njNjpXknD/K9ovHO6gkSdJI8+fP3+vcc8+9dcTmVd13IuK1wC8z84qIOKzcPEARvAzrAA8xjrGuQn72urZHRAeYO96BJUmSui1YsODWRYsWLR9nt1cCsyLiWmArivX5/pxiNmnYTGDFeO1N5BTy11NcpHPTrs23lw1IkqQ+NtUKjzPz4etjlknOPsCRwE0RMRdYBiykKEQe00ROIX8bxRo5FwO7Ae8ALpx4dyVJkjZcZj5IcbHwC4AbgBsZvazmYRM5u+quzLy6jI0en5nviYgbHkVfJUnSFNGbxQA37PiZeRZwVvnzFRRnWk283QnsszoitgRuojiFC2BwfRqRJEnqtYkkOf8OXAS8hOJU8pdSxESSJKnPdehBTU61hx/VRC7QeWZEnJeZ90fEc4BnAl+faAMRsQ9wUmbus8G91Kh+eP1yTvroV7joU8fW3RX1setu/AWnfeZSPn3y67nlF7/m3R/5MkNDQ+y04yzeeuTBDA5OJPSVYM2atXzyjK9y+x2/Zc3qtbx0wfPZeqvHcsqHvsTMmVsCsP9+83jus59ac0/VBmMtBnjciPvdd98AnFpRnzRBp332cs675PtsOmN63V1RHzvrP77Jxd+4hhmbTAPgY2d9jaNe81fMe/qOvOPUL/Gtq29gv+c+reZeql9c9d3reOxmj+Go1/819973O9524qd5+cF7cdALn82LD9yz7u5pHTqdTuWLAVa+2OAoxkpynj7GY0NjPKYe2WH2n/G5U47gyHd+tu6uqI/NnrU1Hzjh1Zz4gXMB+MDbX83g4ACrV6/hzrvvZastNqu5h+onez7rKTx7j50fvj84OMDPl69k5W138sMfJTNnbsVrFh7ADP84Uw+MtRjg4b3siNbfgv124xcr7qy7G+pz85//dFb8+q6H7w8ODrDi13dz5NtPZ7PHbMKc2dvU2Dv1m0022RiABx74PR/66AX8zcv3Yc3qNey3927suMMsLlx8Fed/5Upefej8mnuqYQNM7CykR9tGHZxol/Qntnv8liz+9Ft4xYv25IOnX1R3d9Rn7rjzt/zr+85hr+c9nec/52nsMe/J7LjDLAD2mBcs/8VtNfdQbeEgR9IjHLPoLP73V3cAsOmM6ZWvn6FmWfXb+3jv+7/AoX+zH/v+5a4AvPcDX+DmW34FwPU3LGfHObNq7KH+RFmTU+Wt8tO3RjGRU8gltcjhh+zDO0/9EtOmDbLJ9I15xzEvr7tL6iNf+ep3uP93D/LlxVfy5cXFBaf//tD9OfsLl7PRRgNs8bjNOOLwg2rupdqiMzQ0dg1xRAwAxwNPA44qb6dk5toxn/jH5+8DXAE80LX5nMw8cqznLV26dA6w7Ek7P42NN7ZATZNj+e33190FNUzefm/dXVBDDA6tYdaaXwPsMG/evOVVtzf8e/bLK2dw/9pqJ3Y2HXyIl816AHr02oZNJMl5P7ANsAfFej4vBGYBR0+kgcz8Jq6QLEmSemwiQ7cXUFwU68HMvAc4gOKCnZIkqc8NdHpzq+W1TWCf1Zn50PCdzPw9sKa6LkmSJD16E5muuj4i3ggMRrHs8XHAtZX2SpIk9USTVzyeSJJzDLA78HjgO8BmwLEV9kmSJOlRm8gFOu8B/rEHfZEkSZo04w5yIuIj69qemRM6u0qSJE1dvSgMnsqFx3d23e4F9sYLdEqSpCluItNVi7rvR8T7gMWV9UiSJPVOL666MIWTnEfIzHuBJ1TQF0mSpEkzkZqcj/LH6akOMA/4aZWdkiRJvTFAp/IL8Q7UFOVMZJ2cO7p+HgI+B3y+mu5IkiRNjokMcv4iM/++8p5IkqSeG2ADalc2oI06TKTdXSKippIhSZKkDTORJGcl8JOI+G/gvuGNrpMjSVL/6/Tg7KqaruowepITEdPLH78HnAf8L49cM0eSJGnKGivJ+R6w+8h1ciRJUnMMdHpwdtUUvECndTiSJKlvjZXkbBIRuzHKYCczf1RNlyRJUs80eMXjsQY5OwIXsO6uDZWPS5IkTUljDXJuyMzdetYTSZLUc22/CrkkSVLfGWuQ8+2e9UKSJGmSjTpdlZnH9LIjkiSp99p6CrkkSVLfmshlHSRJUkO18rIOkiRJ/cwkR5KkFvMUckmSpD5jkiNJUst1Gnq5SpMcSZLUSCY5kiS12AA9qMmp9vBTrl1JkqRKmeRIktRiA53qEw/PrpIkSZpEJjmSJLVYp9Op/OyqjteukiRJmjwmOZIktZg1OZIkSX3GQY4kSWokp6skSWqzDtVf1MHpKkmSpMljkiNJUosNdDoMVBy1DHgKuSRJ0uQxyZEkqcU8hVySJKnPmORIktRinR6cXVVTSY5JjiRJaiaTHEmSWmyAHpxdVdNCOSY5kiSpkUxyJElqubpqZqpmkiNJkhrJJEeSpBbryTo5FR9/qrUrSZJUKZMcSZJarCfXrlrP40fEu4BXAEPAGZl5akTMB04FZgDnZeYJ47crSZI0RUTE3sB+wDOAZwJviohdgDOBg4GdgT0i4sDxjmWSI0mSemLx4sWzFy5cOHLzqsxcNXwnM78VEftm5pqIeALFWGUL4KbMXAYQEecAhwCXjtWeSY4kSS3W6fTmBrBkyZIrgWUjbseO7FNmro6IRcANwBXAdsDKrl1WArPHe20OciRJUk/Mnz9/L2CHEbcPr2vfzHwnsA3wRGAnivqcYR3gofHac7pKkqQW62Xh8YIFC25dtGjR8rH2jYgnA5tk5rWZ+buI+DJFEfLart1mAivGa9dBjiRJmkp2BBZFxPMp0puDgU8B74+IuRRTXAspCpHH5HSVJElt1ot6nPUIijLzEuBi4BpgKfDdzDwXOAy4gKJO50bg/PGOZZIjSZKmlMw8CThpxLYrgF3W5zgOciRJarEBvKyDJElSXzHJkSSpxTqdDp2Kz66q+vijMcmRJEmNZJIjSVKLrefJTxvcRh1MciRJUiOZ5KhVfvf7tePvJK2Hv33Nu+vughpi5tabcf7Jh/S83V6ueNxrJjmSJKmRTHIkSWoxa3IkSZL6jIMcSZLUSE5XSZLUcp2q55OGKj7+KExyJElSI5nkSJLUYp1OcWmHStuAWtIckxxJktRIJjmSJLXYANUnHnUlKiY5kiSpkUxyJElqsU6n04OanI41OZIkSZPFJEeSpBbzsg6SJEl9xiRHkqQW61lNTg1MciRJUiOZ5EiS1GIdqk88rMmRJEmaRCY5kiS1mDU5kiRJfcZBjiRJaiSnqyRJajEXA5QkSeozJjmSJLVYp1PcKm2j2sOPyiRHkiQ1kkmOJEktNkCHoYqzlgFPIZckSZo8JjmSJLWYNTmSJEl9xiRHkqQW65T/qm6jDiY5kiSpkUxyJElquaprchiq+PijMMmRJEmNZJIjSVKLDfTg6lWukyNJkjSJTHIkSWqxnqyTU9NCOSY5kiSpkRzkSJKkRnK6SpKkFnO6SpIkqc+Y5EiS1GJe1kGSJKnPmORIktRiA9WvBVi0UQOTHEmS1EgmOZIktVxdNTNVM8mRJEmNZJIjSVKLdXpQk+M6OZIkSZPIJEeSpBYr6nFcJ0eSJKlvmORIktRiAx0YsiZHkiSpf5jkSJLUYtbkSJIk9RkHOZIkqZGcrpIkqcWavBiggxxJkjSlRMQ7gb8p716cmW+JiPnAqcAM4LzMPGG84zhdJUlSi3V6dJuocjBzALAbsCswLyIOBc4EDgZ2BvaIiAPHO5ZJjiRJ6onFixfPXrhw4cjNqzJzVdf9lcDxmfkHgIj4KbATcFNmLiu3nQMcAlw6VnsOciRJarFOp0On6qKZ8vhLliy5ch2PLgJOGr6TmT8Z/jkinkQxbfVRisHPsJXA7PGadbpKkiT1xPz58/cCdhhx+/C69o2IpwKXA/8X+Dkw1PVwB3hovPZMciRJarFenvi0YMGCWxctWrR8vP0i4nnABcCxmXluROwNzOraZSawYrzjOMiRJElTRkQ8EfgK8MrM/Ea5+erioZgLLAMWUhQij8lBjiRJbVfTOjajeDOwCXBqRAxv+yRwGEW6swlwCXD+eAdykCNJkqaMzDwGOGaUh3dZn2M5yJEkqcV6cYHOuqIiz66SJEmNZJIjSVKL9eLaVXXV/JjkSJKkRjLJkSSpxabWiVWTy0FOn/vh9cs56aNf4aJPHVt3V9THfvKzX/KJz36Nj737CN7xwS9y1933AbDyN3fz1Hgi7zr+0Jp7qH5y6IufzcIX7wnA9I034uk7zebpLzmR096+kC0e+xgGBzsc+c7PsfxXd9TcUzVdZYOciJhDsWDPAZl5edf25cA+mbm8qrbb4rTPXs55l3yfTWdMr7sr6mOfv/DbfO2b17DJJhsDPDyguee+B3jTiZ/m6MMPqrN76kNfvOhqvnjR1QC8/y1/w+e/+t8sOvqv+Y+v/YCvLLmG5897EjvNebyDHFWu6pqc1cDpEfHYittppR1m/xmfO+WIuruhPrfdzK1471tf9Sfbzzh3Ca846Dn82Vab19ArNcGuO2/Pk3ecxdkXfodnP2NHttt2Sy78+FEc8sI9uGrpTXV3T8M6PbrVoOpBzgqKi2t9sOJ2WmnBfrsxbaPBuruhPrfvc57GRiO+R3evuo8f/vgWXrTv7jX1Sk1w3OEHcPLplwCw/XZbs+re3/HSN36MW399F8e8Zv+ae6c26MXZVccDfxURfqOlPvFf37ueA/bahcFBT8DUhtl8sxk86c8f/3Bic9dv7+fSb18HwNe+fT277bx9nd1Tl06P/tWh8v+CZeY9wBE4bSX1jR/8z83suftOdXdDfex5u8/lW9/Ph+//97W3cMBznwLAc3efy40/X1lX19QiPfkzLTMvw2krqW/8YsUdbDdzq7q7oT42d/ttWb7ij4XFJ374y7zyoGfz9TOOY/5zduaDn/l6jb1Tt06nN7c69PIU8uOB64CZPWyz8bbfbmsu/8yb6+6G+tysbbfk9JP/6eH7n//IsfV1Ro3w0XOueMT9X952Ny876mM19UZt1bMJ965pq4171aYkSRpfA0+sAipMcsp1cOaM2HYZzV5cUZIkTRGueCxJUpt5gU5JkqT+YpIjSVKL9WYdmw5DFbewLiY5kiSpkUxyJElqsZ6sY9PBJEeSJGmymORIktRida9lUyWTHEmS1EgOciRJUiM5XSVJUpu5GKAkSVJ/McmRJKnFerUYYB1MciRJUiOZ5EiS1GK9WgywDiY5kiSpkUxyJElqMRcDlCRJ6jMmOZIktV1DoxyTHEmS1EgmOZIktVzV6+QMVXr00ZnkSJKkRjLJkSSpxXqxTk7l6/CMwiRHkiQ1kkmOJEkt1uCLkJvkSJKkZjLJkSSpzRoc5ZjkSJKkRnKQI0mSGsnpKkmSWqxT/qu6jTqY5EiSpEYyyZEkqcVcDFCSJKnPmORIktRiDT6D3CRHkiQ1k0mOJElt1uAoxyRHkiQ1kkmOJEkt5jo5kiRJfcYkR5KkFnOdHEmSpD5jkiNJUsvVtY5N1UxyJElSI5nkSJLUZq6TI0mS1F8c5EiSpEZyukqSpBZzMUBJkqQ+Y5IjSVKLuRigJElSnzHJkSSpxabqGeQRsTnwXeDFmbk8IuYDpwIzgPMy84TxjmGSI0mSppSIeDZwFbBTeX8GcCZwMLAzsEdEHDjecUxyJElqux7VzCxevHj2woULR25elZmrRmw7Angj8Lny/rOAmzJzGUBEnAMcAlw6VnsOciRJUk8sWbLkynVsXgSc1L0hM18LEBHDm7YDVnbtshKYPV57TldJktRinR79A5g/f/5ewA4jbh+eQDcHgKFHdBseGu9JJjmSJKknFixYcOuiRYuWb8BTbwVmdd2fCawY70kOciRJarE+WSfnaiAiYi6wDFhIUYg8JqerJEnSlJaZDwKHARcANwA3AueP9zyTHEmSWmyqrpMDkJlzun6+AthlfZ5vkiNJkhrJJEeSpBbrk5qcDWKSI0mSGslBjiRJaiSnqyRJarWpXHr86JjkSJKkRjLJkSSpzXpQeFxTkGOSI0mSmskkR5KkFmtuRc7UHuQMAqz+wx/q7ocaZGjt6rq7oIaZufVmdXdBDbHNlo8Z/nGwzn40yVQe5MwCWH7Lz+ruhySN6vyTD6m7C2qeWcAtvWqsyYsBTuVBzg+AvYCVwNqa+yJJUtUGKQY4P6i7I00xZQc58+bN+z1wVd39kCSph3qW4AzrlP+qbqMOnl0lSZIaacomOZIkqQcafHqVSY4kSWokkxxJklqswUGOSY4kSWomkxxJklqsyevkmORIkqRGcpAjSZIayUFOn4qIORGxfB3bh2rojhoiIvaJiG/W3Q81Q/nfqaGI2H/E9uURMaembmmETo/+1cFBjiSpSquB0yPisXV3RO3jIEeSVKUVwOXAB+vuiEbR6dGtBp5d1d+2i4hr6+6EJI3jeOC6iNg/My+vuzNqDwc5/W1FZu7avcGaHElTTWbeExFHUExbPb3u/uiRXAxQkqRHITMvw2kr9ZhJjiSpV44HrgNm1t0RPVJdi/VVzSRH0kh7RcR9XbdP1t0hNUNm3gMcAWxcd1/UDp2hIUs4JElqm6VLl84Bls3ecWembTy90rZW/+H33PrznwLsMG/evOWVNtbFJEeSJDWSNTmSJLWYF+iUJEnqMw5yJElSIznIkSRJjWRNjlSD8grMt1CsGTKsA5yWmWc+ymNfBJyfmWeVl/3YJzNXjbLv44ALM3O/9WzjFcBRmbnPiO37AB/LzKeN8/whYJvMvGM92jwLuD4zP7A+fZU0tibX5DjIkerzQPdlOSLiCcD1EfHDzPzxZDQw8rIf67Al8KzJaEuSphoHOdIUkZm/ioibgJ0iYnfgH4FNgd9m5r4R8Y/AGyimme+kSFJujIjtgLOB7YD/BbYdPmZ3YhIR/wy8BlgD3AQcBnwGmFEmPvOAnYDTgK2BQeAjw8lSRLwLeFXZ9k3jvZ6I2An4OPBYYBZwLfDKzHyw3OU9EbFH+XpOyMyLyuet83Wux1spSYA1OdKUERHPAeYCV5ebnkox1bRvROxNMUDZKzN3A04BLiz3+zjw35n5VOBo4MnrOPYCikHNc8qppGXAUcDh/DFR6gDnA2/LzHnA3sCbI2LPiDgYeDmwK/Bc4HETeElHAGdn5p7l69oBOKjr8Z9n5u7A3wFnR8Q247xOSRXo9OhfHUxypPoMJyhQ/H/xDuBVmfnLiAD4cbkMPhSDg7nAd8vHALaMiK2A+cCbATLz5oj4xjramg/8R2beXe53HDxcGzRsJ+AvgDO72pgB7AY8BfhyZt5bPu9MigHVWN4K7B8RbymPvR2wWdfjnyz7cn1E3AA8B3j+GK9TktaLgxypPg+MUzNzX9fPg8DnMvOtABExQDFouBsYgkf8mbRmHcdaU+5H+fwtgC1G7DNIMTW2a9d+jwd+C7x/Am2M9EWK/8Z8CbgY2H7EMdZ2/TwArGbs1ympAk0uPHa6SuoPXwcOjYhZ5f0jgSvKn78GvA4gIrYH9l3H85cAL4uIzcv7JwHHUQxWBiOiAyTwQET8XXmsJwLXU9TqXAocEhFblAOPV0+gz38FvCszzyvvP5tiEDPssLKd3fnjNN1Yr1OS1otJjtQHMvOyiDgZuDwiHgLuAV6WmUMR8UbgMxHxU+BWigLfkc+/JCKeAnynnAb6CUXNzO+A75f39wIOBk4rp5imASdm5ncAIuLpwA8pUpX/AbYZp9v/AlwYEfdTpEHfohjMDNsxIq6hSJj+NjPvAsZ6nevxjkmaqA5UXjFTU5DjVcglSWqj4auQ7/Ckp/TkKuTLbroBenwVcpMcSZLarMFRjjU5kiSpkUxyJElqsV6sY1PXOjkmOZIkqZFMciRJarMerJNjTY4kSdIkMsmRJKnFGnxylUmOJElqJpMcSZLarMFRjkmOJElqJAc5kiSpkZyukiSpxVwMUJIkqc+Y5EiS1GKdHiwGWPlig6NwkCNJUout/sPqRrSxLg5yJElqp3uAu5ffklv2qL27yzZ7pjM0NNTL9iRJ0hSxdOnSrYDNe9TcPfPmzburR20BDnIkSVJDeXaVJElqJAc5kiSpkRzkSJKkRnKQI0mSGun/A/TLQmWjAWNUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titles_options = [\n",
    "    (\"Confusion matrix, without normalization\", None),\n",
    "    (\"Normalized confusion matrix\", \"true\"),\n",
    "]\n",
    "for title, normalize in titles_options:\n",
    "    fig, ax = plt.subplots(figsize=(10, 10))\n",
    "\n",
    "    disp = ConfusionMatrixDisplay.from_estimator(\n",
    "        clf,\n",
    "        X_eval,\n",
    "        y_eval,\n",
    "        display_labels=class_names,\n",
    "        cmap=plt.cm.Blues,\n",
    "        normalize=normalize,\n",
    "        ax = ax\n",
    "    )\n",
    "    disp.ax_.set_title(title)\n",
    "    disp.ax_.grid(False)\n",
    "\n",
    "    print(title)\n",
    "    print(disp.confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see from the shade of the plot, our model is predicting the label N much more than others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check for overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 1.0000\n",
      "Test set score: 0.6479\n"
     ]
    }
   ],
   "source": [
    "print('Training set score: {:.4f}'.format(clf.score(X_train, y_train)))\n",
    "print('Test set score: {:.4f}'.format(clf.score(X_eval, y_eval)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature importance\n",
    "\n",
    "Let’s also plot the feature importance to see which features matter more.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_imp = pd.DataFrame(sorted(zip(clf.feature_importances_,X.columns)), columns=['Value','Feature'])\n",
    "\n",
    "plt.figure(figsize=(20, 15))\n",
    "sns.barplot(x=\"Value\", y=\"Feature\", data=feature_imp.sort_values(by=\"Value\", ascending=False))\n",
    "plt.title('LightGBM Features')\n",
    "plt.tight_layout()\n",
    "# plt.savefig('lightgbm_fimp.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# joblib.dump(clf, 'lgb1.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a simple model built. It’s time to predict it on the test data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict on test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we apply the same transformation on the train data to the test data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.concat([test_a ,test_b ,test_c ,test_d ,test_e], keys=[\"A\", \"B\", \"C\", \"D\", \"E\"])\n",
    "test = test.reset_index()\n",
    "test.rename(columns = {\"level_0\": \"region\"}, inplace=True)\n",
    "test.drop(columns=['level_1'], inplace=True)\n",
    "\n",
    "mean_atmos = test['min.atmos.pressure'].mean()\n",
    "test[\"min.atmos.pressure\"] = test['min.atmos.pressure'].fillna(mean_atmos)\n",
    "test = test.ffill(axis = 0)\n",
    "\n",
    "test = feature_eng(test)\n",
    "\n",
    "test = test.pivot(index=[\"date\"], columns=\"region\")\n",
    "test = pd.DataFrame(test.to_records())\n",
    "test.columns = list(map(replace_all, test.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = test.drop('date', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert cat_features to pd.Categorical dtype\n",
    "for col in categorical_cols:\n",
    "    X[col] = pd.Categorical(X[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>date</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0001f2fd</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00177dc1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00b3a048</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>013f131b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01a1b150</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date  label\n",
       "0  0001f2fd      1\n",
       "1  00177dc1      1\n",
       "2  00b3a048      1\n",
       "3  013f131b      2\n",
       "4  01a1b150      2"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_preds = clf.predict(X)\n",
    "submission_df = pd.concat([test['date'], pd.DataFrame(test_preds, columns=['label'])], axis=1)\n",
    "submission_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'H': 0, 'L': 1, 'N': 2}"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le_name_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The labels are still encoded as numeric values; let’s bring the actual label names back.\n",
    "\n",
    "Since we already have the dictionary of the mapping of label names to numeric values, i.e. ‘H’ : 0, we can reverse the dictionary above to map the numbers to the names\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'H', 1: 'L', 2: 'N'}"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inv_map = {v: k for k, v in le_name_map.items()}\n",
    "inv_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>date</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0001f2fd</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00177dc1</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00b3a048</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>013f131b</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01a1b150</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date label\n",
       "0  0001f2fd     L\n",
       "1  00177dc1     L\n",
       "2  00b3a048     L\n",
       "3  013f131b     N\n",
       "4  01a1b150     N"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission_df['label'] = submission_df['label'].map(inv_map)  \n",
    "submission_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solution file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_df.to_csv('solution.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "0f1e841692445df6c0f476977380d4c26cc40d52508098a18c340919add514d9"
  },
  "kernelspec": {
   "display_name": "Python 3.9.10 ('base')",
   "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": "3.9.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}