regression-with-abalone-dataset-competition.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4700e131",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2024-04-11T07:20:46.026233Z",
     "iopub.status.busy": "2024-04-11T07:20:46.025839Z",
     "iopub.status.idle": "2024-04-11T07:20:49.068043Z",
     "shell.execute_reply": "2024-04-11T07:20:49.066527Z"
    },
    "papermill": {
     "duration": 3.052707,
     "end_time": "2024-04-11T07:20:49.070411",
     "exception": false,
     "start_time": "2024-04-11T07:20:46.017704",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anmol\\anaconda3\\Lib\\site-packages\\pandas\\core\\arrays\\masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n",
      "  from pandas.core import (\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.metrics import mean_squared_log_error # metric parameter for this problem statement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "45db9924",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-11T07:20:49.085104Z",
     "iopub.status.busy": "2024-04-11T07:20:49.084575Z",
     "iopub.status.idle": "2024-04-11T07:20:49.279361Z",
     "shell.execute_reply": "2024-04-11T07:20:49.278233Z"
    },
    "papermill": {
     "duration": 0.20557,
     "end_time": "2024-04-11T07:20:49.282433",
     "exception": false,
     "start_time": "2024-04-11T07:20:49.076863",
     "status": "completed"
    },
    "tags": []
   },
   "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>Sex</th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Whole weight.1</th>\n",
       "      <th>Whole weight.2</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Rings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.7715</td>\n",
       "      <td>0.3285</td>\n",
       "      <td>0.1465</td>\n",
       "      <td>0.2400</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>0.630</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.145</td>\n",
       "      <td>1.1300</td>\n",
       "      <td>0.4580</td>\n",
       "      <td>0.2765</td>\n",
       "      <td>0.3200</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.0210</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.595</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.9145</td>\n",
       "      <td>0.3755</td>\n",
       "      <td>0.2055</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.7820</td>\n",
       "      <td>0.3695</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>0.1975</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sex  Length  Diameter  Height  Whole weight  Whole weight.1  Whole weight.2  \\\n",
       "0   F   0.550     0.430   0.150        0.7715          0.3285          0.1465   \n",
       "1   F   0.630     0.490   0.145        1.1300          0.4580          0.2765   \n",
       "2   I   0.160     0.110   0.025        0.0210          0.0055          0.0030   \n",
       "3   M   0.595     0.475   0.150        0.9145          0.3755          0.2055   \n",
       "4   I   0.555     0.425   0.130        0.7820          0.3695          0.1600   \n",
       "\n",
       "   Shell weight  Rings  \n",
       "0        0.2400     11  \n",
       "1        0.3200     11  \n",
       "2        0.0050      6  \n",
       "3        0.2500     10  \n",
       "4        0.1975      9  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./train.csv\")\n",
    "df.drop(columns=\"id\", inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9151c655",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-11T07:20:49.297120Z",
     "iopub.status.busy": "2024-04-11T07:20:49.296739Z",
     "iopub.status.idle": "2024-04-11T07:20:49.325021Z",
     "shell.execute_reply": "2024-04-11T07:20:49.323700Z"
    },
    "papermill": {
     "duration": 0.038447,
     "end_time": "2024-04-11T07:20:49.327391",
     "exception": false,
     "start_time": "2024-04-11T07:20:49.288944",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 90615 entries, 0 to 90614\n",
      "Data columns (total 9 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Sex             90615 non-null  object \n",
      " 1   Length          90615 non-null  float64\n",
      " 2   Diameter        90615 non-null  float64\n",
      " 3   Height          90615 non-null  float64\n",
      " 4   Whole weight    90615 non-null  float64\n",
      " 5   Whole weight.1  90615 non-null  float64\n",
      " 6   Whole weight.2  90615 non-null  float64\n",
      " 7   Shell weight    90615 non-null  float64\n",
      " 8   Rings           90615 non-null  int64  \n",
      "dtypes: float64(7), int64(1), object(1)\n",
      "memory usage: 6.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "570fef2a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-11T07:20:49.342155Z",
     "iopub.status.busy": "2024-04-11T07:20:49.341761Z",
     "iopub.status.idle": "2024-04-11T07:20:49.376913Z",
     "shell.execute_reply": "2024-04-11T07:20:49.375792Z"
    },
    "papermill": {
     "duration": 0.045119,
     "end_time": "2024-04-11T07:20:49.379169",
     "exception": false,
     "start_time": "2024-04-11T07:20:49.334050",
     "status": "completed"
    },
    "tags": []
   },
   "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>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Whole weight.1</th>\n",
       "      <th>Whole weight.2</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Rings</th>\n",
       "      <th>Sex_I</th>\n",
       "      <th>Sex_M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.550</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.7715</td>\n",
       "      <td>0.3285</td>\n",
       "      <td>0.1465</td>\n",
       "      <td>0.2400</td>\n",
       "      <td>11</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.630</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.145</td>\n",
       "      <td>1.1300</td>\n",
       "      <td>0.4580</td>\n",
       "      <td>0.2765</td>\n",
       "      <td>0.3200</td>\n",
       "      <td>11</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.160</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.0210</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.595</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.9145</td>\n",
       "      <td>0.3755</td>\n",
       "      <td>0.2055</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.555</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.7820</td>\n",
       "      <td>0.3695</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>0.1975</td>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Length  Diameter  Height  Whole weight  Whole weight.1  Whole weight.2  \\\n",
       "0   0.550     0.430   0.150        0.7715          0.3285          0.1465   \n",
       "1   0.630     0.490   0.145        1.1300          0.4580          0.2765   \n",
       "2   0.160     0.110   0.025        0.0210          0.0055          0.0030   \n",
       "3   0.595     0.475   0.150        0.9145          0.3755          0.2055   \n",
       "4   0.555     0.425   0.130        0.7820          0.3695          0.1600   \n",
       "\n",
       "   Shell weight  Rings  Sex_I  Sex_M  \n",
       "0        0.2400     11  False  False  \n",
       "1        0.3200     11  False  False  \n",
       "2        0.0050      6   True  False  \n",
       "3        0.2500     10  False   True  \n",
       "4        0.1975      9   True  False  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.get_dummies(df, columns=[\"Sex\"], drop_first=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e87a5fbb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-11T07:20:49.394349Z",
     "iopub.status.busy": "2024-04-11T07:20:49.393946Z",
     "iopub.status.idle": "2024-04-11T07:20:50.044688Z",
     "shell.execute_reply": "2024-04-11T07:20:50.043732Z"
    },
    "papermill": {
     "duration": 0.661064,
     "end_time": "2024-04-11T07:20:50.046973",
     "exception": false,
     "start_time": "2024-04-11T07:20:49.385909",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "plt.figure(figsize=(8, 4))\n",
    "sns.heatmap(corr, annot=True, fmt=\"0.2f\")\n",
    "plt.title(\"Correlation matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8d1e83a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-11T07:20:50.066350Z",
     "iopub.status.busy": "2024-04-11T07:20:50.065716Z",
     "iopub.status.idle": "2024-04-11T07:20:50.073855Z",
     "shell.execute_reply": "2024-04-11T07:20:50.072699Z"
    },
    "papermill": {
     "duration": 0.021034,
     "end_time": "2024-04-11T07:20:50.076479",
     "exception": false,
     "start_time": "2024-04-11T07:20:50.055445",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = df.drop(columns=\"Rings\")\n",
    "y = df[\"Rings\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ab071c5",
   "metadata": {
    "papermill": {
     "duration": 0.00804,
     "end_time": "2024-04-11T07:20:50.092933",
     "exception": false,
     "start_time": "2024-04-11T07:20:50.084893",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Splitting into training & testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4d6f3284",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-11T07:20:50.112457Z",
     "iopub.status.busy": "2024-04-11T07:20:50.111731Z",
     "iopub.status.idle": "2024-04-11T07:20:50.130809Z",
     "shell.execute_reply": "2024-04-11T07:20:50.129509Z"
    },
    "papermill": {
     "duration": 0.032303,
     "end_time": "2024-04-11T07:20:50.133458",
     "exception": false,
     "start_time": "2024-04-11T07:20:50.101155",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29f9c6db",
   "metadata": {
    "papermill": {
     "duration": 0.008081,
     "end_time": "2024-04-11T07:20:50.149866",
     "exception": false,
     "start_time": "2024-04-11T07:20:50.141785",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e132a3c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-11T07:20:50.168350Z",
     "iopub.status.busy": "2024-04-11T07:20:50.167910Z",
     "iopub.status.idle": "2024-04-11T07:21:05.291163Z",
     "shell.execute_reply": "2024-04-11T07:21:05.289992Z"
    },
    "papermill": {
     "duration": 15.135552,
     "end_time": "2024-04-11T07:21:05.293649",
     "exception": false,
     "start_time": "2024-04-11T07:20:50.158097",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(n_jobs=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(n_jobs=-1)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(n_jobs=-1)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = RandomForestRegressor(n_jobs=-1)\n",
    "reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7ed907ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-11T07:21:05.313907Z",
     "iopub.status.busy": "2024-04-11T07:21:05.313006Z",
     "iopub.status.idle": "2024-04-11T07:21:05.573435Z",
     "shell.execute_reply": "2024-04-11T07:21:05.572133Z"
    },
    "papermill": {
     "duration": 0.273014,
     "end_time": "2024-04-11T07:21:05.575999",
     "exception": false,
     "start_time": "2024-04-11T07:21:05.302985",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSLE:  0.15597148081851295\n"
     ]
    }
   ],
   "source": [
    "y_pred = reg.predict(X_test)\n",
    "rmsle = np.sqrt(mean_squared_log_error(y_test, y_pred))\n",
    "print(\"RMSLE: \", rmsle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ebc8d7b1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-11T07:21:05.596049Z",
     "iopub.status.busy": "2024-04-11T07:21:05.595637Z",
     "iopub.status.idle": "2024-04-11T07:21:06.106889Z",
     "shell.execute_reply": "2024-04-11T07:21:06.105590Z"
    },
    "papermill": {
     "duration": 0.524375,
     "end_time": "2024-04-11T07:21:06.109510",
     "exception": false,
     "start_time": "2024-04-11T07:21:05.585135",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anmol\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\anmol\\anaconda3\\Lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(y_test - y_pred, kind=\"kde\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fbe1564",
   "metadata": {
    "papermill": {
     "duration": 0.009518,
     "end_time": "2024-04-11T07:21:06.128922",
     "exception": false,
     "start_time": "2024-04-11T07:21:06.119404",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "697720a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-11T07:21:06.150174Z",
     "iopub.status.busy": "2024-04-11T07:21:06.149768Z",
     "iopub.status.idle": "2024-04-11T07:21:06.676750Z",
     "shell.execute_reply": "2024-04-11T07:21:06.675842Z"
    },
    "papermill": {
     "duration": 0.540509,
     "end_time": "2024-04-11T07:21:06.678986",
     "exception": false,
     "start_time": "2024-04-11T07:21:06.138477",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=-1,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=-1,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=-1,\n",
       "             num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = XGBRegressor(n_jobs=-1)\n",
    "reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "18c1760c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-11T07:21:06.699894Z",
     "iopub.status.busy": "2024-04-11T07:21:06.699466Z",
     "iopub.status.idle": "2024-04-11T07:21:06.745717Z",
     "shell.execute_reply": "2024-04-11T07:21:06.744205Z"
    },
    "papermill": {
     "duration": 0.059319,
     "end_time": "2024-04-11T07:21:06.748032",
     "exception": false,
     "start_time": "2024-04-11T07:21:06.688713",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSLE:  0.1526798095577102\n"
     ]
    }
   ],
   "source": [
    "y_pred = reg.predict(X_test)\n",
    "rmsle = np.sqrt(mean_squared_log_error(y_test, y_pred))\n",
    "print(\"RMSLE: \", rmsle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "22021cc6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-11T07:21:06.769262Z",
     "iopub.status.busy": "2024-04-11T07:21:06.768854Z",
     "iopub.status.idle": "2024-04-11T07:21:07.255819Z",
     "shell.execute_reply": "2024-04-11T07:21:07.254553Z"
    },
    "papermill": {
     "duration": 0.500736,
     "end_time": "2024-04-11T07:21:07.258674",
     "exception": false,
     "start_time": "2024-04-11T07:21:06.757938",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anmol\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\anmol\\anaconda3\\Lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(y_test - y_pred, kind=\"kde\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1546038",
   "metadata": {
    "papermill": {
     "duration": 0.01027,
     "end_time": "2024-04-11T07:21:07.280064",
     "exception": false,
     "start_time": "2024-04-11T07:21:07.269794",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Since XGBoost gives out a better RMSLE score, we'll use that algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e7d0428f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-11T07:21:07.303441Z",
     "iopub.status.busy": "2024-04-11T07:21:07.303003Z",
     "iopub.status.idle": "2024-04-11T07:22:27.278388Z",
     "shell.execute_reply": "2024-04-11T07:22:27.277256Z"
    },
    "papermill": {
     "duration": 80.00003,
     "end_time": "2024-04-11T07:22:27.290909",
     "exception": false,
     "start_time": "2024-04-11T07:21:07.290879",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10,\n",
       "             estimator=XGBRegressor(base_score=None, booster=None,\n",
       "                                    callbacks=None, colsample_bylevel=None,\n",
       "                                    colsample_bynode=None,\n",
       "                                    colsample_bytree=None, device=None,\n",
       "                                    early_stopping_rounds=None,\n",
       "                                    enable_categorical=False, eval_metric=None,\n",
       "                                    feature_types=None, gamma=None,\n",
       "                                    grow_policy=None, importance_type=None,\n",
       "                                    interaction_constraints=None,\n",
       "                                    learning_rate=None,...\n",
       "                                    max_cat_threshold=None,\n",
       "                                    max_cat_to_onehot=None, max_delta_step=None,\n",
       "                                    max_depth=None, max_leaves=None,\n",
       "                                    min_child_weight=None, missing=nan,\n",
       "                                    monotone_constraints=None,\n",
       "                                    multi_strategy=None, n_estimators=None,\n",
       "                                    n_jobs=-1, num_parallel_tree=None,\n",
       "                                    random_state=None, ...),\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.1, 0.01, 0.2],\n",
       "                         &#x27;max_depth&#x27;: [None, 1, 2, 3, 4, 5]},\n",
       "             scoring=&#x27;neg_mean_squared_log_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=10,\n",
       "             estimator=XGBRegressor(base_score=None, booster=None,\n",
       "                                    callbacks=None, colsample_bylevel=None,\n",
       "                                    colsample_bynode=None,\n",
       "                                    colsample_bytree=None, device=None,\n",
       "                                    early_stopping_rounds=None,\n",
       "                                    enable_categorical=False, eval_metric=None,\n",
       "                                    feature_types=None, gamma=None,\n",
       "                                    grow_policy=None, importance_type=None,\n",
       "                                    interaction_constraints=None,\n",
       "                                    learning_rate=None,...\n",
       "                                    max_cat_threshold=None,\n",
       "                                    max_cat_to_onehot=None, max_delta_step=None,\n",
       "                                    max_depth=None, max_leaves=None,\n",
       "                                    min_child_weight=None, missing=nan,\n",
       "                                    monotone_constraints=None,\n",
       "                                    multi_strategy=None, n_estimators=None,\n",
       "                                    n_jobs=-1, num_parallel_tree=None,\n",
       "                                    random_state=None, ...),\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.1, 0.01, 0.2],\n",
       "                         &#x27;max_depth&#x27;: [None, 1, 2, 3, 4, 5]},\n",
       "             scoring=&#x27;neg_mean_squared_log_error&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: XGBRegressor</label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=-1,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">XGBRegressor</label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=-1,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10,\n",
       "             estimator=XGBRegressor(base_score=None, booster=None,\n",
       "                                    callbacks=None, colsample_bylevel=None,\n",
       "                                    colsample_bynode=None,\n",
       "                                    colsample_bytree=None, device=None,\n",
       "                                    early_stopping_rounds=None,\n",
       "                                    enable_categorical=False, eval_metric=None,\n",
       "                                    feature_types=None, gamma=None,\n",
       "                                    grow_policy=None, importance_type=None,\n",
       "                                    interaction_constraints=None,\n",
       "                                    learning_rate=None,...\n",
       "                                    max_cat_threshold=None,\n",
       "                                    max_cat_to_onehot=None, max_delta_step=None,\n",
       "                                    max_depth=None, max_leaves=None,\n",
       "                                    min_child_weight=None, missing=nan,\n",
       "                                    monotone_constraints=None,\n",
       "                                    multi_strategy=None, n_estimators=None,\n",
       "                                    n_jobs=-1, num_parallel_tree=None,\n",
       "                                    random_state=None, ...),\n",
       "             param_grid={'learning_rate': [0.1, 0.01, 0.2],\n",
       "                         'max_depth': [None, 1, 2, 3, 4, 5]},\n",
       "             scoring='neg_mean_squared_log_error')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = XGBRegressor(n_jobs=-1, )\n",
    "param_grid = {\n",
    "    \"max_depth\": [None, 1, 2, 3, 4, 5],\n",
    "    \"learning_rate\": [0.1, 0.01, 0.2]\n",
    "}\n",
    "\n",
    "gscv = GridSearchCV(reg, param_grid, cv=10, scoring=\"neg_mean_squared_log_error\")\n",
    "\n",
    "gscv.fit(X, y) # training on the entire dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7f05ef6f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-11T07:22:27.313988Z",
     "iopub.status.busy": "2024-04-11T07:22:27.313590Z",
     "iopub.status.idle": "2024-04-11T07:22:27.318773Z",
     "shell.execute_reply": "2024-04-11T07:22:27.317765Z"
    },
    "papermill": {
     "duration": 0.019668,
     "end_time": "2024-04-11T07:22:27.320998",
     "exception": false,
     "start_time": "2024-04-11T07:22:27.301330",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score:  -0.022573453001994682\n",
      "Best Params:  {'learning_rate': 0.2, 'max_depth': None}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Score: \", gscv.best_score_)\n",
    "print(\"Best Params: \", gscv.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c596f2a",
   "metadata": {
    "papermill": {
     "duration": 0.010288,
     "end_time": "2024-04-11T07:22:27.342112",
     "exception": false,
     "start_time": "2024-04-11T07:22:27.331824",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Read the testing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0d8184ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-11T07:22:27.364869Z",
     "iopub.status.busy": "2024-04-11T07:22:27.364482Z",
     "iopub.status.idle": "2024-04-11T07:22:27.478577Z",
     "shell.execute_reply": "2024-04-11T07:22:27.477592Z"
    },
    "papermill": {
     "duration": 0.12791,
     "end_time": "2024-04-11T07:22:27.480756",
     "exception": false,
     "start_time": "2024-04-11T07:22:27.352846",
     "status": "completed"
    },
    "tags": []
   },
   "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>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Whole weight.1</th>\n",
       "      <th>Whole weight.2</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Sex_I</th>\n",
       "      <th>Sex_M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.645</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.155</td>\n",
       "      <td>1.2380</td>\n",
       "      <td>0.6185</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.3005</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.580</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.9830</td>\n",
       "      <td>0.4785</td>\n",
       "      <td>0.2195</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.560</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.8395</td>\n",
       "      <td>0.3525</td>\n",
       "      <td>0.1845</td>\n",
       "      <td>0.2405</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.570</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.8740</td>\n",
       "      <td>0.3525</td>\n",
       "      <td>0.1865</td>\n",
       "      <td>0.2350</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.415</td>\n",
       "      <td>0.325</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.3580</td>\n",
       "      <td>0.1575</td>\n",
       "      <td>0.0670</td>\n",
       "      <td>0.1050</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Length  Diameter  Height  Whole weight  Whole weight.1  Whole weight.2  \\\n",
       "0   0.645     0.475   0.155        1.2380          0.6185          0.3125   \n",
       "1   0.580     0.460   0.160        0.9830          0.4785          0.2195   \n",
       "2   0.560     0.420   0.140        0.8395          0.3525          0.1845   \n",
       "3   0.570     0.490   0.145        0.8740          0.3525          0.1865   \n",
       "4   0.415     0.325   0.110        0.3580          0.1575          0.0670   \n",
       "\n",
       "   Shell weight  Sex_I  Sex_M  \n",
       "0        0.3005  False   True  \n",
       "1        0.2750  False   True  \n",
       "2        0.2405  False   True  \n",
       "3        0.2350  False   True  \n",
       "4        0.1050   True  False  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv(\"./test.csv\")\n",
    "test_df.drop(columns=\"id\", inplace=True)\n",
    "test_df = pd.get_dummies(test_df, columns=[\"Sex\"], drop_first=True)\n",
    "test_df = pd.DataFrame(test_df, columns=X.columns)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab64f185",
   "metadata": {
    "papermill": {
     "duration": 0.010287,
     "end_time": "2024-04-11T07:22:27.502178",
     "exception": false,
     "start_time": "2024-04-11T07:22:27.491891",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Reading sample submission file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "21ae538f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-11T07:22:27.525531Z",
     "iopub.status.busy": "2024-04-11T07:22:27.524910Z",
     "iopub.status.idle": "2024-04-11T07:22:27.552897Z",
     "shell.execute_reply": "2024-04-11T07:22:27.551802Z"
    },
    "papermill": {
     "duration": 0.042275,
     "end_time": "2024-04-11T07:22:27.555085",
     "exception": false,
     "start_time": "2024-04-11T07:22:27.512810",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Rings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90615</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90616</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90617</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90618</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90619</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  Rings\n",
       "0  90615     10\n",
       "1  90616     10\n",
       "2  90617     10\n",
       "3  90618     10\n",
       "4  90619     10"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = pd.read_csv(\"./sample_submission.csv\")\n",
    "sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ec5fd839",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-11T07:22:27.579429Z",
     "iopub.status.busy": "2024-04-11T07:22:27.578483Z",
     "iopub.status.idle": "2024-04-11T07:22:27.702517Z",
     "shell.execute_reply": "2024-04-11T07:22:27.700849Z"
    },
    "papermill": {
     "duration": 0.138575,
     "end_time": "2024-04-11T07:22:27.704713",
     "exception": false,
     "start_time": "2024-04-11T07:22:27.566138",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9,  9, 10, ..., 12, 13,  8])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "pred = gscv.predict(test_df)\n",
    "pred = np.array([math.floor(pred_i) for pred_i in pred])\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9b963dca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-11T07:22:27.728794Z",
     "iopub.status.busy": "2024-04-11T07:22:27.728422Z",
     "iopub.status.idle": "2024-04-11T07:22:27.737871Z",
     "shell.execute_reply": "2024-04-11T07:22:27.736869Z"
    },
    "papermill": {
     "duration": 0.023936,
     "end_time": "2024-04-11T07:22:27.740165",
     "exception": false,
     "start_time": "2024-04-11T07:22:27.716229",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Rings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90615</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90616</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90617</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90618</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90619</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  Rings\n",
       "0  90615      9\n",
       "1  90616      9\n",
       "2  90617     10\n",
       "3  90618     10\n",
       "4  90619      7"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample[\"Rings\"] = pred\n",
    "sample.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afcf1414",
   "metadata": {
    "papermill": {
     "duration": 0.010974,
     "end_time": "2024-04-11T07:22:27.762186",
     "exception": false,
     "start_time": "2024-04-11T07:22:27.751212",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Submit the prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "99d80d94",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-11T07:22:27.786078Z",
     "iopub.status.busy": "2024-04-11T07:22:27.785667Z",
     "iopub.status.idle": "2024-04-11T07:22:27.841721Z",
     "shell.execute_reply": "2024-04-11T07:22:27.840524Z"
    },
    "papermill": {
     "duration": 0.070761,
     "end_time": "2024-04-11T07:22:27.844183",
     "exception": false,
     "start_time": "2024-04-11T07:22:27.773422",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sample.to_csv(\"submission-xgboost.csv\", index=False)"
   ]
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [
    {
     "databundleVersionId": 8096274,
     "sourceId": 72489,
     "sourceType": "competition"
    }
   ],
   "dockerImageVersionId": 30684,
   "isGpuEnabled": false,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 105.269016,
   "end_time": "2024-04-11T07:22:28.476200",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-04-11T07:20:43.207184",
   "version": "2.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}