diff --git a/ML_q.ipynb b/ML_q.ipynb
new file mode 100644
index 0000000..d1d96cb
--- /dev/null
+++ b/ML_q.ipynb
@@ -0,0 +1,1605 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "036e7fae-2189-44b0-93ec-4fe42d4d44a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np \n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from sklearn.preprocessing import StandardScaler , OneHotEncoder , OrdinalEncoder\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.pipeline import Pipeline , make_pipeline\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from xgboost import XGBRegressor\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "06eb45d3-c61a-468b-8f1b-51d161c8c581",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>job</th>\n",
+       "      <th>marital</th>\n",
+       "      <th>education</th>\n",
+       "      <th>default</th>\n",
+       "      <th>balance</th>\n",
+       "      <th>housing</th>\n",
+       "      <th>loan</th>\n",
+       "      <th>contact</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>campaign</th>\n",
+       "      <th>pdays</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>poutcome</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>58</td>\n",
+       "      <td>management</td>\n",
+       "      <td>married</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2143</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>5</td>\n",
+       "      <td>may</td>\n",
+       "      <td>261</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>44</td>\n",
+       "      <td>technician</td>\n",
+       "      <td>single</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>no</td>\n",
+       "      <td>29</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>5</td>\n",
+       "      <td>may</td>\n",
+       "      <td>151</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>33</td>\n",
+       "      <td>entrepreneur</td>\n",
+       "      <td>married</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>5</td>\n",
+       "      <td>may</td>\n",
+       "      <td>76</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>47</td>\n",
+       "      <td>blue-collar</td>\n",
+       "      <td>married</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>no</td>\n",
+       "      <td>1506</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>5</td>\n",
+       "      <td>may</td>\n",
+       "      <td>92</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>33</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>single</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>no</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>5</td>\n",
+       "      <td>may</td>\n",
+       "      <td>198</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age           job  marital  education default  balance housing loan  \\\n",
+       "0   58    management  married   tertiary      no     2143     yes   no   \n",
+       "1   44    technician   single  secondary      no       29     yes   no   \n",
+       "2   33  entrepreneur  married  secondary      no        2     yes  yes   \n",
+       "3   47   blue-collar  married    unknown      no     1506     yes   no   \n",
+       "4   33       unknown   single    unknown      no        1      no   no   \n",
+       "\n",
+       "   contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
+       "0  unknown    5   may       261         1     -1         0  unknown  no  \n",
+       "1  unknown    5   may       151         1     -1         0  unknown  no  \n",
+       "2  unknown    5   may        76         1     -1         0  unknown  no  \n",
+       "3  unknown    5   may        92         1     -1         0  unknown  no  \n",
+       "4  unknown    5   may       198         1     -1         0  unknown  no  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"bank.csv\", delimiter=\";\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fd73749-308f-4f7d-8a36-c14fabfd45ca",
+   "metadata": {},
+   "source": [
+    "   ## Description:\n",
+    "   The data is related with direct marketing campaigns of a Portuguese banking institution.<br/> \n",
+    "   The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required,<br/> \n",
+    "   in order to access if the product (bank term deposit) would be (or not) subscribed. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2bbd4e3-790d-4d36-82a1-f95fc05cdec9",
+   "metadata": {},
+   "source": [
+    "   ## Input variables:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e77843f-df08-4a97-90ec-7feb105abc83",
+   "metadata": {},
+   "source": [
+    "<ul> \n",
+    "    <li>1 - age (numeric)</li>\n",
+    "    <li>2 - job : type of job (categorical: \"admin.\",\"unknown\",\"unemployed\",\"management\",\"housemaid\",\"entrepreneur\",\"student\",\n",
+    "                                        \"blue-collar\",\"self-employed\",\"retired\",\"technician\",\"services\") </li>\n",
+    "    <li>3 - marital : marital status (categorical: \"married\",\"divorced\",\"single\"; note: \"divorced\" means divorced or widowed)</li>\n",
+    "    <li>4 - education (categorical: \"unknown\",\"secondary\",\"primary\",\"tertiary\")</li>\n",
+    "    <li>5 - default: has credit in default? (binary: \"yes\",\"no\")</li>\n",
+    "    <li>6 - balance: average yearly balance, in euros (numeric) </li>\n",
+    "    <li>7 - housing: has housing loan? (binary: \"yes\",\"no\")</li>\n",
+    "    <li>8 - loan: has personal loan? (binary: \"yes\",\"no\")</li>\n",
+    "   <li>9 - contact: contact communication type (categorical: \"unknown\",\"telephone\",\"cellular\") </li>\n",
+    "   <li>10 - day: last contact day of the month (numeric)</li>\n",
+    "   <li>11 - month: last contact month of year (categorical: \"jan\", \"feb\", \"mar\", ..., \"nov\", \"dec\")</li>\n",
+    "   <li>12 - duration: last contact duration, in seconds (numeric) </li>\n",
+    "   <li>13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)</li>\n",
+    "   <li>14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not     previously contacted)</li>\n",
+    "   <li>15 - previous: number of contacts performed before this campaign and for this client (numeric)</li>\n",
+    "   <li>16 - poutcome: outcome of the previous marketing campaign (categorical: \"unknown\",\"other\",\"failure\",\"success\")</li>\n",
+    "   <li>17 - y - has the client subscribed a term deposit? (binary: \"yes\",\"no\")</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "525d3b1f-241b-4785-8186-f59b5cc9729f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 45211 entries, 0 to 45210\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column     Non-Null Count  Dtype \n",
+      "---  ------     --------------  ----- \n",
+      " 0   age        45211 non-null  int64 \n",
+      " 1   job        45211 non-null  object\n",
+      " 2   marital    45211 non-null  object\n",
+      " 3   education  45211 non-null  object\n",
+      " 4   default    45211 non-null  object\n",
+      " 5   balance    45211 non-null  int64 \n",
+      " 6   housing    45211 non-null  object\n",
+      " 7   loan       45211 non-null  object\n",
+      " 8   contact    45211 non-null  object\n",
+      " 9   day        45211 non-null  int64 \n",
+      " 10  month      45211 non-null  object\n",
+      " 11  duration   45211 non-null  int64 \n",
+      " 12  campaign   45211 non-null  int64 \n",
+      " 13  pdays      45211 non-null  int64 \n",
+      " 14  previous   45211 non-null  int64 \n",
+      " 15  poutcome   45211 non-null  object\n",
+      " 16  y          45211 non-null  object\n",
+      "dtypes: int64(7), object(10)\n",
+      "memory usage: 5.9+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b0edb6c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>balance</th>\n",
+       "      <th>day</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>campaign</th>\n",
+       "      <th>pdays</th>\n",
+       "      <th>previous</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>45211.000000</td>\n",
+       "      <td>45211.000000</td>\n",
+       "      <td>45211.000000</td>\n",
+       "      <td>45211.000000</td>\n",
+       "      <td>45211.000000</td>\n",
+       "      <td>45211.000000</td>\n",
+       "      <td>45211.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>40.936210</td>\n",
+       "      <td>1362.272058</td>\n",
+       "      <td>15.806419</td>\n",
+       "      <td>258.163080</td>\n",
+       "      <td>2.763841</td>\n",
+       "      <td>40.197828</td>\n",
+       "      <td>0.580323</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>10.618762</td>\n",
+       "      <td>3044.765829</td>\n",
+       "      <td>8.322476</td>\n",
+       "      <td>257.527812</td>\n",
+       "      <td>3.098021</td>\n",
+       "      <td>100.128746</td>\n",
+       "      <td>2.303441</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>-8019.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>72.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>103.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>39.000000</td>\n",
+       "      <td>448.000000</td>\n",
+       "      <td>16.000000</td>\n",
+       "      <td>180.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>48.000000</td>\n",
+       "      <td>1428.000000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>319.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>95.000000</td>\n",
+       "      <td>102127.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>4918.000000</td>\n",
+       "      <td>63.000000</td>\n",
+       "      <td>871.000000</td>\n",
+       "      <td>275.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                age        balance           day      duration      campaign  \\\n",
+       "count  45211.000000   45211.000000  45211.000000  45211.000000  45211.000000   \n",
+       "mean      40.936210    1362.272058     15.806419    258.163080      2.763841   \n",
+       "std       10.618762    3044.765829      8.322476    257.527812      3.098021   \n",
+       "min       18.000000   -8019.000000      1.000000      0.000000      1.000000   \n",
+       "25%       33.000000      72.000000      8.000000    103.000000      1.000000   \n",
+       "50%       39.000000     448.000000     16.000000    180.000000      2.000000   \n",
+       "75%       48.000000    1428.000000     21.000000    319.000000      3.000000   \n",
+       "max       95.000000  102127.000000     31.000000   4918.000000     63.000000   \n",
+       "\n",
+       "              pdays      previous  \n",
+       "count  45211.000000  45211.000000  \n",
+       "mean      40.197828      0.580323  \n",
+       "std      100.128746      2.303441  \n",
+       "min       -1.000000      0.000000  \n",
+       "25%       -1.000000      0.000000  \n",
+       "50%       -1.000000      0.000000  \n",
+       "75%       -1.000000      0.000000  \n",
+       "max      871.000000    275.000000  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "78f9a979",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "integer_columns = df.select_dtypes(include=['int64']).columns \n",
+    "object_columns = df.select_dtypes(include=['object']).columns "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "acda24da",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "int64 columns:\n",
+      " Index(['age', 'balance', 'day', 'duration', 'campaign', 'pdays', 'previous'], dtype='object')\n",
+      "\n",
+      "object columns:\n",
+      " Index(['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact',\n",
+      "       'month', 'poutcome', 'y'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('\\nint64 columns:\\n', integer_columns) \n",
+    "print('\\nobject columns:\\n', object_columns) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8bacf9e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<Axes: title={'center': 'age'}>,\n",
+       "        <Axes: title={'center': 'balance'}>,\n",
+       "        <Axes: title={'center': 'day'}>],\n",
+       "       [<Axes: title={'center': 'duration'}>,\n",
+       "        <Axes: title={'center': 'campaign'}>,\n",
+       "        <Axes: title={'center': 'pdays'}>],\n",
+       "       [<Axes: title={'center': 'previous'}>, <Axes: >, <Axes: >]],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x2000 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[integer_columns].hist(figsize=(20,20))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "71efdac3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x2000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,20))\n",
+    "sns.heatmap(df[integer_columns].corr() , annot=True ,fmt=\"0.0\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "6a04608f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"y\"] = df[\"y\"].map(lambda x:0 if x == \"no\" else 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "e58d918c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1200x1200 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_style('whitegrid')\n",
+    "edgecolor = 'black'\n",
+    "\n",
+    "fig = plt.figure(figsize=(12,12))\n",
+    "\n",
+    "def scatter_plot(a):\n",
+    "    fig, ax = plt.subplots()\n",
+    "    ax.scatter(x = df[a], y = df['y'], edgecolor=edgecolor)\n",
+    "    plt.ylabel('y', fontsize=12)\n",
+    "    plt.xlabel(a, fontsize=12)\n",
+    "    plt.suptitle(\"Scatter Plot of \"+ a + \" and y\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1f6cfab",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "e6b449b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>job</th>\n",
+       "      <th>marital</th>\n",
+       "      <th>education</th>\n",
+       "      <th>default</th>\n",
+       "      <th>balance</th>\n",
+       "      <th>housing</th>\n",
+       "      <th>loan</th>\n",
+       "      <th>contact</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>campaign</th>\n",
+       "      <th>pdays</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>poutcome</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>58</td>\n",
+       "      <td>management</td>\n",
+       "      <td>married</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2143</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>5</td>\n",
+       "      <td>may</td>\n",
+       "      <td>261</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>44</td>\n",
+       "      <td>technician</td>\n",
+       "      <td>single</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>no</td>\n",
+       "      <td>29</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>5</td>\n",
+       "      <td>may</td>\n",
+       "      <td>151</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>33</td>\n",
+       "      <td>entrepreneur</td>\n",
+       "      <td>married</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>5</td>\n",
+       "      <td>may</td>\n",
+       "      <td>76</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>47</td>\n",
+       "      <td>blue-collar</td>\n",
+       "      <td>married</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>no</td>\n",
+       "      <td>1506</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>5</td>\n",
+       "      <td>may</td>\n",
+       "      <td>92</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>33</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>single</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>no</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>5</td>\n",
+       "      <td>may</td>\n",
+       "      <td>198</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age           job  marital  education default  balance housing loan  \\\n",
+       "0   58    management  married   tertiary      no     2143     yes   no   \n",
+       "1   44    technician   single  secondary      no       29     yes   no   \n",
+       "2   33  entrepreneur  married  secondary      no        2     yes  yes   \n",
+       "3   47   blue-collar  married    unknown      no     1506     yes   no   \n",
+       "4   33       unknown   single    unknown      no        1      no   no   \n",
+       "\n",
+       "   contact  day month  duration  campaign  pdays  previous poutcome  y  \n",
+       "0  unknown    5   may       261         1     -1         0  unknown  0  \n",
+       "1  unknown    5   may       151         1     -1         0  unknown  0  \n",
+       "2  unknown    5   may        76         1     -1         0  unknown  0  \n",
+       "3  unknown    5   may        92         1     -1         0  unknown  0  \n",
+       "4  unknown    5   may       198         1     -1         0  unknown  0  "
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "7d02b688",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatter_plot(\"age\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "e8cc5093",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for col in integer_columns:\n",
+    "    scatter_plot(col)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26522f3e",
+   "metadata": {},
+   "source": [
+    "# Handelling missing Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "0ada479a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def missing_data(df,n):    \n",
+    "    total = df.isnull().sum().sort_values(ascending=False)          \n",
+    "    percentage = (df.isnull().sum() / df.isnull().count()).sort_values(ascending=False)*100\n",
+    "    No_unique_val = df.nunique()                                   \n",
+    "    missing_data = pd.concat([total, percentage, No_unique_val], axis=1, \n",
+    "                             keys=['Total No of missing val', '% of Missing val','No of unique val'], sort = False)\n",
+    "    \n",
+    "    print(missing_data.head(n))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "7ac4defa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           Total No of missing val  % of Missing val  No of unique val\n",
+      "age                              0               0.0                77\n",
+      "day                              0               0.0                31\n",
+      "poutcome                         0               0.0                 4\n",
+      "previous                         0               0.0                41\n",
+      "pdays                            0               0.0               559\n",
+      "campaign                         0               0.0                48\n",
+      "duration                         0               0.0              1573\n",
+      "month                            0               0.0                12\n",
+      "contact                          0               0.0                 3\n",
+      "job                              0               0.0                12\n",
+      "loan                             0               0.0                 2\n",
+      "housing                          0               0.0                 2\n",
+      "balance                          0               0.0              7168\n",
+      "default                          0               0.0                 2\n",
+      "education                        0               0.0                 4\n",
+      "marital                          0               0.0                 3\n",
+      "y                                0               0.0                 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "missing_data(df,20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "d1d20841",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df.drop([\"y\"] , axis=1)\n",
+    "y = df[\"y\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "4839b73a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train , X_test , y_train , y_test = train_test_split(X , y , test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "1963a18b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_pipeline = Pipeline([\n",
+    "    (\"impute\", SimpleImputer(strategy=\"median\")),\n",
+    "    (\"standardize\", StandardScaler())\n",
+    "])\n",
+    "\n",
+    "num_attribs = X_train.select_dtypes(include=['int64' , \"float\"]).columns\n",
+    "cat_attribs = X_train.select_dtypes(include=[\"object\"]).columns\n",
+    "\n",
+    "cat_pipeline = make_pipeline(\n",
+    "    SimpleImputer(strategy=\"most_frequent\"),\n",
+    "    OneHotEncoder(handle_unknown=\"ignore\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "3f28fe79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preprocessing = ColumnTransformer([\n",
+    "    (\"num\", num_pipeline, num_attribs),\n",
+    "    (\"cat\", cat_pipeline, cat_attribs)\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ad7bbee",
+   "metadata": {},
+   "source": [
+    "# Linear Regresion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "8bb9f498",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lin_reg = make_pipeline(preprocessing, LinearRegression())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "0dc9a685",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
+       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;impute&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                  (&#x27;standardize&#x27;,\n",
+       "                                                                   StandardScaler())]),\n",
+       "                                                  Index([&#x27;age&#x27;, &#x27;balance&#x27;, &#x27;day&#x27;, &#x27;duration&#x27;, &#x27;campaign&#x27;, &#x27;pdays&#x27;, &#x27;previous&#x27;], dtype=&#x27;object&#x27;)),\n",
+       "                                                 (&#x27;cat&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
+       "                                                                  (&#x27;onehotencoder&#x27;,\n",
+       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
+       "                                                  Index([&#x27;job&#x27;, &#x27;marital&#x27;, &#x27;education&#x27;, &#x27;default&#x27;, &#x27;housing&#x27;, &#x27;loan&#x27;, &#x27;contact&#x27;,\n",
+       "       &#x27;month&#x27;, &#x27;poutcome&#x27;],\n",
+       "      dtype=&#x27;object&#x27;))])),\n",
+       "                (&#x27;linearregression&#x27;, LinearRegression())])</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
+       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;impute&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                  (&#x27;standardize&#x27;,\n",
+       "                                                                   StandardScaler())]),\n",
+       "                                                  Index([&#x27;age&#x27;, &#x27;balance&#x27;, &#x27;day&#x27;, &#x27;duration&#x27;, &#x27;campaign&#x27;, &#x27;pdays&#x27;, &#x27;previous&#x27;], dtype=&#x27;object&#x27;)),\n",
+       "                                                 (&#x27;cat&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
+       "                                                                  (&#x27;onehotencoder&#x27;,\n",
+       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
+       "                                                  Index([&#x27;job&#x27;, &#x27;marital&#x27;, &#x27;education&#x27;, &#x27;default&#x27;, &#x27;housing&#x27;, &#x27;loan&#x27;, &#x27;contact&#x27;,\n",
+       "       &#x27;month&#x27;, &#x27;poutcome&#x27;],\n",
+       "      dtype=&#x27;object&#x27;))])),\n",
+       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
+       "                                 Pipeline(steps=[(&#x27;impute&#x27;,\n",
+       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                 (&#x27;standardize&#x27;,\n",
+       "                                                  StandardScaler())]),\n",
+       "                                 Index([&#x27;age&#x27;, &#x27;balance&#x27;, &#x27;day&#x27;, &#x27;duration&#x27;, &#x27;campaign&#x27;, &#x27;pdays&#x27;, &#x27;previous&#x27;], dtype=&#x27;object&#x27;)),\n",
+       "                                (&#x27;cat&#x27;,\n",
+       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
+       "                                                 (&#x27;onehotencoder&#x27;,\n",
+       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
+       "                                 Index([&#x27;job&#x27;, &#x27;marital&#x27;, &#x27;education&#x27;, &#x27;default&#x27;, &#x27;housing&#x27;, &#x27;loan&#x27;, &#x27;contact&#x27;,\n",
+       "       &#x27;month&#x27;, &#x27;poutcome&#x27;],\n",
+       "      dtype=&#x27;object&#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 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 sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;age&#x27;, &#x27;balance&#x27;, &#x27;day&#x27;, &#x27;duration&#x27;, &#x27;campaign&#x27;, &#x27;pdays&#x27;, &#x27;previous&#x27;], dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator 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 sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator 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 sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;job&#x27;, &#x27;marital&#x27;, &#x27;education&#x27;, &#x27;default&#x27;, &#x27;housing&#x27;, &#x27;loan&#x27;, &#x27;contact&#x27;,\n",
+       "       &#x27;month&#x27;, &#x27;poutcome&#x27;],\n",
+       "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('columntransformer',\n",
+       "                 ColumnTransformer(transformers=[('num',\n",
+       "                                                  Pipeline(steps=[('impute',\n",
+       "                                                                   SimpleImputer(strategy='median')),\n",
+       "                                                                  ('standardize',\n",
+       "                                                                   StandardScaler())]),\n",
+       "                                                  Index(['age', 'balance', 'day', 'duration', 'campaign', 'pdays', 'previous'], dtype='object')),\n",
+       "                                                 ('cat',\n",
+       "                                                  Pipeline(steps=[('simpleimputer',\n",
+       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
+       "                                                                  ('onehotencoder',\n",
+       "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
+       "                                                  Index(['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact',\n",
+       "       'month', 'poutcome'],\n",
+       "      dtype='object'))])),\n",
+       "                ('linearregression', LinearRegression())])"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lin_reg.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "b2e469cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.16291471404118021"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_train, lin_reg.predict(X_train))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b1f3d77",
+   "metadata": {},
+   "source": [
+    "# Randome forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "2d5cb292",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rfc = Pipeline([\n",
+    "    (\"pre\" , preprocessing),\n",
+    "    ('xgboost', RandomForestClassifier())\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "9386bfc7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;pre&#x27;,\n",
+       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;impute&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                  (&#x27;standardize&#x27;,\n",
+       "                                                                   StandardScaler())]),\n",
+       "                                                  Index([&#x27;age&#x27;, &#x27;balance&#x27;, &#x27;day&#x27;, &#x27;duration&#x27;, &#x27;campaign&#x27;, &#x27;pdays&#x27;, &#x27;previous&#x27;], dtype=&#x27;object&#x27;)),\n",
+       "                                                 (&#x27;cat&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
+       "                                                                  (&#x27;onehotencoder&#x27;,\n",
+       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
+       "                                                  Index([&#x27;job&#x27;, &#x27;marital&#x27;, &#x27;education&#x27;, &#x27;default&#x27;, &#x27;housing&#x27;, &#x27;loan&#x27;, &#x27;contact&#x27;,\n",
+       "       &#x27;month&#x27;, &#x27;poutcome&#x27;],\n",
+       "      dtype=&#x27;object&#x27;))])),\n",
+       "                (&#x27;xgboost&#x27;, RandomForestClassifier())])</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;pre&#x27;,\n",
+       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;impute&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                  (&#x27;standardize&#x27;,\n",
+       "                                                                   StandardScaler())]),\n",
+       "                                                  Index([&#x27;age&#x27;, &#x27;balance&#x27;, &#x27;day&#x27;, &#x27;duration&#x27;, &#x27;campaign&#x27;, &#x27;pdays&#x27;, &#x27;previous&#x27;], dtype=&#x27;object&#x27;)),\n",
+       "                                                 (&#x27;cat&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
+       "                                                                  (&#x27;onehotencoder&#x27;,\n",
+       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
+       "                                                  Index([&#x27;job&#x27;, &#x27;marital&#x27;, &#x27;education&#x27;, &#x27;default&#x27;, &#x27;housing&#x27;, &#x27;loan&#x27;, &#x27;contact&#x27;,\n",
+       "       &#x27;month&#x27;, &#x27;poutcome&#x27;],\n",
+       "      dtype=&#x27;object&#x27;))])),\n",
+       "                (&#x27;xgboost&#x27;, RandomForestClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">pre: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
+       "                                 Pipeline(steps=[(&#x27;impute&#x27;,\n",
+       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                 (&#x27;standardize&#x27;,\n",
+       "                                                  StandardScaler())]),\n",
+       "                                 Index([&#x27;age&#x27;, &#x27;balance&#x27;, &#x27;day&#x27;, &#x27;duration&#x27;, &#x27;campaign&#x27;, &#x27;pdays&#x27;, &#x27;previous&#x27;], dtype=&#x27;object&#x27;)),\n",
+       "                                (&#x27;cat&#x27;,\n",
+       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
+       "                                                 (&#x27;onehotencoder&#x27;,\n",
+       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
+       "                                 Index([&#x27;job&#x27;, &#x27;marital&#x27;, &#x27;education&#x27;, &#x27;default&#x27;, &#x27;housing&#x27;, &#x27;loan&#x27;, &#x27;contact&#x27;,\n",
+       "       &#x27;month&#x27;, &#x27;poutcome&#x27;],\n",
+       "      dtype=&#x27;object&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;age&#x27;, &#x27;balance&#x27;, &#x27;day&#x27;, &#x27;duration&#x27;, &#x27;campaign&#x27;, &#x27;pdays&#x27;, &#x27;previous&#x27;], dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;job&#x27;, &#x27;marital&#x27;, &#x27;education&#x27;, &#x27;default&#x27;, &#x27;housing&#x27;, &#x27;loan&#x27;, &#x27;contact&#x27;,\n",
+       "       &#x27;month&#x27;, &#x27;poutcome&#x27;],\n",
+       "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('pre',\n",
+       "                 ColumnTransformer(transformers=[('num',\n",
+       "                                                  Pipeline(steps=[('impute',\n",
+       "                                                                   SimpleImputer(strategy='median')),\n",
+       "                                                                  ('standardize',\n",
+       "                                                                   StandardScaler())]),\n",
+       "                                                  Index(['age', 'balance', 'day', 'duration', 'campaign', 'pdays', 'previous'], dtype='object')),\n",
+       "                                                 ('cat',\n",
+       "                                                  Pipeline(steps=[('simpleimputer',\n",
+       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
+       "                                                                  ('onehotencoder',\n",
+       "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
+       "                                                  Index(['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact',\n",
+       "       'month', 'poutcome'],\n",
+       "      dtype='object'))])),\n",
+       "                ('xgboost', RandomForestClassifier())])"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rfc.fit(X_train , y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "3502eb0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.7648750276487504e-05"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_train, rfc.predict(X_train))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "175ccae2",
+   "metadata": {},
+   "source": [
+    "# XGBoost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "f2a71fb1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xgb = Pipeline([\n",
+    "    (\"pre\" , preprocessing),\n",
+    "    ('xgboost', XGBRegressor(gamma=100))\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "22566169",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;pre&#x27;,\n",
+       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;impute&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                  (&#x27;standardize&#x27;,\n",
+       "                                                                   StandardScaler())]),\n",
+       "                                                  Index([&#x27;age&#x27;, &#x27;balance&#x27;, &#x27;day&#x27;, &#x27;duration&#x27;, &#x27;campaign&#x27;, &#x27;pdays&#x27;, &#x27;previous&#x27;], dtype=&#x27;object&#x27;)),\n",
+       "                                                 (&#x27;cat&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
+       "                                                                  (&#x27;onehotencoder&#x27;,\n",
+       "                                                                   OneHotE...\n",
+       "                              feature_types=None, gamma=100, grow_policy=None,\n",
+       "                              importance_type=None,\n",
+       "                              interaction_constraints=None, learning_rate=None,\n",
+       "                              max_bin=None, 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, multi_strategy=None,\n",
+       "                              n_estimators=None, n_jobs=None,\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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;pre&#x27;,\n",
+       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;impute&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                  (&#x27;standardize&#x27;,\n",
+       "                                                                   StandardScaler())]),\n",
+       "                                                  Index([&#x27;age&#x27;, &#x27;balance&#x27;, &#x27;day&#x27;, &#x27;duration&#x27;, &#x27;campaign&#x27;, &#x27;pdays&#x27;, &#x27;previous&#x27;], dtype=&#x27;object&#x27;)),\n",
+       "                                                 (&#x27;cat&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
+       "                                                                  (&#x27;onehotencoder&#x27;,\n",
+       "                                                                   OneHotE...\n",
+       "                              feature_types=None, gamma=100, grow_policy=None,\n",
+       "                              importance_type=None,\n",
+       "                              interaction_constraints=None, learning_rate=None,\n",
+       "                              max_bin=None, 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, multi_strategy=None,\n",
+       "                              n_estimators=None, n_jobs=None,\n",
+       "                              num_parallel_tree=None, random_state=None, ...))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">pre: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
+       "                                 Pipeline(steps=[(&#x27;impute&#x27;,\n",
+       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                 (&#x27;standardize&#x27;,\n",
+       "                                                  StandardScaler())]),\n",
+       "                                 Index([&#x27;age&#x27;, &#x27;balance&#x27;, &#x27;day&#x27;, &#x27;duration&#x27;, &#x27;campaign&#x27;, &#x27;pdays&#x27;, &#x27;previous&#x27;], dtype=&#x27;object&#x27;)),\n",
+       "                                (&#x27;cat&#x27;,\n",
+       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
+       "                                                 (&#x27;onehotencoder&#x27;,\n",
+       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
+       "                                 Index([&#x27;job&#x27;, &#x27;marital&#x27;, &#x27;education&#x27;, &#x27;default&#x27;, &#x27;housing&#x27;, &#x27;loan&#x27;, &#x27;contact&#x27;,\n",
+       "       &#x27;month&#x27;, &#x27;poutcome&#x27;],\n",
+       "      dtype=&#x27;object&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;age&#x27;, &#x27;balance&#x27;, &#x27;day&#x27;, &#x27;duration&#x27;, &#x27;campaign&#x27;, &#x27;pdays&#x27;, &#x27;previous&#x27;], dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;job&#x27;, &#x27;marital&#x27;, &#x27;education&#x27;, &#x27;default&#x27;, &#x27;housing&#x27;, &#x27;loan&#x27;, &#x27;contact&#x27;,\n",
+       "       &#x27;month&#x27;, &#x27;poutcome&#x27;],\n",
+       "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><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=100, 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=None,\n",
+       "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('pre',\n",
+       "                 ColumnTransformer(transformers=[('num',\n",
+       "                                                  Pipeline(steps=[('impute',\n",
+       "                                                                   SimpleImputer(strategy='median')),\n",
+       "                                                                  ('standardize',\n",
+       "                                                                   StandardScaler())]),\n",
+       "                                                  Index(['age', 'balance', 'day', 'duration', 'campaign', 'pdays', 'previous'], dtype='object')),\n",
+       "                                                 ('cat',\n",
+       "                                                  Pipeline(steps=[('simpleimputer',\n",
+       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
+       "                                                                  ('onehotencoder',\n",
+       "                                                                   OneHotE...\n",
+       "                              feature_types=None, gamma=100, grow_policy=None,\n",
+       "                              importance_type=None,\n",
+       "                              interaction_constraints=None, learning_rate=None,\n",
+       "                              max_bin=None, 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, multi_strategy=None,\n",
+       "                              n_estimators=None, n_jobs=None,\n",
+       "                              num_parallel_tree=None, random_state=None, ...))])"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xgb.fit(X_train , y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "cddd3428",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.17895237255175703"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_train, xgb.predict(X_train))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "fb6f0f5c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "param_grid = {\n",
+    "    'xgboost__n_estimators': [50, 100, 200],\n",
+    "    'xgboost__learning_rate': [0.01, 0.1, 0.2],\n",
+    "    'xgboost__max_depth': [3, 5, 7],\n",
+    "    'xgboost__subsample': [0.8, 1.0],\n",
+    "    'xgboost__colsample_bytree': [0.8, 1.0],\n",
+    "    'xgboost__gamma': [0, 1, 5],\n",
+    "    'xgboost__min_child_weight': [1, 5, 10],\n",
+    "\n",
+    "}\n",
+    "\n",
+    "grid_search = GridSearchCV(xgb, param_grid, cv=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "683527d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
+       "             estimator=Pipeline(steps=[(&#x27;pre&#x27;,\n",
+       "                                        ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
+       "                                                                         Pipeline(steps=[(&#x27;impute&#x27;,\n",
+       "                                                                                          SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                                         (&#x27;standardize&#x27;,\n",
+       "                                                                                          StandardScaler())]),\n",
+       "                                                                         Index([&#x27;age&#x27;, &#x27;balance&#x27;, &#x27;day&#x27;, &#x27;duration&#x27;, &#x27;campaign&#x27;, &#x27;pdays&#x27;, &#x27;previous&#x27;], dtype=&#x27;object&#x27;)),\n",
+       "                                                                        (&#x27;cat&#x27;,\n",
+       "                                                                         Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent...\n",
+       "                                                     monotone_constraints=None,\n",
+       "                                                     multi_strategy=None,\n",
+       "                                                     n_estimators=None,\n",
+       "                                                     n_jobs=None,\n",
+       "                                                     num_parallel_tree=None,\n",
+       "                                                     random_state=None, ...))]),\n",
+       "             param_grid={&#x27;xgboost__colsample_bytree&#x27;: [0.8, 1.0],\n",
+       "                         &#x27;xgboost__gamma&#x27;: [0, 1, 5],\n",
+       "                         &#x27;xgboost__learning_rate&#x27;: [0.01, 0.1, 0.2],\n",
+       "                         &#x27;xgboost__max_depth&#x27;: [3, 5, 7],\n",
+       "                         &#x27;xgboost__min_child_weight&#x27;: [1, 5, 10],\n",
+       "                         &#x27;xgboost__n_estimators&#x27;: [50, 100, 200],\n",
+       "                         &#x27;xgboost__subsample&#x27;: [0.8, 1.0]})</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
+       "             estimator=Pipeline(steps=[(&#x27;pre&#x27;,\n",
+       "                                        ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
+       "                                                                         Pipeline(steps=[(&#x27;impute&#x27;,\n",
+       "                                                                                          SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                                         (&#x27;standardize&#x27;,\n",
+       "                                                                                          StandardScaler())]),\n",
+       "                                                                         Index([&#x27;age&#x27;, &#x27;balance&#x27;, &#x27;day&#x27;, &#x27;duration&#x27;, &#x27;campaign&#x27;, &#x27;pdays&#x27;, &#x27;previous&#x27;], dtype=&#x27;object&#x27;)),\n",
+       "                                                                        (&#x27;cat&#x27;,\n",
+       "                                                                         Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent...\n",
+       "                                                     monotone_constraints=None,\n",
+       "                                                     multi_strategy=None,\n",
+       "                                                     n_estimators=None,\n",
+       "                                                     n_jobs=None,\n",
+       "                                                     num_parallel_tree=None,\n",
+       "                                                     random_state=None, ...))]),\n",
+       "             param_grid={&#x27;xgboost__colsample_bytree&#x27;: [0.8, 1.0],\n",
+       "                         &#x27;xgboost__gamma&#x27;: [0, 1, 5],\n",
+       "                         &#x27;xgboost__learning_rate&#x27;: [0.01, 0.1, 0.2],\n",
+       "                         &#x27;xgboost__max_depth&#x27;: [3, 5, 7],\n",
+       "                         &#x27;xgboost__min_child_weight&#x27;: [1, 5, 10],\n",
+       "                         &#x27;xgboost__n_estimators&#x27;: [50, 100, 200],\n",
+       "                         &#x27;xgboost__subsample&#x27;: [0.8, 1.0]})</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;pre&#x27;,\n",
+       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;impute&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                  (&#x27;standardize&#x27;,\n",
+       "                                                                   StandardScaler())]),\n",
+       "                                                  Index([&#x27;age&#x27;, &#x27;balance&#x27;, &#x27;day&#x27;, &#x27;duration&#x27;, &#x27;campaign&#x27;, &#x27;pdays&#x27;, &#x27;previous&#x27;], dtype=&#x27;object&#x27;)),\n",
+       "                                                 (&#x27;cat&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
+       "                                                                  (&#x27;onehotencoder&#x27;,\n",
+       "                                                                   OneHotE...\n",
+       "                              feature_types=None, gamma=100, grow_policy=None,\n",
+       "                              importance_type=None,\n",
+       "                              interaction_constraints=None, learning_rate=None,\n",
+       "                              max_bin=None, 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, multi_strategy=None,\n",
+       "                              n_estimators=None, n_jobs=None,\n",
+       "                              num_parallel_tree=None, random_state=None, ...))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">pre: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
+       "                                 Pipeline(steps=[(&#x27;impute&#x27;,\n",
+       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                 (&#x27;standardize&#x27;,\n",
+       "                                                  StandardScaler())]),\n",
+       "                                 Index([&#x27;age&#x27;, &#x27;balance&#x27;, &#x27;day&#x27;, &#x27;duration&#x27;, &#x27;campaign&#x27;, &#x27;pdays&#x27;, &#x27;previous&#x27;], dtype=&#x27;object&#x27;)),\n",
+       "                                (&#x27;cat&#x27;,\n",
+       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
+       "                                                 (&#x27;onehotencoder&#x27;,\n",
+       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
+       "                                 Index([&#x27;job&#x27;, &#x27;marital&#x27;, &#x27;education&#x27;, &#x27;default&#x27;, &#x27;housing&#x27;, &#x27;loan&#x27;, &#x27;contact&#x27;,\n",
+       "       &#x27;month&#x27;, &#x27;poutcome&#x27;],\n",
+       "      dtype=&#x27;object&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;age&#x27;, &#x27;balance&#x27;, &#x27;day&#x27;, &#x27;duration&#x27;, &#x27;campaign&#x27;, &#x27;pdays&#x27;, &#x27;previous&#x27;], dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;job&#x27;, &#x27;marital&#x27;, &#x27;education&#x27;, &#x27;default&#x27;, &#x27;housing&#x27;, &#x27;loan&#x27;, &#x27;contact&#x27;,\n",
+       "       &#x27;month&#x27;, &#x27;poutcome&#x27;],\n",
+       "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-45\" type=\"checkbox\" ><label for=\"sk-estimator-id-45\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><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=100, 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=None,\n",
+       "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=5,\n",
+       "             estimator=Pipeline(steps=[('pre',\n",
+       "                                        ColumnTransformer(transformers=[('num',\n",
+       "                                                                         Pipeline(steps=[('impute',\n",
+       "                                                                                          SimpleImputer(strategy='median')),\n",
+       "                                                                                         ('standardize',\n",
+       "                                                                                          StandardScaler())]),\n",
+       "                                                                         Index(['age', 'balance', 'day', 'duration', 'campaign', 'pdays', 'previous'], dtype='object')),\n",
+       "                                                                        ('cat',\n",
+       "                                                                         Pipeline(steps=[('simpleimputer',\n",
+       "                                                                                          SimpleImputer(strategy='most_frequent...\n",
+       "                                                     monotone_constraints=None,\n",
+       "                                                     multi_strategy=None,\n",
+       "                                                     n_estimators=None,\n",
+       "                                                     n_jobs=None,\n",
+       "                                                     num_parallel_tree=None,\n",
+       "                                                     random_state=None, ...))]),\n",
+       "             param_grid={'xgboost__colsample_bytree': [0.8, 1.0],\n",
+       "                         'xgboost__gamma': [0, 1, 5],\n",
+       "                         'xgboost__learning_rate': [0.01, 0.1, 0.2],\n",
+       "                         'xgboost__max_depth': [3, 5, 7],\n",
+       "                         'xgboost__min_child_weight': [1, 5, 10],\n",
+       "                         'xgboost__n_estimators': [50, 100, 200],\n",
+       "                         'xgboost__subsample': [0.8, 1.0]})"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid_search.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "a5912558",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best parameters found:  {'xgboost__colsample_bytree': 1.0, 'xgboost__gamma': 0, 'xgboost__learning_rate': 0.1, 'xgboost__max_depth': 5, 'xgboost__min_child_weight': 10, 'xgboost__n_estimators': 200, 'xgboost__subsample': 1.0}\n",
+      "Best cross-validation score: 0.40\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Best parameters found: \", grid_search.best_params_)\n",
+    "print(\"Best cross-validation score: {:.2f}\".format(grid_search.best_score_))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3728d34c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pandas_q.ipynb b/pandas_q.ipynb
new file mode 100644
index 0000000..d74e30c
--- /dev/null
+++ b/pandas_q.ipynb
@@ -0,0 +1,543 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "w9JF9U0GL6VR"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "foyH8052MUCC",
+    "outputId": "a239f577-8d66-4608-9a11-b55e2bb89ecc"
+   },
+   "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>client_id</th>\n",
+       "      <th>driver_id</th>\n",
+       "      <th>city_id</th>\n",
+       "      <th>status</th>\n",
+       "      <th>request_at</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>2013-10-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "      <td>cancelled_by_driver</td>\n",
+       "      <td>2013-10-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12</td>\n",
+       "      <td>6</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>2013-10-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>cancelled_by_client</td>\n",
+       "      <td>2013-10-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>2013-10-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>11</td>\n",
+       "      <td>6</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>2013-10-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12</td>\n",
+       "      <td>6</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>2013-10-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12</td>\n",
+       "      <td>12</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>2013-10-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10</td>\n",
+       "      <td>12</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>2013-10-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>4</td>\n",
+       "      <td>13</td>\n",
+       "      <td>12</td>\n",
+       "      <td>cancelled_by_driver</td>\n",
+       "      <td>2013-10-03</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id  client_id  driver_id  city_id               status  request_at\n",
+       "0   1          1         10        1            completed  2013-10-01\n",
+       "1   2          2         11        1  cancelled_by_driver  2013-10-01\n",
+       "2   3          3         12        6            completed  2013-10-01\n",
+       "3   4          4         13        6  cancelled_by_client  2013-10-01\n",
+       "4   5          1         10        1            completed  2013-10-02\n",
+       "5   6          2         11        6            completed  2013-10-02\n",
+       "6   7          3         12        6            completed  2013-10-02\n",
+       "7   8          2         12       12            completed  2013-10-03\n",
+       "8   9          3         10       12            completed  2013-10-03\n",
+       "9  10          4         13       12  cancelled_by_driver  2013-10-03"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trips_df = pd.DataFrame({\n",
+    "    'id': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
+    "    'client_id': [1, 2, 3, 4, 1, 2, 3, 2, 3, 4],\n",
+    "    'driver_id': [10, 11, 12, 13, 10, 11, 12, 12, 10, 13],\n",
+    "    'city_id': [1, 1, 6, 6, 1, 6, 6, 12, 12, 12],\n",
+    "    'status': ['completed', 'cancelled_by_driver', 'completed', 'cancelled_by_client', 'completed', 'completed', 'completed', 'completed', 'completed', 'cancelled_by_driver'],\n",
+    "    'request_at': ['2013-10-01', '2013-10-01', '2013-10-01', '2013-10-01', '2013-10-02', '2013-10-02', '2013-10-02', '2013-10-03', '2013-10-03', '2013-10-03']\n",
+    "})\n",
+    "trips_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 300
+    },
+    "id": "7ZbeOjcSMunr",
+    "outputId": "4629471b-16c0-4b56-ea17-b3fb71522a10"
+   },
+   "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>users_id</th>\n",
+       "      <th>banned</th>\n",
+       "      <th>role</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>client</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>True</td>\n",
+       "      <td>client</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>client</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>False</td>\n",
+       "      <td>client</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>driver</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>11</td>\n",
+       "      <td>False</td>\n",
+       "      <td>driver</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>12</td>\n",
+       "      <td>False</td>\n",
+       "      <td>driver</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>13</td>\n",
+       "      <td>False</td>\n",
+       "      <td>driver</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   users_id  banned    role\n",
+       "0         1   False  client\n",
+       "1         2    True  client\n",
+       "2         3   False  client\n",
+       "3         4   False  client\n",
+       "4        10   False  driver\n",
+       "5        11   False  driver\n",
+       "6        12   False  driver\n",
+       "7        13   False  driver"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "users_df = pd.DataFrame({\n",
+    "    'users_id': [1, 2, 3, 4, 10, 11, 12, 13],\n",
+    "    'banned': [False, True, False, False, False, False, False, False],\n",
+    "    'role': ['client', 'client', 'client', 'client', 'driver', 'driver', 'driver', 'driver']\n",
+    "})\n",
+    "users_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "TWucKh9dNuCf"
+   },
+   "source": [
+    "How to calculate the cancellation rate of requests with unbanned users (client and driver both not banned) each day between October 1, 2013, and October 3, 2013? Ensure the solution rounds the cancellation rate to two decimal points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 143
+    },
+    "id": "kKFn90U5N4V7",
+    "outputId": "cb36ff30-fa67-43e1-921f-0e15581e2a3a"
+   },
+   "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>Day</th>\n",
+       "      <th>Cancellation Rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2013-10-01</td>\n",
+       "      <td>0.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2013-10-02</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2013-10-03</td>\n",
+       "      <td>0.50</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Day  Cancellation Rate\n",
+       "0  2013-10-01               0.33\n",
+       "1  2013-10-02               0.00\n",
+       "2  2013-10-03               0.50"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output = pd.DataFrame({\n",
+    "    \"Day\": [\"2013-10-01\", \"2013-10-02\", \"2013-10-03\"],\n",
+    "    \"Cancellation Rate\": [0.33, 0.00, 0.50]\n",
+    "})\n",
+    "output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mNTJ2-RvO82I"
+   },
+   "source": [
+    "Explanation:\n",
+    "\n",
+    "On 2013-10-01: Out of 4 total requests, 2 were canceled. Discounting a request from a banned client, there were 3 unbanned requests, with a cancellation rate of 0.33.\n",
+    "\n",
+    "On 2013-10-02: Among 3 total requests, none were canceled. Ignoring a request from a banned client, there were 2 unbanned requests, resulting in a cancellation rate of 0.00.\n",
+    "\n",
+    "On 2013-10-03: Out of 3 total requests, 1 was canceled. Disregarding a request from a banned client, there were 2 unbanned requests, leading to a cancellation rate of 0.50."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_df = pd.merge(trips_df, users_df, left_on='client_id', right_on='users_id', how='left')\n",
+    "merged_df = pd.merge(merged_df, users_df, left_on='driver_id', right_on='users_id', how='left', suffixes=('_client', '_driver'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\jrahm\\AppData\\Local\\Temp\\ipykernel_22108\\1669844010.py:2: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  filtered_df['request_at'] = pd.to_datetime(filtered_df['request_at'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "filtered_df = merged_df[(~merged_df['banned_client']) & (~merged_df['banned_driver'])]\n",
+    "filtered_df['request_at'] = pd.to_datetime(filtered_df['request_at'])\n",
+    "grouped_df = filtered_df.groupby('request_at')['status'].apply(lambda x: (x == 'cancelled_by_client').sum() / len(x)).reset_index()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output = pd.DataFrame({\n",
+    "    \"Day\": grouped_df['request_at'].dt.strftime('%Y-%m-%d'),\n",
+    "    \"Cancellation Rate\": grouped_df['status'].round(2)\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Day</th>\n",
+       "      <th>Cancellation Rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2013-10-01</td>\n",
+       "      <td>0.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2013-10-02</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2013-10-03</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Day  Cancellation Rate\n",
+       "0  2013-10-01               0.33\n",
+       "1  2013-10-02               0.00\n",
+       "2  2013-10-03               0.00"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python_q.ipynb b/python_q.ipynb
new file mode 100644
index 0000000..c2354a8
--- /dev/null
+++ b/python_q.ipynb
@@ -0,0 +1,117 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "42976f64-9534-4f1c-ba5e-c21c3befa7a6",
+   "metadata": {},
+   "source": [
+    "Find the sum of three integers in the given array 'nums' of length 'n' such that the sum is closest to the target. Return the computed sum. Assume that there is exactly one solution for each input."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efd7fa7c-4865-4480-863b-7203874b40d6",
+   "metadata": {},
+   "source": [
+    "Example 1:\n",
+    "\n",
+    "Input: nums = [-1,2,1,-4], target = 1\n",
+    "Output: 2\n",
+    "Explanation: The sum that is closest to the target is 2. (-1 + 2 + 1 = 2).\n",
+    "\n",
+    "Example 2:\n",
+    "\n",
+    "Input: nums = [0,0,0], target = 1\n",
+    "Output: 0\n",
+    "Explanation: The sum that is closest to the target is 0. (0 + 0 + 0 = 0)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "8320f2ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def threeSumClosest(nums, target):\n",
+    "    nums.sort()\n",
+    "    closest_sum = float('inf')\n",
+    "    result = None\n",
+    "\n",
+    "    for i in range(len(nums) - 2):\n",
+    "        left, right = i + 1, len(nums) - 1\n",
+    "\n",
+    "        while left < right:\n",
+    "            current_sum = nums[i] + nums[left] + nums[right]\n",
+    "\n",
+    "            if abs(current_sum - target) < abs(closest_sum - target):\n",
+    "                closest_sum = current_sum\n",
+    "                result = (nums[i], nums[left], nums[right])\n",
+    "\n",
+    "            if current_sum < target:\n",
+    "                left += 1\n",
+    "            elif current_sum > target:\n",
+    "                right -= 1\n",
+    "            else:\n",
+    "                return result\n",
+    "\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "fb3ee719",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nums1 = [-1, 2, 1, -4]\n",
+    "nums2 = [0, 0, 0]\n",
+    "target1 = 1\n",
+    "target2 = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "9b8dba50",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((-1, 1, 2), (0, 0, 0))"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "threeSumClosest(nums1, target1) , threeSumClosest(nums2, target2)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}