deploy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obejective\n",
    "- Create a streamlit app to predict california house price\n",
    "\n",
    "## Info\n",
    "- Ref: https://github.com/ageron/handson-ml3/blob/main/02_end_to_end_machine_learning_project.ipynb\n",
    "- Column Description\n",
    "    - longitude: A measure of how far west a house is; a higher value is farther west\n",
    "    - latitude: A measure of how far north a house is; a higher value is farther north\n",
    "    - housingMedianAge: Median age of a house within a block; a lower number is a newer building\n",
    "    - totalRooms: Total number of rooms within a block\n",
    "    - totalBedrooms: Total number of bedrooms within a block\n",
    "    - population: Total number of people residing within a block\n",
    "    - households: Total number of households, a group of people residing within a home unit, for a block\n",
    "    - medianIncome: Median income for households within a block of houses (measured in tens of thousands of US Dollars)\n",
    "    - medianHouseValue: Median house value for households within a block (measured in US Dollars)\n",
    "    - oceanProximity: Location of the house w.r.t ocean/sea"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.base import BaseEstimator, TransformerMixin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error, r2_score, root_mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dill\n",
    "import json\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading Data Scource"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='income_cat'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('housing.csv')\n",
    "\n",
    "\n",
    "# df = pd.read_csv('housing.csv')\n",
    "df[\"income_cat\"] = pd.cut(df[\"median_income\"],\n",
    "                               bins=[0., 1.5, 3.0, 4.5, 6., np.inf],\n",
    "                               labels=[1, 2, 3, 4, 5])\n",
    "\n",
    "df[\"income_cat\"].value_counts().sort_index().plot.bar(rot=0, grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income_cat\n",
       "3    0.350568\n",
       "2    0.318868\n",
       "4    0.176287\n",
       "5    0.114480\n",
       "1    0.039798\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "income_cat\n",
       "3    0.350614\n",
       "2    0.318798\n",
       "4    0.176357\n",
       "5    0.114341\n",
       "1    0.039890\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train, test = train_test_split(df, test_size=0.3, stratify=df['income_cat'], random_state=6012024)\n",
    "\n",
    "## Check stratification\n",
    "train['income_cat'].value_counts() / len(train['income_cat'])\n",
    "test['income_cat'].value_counts() / len(test['income_cat'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.681859\n",
       "total_rooms           0.130749\n",
       "housing_median_age    0.107536\n",
       "households            0.062901\n",
       "total_bedrooms        0.046569\n",
       "population           -0.026317\n",
       "longitude            -0.056267\n",
       "latitude             -0.133642\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = train.corr(numeric_only=True)\n",
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x1000 with 0 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation Matrix Heatmap')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(array([0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5]),\n",
       " [Text(0.5, 0, 'longitude'),\n",
       "  Text(1.5, 0, 'latitude'),\n",
       "  Text(2.5, 0, 'housing_median_age'),\n",
       "  Text(3.5, 0, 'total_rooms'),\n",
       "  Text(4.5, 0, 'total_bedrooms'),\n",
       "  Text(5.5, 0, 'population'),\n",
       "  Text(6.5, 0, 'households'),\n",
       "  Text(7.5, 0, 'median_income'),\n",
       "  Text(8.5, 0, 'median_house_value'),\n",
       "  Text(9.5, 0, 'income_cat')])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(array([0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5]),\n",
       " [Text(0, 0.5, 'longitude'),\n",
       "  Text(0, 1.5, 'latitude'),\n",
       "  Text(0, 2.5, 'housing_median_age'),\n",
       "  Text(0, 3.5, 'total_rooms'),\n",
       "  Text(0, 4.5, 'total_bedrooms'),\n",
       "  Text(0, 5.5, 'population'),\n",
       "  Text(0, 6.5, 'households'),\n",
       "  Text(0, 7.5, 'median_income'),\n",
       "  Text(0, 8.5, 'median_house_value'),\n",
       "  Text(0, 9.5, 'income_cat')])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 90.10937499999994, 'Features')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(117.10937499999997, 0.5, 'Features')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming df is your DataFrame\n",
    "correlation_matrix = df.select_dtypes(exclude='object').corr()\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(\n",
    "    correlation_matrix, \n",
    "    annot=True, \n",
    "    fmt=\".2f\", \n",
    "    cmap='coolwarm', \n",
    "    cbar=True, \n",
    "    square=True, \n",
    "    linewidths=.5, \n",
    "    linecolor='white', \n",
    "    annot_kws={\"size\": 10}  # Adjust font size for annotations\n",
    ")\n",
    "\n",
    "# Customize the plot\n",
    "plt.title('Correlation Matrix Heatmap', fontsize=16, fontweight='bold')\n",
    "plt.xticks(rotation=45, ha='right', fontsize=12)\n",
    "plt.yticks(rotation=0, fontsize=12)\n",
    "plt.xlabel('Features', fontsize=14)\n",
    "plt.ylabel('Features', fontsize=14)\n",
    "\n",
    "# Adjust the colorbar\n",
    "cbar = plt.gca().collections[0].colorbar\n",
    "cbar.set_ticks([-1, -0.5, 0, 0.5, 1])\n",
    "cbar.set_label('Correlation Coefficient', fontsize=12)\n",
    "\n",
    "# Tight layout for better spacing\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x800 with 0 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shubham\\AppData\\Local\\Temp\\ipykernel_10432\\3626745619.py:8: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.violinplot(\n",
      "C:\\Users\\Shubham\\AppData\\Local\\Temp\\ipykernel_10432\\3626745619.py:8: FutureWarning: \n",
      "\n",
      "The `scale` parameter has been renamed and will be removed in v0.15.0. Pass `density_norm='width'` for the same effect.\n",
      "  sns.violinplot(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='ocean_proximity', ylabel='median_house_value'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ocean Proximity Analysis for Median House Value')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Ocean Proximity')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Median House Value')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4],\n",
       " [Text(0, 0, 'NEAR BAY'),\n",
       "  Text(1, 0, '<1H OCEAN'),\n",
       "  Text(2, 0, 'INLAND'),\n",
       "  Text(3, 0, 'NEAR OCEAN'),\n",
       "  Text(4, 0, 'ISLAND')])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(array([-100000.,       0.,  100000.,  200000.,  300000.,  400000.,\n",
       "         500000.,  600000.,  700000.]),\n",
       " [Text(0, -100000.0, '−100000'),\n",
       "  Text(0, 0.0, '0'),\n",
       "  Text(0, 100000.0, '100000'),\n",
       "  Text(0, 200000.0, '200000'),\n",
       "  Text(0, 300000.0, '300000'),\n",
       "  Text(0, 400000.0, '400000'),\n",
       "  Text(0, 500000.0, '500000'),\n",
       "  Text(0, 600000.0, '600000'),\n",
       "  Text(0, 700000.0, '700000')])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shubham\\AppData\\Local\\Temp\\ipykernel_10432\\3626745619.py:30: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Ocean Proximity Analysis for Median House Value'}, xlabel='Ocean Proximity', ylabel='Median House Value'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setting the aesthetic style of the plots\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Create a larger figure size for better visibility\n",
    "plt.figure(figsize=(14, 8))\n",
    "\n",
    "# Create a violin plot with enhanced aesthetics\n",
    "sns.violinplot(\n",
    "    x='ocean_proximity', \n",
    "    y='median_house_value', \n",
    "    data=train, \n",
    "    inner='quartile',  # Show quartiles within the violins for more detail\n",
    "    palette='muted',   # Use a muted palette for aesthetic reasons\n",
    "    scale='width'      # Adjust the width of the violins based on the number of observations\n",
    ")\n",
    "\n",
    "# Set titles and labels with larger fonts for better readability\n",
    "plt.title('Ocean Proximity Analysis for Median House Value', fontsize=18, fontweight='bold')\n",
    "plt.xlabel('Ocean Proximity', fontsize=14)\n",
    "plt.ylabel('Median House Value', fontsize=14)\n",
    "\n",
    "# Improve tick visibility\n",
    "plt.xticks(rotation=45, fontsize=12)  # Rotate x labels to prevent overlap\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "# Add grid lines for better readability\n",
    "plt.grid(True, linestyle='--', alpha=0.6)\n",
    "\n",
    "# Add a box plot to show additional statistical information\n",
    "sns.boxplot(\n",
    "    x='ocean_proximity', \n",
    "    y='median_house_value', \n",
    "    data=train, \n",
    "    whis=1.5, \n",
    "    width=0.15, \n",
    "    palette='pastel', \n",
    "    boxprops=dict(alpha=0.7)\n",
    ")\n",
    "\n",
    "# Remove the top and right spines for a cleaner look\n",
    "sns.despine(trim=True)\n",
    "\n",
    "# Tight layout for better spacing\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Preprocessor(BaseEstimator, TransformerMixin): \n",
    "    # Train our custom preprocessors \n",
    "    numerical_columns = ['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
    "       'total_bedrooms', 'population', 'households', 'median_income', ]\n",
    "    categorical_columns = ['ocean_proximity', 'income_cat']\n",
    "    \n",
    "    def fit(self, X, y=None): \n",
    "\n",
    "        # Create and fit simple imputer\n",
    "        self.imputer = SimpleImputer(strategy='median')\n",
    "        self.imputer.fit(X[self.numerical_columns])\n",
    "        \n",
    "        # Create and fit Standard Scaler \n",
    "        self.scaler = StandardScaler()\n",
    "        self.scaler.fit(X[self.numerical_columns]) \n",
    "        \n",
    "        # Create and fit one hot encoder\n",
    "        self.onehot = OneHotEncoder(handle_unknown='ignore')\n",
    "        self.onehot.fit(X[self.categorical_columns])\n",
    "        \n",
    "        return self \n",
    "\n",
    "    def transform(self, X): \n",
    "        \n",
    "        # Apply simple imputer \n",
    "        imputed_cols = self.imputer.transform(X[self.numerical_columns])\n",
    "        onehot_cols = self.onehot.transform(X[self.categorical_columns])\n",
    "        \n",
    "        # Copy the df \n",
    "        transformed_df = X.copy()\n",
    "         \n",
    "        # Apply transformed columns\n",
    "        transformed_df[self.numerical_columns] = imputed_cols\n",
    "        transformed_df[self.numerical_columns] = self.scaler.transform(transformed_df[self.numerical_columns])        \n",
    "        \n",
    "        # Drop existing categorical columns and replace with one hot equiv\n",
    "        transformed_df = transformed_df.drop(self.categorical_columns, axis=1) \n",
    "        transformed_df[self.onehot.get_feature_names_out()] = onehot_cols.toarray().astype(int)\n",
    "        \n",
    "        return transformed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Preprocessor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;Preprocessor<span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>Preprocessor()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "Preprocessor()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity_&lt;1H OCEAN</th>\n",
       "      <th>ocean_proximity_INLAND</th>\n",
       "      <th>ocean_proximity_ISLAND</th>\n",
       "      <th>ocean_proximity_NEAR BAY</th>\n",
       "      <th>ocean_proximity_NEAR OCEAN</th>\n",
       "      <th>income_cat_1</th>\n",
       "      <th>income_cat_2</th>\n",
       "      <th>income_cat_3</th>\n",
       "      <th>income_cat_4</th>\n",
       "      <th>income_cat_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11052</th>\n",
       "      <td>-1.423273</td>\n",
       "      <td>1.015372</td>\n",
       "      <td>1.861228</td>\n",
       "      <td>0.306580</td>\n",
       "      <td>0.784997</td>\n",
       "      <td>-0.214370</td>\n",
       "      <td>0.595966</td>\n",
       "      <td>-0.263125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19511</th>\n",
       "      <td>-1.193655</td>\n",
       "      <td>0.771912</td>\n",
       "      <td>-0.922621</td>\n",
       "      <td>-0.596595</td>\n",
       "      <td>-0.631782</td>\n",
       "      <td>-0.889467</td>\n",
       "      <td>-0.631715</td>\n",
       "      <td>-1.135759</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18564</th>\n",
       "      <td>0.643289</td>\n",
       "      <td>-0.848031</td>\n",
       "      <td>1.065843</td>\n",
       "      <td>-0.809565</td>\n",
       "      <td>-0.781041</td>\n",
       "      <td>-0.536137</td>\n",
       "      <td>-0.736421</td>\n",
       "      <td>-0.002022</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17223</th>\n",
       "      <td>1.297201</td>\n",
       "      <td>-0.885486</td>\n",
       "      <td>-0.684005</td>\n",
       "      <td>0.962893</td>\n",
       "      <td>1.031393</td>\n",
       "      <td>0.774602</td>\n",
       "      <td>1.059291</td>\n",
       "      <td>-0.421214</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1862</th>\n",
       "      <td>0.653272</td>\n",
       "      <td>-0.754392</td>\n",
       "      <td>1.304459</td>\n",
       "      <td>-0.847579</td>\n",
       "      <td>-0.634151</td>\n",
       "      <td>-0.344129</td>\n",
       "      <td>-0.691921</td>\n",
       "      <td>-1.467318</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "11052  -1.423273  1.015372            1.861228     0.306580        0.784997   \n",
       "19511  -1.193655  0.771912           -0.922621    -0.596595       -0.631782   \n",
       "18564   0.643289 -0.848031            1.065843    -0.809565       -0.781041   \n",
       "17223   1.297201 -0.885486           -0.684005     0.962893        1.031393   \n",
       "1862    0.653272 -0.754392            1.304459    -0.847579       -0.634151   \n",
       "\n",
       "       population  households  median_income  ocean_proximity_<1H OCEAN  \\\n",
       "11052   -0.214370    0.595966      -0.263125                          0   \n",
       "19511   -0.889467   -0.631715      -1.135759                          1   \n",
       "18564   -0.536137   -0.736421      -0.002022                          1   \n",
       "17223    0.774602    1.059291      -0.421214                          0   \n",
       "1862    -0.344129   -0.691921      -1.467318                          1   \n",
       "\n",
       "       ocean_proximity_INLAND  ocean_proximity_ISLAND  \\\n",
       "11052                       0                       0   \n",
       "19511                       0                       0   \n",
       "18564                       0                       0   \n",
       "17223                       1                       0   \n",
       "1862                        0                       0   \n",
       "\n",
       "       ocean_proximity_NEAR BAY  ocean_proximity_NEAR OCEAN  income_cat_1  \\\n",
       "11052                         1                           0             0   \n",
       "19511                         0                           0             0   \n",
       "18564                         0                           0             0   \n",
       "17223                         0                           0             0   \n",
       "1862                          0                           0             1   \n",
       "\n",
       "       income_cat_2  income_cat_3  income_cat_4  income_cat_5  \n",
       "11052             0             1             0             0  \n",
       "19511             1             0             0             0  \n",
       "18564             0             1             0             0  \n",
       "17223             0             1             0             0  \n",
       "1862              0             0             0             0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = train.drop('median_house_value', axis=1)\n",
    "y_train = train['median_house_value']\n",
    "preproc = Preprocessor()\n",
    "preproc.fit(X_train)\n",
    "preprocessed_df = preproc.transform(X_train)\n",
    "preprocessed_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;, Preprocessor()),\n",
       "                (&#x27;randomforestregressor&#x27;, RandomForestRegressor())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;, Preprocessor()),\n",
       "                (&#x27;randomforestregressor&#x27;, RandomForestRegressor())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">Preprocessor</label><div class=\"sk-toggleable__content fitted\"><pre>Preprocessor()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor()</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor', Preprocessor()),\n",
       "                ('randomforestregressor', RandomForestRegressor())])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr = make_pipeline(Preprocessor(), RandomForestRegressor())\n",
    "rfr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Set Metrics:\n",
      "Root Mean Squared Error (RMSE): 18726.67\n",
      "Mean Absolute Error (MAE): 12118.22\n",
      "R-squared (R2): 0.97\n"
     ]
    }
   ],
   "source": [
    "# Predictions for training set\n",
    "y_train_hat = rfr.predict(X_train)\n",
    "\n",
    "# Calculate metrics\n",
    "rmse = root_mean_squared_error(y_train, y_train_hat)\n",
    "mae = mean_absolute_error(y_train, y_train_hat)\n",
    "r2 = r2_score(y_train, y_train_hat)\n",
    "\n",
    "# Print the metrics\n",
    "print(f'Train Set Metrics:')\n",
    "print(f'Root Mean Squared Error (RMSE): {rmse:.2f}')\n",
    "print(f'Mean Absolute Error (MAE): {mae:.2f}')\n",
    "print(f'R-squared (R2): {r2:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Metrics:\n",
      "Root Mean Squared Error (RMSE): 48134.42\n",
      "Mean Absolute Error (MAE): 31450.14\n",
      "R-squared (R2): 0.83\n"
     ]
    }
   ],
   "source": [
    "# Prepare the test data\n",
    "X_test = test.drop('median_house_value', axis=1)\n",
    "y_test = test['median_house_value']\n",
    "\n",
    "# Predictions for test set\n",
    "y_test_hat = rfr.predict(X_test)\n",
    "\n",
    "# Calculate metrics for the test set\n",
    "rmse_test = root_mean_squared_error(y_test, y_test_hat)\n",
    "mae_test = mean_absolute_error(y_test, y_test_hat)\n",
    "r2_test = r2_score(y_test, y_test_hat)\n",
    "\n",
    "# Print the metrics\n",
    "print(f'Test Set Metrics:')\n",
    "print(f'Root Mean Squared Error (RMSE): {rmse_test:.2f}')\n",
    "print(f'Mean Absolute Error (MAE): {mae_test:.2f}')\n",
    "print(f'R-squared (R2): {r2_test:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('rfr.pkl', 'wb') as f:\n",
    "    dill.dump(rfr, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"longitude\": -122.29,\n",
      "  \"latitude\": 37.92,\n",
      "  \"housing_median_age\": 32.0,\n",
      "  \"total_rooms\": 1736.0,\n",
      "  \"total_bedrooms\": 234.0,\n",
      "  \"population\": 602.0,\n",
      "  \"households\": 231.0,\n",
      "  \"median_income\": 6.516,\n",
      "  \"ocean_proximity\": \"NEAR BAY\",\n",
      "  \"income_cat\": 5\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "options = {}\n",
    "numerical_columns = ['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
    "       'total_bedrooms', 'population', 'households', 'median_income', ]\n",
    "for column in numerical_columns:\n",
    "    options[column] = (X_train[column].min(),  X_train[column].max())\n",
    "for column in ['ocean_proximity']:\n",
    "    options[column] = sorted(X_train[column].unique().tolist())\n",
    "\n",
    "model = {\n",
    "    'version': '1.0',\n",
    "    'name': 'RandomForestRegressor',    \n",
    "    'Comments': 'Initial model',\n",
    "    'model_file': 'rfr.pkl',\n",
    "    'options': options,\n",
    "}\n",
    "\n",
    "with open('rfr_info.json', 'w') as f:\n",
    "    json.dump(model, f, indent=2)\n",
    "\n",
    "print(json.dumps(X_test.iloc[0].to_dict(), indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "options = {}\n",
    "numerical_columns = ['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
    "       'total_bedrooms', 'population', 'households', 'median_income', ]\n",
    "for column in numerical_columns:\n",
    "    options[column] = (X_train[column].min(),  X_train[column].max())\n",
    "for column in ['ocean_proximity']:\n",
    "    options[column] = sorted(X_train[column].unique().tolist())\n",
    "\n",
    "model = {\n",
    "    'version': '1.0',\n",
    "    'name': 'RandomForestRegressor',    \n",
    "    'Comments': 'Deploy model',\n",
    "    'model_file': 'rfr.pkl',\n",
    "    'options': options,\n",
    "}\n",
    "\n",
    "import json\n",
    "with open('rfr_info.json', 'w') as f:\n",
    "    json.dump(model, f, indent=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"longitude\": -122.29,\n",
      "  \"latitude\": 37.92,\n",
      "  \"housing_median_age\": 32.0,\n",
      "  \"total_rooms\": 1736.0,\n",
      "  \"total_bedrooms\": 234.0,\n",
      "  \"population\": 602.0,\n",
      "  \"households\": 231.0,\n",
      "  \"median_income\": 6.516,\n",
      "  \"ocean_proximity\": \"NEAR BAY\",\n",
      "  \"income_cat\": 5\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "print(json.dumps(X_test.iloc[0].to_dict(), indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 'joblib' Corrupts the .pkl file \n",
    "\n",
    "'''\n",
    "import joblib\n",
    "with open('HomeScope_reduced.pkl', 'wb') as f:\n",
    "    joblib.dump(rfr, f, compress=9)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"longitude\": -122.29,\n",
      "  \"latitude\": 37.92,\n",
      "  \"housing_median_age\": 32.0,\n",
      "  \"total_rooms\": 1736.0,\n",
      "  \"total_bedrooms\": 234.0,\n",
      "  \"population\": 602.0,\n",
      "  \"households\": 231.0,\n",
      "  \"median_income\": 6.516,\n",
      "  \"ocean_proximity\": \"NEAR BAY\",\n",
      "  \"income_cat\": 5\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "## For Fast_API\n",
    "\n",
    "print(json.dumps(X_test.iloc[0].to_dict(), indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "1997 {'prediction': 377652.1}\n",
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      "11582 {'prediction': 377652.1}\n",
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      "20504 {'prediction': 377652.1}\n",
      "20057 {'prediction': 377652.1}\n",
      "206 {'prediction': 377652.1}\n",
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      "2929 {'prediction': 377652.1}\n",
      "2113 {'prediction': 377652.1}\n",
      "1547 {'prediction': 377652.1}\n",
      "19466 {'prediction': 377652.1}\n",
      "8677 {'prediction': 377652.1}\n",
      "2816 {'prediction': 377652.1}\n",
      "10122 {'prediction': 377652.1}\n",
      "5016 {'prediction': 377652.1}\n",
      "19682 {'prediction': 377652.1}\n",
      "18522 {'prediction': 377652.1}\n",
      "12133 {'prediction': 377652.1}\n",
      "6599 {'prediction': 377652.1}\n",
      "10641 {'prediction': 377652.1}\n",
      "14125 {'prediction': 377652.1}\n",
      "6347 {'prediction': 377652.1}\n",
      "3734 {'prediction': 377652.1}\n",
      "12882 {'prediction': 377652.1}\n",
      "16259 {'prediction': 377652.1}\n",
      "15675 {'prediction': 377652.1}\n",
      "11984 {'prediction': 377652.1}\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "for index, row in X_test.iterrows():\n",
    "    data_fast = json.dumps(X_test.iloc[0].to_dict(), indent=2)\n",
    "    r = requests.post('http://127.0.0.1:8000/', data=data_fast)\n",
    "    print(index, r.json())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'prediction': 377652.1}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
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