diff --git "a/notebook.ipynb" "b/notebook.ipynb"
new file mode 100644--- /dev/null
+++ "b/notebook.ipynb"
@@ -0,0 +1,1563 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2da91147",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['id', 'title', 'url', 'price', 'meter_price', 'price_2_payments',\n",
+       "       'price_4_payments', 'price_12_payments', 'rnpl_monthly_price',\n",
+       "       'area_sqm', 'deed_area', 'num_bedrooms', 'num_bathrooms',\n",
+       "       'num_living_rooms', 'num_kitchens', 'num_rooms', 'floor_level',\n",
+       "       'furnished', 'duplex', 'ac', 'lift', 'maid_room', 'driver_room', 'pool',\n",
+       "       'basement', 'backyard', 'playground', 'car_entrance', 'stairs',\n",
+       "       'water_availability', 'electrical_availability',\n",
+       "       'drainage_availability', 'private_roof', 'two_entrances',\n",
+       "       'special_entrance', 'apartment_in_villa', 'street_width', 'direction',\n",
+       "       'city', 'district', 'address', 'latitude', 'longitude', 'category_id',\n",
+       "       'category_ga_listing_type', 'category_ga_property_category',\n",
+       "       'category_is_rent', 'category_name', 'category_en', 'category_plural',\n",
+       "       'category_uri', 'category_path', 'category_keywords',\n",
+       "       'category_description', 'category_index', 'sale_type', 'is_rental',\n",
+       "       'is_sale', 'is_auction', 'is_daily_rental', 'create_time',\n",
+       "       'published_at', 'last_update', 'verified', 'boosted', 'premium',\n",
+       "       'has_img', 'has_video', 'ad_license_number', 'deed_number',\n",
+       "       'rega_licensed', 'plan_no', 'parcel_no', 'user_verified',\n",
+       "       'company_name', 'user_paid_tier', 'description', 'images', 'videos'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from pathlib import Path\n",
+    "\n",
+    "data_dir = Path(\"../data/aqar\")\n",
+    "df=pd.read_csv(data_dir / \"aqar.csv\")\n",
+    "\n",
+    "pd.set_option(\"display.max_columns\",None)\n",
+    "df.columns\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34d0301b",
+   "metadata": {},
+   "source": [
+    "- [x] Use Y-data\n",
+    "- [] try multi layer preceptron(loss-funtion MSE, opt: adamw, Activation: Leaky ReLU)\n",
+    "- [x] try a Layered ML model(Use three diffrent ml models, they output to the forth)\n",
+    "  - Didn't Work Had Worse R^2 Values\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "07504435",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<Axes: title={'center': 'id'}>,\n",
+       "        <Axes: title={'center': 'price'}>,\n",
+       "        <Axes: title={'center': 'meter_price'}>,\n",
+       "        <Axes: title={'center': 'price_2_payments'}>,\n",
+       "        <Axes: title={'center': 'price_4_payments'}>],\n",
+       "       [<Axes: title={'center': 'price_12_payments'}>,\n",
+       "        <Axes: title={'center': 'rnpl_monthly_price'}>,\n",
+       "        <Axes: title={'center': 'area_sqm'}>,\n",
+       "        <Axes: title={'center': 'deed_area'}>,\n",
+       "        <Axes: title={'center': 'num_bedrooms'}>],\n",
+       "       [<Axes: title={'center': 'num_bathrooms'}>,\n",
+       "        <Axes: title={'center': 'num_living_rooms'}>,\n",
+       "        <Axes: title={'center': 'num_kitchens'}>,\n",
+       "        <Axes: title={'center': 'num_rooms'}>,\n",
+       "        <Axes: title={'center': 'floor_level'}>],\n",
+       "       [<Axes: title={'center': 'street_width'}>,\n",
+       "        <Axes: title={'center': 'latitude'}>,\n",
+       "        <Axes: title={'center': 'longitude'}>,\n",
+       "        <Axes: title={'center': 'category_id'}>,\n",
+       "        <Axes: title={'center': 'category_index'}>],\n",
+       "       [<Axes: title={'center': 'create_time'}>,\n",
+       "        <Axes: title={'center': 'published_at'}>,\n",
+       "        <Axes: title={'center': 'last_update'}>,\n",
+       "        <Axes: title={'center': 'ad_license_number'}>,\n",
+       "        <Axes: title={'center': 'user_paid_tier'}>]], dtype=object)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1500 with 25 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.hist(bins=50, figsize=(20,15))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "944597f1",
+   "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>area_sqm</th>\n",
+       "      <th>num_bathrooms</th>\n",
+       "      <th>num_bedrooms</th>\n",
+       "      <th>num_rooms</th>\n",
+       "      <th>location</th>\n",
+       "      <th>lift</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>116.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>الرياض_حي النرجس</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1500000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>700.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>الرياض_حي الملقا</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13500000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>215.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>ابها_حي دره المنسك</td>\n",
+       "      <td>0</td>\n",
+       "      <td>635000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>169.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>ابها_حي ابها الجديده</td>\n",
+       "      <td>0</td>\n",
+       "      <td>510000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>208.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>ابها_حي القريقر</td>\n",
+       "      <td>0</td>\n",
+       "      <td>540000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>127.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>جده_حي البوادي</td>\n",
+       "      <td>1</td>\n",
+       "      <td>558000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>736.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>الرياض_حي المهديه</td>\n",
+       "      <td>1</td>\n",
+       "      <td>717000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>201.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>خميس مشيط_حي النهضه</td>\n",
+       "      <td>0</td>\n",
+       "      <td>550000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>200.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>الرياض_حي عكاظ</td>\n",
+       "      <td>0</td>\n",
+       "      <td>695000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>223.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>خميس مشيط_حي النهضه</td>\n",
+       "      <td>0</td>\n",
+       "      <td>530000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    area_sqm  num_bathrooms  num_bedrooms  num_rooms              location  \\\n",
+       "0      116.0            3.0           4.0        4.0      الرياض_حي النرجس   \n",
+       "1      700.0            5.0           7.0        7.0      الرياض_حي الملقا   \n",
+       "3      215.0            NaN           6.0        6.0    ابها_حي دره المنسك   \n",
+       "4      169.0            NaN           5.0        5.0  ابها_حي ابها الجديده   \n",
+       "5      208.0            NaN           6.0        6.0       ابها_حي القريقر   \n",
+       "6      127.0            3.0           4.0        4.0        جده_حي البوادي   \n",
+       "7      736.0            3.0           2.0        2.0     الرياض_حي المهديه   \n",
+       "8      201.0            NaN           6.0        6.0   خميس مشيط_حي النهضه   \n",
+       "11     200.0            5.0           4.0        4.0        الرياض_حي عكاظ   \n",
+       "12     223.0            NaN           6.0        6.0   خميس مشيط_حي النهضه   \n",
+       "\n",
+       "    lift       price  \n",
+       "0      0   1500000.0  \n",
+       "1      1  13500000.0  \n",
+       "3      0    635000.0  \n",
+       "4      0    510000.0  \n",
+       "5      0    540000.0  \n",
+       "6      1    558000.0  \n",
+       "7      1    717000.0  \n",
+       "8      0    550000.0  \n",
+       "11     0    695000.0  \n",
+       "12     0    530000.0  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "num_feature_cols=[\"area_sqm\",\"num_bathrooms\",\"num_bedrooms\",\"num_rooms\"]\n",
+    "cat_feature_cols=[\"location\",]\n",
+    "bool_feature_cols=[\"lift\"]\n",
+    "\n",
+    "df : pd.DataFrame = df[(df['is_rental'] == False) & (df['is_daily_rental'] == False) & (df['sale_type'] != 'rent') & (df['sale_type'] !='daily')].copy()\n",
+    "pd.set_option('future.no_silent_downcasting', True)\n",
+    "# drop listings of land without buildings\n",
+    "df : pd.DataFrame = df[df['category_ga_property_category'] != 'land'].copy()\n",
+    "# drop listings of commercial buildings\n",
+    "df : pd.DataFrame = df[(df[\"category_ga_listing_type\"]!= \"office\") & (df[\"category_ga_listing_type\"]!=\"store\") & (df[\"category_ga_listing_type\"]!=\"warehouse\") & (df[\"category_ga_listing_type\"]!=\"lounge\")].copy()\n",
+    "\n",
+    "for bool_col in bool_feature_cols:\n",
+    "    df[bool_col] = df[bool_col].astype(int)\n",
+    "df['location'] = df['city'] + '_' + df['district']\n",
+    "\n",
+    "target_col=[\"price\"]\n",
+    "\n",
+    "\n",
+    "df[num_feature_cols + cat_feature_cols + bool_feature_cols + target_col].head(10)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "25689c1c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<Axes: title={'center': 'area_sqm'}>,\n",
+       "        <Axes: title={'center': 'num_bathrooms'}>],\n",
+       "       [<Axes: title={'center': 'num_bedrooms'}>,\n",
+       "        <Axes: title={'center': 'num_rooms'}>],\n",
+       "       [<Axes: title={'center': 'lift'}>,\n",
+       "        <Axes: title={'center': 'price'}>]], dtype=object)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 2000x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[num_feature_cols + cat_feature_cols + bool_feature_cols + target_col].hist(bins=50, figsize=(20,15))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3df09689",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Model Training and Evaluation before preprocessing pipeline\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "import numpy as np\n",
+    "from sklearn.base import BaseEstimator, TransformerMixin, OneToOneFeatureMixin\n",
+    "from sklearn.utils._set_output import _SetOutputMixin\n",
+    "\n",
+    "class RareLabelGrouper(OneToOneFeatureMixin, TransformerMixin, BaseEstimator):\n",
+    "    _parameter_constraints = {}\n",
+    "    \n",
+    "    def __init__(self, tol=0.01, replace_with='Other'):\n",
+    "        self.tol = tol\n",
+    "        self.replace_with = replace_with\n",
+    "\n",
+    "    def fit(self, X, y=None):\n",
+    "        # Store feature names\n",
+    "        if isinstance(X, pd.DataFrame):\n",
+    "            self.feature_names_in_ = X.columns.to_numpy()\n",
+    "        else:\n",
+    "            self.feature_names_in_ = np.array([f\"x{i}\" for i in range(X.shape[1])])\n",
+    "        \n",
+    "        # Learn frequent labels\n",
+    "        self.frequent_labels_ = {}\n",
+    "        X_df = pd.DataFrame(X, columns=self.feature_names_in_)\n",
+    "        for col in X_df.columns:\n",
+    "            counts = pd.Series(X_df[col]).value_counts(normalize=True)\n",
+    "            self.frequent_labels_[col] = counts[counts >= self.tol].index\n",
+    "        return self\n",
+    "\n",
+    "    def transform(self, X):\n",
+    "        X_df = pd.DataFrame(X, columns=self.feature_names_in_).copy()\n",
+    "        for col in X_df.columns:\n",
+    "            known_labels = self.frequent_labels_.get(col, [])\n",
+    "            X_df[col] = X_df[col].where(X_df[col].isin(known_labels), self.replace_with)\n",
+    "        return X_df\n",
+    "    \n",
+    "    def get_feature_names_out(self, input_features=None):\n",
+    "        \"\"\"Required for pandas output support\"\"\"\n",
+    "        if input_features is None:\n",
+    "            return self.feature_names_in_\n",
+    "        return np.asarray(input_features, dtype=object)\n",
+    "\n",
+    "from sklearn.preprocessing import FunctionTransformer\n",
+    "\n",
+    "def compute_ratios(X):\n",
+    "    X = X.copy()\n",
+    "    # Add small epsilon to avoid division by zero\n",
+    "    X['sqm_per_room'] = X['area_sqm'] / (X['num_rooms'].replace(0, 1))\n",
+    "    X['bath_per_bed'] = X['num_bathrooms'] / (X['num_bedrooms'].replace(0, 1))\n",
+    "    return X\n",
+    "\n",
+    "ratio_adder = FunctionTransformer(compute_ratios, validate=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "513a16ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Data Pipeline\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.preprocessing import StandardScaler, OneHotEncoder, RobustScaler, TargetEncoder, PolynomialFeatures\n",
+    "\n",
+    "pipe1= ColumnTransformer([\n",
+    "    (\"num_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
+    "    ]), num_feature_cols),\n",
+    "    (\"cat_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n",
+    "        (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\",sparse_output=False,min_frequency=0.001))\n",
+    "    ]), cat_feature_cols),\n",
+    "    (\"bool_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=0)),\n",
+    "    ]), bool_feature_cols)\n",
+    "])\n",
+    "\n",
+    "pipe1_optimized = ColumnTransformer([\n",
+    "    (\"num_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
+    "        (\"ratios\", ratio_adder),\n",
+    "    ]), num_feature_cols),\n",
+    "    (\"cat_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n",
+    "        (\"rare_grouper\", RareLabelGrouper(tol=0.001, replace_with='other')), \n",
+    "        (\"target_enc\", TargetEncoder())\n",
+    "    ]), cat_feature_cols),\n",
+    "    (\"bool_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=0)),\n",
+    "    ]), bool_feature_cols)\n",
+    "])\n",
+    "\n",
+    "pipe1_optimized_scaled = ColumnTransformer([\n",
+    "    (\"num_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
+    "        (\"ratios\", ratio_adder),\n",
+    "        (\"scaler\", RobustScaler())\n",
+    "    ]), num_feature_cols),\n",
+    "    (\"cat_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n",
+    "        (\"rare_grouper\", RareLabelGrouper(tol=0.001, replace_with='other')), \n",
+    "        (\"target_enc\", TargetEncoder())\n",
+    "    ]), cat_feature_cols),\n",
+    "    (\"bool_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=0)),\n",
+    "    ]), bool_feature_cols)\n",
+    "])\n",
+    "pipe2 = ColumnTransformer([\n",
+    "    (\"num_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
+    "    ]), num_feature_cols),\n",
+    "    (\"cat_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n",
+    "        (\"target_enc\", TargetEncoder())\n",
+    "    ]), cat_feature_cols),\n",
+    "    (\"bool_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=0)),\n",
+    "    ]), bool_feature_cols)\n",
+    "])\n",
+    "\n",
+    "pipe2_optimized = ColumnTransformer([\n",
+    "    (\"num_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
+    "        (\"ratios\", ratio_adder),\n",
+    "    ]), num_feature_cols),\n",
+    "    (\"cat_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n",
+    "        (\"target_enc\", TargetEncoder())\n",
+    "    ]), cat_feature_cols),\n",
+    "    (\"bool_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=0)),\n",
+    "    ]), bool_feature_cols)\n",
+    "])\n",
+    "\n",
+    "\n",
+    "\n",
+    "pipe3 = ColumnTransformer([\n",
+    "    (\"num_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
+    "    ]), num_feature_cols),\n",
+    "    (\"cat_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n",
+    "        (\"rare_grouper\", RareLabelGrouper(tol=0.001, replace_with='other')),\n",
+    "        (\"target_enc\", TargetEncoder())\n",
+    "    ]), cat_feature_cols),\n",
+    "    (\"bool_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=0)),\n",
+    "    ]), bool_feature_cols)\n",
+    "])\n",
+    "pipe4 = ColumnTransformer([\n",
+    "    (\"num_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
+    "        (\"scaler\", RobustScaler())\n",
+    "    ]), num_feature_cols),\n",
+    "    (\"cat_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n",
+    "        (\"target_enc\", TargetEncoder())\n",
+    "    ]), cat_feature_cols),\n",
+    "    (\"bool_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=0)),\n",
+    "    ]), bool_feature_cols)\n",
+    "])\n",
+    "\n",
+    "pipe5_ratio_scaled = ColumnTransformer([\n",
+    "    (\"num_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
+    "        (\"ratios\", ratio_adder),\n",
+    "        (\"scaler\", RobustScaler())\n",
+    "    ]), num_feature_cols),\n",
+    "    (\"cat_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n",
+    "        (\"target_enc\", TargetEncoder())\n",
+    "    ]), cat_feature_cols),\n",
+    "    (\"bool_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=0)),\n",
+    "    ]), bool_feature_cols)\n",
+    "])\n",
+    "\n",
+    "# With polynomial features (degree 2)\n",
+    "pipe6_poly = ColumnTransformer([\n",
+    "    (\"num_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
+    "        (\"ratios\", ratio_adder),\n",
+    "        (\"poly\", PolynomialFeatures(degree=2, include_bias=False)),\n",
+    "        (\"scaler\", RobustScaler())\n",
+    "    ]), num_feature_cols),\n",
+    "    (\"cat_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n",
+    "        (\"rare_grouper\", RareLabelGrouper(tol=0.001, replace_with='other')),\n",
+    "        (\"target_enc\", TargetEncoder())\n",
+    "    ]), cat_feature_cols),\n",
+    "    (\"bool_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=0)),\n",
+    "    ]), bool_feature_cols)\n",
+    "])\n",
+    "\n",
+    "# Different rare label threshold\n",
+    "pipe7_rare_moderate = ColumnTransformer([\n",
+    "    (\"num_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
+    "        (\"ratios\", ratio_adder),\n",
+    "        (\"scaler\", RobustScaler())\n",
+    "    ]), num_feature_cols),\n",
+    "    (\"cat_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n",
+    "        (\"rare_grouper\", RareLabelGrouper(tol=0.005, replace_with='other')),\n",
+    "        (\"target_enc\", TargetEncoder())\n",
+    "    ]), cat_feature_cols),\n",
+    "    (\"bool_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=0)),\n",
+    "    ]), bool_feature_cols)\n",
+    "])\n",
+    "\n",
+    "\n",
+    "catboost_pipe = ColumnTransformer([\n",
+    "    (\"num_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
+    "    ]), num_feature_cols),\n",
+    "\n",
+    "    (\"cat_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n",
+    "        (\"rare_grouper\", RareLabelGrouper(tol=0.001, replace_with='other')),\n",
+    "    ]), cat_feature_cols),\n",
+    "\n",
+    "    (\"bool_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=0)),\n",
+    "    ]), bool_feature_cols)\n",
+    "])\n",
+    "\n",
+    "catboost_pipe_nogroup = ColumnTransformer([\n",
+    "    (\"num_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
+    "    ]), num_feature_cols),\n",
+    "\n",
+    "    (\"cat_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n",
+    "    ]), cat_feature_cols),\n",
+    "\n",
+    "    (\"bool_pipeline\", Pipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=0)),\n",
+    "    ]), bool_feature_cols)\n",
+    "])\n",
+    "# Define features and target\n",
+    "X = df[num_feature_cols + cat_feature_cols + bool_feature_cols]\n",
+    "y = np.log1p(df[target_col])\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c745a523",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pickle\n",
+    "from pathlib import Path\n",
+    "\n",
+    "def build_cache_path(pipeline_name:str, model_name:str) -> Path:\n",
+    "    return Path(f\"cache/models/{pipeline_name}_{model_name}_model.pkl\")\n",
+    "\n",
+    "def cache_model(model, pipeline_name:str, model_name:str):\n",
+    "    cache_path = build_cache_path(pipeline_name, model_name)\n",
+    "    cache_path.parent.mkdir(parents=True, exist_ok=True)\n",
+    "    with open(cache_path, 'wb') as f:\n",
+    "        pickle.dump(model, f)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "807e849c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Training HistGB with pipe1_no_scaling...\n",
+      "Loading cached model for HistGB with pipe1_no_scaling...\n",
+      "\n",
+      "Training XGBoost with pipe1_no_scaling...\n",
+      "Loading cached model for XGBoost with pipe1_no_scaling...\n",
+      "\n",
+      "Training RandomForest with pipe1_no_scaling...\n",
+      "Loading cached model for RandomForest with pipe1_no_scaling...\n",
+      "\n",
+      "Training CatBoost with pipe1_no_scaling...\n",
+      "Skipping CatBoost with pipe1_no_scaling due to incompatible preprocessing.\n",
+      "\n",
+      "Training HistGB with pipe1_optimized...\n",
+      "Loading cached model for HistGB with pipe1_optimized...\n",
+      "\n",
+      "Training XGBoost with pipe1_optimized...\n",
+      "Loading cached model for XGBoost with pipe1_optimized...\n",
+      "\n",
+      "Training RandomForest with pipe1_optimized...\n",
+      "Loading cached model for RandomForest with pipe1_optimized...\n",
+      "\n",
+      "Training CatBoost with pipe1_optimized...\n",
+      "Skipping CatBoost with pipe1_optimized due to incompatible preprocessing.\n",
+      "\n",
+      "Training HistGB with pipe1_optimized_scaled...\n",
+      "Loading cached model for HistGB with pipe1_optimized_scaled...\n",
+      "\n",
+      "Training XGBoost with pipe1_optimized_scaled...\n",
+      "Loading cached model for XGBoost with pipe1_optimized_scaled...\n",
+      "\n",
+      "Training RandomForest with pipe1_optimized_scaled...\n",
+      "Loading cached model for RandomForest with pipe1_optimized_scaled...\n",
+      "\n",
+      "Training CatBoost with pipe1_optimized_scaled...\n",
+      "Skipping CatBoost with pipe1_optimized_scaled due to incompatible preprocessing.\n",
+      "\n",
+      "Training HistGB with pipe2_no_grouper...\n",
+      "Loading cached model for HistGB with pipe2_no_grouper...\n",
+      "\n",
+      "Training XGBoost with pipe2_no_grouper...\n",
+      "Loading cached model for XGBoost with pipe2_no_grouper...\n",
+      "\n",
+      "Training RandomForest with pipe2_no_grouper...\n",
+      "Loading cached model for RandomForest with pipe2_no_grouper...\n",
+      "\n",
+      "Training CatBoost with pipe2_no_grouper...\n",
+      "Skipping CatBoost with pipe2_no_grouper due to incompatible preprocessing.\n",
+      "\n",
+      "Training HistGB with pipe2_optimized...\n",
+      "Loading cached model for HistGB with pipe2_optimized...\n",
+      "\n",
+      "Training XGBoost with pipe2_optimized...\n",
+      "Loading cached model for XGBoost with pipe2_optimized...\n",
+      "\n",
+      "Training RandomForest with pipe2_optimized...\n",
+      "Loading cached model for RandomForest with pipe2_optimized...\n",
+      "\n",
+      "Training CatBoost with pipe2_optimized...\n",
+      "Skipping CatBoost with pipe2_optimized due to incompatible preprocessing.\n",
+      "\n",
+      "Training HistGB with pipe3_with_grouper...\n",
+      "Loading cached model for HistGB with pipe3_with_grouper...\n",
+      "\n",
+      "Training XGBoost with pipe3_with_grouper...\n",
+      "Loading cached model for XGBoost with pipe3_with_grouper...\n",
+      "\n",
+      "Training RandomForest with pipe3_with_grouper...\n",
+      "Loading cached model for RandomForest with pipe3_with_grouper...\n",
+      "\n",
+      "Training CatBoost with pipe3_with_grouper...\n",
+      "Skipping CatBoost with pipe3_with_grouper due to incompatible preprocessing.\n",
+      "\n",
+      "Training HistGB with pipe4_target_enc...\n",
+      "Loading cached model for HistGB with pipe4_target_enc...\n",
+      "\n",
+      "Training XGBoost with pipe4_target_enc...\n",
+      "Loading cached model for XGBoost with pipe4_target_enc...\n",
+      "\n",
+      "Training RandomForest with pipe4_target_enc...\n",
+      "Loading cached model for RandomForest with pipe4_target_enc...\n",
+      "\n",
+      "Training CatBoost with pipe4_target_enc...\n",
+      "Skipping CatBoost with pipe4_target_enc due to incompatible preprocessing.\n",
+      "\n",
+      "Training HistGB with pipe5_ratio_scaled...\n",
+      "Loading cached model for HistGB with pipe5_ratio_scaled...\n",
+      "\n",
+      "Training XGBoost with pipe5_ratio_scaled...\n",
+      "Loading cached model for XGBoost with pipe5_ratio_scaled...\n",
+      "\n",
+      "Training RandomForest with pipe5_ratio_scaled...\n",
+      "Loading cached model for RandomForest with pipe5_ratio_scaled...\n",
+      "\n",
+      "Training CatBoost with pipe5_ratio_scaled...\n",
+      "Skipping CatBoost with pipe5_ratio_scaled due to incompatible preprocessing.\n",
+      "\n",
+      "Training HistGB with pipe6_poly...\n",
+      "Loading cached model for HistGB with pipe6_poly...\n",
+      "\n",
+      "Training XGBoost with pipe6_poly...\n",
+      "Loading cached model for XGBoost with pipe6_poly...\n",
+      "\n",
+      "Training RandomForest with pipe6_poly...\n",
+      "Loading cached model for RandomForest with pipe6_poly...\n",
+      "\n",
+      "Training CatBoost with pipe6_poly...\n",
+      "Skipping CatBoost with pipe6_poly due to incompatible preprocessing.\n",
+      "\n",
+      "Training HistGB with pipe7_rare_moderate...\n",
+      "Loading cached model for HistGB with pipe7_rare_moderate...\n",
+      "\n",
+      "Training XGBoost with pipe7_rare_moderate...\n",
+      "Loading cached model for XGBoost with pipe7_rare_moderate...\n",
+      "\n",
+      "Training RandomForest with pipe7_rare_moderate...\n",
+      "Loading cached model for RandomForest with pipe7_rare_moderate...\n",
+      "\n",
+      "Training CatBoost with pipe7_rare_moderate...\n",
+      "Skipping CatBoost with pipe7_rare_moderate due to incompatible preprocessing.\n",
+      "\n",
+      "Training HistGB with catboost_pipe...\n",
+      "Skipping HistGB with catboost_pipe due to incompatible preprocessing.\n",
+      "\n",
+      "Training XGBoost with catboost_pipe...\n",
+      "Skipping XGBoost with catboost_pipe due to incompatible preprocessing.\n",
+      "\n",
+      "Training RandomForest with catboost_pipe...\n",
+      "Skipping RandomForest with catboost_pipe due to incompatible preprocessing.\n",
+      "\n",
+      "Training CatBoost with catboost_pipe...\n",
+      "Loading cached model for CatBoost with catboost_pipe...\n",
+      "\n",
+      "Training HistGB with catboost_pipe_nogroup...\n",
+      "Skipping HistGB with catboost_pipe_nogroup due to incompatible preprocessing.\n",
+      "\n",
+      "Training XGBoost with catboost_pipe_nogroup...\n",
+      "Skipping XGBoost with catboost_pipe_nogroup due to incompatible preprocessing.\n",
+      "\n",
+      "Training RandomForest with catboost_pipe_nogroup...\n",
+      "Skipping RandomForest with catboost_pipe_nogroup due to incompatible preprocessing.\n",
+      "\n",
+      "Training CatBoost with catboost_pipe_nogroup...\n",
+      "Loading cached model for CatBoost with catboost_pipe_nogroup...\n",
+      "\n",
+      "=== Model Comparison Results (with Hyperparameter Tuning) ===\n",
+      "              Pipeline        Model  Train R2  Test R2  Test R2 Std  Test RMSE  Test MAE       Best Params\n",
+      "    pipe5_ratio_scaled RandomForest  0.844274 0.760393     0.073065   0.354984  0.175395 Loaded from cache\n",
+      "       pipe2_optimized RandomForest  0.848622 0.756708     0.075214   0.357677  0.176309 Loaded from cache\n",
+      "   pipe7_rare_moderate RandomForest  0.840507 0.755344     0.077720   0.358582  0.181427 Loaded from cache\n",
+      "      pipe4_target_enc RandomForest  0.843403 0.755019     0.069325   0.359452  0.177403 Loaded from cache\n",
+      "       pipe1_optimized RandomForest  0.841926 0.753579     0.081702   0.359636  0.180932 Loaded from cache\n",
+      "    pipe3_with_grouper RandomForest  0.831085 0.752273     0.079398   0.360709  0.182901 Loaded from cache\n",
+      "pipe1_optimized_scaled RandomForest  0.842271 0.752246     0.077808   0.360879  0.180542 Loaded from cache\n",
+      "      pipe2_no_grouper RandomForest  0.839584 0.751673     0.071067   0.361771  0.178497 Loaded from cache\n",
+      " catboost_pipe_nogroup     CatBoost  0.858797 0.750937     0.072286   0.362168  0.197576 Loaded from cache\n",
+      "    pipe5_ratio_scaled       HistGB  0.833406 0.746400     0.072051   0.365566  0.198109 Loaded from cache\n",
+      "            pipe6_poly RandomForest  0.859781 0.746290     0.077592   0.365215  0.183992 Loaded from cache\n",
+      "       pipe2_optimized       HistGB  0.844445 0.744533     0.066352   0.367321  0.197047 Loaded from cache\n",
+      "      pipe4_target_enc       HistGB  0.833060 0.741381     0.065579   0.369736  0.197691 Loaded from cache\n",
+      "            pipe6_poly       HistGB  0.860080 0.741144     0.070127   0.369555  0.200769 Loaded from cache\n",
+      "      pipe2_no_grouper       HistGB  0.831199 0.740040     0.064301   0.370737  0.197137 Loaded from cache\n",
+      "         catboost_pipe     CatBoost  0.842461 0.739924     0.072716   0.370111  0.205580 Loaded from cache\n",
+      "            pipe6_poly      XGBoost  0.912639 0.734445     0.071166   0.374361  0.190703 Loaded from cache\n",
+      "   pipe7_rare_moderate       HistGB  0.815826 0.733627     0.075810   0.374638  0.204655 Loaded from cache\n",
+      "    pipe3_with_grouper      XGBoost  0.856736 0.732957     0.068168   0.375587  0.200260 Loaded from cache\n",
+      "    pipe3_with_grouper       HistGB  0.823557 0.732805     0.068836   0.375648  0.200024 Loaded from cache\n",
+      "       pipe1_optimized       HistGB  0.827164 0.731310     0.070967   0.376585  0.204912 Loaded from cache\n",
+      "      pipe1_no_scaling      XGBoost  0.861835 0.730877     0.069195   0.376479  0.209684 Loaded from cache\n",
+      "pipe1_optimized_scaled       HistGB  0.830215 0.730009     0.069313   0.377546  0.204890 Loaded from cache\n",
+      "   pipe7_rare_moderate      XGBoost  0.877704 0.728123     0.066910   0.379104  0.198384 Loaded from cache\n",
+      "      pipe4_target_enc      XGBoost  0.851720 0.725034     0.082287   0.379875  0.192838 Loaded from cache\n",
+      "      pipe2_no_grouper      XGBoost  0.855574 0.723345     0.077918   0.381524  0.186268 Loaded from cache\n",
+      "    pipe5_ratio_scaled      XGBoost  0.878042 0.712105     0.097798   0.387138  0.192608 Loaded from cache\n",
+      "pipe1_optimized_scaled      XGBoost  0.876998 0.708773     0.051019   0.393111  0.190951 Loaded from cache\n",
+      "      pipe1_no_scaling       HistGB  0.780609 0.704237     0.068708   0.395384  0.224694 Loaded from cache\n",
+      "      pipe1_no_scaling RandomForest  0.836642 0.698213     0.079270   0.399046  0.217147 Loaded from cache\n",
+      "       pipe2_optimized      XGBoost  0.851599 0.697398     0.089776   0.398603  0.186426 Loaded from cache\n",
+      "       pipe1_optimized      XGBoost  0.871083 0.695633     0.056585   0.401630  0.204982 Loaded from cache\n",
+      "\n",
+      "=== Best Model ===\n",
+      "Pipeline: pipe5_ratio_scaled\n",
+      "Model: RandomForest\n",
+      "Test R2: 0.7604 (±0.0731)\n",
+      "Test MSE: 0.35\n",
+      "Test MAE: 0.18\n",
+      "Best Params: Loaded from cache\n",
+      "\n",
+      "=== Final Test Set Performance ===\n",
+      "Test R2: 0.5113\n",
+      "Test MSE: 969698792949.38\n"
+     ]
+    },
+    {
+     "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>Pipeline</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Train R2</th>\n",
+       "      <th>Test R2</th>\n",
+       "      <th>Test R2 Std</th>\n",
+       "      <th>Test RMSE</th>\n",
+       "      <th>Test MAE</th>\n",
+       "      <th>Best Params</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>pipe5_ratio_scaled</td>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>0.844274</td>\n",
+       "      <td>0.760393</td>\n",
+       "      <td>0.073065</td>\n",
+       "      <td>0.354984</td>\n",
+       "      <td>0.175395</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>pipe2_optimized</td>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>0.848622</td>\n",
+       "      <td>0.756708</td>\n",
+       "      <td>0.075214</td>\n",
+       "      <td>0.357677</td>\n",
+       "      <td>0.176309</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>pipe7_rare_moderate</td>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>0.840507</td>\n",
+       "      <td>0.755344</td>\n",
+       "      <td>0.077720</td>\n",
+       "      <td>0.358582</td>\n",
+       "      <td>0.181427</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>pipe4_target_enc</td>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>0.843403</td>\n",
+       "      <td>0.755019</td>\n",
+       "      <td>0.069325</td>\n",
+       "      <td>0.359452</td>\n",
+       "      <td>0.177403</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>pipe1_optimized</td>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>0.841926</td>\n",
+       "      <td>0.753579</td>\n",
+       "      <td>0.081702</td>\n",
+       "      <td>0.359636</td>\n",
+       "      <td>0.180932</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>pipe3_with_grouper</td>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>0.831085</td>\n",
+       "      <td>0.752273</td>\n",
+       "      <td>0.079398</td>\n",
+       "      <td>0.360709</td>\n",
+       "      <td>0.182901</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>pipe1_optimized_scaled</td>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>0.842271</td>\n",
+       "      <td>0.752246</td>\n",
+       "      <td>0.077808</td>\n",
+       "      <td>0.360879</td>\n",
+       "      <td>0.180542</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>pipe2_no_grouper</td>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>0.839584</td>\n",
+       "      <td>0.751673</td>\n",
+       "      <td>0.071067</td>\n",
+       "      <td>0.361771</td>\n",
+       "      <td>0.178497</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>catboost_pipe_nogroup</td>\n",
+       "      <td>CatBoost</td>\n",
+       "      <td>0.858797</td>\n",
+       "      <td>0.750937</td>\n",
+       "      <td>0.072286</td>\n",
+       "      <td>0.362168</td>\n",
+       "      <td>0.197576</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>pipe5_ratio_scaled</td>\n",
+       "      <td>HistGB</td>\n",
+       "      <td>0.833406</td>\n",
+       "      <td>0.746400</td>\n",
+       "      <td>0.072051</td>\n",
+       "      <td>0.365566</td>\n",
+       "      <td>0.198109</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>pipe6_poly</td>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>0.859781</td>\n",
+       "      <td>0.746290</td>\n",
+       "      <td>0.077592</td>\n",
+       "      <td>0.365215</td>\n",
+       "      <td>0.183992</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>pipe2_optimized</td>\n",
+       "      <td>HistGB</td>\n",
+       "      <td>0.844445</td>\n",
+       "      <td>0.744533</td>\n",
+       "      <td>0.066352</td>\n",
+       "      <td>0.367321</td>\n",
+       "      <td>0.197047</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>pipe4_target_enc</td>\n",
+       "      <td>HistGB</td>\n",
+       "      <td>0.833060</td>\n",
+       "      <td>0.741381</td>\n",
+       "      <td>0.065579</td>\n",
+       "      <td>0.369736</td>\n",
+       "      <td>0.197691</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>pipe6_poly</td>\n",
+       "      <td>HistGB</td>\n",
+       "      <td>0.860080</td>\n",
+       "      <td>0.741144</td>\n",
+       "      <td>0.070127</td>\n",
+       "      <td>0.369555</td>\n",
+       "      <td>0.200769</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>pipe2_no_grouper</td>\n",
+       "      <td>HistGB</td>\n",
+       "      <td>0.831199</td>\n",
+       "      <td>0.740040</td>\n",
+       "      <td>0.064301</td>\n",
+       "      <td>0.370737</td>\n",
+       "      <td>0.197137</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>catboost_pipe</td>\n",
+       "      <td>CatBoost</td>\n",
+       "      <td>0.842461</td>\n",
+       "      <td>0.739924</td>\n",
+       "      <td>0.072716</td>\n",
+       "      <td>0.370111</td>\n",
+       "      <td>0.205580</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>pipe6_poly</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>0.912639</td>\n",
+       "      <td>0.734445</td>\n",
+       "      <td>0.071166</td>\n",
+       "      <td>0.374361</td>\n",
+       "      <td>0.190703</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>pipe7_rare_moderate</td>\n",
+       "      <td>HistGB</td>\n",
+       "      <td>0.815826</td>\n",
+       "      <td>0.733627</td>\n",
+       "      <td>0.075810</td>\n",
+       "      <td>0.374638</td>\n",
+       "      <td>0.204655</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>pipe3_with_grouper</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>0.856736</td>\n",
+       "      <td>0.732957</td>\n",
+       "      <td>0.068168</td>\n",
+       "      <td>0.375587</td>\n",
+       "      <td>0.200260</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>pipe3_with_grouper</td>\n",
+       "      <td>HistGB</td>\n",
+       "      <td>0.823557</td>\n",
+       "      <td>0.732805</td>\n",
+       "      <td>0.068836</td>\n",
+       "      <td>0.375648</td>\n",
+       "      <td>0.200024</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>pipe1_optimized</td>\n",
+       "      <td>HistGB</td>\n",
+       "      <td>0.827164</td>\n",
+       "      <td>0.731310</td>\n",
+       "      <td>0.070967</td>\n",
+       "      <td>0.376585</td>\n",
+       "      <td>0.204912</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>pipe1_no_scaling</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>0.861835</td>\n",
+       "      <td>0.730877</td>\n",
+       "      <td>0.069195</td>\n",
+       "      <td>0.376479</td>\n",
+       "      <td>0.209684</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>pipe1_optimized_scaled</td>\n",
+       "      <td>HistGB</td>\n",
+       "      <td>0.830215</td>\n",
+       "      <td>0.730009</td>\n",
+       "      <td>0.069313</td>\n",
+       "      <td>0.377546</td>\n",
+       "      <td>0.204890</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>pipe7_rare_moderate</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>0.877704</td>\n",
+       "      <td>0.728123</td>\n",
+       "      <td>0.066910</td>\n",
+       "      <td>0.379104</td>\n",
+       "      <td>0.198384</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>pipe4_target_enc</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>0.851720</td>\n",
+       "      <td>0.725034</td>\n",
+       "      <td>0.082287</td>\n",
+       "      <td>0.379875</td>\n",
+       "      <td>0.192838</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>pipe2_no_grouper</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>0.855574</td>\n",
+       "      <td>0.723345</td>\n",
+       "      <td>0.077918</td>\n",
+       "      <td>0.381524</td>\n",
+       "      <td>0.186268</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>pipe5_ratio_scaled</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>0.878042</td>\n",
+       "      <td>0.712105</td>\n",
+       "      <td>0.097798</td>\n",
+       "      <td>0.387138</td>\n",
+       "      <td>0.192608</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>pipe1_optimized_scaled</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>0.876998</td>\n",
+       "      <td>0.708773</td>\n",
+       "      <td>0.051019</td>\n",
+       "      <td>0.393111</td>\n",
+       "      <td>0.190951</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>pipe1_no_scaling</td>\n",
+       "      <td>HistGB</td>\n",
+       "      <td>0.780609</td>\n",
+       "      <td>0.704237</td>\n",
+       "      <td>0.068708</td>\n",
+       "      <td>0.395384</td>\n",
+       "      <td>0.224694</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>pipe1_no_scaling</td>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>0.836642</td>\n",
+       "      <td>0.698213</td>\n",
+       "      <td>0.079270</td>\n",
+       "      <td>0.399046</td>\n",
+       "      <td>0.217147</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>pipe2_optimized</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>0.851599</td>\n",
+       "      <td>0.697398</td>\n",
+       "      <td>0.089776</td>\n",
+       "      <td>0.398603</td>\n",
+       "      <td>0.186426</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>pipe1_optimized</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>0.871083</td>\n",
+       "      <td>0.695633</td>\n",
+       "      <td>0.056585</td>\n",
+       "      <td>0.401630</td>\n",
+       "      <td>0.204982</td>\n",
+       "      <td>Loaded from cache</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Pipeline         Model  Train R2   Test R2  Test R2 Std  \\\n",
+       "23      pipe5_ratio_scaled  RandomForest  0.844274  0.760393     0.073065   \n",
+       "14         pipe2_optimized  RandomForest  0.848622  0.756708     0.075214   \n",
+       "29     pipe7_rare_moderate  RandomForest  0.840507  0.755344     0.077720   \n",
+       "20        pipe4_target_enc  RandomForest  0.843403  0.755019     0.069325   \n",
+       "5          pipe1_optimized  RandomForest  0.841926  0.753579     0.081702   \n",
+       "17      pipe3_with_grouper  RandomForest  0.831085  0.752273     0.079398   \n",
+       "8   pipe1_optimized_scaled  RandomForest  0.842271  0.752246     0.077808   \n",
+       "11        pipe2_no_grouper  RandomForest  0.839584  0.751673     0.071067   \n",
+       "31   catboost_pipe_nogroup      CatBoost  0.858797  0.750937     0.072286   \n",
+       "21      pipe5_ratio_scaled        HistGB  0.833406  0.746400     0.072051   \n",
+       "26              pipe6_poly  RandomForest  0.859781  0.746290     0.077592   \n",
+       "12         pipe2_optimized        HistGB  0.844445  0.744533     0.066352   \n",
+       "18        pipe4_target_enc        HistGB  0.833060  0.741381     0.065579   \n",
+       "24              pipe6_poly        HistGB  0.860080  0.741144     0.070127   \n",
+       "9         pipe2_no_grouper        HistGB  0.831199  0.740040     0.064301   \n",
+       "30           catboost_pipe      CatBoost  0.842461  0.739924     0.072716   \n",
+       "25              pipe6_poly       XGBoost  0.912639  0.734445     0.071166   \n",
+       "27     pipe7_rare_moderate        HistGB  0.815826  0.733627     0.075810   \n",
+       "16      pipe3_with_grouper       XGBoost  0.856736  0.732957     0.068168   \n",
+       "15      pipe3_with_grouper        HistGB  0.823557  0.732805     0.068836   \n",
+       "3          pipe1_optimized        HistGB  0.827164  0.731310     0.070967   \n",
+       "1         pipe1_no_scaling       XGBoost  0.861835  0.730877     0.069195   \n",
+       "6   pipe1_optimized_scaled        HistGB  0.830215  0.730009     0.069313   \n",
+       "28     pipe7_rare_moderate       XGBoost  0.877704  0.728123     0.066910   \n",
+       "19        pipe4_target_enc       XGBoost  0.851720  0.725034     0.082287   \n",
+       "10        pipe2_no_grouper       XGBoost  0.855574  0.723345     0.077918   \n",
+       "22      pipe5_ratio_scaled       XGBoost  0.878042  0.712105     0.097798   \n",
+       "7   pipe1_optimized_scaled       XGBoost  0.876998  0.708773     0.051019   \n",
+       "0         pipe1_no_scaling        HistGB  0.780609  0.704237     0.068708   \n",
+       "2         pipe1_no_scaling  RandomForest  0.836642  0.698213     0.079270   \n",
+       "13         pipe2_optimized       XGBoost  0.851599  0.697398     0.089776   \n",
+       "4          pipe1_optimized       XGBoost  0.871083  0.695633     0.056585   \n",
+       "\n",
+       "    Test RMSE  Test MAE        Best Params  \n",
+       "23   0.354984  0.175395  Loaded from cache  \n",
+       "14   0.357677  0.176309  Loaded from cache  \n",
+       "29   0.358582  0.181427  Loaded from cache  \n",
+       "20   0.359452  0.177403  Loaded from cache  \n",
+       "5    0.359636  0.180932  Loaded from cache  \n",
+       "17   0.360709  0.182901  Loaded from cache  \n",
+       "8    0.360879  0.180542  Loaded from cache  \n",
+       "11   0.361771  0.178497  Loaded from cache  \n",
+       "31   0.362168  0.197576  Loaded from cache  \n",
+       "21   0.365566  0.198109  Loaded from cache  \n",
+       "26   0.365215  0.183992  Loaded from cache  \n",
+       "12   0.367321  0.197047  Loaded from cache  \n",
+       "18   0.369736  0.197691  Loaded from cache  \n",
+       "24   0.369555  0.200769  Loaded from cache  \n",
+       "9    0.370737  0.197137  Loaded from cache  \n",
+       "30   0.370111  0.205580  Loaded from cache  \n",
+       "25   0.374361  0.190703  Loaded from cache  \n",
+       "27   0.374638  0.204655  Loaded from cache  \n",
+       "16   0.375587  0.200260  Loaded from cache  \n",
+       "15   0.375648  0.200024  Loaded from cache  \n",
+       "3    0.376585  0.204912  Loaded from cache  \n",
+       "1    0.376479  0.209684  Loaded from cache  \n",
+       "6    0.377546  0.204890  Loaded from cache  \n",
+       "28   0.379104  0.198384  Loaded from cache  \n",
+       "19   0.379875  0.192838  Loaded from cache  \n",
+       "10   0.381524  0.186268  Loaded from cache  \n",
+       "22   0.387138  0.192608  Loaded from cache  \n",
+       "7    0.393111  0.190951  Loaded from cache  \n",
+       "0    0.395384  0.224694  Loaded from cache  \n",
+       "2    0.399046  0.217147  Loaded from cache  \n",
+       "13   0.398603  0.186426  Loaded from cache  \n",
+       "4    0.401630  0.204982  Loaded from cache  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from catboost import CatBoostRegressor, Pool\n",
+    "\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.ensemble import GradientBoostingRegressor,HistGradientBoostingRegressor\n",
+    "from xgboost import XGBRegressor\n",
+    "from sklearn.model_selection import cross_validate\n",
+    "import numpy as np\n",
+    "\n",
+    "from sklearn.model_selection import cross_validate, GridSearchCV\n",
+    "import numpy as np\n",
+    "\n",
+    "cb=CatBoostRegressor(loss_function=\"RMSE\",\n",
+    "            eval_metric=\"R2\",\n",
+    "            random_seed=42,\n",
+    "            verbose=False\n",
+    ")\n",
+    "\n",
+    "def evaluate_model(pipe_name, pipe, model_name, best_pipeline):\n",
+    "    result= {}\n",
+    "    fit_params = {}\n",
+    "    if model_name == \"CatBoost\":\n",
+    "        pipe.fit(X_train, y_train)\n",
+    "        feature_names = pipe.get_feature_names_out()\n",
+    "        cat_features_idx = [\n",
+    "            i for i, col in enumerate(feature_names) \n",
+    "            if \"cat_pipeline__\" in col\n",
+    "        ]\n",
+    "        fit_params = {'model__cat_features': cat_features_idx}\n",
+    "    \n",
+    "    cv_results = cross_validate(\n",
+    "        best_pipeline,\n",
+    "        X_train,\n",
+    "        y_train.values.ravel(),\n",
+    "        cv=5,\n",
+    "        scoring=['r2', 'neg_root_mean_squared_error', 'neg_mean_absolute_error'],\n",
+    "        return_train_score=True,\n",
+    "        params=fit_params\n",
+    "    )\n",
+    "    \n",
+    "    result={\n",
+    "        'Pipeline': pipe_name,\n",
+    "        'Model': model_name,\n",
+    "        'Train R2': cv_results['train_r2'].mean(),\n",
+    "        'Test R2': cv_results['test_r2'].mean(),\n",
+    "        'Test R2 Std': cv_results['test_r2'].std(),\n",
+    "        'Test RMSE': -cv_results['test_neg_root_mean_squared_error'].mean(),\n",
+    "        'Test MAE': -cv_results['test_neg_mean_absolute_error'].mean(),\n",
+    "        'Best Params': 'Loaded from cache'\n",
+    "    }\n",
+    "    return result\n",
+    "# Define models with hyperparameter grids\n",
+    "model_configs = {\n",
+    "    \"HistGB\": {\n",
+    "        'model': HistGradientBoostingRegressor(random_state=42),\n",
+    "        'params': {\n",
+    "            'model__max_iter': [300, 500],\n",
+    "            'model__max_depth': [5, 8],\n",
+    "            'model__learning_rate': [0.03, 0.05]\n",
+    "        }\n",
+    "    },\n",
+    "    \"XGBoost\": {\n",
+    "        'model': XGBRegressor(\n",
+    "            random_state=42,\n",
+    "            objective='reg:squarederror',\n",
+    "            tree_method='hist'\n",
+    "        ),\n",
+    "        'params': {\n",
+    "            'model__n_estimators': [300],\n",
+    "            'model__max_depth': [5, 7],\n",
+    "            'model__learning_rate': [0.05]\n",
+    "        }\n",
+    "    },\n",
+    "    \"RandomForest\": {\n",
+    "        'model': RandomForestRegressor(random_state=42),\n",
+    "        'params': {\n",
+    "            'model__n_estimators': [200],\n",
+    "            'model__max_depth': [15, 20],\n",
+    "            'model__min_samples_leaf': [3, 5]\n",
+    "        }\n",
+    "    },\n",
+    "    \"CatBoost\": {\n",
+    "        \"model\": cb,\n",
+    "        \"params\": {\n",
+    "            \"model__iterations\": [500, 800],\n",
+    "            \"model__depth\": [5, 6, 8],\n",
+    "            \"model__learning_rate\": [0.03, 0.05],\n",
+    "            \"model__l2_leaf_reg\": [3, 5, 7]\n",
+    "        }\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "pipelines = {\n",
+    "    'pipe1_no_scaling': pipe1,\n",
+    "    \"pipe1_optimized\": pipe1_optimized,\n",
+    "    \"pipe1_optimized_scaled\": pipe1_optimized_scaled,\n",
+    "    \"pipe2_no_grouper\": pipe2,\n",
+    "    \"pipe2_optimized\": pipe2_optimized,\n",
+    "    \"pipe3_with_grouper\": pipe3,\n",
+    "    \"pipe4_target_enc\": pipe4,\n",
+    "    \"pipe5_ratio_scaled\": pipe5_ratio_scaled,\n",
+    "    \"pipe6_poly\": pipe6_poly,\n",
+    "    \"pipe7_rare_moderate\": pipe7_rare_moderate,\n",
+    "    \"catboost_pipe\": catboost_pipe,\n",
+    "    \"catboost_pipe_nogroup\": catboost_pipe_nogroup\n",
+    "}\n",
+    "\n",
+    "# Store results\n",
+    "results = []\n",
+    "best_models = {}\n",
+    "\n",
+    "for pipe_name, pipe in pipelines.items():\n",
+    "    pipelines[pipe_name] = pipe.set_output(transform=\"pandas\")\n",
+    "    for model_name, config in model_configs.items():\n",
+    "        print(f\"\\nTraining {model_name} with {pipe_name}...\")\n",
+    "        \n",
+    "        cache_path = build_cache_path(pipe_name, model_name)\n",
+    "        if cache_path.exists():\n",
+    "            print(f\"Loading cached model for {model_name} with {pipe_name}...\")\n",
+    "            with open(cache_path, 'rb') as f:\n",
+    "                best_pipeline = pickle.load(f)\n",
+    "            best_models[f\"{pipe_name}_{model_name}\"] = best_pipeline\n",
+    "            results.append(evaluate_model(pipe_name, pipe, model_name, best_pipeline))\n",
+    "            continue\n",
+    "        \n",
+    "        if model_name == \"CatBoost\" and pipe_name not in [\"catboost_pipe\", \"catboost_pipe_nogroup\"]:\n",
+    "            print(f\"Skipping {model_name} with {pipe_name} due to incompatible preprocessing.\")\n",
+    "            continue\n",
+    "        if model_name != \"CatBoost\" and pipe_name in [\"catboost_pipe\", \"catboost_pipe_nogroup\"]:\n",
+    "            print(f\"Skipping {model_name} with {pipe_name} due to incompatible preprocessing.\")\n",
+    "            continue\n",
+    "        \n",
+    "        \n",
+    "            \n",
+    "        \n",
+    "        full_pipeline = Pipeline([\n",
+    "            (\"preprocessing\", pipe),\n",
+    "            (\"model\", config['model'])\n",
+    "        ])\n",
+    "        fit_params = {}\n",
+    "        if model_name == \"CatBoost\":\n",
+    "            pipe.fit(X_train, y_train)\n",
+    "            feature_names = pipe.get_feature_names_out()\n",
+    "            \n",
+    "            cat_features_idx = [\n",
+    "                i for i, col in enumerate(feature_names) \n",
+    "                if \"cat_pipeline__\" in col\n",
+    "            ]\n",
+    "            \n",
+    "            fit_params = {\n",
+    "                'model__cat_features': cat_features_idx,\n",
+    "            }\n",
+    "\n",
+    "        # Tune hyperparameters if params exist\n",
+    "        if config['params']:\n",
+    "            grid_search = GridSearchCV(\n",
+    "                full_pipeline,\n",
+    "                config['params'],\n",
+    "                cv=5,\n",
+    "                scoring='r2',\n",
+    "                n_jobs=-1,\n",
+    "                verbose=1,\n",
+    "            )\n",
+    "            grid_search.fit(X_train, y_train.values.ravel(), **fit_params)\n",
+    "            best_pipeline = grid_search.best_estimator_\n",
+    "            best_params = grid_search.best_params_\n",
+    "            # Cache the best model\n",
+    "            cache_model(best_pipeline, pipe_name, model_name)\n",
+    "        else:\n",
+    "            best_pipeline = full_pipeline\n",
+    "            best_params = {}\n",
+    "        \n",
+    "        # Cross-validation with best model\n",
+    "        cv_results = cross_validate(\n",
+    "            best_pipeline,\n",
+    "            X_train,\n",
+    "            y_train.values.ravel(),\n",
+    "            cv=5,\n",
+    "            scoring=['r2', 'neg_root_mean_squared_error', 'neg_mean_absolute_error'],\n",
+    "            return_train_score=True,\n",
+    "            params=fit_params\n",
+    "        )\n",
+    "        \n",
+    "        # Store results\n",
+    "        results.append({\n",
+    "            'Pipeline': pipe_name,\n",
+    "            'Model': model_name,\n",
+    "            'Train R2': cv_results['train_r2'].mean(),\n",
+    "            'Test R2': cv_results['test_r2'].mean(),\n",
+    "            'Test R2 Std': cv_results['test_r2'].std(),\n",
+    "            'Test RMSE': -cv_results['test_neg_root_mean_squared_error'].mean(),\n",
+    "            'Test MAE': -cv_results['test_neg_mean_absolute_error'].mean(),\n",
+    "            'Best Params': str(best_params)\n",
+    "        })\n",
+    "        \n",
+    "        # Store best model\n",
+    "        best_models[f\"{pipe_name}_{model_name}\"] = best_pipeline\n",
+    "\n",
+    "# Create results DataFrame\n",
+    "results_df = pd.DataFrame(results)\n",
+    "results_df = results_df.sort_values('Test R2', ascending=False)\n",
+    "\n",
+    "print(\"\\n=== Model Comparison Results (with Hyperparameter Tuning) ===\")\n",
+    "print(results_df.to_string(index=False))\n",
+    "\n",
+    "# Find best combination\n",
+    "best = results_df.iloc[0]\n",
+    "print(f\"\\n=== Best Model ===\")\n",
+    "print(f\"Pipeline: {best['Pipeline']}\")\n",
+    "print(f\"Model: {best['Model']}\")\n",
+    "print(f\"Test R2: {best['Test R2']:.4f} (±{best['Test R2 Std']:.4f})\")\n",
+    "print(f\"Test MSE: {best['Test RMSE']:.2f}\")\n",
+    "print(f\"Test MAE: {best['Test MAE']:.2f}\")\n",
+    "print(f\"Best Params: {best['Best Params']}\")\n",
+    "\n",
+    "# Final evaluation on test set\n",
+    "best_model_key = f\"{best['Pipeline']}_{best['Model']}\"\n",
+    "final_model = best_models[best_model_key]\n",
+    "y_pred = np.expm1(final_model.predict(X_test))\n",
+    "test_r2 = r2_score(np.expm1(y_test), y_pred)\n",
+    "test_mse = mean_squared_error(np.expm1(y_test), y_pred)\n",
+    "\n",
+    "print(f\"\\n=== Final Test Set Performance ===\")\n",
+    "print(f\"Test R2: {test_r2:.4f}\")\n",
+    "print(f\"Test MSE: {test_mse:.2f}\")\n",
+    "\n",
+    "        \n",
+    "results_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "51fbbc73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_df.to_csv(\"model_comparison_results.csv\", index=False)\n",
+    "results_df.to_json(\"model_comparison_results.json\", orient=\"records\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "w3 (3.13.7)",
+   "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": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}