diff --git "a/random_forest.ipynb" "b/random_forest.ipynb"
new file mode 100644--- /dev/null
+++ "b/random_forest.ipynb"
@@ -0,0 +1,4662 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# <font color=\"#008B8B\">\n",
+    "  Time Series Forecasting with Random Forest Regressor on Daily Foreign Exchange Rates\n",
+    "</font>\n",
+    "---\n",
+    "\n",
+    "**Random Forest** is an ensemble machine learning algorithm that builds multiple decision trees and combines their outputs to improve prediction accuracy and reduce overfitting. Each tree in the forest is trained on a random subset of the data and features, and their outputs are averaged (for regression tasks) or voted (for classification tasks) to produce the final result.\n",
+    "In the context of **time series forecasting**, Random Forest is adapted to model temporal dependencies by converting the time series into a supervised learning format—using lagged observations as input features to predict future values.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "The purpose of this notebook is to evaluate the feasibility of implementing a machine learning–based algorithm, **Random Forest**, for forecasting tasks. Specifically, we aim to assess how well Random Forest can model and predict **foreign exchange (Forex) rates** using historical time series data.\n",
+    "\n",
+    "\n",
+    "\n",
+    "## Approach and Workflow \n",
+    "\n",
+    "1. **Feature Engineering**  \n",
+    "   Lag features, rolling statistics, and datetime features  \n",
+    "\n",
+    "   ⬇️\n",
+    "\n",
+    "2. **Feature Selection**  \n",
+    "   Using Recursive Feature Elimination (RFE)  \n",
+    "\n",
+    "   ⬇️\n",
+    "\n",
+    "3. **Hyperparameter Tuning**  \n",
+    "   Grid Search CV and Randomized Search CV  \n",
+    "\n",
+    "   ⬇️\n",
+    "\n",
+    "4. **Time Series Cross-Validation**  \n",
+    "   Using `TimeSeriesSplit` for time aware cross validation \n",
+    "\n",
+    "   ⬇️\n",
+    "\n",
+    "5. **Model Training**  \n",
+    "   Train Random Forest using selected features and tuned parameters  \n",
+    "\n",
+    "   ⬇️\n",
+    "\n",
+    "6. **Model Evaluation**  \n",
+    "   Assess performance with metrics  MAE and RMSE, and visualize results\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8061247b",
+   "metadata": {},
+   "source": [
+    "### Lets begin!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "47624f6c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.model_selection import GridSearchCV, TimeSeriesSplit, RandomizedSearchCV\n",
+    "\n",
+    "\n",
+    "from sklearn.ensemble import RandomForestRegressor\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd379311",
+   "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>foreign exchange rates</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1980-01-02</th>\n",
+       "      <td>1.714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-01-03</th>\n",
+       "      <td>1.711</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-01-04</th>\n",
+       "      <td>1.713</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-01-07</th>\n",
+       "      <td>1.708</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-01-08</th>\n",
+       "      <td>1.713</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998-12-24</th>\n",
+       "      <td>1.684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998-12-28</th>\n",
+       "      <td>1.679</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998-12-29</th>\n",
+       "      <td>1.671</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998-12-30</th>\n",
+       "      <td>1.677</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998-12-31</th>\n",
+       "      <td>1.667</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4773 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            foreign exchange rates\n",
+       "Date                              \n",
+       "1980-01-02                   1.714\n",
+       "1980-01-03                   1.711\n",
+       "1980-01-04                   1.713\n",
+       "1980-01-07                   1.708\n",
+       "1980-01-08                   1.713\n",
+       "...                            ...\n",
+       "1998-12-24                   1.684\n",
+       "1998-12-28                   1.679\n",
+       "1998-12-29                   1.671\n",
+       "1998-12-30                   1.677\n",
+       "1998-12-31                   1.667\n",
+       "\n",
+       "[4773 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exhchg_rates= pd.read_csv(\"I:/CQAI/TSA/TSD/TSD/data/daily_foreign_exchnage/daily-foreign-exchange-rates-31-.csv\", parse_dates=[\"Date\"], index_col=\"Date\")\n",
+    "exhchg_rates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f198b59a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "foreign exchange rates    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exhchg_rates.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e83d7f70",
+   "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>foreign exchange rates</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4773.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.946938</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.453933</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.357000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.637000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.782000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.258000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>3.453000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       foreign exchange rates\n",
+       "count             4773.000000\n",
+       "mean                 1.946938\n",
+       "std                  0.453933\n",
+       "min                  1.357000\n",
+       "25%                  1.637000\n",
+       "50%                  1.782000\n",
+       "75%                  2.258000\n",
+       "max                  3.453000"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exhchg_rates.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "15d214bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "exhchg_rates.plot(figsize=(10, 5))\n",
+    "plt.plot(exhchg_rates.index, exhchg_rates['foreign exchange rates'])\n",
+    "plt.title(\"Daily Foreign Exchange Rates\")\n",
+    "plt.xlabel(\"Date\")  \n",
+    "plt.ylabel(\"Price\")\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "442a3239",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(exhchg_rates.loc[\"1988-01-01\":].index, exhchg_rates['foreign exchange rates'].loc[\"1988-01-01\":])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff2eecbe",
+   "metadata": {},
+   "source": [
+    "# Feature Creation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0bb8bc09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_new_features(df):\n",
+    "    df[\"Date\"]=pd.to_datetime(exhchg_rates.index)\n",
+    "    # Extract date features\n",
+    "    df[\"Month\"]=df[\"Date\"].dt.month\n",
+    "    df[\"Year\"]=df[\"Date\"].dt.year\n",
+    "    df[\"Day\"]=df[\"Date\"].dt.day\n",
+    "    df[\"DayOfWeek\"]=df[\"Date\"].dt.dayofweek\n",
+    "    df[\"DayOfYear\"]=df[\"Date\"].dt.dayofyear\n",
+    "    df[\"WeekOfYear\"]=df[\"Date\"].dt.isocalendar().week\n",
+    "   \n",
+    "    \n",
+    "    \n",
+    "    # Calculate rolling mean and deviation\n",
+    "    df[\"rolling_mean\"] = df['foreign exchange rates'].rolling(window=7).mean()\n",
+    "    df[\"rolling_std\"] = df['foreign exchange rates'].rolling(window=7).std()\n",
+    "    \n",
+    "    #Lagged features\n",
+    "    for i in range(10,0,-1):\n",
+    "      df['t-'+str(i)] = df['foreign exchange rates'].shift(i)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9de40564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = exhchg_rates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "131d8344",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "create_new_features(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f740ffb1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=df[150:]  # droppping first 150 rows to avoid NaN values in the lagged features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d25feed6",
+   "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>foreign exchange rates</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Month</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Day</th>\n",
+       "      <th>DayOfWeek</th>\n",
+       "      <th>DayOfYear</th>\n",
+       "      <th>WeekOfYear</th>\n",
+       "      <th>rolling_mean</th>\n",
+       "      <th>rolling_std</th>\n",
+       "      <th>t-10</th>\n",
+       "      <th>t-9</th>\n",
+       "      <th>t-8</th>\n",
+       "      <th>t-7</th>\n",
+       "      <th>t-6</th>\n",
+       "      <th>t-5</th>\n",
+       "      <th>t-4</th>\n",
+       "      <th>t-3</th>\n",
+       "      <th>t-2</th>\n",
+       "      <th>t-1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1980-08-05</th>\n",
+       "      <td>1.776</td>\n",
+       "      <td>1980-08-05</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>218</td>\n",
+       "      <td>32</td>\n",
+       "      <td>1.772143</td>\n",
+       "      <td>0.019769</td>\n",
+       "      <td>1.739</td>\n",
+       "      <td>1.740</td>\n",
+       "      <td>1.735</td>\n",
+       "      <td>1.739</td>\n",
+       "      <td>1.734</td>\n",
+       "      <td>1.759</td>\n",
+       "      <td>1.777</td>\n",
+       "      <td>1.781</td>\n",
+       "      <td>1.793</td>\n",
+       "      <td>1.785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-08-06</th>\n",
+       "      <td>1.769</td>\n",
+       "      <td>1980-08-06</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>219</td>\n",
+       "      <td>32</td>\n",
+       "      <td>1.777143</td>\n",
+       "      <td>0.010991</td>\n",
+       "      <td>1.740</td>\n",
+       "      <td>1.735</td>\n",
+       "      <td>1.739</td>\n",
+       "      <td>1.734</td>\n",
+       "      <td>1.759</td>\n",
+       "      <td>1.777</td>\n",
+       "      <td>1.781</td>\n",
+       "      <td>1.793</td>\n",
+       "      <td>1.785</td>\n",
+       "      <td>1.776</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-08-07</th>\n",
+       "      <td>1.782</td>\n",
+       "      <td>1980-08-07</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>220</td>\n",
+       "      <td>32</td>\n",
+       "      <td>1.780429</td>\n",
+       "      <td>0.007569</td>\n",
+       "      <td>1.735</td>\n",
+       "      <td>1.739</td>\n",
+       "      <td>1.734</td>\n",
+       "      <td>1.759</td>\n",
+       "      <td>1.777</td>\n",
+       "      <td>1.781</td>\n",
+       "      <td>1.793</td>\n",
+       "      <td>1.785</td>\n",
+       "      <td>1.776</td>\n",
+       "      <td>1.769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-08-08</th>\n",
+       "      <td>1.783</td>\n",
+       "      <td>1980-08-08</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>221</td>\n",
+       "      <td>32</td>\n",
+       "      <td>1.781286</td>\n",
+       "      <td>0.007455</td>\n",
+       "      <td>1.739</td>\n",
+       "      <td>1.734</td>\n",
+       "      <td>1.759</td>\n",
+       "      <td>1.777</td>\n",
+       "      <td>1.781</td>\n",
+       "      <td>1.793</td>\n",
+       "      <td>1.785</td>\n",
+       "      <td>1.776</td>\n",
+       "      <td>1.769</td>\n",
+       "      <td>1.782</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-08-11</th>\n",
+       "      <td>1.783</td>\n",
+       "      <td>1980-08-11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>224</td>\n",
+       "      <td>33</td>\n",
+       "      <td>1.781571</td>\n",
+       "      <td>0.007480</td>\n",
+       "      <td>1.734</td>\n",
+       "      <td>1.759</td>\n",
+       "      <td>1.777</td>\n",
+       "      <td>1.781</td>\n",
+       "      <td>1.793</td>\n",
+       "      <td>1.785</td>\n",
+       "      <td>1.776</td>\n",
+       "      <td>1.769</td>\n",
+       "      <td>1.782</td>\n",
+       "      <td>1.783</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998-12-24</th>\n",
+       "      <td>1.684</td>\n",
+       "      <td>1998-12-24</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1998</td>\n",
+       "      <td>24</td>\n",
+       "      <td>3</td>\n",
+       "      <td>358</td>\n",
+       "      <td>52</td>\n",
+       "      <td>1.672286</td>\n",
+       "      <td>0.006775</td>\n",
+       "      <td>1.655</td>\n",
+       "      <td>1.649</td>\n",
+       "      <td>1.650</td>\n",
+       "      <td>1.659</td>\n",
+       "      <td>1.667</td>\n",
+       "      <td>1.666</td>\n",
+       "      <td>1.665</td>\n",
+       "      <td>1.674</td>\n",
+       "      <td>1.675</td>\n",
+       "      <td>1.675</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998-12-28</th>\n",
+       "      <td>1.679</td>\n",
+       "      <td>1998-12-28</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1998</td>\n",
+       "      <td>28</td>\n",
+       "      <td>0</td>\n",
+       "      <td>362</td>\n",
+       "      <td>53</td>\n",
+       "      <td>1.674000</td>\n",
+       "      <td>0.006733</td>\n",
+       "      <td>1.649</td>\n",
+       "      <td>1.650</td>\n",
+       "      <td>1.659</td>\n",
+       "      <td>1.667</td>\n",
+       "      <td>1.666</td>\n",
+       "      <td>1.665</td>\n",
+       "      <td>1.674</td>\n",
+       "      <td>1.675</td>\n",
+       "      <td>1.675</td>\n",
+       "      <td>1.684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998-12-29</th>\n",
+       "      <td>1.671</td>\n",
+       "      <td>1998-12-29</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1998</td>\n",
+       "      <td>29</td>\n",
+       "      <td>1</td>\n",
+       "      <td>363</td>\n",
+       "      <td>53</td>\n",
+       "      <td>1.674714</td>\n",
+       "      <td>0.005964</td>\n",
+       "      <td>1.650</td>\n",
+       "      <td>1.659</td>\n",
+       "      <td>1.667</td>\n",
+       "      <td>1.666</td>\n",
+       "      <td>1.665</td>\n",
+       "      <td>1.674</td>\n",
+       "      <td>1.675</td>\n",
+       "      <td>1.675</td>\n",
+       "      <td>1.684</td>\n",
+       "      <td>1.679</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998-12-30</th>\n",
+       "      <td>1.677</td>\n",
+       "      <td>1998-12-30</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1998</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>364</td>\n",
+       "      <td>53</td>\n",
+       "      <td>1.676429</td>\n",
+       "      <td>0.004158</td>\n",
+       "      <td>1.659</td>\n",
+       "      <td>1.667</td>\n",
+       "      <td>1.666</td>\n",
+       "      <td>1.665</td>\n",
+       "      <td>1.674</td>\n",
+       "      <td>1.675</td>\n",
+       "      <td>1.675</td>\n",
+       "      <td>1.684</td>\n",
+       "      <td>1.679</td>\n",
+       "      <td>1.671</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998-12-31</th>\n",
+       "      <td>1.667</td>\n",
+       "      <td>1998-12-31</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1998</td>\n",
+       "      <td>31</td>\n",
+       "      <td>3</td>\n",
+       "      <td>365</td>\n",
+       "      <td>53</td>\n",
+       "      <td>1.675429</td>\n",
+       "      <td>0.005473</td>\n",
+       "      <td>1.667</td>\n",
+       "      <td>1.666</td>\n",
+       "      <td>1.665</td>\n",
+       "      <td>1.674</td>\n",
+       "      <td>1.675</td>\n",
+       "      <td>1.675</td>\n",
+       "      <td>1.684</td>\n",
+       "      <td>1.679</td>\n",
+       "      <td>1.671</td>\n",
+       "      <td>1.677</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4623 rows × 20 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            foreign exchange rates       Date  Month  Year  Day  DayOfWeek  \\\n",
+       "Date                                                                         \n",
+       "1980-08-05                   1.776 1980-08-05      8  1980    5          1   \n",
+       "1980-08-06                   1.769 1980-08-06      8  1980    6          2   \n",
+       "1980-08-07                   1.782 1980-08-07      8  1980    7          3   \n",
+       "1980-08-08                   1.783 1980-08-08      8  1980    8          4   \n",
+       "1980-08-11                   1.783 1980-08-11      8  1980   11          0   \n",
+       "...                            ...        ...    ...   ...  ...        ...   \n",
+       "1998-12-24                   1.684 1998-12-24     12  1998   24          3   \n",
+       "1998-12-28                   1.679 1998-12-28     12  1998   28          0   \n",
+       "1998-12-29                   1.671 1998-12-29     12  1998   29          1   \n",
+       "1998-12-30                   1.677 1998-12-30     12  1998   30          2   \n",
+       "1998-12-31                   1.667 1998-12-31     12  1998   31          3   \n",
+       "\n",
+       "            DayOfYear  WeekOfYear  rolling_mean  rolling_std   t-10    t-9  \\\n",
+       "Date                                                                         \n",
+       "1980-08-05        218          32      1.772143     0.019769  1.739  1.740   \n",
+       "1980-08-06        219          32      1.777143     0.010991  1.740  1.735   \n",
+       "1980-08-07        220          32      1.780429     0.007569  1.735  1.739   \n",
+       "1980-08-08        221          32      1.781286     0.007455  1.739  1.734   \n",
+       "1980-08-11        224          33      1.781571     0.007480  1.734  1.759   \n",
+       "...               ...         ...           ...          ...    ...    ...   \n",
+       "1998-12-24        358          52      1.672286     0.006775  1.655  1.649   \n",
+       "1998-12-28        362          53      1.674000     0.006733  1.649  1.650   \n",
+       "1998-12-29        363          53      1.674714     0.005964  1.650  1.659   \n",
+       "1998-12-30        364          53      1.676429     0.004158  1.659  1.667   \n",
+       "1998-12-31        365          53      1.675429     0.005473  1.667  1.666   \n",
+       "\n",
+       "              t-8    t-7    t-6    t-5    t-4    t-3    t-2    t-1  \n",
+       "Date                                                                \n",
+       "1980-08-05  1.735  1.739  1.734  1.759  1.777  1.781  1.793  1.785  \n",
+       "1980-08-06  1.739  1.734  1.759  1.777  1.781  1.793  1.785  1.776  \n",
+       "1980-08-07  1.734  1.759  1.777  1.781  1.793  1.785  1.776  1.769  \n",
+       "1980-08-08  1.759  1.777  1.781  1.793  1.785  1.776  1.769  1.782  \n",
+       "1980-08-11  1.777  1.781  1.793  1.785  1.776  1.769  1.782  1.783  \n",
+       "...           ...    ...    ...    ...    ...    ...    ...    ...  \n",
+       "1998-12-24  1.650  1.659  1.667  1.666  1.665  1.674  1.675  1.675  \n",
+       "1998-12-28  1.659  1.667  1.666  1.665  1.674  1.675  1.675  1.684  \n",
+       "1998-12-29  1.667  1.666  1.665  1.674  1.675  1.675  1.684  1.679  \n",
+       "1998-12-30  1.666  1.665  1.674  1.675  1.675  1.684  1.679  1.671  \n",
+       "1998-12-31  1.665  1.674  1.675  1.675  1.684  1.679  1.671  1.677  \n",
+       "\n",
+       "[4623 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "da0ee8d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "foreign exchange rates    0\n",
+       "Date                      0\n",
+       "Month                     0\n",
+       "Year                      0\n",
+       "Day                       0\n",
+       "DayOfWeek                 0\n",
+       "DayOfYear                 0\n",
+       "WeekOfYear                0\n",
+       "rolling_mean              0\n",
+       "rolling_std               0\n",
+       "t-10                      0\n",
+       "t-9                       0\n",
+       "t-8                       0\n",
+       "t-7                       0\n",
+       "t-6                       0\n",
+       "t-5                       0\n",
+       "t-4                       0\n",
+       "t-3                       0\n",
+       "t-2                       0\n",
+       "t-1                       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "284ed07b",
+   "metadata": {},
+   "source": [
+    "## Feature Selection Using RFE (Recursive Feature Elimination)\n",
+    "\n",
+    "\n",
+    "\n",
+    "**Recursive Feature Elimination (RFE)** is a feature selection technique that helps identify the most important features for a predictive model. It works by recursively removing the least important features based on model performance, until the desired number of features is reached.\n",
+    "\n",
+    "RFE is useful when:\n",
+    "- You want to reduce overfitting.\n",
+    "- You want to speed up training by reducing feature dimensionality.\n",
+    "- You want to interpret which features have the most predictive power.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### How RFE Works (Step-by-Step)\n",
+    "\n",
+    "1. **Fit a model** (e.g., RandomForest) on the full set of features.\n",
+    "2. **Rank features** by importance \n",
+    "3. **Remove the least important feature(s)**.\n",
+    "4. **Repeat** the process until a specified number of features is selected.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "72ca1cb5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "train_df = df[:3124]\n",
+    "test_df = df[3124:]\n",
+    "\n",
+    "FEATURES = ['Month','Year','Day','DayOfYear',  'DayOfWeek', 'WeekOfYear','rolling_mean','t-1','t-2', 't-3', 't-4', 't-5', 't-6', 't-7'\n",
+    "                ]\n",
+    "TARGET = 'foreign exchange rates'\n",
+    "\n",
+    "x_train =train_df[FEATURES]\n",
+    "\n",
+    "y_train =train_df[TARGET]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "06a6e1a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((3124, 14), (3124,))"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train.shape, y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a29bc553",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Columns with predictive power: ['Month', 'Year', 'DayOfYear', 'WeekOfYear', 'rolling_mean', 'rolling_std', 't-7']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.feature_selection import RFE\n",
+    "rfe = RFE(RandomForestRegressor(n_estimators=500, random_state=1))\n",
+    "# opetional customization to specify no of parameters to select with n_features_to_select parameter\n",
+    "\n",
+    "\n",
+    "fit = rfe.fit(x_train, y_train)\n",
+    "names = df.columns\n",
+    "columns=[]\n",
+    "for i in range(len(fit.support_)):\n",
+    "    if fit.support_[i]:\n",
+    "        columns.append(names[i])\n",
+    "#fit.support_ gives a list of True/False values indicating which features were selected.\n",
+    "\n",
+    "#he final columns list contains the names of those important features.\n",
+    "\n",
+    "\n",
+    "\n",
+    "print(\"Columns with predictive power:\", columns )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36a4203a",
+   "metadata": {},
+   "source": [
+    "## Parameter Tuning: Grid Search CV and Randomized Search CV\n",
+    "\n",
+    "In machine learning, **hyperparameter tuning** is crucial to optimize model performance. For Random Forest (and many other models), hyperparameters like the number of trees, tree depth etc significantly affect performance.\n",
+    "\n",
+    "Two popular methods for tuning these parameters are:\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Grid Search CV\n",
+    "\n",
+    "**What it does:**  \n",
+    "Grid Search Cross-Validation exhaustively searches through a predefined set of hyperparameter values. It evaluates every possible combination using cross-validation to find the best-performing configuration.\n",
+    "\n",
+    "**How it works:**\n",
+    "- Define a grid of hyperparameters (e.g., `n_estimators = [100, 200, 300]`, `max_depth = [5, 10, 15]`)\n",
+    "- Train and evaluate a model on each combination using cross-validation\n",
+    "- Select the best-performing set of hyperparameters\n",
+    "\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Randomized Search CV\n",
+    "\n",
+    "**What it does:**  \n",
+    "Randomized Search CV samples a fixed number of random combinations from a specified distribution or parameter grid. It does not try all possible combinations but explores the space more efficiently.\n",
+    "\n",
+    "**How it works:**\n",
+    "- Specify distributions or lists for hyperparameters\n",
+    "- Randomly sample one combination per iteration; for example, with 50 iterations, 50 different random combinations will be tried.\n",
+    "- Train and evaluate models on those samples\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2cc5da7",
+   "metadata": {},
+   "source": [
+    "# Grid search cv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ccd19615",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# No of decision trees  in random forest\n",
+    "n_estimators = [50,100,500]\n",
+    "\n",
+    "# Number of features to consider at every split\n",
+    "\"\"\"Adds randomness to each tree.\n",
+    "Helps reduce correlation between trees → improves ensemble performance.\n",
+    "Lower values → more randomness and diversity.\n",
+    "Higher values → trees are stronger individually but more correlated.\"\"\"\n",
+    "max_features = [0.2,0.6,1.0]\n",
+    "\n",
+    "\n",
+    "# Maximum number of levels in tree\n",
+    "max_depth = [8,10,12]\n",
+    "\n",
+    "# The fraction (or absolute number) of training samples to draw (with replacement) to train each individual tree.\n",
+    "\"\"\"Controls the subsampling of data per tree.\n",
+    "Can speed up training and help reduce overfitting.\n",
+    "More subsampling → more diversity → better generalization.\"\"\"\n",
+    "max_samples = [0.5,0.75,1.0]\n",
+    "\n",
+    "\n",
+    "\n",
+    "# total combinations 3*3*3*3 = 81"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "7ca50319",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "param_grid = {'n_estimators': n_estimators,\n",
+    "               'max_features': max_features,\n",
+    "               'max_depth': max_depth,\n",
+    "              'max_samples':max_samples\n",
+    "             }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "fd635223",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rf = RandomForestRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ca41c49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "\n",
+    "rf_grid = GridSearchCV(estimator = rf, \n",
+    "                            param_grid = param_grid, \n",
+    "                            cv = TimeSeriesSplit(n_splits=5), \n",
+    "                            verbose=2, \n",
+    "                            n_jobs = -1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24b57a70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "x_train =train_df[columns]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0dfb3449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 81 candidates, totalling 405 fits\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\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: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\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: 0.5em;\n",
+       "  text-align: center;\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>GridSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None),\n",
+       "             estimator=RandomForestRegressor(), n_jobs=-1,\n",
+       "             param_grid={&#x27;max_depth&#x27;: [8, 10, 12],\n",
+       "                         &#x27;max_features&#x27;: [0.2, 0.6, 1.0],\n",
+       "                         &#x27;max_samples&#x27;: [0.5, 0.75, 1.0],\n",
+       "                         &#x27;n_estimators&#x27;: [50, 100, 500]},\n",
+       "             verbose=2)</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-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None),\n",
+       "             estimator=RandomForestRegressor(), n_jobs=-1,\n",
+       "             param_grid={&#x27;max_depth&#x27;: [8, 10, 12],\n",
+       "                         &#x27;max_features&#x27;: [0.2, 0.6, 1.0],\n",
+       "                         &#x27;max_samples&#x27;: [0.5, 0.75, 1.0],\n",
+       "                         &#x27;n_estimators&#x27;: [50, 100, 500]},\n",
+       "             verbose=2)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: RandomForestRegressor</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_depth=12, max_samples=0.5, n_estimators=50)</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\"><div><div>RandomForestRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_depth=12, max_samples=0.5, n_estimators=50)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None),\n",
+       "             estimator=RandomForestRegressor(), n_jobs=-1,\n",
+       "             param_grid={'max_depth': [8, 10, 12],\n",
+       "                         'max_features': [0.2, 0.6, 1.0],\n",
+       "                         'max_samples': [0.5, 0.75, 1.0],\n",
+       "                         'n_estimators': [50, 100, 500]},\n",
+       "             verbose=2)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "# 81 parameters to be tuned across 5 folds = 5*81 model fits\n",
+    "rf_grid.fit(x_train,y_train)\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "39b72cbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'max_depth': 9, 'max_features': 1.0, 'max_samples': 0.75, 'n_estimators': 50}"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rf_grid.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "1094f4f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5457005420716959"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rf_grid.best_score_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e770141c",
+   "metadata": {},
+   "source": [
+    "# randomized serach cv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "4acf42b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "n_estimators = [50,100,500]\n",
+    "max_features = [0.2,0.6,1.0]\n",
+    "max_depth = [8,10,12]\n",
+    "max_samples = [0.5,0.75,1.0]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "07365615",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "param_grid = {'n_estimators': n_estimators,\n",
+    "               'max_features': max_features,\n",
+    "               'max_depth': max_depth,\n",
+    "              'max_samples':max_samples,\n",
+    "              \n",
+    "             \n",
+    "             }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "675e475b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import RandomizedSearchCV\n",
+    "\n",
+    "rf_rndm = RandomizedSearchCV(estimator = rf, \n",
+    "                       param_distributions = param_grid, \n",
+    "                       cv = TimeSeriesSplit(n_splits=5),  # We use Time series split cross validation with 5 folds\n",
+    "                       n_iter=50,                \n",
+    "                       verbose=2, \n",
+    "                       n_jobs = -1,)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c8ab455c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\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: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\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: 0.5em;\n",
+       "  text-align: center;\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>RandomizedSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None),\n",
+       "                   estimator=RandomForestRegressor(), n_iter=50, n_jobs=-1,\n",
+       "                   param_distributions={&#x27;max_depth&#x27;: [8, 10, 12],\n",
+       "                                        &#x27;max_features&#x27;: [0.2, 0.6, 1.0],\n",
+       "                                        &#x27;max_samples&#x27;: [0.5, 0.75, 1.0],\n",
+       "                                        &#x27;n_estimators&#x27;: [50, 100, 500]},\n",
+       "                   verbose=2)</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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomizedSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None),\n",
+       "                   estimator=RandomForestRegressor(), n_iter=50, n_jobs=-1,\n",
+       "                   param_distributions={&#x27;max_depth&#x27;: [8, 10, 12],\n",
+       "                                        &#x27;max_features&#x27;: [0.2, 0.6, 1.0],\n",
+       "                                        &#x27;max_samples&#x27;: [0.5, 0.75, 1.0],\n",
+       "                                        &#x27;n_estimators&#x27;: [50, 100, 500]},\n",
+       "                   verbose=2)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: RandomForestRegressor</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_depth=10, max_samples=1.0, n_estimators=50)</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-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_depth=10, max_samples=1.0, n_estimators=50)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomizedSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None),\n",
+       "                   estimator=RandomForestRegressor(), n_iter=50, n_jobs=-1,\n",
+       "                   param_distributions={'max_depth': [8, 10, 12],\n",
+       "                                        'max_features': [0.2, 0.6, 1.0],\n",
+       "                                        'max_samples': [0.5, 0.75, 1.0],\n",
+       "                                        'n_estimators': [50, 100, 500]},\n",
+       "                   verbose=2)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rf_rndm.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "15571523",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'n_estimators': 50, 'max_samples': 1.0, 'max_features': 1.0, 'max_depth': 10}"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rf_rndm.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "b5ce9d9c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5455278112464174"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rf_rndm.best_score_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91e0fc8d",
+   "metadata": {},
+   "source": [
+    "# WHat  is Cross validation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec702ca7",
+   "metadata": {},
+   "source": [
+    "- In traditional cross-validation (like K-Fold), the dataset is randomly split into training and validation folds. However, in time series forecasting, this approach isn't valid because time order matters — you can't use future data to predict the past.\n",
+    "\n",
+    "- Instead, we use Time Series Cross-Validation, which respects temporal order by only allowing models to train on past data and validate on future data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5702effe",
+   "metadata": {},
+   "source": [
+    "### TimeSeriesSplit: How It Works\n",
+    "\n",
+    "- The `TimeSeriesSplit` method in **scikit-learn** creates multiple train-test splits while **preserving the time order**.\n",
+    "- Each fold **expands the training window** forward in time and keeps a separate validation (test) set.\n",
+    "\n",
+    "**Parameters used:**\n",
+    "\n",
+    "- `n_splits=5`: The data is split into 5 train/test folds.\n",
+    "- `test_size=150`: Each test set contains 150 time steps (e.g., 6 days × 25 observations per day).\n",
+    "- `gap=1`: There’s a 1-step gap between the end of the training set and the start of the test set to **avoid data leakage**.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "10dc899d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import TimeSeriesSplit\n",
+    "\n",
+    "tss = TimeSeriesSplit(n_splits=5, test_size=6*25, gap=1)\n",
+    "exhchg_rates = exhchg_rates.sort_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "f0a73bb3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(5, 1, figsize=(15, 15), sharex=True)\n",
+    "\n",
+    "fold = 0\n",
+    "#For each split, the training and testing indices are generated.\n",
+    "\n",
+    "#Data is selected using .iloc so indexing is position-based, not label-based.\n",
+    "for train_idx, val_idx in tss.split(exhchg_rates):\n",
+    "    train = exhchg_rates.iloc[train_idx]\n",
+    "    test = exhchg_rates.iloc[val_idx]\n",
+    "    train['foreign exchange rates'].plot(ax=axs[fold],\n",
+    "                        label='Training Set',\n",
+    "                        title=f'Data Train/Test Split Fold {fold}')\n",
+    "    test['foreign exchange rates'].plot(ax=axs[fold],\n",
+    "                       label='Test Set')\n",
+    "    axs[fold].axvline(test.index.min(), color='black', ls='--')\n",
+    "    axs[fold].legend()\n",
+    "    fold += 1\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ec46ce7",
+   "metadata": {},
+   "source": [
+    "# Final Visualization\n",
+    "- The plot shows how each fold uses past data to train and a future segment to test, illustrating time-aware cross-validation — a critical step in building reliable time series models like Random Forests.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "126c51b6",
+   "metadata": {},
+   "source": [
+    "# Model Training using  Time-aware cross validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "60b96a5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import TimeSeriesSplit\n",
+    "\n",
+    "\n",
+    "tss = TimeSeriesSplit(n_splits=5, test_size=12*25, gap=1)\n",
+    "\n",
+    "import numpy as np\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "fold = 0\n",
+    "preds = []\n",
+    "scores = []\n",
+    "for train_idx, val_idx in tss.split(df):\n",
+    "    train = df.iloc[train_idx]\n",
+    "    test = df.iloc[val_idx]\n",
+    "\n",
+    "    #train = create_new_features(train)\n",
+    "    #test = create_new_features(test)\n",
+    "    test = test.dropna()\n",
+    "    train = train.dropna()\n",
+    "    columns =['Month', 'Year', 'DayOfYear', 'WeekOfYear', 'rolling_mean', 'rolling_std', 't-7']\n",
+    "    Target =\"foreign exchange rates\"\n",
+    "\n",
+    "    X_train = train[columns]\n",
+    "    Y_train = train[Target]\n",
+    "\n",
+    "    X_test = test[columns]\n",
+    "    Y_test = test[Target]\n",
+    "\n",
+    "\n",
+    "\n",
+    "    # we use the estimated best parameters  from grid search cv\n",
+    "    mdl = RandomForestRegressor(n_estimators=50, random_state=1, n_jobs=6,max_samples= 0.5,max_features= 1.0,max_depth=9)\n",
+    "    \n",
+    "    mdl.fit(X_train, Y_train,\n",
+    "            \n",
+    "           )\n",
+    "\n",
+    "    y_pred = mdl.predict(X_test)\n",
+    "    preds.append(y_pred)\n",
+    "    score = np.sqrt(mean_squared_error(Y_test, y_pred))\n",
+    "    scores.append(score)\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "8947ec65",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Score across folds 0.0139\n",
+      "Fold scores:[0.014152135406700584, 0.016659568798973325, 0.01455723762019069, 0.012262042183823357, 0.011844251233982426]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#average mean sqaured error acorss 5 folds\n",
+    "\n",
+    "print(f'Score across folds {np.mean(scores):0.4f}')\n",
+    "print(f'Fold scores:{scores}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "313819ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "mode": "lines",
+         "name": "actual ",
+         "type": "scatter",
+         "x": [
+          "1997-10-22T00:00:00.000000000",
+          "1997-10-23T00:00:00.000000000",
+          "1997-10-24T00:00:00.000000000",
+          "1997-10-27T00:00:00.000000000",
+          "1997-10-28T00:00:00.000000000",
+          "1997-10-29T00:00:00.000000000",
+          "1997-10-30T00:00:00.000000000",
+          "1997-10-31T00:00:00.000000000",
+          "1997-11-03T00:00:00.000000000",
+          "1997-11-04T00:00:00.000000000",
+          "1997-11-05T00:00:00.000000000",
+          "1997-11-06T00:00:00.000000000",
+          "1997-11-07T00:00:00.000000000",
+          "1997-11-10T00:00:00.000000000",
+          "1997-11-12T00:00:00.000000000",
+          "1997-11-13T00:00:00.000000000",
+          "1997-11-14T00:00:00.000000000",
+          "1997-11-17T00:00:00.000000000",
+          "1997-11-18T00:00:00.000000000",
+          "1997-11-19T00:00:00.000000000",
+          "1997-11-20T00:00:00.000000000",
+          "1997-11-21T00:00:00.000000000",
+          "1997-11-24T00:00:00.000000000",
+          "1997-11-25T00:00:00.000000000",
+          "1997-11-26T00:00:00.000000000",
+          "1997-11-28T00:00:00.000000000",
+          "1997-12-01T00:00:00.000000000",
+          "1997-12-02T00:00:00.000000000",
+          "1997-12-03T00:00:00.000000000",
+          "1997-12-04T00:00:00.000000000",
+          "1997-12-05T00:00:00.000000000",
+          "1997-12-08T00:00:00.000000000",
+          "1997-12-09T00:00:00.000000000",
+          "1997-12-10T00:00:00.000000000",
+          "1997-12-11T00:00:00.000000000",
+          "1997-12-12T00:00:00.000000000",
+          "1997-12-15T00:00:00.000000000",
+          "1997-12-16T00:00:00.000000000",
+          "1997-12-17T00:00:00.000000000",
+          "1997-12-18T00:00:00.000000000",
+          "1997-12-19T00:00:00.000000000",
+          "1997-12-22T00:00:00.000000000",
+          "1997-12-23T00:00:00.000000000",
+          "1997-12-24T00:00:00.000000000",
+          "1997-12-26T00:00:00.000000000",
+          "1997-12-29T00:00:00.000000000",
+          "1997-12-30T00:00:00.000000000",
+          "1997-12-31T00:00:00.000000000",
+          "1998-01-02T00:00:00.000000000",
+          "1998-01-05T00:00:00.000000000",
+          "1998-01-06T00:00:00.000000000",
+          "1998-01-07T00:00:00.000000000",
+          "1998-01-08T00:00:00.000000000",
+          "1998-01-09T00:00:00.000000000",
+          "1998-01-12T00:00:00.000000000",
+          "1998-01-13T00:00:00.000000000",
+          "1998-01-14T00:00:00.000000000",
+          "1998-01-15T00:00:00.000000000",
+          "1998-01-16T00:00:00.000000000",
+          "1998-01-20T00:00:00.000000000",
+          "1998-01-21T00:00:00.000000000",
+          "1998-01-22T00:00:00.000000000",
+          "1998-01-23T00:00:00.000000000",
+          "1998-01-26T00:00:00.000000000",
+          "1998-01-27T00:00:00.000000000",
+          "1998-01-28T00:00:00.000000000",
+          "1998-01-29T00:00:00.000000000",
+          "1998-01-30T00:00:00.000000000",
+          "1998-02-02T00:00:00.000000000",
+          "1998-02-03T00:00:00.000000000",
+          "1998-02-04T00:00:00.000000000",
+          "1998-02-05T00:00:00.000000000",
+          "1998-02-06T00:00:00.000000000",
+          "1998-02-09T00:00:00.000000000",
+          "1998-02-10T00:00:00.000000000",
+          "1998-02-11T00:00:00.000000000",
+          "1998-02-12T00:00:00.000000000",
+          "1998-02-13T00:00:00.000000000",
+          "1998-02-17T00:00:00.000000000",
+          "1998-02-18T00:00:00.000000000",
+          "1998-02-19T00:00:00.000000000",
+          "1998-02-20T00:00:00.000000000",
+          "1998-02-23T00:00:00.000000000",
+          "1998-02-24T00:00:00.000000000",
+          "1998-02-25T00:00:00.000000000",
+          "1998-02-26T00:00:00.000000000",
+          "1998-02-27T00:00:00.000000000",
+          "1998-03-02T00:00:00.000000000",
+          "1998-03-03T00:00:00.000000000",
+          "1998-03-04T00:00:00.000000000",
+          "1998-03-05T00:00:00.000000000",
+          "1998-03-06T00:00:00.000000000",
+          "1998-03-09T00:00:00.000000000",
+          "1998-03-10T00:00:00.000000000",
+          "1998-03-11T00:00:00.000000000",
+          "1998-03-12T00:00:00.000000000",
+          "1998-03-13T00:00:00.000000000",
+          "1998-03-16T00:00:00.000000000",
+          "1998-03-17T00:00:00.000000000",
+          "1998-03-18T00:00:00.000000000",
+          "1998-03-19T00:00:00.000000000",
+          "1998-03-20T00:00:00.000000000",
+          "1998-03-23T00:00:00.000000000",
+          "1998-03-24T00:00:00.000000000",
+          "1998-03-25T00:00:00.000000000",
+          "1998-03-26T00:00:00.000000000",
+          "1998-03-27T00:00:00.000000000",
+          "1998-03-30T00:00:00.000000000",
+          "1998-03-31T00:00:00.000000000",
+          "1998-04-01T00:00:00.000000000",
+          "1998-04-02T00:00:00.000000000",
+          "1998-04-03T00:00:00.000000000",
+          "1998-04-06T00:00:00.000000000",
+          "1998-04-07T00:00:00.000000000",
+          "1998-04-08T00:00:00.000000000",
+          "1998-04-09T00:00:00.000000000",
+          "1998-04-10T00:00:00.000000000",
+          "1998-04-13T00:00:00.000000000",
+          "1998-04-14T00:00:00.000000000",
+          "1998-04-15T00:00:00.000000000",
+          "1998-04-16T00:00:00.000000000",
+          "1998-04-17T00:00:00.000000000",
+          "1998-04-20T00:00:00.000000000",
+          "1998-04-21T00:00:00.000000000",
+          "1998-04-22T00:00:00.000000000",
+          "1998-04-23T00:00:00.000000000",
+          "1998-04-24T00:00:00.000000000",
+          "1998-04-27T00:00:00.000000000",
+          "1998-04-28T00:00:00.000000000",
+          "1998-04-29T00:00:00.000000000",
+          "1998-04-30T00:00:00.000000000",
+          "1998-05-01T00:00:00.000000000",
+          "1998-05-04T00:00:00.000000000",
+          "1998-05-05T00:00:00.000000000",
+          "1998-05-06T00:00:00.000000000",
+          "1998-05-07T00:00:00.000000000",
+          "1998-05-08T00:00:00.000000000",
+          "1998-05-11T00:00:00.000000000",
+          "1998-05-12T00:00:00.000000000",
+          "1998-05-13T00:00:00.000000000",
+          "1998-05-14T00:00:00.000000000",
+          "1998-05-15T00:00:00.000000000",
+          "1998-05-18T00:00:00.000000000",
+          "1998-05-19T00:00:00.000000000",
+          "1998-05-20T00:00:00.000000000",
+          "1998-05-21T00:00:00.000000000",
+          "1998-05-22T00:00:00.000000000",
+          "1998-05-26T00:00:00.000000000",
+          "1998-05-27T00:00:00.000000000",
+          "1998-05-28T00:00:00.000000000",
+          "1998-05-29T00:00:00.000000000",
+          "1998-06-01T00:00:00.000000000",
+          "1998-06-02T00:00:00.000000000",
+          "1998-06-03T00:00:00.000000000",
+          "1998-06-04T00:00:00.000000000",
+          "1998-06-05T00:00:00.000000000",
+          "1998-06-08T00:00:00.000000000",
+          "1998-06-09T00:00:00.000000000",
+          "1998-06-10T00:00:00.000000000",
+          "1998-06-11T00:00:00.000000000",
+          "1998-06-12T00:00:00.000000000",
+          "1998-06-15T00:00:00.000000000",
+          "1998-06-16T00:00:00.000000000",
+          "1998-06-17T00:00:00.000000000",
+          "1998-06-18T00:00:00.000000000",
+          "1998-06-19T00:00:00.000000000",
+          "1998-06-22T00:00:00.000000000",
+          "1998-06-23T00:00:00.000000000",
+          "1998-06-24T00:00:00.000000000",
+          "1998-06-25T00:00:00.000000000",
+          "1998-06-26T00:00:00.000000000",
+          "1998-06-29T00:00:00.000000000",
+          "1998-06-30T00:00:00.000000000",
+          "1998-07-01T00:00:00.000000000",
+          "1998-07-02T00:00:00.000000000",
+          "1998-07-03T00:00:00.000000000",
+          "1998-07-06T00:00:00.000000000",
+          "1998-07-07T00:00:00.000000000",
+          "1998-07-08T00:00:00.000000000",
+          "1998-07-09T00:00:00.000000000",
+          "1998-07-10T00:00:00.000000000",
+          "1998-07-13T00:00:00.000000000",
+          "1998-07-14T00:00:00.000000000",
+          "1998-07-15T00:00:00.000000000",
+          "1998-07-16T00:00:00.000000000",
+          "1998-07-17T00:00:00.000000000",
+          "1998-07-20T00:00:00.000000000",
+          "1998-07-21T00:00:00.000000000",
+          "1998-07-22T00:00:00.000000000",
+          "1998-07-23T00:00:00.000000000",
+          "1998-07-24T00:00:00.000000000",
+          "1998-07-27T00:00:00.000000000",
+          "1998-07-28T00:00:00.000000000",
+          "1998-07-29T00:00:00.000000000",
+          "1998-07-30T00:00:00.000000000",
+          "1998-07-31T00:00:00.000000000",
+          "1998-08-03T00:00:00.000000000",
+          "1998-08-04T00:00:00.000000000",
+          "1998-08-05T00:00:00.000000000",
+          "1998-08-06T00:00:00.000000000",
+          "1998-08-07T00:00:00.000000000",
+          "1998-08-10T00:00:00.000000000",
+          "1998-08-11T00:00:00.000000000",
+          "1998-08-12T00:00:00.000000000",
+          "1998-08-13T00:00:00.000000000",
+          "1998-08-14T00:00:00.000000000",
+          "1998-08-17T00:00:00.000000000",
+          "1998-08-18T00:00:00.000000000",
+          "1998-08-19T00:00:00.000000000",
+          "1998-08-20T00:00:00.000000000",
+          "1998-08-21T00:00:00.000000000",
+          "1998-08-24T00:00:00.000000000",
+          "1998-08-25T00:00:00.000000000",
+          "1998-08-26T00:00:00.000000000",
+          "1998-08-27T00:00:00.000000000",
+          "1998-08-28T00:00:00.000000000",
+          "1998-08-31T00:00:00.000000000",
+          "1998-09-01T00:00:00.000000000",
+          "1998-09-02T00:00:00.000000000",
+          "1998-09-03T00:00:00.000000000",
+          "1998-09-04T00:00:00.000000000",
+          "1998-09-08T00:00:00.000000000",
+          "1998-09-09T00:00:00.000000000",
+          "1998-09-10T00:00:00.000000000",
+          "1998-09-11T00:00:00.000000000",
+          "1998-09-14T00:00:00.000000000",
+          "1998-09-15T00:00:00.000000000",
+          "1998-09-16T00:00:00.000000000",
+          "1998-09-17T00:00:00.000000000",
+          "1998-09-18T00:00:00.000000000",
+          "1998-09-21T00:00:00.000000000",
+          "1998-09-22T00:00:00.000000000",
+          "1998-09-23T00:00:00.000000000",
+          "1998-09-24T00:00:00.000000000",
+          "1998-09-25T00:00:00.000000000",
+          "1998-09-28T00:00:00.000000000",
+          "1998-09-29T00:00:00.000000000",
+          "1998-09-30T00:00:00.000000000",
+          "1998-10-01T00:00:00.000000000",
+          "1998-10-02T00:00:00.000000000",
+          "1998-10-05T00:00:00.000000000",
+          "1998-10-06T00:00:00.000000000",
+          "1998-10-07T00:00:00.000000000",
+          "1998-10-08T00:00:00.000000000",
+          "1998-10-09T00:00:00.000000000",
+          "1998-10-13T00:00:00.000000000",
+          "1998-10-14T00:00:00.000000000",
+          "1998-10-15T00:00:00.000000000",
+          "1998-10-16T00:00:00.000000000",
+          "1998-10-19T00:00:00.000000000",
+          "1998-10-20T00:00:00.000000000",
+          "1998-10-21T00:00:00.000000000",
+          "1998-10-22T00:00:00.000000000",
+          "1998-10-23T00:00:00.000000000",
+          "1998-10-26T00:00:00.000000000",
+          "1998-10-27T00:00:00.000000000",
+          "1998-10-28T00:00:00.000000000",
+          "1998-10-29T00:00:00.000000000",
+          "1998-10-30T00:00:00.000000000",
+          "1998-11-02T00:00:00.000000000",
+          "1998-11-03T00:00:00.000000000",
+          "1998-11-04T00:00:00.000000000",
+          "1998-11-05T00:00:00.000000000",
+          "1998-11-06T00:00:00.000000000",
+          "1998-11-09T00:00:00.000000000",
+          "1998-11-10T00:00:00.000000000",
+          "1998-11-12T00:00:00.000000000",
+          "1998-11-13T00:00:00.000000000",
+          "1998-11-16T00:00:00.000000000",
+          "1998-11-17T00:00:00.000000000",
+          "1998-11-18T00:00:00.000000000",
+          "1998-11-19T00:00:00.000000000",
+          "1998-11-20T00:00:00.000000000",
+          "1998-11-23T00:00:00.000000000",
+          "1998-11-24T00:00:00.000000000",
+          "1998-11-25T00:00:00.000000000",
+          "1998-11-27T00:00:00.000000000",
+          "1998-11-30T00:00:00.000000000",
+          "1998-12-01T00:00:00.000000000",
+          "1998-12-02T00:00:00.000000000",
+          "1998-12-03T00:00:00.000000000",
+          "1998-12-04T00:00:00.000000000",
+          "1998-12-07T00:00:00.000000000",
+          "1998-12-08T00:00:00.000000000",
+          "1998-12-09T00:00:00.000000000",
+          "1998-12-10T00:00:00.000000000",
+          "1998-12-11T00:00:00.000000000",
+          "1998-12-14T00:00:00.000000000",
+          "1998-12-15T00:00:00.000000000",
+          "1998-12-16T00:00:00.000000000",
+          "1998-12-17T00:00:00.000000000",
+          "1998-12-18T00:00:00.000000000",
+          "1998-12-21T00:00:00.000000000",
+          "1998-12-22T00:00:00.000000000",
+          "1998-12-23T00:00:00.000000000",
+          "1998-12-24T00:00:00.000000000",
+          "1998-12-28T00:00:00.000000000",
+          "1998-12-29T00:00:00.000000000",
+          "1998-12-30T00:00:00.000000000",
+          "1998-12-31T00:00:00.000000000"
+         ],
+         "y": {
+          "bdata": "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",
+          "dtype": "f8"
+         }
+        },
+        {
+         "mode": "lines",
+         "name": "Forecasted",
+         "type": "scatter",
+         "x": [
+          "1997-10-22T00:00:00.000000000",
+          "1997-10-23T00:00:00.000000000",
+          "1997-10-24T00:00:00.000000000",
+          "1997-10-27T00:00:00.000000000",
+          "1997-10-28T00:00:00.000000000",
+          "1997-10-29T00:00:00.000000000",
+          "1997-10-30T00:00:00.000000000",
+          "1997-10-31T00:00:00.000000000",
+          "1997-11-03T00:00:00.000000000",
+          "1997-11-04T00:00:00.000000000",
+          "1997-11-05T00:00:00.000000000",
+          "1997-11-06T00:00:00.000000000",
+          "1997-11-07T00:00:00.000000000",
+          "1997-11-10T00:00:00.000000000",
+          "1997-11-12T00:00:00.000000000",
+          "1997-11-13T00:00:00.000000000",
+          "1997-11-14T00:00:00.000000000",
+          "1997-11-17T00:00:00.000000000",
+          "1997-11-18T00:00:00.000000000",
+          "1997-11-19T00:00:00.000000000",
+          "1997-11-20T00:00:00.000000000",
+          "1997-11-21T00:00:00.000000000",
+          "1997-11-24T00:00:00.000000000",
+          "1997-11-25T00:00:00.000000000",
+          "1997-11-26T00:00:00.000000000",
+          "1997-11-28T00:00:00.000000000",
+          "1997-12-01T00:00:00.000000000",
+          "1997-12-02T00:00:00.000000000",
+          "1997-12-03T00:00:00.000000000",
+          "1997-12-04T00:00:00.000000000",
+          "1997-12-05T00:00:00.000000000",
+          "1997-12-08T00:00:00.000000000",
+          "1997-12-09T00:00:00.000000000",
+          "1997-12-10T00:00:00.000000000",
+          "1997-12-11T00:00:00.000000000",
+          "1997-12-12T00:00:00.000000000",
+          "1997-12-15T00:00:00.000000000",
+          "1997-12-16T00:00:00.000000000",
+          "1997-12-17T00:00:00.000000000",
+          "1997-12-18T00:00:00.000000000",
+          "1997-12-19T00:00:00.000000000",
+          "1997-12-22T00:00:00.000000000",
+          "1997-12-23T00:00:00.000000000",
+          "1997-12-24T00:00:00.000000000",
+          "1997-12-26T00:00:00.000000000",
+          "1997-12-29T00:00:00.000000000",
+          "1997-12-30T00:00:00.000000000",
+          "1997-12-31T00:00:00.000000000",
+          "1998-01-02T00:00:00.000000000",
+          "1998-01-05T00:00:00.000000000",
+          "1998-01-06T00:00:00.000000000",
+          "1998-01-07T00:00:00.000000000",
+          "1998-01-08T00:00:00.000000000",
+          "1998-01-09T00:00:00.000000000",
+          "1998-01-12T00:00:00.000000000",
+          "1998-01-13T00:00:00.000000000",
+          "1998-01-14T00:00:00.000000000",
+          "1998-01-15T00:00:00.000000000",
+          "1998-01-16T00:00:00.000000000",
+          "1998-01-20T00:00:00.000000000",
+          "1998-01-21T00:00:00.000000000",
+          "1998-01-22T00:00:00.000000000",
+          "1998-01-23T00:00:00.000000000",
+          "1998-01-26T00:00:00.000000000",
+          "1998-01-27T00:00:00.000000000",
+          "1998-01-28T00:00:00.000000000",
+          "1998-01-29T00:00:00.000000000",
+          "1998-01-30T00:00:00.000000000",
+          "1998-02-02T00:00:00.000000000",
+          "1998-02-03T00:00:00.000000000",
+          "1998-02-04T00:00:00.000000000",
+          "1998-02-05T00:00:00.000000000",
+          "1998-02-06T00:00:00.000000000",
+          "1998-02-09T00:00:00.000000000",
+          "1998-02-10T00:00:00.000000000",
+          "1998-02-11T00:00:00.000000000",
+          "1998-02-12T00:00:00.000000000",
+          "1998-02-13T00:00:00.000000000",
+          "1998-02-17T00:00:00.000000000",
+          "1998-02-18T00:00:00.000000000",
+          "1998-02-19T00:00:00.000000000",
+          "1998-02-20T00:00:00.000000000",
+          "1998-02-23T00:00:00.000000000",
+          "1998-02-24T00:00:00.000000000",
+          "1998-02-25T00:00:00.000000000",
+          "1998-02-26T00:00:00.000000000",
+          "1998-02-27T00:00:00.000000000",
+          "1998-03-02T00:00:00.000000000",
+          "1998-03-03T00:00:00.000000000",
+          "1998-03-04T00:00:00.000000000",
+          "1998-03-05T00:00:00.000000000",
+          "1998-03-06T00:00:00.000000000",
+          "1998-03-09T00:00:00.000000000",
+          "1998-03-10T00:00:00.000000000",
+          "1998-03-11T00:00:00.000000000",
+          "1998-03-12T00:00:00.000000000",
+          "1998-03-13T00:00:00.000000000",
+          "1998-03-16T00:00:00.000000000",
+          "1998-03-17T00:00:00.000000000",
+          "1998-03-18T00:00:00.000000000",
+          "1998-03-19T00:00:00.000000000",
+          "1998-03-20T00:00:00.000000000",
+          "1998-03-23T00:00:00.000000000",
+          "1998-03-24T00:00:00.000000000",
+          "1998-03-25T00:00:00.000000000",
+          "1998-03-26T00:00:00.000000000",
+          "1998-03-27T00:00:00.000000000",
+          "1998-03-30T00:00:00.000000000",
+          "1998-03-31T00:00:00.000000000",
+          "1998-04-01T00:00:00.000000000",
+          "1998-04-02T00:00:00.000000000",
+          "1998-04-03T00:00:00.000000000",
+          "1998-04-06T00:00:00.000000000",
+          "1998-04-07T00:00:00.000000000",
+          "1998-04-08T00:00:00.000000000",
+          "1998-04-09T00:00:00.000000000",
+          "1998-04-10T00:00:00.000000000",
+          "1998-04-13T00:00:00.000000000",
+          "1998-04-14T00:00:00.000000000",
+          "1998-04-15T00:00:00.000000000",
+          "1998-04-16T00:00:00.000000000",
+          "1998-04-17T00:00:00.000000000",
+          "1998-04-20T00:00:00.000000000",
+          "1998-04-21T00:00:00.000000000",
+          "1998-04-22T00:00:00.000000000",
+          "1998-04-23T00:00:00.000000000",
+          "1998-04-24T00:00:00.000000000",
+          "1998-04-27T00:00:00.000000000",
+          "1998-04-28T00:00:00.000000000",
+          "1998-04-29T00:00:00.000000000",
+          "1998-04-30T00:00:00.000000000",
+          "1998-05-01T00:00:00.000000000",
+          "1998-05-04T00:00:00.000000000",
+          "1998-05-05T00:00:00.000000000",
+          "1998-05-06T00:00:00.000000000",
+          "1998-05-07T00:00:00.000000000",
+          "1998-05-08T00:00:00.000000000",
+          "1998-05-11T00:00:00.000000000",
+          "1998-05-12T00:00:00.000000000",
+          "1998-05-13T00:00:00.000000000",
+          "1998-05-14T00:00:00.000000000",
+          "1998-05-15T00:00:00.000000000",
+          "1998-05-18T00:00:00.000000000",
+          "1998-05-19T00:00:00.000000000",
+          "1998-05-20T00:00:00.000000000",
+          "1998-05-21T00:00:00.000000000",
+          "1998-05-22T00:00:00.000000000",
+          "1998-05-26T00:00:00.000000000",
+          "1998-05-27T00:00:00.000000000",
+          "1998-05-28T00:00:00.000000000",
+          "1998-05-29T00:00:00.000000000",
+          "1998-06-01T00:00:00.000000000",
+          "1998-06-02T00:00:00.000000000",
+          "1998-06-03T00:00:00.000000000",
+          "1998-06-04T00:00:00.000000000",
+          "1998-06-05T00:00:00.000000000",
+          "1998-06-08T00:00:00.000000000",
+          "1998-06-09T00:00:00.000000000",
+          "1998-06-10T00:00:00.000000000",
+          "1998-06-11T00:00:00.000000000",
+          "1998-06-12T00:00:00.000000000",
+          "1998-06-15T00:00:00.000000000",
+          "1998-06-16T00:00:00.000000000",
+          "1998-06-17T00:00:00.000000000",
+          "1998-06-18T00:00:00.000000000",
+          "1998-06-19T00:00:00.000000000",
+          "1998-06-22T00:00:00.000000000",
+          "1998-06-23T00:00:00.000000000",
+          "1998-06-24T00:00:00.000000000",
+          "1998-06-25T00:00:00.000000000",
+          "1998-06-26T00:00:00.000000000",
+          "1998-06-29T00:00:00.000000000",
+          "1998-06-30T00:00:00.000000000",
+          "1998-07-01T00:00:00.000000000",
+          "1998-07-02T00:00:00.000000000",
+          "1998-07-03T00:00:00.000000000",
+          "1998-07-06T00:00:00.000000000",
+          "1998-07-07T00:00:00.000000000",
+          "1998-07-08T00:00:00.000000000",
+          "1998-07-09T00:00:00.000000000",
+          "1998-07-10T00:00:00.000000000",
+          "1998-07-13T00:00:00.000000000",
+          "1998-07-14T00:00:00.000000000",
+          "1998-07-15T00:00:00.000000000",
+          "1998-07-16T00:00:00.000000000",
+          "1998-07-17T00:00:00.000000000",
+          "1998-07-20T00:00:00.000000000",
+          "1998-07-21T00:00:00.000000000",
+          "1998-07-22T00:00:00.000000000",
+          "1998-07-23T00:00:00.000000000",
+          "1998-07-24T00:00:00.000000000",
+          "1998-07-27T00:00:00.000000000",
+          "1998-07-28T00:00:00.000000000",
+          "1998-07-29T00:00:00.000000000",
+          "1998-07-30T00:00:00.000000000",
+          "1998-07-31T00:00:00.000000000",
+          "1998-08-03T00:00:00.000000000",
+          "1998-08-04T00:00:00.000000000",
+          "1998-08-05T00:00:00.000000000",
+          "1998-08-06T00:00:00.000000000",
+          "1998-08-07T00:00:00.000000000",
+          "1998-08-10T00:00:00.000000000",
+          "1998-08-11T00:00:00.000000000",
+          "1998-08-12T00:00:00.000000000",
+          "1998-08-13T00:00:00.000000000",
+          "1998-08-14T00:00:00.000000000",
+          "1998-08-17T00:00:00.000000000",
+          "1998-08-18T00:00:00.000000000",
+          "1998-08-19T00:00:00.000000000",
+          "1998-08-20T00:00:00.000000000",
+          "1998-08-21T00:00:00.000000000",
+          "1998-08-24T00:00:00.000000000",
+          "1998-08-25T00:00:00.000000000",
+          "1998-08-26T00:00:00.000000000",
+          "1998-08-27T00:00:00.000000000",
+          "1998-08-28T00:00:00.000000000",
+          "1998-08-31T00:00:00.000000000",
+          "1998-09-01T00:00:00.000000000",
+          "1998-09-02T00:00:00.000000000",
+          "1998-09-03T00:00:00.000000000",
+          "1998-09-04T00:00:00.000000000",
+          "1998-09-08T00:00:00.000000000",
+          "1998-09-09T00:00:00.000000000",
+          "1998-09-10T00:00:00.000000000",
+          "1998-09-11T00:00:00.000000000",
+          "1998-09-14T00:00:00.000000000",
+          "1998-09-15T00:00:00.000000000",
+          "1998-09-16T00:00:00.000000000",
+          "1998-09-17T00:00:00.000000000",
+          "1998-09-18T00:00:00.000000000",
+          "1998-09-21T00:00:00.000000000",
+          "1998-09-22T00:00:00.000000000",
+          "1998-09-23T00:00:00.000000000",
+          "1998-09-24T00:00:00.000000000",
+          "1998-09-25T00:00:00.000000000",
+          "1998-09-28T00:00:00.000000000",
+          "1998-09-29T00:00:00.000000000",
+          "1998-09-30T00:00:00.000000000",
+          "1998-10-01T00:00:00.000000000",
+          "1998-10-02T00:00:00.000000000",
+          "1998-10-05T00:00:00.000000000",
+          "1998-10-06T00:00:00.000000000",
+          "1998-10-07T00:00:00.000000000",
+          "1998-10-08T00:00:00.000000000",
+          "1998-10-09T00:00:00.000000000",
+          "1998-10-13T00:00:00.000000000",
+          "1998-10-14T00:00:00.000000000",
+          "1998-10-15T00:00:00.000000000",
+          "1998-10-16T00:00:00.000000000",
+          "1998-10-19T00:00:00.000000000",
+          "1998-10-20T00:00:00.000000000",
+          "1998-10-21T00:00:00.000000000",
+          "1998-10-22T00:00:00.000000000",
+          "1998-10-23T00:00:00.000000000",
+          "1998-10-26T00:00:00.000000000",
+          "1998-10-27T00:00:00.000000000",
+          "1998-10-28T00:00:00.000000000",
+          "1998-10-29T00:00:00.000000000",
+          "1998-10-30T00:00:00.000000000",
+          "1998-11-02T00:00:00.000000000",
+          "1998-11-03T00:00:00.000000000",
+          "1998-11-04T00:00:00.000000000",
+          "1998-11-05T00:00:00.000000000",
+          "1998-11-06T00:00:00.000000000",
+          "1998-11-09T00:00:00.000000000",
+          "1998-11-10T00:00:00.000000000",
+          "1998-11-12T00:00:00.000000000",
+          "1998-11-13T00:00:00.000000000",
+          "1998-11-16T00:00:00.000000000",
+          "1998-11-17T00:00:00.000000000",
+          "1998-11-18T00:00:00.000000000",
+          "1998-11-19T00:00:00.000000000",
+          "1998-11-20T00:00:00.000000000",
+          "1998-11-23T00:00:00.000000000",
+          "1998-11-24T00:00:00.000000000",
+          "1998-11-25T00:00:00.000000000",
+          "1998-11-27T00:00:00.000000000",
+          "1998-11-30T00:00:00.000000000",
+          "1998-12-01T00:00:00.000000000",
+          "1998-12-02T00:00:00.000000000",
+          "1998-12-03T00:00:00.000000000",
+          "1998-12-04T00:00:00.000000000",
+          "1998-12-07T00:00:00.000000000",
+          "1998-12-08T00:00:00.000000000",
+          "1998-12-09T00:00:00.000000000",
+          "1998-12-10T00:00:00.000000000",
+          "1998-12-11T00:00:00.000000000",
+          "1998-12-14T00:00:00.000000000",
+          "1998-12-15T00:00:00.000000000",
+          "1998-12-16T00:00:00.000000000",
+          "1998-12-17T00:00:00.000000000",
+          "1998-12-18T00:00:00.000000000",
+          "1998-12-21T00:00:00.000000000",
+          "1998-12-22T00:00:00.000000000",
+          "1998-12-23T00:00:00.000000000",
+          "1998-12-24T00:00:00.000000000",
+          "1998-12-28T00:00:00.000000000",
+          "1998-12-29T00:00:00.000000000",
+          "1998-12-30T00:00:00.000000000",
+          "1998-12-31T00:00:00.000000000"
+         ],
+         "y": {
+          "bdata": "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",
+          "dtype": "f8"
+         }
+        }
+       ],
+       "layout": {
+        "template": {
+         "data": {
+          "bar": [
+           {
+            "error_x": {
+             "color": "#2a3f5f"
+            },
+            "error_y": {
+             "color": "#2a3f5f"
+            },
+            "marker": {
+             "line": {
+              "color": "#E5ECF6",
+              "width": 0.5
+             },
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
+            },
+            "type": "bar"
+           }
+          ],
+          "barpolar": [
+           {
+            "marker": {
+             "line": {
+              "color": "#E5ECF6",
+              "width": 0.5
+             },
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
+            },
+            "type": "barpolar"
+           }
+          ],
+          "carpet": [
+           {
+            "aaxis": {
+             "endlinecolor": "#2a3f5f",
+             "gridcolor": "white",
+             "linecolor": "white",
+             "minorgridcolor": "white",
+             "startlinecolor": "#2a3f5f"
+            },
+            "baxis": {
+             "endlinecolor": "#2a3f5f",
+             "gridcolor": "white",
+             "linecolor": "white",
+             "minorgridcolor": "white",
+             "startlinecolor": "#2a3f5f"
+            },
+            "type": "carpet"
+           }
+          ],
+          "choropleth": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "choropleth"
+           }
+          ],
+          "contour": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "contour"
+           }
+          ],
+          "contourcarpet": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "contourcarpet"
+           }
+          ],
+          "heatmap": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "heatmap"
+           }
+          ],
+          "histogram": [
+           {
+            "marker": {
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
+            },
+            "type": "histogram"
+           }
+          ],
+          "histogram2d": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "histogram2d"
+           }
+          ],
+          "histogram2dcontour": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "histogram2dcontour"
+           }
+          ],
+          "mesh3d": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "mesh3d"
+           }
+          ],
+          "parcoords": [
+           {
+            "line": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "parcoords"
+           }
+          ],
+          "pie": [
+           {
+            "automargin": true,
+            "type": "pie"
+           }
+          ],
+          "scatter": [
+           {
+            "fillpattern": {
+             "fillmode": "overlay",
+             "size": 10,
+             "solidity": 0.2
+            },
+            "type": "scatter"
+           }
+          ],
+          "scatter3d": [
+           {
+            "line": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatter3d"
+           }
+          ],
+          "scattercarpet": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattercarpet"
+           }
+          ],
+          "scattergeo": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattergeo"
+           }
+          ],
+          "scattergl": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattergl"
+           }
+          ],
+          "scattermap": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattermap"
+           }
+          ],
+          "scattermapbox": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattermapbox"
+           }
+          ],
+          "scatterpolar": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterpolar"
+           }
+          ],
+          "scatterpolargl": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterpolargl"
+           }
+          ],
+          "scatterternary": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterternary"
+           }
+          ],
+          "surface": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "surface"
+           }
+          ],
+          "table": [
+           {
+            "cells": {
+             "fill": {
+              "color": "#EBF0F8"
+             },
+             "line": {
+              "color": "white"
+             }
+            },
+            "header": {
+             "fill": {
+              "color": "#C8D4E3"
+             },
+             "line": {
+              "color": "white"
+             }
+            },
+            "type": "table"
+           }
+          ]
+         },
+         "layout": {
+          "annotationdefaults": {
+           "arrowcolor": "#2a3f5f",
+           "arrowhead": 0,
+           "arrowwidth": 1
+          },
+          "autotypenumbers": "strict",
+          "coloraxis": {
+           "colorbar": {
+            "outlinewidth": 0,
+            "ticks": ""
+           }
+          },
+          "colorscale": {
+           "diverging": [
+            [
+             0,
+             "#8e0152"
+            ],
+            [
+             0.1,
+             "#c51b7d"
+            ],
+            [
+             0.2,
+             "#de77ae"
+            ],
+            [
+             0.3,
+             "#f1b6da"
+            ],
+            [
+             0.4,
+             "#fde0ef"
+            ],
+            [
+             0.5,
+             "#f7f7f7"
+            ],
+            [
+             0.6,
+             "#e6f5d0"
+            ],
+            [
+             0.7,
+             "#b8e186"
+            ],
+            [
+             0.8,
+             "#7fbc41"
+            ],
+            [
+             0.9,
+             "#4d9221"
+            ],
+            [
+             1,
+             "#276419"
+            ]
+           ],
+           "sequential": [
+            [
+             0,
+             "#0d0887"
+            ],
+            [
+             0.1111111111111111,
+             "#46039f"
+            ],
+            [
+             0.2222222222222222,
+             "#7201a8"
+            ],
+            [
+             0.3333333333333333,
+             "#9c179e"
+            ],
+            [
+             0.4444444444444444,
+             "#bd3786"
+            ],
+            [
+             0.5555555555555556,
+             "#d8576b"
+            ],
+            [
+             0.6666666666666666,
+             "#ed7953"
+            ],
+            [
+             0.7777777777777778,
+             "#fb9f3a"
+            ],
+            [
+             0.8888888888888888,
+             "#fdca26"
+            ],
+            [
+             1,
+             "#f0f921"
+            ]
+           ],
+           "sequentialminus": [
+            [
+             0,
+             "#0d0887"
+            ],
+            [
+             0.1111111111111111,
+             "#46039f"
+            ],
+            [
+             0.2222222222222222,
+             "#7201a8"
+            ],
+            [
+             0.3333333333333333,
+             "#9c179e"
+            ],
+            [
+             0.4444444444444444,
+             "#bd3786"
+            ],
+            [
+             0.5555555555555556,
+             "#d8576b"
+            ],
+            [
+             0.6666666666666666,
+             "#ed7953"
+            ],
+            [
+             0.7777777777777778,
+             "#fb9f3a"
+            ],
+            [
+             0.8888888888888888,
+             "#fdca26"
+            ],
+            [
+             1,
+             "#f0f921"
+            ]
+           ]
+          },
+          "colorway": [
+           "#636efa",
+           "#EF553B",
+           "#00cc96",
+           "#ab63fa",
+           "#FFA15A",
+           "#19d3f3",
+           "#FF6692",
+           "#B6E880",
+           "#FF97FF",
+           "#FECB52"
+          ],
+          "font": {
+           "color": "#2a3f5f"
+          },
+          "geo": {
+           "bgcolor": "white",
+           "lakecolor": "white",
+           "landcolor": "#E5ECF6",
+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "white"
+          },
+          "hoverlabel": {
+           "align": "left"
+          },
+          "hovermode": "closest",
+          "mapbox": {
+           "style": "light"
+          },
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "#E5ECF6",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pred=pd.Series(y_pred, index=Y_test.index)\n",
+    "import plotly.graph_objects as go\n",
+    "fig = go.Figure()\n",
+    "fig.add_trace(go.Scatter(x=Y_test.index, y=Y_test.values, mode='lines', name='actual '))\n",
+    "fig.add_trace(go.Scatter(x=pred.index, y=pred.values, mode='lines', name='Forecasted'))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d377c04b",
+   "metadata": {},
+   "source": [
+    "# Future Predicitons -- A rough estimate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "753aab7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_whole = df[columns]\n",
+    "y_whole = df['foreign exchange rates']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "f7c52f71",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 81 candidates, totalling 405 fits\n",
+      "best Parameters: {'max_depth': 10, 'max_features': 1.0, 'max_samples': 0.5, 'n_estimators': 50}\n",
+      "best score: 0.7110295623806835\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Grid search cv\n",
+    "\n",
+    "rf_grid.fit(X_whole,y_whole)\n",
+    "print(\"best Parameters:\",rf_grid.best_params_)\n",
+    "\n",
+    "print(\"best score:\",rf_grid.best_score_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "0cbe550d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
+      "best Parameters: {'n_estimators': 50, 'max_samples': 0.5, 'max_features': 1.0, 'max_depth': 12}\n",
+      "best score: 0.705785035884448\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Randomized search cv\n",
+    "\n",
+    "rf_rndm.fit(X_whole,y_whole)\n",
+    "\n",
+    "print(\"best Parameters:\",rf_rndm.best_params_)\n",
+    "\n",
+    "print(\"best score:\",rf_rndm.best_score_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "a7b68b23",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\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-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 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-4 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-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 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-4 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-4 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-4 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-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 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-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 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: 0.5em;\n",
+       "  text-align: center;\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-4 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-4 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-4 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-4 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-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=12, max_samples=0.5, n_estimators=50, n_jobs=6,\n",
+       "                      random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_depth=12, max_samples=0.5, n_estimators=50, n_jobs=6,\n",
+       "                      random_state=1)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestRegressor(max_depth=12, max_samples=0.5, n_estimators=50, n_jobs=6,\n",
+       "                      random_state=1)"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "reg = mdl = RandomForestRegressor(n_estimators=50, random_state=1, n_jobs=6,max_samples= 0.5,max_features= 1.0,max_depth=12)\n",
+    "    \n",
+    "reg.fit(X_whole, y_whole)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "66919e4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Timestamp('1998-12-31 00:00:00')"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.index[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "2f04fafb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "future_index = pd.date_range('1998-12-31','2000-12-31', freq='D')#Creates a daily date index from 1998-12-31 to 2000-12-31\n",
+    "future_df = pd.DataFrame(index=future_index)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "b587a6b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(732, 0)"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "future_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "53eedc7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "past_df =exhchg_rates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "506602fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "concatenated_df = pd.concat([past_df, future_df])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "c07fefb8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def create_new_features(df):\n",
+    "    df[\"Date\"]=pd.to_datetime(concatenated_df.index)\n",
+    "    df[\"Month\"]=df[\"Date\"].dt.month\n",
+    "    df[\"Year\"]=df[\"Date\"].dt.year\n",
+    "\n",
+    "    df[\"DayOfYear\"]=df[\"Date\"].dt.dayofyear\n",
+    "    df[\"WeekOfYear\"]=df[\"Date\"].dt.isocalendar().week\n",
+    "\n",
+    "\n",
+    "\n",
+    "    # Calculate rolling mean and deviation\n",
+    "    df[\"rolling_mean\"] = df['foreign exchange rates'].rolling(window=7).mean()\n",
+    "    df[\"rolling_std\"] = df['foreign exchange rates'].rolling(window=7).std()\n",
+    "\n",
+    "    df['t-7']= df['foreign exchange rates'].shift(7)\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "d8e1b200",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "concatenated_df = create_new_features(concatenated_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "f58e3eb5",
+   "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>foreign exchange rates</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Month</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Day</th>\n",
+       "      <th>DayOfWeek</th>\n",
+       "      <th>DayOfYear</th>\n",
+       "      <th>WeekOfYear</th>\n",
+       "      <th>rolling_mean</th>\n",
+       "      <th>rolling_std</th>\n",
+       "      <th>t-10</th>\n",
+       "      <th>t-9</th>\n",
+       "      <th>t-8</th>\n",
+       "      <th>t-7</th>\n",
+       "      <th>t-6</th>\n",
+       "      <th>t-5</th>\n",
+       "      <th>t-4</th>\n",
+       "      <th>t-3</th>\n",
+       "      <th>t-2</th>\n",
+       "      <th>t-1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2000-12-27</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2000-12-27</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>362</td>\n",
+       "      <td>52</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-12-28</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2000-12-28</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>363</td>\n",
+       "      <td>52</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-12-29</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2000-12-29</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>364</td>\n",
+       "      <td>52</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-12-30</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2000-12-30</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>365</td>\n",
+       "      <td>52</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-12-31</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2000-12-31</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>366</td>\n",
+       "      <td>52</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            foreign exchange rates       Date  Month  Year  Day  DayOfWeek  \\\n",
+       "2000-12-27                     NaN 2000-12-27     12  2000  NaN        NaN   \n",
+       "2000-12-28                     NaN 2000-12-28     12  2000  NaN        NaN   \n",
+       "2000-12-29                     NaN 2000-12-29     12  2000  NaN        NaN   \n",
+       "2000-12-30                     NaN 2000-12-30     12  2000  NaN        NaN   \n",
+       "2000-12-31                     NaN 2000-12-31     12  2000  NaN        NaN   \n",
+       "\n",
+       "            DayOfYear  WeekOfYear  rolling_mean  rolling_std  t-10  t-9  t-8  \\\n",
+       "2000-12-27        362          52           NaN          NaN   NaN  NaN  NaN   \n",
+       "2000-12-28        363          52           NaN          NaN   NaN  NaN  NaN   \n",
+       "2000-12-29        364          52           NaN          NaN   NaN  NaN  NaN   \n",
+       "2000-12-30        365          52           NaN          NaN   NaN  NaN  NaN   \n",
+       "2000-12-31        366          52           NaN          NaN   NaN  NaN  NaN   \n",
+       "\n",
+       "            t-7  t-6  t-5  t-4  t-3  t-2  t-1  \n",
+       "2000-12-27  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
+       "2000-12-28  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
+       "2000-12-29  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
+       "2000-12-30  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
+       "2000-12-31  NaN  NaN  NaN  NaN  NaN  NaN  NaN  "
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "concatenated_df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "d0fc64b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "future_rows = concatenated_df['1998-12-31':].copy()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "fd6b2379",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "future_rows['pred'] = reg.predict(future_rows[columns])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "f7803b33",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "future_rows['pred'].plot(figsize=(10, 5),\n",
+    "                               \n",
+    "                               ms=1,\n",
+    "                               lw=1,\n",
+    "                               title='Future Predictions')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff3ef913",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": 5
+}