diff --git "a/Time_Series_Forcasting_XGBoost.ipynb" "b/Time_Series_Forcasting_XGBoost.ipynb"
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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "dM9_ky3Urx-0",
+      "metadata": {
+        "id": "dM9_ky3Urx-0"
+      },
+      "source": [
+        "# <font color='367588'> Multivariate Time Series Forecasting using XGBoost Regressor: A Machine Learning Approach</font>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ec02f7eb",
+      "metadata": {},
+      "source": [
+        "#### The notebook aims to implement a **Machine learning based model** (**XGBoost Regressor**) on historical stock data of **INTEL**, spanning   **1980 to 2023**.\n",
+        "\n",
+        "#### The primary objective is to forecast the **closing prices** of the stock capturing temporal patterns through relevant predictive variables."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "504890d4",
+      "metadata": {},
+      "source": [
+        "#### XGBoost Regressor is a powerful machine learning algorithm based on the gradient boosting framework. It is an ensemble learning method that combines the predictions of multiple weak models (decision trees) to create a strong predictive model -> each tree is trained to correct the errors of the previous ones.\n",
+        "\n",
+        "--- \n",
+        "\n",
+        "### XGBoost can be used for time series forecasting by  reframing the problem into a <span style=\"color:#367588;\">supervised learning task </span> -> \"transforming the time series data into a <span style=\"color:#367588;\">supervised learning format\"</span> This involves creating lagged features (past values) of the target variable and optionally for other relevant input variablesvto enable the model to learn patterns and predict future values.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7d287fa1",
+      "metadata": {},
+      "source": [
+        "\n",
+        "\n",
+        "# <span style=\"color:#367588;\">Approach</span>\n",
+        "\n",
+        "### <span style=\"color:#367588;\">Data preparation:</span>\n",
+        "\n",
+        "* Creating additional new features from <span style=\"color:#FFA500;\">Date components</span> and <span style=\"color:#FFA500;\">rolling statistics</span>\n",
+        "* Creating <span style=\"color:#FFA500;\">lagged features</span> for all the relevant time series variables ,in our case, the <span style=\"color:#FFA500;\">\"Closing Price\" and \"High\"</span> to serve as target and input variables respectively.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### <span style=\"color:#367588;\">Defining the target variable</span>\n",
+        "\n",
+        "* Defining the target variable as the <span style=\"color:#FFA500;\">future value</span> we want to predict\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### <span style=\"color:#367588;\">Chronological Splitting</span>\n",
+        "\n",
+        "* Preserve <span style=\"color:#FFA500;\">temporal order</span> when splitting data into train/test sets\n",
+        "\n",
+        "\n",
+        "---\n",
+        "### <span style=\"color:#367588;\">Defining Evaluation metrics and Creating a Baseline  model </span>\n",
+        "\n",
+        "* We define a very basic **naive** model for later comparison with the trained model across the defined evaluation metrics\n",
+        "\n",
+        "---\n",
+        "### <span style=\"color:#367588;\">Model training</span>\n",
+        "\n",
+        "* Train the <span style=\"color:#FFA500;\">XGBoost regressor</span> on this structured dataset to learn <span style=\"color:#FFA500;\">patterns and dependencies</span> across multiple time series\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### <span style=\"color:#367588;\">Making Predictions</span>\n",
+        "\n",
+        "* Making <span style=\"color:#367588;\">predictions one step ahead</span> in the future\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### <span style=\"color:#367588;\">Evaluate the model</span>\n",
+        "\n",
+        "* Evaluate the model using appropriate performance metrics\n",
+        "\n",
+        "---\n",
+        "### <span style=\"color:#367588;\">Hyperparameter Tuning/Optimization</span>\n",
+        "\n",
+        "* Using Grid Search  CV to find best parameters\n",
+        "\n",
+        "\n",
+        "### <span style=\"color:#367588;\">Making Predictions (n steps ahead)</span>\n",
+        "\n",
+        "* Making predictions for <span style=\"color:#FFA500;\">n steps ahead</span> in the future\n",
+        "\n",
+        "---\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cd45fb74",
+      "metadata": {},
+      "source": [
+        "# <font color='FFA500'>Importing required libraries</font>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "qggkhEYpryK7",
+      "metadata": {
+        "id": "qggkhEYpryK7"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "from sklearn.model_selection import GridSearchCV\n",
+        "from xgboost import plot_importance\n",
+        "import xgboost as xgb\n",
+        "import seaborn as sns\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "de09a572",
+      "metadata": {},
+      "source": [
+        "# <font color='FFA500'>Data Loading and Visualization</font>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "452fda19",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 203
+        },
+        "id": "452fda19",
+        "outputId": "3ef649d3-ec89-4e18-e369-70b37cfe2021"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(         Date      High     Close\n",
+              " 0  1980-03-18  0.328125  0.322917\n",
+              " 1  1980-03-19  0.335938  0.330729\n",
+              " 2  1980-03-20  0.334635  0.329427\n",
+              " 3  1980-03-21  0.322917  0.317708\n",
+              " 4  1980-03-24  0.316406  0.311198,\n",
+              " (10919, 3))"
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df = pd.read_csv('I:/CQAI/TSA/TSD/TSD/archive (1)/INTEL (1980 - 11.07.2023).csv',usecols ={\"Date\",\"Close\",\"High\"})\n",
+        "\n",
+        "df.head(),df.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "d955833b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "df[\"Date\"]=pd.to_datetime(df[\"Date\"]) #convert date column to pandas datetime format"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "df2e37e1",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x1f9b9c9f800>]"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "\n",
+        "plt.figure(figsize=(10, 4))\n",
+        "\n",
+        "plt.plot(df[\"Date\"],df[\"Close\"],label=\"Nividia Closing price\", color=\"blue\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d1283281",
+      "metadata": {},
+      "source": [
+        "\n",
+        "\n",
+        "## <font color='FFA500'>Feature Engineering</font>\n",
+        "\n",
+        "Features created:\n",
+        "\n",
+        "* **Date features**: Extracting components like\n",
+        "dayofweek, year, day_of_year, sin_day, cos_day. These help the model understand seasonality and calendar effects.\n",
+        "\n",
+        "* **Lag features**: Using past values of a variable as predictors (e.g., `Close_t-1`, `High_t-1`, etc.)\n",
+        "* **Rolling statistics**: Applying moving averagesto smooth and represent recent trends\n",
+        "* **Date Features**: Capturing seasonality and cyclic behavior in the time series data\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "98b56b8d",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "98b56b8d",
+        "outputId": "566561b1-4cd2-4b6d-e6a9-70155c175625"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "# Creating Date features\n",
+        "df['dayofweek'] = df['Date'].dt.dayofweek\n",
+        "df['year'] = df['Date'].dt.year\n",
+        "df['dayofyear'] = df['Date'].dt.dayofyear\n",
+        "df['sin_day'] = np.sin(df['dayofyear'])\n",
+        "df['cos_day'] = np.cos(df['dayofyear'])\n",
+        "df['month'] = df['Date'].dt.month\n",
+        "df['year'] = df['Date'].dt.year\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "9baf7e8b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#one step_behind from close\n",
+        "df[\"Close_t-1\"] = df.Close.shift(1)\n",
+        "df[\"High_t-1\"] = df.High.shift(1)\n",
+        "\n",
+        "#Differnced feature\n",
+        "df[\"Close_diff\"] = df.Close.diff(1)\n",
+        "\n",
+        "#rolling feature\n",
+        "df[\"Rolling_mean\"]=df.Close.rolling(20).mean().reset_index(level=0, drop=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "71f4ca63",
+      "metadata": {},
+      "source": [
+        "\n",
+        "## <font color='FFA500'>Transform to a Supervised Learning Problem </font >\n",
+        "\n",
+        "Now we will create our target feature -> a  new column called **`next_day_close`** by shifting the **`Close`** values one step back.\n",
+        "\n",
+        "This new column will contain the **one-step-ahead future values** of the `Close` price, making it suitable for supervised learning.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "f1808121",
+      "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>Date</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>dayofweek</th>\n",
+              "      <th>year</th>\n",
+              "      <th>dayofyear</th>\n",
+              "      <th>sin_day</th>\n",
+              "      <th>cos_day</th>\n",
+              "      <th>month</th>\n",
+              "      <th>Close_t-1</th>\n",
+              "      <th>High_t-1</th>\n",
+              "      <th>Close_diff</th>\n",
+              "      <th>Rolling_mean</th>\n",
+              "      <th>Close_next_day</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1980-03-18</td>\n",
+              "      <td>0.328125</td>\n",
+              "      <td>0.322917</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1980</td>\n",
+              "      <td>78</td>\n",
+              "      <td>0.513978</td>\n",
+              "      <td>-0.857803</td>\n",
+              "      <td>3</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.330729</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1980-03-19</td>\n",
+              "      <td>0.335938</td>\n",
+              "      <td>0.330729</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1980</td>\n",
+              "      <td>79</td>\n",
+              "      <td>-0.444113</td>\n",
+              "      <td>-0.895971</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0.322917</td>\n",
+              "      <td>0.328125</td>\n",
+              "      <td>0.007812</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.329427</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1980-03-20</td>\n",
+              "      <td>0.334635</td>\n",
+              "      <td>0.329427</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1980</td>\n",
+              "      <td>80</td>\n",
+              "      <td>-0.993889</td>\n",
+              "      <td>-0.110387</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0.330729</td>\n",
+              "      <td>0.335938</td>\n",
+              "      <td>-0.001302</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.317708</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1980-03-21</td>\n",
+              "      <td>0.322917</td>\n",
+              "      <td>0.317708</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1980</td>\n",
+              "      <td>81</td>\n",
+              "      <td>-0.629888</td>\n",
+              "      <td>0.776686</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0.329427</td>\n",
+              "      <td>0.334635</td>\n",
+              "      <td>-0.011719</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.311198</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1980-03-24</td>\n",
+              "      <td>0.316406</td>\n",
+              "      <td>0.311198</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1980</td>\n",
+              "      <td>84</td>\n",
+              "      <td>0.733190</td>\n",
+              "      <td>-0.680023</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0.317708</td>\n",
+              "      <td>0.322917</td>\n",
+              "      <td>-0.006510</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.312500</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "        Date      High     Close  dayofweek  year  dayofyear   sin_day  \\\n",
+              "0 1980-03-18  0.328125  0.322917          1  1980         78  0.513978   \n",
+              "1 1980-03-19  0.335938  0.330729          2  1980         79 -0.444113   \n",
+              "2 1980-03-20  0.334635  0.329427          3  1980         80 -0.993889   \n",
+              "3 1980-03-21  0.322917  0.317708          4  1980         81 -0.629888   \n",
+              "4 1980-03-24  0.316406  0.311198          0  1980         84  0.733190   \n",
+              "\n",
+              "    cos_day  month  Close_t-1  High_t-1  Close_diff  Rolling_mean  \\\n",
+              "0 -0.857803      3        NaN       NaN         NaN           NaN   \n",
+              "1 -0.895971      3   0.322917  0.328125    0.007812           NaN   \n",
+              "2 -0.110387      3   0.330729  0.335938   -0.001302           NaN   \n",
+              "3  0.776686      3   0.329427  0.334635   -0.011719           NaN   \n",
+              "4 -0.680023      3   0.317708  0.322917   -0.006510           NaN   \n",
+              "\n",
+              "   Close_next_day  \n",
+              "0        0.330729  \n",
+              "1        0.329427  \n",
+              "2        0.317708  \n",
+              "3        0.311198  \n",
+              "4        0.312500  "
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df['Close_next_day'] = df.Close.shift(-1)\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "8184344c",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "df = df.dropna(subset=['Close_next_day'])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "807c4055",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "10913    33.619999\n",
+              "10914    32.509998\n",
+              "10915    31.969999\n",
+              "10916    31.850000\n",
+              "10917    32.740002\n",
+              "Name: Close_next_day, dtype: float64"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df['Close_next_day'].tail()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2ac05eed",
+      "metadata": {},
+      "source": [
+        "\n",
+        "## <font color='FFA500'>Train/Test Split</font >\n",
+        "\n",
+        "This step involves **chronological splitting** of the dataset.\n",
+        "\n",
+        "We keep the **major portion** of the data for **training**, and use the **later sequential portion** for **testing**.\n",
+        "\n",
+        "This preserves the **temporal order** and avoids data leakage, which is essential for time series forecasting tasks.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "eA3q5pVfmUal",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eA3q5pVfmUal",
+        "outputId": "19b69c13-d7ec-43c6-b772-806f57db290d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "10918"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "len(df)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "7571764a",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "8734\n"
+          ]
+        }
+      ],
+      "source": [
+        "#we define a split value between train and test\n",
+        "\n",
+        "split_idx = int(len(df) * 0.8)\n",
+        "print(split_idx)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "96aa41aa",
+      "metadata": {
+        "id": "96aa41aa"
+      },
+      "outputs": [],
+      "source": [
+        "# Train data\n",
+        "\n",
+        "df_train=df[:split_idx]\n",
+        "X_train=df_train.drop(['Close_next_day','Date'],axis=1).copy()\n",
+        "\n",
+        "#Setting Close_next_day as the target feature\n",
+        "y_train=df_train['Close_next_day'].copy()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "840e2c99",
+      "metadata": {
+        "id": "840e2c99"
+      },
+      "outputs": [],
+      "source": [
+        "#Test data\n",
+        "\n",
+        "df_test=df[split_idx:]\n",
+        "X_test=df_test.drop(['Close_next_day','Date'],axis=1).copy()\n",
+        "y_test=df_test['Close_next_day'].copy()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "892c14ca",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "892c14ca",
+        "outputId": "3ef15986-784c-46c7-a023-83d22bb9e94d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "((8734, 12), (2184, 12))"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "X_train.shape,X_test.shape,"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "578cf9d8",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "((2184,), (8734,))"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "y_test.shape,y_train.shape"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "312f31df",
+      "metadata": {},
+      "source": [
+        "# <font color='FFA500'>Evaluation metrics </font>\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ecb03afa",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.metrics import mean_squared_error as MSE\n",
+        "\n",
+        "\n",
+        "def rmse(y_true, y_pred):\n",
+        "    rmse = np.sqrt(MSE(y_true, y_pred))\n",
+        "    print(\"Mean Value of Test Dataset:\", y_test.mean())\n",
+        "    print(\"RMSE : % f\" %(rmse))\n",
+        "\n",
+        "def mape(y_true, y_pred):\n",
+        "    ape = np.abs((y_true - y_pred) / y_true)\n",
+        "    \n",
+        "    ape[~np.isfinite(ape)] = 1.\n",
+        "    print(\"Mape\",np.mean(ape))\n",
+        "\n",
+        "def wmape(y_true, y_pred):\n",
+        "    print(\"Wmape\",np.sum(np.abs(y_true - y_pred)) / np.sum(np.abs(y_true)))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "86905cf6",
+      "metadata": {},
+      "source": [
+        "# <font color='FFA500'>Creating the Basline for Later evaluation</font>\n",
+        "\n",
+        "The baseline model assumes that todays closing price will be the same as next days closing price. In this setup, the **predicted value** is set as today’s close, while the **true value** is the actual closing price of the following day. This provides a simple benchmark, representing the maximum error a naïve model might produce. Ideally, our trained model should achieve a lower error than this baseline.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "id": "98e0b35d",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "y_pred = df_test[\"Close\"]\n",
+        "y_true = df_test['Close_next_day']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "id": "759e507d",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mape 0.01403404955533739\n",
+            "Wmape 0.014251380880077104\n"
+          ]
+        }
+      ],
+      "source": [
+        "mape(y_true,y_pred)\n",
+        "wmape(y_true,y_pred)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d920d2e2",
+      "metadata": {},
+      "source": [
+        "## <font color='FFA500'>Handling Missing Values Before Training</font>\n",
+        "\n",
+        "Before training, we re-inspect our dataset and impute the **NaN values** using the **`SimpleImputer`** with the default **mean strategy**.\n",
+        "\n",
+        "These missing values were introduced as a result of the **new features** we added — such as **lagged features** and **rolling statistics** — which naturally create missing values at the beginning of the time series.\n",
+        "\n",
+        "### Why this is important:\n",
+        "Imputing ensures that **each training example is complete**, allowing the model to learn effectively from all available data.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "id": "64ab8415",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "High             0\n",
+              "Close            0\n",
+              "dayofweek        0\n",
+              "year             0\n",
+              "dayofyear        0\n",
+              "sin_day          0\n",
+              "cos_day          0\n",
+              "month            0\n",
+              "Close_t-1        1\n",
+              "High_t-1         1\n",
+              "Close_diff       1\n",
+              "Rolling_mean    19\n",
+              "dtype: int64"
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "X_train.isnull().sum()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "id": "cda45109",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "imputer = SimpleImputer()\n",
+        "Xtr = imputer.fit_transform(X_train)\n",
+        "\n",
+        "\n",
+        "Xtst = imputer.transform(X_test)#This ensures that  preprocessing is consistent between training and testing.\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8f256f46",
+      "metadata": {},
+      "source": [
+        "## <font color='FFA500'>Model Training using `XGBRegressor` class from xgboost Library</font>\n",
+        "\n",
+        "\n",
+        "### Parameters used:\n",
+        "\n",
+        "- Objective: regression with squared loss.\n",
+        "- n_estimators: The number of boosting (tree) rounds\n",
+        "- learning_rate: The step size shrinkage used to prevent overfitting\n",
+        "\n",
+        "check out the documentation :https://xgboost.readthedocs.io/en/stable/parameter.html#\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "id": "73cb55a1",
+      "metadata": {
+        "id": "73cb55a1"
+      },
+      "outputs": [],
+      "source": [
+        "def fit_Regressor(x,y,n):\n",
+        "    reg = xgb.XGBRegressor(\n",
+        "        objective='reg:squarederror',\n",
+        "        n_estimators=n,\n",
+        "        learning_rate=0.01,\n",
+        "    )\n",
+        "\n",
+        "    reg.fit(x, y,\n",
+        "            verbose=True,  #eval_set=[(X_train, y_train), (X_test, y_test)] , eval_metric='mae')\n",
+        "        ) \n",
+        "    return reg\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "5242ce9d",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "reg =fit_Regressor(Xtr,y_train,230)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "id": "6986b256",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#Predict for test data\n",
+        "yhat = reg.predict(Xtst)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "id": "e6610b92",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "e6610b92",
+        "outputId": "cbfb25e0-9508-4b4e-8a77-4b8abcce4745"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([32.18184 , 32.428726, 32.500183, ..., 31.38361 , 30.459702,\n",
+              "       30.474905], dtype=float32)"
+            ]
+          },
+          "execution_count": 25,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "yhat"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "id": "ee6ddb45",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ee6ddb45",
+        "outputId": "feacc9bd-eaf9-4e62-ea7a-d68290191619"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "8734     34.310001\n",
+              "8735     34.540001\n",
+              "8736     33.759998\n",
+              "8737     33.820000\n",
+              "8738     33.580002\n",
+              "           ...    \n",
+              "10913    33.619999\n",
+              "10914    32.509998\n",
+              "10915    31.969999\n",
+              "10916    31.850000\n",
+              "10917    32.740002\n",
+              "Name: Close_next_day, Length: 2184, dtype: float64"
+            ]
+          },
+          "execution_count": 26,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "y_test"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1c12c8d7",
+      "metadata": {},
+      "source": [
+        "# <font color='FFA500'>Perform  Evaluation on Model predictions</font>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "id": "139477ee",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mean Value of Test Dataset: 43.15017851923077\n",
+            "RMSE :  3.395802\n",
+            "Mape 0.06181969728770354\n",
+            "Wmape 0.06443764574924396\n"
+          ]
+        }
+      ],
+      "source": [
+        "rmse(y_test, yhat)\n",
+        "\n",
+        "mape(y_test, yhat)\n",
+        "wmape(y_test, yhat)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2d279780",
+      "metadata": {},
+      "source": [
+        "#### From the above results we see the mape and wmape for the trained model is high than the baseline which suggests model predictions are very poor "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1d61d360",
+      "metadata": {},
+      "source": [
+        "# <font color='FFA500'>Plotting the Forecast (Original vs Predicted)</font>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "id": "43d553b8",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 293
+        },
+        "id": "43d553b8",
+        "outputId": "bb311728-bea2-4dda-f0d7-66637eaca3c1"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<matplotlib.legend.Legend at 0x1f9bf332d80>"
+            ]
+          },
+          "execution_count": 28,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.plot(y_test.values, label='Original')\n",
+        "plt.plot(yhat, color='red', label='XGboost')\n",
+        "plt.legend()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "813e5cb8",
+      "metadata": {},
+      "source": [
+        "## <font color='FFA500'>Hyperparameter Tuning </font>\n",
+        "\n",
+        "After training our initial model, we perform **hyperparameter tuning** to improve the model’s forecasting accuracy and generalization.\n",
+        "\n",
+        "We use **`GridSearchCV`** from `sklearn.model_selection`, which automates the search for the best combination of hyperparameters.\n",
+        "\n",
+        "\n",
+        "\n",
+        "`GridSearchCV` systematically tests multiple combinations of hyperparameters using **cross-validation**.\n",
+        "\n",
+        "- Splits the training data into multiple folds (e.g., 5)\n",
+        "- Trains the model on a subset and validates it on the rest\n",
+        "- Repeats this process for each combination of parameters\n",
+        "- Returns the set of parameters that **produces the best average performance**\n",
+        "\n",
+        "\n",
+        "This ensures that the model does not overfit or underfit, and that the chosen parameters are optimal for generalizing to unseen data.\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "id": "197d6758",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "197d6758",
+        "outputId": "157b2232-bce1-43d0-f9d2-df10af438e1d"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "Xtr = pd.DataFrame(Xtr, columns=X_train.columns)\n",
+        "Xtst = pd.DataFrame(Xtst, columns=X_test.columns)\n",
+        "\n",
+        "\n",
+        "params = {\n",
+        "    'n_estimators': [600],# no of decision trees\n",
+        "    'subsample': [0.6, 0.7, 0.8, 0.9],\n",
+        "    'colsample_bytree': [0.6, 0.7, 0.8, 0.9],\n",
+        "    'max_depth': [2,3],\n",
+        "    'gamma': [0.3, 0.4],\n",
+        "    'min_child_weight': [4,5]\n",
+        "}\n",
+        "# Initialize XGB and GridSearch\n",
+        "xgb_reg = xgb.XGBRegressor(nthread=-1, objective='reg:squarederror')\n",
+        "grid = GridSearchCV(xgb_reg, params)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "id": "f6e4884f",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "grid.fit(Xtr, y_train)\n",
+        "gridcv_xgb = grid.best_estimator_\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "044d43d4",
+      "metadata": {},
+      "source": [
+        "## <font color='FFA500'>About Hyperparameters</font>\n",
+        "\n",
+        "### `min_child_weight`\n",
+        "Minimum sum of instance weight (Hessian) needed in a child node.  \n",
+        "➤ Larger values prevent the model from learning patterns that are too specific to individual samples (i.e., helps avoid overfitting).\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### `gamma`\n",
+        "Minimum loss reduction required to make a further partition on a leaf node.  \n",
+        "➤ Acts as a regularization parameter — higher values make the algorithm more conservative by limiting tree growth.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### `subsample`\n",
+        "Fraction of the training data used to build each tree.  \n",
+        "➤ Adding this randomness reduces overfitting. Typical values are between **0.5 and 1.0**.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### `colsample_bytree`\n",
+        "Fraction of features (columns) randomly sampled for each tree.  \n",
+        "➤ Encourages diversity among trees and reduces overfitting by randomly ignoring some features at each iteration.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### `max_depth`\n",
+        "Maximum depth of a decision tree.  \n",
+        "➤ Controls the complexity of the model — deeper trees can model more intricate patterns but are prone to overfitting.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a8e2e11e",
+      "metadata": {},
+      "source": [
+        "## <font color='FFA500'>Feature Importance Plot</font>\n",
+        "\n",
+        "After training our best model using `GridSearchCV`, we visualize which features had the most impact on the model's predictions.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "id": "7da62198",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 295
+        },
+        "id": "7da62198",
+        "outputId": "cde72905-5bd3-4c35-e8e2-2c04f6f86cfb"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "\n",
+        "_ = plot_importance(gridcv_xgb)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "id": "3e5e8220",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3e5e8220",
+        "outputId": "81e27a2d-025f-4628-ba31-8bca4279929a"
+      },
+      "outputs": [
+        {
+          "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>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=0.9, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             feature_weights=None, gamma=0.3, grow_policy=None,\n",
+              "             importance_type=None, interaction_constraints=None,\n",
+              "             learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
+              "             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,\n",
+              "             max_leaves=None, min_child_weight=5, missing=nan,\n",
+              "             monotone_constraints=None, multi_strategy=None, n_estimators=600,\n",
+              "             n_jobs=None, nthread=-1, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>XGBRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.0.0/python/python_api.html#xgboost.XGBRegressor\">?<span>Documentation for XGBRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=0.9, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             feature_weights=None, gamma=0.3, grow_policy=None,\n",
+              "             importance_type=None, interaction_constraints=None,\n",
+              "             learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
+              "             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,\n",
+              "             max_leaves=None, min_child_weight=5, missing=nan,\n",
+              "             monotone_constraints=None, multi_strategy=None, n_estimators=600,\n",
+              "             n_jobs=None, nthread=-1, ...)</pre></div> </div></div></div></div>"
+            ],
+            "text/plain": [
+              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=0.9, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             feature_weights=None, gamma=0.3, grow_policy=None,\n",
+              "             importance_type=None, interaction_constraints=None,\n",
+              "             learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
+              "             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,\n",
+              "             max_leaves=None, min_child_weight=5, missing=nan,\n",
+              "             monotone_constraints=None, multi_strategy=None, n_estimators=600,\n",
+              "             n_jobs=None, nthread=-1, ...)"
+            ]
+          },
+          "execution_count": 32,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "gridcv_xgb"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "id": "9d17515c",
+      "metadata": {
+        "id": "9d17515c"
+      },
+      "outputs": [],
+      "source": [
+        "yhat = grid.predict(Xtst)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "id": "9m3olnq0LyFO",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9m3olnq0LyFO",
+        "outputId": "fb7dc17b-bdb0-484c-80a2-f9d3d94a2fde"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mean Value of Test Dataset: 43.15017851923077\n",
+            "RMSE :  2.094299\n",
+            "Mape 0.03131156751819206\n",
+            "Wmape 0.033588469781125375\n"
+          ]
+        }
+      ],
+      "source": [
+        "rmse(y_test, yhat)\n",
+        "\n",
+        "mape(y_test, yhat)\n",
+        "wmape(y_test, yhat)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f911b510",
+      "metadata": {},
+      "source": [
+        "#### From the above results we see that after tuning parameters the mape and wmape dropped form 0.06 to 0.03 which is progressively better and more closer to our baseline models metrics."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "id": "ea67317a",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 293
+        },
+        "id": "ea67317a",
+        "outputId": "7c6ffd72-8517-461f-b503-4b04085bfc0a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<matplotlib.legend.Legend at 0x1f9bf465e50>"
+            ]
+          },
+          "execution_count": 35,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.plot(y_test.values, label='Original')\n",
+        "plt.plot(yhat, color='red', label='XGboost')\n",
+        "plt.legend()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6b480c5c",
+      "metadata": {},
+      "source": [
+        "# <font color='FFA500'>Make Weekly Predictions (Target variable = next_week_close) </font>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "id": "ed4b906d",
+      "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>Date</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>dayofweek</th>\n",
+              "      <th>year</th>\n",
+              "      <th>dayofyear</th>\n",
+              "      <th>sin_day</th>\n",
+              "      <th>cos_day</th>\n",
+              "      <th>month</th>\n",
+              "      <th>Close_t-1</th>\n",
+              "      <th>High_t-1</th>\n",
+              "      <th>Close_diff</th>\n",
+              "      <th>Rolling_mean</th>\n",
+              "      <th>Close_next_day</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1980-03-18</td>\n",
+              "      <td>0.328125</td>\n",
+              "      <td>0.322917</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1980</td>\n",
+              "      <td>78</td>\n",
+              "      <td>0.513978</td>\n",
+              "      <td>-0.857803</td>\n",
+              "      <td>3</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.330729</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>1980-03-25</td>\n",
+              "      <td>0.317708</td>\n",
+              "      <td>0.312500</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1980</td>\n",
+              "      <td>85</td>\n",
+              "      <td>-0.176076</td>\n",
+              "      <td>-0.984377</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0.311198</td>\n",
+              "      <td>0.316406</td>\n",
+              "      <td>0.001302</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.309896</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>1980-04-01</td>\n",
+              "      <td>0.328125</td>\n",
+              "      <td>0.322917</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1980</td>\n",
+              "      <td>92</td>\n",
+              "      <td>-0.779466</td>\n",
+              "      <td>-0.626444</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0.321615</td>\n",
+              "      <td>0.326823</td>\n",
+              "      <td>0.001302</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.325521</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>1980-04-08</td>\n",
+              "      <td>0.317708</td>\n",
+              "      <td>0.312500</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1980</td>\n",
+              "      <td>99</td>\n",
+              "      <td>-0.999207</td>\n",
+              "      <td>0.039821</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0.311198</td>\n",
+              "      <td>0.316406</td>\n",
+              "      <td>0.001302</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.305990</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>1980-04-15</td>\n",
+              "      <td>0.308594</td>\n",
+              "      <td>0.303385</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1980</td>\n",
+              "      <td>106</td>\n",
+              "      <td>-0.727143</td>\n",
+              "      <td>0.686487</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0.307292</td>\n",
+              "      <td>0.312500</td>\n",
+              "      <td>-0.003907</td>\n",
+              "      <td>0.314193</td>\n",
+              "      <td>0.291667</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",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10891</th>\n",
+              "      <td>2023-05-30</td>\n",
+              "      <td>30.040001</td>\n",
+              "      <td>29.990000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2023</td>\n",
+              "      <td>150</td>\n",
+              "      <td>-0.714876</td>\n",
+              "      <td>0.699251</td>\n",
+              "      <td>5</td>\n",
+              "      <td>29.000000</td>\n",
+              "      <td>29.090000</td>\n",
+              "      <td>0.990000</td>\n",
+              "      <td>29.699000</td>\n",
+              "      <td>31.440001</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10896</th>\n",
+              "      <td>2023-06-06</td>\n",
+              "      <td>31.450001</td>\n",
+              "      <td>30.959999</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2023</td>\n",
+              "      <td>157</td>\n",
+              "      <td>-0.079549</td>\n",
+              "      <td>0.996831</td>\n",
+              "      <td>6</td>\n",
+              "      <td>29.860001</td>\n",
+              "      <td>31.400000</td>\n",
+              "      <td>1.099998</td>\n",
+              "      <td>29.763000</td>\n",
+              "      <td>31.280001</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10901</th>\n",
+              "      <td>2023-06-13</td>\n",
+              "      <td>33.950001</td>\n",
+              "      <td>33.910000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2023</td>\n",
+              "      <td>164</td>\n",
+              "      <td>0.594933</td>\n",
+              "      <td>0.803775</td>\n",
+              "      <td>6</td>\n",
+              "      <td>33.070000</td>\n",
+              "      <td>33.299999</td>\n",
+              "      <td>0.840000</td>\n",
+              "      <td>30.450000</td>\n",
+              "      <td>35.580002</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10905</th>\n",
+              "      <td>2023-06-20</td>\n",
+              "      <td>37.110001</td>\n",
+              "      <td>35.000000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2023</td>\n",
+              "      <td>171</td>\n",
+              "      <td>0.976591</td>\n",
+              "      <td>0.215105</td>\n",
+              "      <td>6</td>\n",
+              "      <td>36.369999</td>\n",
+              "      <td>36.799999</td>\n",
+              "      <td>-1.369999</td>\n",
+              "      <td>31.703500</td>\n",
+              "      <td>32.900002</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10910</th>\n",
+              "      <td>2023-06-27</td>\n",
+              "      <td>34.230000</td>\n",
+              "      <td>34.099998</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2023</td>\n",
+              "      <td>178</td>\n",
+              "      <td>0.877575</td>\n",
+              "      <td>-0.479439</td>\n",
+              "      <td>6</td>\n",
+              "      <td>33.340000</td>\n",
+              "      <td>33.990002</td>\n",
+              "      <td>0.759998</td>\n",
+              "      <td>32.746500</td>\n",
+              "      <td>33.570000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>2238 rows × 14 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            Date       High      Close  dayofweek  year  dayofyear   sin_day  \\\n",
+              "0     1980-03-18   0.328125   0.322917          1  1980         78  0.513978   \n",
+              "5     1980-03-25   0.317708   0.312500          1  1980         85 -0.176076   \n",
+              "10    1980-04-01   0.328125   0.322917          1  1980         92 -0.779466   \n",
+              "14    1980-04-08   0.317708   0.312500          1  1980         99 -0.999207   \n",
+              "19    1980-04-15   0.308594   0.303385          1  1980        106 -0.727143   \n",
+              "...          ...        ...        ...        ...   ...        ...       ...   \n",
+              "10891 2023-05-30  30.040001  29.990000          1  2023        150 -0.714876   \n",
+              "10896 2023-06-06  31.450001  30.959999          1  2023        157 -0.079549   \n",
+              "10901 2023-06-13  33.950001  33.910000          1  2023        164  0.594933   \n",
+              "10905 2023-06-20  37.110001  35.000000          1  2023        171  0.976591   \n",
+              "10910 2023-06-27  34.230000  34.099998          1  2023        178  0.877575   \n",
+              "\n",
+              "        cos_day  month  Close_t-1   High_t-1  Close_diff  Rolling_mean  \\\n",
+              "0     -0.857803      3        NaN        NaN         NaN           NaN   \n",
+              "5     -0.984377      3   0.311198   0.316406    0.001302           NaN   \n",
+              "10    -0.626444      4   0.321615   0.326823    0.001302           NaN   \n",
+              "14     0.039821      4   0.311198   0.316406    0.001302           NaN   \n",
+              "19     0.686487      4   0.307292   0.312500   -0.003907      0.314193   \n",
+              "...         ...    ...        ...        ...         ...           ...   \n",
+              "10891  0.699251      5  29.000000  29.090000    0.990000     29.699000   \n",
+              "10896  0.996831      6  29.860001  31.400000    1.099998     29.763000   \n",
+              "10901  0.803775      6  33.070000  33.299999    0.840000     30.450000   \n",
+              "10905  0.215105      6  36.369999  36.799999   -1.369999     31.703500   \n",
+              "10910 -0.479439      6  33.340000  33.990002    0.759998     32.746500   \n",
+              "\n",
+              "       Close_next_day  \n",
+              "0            0.330729  \n",
+              "5            0.309896  \n",
+              "10           0.325521  \n",
+              "14           0.305990  \n",
+              "19           0.291667  \n",
+              "...               ...  \n",
+              "10891       31.440001  \n",
+              "10896       31.280001  \n",
+              "10901       35.580002  \n",
+              "10905       32.900002  \n",
+              "10910       33.570000  \n",
+              "\n",
+              "[2238 rows x 14 columns]"
+            ]
+          },
+          "execution_count": 36,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "weekly_data = df[df['dayofweek'] == 1].copy()\n",
+        "weekly_data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "id": "6c356bcc",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "2238"
+            ]
+          },
+          "execution_count": 37,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "len(weekly_data)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "id": "9fb4ae19",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "2014\n"
+          ]
+        }
+      ],
+      "source": [
+        "split_idx = int(len(weekly_data) * 0.9)\n",
+        "print(split_idx)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "id": "4989c0d9",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "weekly_data.rename(columns ={'Close_next_day':'Close_next_week'},inplace=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "id": "b1487820",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "df_train=weekly_data[:split_idx]\n",
+        "X_train=df_train.drop(['Close_next_week','Date',],axis=1).copy()\n",
+        "y_train=df_train['Close_next_week'].copy()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "id": "e368a407",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "df_test=weekly_data[split_idx:]\n",
+        "X_test=df_test.drop(['Close_next_week','Date',],axis=1).copy()\n",
+        "y_test=df_test['Close_next_week'].copy()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "0817e6b3",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.impute import SimpleImputer\n",
+        "imputer = SimpleImputer()\n",
+        "Xtr = imputer.fit_transform(X_train)\n",
+        "\n",
+        "\n",
+        "Xtst = imputer.transform(X_test) #This ensures that  preprocessing is consistent between training and testing.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "19613d8a",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "reg = fit_Regressor(Xtr,y_train,600)  #no of estimators = 600 \"from grid search cv \""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "id": "2553e76e",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#Predict for test data\n",
+        "yhat = reg.predict(Xtst)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "id": "d95fdd36",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "((224,), (224,))"
+            ]
+          },
+          "execution_count": 46,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "y_test.shape, yhat.shape\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 47,
+      "id": "66391990",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mean Value of Test Dataset: 47.93718738839285\n",
+            "RMSE :  1.593557\n",
+            "Mape 0.023720529593762415\n",
+            "Wmape 0.024556221123795293\n"
+          ]
+        }
+      ],
+      "source": [
+        "rmse(y_test, yhat)\n",
+        "mape(y_test, yhat)\n",
+        "wmape(y_test, yhat)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "id": "d8004acb",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<matplotlib.legend.Legend at 0x1f9bf468b60>"
+            ]
+          },
+          "execution_count": 48,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.plot(y_test.values, label='Original')\n",
+        "plt.plot(yhat, color='red', label='XGboost')\n",
+        "plt.legend()"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "collapsed_sections": [],
+      "name": "Time Series Forcasting using Grid Search.ipynb",
+      "provenance": []
+    },
+    "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
+}