11_kod_dane_czasowe.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3e0b789b",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Praca z danymi czasowymi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b7f1f7d9",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from warnings import filterwarnings\n",
    "filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "29376122",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     y\n",
       "0  112\n",
       "1  118\n",
       "2  132\n",
       "3  129\n",
       "4  121"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "data = pd.read_csv('data/airline-passengers.csv', usecols=[1])\n",
    "data.columns = [\"y\"]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "99744aeb",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(data.shape[0]), data.y)\n",
    "plt.xticks(np.arange(0,150, 12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cf4bbafb",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        NaN\n",
       "1      112.0\n",
       "2      118.0\n",
       "3      132.0\n",
       "4      129.0\n",
       "       ...  \n",
       "139    622.0\n",
       "140    606.0\n",
       "141    508.0\n",
       "142    461.0\n",
       "143    390.0\n",
       "Name: y, Length: 144, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.y.shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "25f4a583",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>y</th>\n",
       "      <th>y_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>112</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>118</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>132</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>129</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>121</td>\n",
       "      <td>129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>606</td>\n",
       "      <td>622.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>508</td>\n",
       "      <td>606.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>461</td>\n",
       "      <td>508.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>390</td>\n",
       "      <td>461.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>432</td>\n",
       "      <td>390.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       y    y_1\n",
       "0    112    NaN\n",
       "1    118  112.0\n",
       "2    132  118.0\n",
       "3    129  132.0\n",
       "4    121  129.0\n",
       "..   ...    ...\n",
       "139  606  622.0\n",
       "140  508  606.0\n",
       "141  461  508.0\n",
       "142  390  461.0\n",
       "143  432  390.0\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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   "source": [
    "data[\"y_1\"] = data.y.shift(1)\n",
    "data"
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  {
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   "id": "20b89e10",
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   "source": [
    "Dla 12 kroków:"
   ]
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   "id": "16ffa034",
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       "     y    y_1    y_2    y_3    y_4  y_5  y_6  y_7  y_8  y_9  y_10  y_11  y_12\n",
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    "for i in range(2, 13):\n",
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   "execution_count": 8,
   "id": "711224e3",
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       "<p>132 rows × 13 columns</p>\n",
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       "       y    y_1    y_2    y_3    y_4    y_5    y_6    y_7    y_8    y_9  \\\n",
       "12   115  118.0  104.0  119.0  136.0  148.0  148.0  135.0  121.0  129.0   \n",
       "13   126  115.0  118.0  104.0  119.0  136.0  148.0  148.0  135.0  121.0   \n",
       "14   141  126.0  115.0  118.0  104.0  119.0  136.0  148.0  148.0  135.0   \n",
       "15   135  141.0  126.0  115.0  118.0  104.0  119.0  136.0  148.0  148.0   \n",
       "16   125  135.0  141.0  126.0  115.0  118.0  104.0  119.0  136.0  148.0   \n",
       "..   ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "139  606  622.0  535.0  472.0  461.0  419.0  391.0  417.0  405.0  362.0   \n",
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       "143  432  390.0  461.0  508.0  606.0  622.0  535.0  472.0  461.0  419.0   \n",
       "\n",
       "      y_10   y_11   y_12  \n",
       "12   132.0  118.0  112.0  \n",
       "13   129.0  132.0  118.0  \n",
       "14   121.0  129.0  132.0  \n",
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       "[132 rows x 13 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.dropna()\n",
    "data"
   ]
  },
  {
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   "execution_count": 10,
   "id": "c0e8023e",
   "metadata": {
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   },
   "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>y_1</th>\n",
       "      <th>y_2</th>\n",
       "      <th>y_3</th>\n",
       "      <th>y_4</th>\n",
       "      <th>y_5</th>\n",
       "      <th>y_6</th>\n",
       "      <th>y_7</th>\n",
       "      <th>y_8</th>\n",
       "      <th>y_9</th>\n",
       "      <th>y_10</th>\n",
       "      <th>y_11</th>\n",
       "      <th>y_12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>118.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>115.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>126.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>141.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>135.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      y_1    y_2    y_3    y_4    y_5    y_6    y_7    y_8    y_9   y_10  \\\n",
       "12  118.0  104.0  119.0  136.0  148.0  148.0  135.0  121.0  129.0  132.0   \n",
       "13  115.0  118.0  104.0  119.0  136.0  148.0  148.0  135.0  121.0  129.0   \n",
       "14  126.0  115.0  118.0  104.0  119.0  136.0  148.0  148.0  135.0  121.0   \n",
       "15  141.0  126.0  115.0  118.0  104.0  119.0  136.0  148.0  148.0  135.0   \n",
       "16  135.0  141.0  126.0  115.0  118.0  104.0  119.0  136.0  148.0  148.0   \n",
       "\n",
       "     y_11   y_12  \n",
       "12  118.0  112.0  \n",
       "13  132.0  118.0  \n",
       "14  129.0  132.0  \n",
       "15  121.0  129.0  \n",
       "16  135.0  121.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data.loc[:,\"y_1\":\"y_12\"]\n",
    "y = data.y\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "387b6fda",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "n_train = 108\n",
    "X_train, y_train = X.iloc[:n_train], y.iloc[:n_train]\n",
    "X_test, y_test = X.iloc[n_train:], y.iloc[n_train:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fad36502",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_percentage_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "80ad059e",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=5)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestRegressor(max_depth=5)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5aac54e9",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07538070654697888"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "err = mean_absolute_percentage_error(y_test, y_pred)\n",
    "err"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0db47ba5",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = data.shape[0]\n",
    "plt.plot(range(N), data.y)\n",
    "plt.plot(range(n_train, N), y_pred, \n",
    "         color = \"red\", label=f\"prediction with error: {err.round(2)}\")\n",
    "\n",
    "plt.xticks(np.arange(0,150, 12))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8b8ad30e",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(N), data.y)\n",
    "plt.plot(range(n_train, N), X_test.y_1, color = \"red\")\n",
    "\n",
    "plt.xticks(np.arange(0,150, 12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dd060e2b",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "y_pred_dummy = X_test.y_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e5f7f7e2",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09729930052564972"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_percentage_error(y_test, y_pred_dummy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf67842d",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Inżynieria cech dla szeregów czasowych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5d0356b9",
   "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>y_1</th>\n",
       "      <th>y_2</th>\n",
       "      <th>y_3</th>\n",
       "      <th>y_4</th>\n",
       "      <th>y_5</th>\n",
       "      <th>y_6</th>\n",
       "      <th>y_7</th>\n",
       "      <th>y_8</th>\n",
       "      <th>y_9</th>\n",
       "      <th>y_10</th>\n",
       "      <th>y_11</th>\n",
       "      <th>y_12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>118.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>115.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      y_1    y_2    y_3    y_4    y_5    y_6    y_7    y_8    y_9   y_10  \\\n",
       "12  118.0  104.0  119.0  136.0  148.0  148.0  135.0  121.0  129.0  132.0   \n",
       "13  115.0  118.0  104.0  119.0  136.0  148.0  148.0  135.0  121.0  129.0   \n",
       "\n",
       "     y_11   y_12  \n",
       "12  118.0  112.0  \n",
       "13  132.0  118.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3d1d4458",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>y_1</th>\n",
       "      <th>y_2</th>\n",
       "      <th>y_3</th>\n",
       "      <th>y_4</th>\n",
       "      <th>y_5</th>\n",
       "      <th>y_6</th>\n",
       "      <th>y_7</th>\n",
       "      <th>y_8</th>\n",
       "      <th>y_9</th>\n",
       "      <th>y_10</th>\n",
       "      <th>y_11</th>\n",
       "      <th>y_12</th>\n",
       "      <th>mean_1_12</th>\n",
       "      <th>var_1_12</th>\n",
       "      <th>range_1_12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>118.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>126.666667</td>\n",
       "      <td>172.555556</td>\n",
       "      <td>42.611111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>115.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>126.916667</td>\n",
       "      <td>165.909722</td>\n",
       "      <td>36.207837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      y_1    y_2    y_3    y_4    y_5    y_6    y_7    y_8    y_9   y_10  \\\n",
       "12  118.0  104.0  119.0  136.0  148.0  148.0  135.0  121.0  129.0  132.0   \n",
       "13  115.0  118.0  104.0  119.0  136.0  148.0  148.0  135.0  121.0  129.0   \n",
       "\n",
       "     y_11   y_12   mean_1_12    var_1_12  range_1_12  \n",
       "12  118.0  112.0  126.666667  172.555556   42.611111  \n",
       "13  132.0  118.0  126.916667  165.909722   36.207837  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[\"mean_1_12\"] = X.mean(1)\n",
    "X[\"var_1_12\"] = X.var(1)\n",
    "X[\"range_1_12\"] = X.max(1) - X.mean(1)\n",
    "\n",
    "X.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "238a6a14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0719714954231676"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestRegressor(max_depth=5)\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "err = mean_absolute_percentage_error(y_test, y_pred)\n",
    "err"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "620c3a79",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[340, 318, 362, 348, 363, 435, 491, 505, 404, 359, 310, 337]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds = list(y_train.iloc[-12:])\n",
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4682c176",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([351.2882696])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(np.array(y_preds).reshape((1,-1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "54002b56",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "for i in range(X_test.shape[0]):\n",
    "    y_pred_i = model.predict(np.array(y_preds[-12:]).reshape((1,-1)))[0]\n",
    "    y_preds.append(y_pred_i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c2094d28",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[351.28826960268833,\n",
       " 347.9865280468585,\n",
       " 350.537410622616,\n",
       " 348.1955474433117,\n",
       " 355.7343366541009,\n",
       " 389.30885437438366,\n",
       " 442.1653333333334,\n",
       " 471.62666666666667,\n",
       " 470.14849206349203,\n",
       " 463.0564920634921,\n",
       " 460.05077777777774,\n",
       " 459.0801587301587,\n",
       " 462.93682539682544,\n",
       " 462.606,\n",
       " 460.466,\n",
       " 463.6726666666667,\n",
       " 465.2226666666667,\n",
       " 468.2426666666667,\n",
       " 479.856,\n",
       " 481.325,\n",
       " 481.325,\n",
       " 481.325,\n",
       " 480.765,\n",
       " 480.765]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds[12:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "14212081",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(N), data.y)\n",
    "plt.plot(range(n_train, N), y_preds[12:], color = \"red\")\n",
    "\n",
    "plt.xticks(np.arange(0,150, 12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cabadf25",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "?TimeSeriesSplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "53296430",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "tscv = TimeSeriesSplit(n_splits=5, test_size=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ba72946e",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: [0 1 2 3 4 5] TEST: [6 7]\n",
      "TRAIN: [0 1 2 3 4 5 6 7] TEST: [8 9]\n",
      "TRAIN: [0 1 2 3 4 5 6 7 8 9] TEST: [10 11]\n",
      "TRAIN: [ 0  1  2  3  4  5  6  7  8  9 10 11] TEST: [12 13]\n",
      "TRAIN: [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13] TEST: [14 15]\n"
     ]
    }
   ],
   "source": [
    "X = range(16)\n",
    "for train_index, test_index in tscv.split(X):\n",
    "    print(\"TRAIN:\", train_index, \"TEST:\", test_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2218be73",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: [1 2 3 4 5] TEST: [6 7]\n",
      "TRAIN: [3 4 5 6 7] TEST: [8 9]\n",
      "TRAIN: [5 6 7 8 9] TEST: [10 11]\n",
      "TRAIN: [ 7  8  9 10 11] TEST: [12 13]\n",
      "TRAIN: [ 9 10 11 12 13] TEST: [14 15]\n"
     ]
    }
   ],
   "source": [
    "tscv = TimeSeriesSplit(n_splits=5, max_train_size=5, test_size=2)\n",
    "\n",
    "for train_index, test_index in tscv.split(X):\n",
    "    print(\"TRAIN:\", train_index, \"TEST:\", test_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ca319d7",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Problem predykcji długoterminowych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b72e3dfc",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_preds = list(y_train.iloc[-12:])\n",
    "for i in range(100):\n",
    "    y_pred_i = model.predict(np.array(y_preds[-12:]).reshape((1,-1)))[0]\n",
    "    y_preds.append(y_pred_i)\n",
    "    \n",
    "    \n",
    "plt.plot(range(N), data.y)\n",
    "plt.plot(range(n_train, n_train+100), y_preds[12:], color = \"red\")\n",
    "\n",
    "plt.xticks(np.arange(0, 250, 24))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7c23d1d3",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f9a951c3",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "trend_model = make_pipeline(PolynomialFeatures(2, include_bias=False), \n",
    "                            LinearRegression())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f56cd87b",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n",
       "                ('linearregression', LinearRegression())])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trend_model.fit(np.array(range(n_train)).reshape((-1,1)), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "992be519",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_train = np.array(range(n_train)).reshape((-1,1))\n",
    "\n",
    "trend_pred = trend_model.predict(t_train)\n",
    "\n",
    "plt.plot(range(N), data.y)\n",
    "plt.plot(range(n_train), trend_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "85d68c76",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "residuals_train = y_train-trend_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ae36b484",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(n_train), residuals_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "03153376",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=5)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "residual_model = RandomForestRegressor(max_depth=5)\n",
    "residual_model.fit(X_train, residuals_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1f5aa678",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "t_test = np.array(range(n_train, N)).reshape((-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c6cde64e",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "y_test_pred = trend_model.predict(t_test) + residual_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c54922cb",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(N), data.y)\n",
    "plt.plot(range(n_train, N), y_test_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a77878cf",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09527093549440042"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_percentage_error(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de290a44",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Wielowymiarowe dane czasowe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "03bd762c",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>season</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>demand</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>instant</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>1</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               date  season  year  month  hour  holiday  weekday  workingday  \\\n",
       "instant                                                                        \n",
       "1        2011-01-01       1     0      1     0        0        6           0   \n",
       "2        2011-01-01       1     0      1     1        0        6           0   \n",
       "3        2011-01-01       1     0      1     2        0        6           0   \n",
       "4        2011-01-01       1     0      1     3        0        6           0   \n",
       "5        2011-01-01       1     0      1     4        0        6           0   \n",
       "\n",
       "         weathersit  temp   atemp   hum  windspeed  casual  registered  demand  \n",
       "instant                                                                         \n",
       "1                 1  0.24  0.2879  0.81        0.0       3          13      16  \n",
       "2                 1  0.22  0.2727  0.80        0.0       8          32      40  \n",
       "3                 1  0.22  0.2727  0.80        0.0       5          27      32  \n",
       "4                 1  0.24  0.2879  0.75        0.0       3          10      13  \n",
       "5                 1  0.24  0.2879  0.75        0.0       0           1       1  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"data/Bike-Sharing-Dataset/hour.csv\", index_col=0)\n",
    "data[\"weekday\"] = data.weekday.replace({0:7})\n",
    "data = data.rename({\"dteday\": \"date\", \"yr\": \"year\", \"mnth\": \"month\", \"hr\":\"hour\", \n",
    "                    \"cnt\": \"demand\"}, axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed39befb",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Źródło: https://archive.ics.uci.edu/ml/datasets/bike+sharing+dataset#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "43c94697",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17379, 14)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop([\"casual\", \"registered\"], axis=1)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7a82162d",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>17376</th>\n",
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       "      <td>0.1642</td>\n",
       "      <td>89</td>\n",
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       "      <td>0.26</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17379</th>\n",
       "      <td>2012-12-31</td>\n",
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       "      <td>23</td>\n",
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      ],
      "text/plain": [
       "               date  season  year  month  hour  holiday  weekday  workingday  \\\n",
       "instant                                                                        \n",
       "17375    2012-12-31       1     1     12    19        0        1           1   \n",
       "17376    2012-12-31       1     1     12    20        0        1           1   \n",
       "17377    2012-12-31       1     1     12    21        0        1           1   \n",
       "17378    2012-12-31       1     1     12    22        0        1           1   \n",
       "17379    2012-12-31       1     1     12    23        0        1           1   \n",
       "\n",
       "         weathersit  temp   atemp   hum  windspeed  demand  \n",
       "instant                                                     \n",
       "17375             2  0.26  0.2576  0.60     0.1642     119  \n",
       "17376             2  0.26  0.2576  0.60     0.1642      89  \n",
       "17377             1  0.26  0.2576  0.60     0.1642      90  \n",
       "17378             1  0.26  0.2727  0.56     0.1343      61  \n",
       "17379             1  0.26  0.2727  0.65     0.1343      49  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bd23cee3",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "def create_date_with_hour(date, hour):\n",
    "    date_parsed = datetime.strptime(date, \"%Y-%m-%d\")\n",
    "    date_with_hour = datetime(date_parsed.year, date_parsed.month, date_parsed.day, hour)\n",
    "    return date_with_hour\n",
    "\n",
    "data[\"date\"] = data[[\"date\", \"hour\"]].apply(lambda row: create_date_with_hour(row[\"date\"], \n",
    "                                                                              row[\"hour\"]), \n",
    "                                            axis=1)\n",
    "data = data.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "62ea177e",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
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       "      <th>2011-01-01 01:00:00</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
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       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>32</td>\n",
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       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>1</td>\n",
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       "      <td>6</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     season  year  month  hour  holiday  weekday  workingday  \\\n",
       "date                                                                           \n",
       "2011-01-01 00:00:00       1     0      1     0        0        6           0   \n",
       "2011-01-01 01:00:00       1     0      1     1        0        6           0   \n",
       "2011-01-01 02:00:00       1     0      1     2        0        6           0   \n",
       "2011-01-01 03:00:00       1     0      1     3        0        6           0   \n",
       "2011-01-01 04:00:00       1     0      1     4        0        6           0   \n",
       "\n",
       "                     weathersit  temp   atemp   hum  windspeed  demand  \n",
       "date                                                                    \n",
       "2011-01-01 00:00:00           1  0.24  0.2879  0.81        0.0      16  \n",
       "2011-01-01 01:00:00           1  0.22  0.2727  0.80        0.0      40  \n",
       "2011-01-01 02:00:00           1  0.22  0.2727  0.80        0.0      32  \n",
       "2011-01-01 03:00:00           1  0.24  0.2879  0.75        0.0      13  \n",
       "2011-01-01 04:00:00           1  0.24  0.2879  0.75        0.0       1  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "095935c7",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(240), data.demand.iloc[:240])\n",
    "plt.xticks(np.arange(0, 240, 24))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f7a9f6b7",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 4))\n",
    "average_week_demand = data.groupby([\"weekday\", \"hour\"]).mean()[\"demand\"]\n",
    "average_week_demand.plot(ax=ax)\n",
    "_ = ax.set(\n",
    "    title=\"Average hourly bike demand during the week\",\n",
    "    xticks=[i * 24 for i in range(7)],\n",
    "    xticklabels=[\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"],\n",
    "    xlabel=\"Time of the week\",\n",
    "    ylabel=\"Number of bike rentals\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1908ef83",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Definicja problemu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f4f74290",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>season</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>demand</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     season  year  month  hour  holiday  weekday  workingday  \\\n",
       "date                                                                           \n",
       "2011-01-01 00:00:00       1     0      1     0        0        6           0   \n",
       "2011-01-01 01:00:00       1     0      1     1        0        6           0   \n",
       "2011-01-01 02:00:00       1     0      1     2        0        6           0   \n",
       "2011-01-01 03:00:00       1     0      1     3        0        6           0   \n",
       "2011-01-01 04:00:00       1     0      1     4        0        6           0   \n",
       "\n",
       "                     weathersit  temp   atemp   hum  windspeed  demand  \n",
       "date                                                                    \n",
       "2011-01-01 00:00:00           1  0.24  0.2879  0.81        0.0      16  \n",
       "2011-01-01 01:00:00           1  0.22  0.2727  0.80        0.0      40  \n",
       "2011-01-01 02:00:00           1  0.22  0.2727  0.80        0.0      32  \n",
       "2011-01-01 03:00:00           1  0.24  0.2879  0.75        0.0      13  \n",
       "2011-01-01 04:00:00           1  0.24  0.2879  0.75        0.0       1  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "763280b0",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(240), data.hour.iloc[:240])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5f91494c",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from datetime import timedelta\n",
    "\n",
    "def get_next(date, data):\n",
    "    next_hour = date + timedelta(hours=1)\n",
    "    if next_hour in data.index:\n",
    "        return data.demand.loc[next_hour]\n",
    "    else:\n",
    "        return np.nan\n",
    "    \n",
    "data[\"y\"] = data.apply(lambda x: get_next(x.name, data), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8a3b335a",
   "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>season</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>demand</th>\n",
       "      <th>y</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     season  year  month  hour  holiday  weekday  workingday  \\\n",
       "date                                                                           \n",
       "2011-01-01 00:00:00       1     0      1     0        0        6           0   \n",
       "2011-01-01 01:00:00       1     0      1     1        0        6           0   \n",
       "2011-01-01 02:00:00       1     0      1     2        0        6           0   \n",
       "2011-01-01 03:00:00       1     0      1     3        0        6           0   \n",
       "2011-01-01 04:00:00       1     0      1     4        0        6           0   \n",
       "\n",
       "                     weathersit  temp   atemp   hum  windspeed  demand     y  \n",
       "date                                                                          \n",
       "2011-01-01 00:00:00           1  0.24  0.2879  0.81        0.0      16  40.0  \n",
       "2011-01-01 01:00:00           1  0.22  0.2727  0.80        0.0      40  32.0  \n",
       "2011-01-01 02:00:00           1  0.22  0.2727  0.80        0.0      32  13.0  \n",
       "2011-01-01 03:00:00           1  0.24  0.2879  0.75        0.0      13   1.0  \n",
       "2011-01-01 04:00:00           1  0.24  0.2879  0.75        0.0       1   1.0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "45e84b5b",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "                     season  year  month  hour  holiday  weekday  workingday  \\\n",
       "date                                                                           \n",
       "2011-01-02 01:00:00       1     0      1     1        0        7           0   \n",
       "2011-01-02 02:00:00       1     0      1     2        0        7           0   \n",
       "2011-01-02 03:00:00       1     0      1     3        0        7           0   \n",
       "2011-01-02 04:00:00       1     0      1     4        0        7           0   \n",
       "2011-01-02 06:00:00       1     0      1     6        0        7           0   \n",
       "\n",
       "                     weathersit  temp   atemp   hum  windspeed  demand    y  \n",
       "date                                                                         \n",
       "2011-01-02 01:00:00           2  0.44  0.4394  0.94     0.2537      17  9.0  \n",
       "2011-01-02 02:00:00           2  0.42  0.4242  1.00     0.2836       9  6.0  \n",
       "2011-01-02 03:00:00           2  0.46  0.4545  0.94     0.1940       6  3.0  \n",
       "2011-01-02 04:00:00           2  0.46  0.4545  0.94     0.1940       3  NaN  \n",
       "2011-01-02 06:00:00           3  0.42  0.4242  0.77     0.2985       2  1.0  "
      ]
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     "execution_count": 54,
     "metadata": {},
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   "source": [
    "data.iloc[25:30]"
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   "cell_type": "code",
   "execution_count": 55,
   "id": "351bf9ed",
   "metadata": {
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     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17303, 14)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.dropna()\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "01169c0a",
   "metadata": {
    "slideshow": {
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    }
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "                     season  month  hour  holiday  weekday  workingday  \\\n",
       "date                                                                     \n",
       "2011-01-01 00:00:00       1      1     0        0        6           0   \n",
       "2011-01-01 01:00:00       1      1     1        0        6           0   \n",
       "2011-01-01 02:00:00       1      1     2        0        6           0   \n",
       "2011-01-01 03:00:00       1      1     3        0        6           0   \n",
       "2011-01-01 04:00:00       1      1     4        0        6           0   \n",
       "\n",
       "                     weathersit  temp   atemp   hum  windspeed  demand     y  \n",
       "date                                                                          \n",
       "2011-01-01 00:00:00           1  0.24  0.2879  0.81        0.0      16  40.0  \n",
       "2011-01-01 01:00:00           1  0.22  0.2727  0.80        0.0      40  32.0  \n",
       "2011-01-01 02:00:00           1  0.22  0.2727  0.80        0.0      32  13.0  \n",
       "2011-01-01 03:00:00           1  0.24  0.2879  0.75        0.0      13   1.0  \n",
       "2011-01-01 04:00:00           1  0.24  0.2879  0.75        0.0       1   1.0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop([\"year\"], axis=1) \n",
    "# zmienne season, weekday i opcjonalnie month - kategoryczne\n",
    "\n",
    "data.head()"
   ]
  }
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