Forecasting(model based methods(AirlinesData)).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot\n",
    "from numpy import sqrt\n",
    "import warnings\n",
    "import itertools\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "airlines = pd.read_excel('E:/DataScience/Assignments/Forecasting/AirlinesData.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1abfecb4400>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "airlines.Passengers.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "airlines[\"Date\"] = pd.to_datetime(airlines.Month,format=\"%b-%y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "airlines[\"month\"] = airlines.Date.dt.strftime(\"%b\")\n",
    "airlines[\"year\"] = airlines.Date.dt.strftime(\"%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1abfc454b50>"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "heatmap_y_month = pd.pivot_table(data=airlines,values=\"Passengers\",index=\"year\",columns=\"month\",aggfunc=\"mean\",fill_value=0)\n",
    "sns.heatmap(heatmap_y_month,annot=True,fmt=\"g\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1abff50bb50>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.subplot(211)\n",
    "sns.boxplot(x=\"month\",y=\"Passengers\",data=airlines)\n",
    "plt.subplot(212)\n",
    "sns.boxplot(x=\"year\",y=\"Passengers\",data=airlines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1abff9c3c70>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,3))\n",
    "sns.lineplot(x=\"year\",y=\"Passengers\",data=airlines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "airlines['i'] = range(1, len(airlines) + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "airlines['log_passenger'] = np.log(airlines.Passengers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "airlines['i_square'] = airlines.i**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "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>Month</th>\n",
       "      <th>Passengers</th>\n",
       "      <th>Date</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>i</th>\n",
       "      <th>log_passenger</th>\n",
       "      <th>i_square</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>112</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>Jan</td>\n",
       "      <td>1995</td>\n",
       "      <td>1</td>\n",
       "      <td>4.718499</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1995-02-01</td>\n",
       "      <td>118</td>\n",
       "      <td>1995-02-01</td>\n",
       "      <td>Feb</td>\n",
       "      <td>1995</td>\n",
       "      <td>2</td>\n",
       "      <td>4.770685</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1995-03-01</td>\n",
       "      <td>132</td>\n",
       "      <td>1995-03-01</td>\n",
       "      <td>Mar</td>\n",
       "      <td>1995</td>\n",
       "      <td>3</td>\n",
       "      <td>4.882802</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1995-04-01</td>\n",
       "      <td>129</td>\n",
       "      <td>1995-04-01</td>\n",
       "      <td>Apr</td>\n",
       "      <td>1995</td>\n",
       "      <td>4</td>\n",
       "      <td>4.859812</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1995-05-01</td>\n",
       "      <td>121</td>\n",
       "      <td>1995-05-01</td>\n",
       "      <td>May</td>\n",
       "      <td>1995</td>\n",
       "      <td>5</td>\n",
       "      <td>4.795791</td>\n",
       "      <td>25</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2002-08-01</td>\n",
       "      <td>405</td>\n",
       "      <td>2002-08-01</td>\n",
       "      <td>Aug</td>\n",
       "      <td>2002</td>\n",
       "      <td>92</td>\n",
       "      <td>6.003887</td>\n",
       "      <td>8464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2002-09-01</td>\n",
       "      <td>355</td>\n",
       "      <td>2002-09-01</td>\n",
       "      <td>Sep</td>\n",
       "      <td>2002</td>\n",
       "      <td>93</td>\n",
       "      <td>5.872118</td>\n",
       "      <td>8649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>2002-10-01</td>\n",
       "      <td>306</td>\n",
       "      <td>2002-10-01</td>\n",
       "      <td>Oct</td>\n",
       "      <td>2002</td>\n",
       "      <td>94</td>\n",
       "      <td>5.723585</td>\n",
       "      <td>8836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2002-11-01</td>\n",
       "      <td>271</td>\n",
       "      <td>2002-11-01</td>\n",
       "      <td>Nov</td>\n",
       "      <td>2002</td>\n",
       "      <td>95</td>\n",
       "      <td>5.602119</td>\n",
       "      <td>9025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2002-12-01</td>\n",
       "      <td>306</td>\n",
       "      <td>2002-12-01</td>\n",
       "      <td>Dec</td>\n",
       "      <td>2002</td>\n",
       "      <td>96</td>\n",
       "      <td>5.723585</td>\n",
       "      <td>9216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Month  Passengers       Date month  year   i  log_passenger  i_square\n",
       "0  1995-01-01         112 1995-01-01   Jan  1995   1       4.718499         1\n",
       "1  1995-02-01         118 1995-02-01   Feb  1995   2       4.770685         4\n",
       "2  1995-03-01         132 1995-03-01   Mar  1995   3       4.882802         9\n",
       "3  1995-04-01         129 1995-04-01   Apr  1995   4       4.859812        16\n",
       "4  1995-05-01         121 1995-05-01   May  1995   5       4.795791        25\n",
       "..        ...         ...        ...   ...   ...  ..            ...       ...\n",
       "91 2002-08-01         405 2002-08-01   Aug  2002  92       6.003887      8464\n",
       "92 2002-09-01         355 2002-09-01   Sep  2002  93       5.872118      8649\n",
       "93 2002-10-01         306 2002-10-01   Oct  2002  94       5.723585      8836\n",
       "94 2002-11-01         271 2002-11-01   Nov  2002  95       5.602119      9025\n",
       "95 2002-12-01         306 2002-12-01   Dec  2002  96       5.723585      9216\n",
       "\n",
       "[96 rows x 8 columns]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "airlines=pd.get_dummies(airlines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Passengers</th>\n",
       "      <th>Date</th>\n",
       "      <th>i</th>\n",
       "      <th>log_passenger</th>\n",
       "      <th>i_square</th>\n",
       "      <th>month_Apr</th>\n",
       "      <th>month_Aug</th>\n",
       "      <th>month_Dec</th>\n",
       "      <th>month_Feb</th>\n",
       "      <th>...</th>\n",
       "      <th>month_Oct</th>\n",
       "      <th>month_Sep</th>\n",
       "      <th>year_1995</th>\n",
       "      <th>year_1996</th>\n",
       "      <th>year_1997</th>\n",
       "      <th>year_1998</th>\n",
       "      <th>year_1999</th>\n",
       "      <th>year_2000</th>\n",
       "      <th>year_2001</th>\n",
       "      <th>year_2002</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>112</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>4.718499</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1995-02-01</td>\n",
       "      <td>118</td>\n",
       "      <td>1995-02-01</td>\n",
       "      <td>2</td>\n",
       "      <td>4.770685</td>\n",
       "      <td>4</td>\n",
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       "      <th>2</th>\n",
       "      <td>1995-03-01</td>\n",
       "      <td>132</td>\n",
       "      <td>1995-03-01</td>\n",
       "      <td>3</td>\n",
       "      <td>4.882802</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1995-04-01</td>\n",
       "      <td>129</td>\n",
       "      <td>1995-04-01</td>\n",
       "      <td>4</td>\n",
       "      <td>4.859812</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1995-05-01</td>\n",
       "      <td>121</td>\n",
       "      <td>1995-05-01</td>\n",
       "      <td>5</td>\n",
       "      <td>4.795791</td>\n",
       "      <td>25</td>\n",
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       "      <th>91</th>\n",
       "      <td>2002-08-01</td>\n",
       "      <td>405</td>\n",
       "      <td>2002-08-01</td>\n",
       "      <td>92</td>\n",
       "      <td>6.003887</td>\n",
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       "      <th>92</th>\n",
       "      <td>2002-09-01</td>\n",
       "      <td>355</td>\n",
       "      <td>2002-09-01</td>\n",
       "      <td>93</td>\n",
       "      <td>5.872118</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>2002-10-01</td>\n",
       "      <td>306</td>\n",
       "      <td>2002-10-01</td>\n",
       "      <td>94</td>\n",
       "      <td>5.723585</td>\n",
       "      <td>8836</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2002-11-01</td>\n",
       "      <td>271</td>\n",
       "      <td>2002-11-01</td>\n",
       "      <td>95</td>\n",
       "      <td>5.602119</td>\n",
       "      <td>9025</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2002-12-01</td>\n",
       "      <td>306</td>\n",
       "      <td>2002-12-01</td>\n",
       "      <td>96</td>\n",
       "      <td>5.723585</td>\n",
       "      <td>9216</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Month  Passengers       Date   i  log_passenger  i_square  month_Apr  \\\n",
       "0  1995-01-01         112 1995-01-01   1       4.718499         1          0   \n",
       "1  1995-02-01         118 1995-02-01   2       4.770685         4          0   \n",
       "2  1995-03-01         132 1995-03-01   3       4.882802         9          0   \n",
       "3  1995-04-01         129 1995-04-01   4       4.859812        16          1   \n",
       "4  1995-05-01         121 1995-05-01   5       4.795791        25          0   \n",
       "..        ...         ...        ...  ..            ...       ...        ...   \n",
       "91 2002-08-01         405 2002-08-01  92       6.003887      8464          0   \n",
       "92 2002-09-01         355 2002-09-01  93       5.872118      8649          0   \n",
       "93 2002-10-01         306 2002-10-01  94       5.723585      8836          0   \n",
       "94 2002-11-01         271 2002-11-01  95       5.602119      9025          0   \n",
       "95 2002-12-01         306 2002-12-01  96       5.723585      9216          0   \n",
       "\n",
       "    month_Aug  month_Dec  month_Feb  ...  month_Oct  month_Sep  year_1995  \\\n",
       "0           0          0          0  ...          0          0          1   \n",
       "1           0          0          1  ...          0          0          1   \n",
       "2           0          0          0  ...          0          0          1   \n",
       "3           0          0          0  ...          0          0          1   \n",
       "4           0          0          0  ...          0          0          1   \n",
       "..        ...        ...        ...  ...        ...        ...        ...   \n",
       "91          1          0          0  ...          0          0          0   \n",
       "92          0          0          0  ...          0          1          0   \n",
       "93          0          0          0  ...          1          0          0   \n",
       "94          0          0          0  ...          0          0          0   \n",
       "95          0          1          0  ...          0          0          0   \n",
       "\n",
       "    year_1996  year_1997  year_1998  year_1999  year_2000  year_2001  \\\n",
       "0           0          0          0          0          0          0   \n",
       "1           0          0          0          0          0          0   \n",
       "2           0          0          0          0          0          0   \n",
       "3           0          0          0          0          0          0   \n",
       "4           0          0          0          0          0          0   \n",
       "..        ...        ...        ...        ...        ...        ...   \n",
       "91          0          0          0          0          0          0   \n",
       "92          0          0          0          0          0          0   \n",
       "93          0          0          0          0          0          0   \n",
       "94          0          0          0          0          0          0   \n",
       "95          0          0          0          0          0          0   \n",
       "\n",
       "    year_2002  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  \n",
       "..        ...  \n",
       "91          1  \n",
       "92          1  \n",
       "93          1  \n",
       "94          1  \n",
       "95          1  \n",
       "\n",
       "[96 rows x 26 columns]"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train = airlines.head(60)\n",
    "Test = airlines.tail(36)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44.35662671788453"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model = smf.ols('Passengers~i',data=Train).fit()\n",
    "pred_linear =  pd.Series(linear_model.predict(pd.DataFrame(Test['i'])))\n",
    "rmse_linear = np.sqrt(np.mean((np.array(Test['Passengers'])-np.array(pred_linear))**2))\n",
    "rmse_linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42.91199647698261"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Exp = smf.ols(\"log_passenger~i\",data=Train).fit()\n",
    "pred_Exp = pd.Series(Exp.predict(pd.DataFrame(Test['i'])))\n",
    "rmse_Exp = np.sqrt(np.mean((np.array(Test['Passengers'])-np.array(np.exp(pred_Exp)))**2))\n",
    "rmse_Exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40.31203210939926"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Quad = smf.ols('Passengers~i+i_square',data=Train).fit()\n",
    "pred_Quad = pd.Series(Quad.predict(Test[[\"i\",\"i_square\"]]))\n",
    "rmse_Quad = np.sqrt(np.mean((np.array(Test['Passengers'])-np.array(pred_Quad))**2))\n",
    "rmse_Quad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "119.97073717276962"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_sea = smf.ols('Passengers~month_Jan+month_Feb+month_Mar+month_Apr+month_May+month_Jun+month_Jul+month_Aug+month_Sep+month_Oct+month_Nov',data=Train).fit()\n",
    "pred_add_sea = pd.Series(add_sea.predict(Test[['month_Jan','month_Feb','month_Mar','month_Apr','month_May','month_Jun','month_Jul','month_Aug','month_Sep','month_Oct','month_Nov']]))\n",
    "rmse_add_sea = np.sqrt(np.mean((np.array(Test['Passengers'])-np.array(pred_add_sea))**2))\n",
    "rmse_add_sea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.49269369204626"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_sea_Quad = smf.ols('Passengers~i+i_square+month_Jan+month_Feb+month_Mar+month_Apr+month_May+month_Jun+month_Jul+month_Aug+month_Sep+month_Oct+month_Nov',data=Train).fit()\n",
    "pred_add_sea_quad = pd.Series(add_sea_Quad.predict(Test[['month_Jan','month_Feb','month_Mar','month_Apr','month_May','month_Jun','month_Jul','month_Aug','month_Sep','month_Oct','month_Nov','i','i_square']]))\n",
    "rmse_add_sea_quad = np.sqrt(np.mean((np.array(Test['Passengers'])-np.array(pred_add_sea_quad))**2))\n",
    "rmse_add_sea_quad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123.66785534535846"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mul_sea = smf.ols('log_passenger~month_Jan+month_Feb+month_Mar+month_Apr+month_May+month_Jun+month_Jul+month_Aug+month_Sep+month_Oct+month_Nov',data=Train).fit()\n",
    "pred_mult_sea = pd.Series(mul_sea.predict(Test))\n",
    "rmse_mult_sea = np.sqrt(np.mean((np.array(Test['Passengers'])-np.array(np.exp(pred_mult_sea)))**2))\n",
    "rmse_mult_sea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.100380469558495"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mul_add_sea = smf.ols('log_passenger~i+month_Jan+month_Feb+month_Mar+month_Apr+month_May+month_Jun+month_Jul+month_Aug+month_Sep+month_Oct+month_Nov',data=Train).fit()\n",
    "pred_mult_add_sea = pd.Series(mul_add_sea.predict(Test))\n",
    "rmse_mult_add_sea = np.sqrt(np.mean((np.array(Test['Passengers'])-np.array(np.exp(pred_mult_add_sea)))**2))\n",
    "rmse_mult_add_sea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "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>MODEL</th>\n",
       "      <th>RMSE_Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rmse_add_sea_quad</td>\n",
       "      <td>25.492694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>rmse_mult_add_sea</td>\n",
       "      <td>26.100380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rmse_Quad</td>\n",
       "      <td>40.312032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rmse_Exp</td>\n",
       "      <td>42.911996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rmse_linear</td>\n",
       "      <td>44.356627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rmse_add_sea</td>\n",
       "      <td>119.970737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>rmse_mult_sea</td>\n",
       "      <td>123.667855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               MODEL  RMSE_Values\n",
       "4  rmse_add_sea_quad    25.492694\n",
       "6  rmse_mult_add_sea    26.100380\n",
       "2          rmse_Quad    40.312032\n",
       "1           rmse_Exp    42.911996\n",
       "0        rmse_linear    44.356627\n",
       "3       rmse_add_sea   119.970737\n",
       "5      rmse_mult_sea   123.667855"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\"MODEL\":pd.Series([\"rmse_linear\",\"rmse_Exp\",\"rmse_Quad\",\"rmse_add_sea\",\"rmse_add_sea_quad\",\"rmse_mult_sea\",\"rmse_mult_add_sea\"]),\"RMSE_Values\":pd.Series([rmse_linear,rmse_Exp,rmse_Quad,rmse_add_sea,rmse_add_sea_quad,rmse_mult_sea,rmse_mult_add_sea])}\n",
    "table_rmse=pd.DataFrame(data)\n",
    "table_rmse.sort_values(['RMSE_Values'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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