Forecasting Trends .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import library\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.formula.api as smf "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read csv file\n",
    "Amtrak = pd.read_csv(\"~/Downloads/Data Science/data set/Amtrak.csv\")\n",
    "month =['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = Amtrak[\"Month\"][0]\n",
    "p[0:3]\n",
    "Amtrak['months']= 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/indexing.py:205: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "for i in range(159):\n",
    "    p = Amtrak[\"Month\"][i]\n",
    "    Amtrak['months'][i]= p[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "     Apr  Aug  Dec  Feb  Jan  Jul  Jun  Mar  May  Nov  Oct  Sep\n",
       "0      0    0    0    0    1    0    0    0    0    0    0    0\n",
       "1      0    0    0    1    0    0    0    0    0    0    0    0\n",
       "2      0    0    0    0    0    0    0    1    0    0    0    0\n",
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       "158    0    0    0    0    0    0    0    1    0    0    0    0\n",
       "\n",
       "[159 rows x 12 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_dummies = pd.DataFrame(pd.get_dummies(Amtrak['months']))\n",
    "month_dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <td>Jan-91</td>\n",
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       "      <th>154</th>\n",
       "      <td>Nov-03</td>\n",
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       "      <td>1</td>\n",
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       "      <td>Jan-04</td>\n",
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       "      <td>Feb-04</td>\n",
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       "      <td>Mar-04</td>\n",
       "      <td>2132</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "<p>159 rows × 15 columns</p>\n",
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      ],
      "text/plain": [
       "      Month  Ridership  months  Apr  Aug  Dec  Feb  Jan  Jul  Jun  Mar  May  \\\n",
       "0    Jan-91        1709    Jan    0    0    0    0    1    0    0    0    0   \n",
       "1    Feb-91        1621    Feb    0    0    0    1    0    0    0    0    0   \n",
       "2    Mar-91        1973    Mar    0    0    0    0    0    0    0    1    0   \n",
       "3    Apr-91        1812    Apr    1    0    0    0    0    0    0    0    0   \n",
       "4    May-91        1975    May    0    0    0    0    0    0    0    0    1   \n",
       "..      ...         ...    ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "154  Nov-03        2076    Nov    0    0    0    0    0    0    0    0    0   \n",
       "155  Dec-03        2141    Dec    0    0    1    0    0    0    0    0    0   \n",
       "156  Jan-04        1832    Jan    0    0    0    0    1    0    0    0    0   \n",
       "157  Feb-04        1838    Feb    0    0    0    1    0    0    0    0    0   \n",
       "158  Mar-04        2132    Mar    0    0    0    0    0    0    0    1    0   \n",
       "\n",
       "     Nov  Oct  Sep  \n",
       "0      0    0    0  \n",
       "1      0    0    0  \n",
       "2      0    0    0  \n",
       "3      0    0    0  \n",
       "4      0    0    0  \n",
       "..   ...  ...  ...  \n",
       "154    1    0    0  \n",
       "155    0    0    0  \n",
       "156    0    0    0  \n",
       "157    0    0    0  \n",
       "158    0    0    0  \n",
       "\n",
       "[159 rows x 15 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Amtrak1 = pd.concat([Amtrak,month_dummies],axis = 1)\n",
    "Amtrak1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Month', 'Ridership ', 'months', 'Apr', 'Aug', 'Dec', 'Feb', 'Jan',\n",
       "       'Jul', 'Jun', 'Mar', 'May', 'Nov', 'Oct', 'Sep', 't', 't_squared'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Amtrak1[\"t\"] = np.arange(1,160)\n",
    "Amtrak1[\"t_squared\"] = Amtrak1[\"t\"]*Amtrak1[\"t\"]\n",
    "Amtrak1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122930190>"
      ]
     },
     "execution_count": 8,
     "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": [
    "Amtrak1[\"log_Rider\"] = np.log(Amtrak1[\"Ridership \"])\n",
    "Amtrak1.rename(columns={\"Ridership \": 'Ridership'}, inplace=True)\n",
    "Amtrak1.Ridership.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train = Amtrak1.head(147)\n",
    "Test = Amtrak1.tail(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "209.92559265462572"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to change the index value in pandas data frame \n",
    "# Test.set_index(np.arange(1,13))\n",
    "\n",
    "#LINEAR \n",
    "linear_model = smf.ols('Ridership~t',data=Train).fit()\n",
    "pred_linear =  pd.Series(linear_model.predict(pd.DataFrame(Test['t'])))\n",
    "rmse_linear = np.sqrt(np.mean((np.array(Test['Ridership'])-np.array(pred_linear))**2))\n",
    "rmse_linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "217.05263566813343"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Exponential\n",
    "Exp = smf.ols('log_Rider~t',data=Train).fit()\n",
    "pred_Exp = pd.Series(Exp.predict(pd.DataFrame(Test['t'])))\n",
    "rmse_Exp = np.sqrt(np.mean((np.array(Test['Ridership'])-np.array(np.exp(pred_Exp)))**2))\n",
    "rmse_Exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "137.15462741356137"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Quadratic\n",
    "Quad = smf.ols('Ridership~t+t_squared',data=Train).fit()\n",
    "pred_Quad = pd.Series(Quad.predict(Test[[\"t\",\"t_squared\"]]))\n",
    "rmse_Quad = np.sqrt(np.mean((np.array(Test['Ridership'])-np.array(pred_Quad))**2))\n",
    "rmse_Quad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "264.6643900568775"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Additive seasonality\n",
    "\n",
    "add_sea = smf.ols('Ridership~Jan+Feb+Mar+Apr+May+Jun+Jul+Aug+Sep+Oct+Nov',data=Train).fit()\n",
    "pred_add_sea = pd.Series(add_sea.predict(Test[['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov']]))\n",
    "rmse_add_sea = np.sqrt(np.mean((np.array(Test['Ridership'])-np.array(pred_add_sea))**2))\n",
    "rmse_add_sea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.60724584141619"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Additive Seasonality Quadratic\n",
    "\n",
    "add_sea_Quad = smf.ols('Ridership~t+t_squared+Jan+Feb+Mar+Apr+May+Jun+Jul+Aug+Sep+Oct+Nov',data=Train).fit()\n",
    "pred_add_sea_quad = pd.Series(add_sea_Quad.predict(Test[['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','t','t_squared']]))\n",
    "rmse_add_sea_quad = np.sqrt(np.mean((np.array(Test['Ridership'])-np.array(pred_add_sea_quad))**2))\n",
    "rmse_add_sea_quad "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "268.1970325309181"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multiplicative Seasonality\n",
    "\n",
    "Mul_sea = smf.ols('log_Rider~Jan+Feb+Mar+Apr+May+Jun+Jul+Aug+Sep+Oct+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['Ridership'])-np.array(np.exp(pred_Mult_sea)))**2))\n",
    "rmse_Mult_sea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "172.76726784671988"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Multiplicative Additive Seasonality\n",
    "Mul_Add_sea = smf.ols('log_Rider~t+Jan+Feb+Mar+Apr+May+Jun+Jul+Aug+Sep+Oct+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['Ridership'])-np.array(np.exp(pred_Mult_add_sea)))**2))\n",
    "rmse_Mult_add_sea "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>0</th>\n",
       "      <td>rmse_linear</td>\n",
       "      <td>209.925593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rmse_Exp</td>\n",
       "      <td>217.052636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rmse_Quad</td>\n",
       "      <td>137.154627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rmse_add_sea</td>\n",
       "      <td>264.664390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rmse_add_sea_quad</td>\n",
       "      <td>50.607246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>rmse_Mult_sea</td>\n",
       "      <td>268.197033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>rmse_Mult_add_sea</td>\n",
       "      <td>172.767268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               MODEL  RMSE_Values\n",
       "0        rmse_linear   209.925593\n",
       "1           rmse_Exp   217.052636\n",
       "2          rmse_Quad   137.154627\n",
       "3       rmse_add_sea   264.664390\n",
       "4  rmse_add_sea_quad    50.607246\n",
       "5      rmse_Mult_sea   268.197033\n",
       "6  rmse_Mult_add_sea   172.767268"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Testing\n",
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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