Regresion Lineal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "from datetime import datetime\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Prometidas</th>\n",
       "      <th>Facturadas</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01 01:15:00</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 01:30:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 01:45:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 02:00:00</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 02:15:00</th>\n",
       "      <td>133</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Prometidas  Facturadas\n",
       "Timestamp                                  \n",
       "2010-01-01 01:15:00           0           1\n",
       "2010-01-01 01:30:00           0           0\n",
       "2010-01-01 01:45:00           0           0\n",
       "2010-01-01 02:00:00           6           1\n",
       "2010-01-01 02:15:00         133         305"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'Prometidas vs Facturadas.csv'\n",
    "df = pd.read_csv(filename, index_col=[0], \n",
    "                 date_parser=lambda x: datetime.strptime(x, \"%m/%d/%Y %H:%M\"))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f610348b7b8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(figsize=(18,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check for missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f6103f5bc88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f610384a748>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remove outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "std_dev = 3\n",
    "df_new = df[(np.abs(stats.zscore(df)) < float(std_dev)).all(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot cleaned data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f610365aeb8>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_new.plot(figsize=(18,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validate linear relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f61028f8588>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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30lz6cheJ37h1L9t2T3Y8FjObPRpOApIWS/qupEclPSzpz1L7JyVNSnoo3S7LvGajpHFJ+yWtbsUXaJVG5sX30lx6l6bMrBHNlIOOAf89In4k6bXALkk703Ofj4jPZjtLOgdYC5wLLAS+I+lNEXHyL1eXNDIvvpfm0vdaacrMZoeGk0BEHAQOpuVfSnoUqPbrdwVwe0S8DDwlaRy4APhBozG0WqV58dVq7b0yl77SReJ9mgczq6YlYwKSlgArgQdS0zWS9ki6RdK81DYCPJt52QTVk0ZPmC219l4qTZnZ7NF0EpD068DXgY9HxC+Am4A3Aiso7il8rtS1zMujwnuulzQmaezw4cPNhtiURmvt23ZPsuqG+1i64RusuuG+tieNNStHuP7K8xgZGkTAyNAg1195Xk/spZhZ72pqiqikuRQTwG0RsRUgIp7LPP8l4O70cAJYnHn5IuBAufeNiM3AZoBCoVA2UXRKI7X2v9y2l9vuf+ZEhpvpILJGp3aWe92/bXjnzF/KzCxpZnaQgH8AHo2Iv820L8h0ey+wLy1vB9ZKOkPSUmAZ8MNGP79T6p0Gum335EkJoKTS3kOj5abZUqYys97WTDloFfAB4J3TpoN+RtJeSXuAdwB/DhARDwN3AI8A3wI+1iszg6qpt9a+acf+8jUuyu89NFpu8pRQM2uFZmYH/Svl6/z3VHnNp4FPN/qZtWrlkbP1TgOtViYqt/fQ6NROTwk1s1bou9NG1HpSt1oTRb0JpdJUTYB3/M5wzf1Pk1i64RsVP9NTQs2sFfrutBG1lElqrac3Une/dvXysrtHAFseePbEbKG/3LaXVTfcx+SRqbL9j0dU/UxPCTWzVui7JFBLmaTWevontz9cd919zcqRimMC2R/2f7r/mRN/yQev1tUGdGpKKPeZnhJqZq3Qd+WgSmWSAFbdcB/Xrl5eMVFMHplixV99mxenjjJ05lyOTB0t269a3X3b7kkGJI5HfTNbg+IPeT21/l45WtnMZq++2xM48/TKX6lUWvmNwbkV+xyZOkoAP3+pfAKA6tNDN27dW3cCKCmNO5RzmuTpn2bWcn23J/D4oV9VfX7q6HFePnacuaeJo6809mP981+9zMpPfZufv3T0xF/9I0OD/OrlY6eUj+pRGgTODmyXHI/wVcvMrOX6bk+gFq9ki/ANeOnoKyf2FEp/9U8emapYPqpFaVC3VOuvdWzAzKwZuUwCAEePR9kf2k4qffr0Qd01K0d4pUJJyccBmFkr9V05qB7HIxicO9BUCacZn//DFRVLOz4OwMw6Ibd7AlCcjplNAKe1eMdgZGiQeWeWH4QeGRqsWtv3cQBm1gm5TgLTZ/E0OE5c1sjQIP+24Z184vfPbejH3McBmFkn5KocNO/MuVWnfrZK9ke+mUtQ+jgAM2u3XCWBBqfv12WkzI+8f8zNrFflKgk0M4WzFgJf1MXMZpVcjwm0mmfumNlsk6s9gXZqZOZOK697YGbWCCeBFig3DjCTWq97YGbWTi4HNak0FbTeH25fHtLMekHHk4CkSyTtlzQuaUOnP7/VGj2Ngy8PaWa9oKPlIEkDwBeAdwMTwIOStkfEI52Mo5UaHQye6bQQHi8ws07o9J7ABcB4RDwZEf8PuB24osMxtEwzp3GodlqIRi5raWbWiE4ngRHg2czjidQ26zR7Godqp4XweIGZdUqnZweVO0XbKcfxSloPrAcYHR1td0x1a9VBYZWOJPZ4gZl1Sqf3BCaAxZnHi4AD0ztFxOaIKEREYXh4uGPB1ardB4VVen8fjGZmrdbpJPAgsEzSUkmnA2uB7R2OoSlzT1PbT+fs00ibWad0NAlExDHgGmAH8ChwR0Q83MrPePqG/9LU608TrHrjWQyVuRj90OBcNr3vzW2fpePTSJtZpyg6cWrNJhQKhRgbG+t2GGZms4akXRFRqKWvjxg2M8sxJwEzsxxzEjAzyzEnATOzHHMSMDPLsZ6fHSTpMPDTBl8+H/hZC8NpB8fYGo6xNRxj63Qzzt+OiJqOtO35JNAMSWO1TpPqFsfYGo6xNRxj68yWOF0OMjPLMScBM7Mc6/cksLnbAdTAMbaGY2wNx9g6syLOvh4TMDOz6vp9T8DMzKroyyTQKxezl7RY0nclPSrpYUl/ltrPkrRT0uPpfl5ql6QbU9x7JJ3fwVgHJO2WdHd6vFTSAynGr6ZTfyPpjPR4PD2/pEPxDUn6mqTH0vp8a6+tR0l/nv6d90naIunXemE9SrpF0iFJ+zJtda87SetS/8clretAjJvSv/ceSXdKGso8tzHFuF/S6kx727b9cjFmnvsfkkLS/PS4K+uxIRHRVzdgAHgCOBs4HfgxcE6XYlkAnJ+WXwv8BDgH+AywIbVvAP4mLV8GfJPixcsuBB7oYKz/Dfhn4O70+A5gbVq+GfhoWv5T4Oa0vBb4aofiuxX447R8OjDUS+uR4mVSnwIGM+vvj3phPQJvB84H9mXa6lp3wFnAk+l+Xlqe1+YYLwbmpOW/ycR4TtquzwCWpu19oN3bfrkYU/tiiqfH/ykwv5vrsaHv1c0Pb8sXgrcCOzKPNwIbux1XiuUu4N3AfmBBalsA7E/LXwSuyvQ/0a/NcS0C7gXeCdyd/uP+LLMBnlin6T/7W9PynNRPbY7vdekHVtPae2Y98ur1s89K6+VuYHWvrEdgybQf2LrWHXAV8MVM+0n92hHjtOfeC9yWlk/apkvrshPbfrkYga8Bbwae5tUk0LX1WO+tH8tBPXkx+7S7vxJ4AHhDRBwESPevT926FfvfAf8TeCU9/k3gSBQvAjQ9jhMxpudfTP3b6WzgMPB/U8nq7yW9hh5ajxExCXwWeAY4SHG97KK31mNWveuu29vVhyj+ZU2VWDoeo6TLgcmI+PG0p3omxpn0YxKo6WL2nSTp14GvAx+PiF9U61qmra2xS3oPcCgidtUYRzfW7xyKu+E3RcRK4FcUSxiVdGM9zgOuoFieWAi8Bri0Shw99/80qRRX1+KVdB1wDLit1FQhlo7GKOlM4Drgf5V7ukIsPffv3o9JoKaL2XeKpLkUE8BtEbE1NT8naUF6fgFwKLV3I/ZVwOWSngZup1gS+jtgSNKcMnGciDE9/xvAC22OcQKYiIgH0uOvUUwKvbQefw94KiIOR8RRYCvwn+it9ZhV77rrynaVBk7fA7w/Uv2kh2J8I8Wk/+O0/SwCfiTpt3ooxhn1YxLomYvZSxLwD8CjEfG3mae2A6VZAesojhWU2q9OMwsuBF4s7bK3S0RsjIhFEbGE4rq6LyLeD3wX+IMKMZZi/4PUv61/yUTEvwPPSlqemt4FPEIPrUeKZaALJZ2Z/t1LMfbMepym3nW3A7hY0ry013NxamsbSZcAfwFcHhEvTYt9bZphtRRYBvyQDm/7EbE3Il4fEUvS9jNBcSLIv9ND63FG3RyQaNeN4sj8TyjOFLiui3H8Z4q7enuAh9LtMoq133uBx9P9Wam/gC+kuPcChQ7HexGvzg46m+KGNQ78C3BGav+19Hg8PX92h2JbAYyldbmN4syKnlqPwF8BjwH7gH+kOHul6+sR2EJxnOIoxR+qDzey7ijW5cfT7YMdiHGcYv28tO3cnOl/XYpxP3Bppr1t2365GKc9/zSvDgx3ZT02cvMRw2ZmOdaP5SAzM6uRk4CZWY45CZiZ5ZiTgJlZjjkJmJnlmJOAmVmOOQmYmeWYk4CZWY79fwgp+4luFf7SAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Prometidas'], df['Facturadas'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot a day picked at random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Monday\n"
     ]
    },
    {
     "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": [
    "df.loc['2010-01-01', ['Prometidas']].plot()\n",
    "\n",
    "# Prints the day\n",
    "print(df.loc['2010-02-01'].index.day_name()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Monday\n"
     ]
    },
    {
     "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": [
    "df.loc['2010-01-01', ['Facturadas']].plot()\n",
    "print(df.loc['2010-02-01'].index.day_name()[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit Data to Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.9167062664026668, 0.6693931982572362, 0.38107685613368725]\n"
     ]
    }
   ],
   "source": [
    "X = pd.DataFrame(df['Prometidas'])\n",
    "y = pd.DataFrame(df['Facturadas'])\n",
    "\n",
    "model = LinearRegression()\n",
    "scores = []\n",
    "kfold = KFold(n_splits=3, shuffle=True, random_state=42)\n",
    "for i, (train, test) in enumerate(kfold.split(X, y)):\n",
    "    model.fit(X.iloc[train,:], y.iloc[train,:])\n",
    "    score = model.score(X.iloc[test,:], y.iloc[test,:])\n",
    "    scores.append(score)\n",
    "\n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add time of day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>Prometidas</th>\n",
       "      <th>tod_1</th>\n",
       "      <th>tod_2</th>\n",
       "      <th>tod_3</th>\n",
       "      <th>tod_4</th>\n",
       "      <th>tod_5</th>\n",
       "      <th>tod_6</th>\n",
       "      <th>tod_7</th>\n",
       "      <th>tod_8</th>\n",
       "      <th>tod_9</th>\n",
       "      <th>...</th>\n",
       "      <th>tod_14</th>\n",
       "      <th>tod_15</th>\n",
       "      <th>tod_16</th>\n",
       "      <th>tod_17</th>\n",
       "      <th>tod_18</th>\n",
       "      <th>tod_19</th>\n",
       "      <th>tod_20</th>\n",
       "      <th>tod_21</th>\n",
       "      <th>tod_22</th>\n",
       "      <th>tod_23</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01 01:15:00</th>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 01:30:00</th>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 01:45:00</th>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 02:00:00</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 02:15:00</th>\n",
       "      <td>133</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Prometidas  tod_1  tod_2  tod_3  tod_4  tod_5  tod_6  \\\n",
       "Timestamp                                                                   \n",
       "2010-01-01 01:15:00           0      1      0      0      0      0      0   \n",
       "2010-01-01 01:30:00           0      1      0      0      0      0      0   \n",
       "2010-01-01 01:45:00           0      1      0      0      0      0      0   \n",
       "2010-01-01 02:00:00           6      0      1      0      0      0      0   \n",
       "2010-01-01 02:15:00         133      0      1      0      0      0      0   \n",
       "\n",
       "                     tod_7  tod_8  tod_9   ...    tod_14  tod_15  tod_16  \\\n",
       "Timestamp                                  ...                             \n",
       "2010-01-01 01:15:00      0      0      0   ...         0       0       0   \n",
       "2010-01-01 01:30:00      0      0      0   ...         0       0       0   \n",
       "2010-01-01 01:45:00      0      0      0   ...         0       0       0   \n",
       "2010-01-01 02:00:00      0      0      0   ...         0       0       0   \n",
       "2010-01-01 02:15:00      0      0      0   ...         0       0       0   \n",
       "\n",
       "                     tod_17  tod_18  tod_19  tod_20  tod_21  tod_22  tod_23  \n",
       "Timestamp                                                                    \n",
       "2010-01-01 01:15:00       0       0       0       0       0       0       0  \n",
       "2010-01-01 01:30:00       0       0       0       0       0       0       0  \n",
       "2010-01-01 01:45:00       0       0       0       0       0       0       0  \n",
       "2010-01-01 02:00:00       0       0       0       0       0       0       0  \n",
       "2010-01-01 02:15:00       0       0       0       0       0       0       0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.DataFrame(df['Prometidas'])\n",
    "\n",
    "var = 'tod'\n",
    "X[var] = X.index.hour\n",
    "\n",
    "# drop_first = True removes multi-collinearity\n",
    "add_var = pd.get_dummies(X[var], prefix=var, drop_first=True)\n",
    "\n",
    "# Add all the columns to the model data\n",
    "X = X.join(add_var)\n",
    "\n",
    "# Drop the original column that was expanded\n",
    "X.drop(columns=[var], inplace=True)\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit Data to Multiple Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.9150350254532037, 0.657270555789474, 0.38002649040667197]\n"
     ]
    }
   ],
   "source": [
    "y = pd.DataFrame(df['Facturadas'])\n",
    "\n",
    "model = LinearRegression()\n",
    "scores = []\n",
    "kfold = KFold(n_splits=3, shuffle=True, random_state=42)\n",
    "for i, (train, test) in enumerate(kfold.split(X, y)):\n",
    "    model.fit(X.iloc[train,:], y.iloc[train,:])\n",
    "    scores.append(model.score(X.iloc[test,:], y.iloc[test,:]))\n",
    "\n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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