From feaa2bd6d0bade75b9731c7cae2ac518634c3a13 Mon Sep 17 00:00:00 2001
From: JulianTrufero <jgtrufero@gmail.com>
Date: Thu, 14 Oct 2021 11:57:14 +0200
Subject: [PATCH 1/6] update

---
 Untitled.ipynb | 716 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 716 insertions(+)
 create mode 100644 Untitled.ipynb

diff --git a/Untitled.ipynb b/Untitled.ipynb
new file mode 100644
index 0000000..db6e8fe
--- /dev/null
+++ b/Untitled.ipynb
@@ -0,0 +1,716 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import statsmodels.formula.api as smf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "consume_data = pd.read_csv('/home/julian/Cursos/Ironhack/Career/reto_data/consume_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### EDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(376, 11)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "consume_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 376 entries, 0 to 375\n",
+      "Data columns (total 11 columns):\n",
+      " #   Column        Non-Null Count  Dtype  \n",
+      "---  ------        --------------  -----  \n",
+      " 0   consume_id    376 non-null    int64  \n",
+      " 1   distance      376 non-null    float64\n",
+      " 2   consume       376 non-null    float64\n",
+      " 3   speed         376 non-null    int64  \n",
+      " 4   temp_inside   376 non-null    float64\n",
+      " 5   temp_outside  376 non-null    int64  \n",
+      " 6   gas_type      376 non-null    object \n",
+      " 7   AC            376 non-null    int64  \n",
+      " 8   rain          376 non-null    int64  \n",
+      " 9   sun           376 non-null    int64  \n",
+      " 10  snow          376 non-null    int64  \n",
+      "dtypes: float64(3), int64(7), object(1)\n",
+      "memory usage: 32.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "consume_data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "consume_id  distance  consume  speed  temp_inside  temp_outside  gas_type  AC     rain   sun    snow \n",
+       "False       False     False    False  False        False         False     False  False  False  False    376\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "consume_data.isna().value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SP98    219\n",
+       "E10     157\n",
+       "Name: gas_type, dtype: int64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "consume_data.gas_type.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "consume_data = pd.get_dummies(consume_data, columns=['gas_type'], drop_first=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "consume_data.drop(columns=['consume_id'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "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>distance</th>\n",
+       "      <th>consume</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>temp_inside</th>\n",
+       "      <th>temp_outside</th>\n",
+       "      <th>AC</th>\n",
+       "      <th>rain</th>\n",
+       "      <th>sun</th>\n",
+       "      <th>snow</th>\n",
+       "      <th>gas_type_SP98</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>12</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>1</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>13</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>2</th>\n",
+       "      <td>11.2</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>38</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>15</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",
+       "</div>"
+      ],
+      "text/plain": [
+       "   distance  consume  speed  temp_inside  temp_outside  AC  rain  sun  snow  \\\n",
+       "0      28.0      5.0     26         21.5            12   0     0    0     0   \n",
+       "1      12.0      4.2     30         21.5            13   0     0    0     0   \n",
+       "2      11.2      5.5     38         21.5            15   0     0    0     0   \n",
+       "\n",
+       "   gas_type_SP98  \n",
+       "0              0  \n",
+       "1              0  \n",
+       "2              0  "
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "consume_data.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "consume = {'Minimum': consume_data.consume.min(),\n",
+    "          'Maximum': consume_data.consume.max(),\n",
+    "          'Mean': consume_data.consume.mean(),\n",
+    "          'Median': consume_data.consume.median(),\n",
+    "          'Mode': consume_data.consume.mode()}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "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>Minimum</th>\n",
+       "      <th>Maximum</th>\n",
+       "      <th>Mean</th>\n",
+       "      <th>Median</th>\n",
+       "      <th>Mode</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Consume</th>\n",
+       "      <td>3.3</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>4.925266</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Minimum  Maximum      Mean  Median  Mode\n",
+       "Consume      3.3     12.2  4.925266     4.7   4.5"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "consume_descr = pd.DataFrame(consume) \n",
+    "consume_descr = consume_descr.rename(index={0:'Consume'})\n",
+    "consume_descr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='consume', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 41,
+     "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": [
+    "sns.histplot(x='consume', data=consume_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corr_matrix = consume_data.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "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>distance</th>\n",
+       "      <th>consume</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>temp_inside</th>\n",
+       "      <th>temp_outside</th>\n",
+       "      <th>AC</th>\n",
+       "      <th>rain</th>\n",
+       "      <th>sun</th>\n",
+       "      <th>snow</th>\n",
+       "      <th>gas_type_SP98</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>distance</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.131379</td>\n",
+       "      <td>0.565505</td>\n",
+       "      <td>0.075305</td>\n",
+       "      <td>0.086774</td>\n",
+       "      <td>0.019030</td>\n",
+       "      <td>-0.030784</td>\n",
+       "      <td>0.078627</td>\n",
+       "      <td>-0.034428</td>\n",
+       "      <td>-0.047877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>consume</th>\n",
+       "      <td>-0.131379</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.234537</td>\n",
+       "      <td>-0.161991</td>\n",
+       "      <td>-0.318758</td>\n",
+       "      <td>0.089472</td>\n",
+       "      <td>0.251756</td>\n",
+       "      <td>-0.169173</td>\n",
+       "      <td>0.072308</td>\n",
+       "      <td>-0.020928</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>speed</th>\n",
+       "      <td>0.565505</td>\n",
+       "      <td>-0.234537</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.059725</td>\n",
+       "      <td>0.019327</td>\n",
+       "      <td>-0.024485</td>\n",
+       "      <td>-0.004173</td>\n",
+       "      <td>0.077181</td>\n",
+       "      <td>0.032500</td>\n",
+       "      <td>-0.099495</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>temp_inside</th>\n",
+       "      <td>0.075305</td>\n",
+       "      <td>-0.161991</td>\n",
+       "      <td>0.059725</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.361308</td>\n",
+       "      <td>0.313197</td>\n",
+       "      <td>-0.038269</td>\n",
+       "      <td>0.246120</td>\n",
+       "      <td>0.097156</td>\n",
+       "      <td>0.010340</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>temp_outside</th>\n",
+       "      <td>0.086774</td>\n",
+       "      <td>-0.318758</td>\n",
+       "      <td>0.019327</td>\n",
+       "      <td>0.361308</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.180096</td>\n",
+       "      <td>-0.184763</td>\n",
+       "      <td>0.349988</td>\n",
+       "      <td>-0.162948</td>\n",
+       "      <td>0.156990</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AC</th>\n",
+       "      <td>0.019030</td>\n",
+       "      <td>0.089472</td>\n",
+       "      <td>-0.024485</td>\n",
+       "      <td>0.313197</td>\n",
+       "      <td>0.180096</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.242197</td>\n",
+       "      <td>0.085928</td>\n",
+       "      <td>0.063170</td>\n",
+       "      <td>0.096943</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rain</th>\n",
+       "      <td>-0.030784</td>\n",
+       "      <td>0.251756</td>\n",
+       "      <td>-0.004173</td>\n",
+       "      <td>-0.038269</td>\n",
+       "      <td>-0.184763</td>\n",
+       "      <td>0.242197</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.111916</td>\n",
+       "      <td>0.277739</td>\n",
+       "      <td>0.052789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sun</th>\n",
+       "      <td>0.078627</td>\n",
+       "      <td>-0.169173</td>\n",
+       "      <td>0.077181</td>\n",
+       "      <td>0.246120</td>\n",
+       "      <td>0.349988</td>\n",
+       "      <td>0.085928</td>\n",
+       "      <td>-0.111916</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.031083</td>\n",
+       "      <td>0.038120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>snow</th>\n",
+       "      <td>-0.034428</td>\n",
+       "      <td>0.072308</td>\n",
+       "      <td>0.032500</td>\n",
+       "      <td>0.097156</td>\n",
+       "      <td>-0.162948</td>\n",
+       "      <td>0.063170</td>\n",
+       "      <td>0.277739</td>\n",
+       "      <td>-0.031083</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.035231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas_type_SP98</th>\n",
+       "      <td>-0.047877</td>\n",
+       "      <td>-0.020928</td>\n",
+       "      <td>-0.099495</td>\n",
+       "      <td>0.010340</td>\n",
+       "      <td>0.156990</td>\n",
+       "      <td>0.096943</td>\n",
+       "      <td>0.052789</td>\n",
+       "      <td>0.038120</td>\n",
+       "      <td>0.035231</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               distance   consume     speed  temp_inside  temp_outside  \\\n",
+       "distance       1.000000 -0.131379  0.565505     0.075305      0.086774   \n",
+       "consume       -0.131379  1.000000 -0.234537    -0.161991     -0.318758   \n",
+       "speed          0.565505 -0.234537  1.000000     0.059725      0.019327   \n",
+       "temp_inside    0.075305 -0.161991  0.059725     1.000000      0.361308   \n",
+       "temp_outside   0.086774 -0.318758  0.019327     0.361308      1.000000   \n",
+       "AC             0.019030  0.089472 -0.024485     0.313197      0.180096   \n",
+       "rain          -0.030784  0.251756 -0.004173    -0.038269     -0.184763   \n",
+       "sun            0.078627 -0.169173  0.077181     0.246120      0.349988   \n",
+       "snow          -0.034428  0.072308  0.032500     0.097156     -0.162948   \n",
+       "gas_type_SP98 -0.047877 -0.020928 -0.099495     0.010340      0.156990   \n",
+       "\n",
+       "                     AC      rain       sun      snow  gas_type_SP98  \n",
+       "distance       0.019030 -0.030784  0.078627 -0.034428      -0.047877  \n",
+       "consume        0.089472  0.251756 -0.169173  0.072308      -0.020928  \n",
+       "speed         -0.024485 -0.004173  0.077181  0.032500      -0.099495  \n",
+       "temp_inside    0.313197 -0.038269  0.246120  0.097156       0.010340  \n",
+       "temp_outside   0.180096 -0.184763  0.349988 -0.162948       0.156990  \n",
+       "AC             1.000000  0.242197  0.085928  0.063170       0.096943  \n",
+       "rain           0.242197  1.000000 -0.111916  0.277739       0.052789  \n",
+       "sun            0.085928 -0.111916  1.000000 -0.031083       0.038120  \n",
+       "snow           0.063170  0.277739 -0.031083  1.000000       0.035231  \n",
+       "gas_type_SP98  0.096943  0.052789  0.038120  0.035231       1.000000  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "mascara = np.triu(np.ones_like(corr_matrix, dtype=bool))\n",
+    "color_map = sns.diverging_palette(0, 10, as_cmap=True) \n",
+    "\n",
+    "sns.heatmap(corr_matrix, \n",
+    "            mask=mascara,\n",
+    "            cmap=\"viridis\",\n",
+    "            vmax=1,\n",
+    "            vmin=-1,\n",
+    "            square=True, \n",
+    "            linewidth=.5, \n",
+    "            cbar_kws={\"shrink\": .5} \n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### El consumo esta mayormente correlacionado con temp_outside (negativamente), rain (positivamente), speed (negativamente), sun (negativamente), temp_inside (negativamente)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x7f834952eeb0>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x1800 with 110 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(consume_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='consume', ylabel='gas_type_SP98'>"
+      ]
+     },
+     "execution_count": 44,
+     "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": [
+    "sns.scatterplot(x=consume_data.consume, y=consume_data.gas_type_SP98)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ironcon",
+   "language": "python",
+   "name": "ironcon"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 5c088864055d913a78acb730825c69cc52ad70a9 Mon Sep 17 00:00:00 2001
From: JulianTrufero <jgtrufero@gmail.com>
Date: Thu, 14 Oct 2021 14:11:53 +0200
Subject: [PATCH 2/6] update

---
 Untitled.ipynb |  716 ---------------------------
 consume.ipynb  | 1281 ++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 1281 insertions(+), 716 deletions(-)
 delete mode 100644 Untitled.ipynb
 create mode 100644 consume.ipynb

diff --git a/Untitled.ipynb b/Untitled.ipynb
deleted file mode 100644
index db6e8fe..0000000
--- a/Untitled.ipynb
+++ /dev/null
@@ -1,716 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import seaborn as sns\n",
-    "import statsmodels.formula.api as smf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "consume_data = pd.read_csv('/home/julian/Cursos/Ironhack/Career/reto_data/consume_data.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### EDA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(376, 11)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "consume_data.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 376 entries, 0 to 375\n",
-      "Data columns (total 11 columns):\n",
-      " #   Column        Non-Null Count  Dtype  \n",
-      "---  ------        --------------  -----  \n",
-      " 0   consume_id    376 non-null    int64  \n",
-      " 1   distance      376 non-null    float64\n",
-      " 2   consume       376 non-null    float64\n",
-      " 3   speed         376 non-null    int64  \n",
-      " 4   temp_inside   376 non-null    float64\n",
-      " 5   temp_outside  376 non-null    int64  \n",
-      " 6   gas_type      376 non-null    object \n",
-      " 7   AC            376 non-null    int64  \n",
-      " 8   rain          376 non-null    int64  \n",
-      " 9   sun           376 non-null    int64  \n",
-      " 10  snow          376 non-null    int64  \n",
-      "dtypes: float64(3), int64(7), object(1)\n",
-      "memory usage: 32.4+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "consume_data.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "consume_id  distance  consume  speed  temp_inside  temp_outside  gas_type  AC     rain   sun    snow \n",
-       "False       False     False    False  False        False         False     False  False  False  False    376\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "consume_data.isna().value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SP98    219\n",
-       "E10     157\n",
-       "Name: gas_type, dtype: int64"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "consume_data.gas_type.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "consume_data = pd.get_dummies(consume_data, columns=['gas_type'], drop_first=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "consume_data.drop(columns=['consume_id'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "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>distance</th>\n",
-       "      <th>consume</th>\n",
-       "      <th>speed</th>\n",
-       "      <th>temp_inside</th>\n",
-       "      <th>temp_outside</th>\n",
-       "      <th>AC</th>\n",
-       "      <th>rain</th>\n",
-       "      <th>sun</th>\n",
-       "      <th>snow</th>\n",
-       "      <th>gas_type_SP98</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>28.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>26</td>\n",
-       "      <td>21.5</td>\n",
-       "      <td>12</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>1</th>\n",
-       "      <td>12.0</td>\n",
-       "      <td>4.2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>21.5</td>\n",
-       "      <td>13</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>2</th>\n",
-       "      <td>11.2</td>\n",
-       "      <td>5.5</td>\n",
-       "      <td>38</td>\n",
-       "      <td>21.5</td>\n",
-       "      <td>15</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",
-       "</div>"
-      ],
-      "text/plain": [
-       "   distance  consume  speed  temp_inside  temp_outside  AC  rain  sun  snow  \\\n",
-       "0      28.0      5.0     26         21.5            12   0     0    0     0   \n",
-       "1      12.0      4.2     30         21.5            13   0     0    0     0   \n",
-       "2      11.2      5.5     38         21.5            15   0     0    0     0   \n",
-       "\n",
-       "   gas_type_SP98  \n",
-       "0              0  \n",
-       "1              0  \n",
-       "2              0  "
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "consume_data.head(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "consume = {'Minimum': consume_data.consume.min(),\n",
-    "          'Maximum': consume_data.consume.max(),\n",
-    "          'Mean': consume_data.consume.mean(),\n",
-    "          'Median': consume_data.consume.median(),\n",
-    "          'Mode': consume_data.consume.mode()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "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>Minimum</th>\n",
-       "      <th>Maximum</th>\n",
-       "      <th>Mean</th>\n",
-       "      <th>Median</th>\n",
-       "      <th>Mode</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Consume</th>\n",
-       "      <td>3.3</td>\n",
-       "      <td>12.2</td>\n",
-       "      <td>4.925266</td>\n",
-       "      <td>4.7</td>\n",
-       "      <td>4.5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Minimum  Maximum      Mean  Median  Mode\n",
-       "Consume      3.3     12.2  4.925266     4.7   4.5"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "consume_descr = pd.DataFrame(consume) \n",
-    "consume_descr = consume_descr.rename(index={0:'Consume'})\n",
-    "consume_descr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:xlabel='consume', ylabel='Count'>"
-      ]
-     },
-     "execution_count": 41,
-     "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": [
-    "sns.histplot(x='consume', data=consume_data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corr_matrix = consume_data.corr()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "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>distance</th>\n",
-       "      <th>consume</th>\n",
-       "      <th>speed</th>\n",
-       "      <th>temp_inside</th>\n",
-       "      <th>temp_outside</th>\n",
-       "      <th>AC</th>\n",
-       "      <th>rain</th>\n",
-       "      <th>sun</th>\n",
-       "      <th>snow</th>\n",
-       "      <th>gas_type_SP98</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>distance</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.131379</td>\n",
-       "      <td>0.565505</td>\n",
-       "      <td>0.075305</td>\n",
-       "      <td>0.086774</td>\n",
-       "      <td>0.019030</td>\n",
-       "      <td>-0.030784</td>\n",
-       "      <td>0.078627</td>\n",
-       "      <td>-0.034428</td>\n",
-       "      <td>-0.047877</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>consume</th>\n",
-       "      <td>-0.131379</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.234537</td>\n",
-       "      <td>-0.161991</td>\n",
-       "      <td>-0.318758</td>\n",
-       "      <td>0.089472</td>\n",
-       "      <td>0.251756</td>\n",
-       "      <td>-0.169173</td>\n",
-       "      <td>0.072308</td>\n",
-       "      <td>-0.020928</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>speed</th>\n",
-       "      <td>0.565505</td>\n",
-       "      <td>-0.234537</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.059725</td>\n",
-       "      <td>0.019327</td>\n",
-       "      <td>-0.024485</td>\n",
-       "      <td>-0.004173</td>\n",
-       "      <td>0.077181</td>\n",
-       "      <td>0.032500</td>\n",
-       "      <td>-0.099495</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>temp_inside</th>\n",
-       "      <td>0.075305</td>\n",
-       "      <td>-0.161991</td>\n",
-       "      <td>0.059725</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.361308</td>\n",
-       "      <td>0.313197</td>\n",
-       "      <td>-0.038269</td>\n",
-       "      <td>0.246120</td>\n",
-       "      <td>0.097156</td>\n",
-       "      <td>0.010340</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>temp_outside</th>\n",
-       "      <td>0.086774</td>\n",
-       "      <td>-0.318758</td>\n",
-       "      <td>0.019327</td>\n",
-       "      <td>0.361308</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.180096</td>\n",
-       "      <td>-0.184763</td>\n",
-       "      <td>0.349988</td>\n",
-       "      <td>-0.162948</td>\n",
-       "      <td>0.156990</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>AC</th>\n",
-       "      <td>0.019030</td>\n",
-       "      <td>0.089472</td>\n",
-       "      <td>-0.024485</td>\n",
-       "      <td>0.313197</td>\n",
-       "      <td>0.180096</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.242197</td>\n",
-       "      <td>0.085928</td>\n",
-       "      <td>0.063170</td>\n",
-       "      <td>0.096943</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rain</th>\n",
-       "      <td>-0.030784</td>\n",
-       "      <td>0.251756</td>\n",
-       "      <td>-0.004173</td>\n",
-       "      <td>-0.038269</td>\n",
-       "      <td>-0.184763</td>\n",
-       "      <td>0.242197</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.111916</td>\n",
-       "      <td>0.277739</td>\n",
-       "      <td>0.052789</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sun</th>\n",
-       "      <td>0.078627</td>\n",
-       "      <td>-0.169173</td>\n",
-       "      <td>0.077181</td>\n",
-       "      <td>0.246120</td>\n",
-       "      <td>0.349988</td>\n",
-       "      <td>0.085928</td>\n",
-       "      <td>-0.111916</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.031083</td>\n",
-       "      <td>0.038120</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>snow</th>\n",
-       "      <td>-0.034428</td>\n",
-       "      <td>0.072308</td>\n",
-       "      <td>0.032500</td>\n",
-       "      <td>0.097156</td>\n",
-       "      <td>-0.162948</td>\n",
-       "      <td>0.063170</td>\n",
-       "      <td>0.277739</td>\n",
-       "      <td>-0.031083</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.035231</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gas_type_SP98</th>\n",
-       "      <td>-0.047877</td>\n",
-       "      <td>-0.020928</td>\n",
-       "      <td>-0.099495</td>\n",
-       "      <td>0.010340</td>\n",
-       "      <td>0.156990</td>\n",
-       "      <td>0.096943</td>\n",
-       "      <td>0.052789</td>\n",
-       "      <td>0.038120</td>\n",
-       "      <td>0.035231</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               distance   consume     speed  temp_inside  temp_outside  \\\n",
-       "distance       1.000000 -0.131379  0.565505     0.075305      0.086774   \n",
-       "consume       -0.131379  1.000000 -0.234537    -0.161991     -0.318758   \n",
-       "speed          0.565505 -0.234537  1.000000     0.059725      0.019327   \n",
-       "temp_inside    0.075305 -0.161991  0.059725     1.000000      0.361308   \n",
-       "temp_outside   0.086774 -0.318758  0.019327     0.361308      1.000000   \n",
-       "AC             0.019030  0.089472 -0.024485     0.313197      0.180096   \n",
-       "rain          -0.030784  0.251756 -0.004173    -0.038269     -0.184763   \n",
-       "sun            0.078627 -0.169173  0.077181     0.246120      0.349988   \n",
-       "snow          -0.034428  0.072308  0.032500     0.097156     -0.162948   \n",
-       "gas_type_SP98 -0.047877 -0.020928 -0.099495     0.010340      0.156990   \n",
-       "\n",
-       "                     AC      rain       sun      snow  gas_type_SP98  \n",
-       "distance       0.019030 -0.030784  0.078627 -0.034428      -0.047877  \n",
-       "consume        0.089472  0.251756 -0.169173  0.072308      -0.020928  \n",
-       "speed         -0.024485 -0.004173  0.077181  0.032500      -0.099495  \n",
-       "temp_inside    0.313197 -0.038269  0.246120  0.097156       0.010340  \n",
-       "temp_outside   0.180096 -0.184763  0.349988 -0.162948       0.156990  \n",
-       "AC             1.000000  0.242197  0.085928  0.063170       0.096943  \n",
-       "rain           0.242197  1.000000 -0.111916  0.277739       0.052789  \n",
-       "sun            0.085928 -0.111916  1.000000 -0.031083       0.038120  \n",
-       "snow           0.063170  0.277739 -0.031083  1.000000       0.035231  \n",
-       "gas_type_SP98  0.096943  0.052789  0.038120  0.035231       1.000000  "
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "corr_matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "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": [
-    "mascara = np.triu(np.ones_like(corr_matrix, dtype=bool))\n",
-    "color_map = sns.diverging_palette(0, 10, as_cmap=True) \n",
-    "\n",
-    "sns.heatmap(corr_matrix, \n",
-    "            mask=mascara,\n",
-    "            cmap=\"viridis\",\n",
-    "            vmax=1,\n",
-    "            vmin=-1,\n",
-    "            square=True, \n",
-    "            linewidth=.5, \n",
-    "            cbar_kws={\"shrink\": .5} \n",
-    ");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### El consumo esta mayormente correlacionado con temp_outside (negativamente), rain (positivamente), speed (negativamente), sun (negativamente), temp_inside (negativamente)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.axisgrid.PairGrid at 0x7f834952eeb0>"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1800x1800 with 110 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.pairplot(consume_data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:xlabel='consume', ylabel='gas_type_SP98'>"
-      ]
-     },
-     "execution_count": 44,
-     "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": [
-    "sns.scatterplot(x=consume_data.consume, y=consume_data.gas_type_SP98)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "ironcon",
-   "language": "python",
-   "name": "ironcon"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/consume.ipynb b/consume.ipynb
new file mode 100644
index 0000000..b76d8c9
--- /dev/null
+++ b/consume.ipynb
@@ -0,0 +1,1281 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import statsmodels.api as sm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "consume_data = pd.read_csv('/home/julian/Cursos/Ironhack/Career/reto_data/consume_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### EDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(376, 11)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "consume_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 376 entries, 0 to 375\n",
+      "Data columns (total 11 columns):\n",
+      " #   Column        Non-Null Count  Dtype  \n",
+      "---  ------        --------------  -----  \n",
+      " 0   consume_id    376 non-null    int64  \n",
+      " 1   distance      376 non-null    float64\n",
+      " 2   consume       376 non-null    float64\n",
+      " 3   speed         376 non-null    int64  \n",
+      " 4   temp_inside   376 non-null    float64\n",
+      " 5   temp_outside  376 non-null    int64  \n",
+      " 6   gas_type      376 non-null    object \n",
+      " 7   AC            376 non-null    int64  \n",
+      " 8   rain          376 non-null    int64  \n",
+      " 9   sun           376 non-null    int64  \n",
+      " 10  snow          376 non-null    int64  \n",
+      "dtypes: float64(3), int64(7), object(1)\n",
+      "memory usage: 32.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "consume_data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "consume_id  distance  consume  speed  temp_inside  temp_outside  gas_type  AC     rain   sun    snow \n",
+       "False       False     False    False  False        False         False     False  False  False  False    376\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "consume_data.isna().value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SP98    219\n",
+       "E10     157\n",
+       "Name: gas_type, dtype: int64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "consume_data.gas_type.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "consume_data = pd.get_dummies(consume_data, columns=['gas_type'], drop_first=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "consume_data.drop(columns=['consume_id'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "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>distance</th>\n",
+       "      <th>consume</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>temp_inside</th>\n",
+       "      <th>temp_outside</th>\n",
+       "      <th>AC</th>\n",
+       "      <th>rain</th>\n",
+       "      <th>sun</th>\n",
+       "      <th>snow</th>\n",
+       "      <th>gas_type_SP98</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>12</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>1</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>13</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>2</th>\n",
+       "      <td>11.2</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>38</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>15</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",
+       "</div>"
+      ],
+      "text/plain": [
+       "   distance  consume  speed  temp_inside  temp_outside  AC  rain  sun  snow  \\\n",
+       "0      28.0      5.0     26         21.5            12   0     0    0     0   \n",
+       "1      12.0      4.2     30         21.5            13   0     0    0     0   \n",
+       "2      11.2      5.5     38         21.5            15   0     0    0     0   \n",
+       "\n",
+       "   gas_type_SP98  \n",
+       "0              0  \n",
+       "1              0  \n",
+       "2              0  "
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "consume_data.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "consume = {'Minimum': consume_data.consume.min(),\n",
+    "          'Maximum': consume_data.consume.max(),\n",
+    "          'Mean': consume_data.consume.mean(),\n",
+    "          'Median': consume_data.consume.median(),\n",
+    "          'Mode': consume_data.consume.mode()}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "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>Minimum</th>\n",
+       "      <th>Maximum</th>\n",
+       "      <th>Mean</th>\n",
+       "      <th>Median</th>\n",
+       "      <th>Mode</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Consume</th>\n",
+       "      <td>3.3</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>4.925266</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Minimum  Maximum      Mean  Median  Mode\n",
+       "Consume      3.3     12.2  4.925266     4.7   4.5"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "consume_descr = pd.DataFrame(consume) \n",
+    "consume_descr = consume_descr.rename(index={0:'Consume'})\n",
+    "consume_descr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='consume', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 41,
+     "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": [
+    "sns.histplot(x='consume', data=consume_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corr_matrix = consume_data.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "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>distance</th>\n",
+       "      <th>consume</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>temp_inside</th>\n",
+       "      <th>temp_outside</th>\n",
+       "      <th>AC</th>\n",
+       "      <th>rain</th>\n",
+       "      <th>sun</th>\n",
+       "      <th>snow</th>\n",
+       "      <th>gas_type_SP98</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>distance</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.131379</td>\n",
+       "      <td>0.565505</td>\n",
+       "      <td>0.075305</td>\n",
+       "      <td>0.086774</td>\n",
+       "      <td>0.019030</td>\n",
+       "      <td>-0.030784</td>\n",
+       "      <td>0.078627</td>\n",
+       "      <td>-0.034428</td>\n",
+       "      <td>-0.047877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>consume</th>\n",
+       "      <td>-0.131379</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.234537</td>\n",
+       "      <td>-0.161991</td>\n",
+       "      <td>-0.318758</td>\n",
+       "      <td>0.089472</td>\n",
+       "      <td>0.251756</td>\n",
+       "      <td>-0.169173</td>\n",
+       "      <td>0.072308</td>\n",
+       "      <td>-0.020928</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>speed</th>\n",
+       "      <td>0.565505</td>\n",
+       "      <td>-0.234537</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.059725</td>\n",
+       "      <td>0.019327</td>\n",
+       "      <td>-0.024485</td>\n",
+       "      <td>-0.004173</td>\n",
+       "      <td>0.077181</td>\n",
+       "      <td>0.032500</td>\n",
+       "      <td>-0.099495</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>temp_inside</th>\n",
+       "      <td>0.075305</td>\n",
+       "      <td>-0.161991</td>\n",
+       "      <td>0.059725</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.361308</td>\n",
+       "      <td>0.313197</td>\n",
+       "      <td>-0.038269</td>\n",
+       "      <td>0.246120</td>\n",
+       "      <td>0.097156</td>\n",
+       "      <td>0.010340</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>temp_outside</th>\n",
+       "      <td>0.086774</td>\n",
+       "      <td>-0.318758</td>\n",
+       "      <td>0.019327</td>\n",
+       "      <td>0.361308</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.180096</td>\n",
+       "      <td>-0.184763</td>\n",
+       "      <td>0.349988</td>\n",
+       "      <td>-0.162948</td>\n",
+       "      <td>0.156990</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AC</th>\n",
+       "      <td>0.019030</td>\n",
+       "      <td>0.089472</td>\n",
+       "      <td>-0.024485</td>\n",
+       "      <td>0.313197</td>\n",
+       "      <td>0.180096</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.242197</td>\n",
+       "      <td>0.085928</td>\n",
+       "      <td>0.063170</td>\n",
+       "      <td>0.096943</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rain</th>\n",
+       "      <td>-0.030784</td>\n",
+       "      <td>0.251756</td>\n",
+       "      <td>-0.004173</td>\n",
+       "      <td>-0.038269</td>\n",
+       "      <td>-0.184763</td>\n",
+       "      <td>0.242197</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.111916</td>\n",
+       "      <td>0.277739</td>\n",
+       "      <td>0.052789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sun</th>\n",
+       "      <td>0.078627</td>\n",
+       "      <td>-0.169173</td>\n",
+       "      <td>0.077181</td>\n",
+       "      <td>0.246120</td>\n",
+       "      <td>0.349988</td>\n",
+       "      <td>0.085928</td>\n",
+       "      <td>-0.111916</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.031083</td>\n",
+       "      <td>0.038120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>snow</th>\n",
+       "      <td>-0.034428</td>\n",
+       "      <td>0.072308</td>\n",
+       "      <td>0.032500</td>\n",
+       "      <td>0.097156</td>\n",
+       "      <td>-0.162948</td>\n",
+       "      <td>0.063170</td>\n",
+       "      <td>0.277739</td>\n",
+       "      <td>-0.031083</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.035231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas_type_SP98</th>\n",
+       "      <td>-0.047877</td>\n",
+       "      <td>-0.020928</td>\n",
+       "      <td>-0.099495</td>\n",
+       "      <td>0.010340</td>\n",
+       "      <td>0.156990</td>\n",
+       "      <td>0.096943</td>\n",
+       "      <td>0.052789</td>\n",
+       "      <td>0.038120</td>\n",
+       "      <td>0.035231</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               distance   consume     speed  temp_inside  temp_outside  \\\n",
+       "distance       1.000000 -0.131379  0.565505     0.075305      0.086774   \n",
+       "consume       -0.131379  1.000000 -0.234537    -0.161991     -0.318758   \n",
+       "speed          0.565505 -0.234537  1.000000     0.059725      0.019327   \n",
+       "temp_inside    0.075305 -0.161991  0.059725     1.000000      0.361308   \n",
+       "temp_outside   0.086774 -0.318758  0.019327     0.361308      1.000000   \n",
+       "AC             0.019030  0.089472 -0.024485     0.313197      0.180096   \n",
+       "rain          -0.030784  0.251756 -0.004173    -0.038269     -0.184763   \n",
+       "sun            0.078627 -0.169173  0.077181     0.246120      0.349988   \n",
+       "snow          -0.034428  0.072308  0.032500     0.097156     -0.162948   \n",
+       "gas_type_SP98 -0.047877 -0.020928 -0.099495     0.010340      0.156990   \n",
+       "\n",
+       "                     AC      rain       sun      snow  gas_type_SP98  \n",
+       "distance       0.019030 -0.030784  0.078627 -0.034428      -0.047877  \n",
+       "consume        0.089472  0.251756 -0.169173  0.072308      -0.020928  \n",
+       "speed         -0.024485 -0.004173  0.077181  0.032500      -0.099495  \n",
+       "temp_inside    0.313197 -0.038269  0.246120  0.097156       0.010340  \n",
+       "temp_outside   0.180096 -0.184763  0.349988 -0.162948       0.156990  \n",
+       "AC             1.000000  0.242197  0.085928  0.063170       0.096943  \n",
+       "rain           0.242197  1.000000 -0.111916  0.277739       0.052789  \n",
+       "sun            0.085928 -0.111916  1.000000 -0.031083       0.038120  \n",
+       "snow           0.063170  0.277739 -0.031083  1.000000       0.035231  \n",
+       "gas_type_SP98  0.096943  0.052789  0.038120  0.035231       1.000000  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "mascara = np.triu(np.ones_like(corr_matrix, dtype=bool))\n",
+    "color_map = sns.diverging_palette(0, 10, as_cmap=True) \n",
+    "\n",
+    "sns.heatmap(corr_matrix, \n",
+    "            mask=mascara,\n",
+    "            cmap=\"viridis\",\n",
+    "            vmax=1,\n",
+    "            vmin=-1,\n",
+    "            square=True, \n",
+    "            linewidth=.5, \n",
+    "            cbar_kws={\"shrink\": .5} \n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### El consumo esta mayormente correlacionado con temp_outside (negativamente), rain (positivamente), speed (negativamente), sun (negativamente), temp_inside (negativamente)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CONSUME VS GAS_TYPE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "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>consume</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas_type_SP98</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4.950955</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.906849</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                consume\n",
+       "gas_type_SP98          \n",
+       "0              4.950955\n",
+       "1              4.906849"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gtype_gr = consume_data.groupby('gas_type_SP98').agg({'consume': 'mean'})\n",
+    "gtype_gr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='consume', ylabel='gas_type_SP98'>"
+      ]
+     },
+     "execution_count": 44,
+     "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": [
+    "sns.scatterplot(x=consume_data.consume, y=consume_data.gas_type_SP98)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CONSUME VS AC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "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>consume</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AC</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4.897391</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5.235484</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     consume\n",
+       "AC          \n",
+       "0   4.897391\n",
+       "1   5.235484"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ac_group = consume_data.groupby('AC').agg({'consume': 'mean'})\n",
+    "ac_group"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CONSUME VS RAIN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "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>consume</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rain</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4.827576</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5.626087</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       consume\n",
+       "rain          \n",
+       "0     4.827576\n",
+       "1     5.626087"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rain_gr = consume_data.groupby('rain').agg({'consume': 'mean'})\n",
+    "rain_gr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### CONSUME COST"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def price(x):\n",
+    "    E10_price = 1.379\n",
+    "    SP98_price = 1.459\n",
+    "    if x == 0:\n",
+    "        return E10_price\n",
+    "    else:\n",
+    "        return SP98_price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "consume_data['price'] = consume_data.gas_type_SP98.apply(price)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "consume_data['cost_per_distance'] =(((consume_data.consume)/100) * (consume_data.distance)) * consume_data.price "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "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>distance</th>\n",
+       "      <th>consume</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>temp_inside</th>\n",
+       "      <th>temp_outside</th>\n",
+       "      <th>AC</th>\n",
+       "      <th>rain</th>\n",
+       "      <th>sun</th>\n",
+       "      <th>snow</th>\n",
+       "      <th>gas_type_SP98</th>\n",
+       "      <th>price</th>\n",
+       "      <th>cost_per_distance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.379</td>\n",
+       "      <td>1.930600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.379</td>\n",
+       "      <td>0.695016</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>11.2</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>38</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.379</td>\n",
+       "      <td>0.849464</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   distance  consume  speed  temp_inside  temp_outside  AC  rain  sun  snow  \\\n",
+       "0      28.0      5.0     26         21.5            12   0     0    0     0   \n",
+       "1      12.0      4.2     30         21.5            13   0     0    0     0   \n",
+       "2      11.2      5.5     38         21.5            15   0     0    0     0   \n",
+       "\n",
+       "   gas_type_SP98  price  cost_per_distance  \n",
+       "0              0  1.379           1.930600  \n",
+       "1              0  1.379           0.695016  \n",
+       "2              0  1.379           0.849464  "
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "consume_data.gro"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "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>cost_per_distance</th>\n",
+       "      <th>distance</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas_type_SP98</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.396125</td>\n",
+       "      <td>21.095541</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.305041</td>\n",
+       "      <td>18.866667</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               cost_per_distance   distance\n",
+       "gas_type_SP98                              \n",
+       "0                       1.396125  21.095541\n",
+       "1                       1.305041  18.866667"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "price_gr = consume_data.groupby('gas_type_SP98').agg({'cost_per_distance': 'mean', 'distance': 'mean'})\n",
+    "price_gr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### REGRESSION ANALYSIS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = consume_data.drop(columns=['consume', 'price', 'cost_per_distance'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = consume_data.consume"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "linear_model = sm.OLS(y,X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = linear_model.fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>         <td>consume</td>     <th>  R-squared (uncentered):</th>      <td>   0.962</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.961</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   1041.</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Thu, 14 Oct 2021</td> <th>  Prob (F-statistic):</th>          <td>3.68e-255</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>14:06:14</td>     <th>  Log-Likelihood:    </th>          <td> -524.89</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   376</td>      <th>  AIC:               </th>          <td>   1068.</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   367</td>      <th>  BIC:               </th>          <td>   1103.</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     9</td>      <th>                     </th>              <td> </td>    \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>distance</th>      <td>    0.0007</td> <td>    0.003</td> <td>    0.241</td> <td> 0.810</td> <td>   -0.005</td> <td>    0.006</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>speed</th>         <td>   -0.0154</td> <td>    0.005</td> <td>   -3.397</td> <td> 0.001</td> <td>   -0.024</td> <td>   -0.006</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>temp_inside</th>   <td>    0.2756</td> <td>    0.010</td> <td>   28.897</td> <td> 0.000</td> <td>    0.257</td> <td>    0.294</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>temp_outside</th>  <td>   -0.0509</td> <td>    0.008</td> <td>   -6.121</td> <td> 0.000</td> <td>   -0.067</td> <td>   -0.035</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>AC</th>            <td>    0.0981</td> <td>    0.197</td> <td>    0.497</td> <td> 0.619</td> <td>   -0.290</td> <td>    0.486</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>rain</th>          <td>    0.6467</td> <td>    0.171</td> <td>    3.791</td> <td> 0.000</td> <td>    0.311</td> <td>    0.982</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>sun</th>           <td>   -0.2943</td> <td>    0.199</td> <td>   -1.481</td> <td> 0.140</td> <td>   -0.685</td> <td>    0.097</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>snow</th>          <td>   -0.6379</td> <td>    0.524</td> <td>   -1.218</td> <td> 0.224</td> <td>   -1.667</td> <td>    0.392</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>gas_type_SP98</th> <td>    0.0371</td> <td>    0.105</td> <td>    0.352</td> <td> 0.725</td> <td>   -0.170</td> <td>    0.244</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>250.941</td> <th>  Durbin-Watson:     </th> <td>   1.924</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>3120.675</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td> 2.663</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td>16.070</td>  <th>  Cond. No.          </th> <td>    573.</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                                 OLS Regression Results                                \n",
+       "=======================================================================================\n",
+       "Dep. Variable:                consume   R-squared (uncentered):                   0.962\n",
+       "Model:                            OLS   Adj. R-squared (uncentered):              0.961\n",
+       "Method:                 Least Squares   F-statistic:                              1041.\n",
+       "Date:                Thu, 14 Oct 2021   Prob (F-statistic):                   3.68e-255\n",
+       "Time:                        14:06:14   Log-Likelihood:                         -524.89\n",
+       "No. Observations:                 376   AIC:                                      1068.\n",
+       "Df Residuals:                     367   BIC:                                      1103.\n",
+       "Df Model:                           9                                                  \n",
+       "Covariance Type:            nonrobust                                                  \n",
+       "=================================================================================\n",
+       "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "---------------------------------------------------------------------------------\n",
+       "distance          0.0007      0.003      0.241      0.810      -0.005       0.006\n",
+       "speed            -0.0154      0.005     -3.397      0.001      -0.024      -0.006\n",
+       "temp_inside       0.2756      0.010     28.897      0.000       0.257       0.294\n",
+       "temp_outside     -0.0509      0.008     -6.121      0.000      -0.067      -0.035\n",
+       "AC                0.0981      0.197      0.497      0.619      -0.290       0.486\n",
+       "rain              0.6467      0.171      3.791      0.000       0.311       0.982\n",
+       "sun              -0.2943      0.199     -1.481      0.140      -0.685       0.097\n",
+       "snow             -0.6379      0.524     -1.218      0.224      -1.667       0.392\n",
+       "gas_type_SP98     0.0371      0.105      0.352      0.725      -0.170       0.244\n",
+       "==============================================================================\n",
+       "Omnibus:                      250.941   Durbin-Watson:                   1.924\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3120.675\n",
+       "Skew:                           2.663   Prob(JB):                         0.00\n",
+       "Kurtosis:                      16.070   Cond. No.                         573.\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
+       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ironcon",
+   "language": "python",
+   "name": "ironcon"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 7b935deca8970136ef84a655f9b1c7896da63bfa Mon Sep 17 00:00:00 2001
From: JulianTrufero <jgtrufero@gmail.com>
Date: Thu, 14 Oct 2021 17:17:30 +0200
Subject: [PATCH 3/6] update

---
 consume.ipynb | 300 +++++++++++++++++++++++++++-----------------------
 1 file changed, 165 insertions(+), 135 deletions(-)

diff --git a/consume.ipynb b/consume.ipynb
index b76d8c9..f6f8ba7 100644
--- a/consume.ipynb
+++ b/consume.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -137,7 +137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -864,7 +864,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -879,7 +879,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -888,7 +888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -897,7 +897,81 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 27,
+   "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>cost_per_distance</th>\n",
+       "      <th>distance</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas_type_SP98</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.396125</td>\n",
+       "      <td>21.095541</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.305041</td>\n",
+       "      <td>18.866667</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               cost_per_distance   distance\n",
+       "gas_type_SP98                              \n",
+       "0                       1.396125  21.095541\n",
+       "1                       1.305041  18.866667"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "price_gr = consume_data.groupby('gas_type_SP98').agg({'cost_per_distance': 'mean', 'distance': 'mean'})\n",
+    "price_gr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### REGRESSION ANALYSIS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -997,101 +1071,45 @@
        "2              0  1.379           0.849464  "
       ]
      },
-     "execution_count": 87,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "consume_data.gro"
+    "consume_data.head(3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
-   "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>cost_per_distance</th>\n",
-       "      <th>distance</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gas_type_SP98</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1.396125</td>\n",
-       "      <td>21.095541</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1.305041</td>\n",
-       "      <td>18.866667</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               cost_per_distance   distance\n",
-       "gas_type_SP98                              \n",
-       "0                       1.396125  21.095541\n",
-       "1                       1.305041  18.866667"
-      ]
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "price_gr = consume_data.groupby('gas_type_SP98').agg({'cost_per_distance': 'mean', 'distance': 'mean'})\n",
-    "price_gr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": 39,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "### REGRESSION ANALYSIS"
+    "X = consume_data.drop(columns=['consume', 'price', 'cost_per_distance'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 40,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/julian/miniconda3/envs/ironcon/lib/python3.8/site-packages/statsmodels/tsa/tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
+      "  x = pd.concat(x[::order], 1)\n"
+     ]
+    }
+   ],
    "source": [
-    "X = consume_data.drop(columns=['consume', 'price', 'cost_per_distance'])"
+    "X = sm.add_constant(X)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1100,7 +1118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1109,7 +1127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1118,7 +1136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -1127,31 +1145,31 @@
        "<table class=\"simpletable\">\n",
        "<caption>OLS Regression Results</caption>\n",
        "<tr>\n",
-       "  <th>Dep. Variable:</th>         <td>consume</td>     <th>  R-squared (uncentered):</th>      <td>   0.962</td> \n",
+       "  <th>Dep. Variable:</th>         <td>consume</td>     <th>  R-squared:         </th> <td>   0.208</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.961</td> \n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.188</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   1041.</td> \n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   10.68</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Thu, 14 Oct 2021</td> <th>  Prob (F-statistic):</th>          <td>3.68e-255</td>\n",
+       "  <th>Date:</th>             <td>Thu, 14 Oct 2021</td> <th>  Prob (F-statistic):</th> <td>9.76e-15</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>14:06:14</td>     <th>  Log-Likelihood:    </th>          <td> -524.89</td> \n",
+       "  <th>Time:</th>                 <td>15:49:30</td>     <th>  Log-Likelihood:    </th> <td> -504.20</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>No. Observations:</th>      <td>   376</td>      <th>  AIC:               </th>          <td>   1068.</td> \n",
+       "  <th>No. Observations:</th>      <td>   376</td>      <th>  AIC:               </th> <td>   1028.</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Df Residuals:</th>          <td>   367</td>      <th>  BIC:               </th>          <td>   1103.</td> \n",
+       "  <th>Df Residuals:</th>          <td>   366</td>      <th>  BIC:               </th> <td>   1068.</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Df Model:</th>              <td>     9</td>      <th>                     </th>              <td> </td>    \n",
+       "  <th>Df Model:</th>              <td>     9</td>      <th>                     </th>     <td> </td>   \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
@@ -1159,88 +1177,93 @@
        "        <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>distance</th>      <td>    0.0007</td> <td>    0.003</td> <td>    0.241</td> <td> 0.810</td> <td>   -0.005</td> <td>    0.006</td>\n",
+       "  <th>const</th>         <td>    7.7409</td> <td>    1.186</td> <td>    6.526</td> <td> 0.000</td> <td>    5.408</td> <td>   10.073</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>speed</th>         <td>   -0.0154</td> <td>    0.005</td> <td>   -3.397</td> <td> 0.001</td> <td>   -0.024</td> <td>   -0.006</td>\n",
+       "  <th>distance</th>      <td>    0.0018</td> <td>    0.003</td> <td>    0.683</td> <td> 0.495</td> <td>   -0.003</td> <td>    0.007</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>temp_inside</th>   <td>    0.2756</td> <td>    0.010</td> <td>   28.897</td> <td> 0.000</td> <td>    0.257</td> <td>    0.294</td>\n",
+       "  <th>speed</th>         <td>   -0.0184</td> <td>    0.004</td> <td>   -4.259</td> <td> 0.000</td> <td>   -0.027</td> <td>   -0.010</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>temp_outside</th>  <td>   -0.0509</td> <td>    0.008</td> <td>   -6.121</td> <td> 0.000</td> <td>   -0.067</td> <td>   -0.035</td>\n",
+       "  <th>temp_inside</th>   <td>   -0.0776</td> <td>    0.055</td> <td>   -1.414</td> <td> 0.158</td> <td>   -0.185</td> <td>    0.030</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>AC</th>            <td>    0.0981</td> <td>    0.197</td> <td>    0.497</td> <td> 0.619</td> <td>   -0.290</td> <td>    0.486</td>\n",
+       "  <th>temp_outside</th>  <td>   -0.0394</td> <td>    0.008</td> <td>   -4.873</td> <td> 0.000</td> <td>   -0.055</td> <td>   -0.023</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>rain</th>          <td>    0.6467</td> <td>    0.171</td> <td>    3.791</td> <td> 0.000</td> <td>    0.311</td> <td>    0.982</td>\n",
+       "  <th>AC</th>            <td>    0.4471</td> <td>    0.195</td> <td>    2.298</td> <td> 0.022</td> <td>    0.065</td> <td>    0.830</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>sun</th>           <td>   -0.2943</td> <td>    0.199</td> <td>   -1.481</td> <td> 0.140</td> <td>   -0.685</td> <td>    0.097</td>\n",
+       "  <th>rain</th>          <td>    0.5432</td> <td>    0.162</td> <td>    3.344</td> <td> 0.001</td> <td>    0.224</td> <td>    0.863</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>snow</th>          <td>   -0.6379</td> <td>    0.524</td> <td>   -1.218</td> <td> 0.224</td> <td>   -1.667</td> <td>    0.392</td>\n",
+       "  <th>sun</th>           <td>   -0.1210</td> <td>    0.190</td> <td>   -0.636</td> <td> 0.525</td> <td>   -0.495</td> <td>    0.253</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>gas_type_SP98</th> <td>    0.0371</td> <td>    0.105</td> <td>    0.352</td> <td> 0.725</td> <td>   -0.170</td> <td>    0.244</td>\n",
+       "  <th>snow</th>          <td>   -0.1020</td> <td>    0.503</td> <td>   -0.203</td> <td> 0.839</td> <td>   -1.091</td> <td>    0.887</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>gas_type_SP98</th> <td>   -0.0408</td> <td>    0.101</td> <td>   -0.406</td> <td> 0.685</td> <td>   -0.239</td> <td>    0.157</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
-       "  <th>Omnibus:</th>       <td>250.941</td> <th>  Durbin-Watson:     </th> <td>   1.924</td>\n",
+       "  <th>Omnibus:</th>       <td>248.482</td> <th>  Durbin-Watson:     </th> <td>   2.096</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>3120.675</td>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>2919.818</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Skew:</th>          <td> 2.663</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
+       "  <th>Skew:</th>          <td> 2.651</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Kurtosis:</th>      <td>16.070</td>  <th>  Cond. No.          </th> <td>    573.</td>\n",
+       "  <th>Kurtosis:</th>      <td>15.580</td>  <th>  Cond. No.          </th> <td>1.37e+03</td>\n",
        "</tr>\n",
-       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.37e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
       ],
       "text/plain": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
        "\"\"\"\n",
-       "                                 OLS Regression Results                                \n",
-       "=======================================================================================\n",
-       "Dep. Variable:                consume   R-squared (uncentered):                   0.962\n",
-       "Model:                            OLS   Adj. R-squared (uncentered):              0.961\n",
-       "Method:                 Least Squares   F-statistic:                              1041.\n",
-       "Date:                Thu, 14 Oct 2021   Prob (F-statistic):                   3.68e-255\n",
-       "Time:                        14:06:14   Log-Likelihood:                         -524.89\n",
-       "No. Observations:                 376   AIC:                                      1068.\n",
-       "Df Residuals:                     367   BIC:                                      1103.\n",
-       "Df Model:                           9                                                  \n",
-       "Covariance Type:            nonrobust                                                  \n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                consume   R-squared:                       0.208\n",
+       "Model:                            OLS   Adj. R-squared:                  0.188\n",
+       "Method:                 Least Squares   F-statistic:                     10.68\n",
+       "Date:                Thu, 14 Oct 2021   Prob (F-statistic):           9.76e-15\n",
+       "Time:                        15:49:30   Log-Likelihood:                -504.20\n",
+       "No. Observations:                 376   AIC:                             1028.\n",
+       "Df Residuals:                     366   BIC:                             1068.\n",
+       "Df Model:                           9                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
        "=================================================================================\n",
        "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
        "---------------------------------------------------------------------------------\n",
-       "distance          0.0007      0.003      0.241      0.810      -0.005       0.006\n",
-       "speed            -0.0154      0.005     -3.397      0.001      -0.024      -0.006\n",
-       "temp_inside       0.2756      0.010     28.897      0.000       0.257       0.294\n",
-       "temp_outside     -0.0509      0.008     -6.121      0.000      -0.067      -0.035\n",
-       "AC                0.0981      0.197      0.497      0.619      -0.290       0.486\n",
-       "rain              0.6467      0.171      3.791      0.000       0.311       0.982\n",
-       "sun              -0.2943      0.199     -1.481      0.140      -0.685       0.097\n",
-       "snow             -0.6379      0.524     -1.218      0.224      -1.667       0.392\n",
-       "gas_type_SP98     0.0371      0.105      0.352      0.725      -0.170       0.244\n",
+       "const             7.7409      1.186      6.526      0.000       5.408      10.073\n",
+       "distance          0.0018      0.003      0.683      0.495      -0.003       0.007\n",
+       "speed            -0.0184      0.004     -4.259      0.000      -0.027      -0.010\n",
+       "temp_inside      -0.0776      0.055     -1.414      0.158      -0.185       0.030\n",
+       "temp_outside     -0.0394      0.008     -4.873      0.000      -0.055      -0.023\n",
+       "AC                0.4471      0.195      2.298      0.022       0.065       0.830\n",
+       "rain              0.5432      0.162      3.344      0.001       0.224       0.863\n",
+       "sun              -0.1210      0.190     -0.636      0.525      -0.495       0.253\n",
+       "snow             -0.1020      0.503     -0.203      0.839      -1.091       0.887\n",
+       "gas_type_SP98    -0.0408      0.101     -0.406      0.685      -0.239       0.157\n",
        "==============================================================================\n",
-       "Omnibus:                      250.941   Durbin-Watson:                   1.924\n",
-       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3120.675\n",
-       "Skew:                           2.663   Prob(JB):                         0.00\n",
-       "Kurtosis:                      16.070   Cond. No.                         573.\n",
+       "Omnibus:                      248.482   Durbin-Watson:                   2.096\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2919.818\n",
+       "Skew:                           2.651   Prob(JB):                         0.00\n",
+       "Kurtosis:                      15.580   Cond. No.                     1.37e+03\n",
        "==============================================================================\n",
        "\n",
        "Notes:\n",
-       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
-       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "[2] The condition number is large, 1.37e+03. This might indicate that there are\n",
+       "strong multicollinearity or other numerical problems.\n",
        "\"\"\""
       ]
      },
-     "execution_count": 102,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1249,6 +1272,13 @@
     "results.summary()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Del análisis de regresión podemos observar que las variables que resultan significativas para estimar la media de consumo son rain, AC, temp_outside y speed. Lo cual coincide con el resultado de la matriz de correlaciones, son las variables que aparecen mas correlacionadas con la variable consumo"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

From e4033dab27137bdd4517bfb7cedcafce23b902fe Mon Sep 17 00:00:00 2001
From: JulianTrufero <jgtrufero@gmail.com>
Date: Sat, 16 Oct 2021 16:58:57 +0200
Subject: [PATCH 4/6] update

---
 consume.ipynb | 841 +++++++++++++++++++++++++++++++++++++-------------
 1 file changed, 618 insertions(+), 223 deletions(-)

diff --git a/consume.ipynb b/consume.ipynb
index f6f8ba7..1d09b41 100644
--- a/consume.ipynb
+++ b/consume.ipynb
@@ -2,19 +2,20 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
     "import seaborn as sns\n",
-    "import statsmodels.api as sm"
+    "from sklearn.preprocessing import StandardScaler\n",
+    "import statsmodels.formula.api as smf"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -39,7 +40,7 @@
        "(376, 11)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -50,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -84,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -95,7 +96,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -106,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -117,7 +118,7 @@
        "Name: gas_type, dtype: int64"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -126,9 +127,17 @@
     "consume_data.gas_type.value_counts()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- We can see that all variabels are numerical, except for gas_type, which we'll transform into a dummy variable. On the other hand, we don'thave null values.\n",
+    "- We'll also drop consume_id column as is an index column."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -137,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -146,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -238,7 +247,7 @@
        "2              0  "
       ]
      },
-     "execution_count": 34,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -249,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -262,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -311,7 +320,7 @@
        "Consume      3.3     12.2  4.925266     4.7   4.5"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -324,7 +333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -333,7 +342,7 @@
        "<AxesSubplot:xlabel='consume', ylabel='Count'>"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -356,7 +365,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='consume'>"
+      ]
+     },
+     "execution_count": 11,
+     "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": [
+    "sns.boxplot(x=consume_data.consume)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- We can observe that we have some outliers in the consume column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -365,7 +413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -562,7 +610,7 @@
        "gas_type_SP98  0.096943  0.052789  0.038120  0.035231       1.000000  "
       ]
      },
-     "execution_count": 37,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -573,7 +621,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -608,99 +656,62 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### El consumo esta mayormente correlacionado con temp_outside (negativamente), rain (positivamente), speed (negativamente), sun (negativamente), temp_inside (negativamente)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### CONSUME VS GAS_TYPE"
+    "- We can see through the correlations heatmap that `consume` is mostly correlated with `temp_outside` (negatively), `rain` (positively), `speed` (negatively), `sun` (negatively), and `temp_inside` (negatively).\n",
+    "- We can also observe that `speed` and `distance` are correlated, and `temp_inside` wit `AC`. Both are not surprise."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": false
+   },
    "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>consume</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gas_type_SP98</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>4.950955</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.906849</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "                consume\n",
-       "gas_type_SP98          \n",
-       "0              4.950955\n",
-       "1              4.906849"
+       "<AxesSubplot:xlabel='temp_outside', ylabel='consume'>"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 9,
      "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": [
-    "gtype_gr = consume_data.groupby('gas_type_SP98').agg({'consume': 'mean'})\n",
-    "gtype_gr"
+    "sns.scatterplot(x=consume_data.temp_outside, y=consume_data.consume, hue=consume_data.gas_type_SP98)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "scrolled": false
-   },
+   "execution_count": 53,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<AxesSubplot:xlabel='consume', ylabel='gas_type_SP98'>"
+       "<AxesSubplot:xlabel='temp_inside', ylabel='consume'>"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -712,19 +723,26 @@
     }
    ],
    "source": [
-    "sns.scatterplot(x=consume_data.consume, y=consume_data.gas_type_SP98)"
+    "sns.scatterplot(x=consume_data.temp_inside, y=consume_data.consume, hue=consume_data.gas_type_SP98)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### CONSUME VS AC"
+    "#### CONSUME VS GAS_TYPE"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -751,50 +769,50 @@
        "      <th>consume</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>AC</th>\n",
+       "      <th>gas_type_SP98</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>4.897391</td>\n",
+       "      <td>4.950955</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>5.235484</td>\n",
+       "      <td>4.906849</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "     consume\n",
-       "AC          \n",
-       "0   4.897391\n",
-       "1   5.235484"
+       "                consume\n",
+       "gas_type_SP98          \n",
+       "0              4.950955\n",
+       "1              4.906849"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "ac_group = consume_data.groupby('AC').agg({'consume': 'mean'})\n",
-    "ac_group"
+    "gtype_gr = consume_data.groupby('gas_type_SP98').agg({'consume': 'mean'})\n",
+    "gtype_gr"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### CONSUME VS RAIN"
+    "#### CONSUME VS AC"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -821,83 +839,72 @@
        "      <th>consume</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>rain</th>\n",
+       "      <th>AC</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>4.827576</td>\n",
+       "      <td>4.897391</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>5.626087</td>\n",
+       "      <td>5.235484</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "       consume\n",
-       "rain          \n",
-       "0     4.827576\n",
-       "1     5.626087"
+       "     consume\n",
+       "AC          \n",
+       "0   4.897391\n",
+       "1   5.235484"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "rain_gr = consume_data.groupby('rain').agg({'consume': 'mean'})\n",
-    "rain_gr"
+    "ac_group = consume_data.groupby('AC').agg({'consume': 'mean'})\n",
+    "ac_group"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### CONSUME COST"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def price(x):\n",
-    "    E10_price = 1.379\n",
-    "    SP98_price = 1.459\n",
-    "    if x == 0:\n",
-    "        return E10_price\n",
-    "    else:\n",
-    "        return SP98_price"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "consume_data['price'] = consume_data.gas_type_SP98.apply(price)"
+    "#### CONSUME VS RAIN"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 57,
    "metadata": {},
-   "outputs": [],
+   "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": [
-    "consume_data['cost_per_distance'] =(((consume_data.consume)/100) * (consume_data.distance)) * consume_data.price "
+    "sns.scatterplot(y=consume_data.rain, x=consume_data.consume, hue=consume_data.gas_type_SP98);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -921,45 +928,41 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>cost_per_distance</th>\n",
-       "      <th>distance</th>\n",
+       "      <th>consume</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>gas_type_SP98</th>\n",
-       "      <th></th>\n",
+       "      <th>rain</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1.396125</td>\n",
-       "      <td>21.095541</td>\n",
+       "      <td>4.827576</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1.305041</td>\n",
-       "      <td>18.866667</td>\n",
+       "      <td>5.626087</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "               cost_per_distance   distance\n",
-       "gas_type_SP98                              \n",
-       "0                       1.396125  21.095541\n",
-       "1                       1.305041  18.866667"
+       "       consume\n",
+       "rain          \n",
+       "0     4.827576\n",
+       "1     5.626087"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "price_gr = consume_data.groupby('gas_type_SP98').agg({'cost_per_distance': 'mean', 'distance': 'mean'})\n",
-    "price_gr"
+    "rain_gr = consume_data.groupby('rain').agg({'consume': 'mean'})\n",
+    "rain_gr"
    ]
   },
   {
@@ -971,7 +974,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1005,8 +1008,6 @@
        "      <th>sun</th>\n",
        "      <th>snow</th>\n",
        "      <th>gas_type_SP98</th>\n",
-       "      <th>price</th>\n",
-       "      <th>cost_per_distance</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1022,8 +1023,6 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>1.379</td>\n",
-       "      <td>1.930600</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -1037,8 +1036,6 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>1.379</td>\n",
-       "      <td>0.695016</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -1052,8 +1049,6 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>1.379</td>\n",
-       "      <td>0.849464</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1065,13 +1060,13 @@
        "1      12.0      4.2     30         21.5            13   0     0    0     0   \n",
        "2      11.2      5.5     38         21.5            15   0     0    0     0   \n",
        "\n",
-       "   gas_type_SP98  price  cost_per_distance  \n",
-       "0              0  1.379           1.930600  \n",
-       "1              0  1.379           0.695016  \n",
-       "2              0  1.379           0.849464  "
+       "   gas_type_SP98  \n",
+       "0              0  \n",
+       "1              0  \n",
+       "2              0  "
       ]
      },
-     "execution_count": 28,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1086,58 +1081,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "X = consume_data.drop(columns=['consume', 'price', 'cost_per_distance'])"
+    "linear_model = smf.ols(\"consume ~ distance + speed + temp_inside + temp_outside + AC + rain + sun + snow + gas_type_SP98\", data=consume_data).fit()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 40,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/julian/miniconda3/envs/ironcon/lib/python3.8/site-packages/statsmodels/tsa/tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
-      "  x = pd.concat(x[::order], 1)\n"
-     ]
-    }
-   ],
-   "source": [
-    "X = sm.add_constant(X)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y = consume_data.consume"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "linear_model = sm.OLS(y,X)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = linear_model.fit()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1154,10 +1104,10 @@
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   10.68</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Thu, 14 Oct 2021</td> <th>  Prob (F-statistic):</th> <td>9.76e-15</td>\n",
+       "  <th>Date:</th>             <td>Sat, 16 Oct 2021</td> <th>  Prob (F-statistic):</th> <td>9.76e-15</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>15:49:30</td>     <th>  Log-Likelihood:    </th> <td> -504.20</td>\n",
+       "  <th>Time:</th>                 <td>12:49:05</td>     <th>  Log-Likelihood:    </th> <td> -504.20</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>   376</td>      <th>  AIC:               </th> <td>   1028.</td>\n",
@@ -1177,7 +1127,7 @@
        "        <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>const</th>         <td>    7.7409</td> <td>    1.186</td> <td>    6.526</td> <td> 0.000</td> <td>    5.408</td> <td>   10.073</td>\n",
+       "  <th>Intercept</th>     <td>    7.7409</td> <td>    1.186</td> <td>    6.526</td> <td> 0.000</td> <td>    5.408</td> <td>   10.073</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>distance</th>      <td>    0.0018</td> <td>    0.003</td> <td>    0.683</td> <td> 0.495</td> <td>   -0.003</td> <td>    0.007</td>\n",
@@ -1230,8 +1180,8 @@
        "Dep. Variable:                consume   R-squared:                       0.208\n",
        "Model:                            OLS   Adj. R-squared:                  0.188\n",
        "Method:                 Least Squares   F-statistic:                     10.68\n",
-       "Date:                Thu, 14 Oct 2021   Prob (F-statistic):           9.76e-15\n",
-       "Time:                        15:49:30   Log-Likelihood:                -504.20\n",
+       "Date:                Sat, 16 Oct 2021   Prob (F-statistic):           9.76e-15\n",
+       "Time:                        12:49:05   Log-Likelihood:                -504.20\n",
        "No. Observations:                 376   AIC:                             1028.\n",
        "Df Residuals:                     366   BIC:                             1068.\n",
        "Df Model:                           9                                         \n",
@@ -1239,7 +1189,7 @@
        "=================================================================================\n",
        "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
        "---------------------------------------------------------------------------------\n",
-       "const             7.7409      1.186      6.526      0.000       5.408      10.073\n",
+       "Intercept         7.7409      1.186      6.526      0.000       5.408      10.073\n",
        "distance          0.0018      0.003      0.683      0.495      -0.003       0.007\n",
        "speed            -0.0184      0.004     -4.259      0.000      -0.027      -0.010\n",
        "temp_inside      -0.0776      0.055     -1.414      0.158      -0.185       0.030\n",
@@ -1263,15 +1213,353 @@
        "\"\"\""
       ]
      },
-     "execution_count": 44,
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linear_model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "consume_data['log_consume'] = np.log(consume_data.consume)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "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": [
+    "sns.histplot(consume_data.log_consume);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "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>distance</th>\n",
+       "      <th>consume</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>temp_inside</th>\n",
+       "      <th>temp_outside</th>\n",
+       "      <th>AC</th>\n",
+       "      <th>rain</th>\n",
+       "      <th>sun</th>\n",
+       "      <th>snow</th>\n",
+       "      <th>gas_type_SP98</th>\n",
+       "      <th>log_consume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.357285</td>\n",
+       "      <td>0.071907</td>\n",
+       "      <td>-1.165108</td>\n",
+       "      <td>-0.425643</td>\n",
+       "      <td>0.093456</td>\n",
+       "      <td>-0.299758</td>\n",
+       "      <td>-0.373355</td>\n",
+       "      <td>-0.299758</td>\n",
+       "      <td>-0.103695</td>\n",
+       "      <td>-1.181061</td>\n",
+       "      <td>0.182583</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.339631</td>\n",
+       "      <td>-0.697827</td>\n",
+       "      <td>-0.873055</td>\n",
+       "      <td>-0.425643</td>\n",
+       "      <td>0.235147</td>\n",
+       "      <td>-0.299758</td>\n",
+       "      <td>-0.373355</td>\n",
+       "      <td>-0.299758</td>\n",
+       "      <td>-0.103695</td>\n",
+       "      <td>-1.181061</td>\n",
+       "      <td>-0.794181</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.374477</td>\n",
+       "      <td>0.552990</td>\n",
+       "      <td>-0.288947</td>\n",
+       "      <td>-0.425643</td>\n",
+       "      <td>0.518530</td>\n",
+       "      <td>-0.299758</td>\n",
+       "      <td>-0.373355</td>\n",
+       "      <td>-0.299758</td>\n",
+       "      <td>-0.103695</td>\n",
+       "      <td>-1.181061</td>\n",
+       "      <td>0.716531</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.300429</td>\n",
+       "      <td>-0.986478</td>\n",
+       "      <td>-0.434974</td>\n",
+       "      <td>-0.425643</td>\n",
+       "      <td>0.376839</td>\n",
+       "      <td>-0.299758</td>\n",
+       "      <td>-0.373355</td>\n",
+       "      <td>-0.299758</td>\n",
+       "      <td>-0.103695</td>\n",
+       "      <td>-1.181061</td>\n",
+       "      <td>-1.209349</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.056509</td>\n",
+       "      <td>-0.409177</td>\n",
+       "      <td>0.295161</td>\n",
+       "      <td>-0.425643</td>\n",
+       "      <td>0.518530</td>\n",
+       "      <td>-0.299758</td>\n",
+       "      <td>-0.373355</td>\n",
+       "      <td>-0.299758</td>\n",
+       "      <td>-0.103695</td>\n",
+       "      <td>-1.181061</td>\n",
+       "      <td>-0.407669</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   distance   consume     speed  temp_inside  temp_outside        AC  \\\n",
+       "0  0.357285  0.071907 -1.165108    -0.425643      0.093456 -0.299758   \n",
+       "1 -0.339631 -0.697827 -0.873055    -0.425643      0.235147 -0.299758   \n",
+       "2 -0.374477  0.552990 -0.288947    -0.425643      0.518530 -0.299758   \n",
+       "3 -0.300429 -0.986478 -0.434974    -0.425643      0.376839 -0.299758   \n",
+       "4 -0.056509 -0.409177  0.295161    -0.425643      0.518530 -0.299758   \n",
+       "\n",
+       "       rain       sun      snow  gas_type_SP98  log_consume  \n",
+       "0 -0.373355 -0.299758 -0.103695      -1.181061     0.182583  \n",
+       "1 -0.373355 -0.299758 -0.103695      -1.181061    -0.794181  \n",
+       "2 -0.373355 -0.299758 -0.103695      -1.181061     0.716531  \n",
+       "3 -0.373355 -0.299758 -0.103695      -1.181061    -1.209349  \n",
+       "4 -0.373355 -0.299758 -0.103695      -1.181061    -0.407669  "
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "consume_data_scaled=pd.DataFrame(scaler.fit_transform(consume_data), columns=consume_data.columns)\n",
+    "consume_data_scaled.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "linear_model_2 = smf.ols(\"log_consume ~ speed  + temp_outside + AC + rain + sun + snow + gas_type_SP98\", data=consume_data).fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>       <td>log_consume</td>   <th>  R-squared:         </th> <td>   0.229</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.214</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   15.62</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Sat, 16 Oct 2021</td> <th>  Prob (F-statistic):</th> <td>6.11e-18</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>16:25:35</td>     <th>  Log-Likelihood:    </th> <td>  163.29</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   376</td>      <th>  AIC:               </th> <td>  -310.6</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   368</td>      <th>  BIC:               </th> <td>  -279.1</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     7</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Intercept</th>     <td>    1.7727</td> <td>    0.032</td> <td>   56.115</td> <td> 0.000</td> <td>    1.711</td> <td>    1.835</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>speed</th>         <td>   -0.0026</td> <td>    0.001</td> <td>   -4.271</td> <td> 0.000</td> <td>   -0.004</td> <td>   -0.001</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>temp_outside</th>  <td>   -0.0085</td> <td>    0.001</td> <td>   -6.511</td> <td> 0.000</td> <td>   -0.011</td> <td>   -0.006</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>AC</th>            <td>    0.0664</td> <td>    0.032</td> <td>    2.101</td> <td> 0.036</td> <td>    0.004</td> <td>    0.128</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>rain</th>          <td>    0.0958</td> <td>    0.027</td> <td>    3.500</td> <td> 0.001</td> <td>    0.042</td> <td>    0.150</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>sun</th>           <td>   -0.0320</td> <td>    0.032</td> <td>   -1.005</td> <td> 0.316</td> <td>   -0.095</td> <td>    0.031</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>snow</th>          <td>   -0.0436</td> <td>    0.084</td> <td>   -0.521</td> <td> 0.602</td> <td>   -0.208</td> <td>    0.121</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>gas_type_SP98</th> <td>   -0.0095</td> <td>    0.017</td> <td>   -0.563</td> <td> 0.574</td> <td>   -0.043</td> <td>    0.024</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>111.195</td> <th>  Durbin-Watson:     </th> <td>   2.112</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 366.740</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td> 1.317</td>  <th>  Prob(JB):          </th> <td>2.31e-80</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 7.059</td>  <th>  Cond. No.          </th> <td>    468.</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:            log_consume   R-squared:                       0.229\n",
+       "Model:                            OLS   Adj. R-squared:                  0.214\n",
+       "Method:                 Least Squares   F-statistic:                     15.62\n",
+       "Date:                Sat, 16 Oct 2021   Prob (F-statistic):           6.11e-18\n",
+       "Time:                        16:25:35   Log-Likelihood:                 163.29\n",
+       "No. Observations:                 376   AIC:                            -310.6\n",
+       "Df Residuals:                     368   BIC:                            -279.1\n",
+       "Df Model:                           7                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "=================================================================================\n",
+       "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "---------------------------------------------------------------------------------\n",
+       "Intercept         1.7727      0.032     56.115      0.000       1.711       1.835\n",
+       "speed            -0.0026      0.001     -4.271      0.000      -0.004      -0.001\n",
+       "temp_outside     -0.0085      0.001     -6.511      0.000      -0.011      -0.006\n",
+       "AC                0.0664      0.032      2.101      0.036       0.004       0.128\n",
+       "rain              0.0958      0.027      3.500      0.001       0.042       0.150\n",
+       "sun              -0.0320      0.032     -1.005      0.316      -0.095       0.031\n",
+       "snow             -0.0436      0.084     -0.521      0.602      -0.208       0.121\n",
+       "gas_type_SP98    -0.0095      0.017     -0.563      0.574      -0.043       0.024\n",
+       "==============================================================================\n",
+       "Omnibus:                      111.195   Durbin-Watson:                   2.112\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              366.740\n",
+       "Skew:                           1.317   Prob(JB):                     2.31e-80\n",
+       "Kurtosis:                       7.059   Cond. No.                         468.\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "results.summary()"
+    "linear_model_2.summary()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1279,6 +1567,113 @@
     "#### Del análisis de regresión podemos observar que las variables que resultan significativas para estimar la media de consumo son rain, AC, temp_outside y speed. Lo cual coincide con el resultado de la matriz de correlaciones, son las variables que aparecen mas correlacionadas con la variable consumo"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### CONSUME COST"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def price(x):\n",
+    "    E10_price = 1.379\n",
+    "    SP98_price = 1.459\n",
+    "    if x == 0:\n",
+    "        return E10_price\n",
+    "    else:\n",
+    "        return SP98_price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "consume_data['price'] = consume_data.gas_type_SP98.apply(price)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "consume_data['cost_per_distance'] =(((consume_data.consume)/100) * (consume_data.distance)) * consume_data.price "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "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>cost_per_distance</th>\n",
+       "      <th>distance</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas_type_SP98</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.396125</td>\n",
+       "      <td>21.095541</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.305041</td>\n",
+       "      <td>18.866667</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               cost_per_distance   distance\n",
+       "gas_type_SP98                              \n",
+       "0                       1.396125  21.095541\n",
+       "1                       1.305041  18.866667"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "price_gr = consume_data.groupby('gas_type_SP98').agg({'cost_per_distance': 'mean', 'distance': 'mean'})\n",
+    "price_gr"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

From 34bae0354c8879e63519b0841e44d8381369ce85 Mon Sep 17 00:00:00 2001
From: JulianTrufero <jgtrufero@gmail.com>
Date: Sun, 17 Oct 2021 14:06:37 +0200
Subject: [PATCH 5/6] update

---
 consume.ipynb | 160 +++++++++++++++++++-------------------------------
 1 file changed, 60 insertions(+), 100 deletions(-)

diff --git a/consume.ipynb b/consume.ipynb
index 1d09b41..82d10ce 100644
--- a/consume.ipynb
+++ b/consume.ipynb
@@ -1,11 +1,26 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## COBIFY - Best Fuel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Let's import the libraries we'll use and the csv file with the data."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
+    "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
     "import numpy as np\n",
     "import seaborn as sns\n",
@@ -15,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,27 +41,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### EDA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(376, 11)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "consume_data.shape"
+    "### Exploratory Data Analysis"
    ]
   },
   {
@@ -131,13 +126,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "- We can see that all variabels are numerical, except for gas_type, which we'll transform into a dummy variable. On the other hand, we don'thave null values.\n",
-    "- We'll also drop consume_id column as is an index column."
+    "- We can see that all variabels are numerical, except for gas_type, which we'll transform into a dummy variable. On the other hand, we don't have null values.\n",
+    "- We'll also drop consume_id column as is an index column.\n",
+    "- 58% of cars use SP98 gas."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -146,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -155,8 +151,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
@@ -247,7 +245,7 @@
        "2              0  "
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -258,20 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "consume = {'Minimum': consume_data.consume.min(),\n",
-    "          'Maximum': consume_data.consume.max(),\n",
-    "          'Mean': consume_data.consume.mean(),\n",
-    "          'Median': consume_data.consume.median(),\n",
-    "          'Mode': consume_data.consume.mode()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -320,12 +305,17 @@
        "Consume      3.3     12.2  4.925266     4.7   4.5"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "consume = {'Minimum': consume_data.consume.min(),\n",
+    "          'Maximum': consume_data.consume.max(),\n",
+    "          'Mean': consume_data.consume.mean(),\n",
+    "          'Median': consume_data.consume.median(),\n",
+    "          'Mode': consume_data.consume.mode()}\n",
     "consume_descr = pd.DataFrame(consume) \n",
     "consume_descr = consume_descr.rename(index={0:'Consume'})\n",
     "consume_descr"
@@ -399,7 +389,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "- We can observe that we have some outliers in the consume column"
+    "- From the histogram and boxplot, we can observe that consume variable has some outliers, which we'll have to take into account when doing the regression analysis"
    ]
   },
   {
@@ -657,26 +647,17 @@
    "metadata": {},
    "source": [
     "- We can see through the correlations heatmap that `consume` is mostly correlated with `temp_outside` (negatively), `rain` (positively), `speed` (negatively), `sun` (negatively), and `temp_inside` (negatively).\n",
-    "- We can also observe that `speed` and `distance` are correlated, and `temp_inside` wit `AC`. Both are not surprise."
+    "- We can also observe that `speed` and `distance` are correlated, and `temp_inside` wit `AC`. Both are not surprise.\n",
+    "- Next we'll plot some of these variables against the consume."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:xlabel='temp_outside', ylabel='consume'>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
     {
      "data": {
       "image/png": 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\n",
@@ -691,29 +672,28 @@
     }
    ],
    "source": [
-    "sns.scatterplot(x=consume_data.temp_outside, y=consume_data.consume, hue=consume_data.gas_type_SP98)"
+    "sns.scatterplot(x=consume_data.temp_outside, y=consume_data.consume, hue=consume_data.gas_type_SP98);"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 53,
+   "cell_type": "markdown",
    "metadata": {},
+   "source": [
+    "- We can see how lower outside temperatures accounts for higher consumption levels. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "<AxesSubplot:xlabel='temp_inside', ylabel='consume'>"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1080x360 with 2 Axes>"
       ]
      },
      "metadata": {
@@ -723,15 +703,17 @@
     }
    ],
    "source": [
-    "sns.scatterplot(x=consume_data.temp_inside, y=consume_data.consume, hue=consume_data.gas_type_SP98)"
+    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\n",
+    "sns.scatterplot(data=consume_data, x=\"temp_inside\", y=\"consume\", hue=\"gas_type_SP98\", ax=axs[0])\n",
+    "sns.scatterplot(data=consume_data, x=\"temp_inside\", y=\"consume\", hue=\"AC\", ax=axs[1]);"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "- We can see that for lower inside temperature values, there are higher consumption values. On the other hand, we can see that most of the cars had the AC off, but those with the AC on, accounts for higher consumption values"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -880,28 +862,6 @@
     "#### CONSUME VS RAIN"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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ybNkyXn75ZZKTk/fP//vf/86SJUvIzMzk5z//OXv27OHOO+9k3rx5rFixgl27djFv3jwA/vd//5epU6eyaNEiZs+ezTe/+c0WqTGaQfAZMNjMBphZG+Aa4LUGfV4FzjKzODNrB4wDVkWxJhE5wcyYMYOTTz6Z888/n2nTpvGrX/2KJ554gjFjxpCRkcEVV1xBaWkpAH/5y19IT08nIyODCRMmNDleZWUl9957L88//zyjRo3i+eefZ/Dgwew77FxbW8ugQYPIzc1l+vTp3HbbbZx11lkMGTKEN954A4CamhruvPNOxowZw8iRI3nssccOWv+OHTvo1asXMTHBy21aWhqdO3du1G/ChAmsX7+ejRs3MmTIEPYd/TjvvPN48cUXgeD4f2FhIQAFBQX07t37SFZpY+4etS/gImAtsAH4QaTtNuC2On3uBFYCy4H/OtyYp512movIl8/KlSsbtX322WeekZHhpaWlXlhY6IMGDfL777/fc3Nz9/f5wQ9+4A899JC7u6enp3t2dra7u+fn5x90WU8//bTffvvt+6d//OMf+wMPPODu7nPnzvXLL7/c3d1vuOEGnzx5stfU1PjatWs9NTXVy8rK/LHHHvMZM2a4u3t5ebmfdtppvnHjxiaXtXXrVu/Xr59nZGT49773PV+4cOH+ef369fOcnBx3d7/99tv9rrvu8ry8PE9NTfVNmzZ5VVWVX3755X7xxRe7u/v27ds9PT3dU1NTPTk52bOyspq9LoEsP8jralQ/Wezub7n7EHc/yd1/Fml71N0frdPnfncf5u7p7v6baNYjIieWjz76iMsuu4y2bduSlJTEJZdcAsDy5cs566yzGDFiBM899xwrVqwA4IwzzmD69Ok88cQT1NTUNHs5N910E8888wwATz31FDfeeOP+eVOnTiUmJobBgwczcOBAVq9ezTvvvMMzzzzDqFGjGDduHHv27GHdunVNjp2WlsaaNWv4xS9+QUxMDJMmTdp/qAdg4sSJjBo1isLCQu655x46d+7MI488wtVXX81ZZ51F//79iYsLPvI1a9Yspk+fTnZ2Nm+99RbXX389tbW1X2ylNkGfLBaR41awIdvY9OnTeeWVV8jIyGDmzJm8//77ADz66KN88sknvPnmm4waNYrFixfTtWvXwy6nT58+9OjRg/fee49PPvmE5557bv+8hm/HNDPcnd/+9rdMnjy5Wc8jISGBKVOmMGXKFHr06MErr7zCpEmTgOAcQbdu3er1v+SSS/aH3uOPP05sbCwATz75JHPmzAFg/PjxlJeXk5ubS/fu3ZtVx8HoWkMictw688wzef311ykvL6e4uJg333wTgKKiInr16kVVVVW9F+0NGzYwbtw4fvrTn9KtWze2bt3a5LhJSUkUFRXVa7v55pu57rrrmDp16v4XXgjOO9TW1rJhwwY2btzI0KFDmTx5Mo888ghVVVUArF27lpKSkiaXtXDhQrZvD94nU1tby9KlS+nXr98hn/fu3cFHqvLz8/n973/PzTffDEDfvn33702sWrWK8vJyWuKdlNojEJHj1pgxY7j00kvJyMigX79+ZGZm0qlTJ2bMmMG4cePo168fI0aM2P+ifuedd7Ju3TrcnUmTJpGR0fSbECdOnMh9993HqFGjuOeee7j66qu59NJLufHGG+sdFgIYOnQoZ599Nrt27eLRRx8lMTGRm2++mc8//5zRo0fj7qSkpPDKK680uazdu3dzyy23UFFRAcDYsWO54447Dvm8v/Od77BkyRIA7r33XoYMCd5V/+tf/5pbbrmFBx54ADNj5syZLfIhPDvYrtfxKjMz07Oyslq7DBFpYatWreKUU05p1F5cXEyHDh0oLS1lwoQJPP7444wePbrFl5+VlcV3v/tdPvzww/1t06dP5+KLL+bKK69s8eVFU1Pr0swWuHtmU/21RyAix7Vbb72VlStXUl5ezg033BCVELjvvvt45JFH6h1mChPtEYjIceFgewRHY+7cudx999312gYMGMDLL7/cossBWLZsGddff329toSEBD755JMWX9bhaI9ARCRi8uTJzX5nz9EaMWIEixcvPibLaml615CISMg1a4/AzFKAW4D+dR/j7jdFpywRETlWmnto6FXgQ+BvQPM/riciIse95h4aaufud7v7C+7+4r6vqFYmIvIlMGfOHIYOHcqgQYO4775Gd+s9LjQ3CN4ws4uiWomIyJdMTU0Nt99+O2+//TYrV65k1qxZrFy5srXLaqS5h4a+A/y3mVUAVQQ3nXF37xi1ykREjqFXFm3j/rlr2L63jN7Jbblz8lC+cmrDmyp+MZ9++imDBg1i4MCBAFxzzTW8+uqrDBs27DCPPLaaFQTunhTtQkREWssri7Zxz0vLKKsKToFu21vGPS8tAziqMNi2bRt9+hy4P1daWlqrfK7gcA4ZBGZ2sruvNrMmP8rn7gujU5aIyLFz/9w1+0Ngn7KqGu6fu+aogqCpD+y2xLWBWtrh9gi+B9wK/LqJeQ6c2+IViYgcY9v3ln2h9uZKS0urdwXU7OzslrurWAs6ZBC4+62R7xOPTTkiIsde7+S2bGviRb93ctujGnfMmDGsW7eOTZs2kZqayuzZs/nzn/98VGNGQ7MvMWFm6cAwIHFfm7s/E42iRESOpTsnD613jgCgbXwsd04eelTjxsXF8fDDDzN58mRqamq46aabGD58+NGW2+Ka+8niHwHnEATBW8AU4CNAQSAiJ7x95wFa+l1DABdddBEXXXR8v/u+uXsEVwIZwCJ3v9HMegB/iF5ZIiLH1ldOTW2RF/4TUXM/UFbu7rVAtZl1BHYDA6NXloiIHCuH3SOw4L1OS80sGXgCWAAUA59GtzQRETkWDhsE7u5mNsrd9wKPmtkcoKO7L416dSIiEnXNPTQ038zGALj75woBEZEvj+aeLJ4I/KeZbQZKOHCtoZFRq0xERI6J5gbBlKhWISLyJXTTTTfxxhtv0L17d5YvX97a5RxUsw4Nufvmpr6iXZyIyIls+vTpzJkzp7XLOCzds1hEBGDpC/BAOvw4Ofi+9IWjHnLChAl06dLl6GuLsmZfYkJE5Etr6Qvw+rehKnK9oYKtwTTAyKmtV9cxoj0CEZF5Pz0QAvtUlQXtIaAgEBEpyP5i7V8yUQ0CM7vQzNaY2Xoz+/4h+o0xsxozuzKa9YiINKlT2hdr/5KJWhCYWSzwO4K3ng4DpplZoxt1Rvr9EpgbrVpERA5p0r0Q3+DeA/Ftg/ajMG3aNMaPH8+aNWtIS0vjySefPKrxoiWaJ4vHAuvdfSOAmc0GLgNWNuj3LeBFYEwUaxERObh9J4Tn/TQ4HNQpLQiBozxRPGvWrBYoLvqiGQSpwNY609nAuLodzCwV+CrBLS8PGgRmdivBLTPp27dvixcqIsLIqaF4h1BTonmOoKk7NDe8k/NvgLvdvaaJvgce5P64u2e6e2ZKSkpL1SciIkR3jyAb6FNnOg3Y3qBPJjA7uNI13YCLzKza3V+JYl0iIlJHNIPgM2CwmQ0AtgHXANfW7eDuA/b9bGYzgTcUAiLh5e5ENgzlCLk3PPByeFE7NOTu1cAdBO8GWgW84O4rzOw2M7stWssVkRNTYmIie/bsOaIXMgm4O3v27CExMfELPc5OtJWemZnpWVlZrV2GiLSwqqoqsrOzKS8vb+1STmiJiYmkpaURHx9fr93MFrh7ZlOP0bWGROS4EB8fz4ABAw7fUVqcLjEhIhJyCgIRkZBTEIiIhJyCQEQk5BQEIiIhpyAQEQk5BYGISMgpCEREQk5BICIScgoCEZGQUxCIiIScgkBEJOQUBCIiIacgEBEJOQWBiEjIKQhEREJOQSAiEnIKAhGRkFMQiIiEnIJARCTkFAQiIiGnIBARCTkFgYhIyCkIRERCTkEgIhJyCgIRkZBTEIiIhJyCQEQk5BQEIiIhF9UgMLMLzWyNma03s+83Mf9rZrY08vWxmWVEsx4REWksakFgZrHA74ApwDBgmpkNa9BtE3C2u48EZgCPR6seERFpWjT3CMYC6919o7tXArOBy+p2cPeP3T0/MjkfSItiPSIi0oRoBkEqsLXOdHak7WD+A3i7qRlmdquZZZlZVk5OTguWKCIi0QwCa6LNm+xoNpEgCO5uar67P+7ume6emZKS0oIliohIXBTHzgb61JlOA7Y37GRmI4E/AFPcfU8U6xERkSZEc4/gM2CwmQ0wszbANcBrdTuYWV/gJeB6d18bxVpEROQgorZH4O7VZnYHMBeIBZ5y9xVmdltk/qPAvUBX4PdmBlDt7pnRqklERBoz9yYP2x+3MjMzPSsrq7XLEBE5oZjZgoNtaOuTxSIiIacgEBEJOQWBiEjIKQhEREJOQSAiEnIKAhGRkFMQiIiEnIJARCTkFAQiIiGnIBARCTkFgYhIyCkIRERCTkEgIhJyCgIRkZBTEIiIhJyCQEQk5BQEIiIhpyAQEQk5BYGISMgpCEREQk5BICIScgoCEZGQUxCIiIScgkBEJOQUBCIiIacgEBEJOQWBiEjIKQhEREJOQSAiEnIKAhGRkAtPENTWQkVJ8/ru3b6/b01xLtUFOwCoqqigOG8HVZWVAJQV7KaieA8AlWWFVORl7x+iJj+b6pI8AEpL8inNzzkwL+9zqkoLgjEKcykvyomMUUxF3lYqKioAKCrIpbRoLwAlZSXk5OfvHyMvP4/isuLgcUW5VEZqrKiooCZvM5VlQf17iwrZW1gYLKu0hJK8HfvHqCjYSUVpMGZ5SRF79x4Yvzx/O1XlZcHjivMpLgieS3VlJSV5u6iOrIOaolxqSvMiNVayY2/p/jFKS0uprqoK1l11LWVVNQfWceFuqI7MKyuisrRw//hle3cfqLGsmKqK8siKq4bKZv4OW0B5SSE11dVRGz+vpILq6tqWG7C6EqrKD9+vtgYqiltuuV9EdSVUlbXOsqOtqjR4fieguGgObmYXAg8CscAf3P2+BvMtMv8ioBSY7u4LW7yQXSsh6ynY/E845VLIuBq6DGzcb9sCWDMH1ryJj/sWJLYn5tPHobIUP/su4jZ9QPzG9/A+p+PpV5D43gxo0x4ffwfxS2Zju5bj4++Asnxil87GO/bGx9xM2+xF2Jo38Avvg7VziV3/LjE9R+Ijp5L4zwehqgQfextx+ZuIWfEytad+nZo27Wm/4CmqE7pQOPYOnt+dyouLdvHAlO6k7XiXLqtmU5E6nuqh5xP/6aNQlo9f8HParHoN2/QPYlJPwzOm8d158VTU1vDgOfF0Xf4kMTsW40MuhLRMEv7xS7xdCj7uVh7b1Je5q3O5anRPpnRYT8/5Myg97RuUt+tB4mcP06a6hMLRt1HTsS+d3/0OPuBs/KRzifnHL6FtF5aPvY8nFxaxamcR/zmhP3uLy/jLot307xzHtNMH8uLC7azbXcxDF3Sk/9aXiF33Dp6aSc2wy7GPHsBqKyk+7TZiKwtpm/UItYPOw/udQfz7v6C600CqM68ndvGfsJw1MPrrwe+xY68W/1MBKNqxAVvxVzqsfYWyHqdRk3kzHfqNarHxl2UX8OribXy4LpeMPp24ZmxfRvftfOQDVlfClo/hnw9CeSGMvx1OOg/admzcd8cS+PRx2LYQRkyFEVdAct8jX3Zz1VTDln/BP38DpXvg9G/CoPOh3VE87+NFaV7wuvHpY9ChJ5zxbeg7HmJOnO1sc/foDGwWC6wFzgeygc+Aae6+sk6fi4BvEQTBOOBBdx93qHEzMzM9Kyur+YUUZMNTU6Bgy4G2wZPhiichMelAW1kBzLkblswKpqfNhtnTwB1GToWcNcE/0T4pQ2HsrcH4i56FklzolAYnXwyfPHqgX1wiTJsFOWth499h7ZwD8zr0CMb++LfB9Hk/ho8ehHG3wj9+eaBfTCx7pr7GPfPj+b9ur9Ih63eR/j+B934abOFd/kQQdlv+tf9h3nkAFZc/zaylRdyw6j+JKd5+YMxBk4LntuE9iG1D5dRZDHk62BO584xkvrn+NgovfJBOL1we9IsovPQpOn7wY9i7BXqkQ890NiWdxlc/7s/e0ir6dmnH2UNSeHb+5v2PaRsfyzfOOYnVm3fwq9jf0m7TOwfq6JgKwy6D+b8HoPgrM+nw2s1QWw39/g0Sk6HXSPj4YaissxV75vfg3B+2+D9bZVkJta9+i8TVLx5obJ9C0XVzSOo16KjHzyks545Zi/hkU97+trTObZl54xgGdU86xCMPYfPHMPOier8nrpwJ6V+t3y9vI/xhUvDCtc+o6+Di/4O4hCNbdnNt/RSevjD4W93nK4/CqGnRXe6xsGAmvP6dA9MxcXDTO5B2WquV1BQzW+DumU3Ni2ZkjQXWu/tGd68EZgOXNehzGfCMB+YDyWbWspt5uWvrhwDAurmQv6l+W85qWPZC8HP6VbDh7wf+sToPqB8CEARDfDuIbROEAMDQi2Dp8/X7VZcHIdB1YP0QACjeFYyxz4b34LTpsOq1+v1qa2iz5UO+d3oHOix6Imhr2xkKtx34x2rTvl4IAFj+JhL2biIjcVf9EABYPw/SxgQ/11QSn7Oc5LbxADy2oJj8s2cQt25u/RcXoP3Cxyib/OtgYtdySO7HWgtCAOD8YT14aWF2vceUVdVQU+tc0reyfghA8BwSDrwAJqx4HkZeHUxs/hh6jghqqGxwKGP+74MQbmHluRtJXPNS/caSHGp3r26R8dflFNcLAYDs/DLW7io68kHXNv498a/fNj4Es3t1/RAAWPJn2LuZqNv4j/ohAMHeQflRPO/jQWlesCdWV201bJ3fOvUcoWgGQSqwtc50dqTti/bBzG41sywzy8rJyWk4+9Bi2jRusxiIbXBULCYWYiNbRWX5wQtr3f5Njh1bf151BcS3bdwvNh6ICbYUmqpln/i2wXHGJsbw+HaUVxPsYUDwxxZb57nFxIFZk8uusSaWGxMLfuD4tMe2obommE6Mj8XK9+J110FETZskYuq+KJsRbwfGqayuJTE+tvHiDKqIaXpd1mmrie944DzAvufT1POKS2x6fR4li4lretzY+BYZPz7Gmnw6bWKP4l+xTYcm2pIar+umnkNsfFTWY+N62jVuS0g6oQ6fNCkmrv7G3D5NvQ4cx6L5W2jiz52Gx6Ga0wd3f9zdM909MyUl5YtVkTI0OF5XV+bNjc8R9BgRHLcE2PA36H/WgV9w9qcwZHL9/iedC3s2QkkOdBsStK16DUbfUL9f+5Rg/sa/w+jpDZY5HAojW+oxsTBgAix4GoZfXr9fmw6Up53Bj97Pp/Dfvh+0VRQFewX7tqZ3r4b0K+o9zPuOZ2/7fryf35WKHqPrjznyGlj3bvBz285UpIyguDLYYrtrXCKdP/oJ1QPPOxA8AGZUjPkmCa9/I5gedB5kZ3Fy2RIGdQtC9K1lO7h2XP1jzt2TEqioruWZ1THkpd9Yv45eo4LDTJF1UD38Clj5SjA9/HLY+H6wZduxwfbBuT+ETr1paYndT6Ik8456bVXd04ntMaxFxj+5Z0cuP7X+cxndN5mhPY/wsBDA4PPrv/CYwZn/1fhwT4/hkHJK/bYzvwfJ/Y582c01YEK9PT8AJtxVf4PrRJTYESb+d4O2TtDn9Nap5whF8xzBeODH7j45Mn0PgLv/ok6fx4D33X1WZHoNcI6772hiSOAIzhEA5G8Odk23L4IBZ0G/MyCpR+N+u1bD9gXw+Ycw6Hy8XWfY8H7wojvsMjx/E2yZD71PhZ4jYcVLENcWhkzGtmUFh5dOvjjYWl//N0jqDQMm4NsXQ+4aOPVrwcnOzR9Dj2GQehqsfhtqKvAhU/DCbGzzx/hJk6hNTKZyzTxqE5OpPul85uR0Y+HWvVw/Kpl+pcuI2/geNSmnkNh7OPEb/xbsxWRMCw7XbP0Ueo3EU8fwk8XtiIsxbh3udNv+d2zXcuh/JtYxFVa8DB264wMmMnN7T1bvKGHS0M6Mj19P0uoXYOBECtr1I27j36CqlJpBk4mLjaPtsmcgbSzW7SRY+Rq06cDng67n3a0xrNlVxIXDu0N1JR+t203fzomMGtiLhVuL2FFQznXD4ui/dz4xm/+J98qgquepVK2eC15D7aALSCjfRdz6v+F9x1PbeQCVK97A23cn4aQzid+5CPZsCF74+p4e/MNFQfGeHfjmj4nd/CE1KcNh4Nkk9R7SYuNvyCnik415LNicz7BeHfm3QV05pddRPpfti2HdO8Hf6pApkJYJcU3sDe/ZEBz2zFkFA88JNpLadzu6ZTfXjqXBxkdZHgy9EFLHQHzi4R93vKsqC/7n1s4NNvwGnw8901u7qkYOdY4gmkEQR3CyeBKwjeBk8bXuvqJOn38H7uDAyeKH3H3socY9oiAQEQm5QwVB1A4Ounu1md0BzCV4++hT7r7CzG6LzH8UeIsgBNYTvH30xoONJyIi0RHVs0Tu/hbBi33dtkfr/OzA7dGsQUREDu0EP2UvIiJHS0EgIhJyCgIRkZBTEIiIhFzU3j4aLWaWAxyDz8QfkW5AbmsXcZzROqlP66MxrZP6orU++rl7k5/IPeGC4HhmZlkHe59uWGmd1Kf10ZjWSX2tsT50aEhEJOQUBCIiIacgaFmPt3YBxyGtk/q0PhrTOqnvmK8PnSMQEQk57RGIiIScgkBEJOQUBC3IzGLNbJGZvdHatbQ2M0s2s7+a2WozWxW5P0Womdl3zWyFmS03s1lm9iW4GH/zmdlTZrbbzJbXaetiZu+a2brI9y/B3eyb7yDr5P7I/81SM3vZzJKjXYeCoGV9B1jV2kUcJx4E5rj7yUAGIV8vZpYKfBvIdPd0gkuzX9O6VR1zM4ELG7R9H5jn7oOBeZHpMJlJ43XyLpDu7iMJ7ulyT7SLUBC0EDNLA/4d+ENr19LazKwjMAF4EsDdK919b6sWdXyIA9pGbtrUDtjeyvUcU+7+AZDXoPky4I+Rn/8IfOVY1tTamlon7v6Ou1dHJucDadGuQ0HQcn4D3AXUHqZfGAwEcoCnI4fK/mBmJ/jNaY+Ou28DfgVsAXYABe7+TutWdVzose/WtJHv3Vu5nuPNTcDb0V6IgqAFmNnFwG53X9DatRwn4oDRwCPufipQQvh2+euJHPu+DBgA9Abam9l1rVuVHM/M7AdANfBctJelIGgZZwCXmtnnwGzgXDP7U+uW1KqygWx3/yQy/VeCYAiz84BN7p7j7lXAS8C/tXJNx4NdZtYLIPJ9dyvXc1wwsxuAi4Gv+TH4sJeCoAW4+z3unubu/QlOAL7n7qHd2nP3ncBWMxsaaZoErGzFko4HW4DTzaydmRnBOgn1CfSI14AbIj/fALzairUcF8zsQuBu4FJ3Lz0Wy4zqPYsl1L4FPGdmbYCNwI2tXE+rcvdPzOyvwEKC3f1FhOzSCmY2CzgH6GZm2cCPgPuAF8zsPwjC8qrWq/DYO8g6uQdIAN4NthmY7+63RbUOXWJCRCTcdGhIRCTkFAQiIiGnIBARCTkFgYhIyCkIRERCTkEgIhJyCgIRkZBTEEgomdnXI9d7X2Jmz5pZPzObF2mbZ2Z9I/1mmtlDZvaxmW00sysj7b3M7AMzWxy5v8BZkfbiOsu40sxm1hnnETP7e2ScsyPXol+1r0+k3wVm9i8zW2hmfzGzDsdyvUg4KQgkdMxsOPAD4Fx3zyC4j8TDwDORa8A/BzxU5yG9gDMJrv1yX6TtWmCuu48iuN/C4mYsujNwLvBd4HXgAWA4MMLMRplZN+B/gPPcfTSQBXzvyJ+pSPPoEhMSRucCf3X3XAB3z4vcQe3yyPxngf+/Tv9X3L0WWGlmPSJtnwFPmVl8ZP7iZiz3dXd3M1sG7HL3ZQBmtgLoT3Dd+WHAPyOXFmgD/OvIn6ZI82iPQMLIgMNdW6Xu/IoGj913Q5EJwDbgWTP7ehOPa3gryn3j1DYYs5Zgo8yAd919VORrmLv/x+GejMjRUhBIGM0DpppZVwjumwt8zIFbR34N+OhQA5hZP4J7UDxBcCe2fZfZ3mVmp5hZDPDVL1jXfOAMMxsUWUY7MxvyBccQ+cJ0aEhCx91XmNnPgH+YWQ3BlUC/TXCo506Cu6sd7mqp5wB3mlkVUAzs2yP4PvAGsBVYDjT7ZK+755jZdGCWmSVEmv+H4L61IlGjq4+KiIScDg2JiIScgkBEJOQUBCIiIacgEBEJOQWBiEjIKQhEREJOQSAiEnL/D2g2uRCn7umXAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.scatterplot(y=consume_data.rain, x=consume_data.consume, hue=consume_data.gas_type_SP98);"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 19,

From 6e6ecc76ae171f81f6b525e6dc56e691817a2632 Mon Sep 17 00:00:00 2001
From: JulianTrufero <jgtrufero@gmail.com>
Date: Sun, 17 Oct 2021 21:19:23 +0200
Subject: [PATCH 6/6] update

---
 consume.ipynb | 416 +++++++++++---------------------------------------
 1 file changed, 89 insertions(+), 327 deletions(-)

diff --git a/consume.ipynb b/consume.ipynb
index 82d10ce..f244f6b 100644
--- a/consume.ipynb
+++ b/consume.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -133,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -142,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -151,9 +151,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {
-    "scrolled": true
+    "scrolled": false
    },
    "outputs": [
     {
@@ -245,7 +245,7 @@
        "2              0  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -256,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -305,7 +305,7 @@
        "Consume      3.3     12.2  4.925266     4.7   4.5"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -323,7 +323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -332,7 +332,7 @@
        "<AxesSubplot:xlabel='consume', ylabel='Count'>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -355,7 +355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -364,7 +364,7 @@
        "<AxesSubplot:xlabel='consume'>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -653,7 +653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 16,
    "metadata": {
     "scrolled": false
    },
@@ -684,7 +684,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 17,
    "metadata": {
     "scrolled": true
    },
@@ -715,16 +715,9 @@
     "- We can see that for lower inside temperature values, there are higher consumption values. On the other hand, we can see that most of the cars had the AC off, but those with the AC on, accounts for higher consumption values"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### CONSUME VS GAS_TYPE"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -775,7 +768,7 @@
        "1              4.906849"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -785,16 +778,9 @@
     "gtype_gr"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### CONSUME VS AC"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -845,7 +831,7 @@
        "1   5.235484"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -855,16 +841,9 @@
     "ac_group"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### CONSUME VS RAIN"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -915,7 +894,7 @@
        "1     5.626087"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -925,6 +904,14 @@
     "rain_gr"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- From the above data frames we can see that the consumption is higher in average when: the AC is on, is raining\n",
+    "- But also the consumption is higher in average when the car is using the ethanol type of gas. Although is higher in a small proportion, it's considerable given that most of the cars in the data set use non-ethanol gas. "
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -933,111 +920,15 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 19,
+   "cell_type": "markdown",
    "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>distance</th>\n",
-       "      <th>consume</th>\n",
-       "      <th>speed</th>\n",
-       "      <th>temp_inside</th>\n",
-       "      <th>temp_outside</th>\n",
-       "      <th>AC</th>\n",
-       "      <th>rain</th>\n",
-       "      <th>sun</th>\n",
-       "      <th>snow</th>\n",
-       "      <th>gas_type_SP98</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>28.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>26</td>\n",
-       "      <td>21.5</td>\n",
-       "      <td>12</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>1</th>\n",
-       "      <td>12.0</td>\n",
-       "      <td>4.2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>21.5</td>\n",
-       "      <td>13</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>2</th>\n",
-       "      <td>11.2</td>\n",
-       "      <td>5.5</td>\n",
-       "      <td>38</td>\n",
-       "      <td>21.5</td>\n",
-       "      <td>15</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",
-       "</div>"
-      ],
-      "text/plain": [
-       "   distance  consume  speed  temp_inside  temp_outside  AC  rain  sun  snow  \\\n",
-       "0      28.0      5.0     26         21.5            12   0     0    0     0   \n",
-       "1      12.0      4.2     30         21.5            13   0     0    0     0   \n",
-       "2      11.2      5.5     38         21.5            15   0     0    0     0   \n",
-       "\n",
-       "   gas_type_SP98  \n",
-       "0              0  \n",
-       "1              0  \n",
-       "2              0  "
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "consume_data.head(3)"
+    "- We'll model the `consume` variable as a linear regression over the other variables to check which of them has statistical significance in order to explain the consume of the car."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1046,7 +937,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1064,10 +955,10 @@
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   10.68</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Sat, 16 Oct 2021</td> <th>  Prob (F-statistic):</th> <td>9.76e-15</td>\n",
+       "  <th>Date:</th>             <td>Sun, 17 Oct 2021</td> <th>  Prob (F-statistic):</th> <td>9.76e-15</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>12:49:05</td>     <th>  Log-Likelihood:    </th> <td> -504.20</td>\n",
+       "  <th>Time:</th>                 <td>20:45:17</td>     <th>  Log-Likelihood:    </th> <td> -504.20</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>   376</td>      <th>  AIC:               </th> <td>   1028.</td>\n",
@@ -1140,8 +1031,8 @@
        "Dep. Variable:                consume   R-squared:                       0.208\n",
        "Model:                            OLS   Adj. R-squared:                  0.188\n",
        "Method:                 Least Squares   F-statistic:                     10.68\n",
-       "Date:                Sat, 16 Oct 2021   Prob (F-statistic):           9.76e-15\n",
-       "Time:                        12:49:05   Log-Likelihood:                -504.20\n",
+       "Date:                Sun, 17 Oct 2021   Prob (F-statistic):           9.76e-15\n",
+       "Time:                        20:45:17   Log-Likelihood:                -504.20\n",
        "No. Observations:                 376   AIC:                             1028.\n",
        "Df Residuals:                     366   BIC:                             1068.\n",
        "Df Model:                           9                                         \n",
@@ -1173,7 +1064,7 @@
        "\"\"\""
       ]
      },
-     "execution_count": 40,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1182,9 +1073,19 @@
     "linear_model.summary()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- From the summary we can see that a linear model is not very good to model the consumption, as we got a R2 of 0.208. \n",
+    "- We can see from the p-values that `distance` and `temp_inside` are not significant, which is no suprise given the correlation they have with `speed` and `AC` respectively.\n",
+    "- We can also see that `sun`, `snow` and `gas_type` are non-significant.\n",
+    "- We'll perform another regression but this time without `distance`. We'll log-transform the target variable to see if we can reduce the potential influence of outliers and standarize the data set."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1193,7 +1094,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1215,7 +1116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1224,154 +1125,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
-   "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>distance</th>\n",
-       "      <th>consume</th>\n",
-       "      <th>speed</th>\n",
-       "      <th>temp_inside</th>\n",
-       "      <th>temp_outside</th>\n",
-       "      <th>AC</th>\n",
-       "      <th>rain</th>\n",
-       "      <th>sun</th>\n",
-       "      <th>snow</th>\n",
-       "      <th>gas_type_SP98</th>\n",
-       "      <th>log_consume</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.357285</td>\n",
-       "      <td>0.071907</td>\n",
-       "      <td>-1.165108</td>\n",
-       "      <td>-0.425643</td>\n",
-       "      <td>0.093456</td>\n",
-       "      <td>-0.299758</td>\n",
-       "      <td>-0.373355</td>\n",
-       "      <td>-0.299758</td>\n",
-       "      <td>-0.103695</td>\n",
-       "      <td>-1.181061</td>\n",
-       "      <td>0.182583</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-0.339631</td>\n",
-       "      <td>-0.697827</td>\n",
-       "      <td>-0.873055</td>\n",
-       "      <td>-0.425643</td>\n",
-       "      <td>0.235147</td>\n",
-       "      <td>-0.299758</td>\n",
-       "      <td>-0.373355</td>\n",
-       "      <td>-0.299758</td>\n",
-       "      <td>-0.103695</td>\n",
-       "      <td>-1.181061</td>\n",
-       "      <td>-0.794181</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-0.374477</td>\n",
-       "      <td>0.552990</td>\n",
-       "      <td>-0.288947</td>\n",
-       "      <td>-0.425643</td>\n",
-       "      <td>0.518530</td>\n",
-       "      <td>-0.299758</td>\n",
-       "      <td>-0.373355</td>\n",
-       "      <td>-0.299758</td>\n",
-       "      <td>-0.103695</td>\n",
-       "      <td>-1.181061</td>\n",
-       "      <td>0.716531</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-0.300429</td>\n",
-       "      <td>-0.986478</td>\n",
-       "      <td>-0.434974</td>\n",
-       "      <td>-0.425643</td>\n",
-       "      <td>0.376839</td>\n",
-       "      <td>-0.299758</td>\n",
-       "      <td>-0.373355</td>\n",
-       "      <td>-0.299758</td>\n",
-       "      <td>-0.103695</td>\n",
-       "      <td>-1.181061</td>\n",
-       "      <td>-1.209349</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-0.056509</td>\n",
-       "      <td>-0.409177</td>\n",
-       "      <td>0.295161</td>\n",
-       "      <td>-0.425643</td>\n",
-       "      <td>0.518530</td>\n",
-       "      <td>-0.299758</td>\n",
-       "      <td>-0.373355</td>\n",
-       "      <td>-0.299758</td>\n",
-       "      <td>-0.103695</td>\n",
-       "      <td>-1.181061</td>\n",
-       "      <td>-0.407669</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   distance   consume     speed  temp_inside  temp_outside        AC  \\\n",
-       "0  0.357285  0.071907 -1.165108    -0.425643      0.093456 -0.299758   \n",
-       "1 -0.339631 -0.697827 -0.873055    -0.425643      0.235147 -0.299758   \n",
-       "2 -0.374477  0.552990 -0.288947    -0.425643      0.518530 -0.299758   \n",
-       "3 -0.300429 -0.986478 -0.434974    -0.425643      0.376839 -0.299758   \n",
-       "4 -0.056509 -0.409177  0.295161    -0.425643      0.518530 -0.299758   \n",
-       "\n",
-       "       rain       sun      snow  gas_type_SP98  log_consume  \n",
-       "0 -0.373355 -0.299758 -0.103695      -1.181061     0.182583  \n",
-       "1 -0.373355 -0.299758 -0.103695      -1.181061    -0.794181  \n",
-       "2 -0.373355 -0.299758 -0.103695      -1.181061     0.716531  \n",
-       "3 -0.373355 -0.299758 -0.103695      -1.181061    -1.209349  \n",
-       "4 -0.373355 -0.299758 -0.103695      -1.181061    -0.407669  "
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": 39,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
    "source": [
-    "consume_data_scaled=pd.DataFrame(scaler.fit_transform(consume_data), columns=consume_data.columns)\n",
-    "consume_data_scaled.head()"
+    "consume_data_scaled=pd.DataFrame(scaler.fit_transform(consume_data), columns=consume_data.columns)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1380,7 +1145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1398,10 +1163,10 @@
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   15.62</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Sat, 16 Oct 2021</td> <th>  Prob (F-statistic):</th> <td>6.11e-18</td>\n",
+       "  <th>Date:</th>             <td>Sun, 17 Oct 2021</td> <th>  Prob (F-statistic):</th> <td>6.11e-18</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>16:25:35</td>     <th>  Log-Likelihood:    </th> <td>  163.29</td>\n",
+       "  <th>Time:</th>                 <td>20:45:18</td>     <th>  Log-Likelihood:    </th> <td>  163.29</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>   376</td>      <th>  AIC:               </th> <td>  -310.6</td>\n",
@@ -1468,8 +1233,8 @@
        "Dep. Variable:            log_consume   R-squared:                       0.229\n",
        "Model:                            OLS   Adj. R-squared:                  0.214\n",
        "Method:                 Least Squares   F-statistic:                     15.62\n",
-       "Date:                Sat, 16 Oct 2021   Prob (F-statistic):           6.11e-18\n",
-       "Time:                        16:25:35   Log-Likelihood:                 163.29\n",
+       "Date:                Sun, 17 Oct 2021   Prob (F-statistic):           6.11e-18\n",
+       "Time:                        20:45:18   Log-Likelihood:                 163.29\n",
        "No. Observations:                 376   AIC:                            -310.6\n",
        "Df Residuals:                     368   BIC:                            -279.1\n",
        "Df Model:                           7                                         \n",
@@ -1497,7 +1262,7 @@
        "\"\"\""
       ]
      },
-     "execution_count": 64,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1507,36 +1272,37 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "- This time we can see that we got a R2 of 0.229, a slightly improvement,   \n",
+    "- We can see from the p-values that `sun`, `snow` and `gas_type` are still non-significant."
+   ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "#### From the regression analisis, we can say that the variables whic are significant to predict the mean of consumption are `rain`, `AC`, `temp_outside`  and `speed`. Which goes in line with the correlation analysis."
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Del análisis de regresión podemos observar que las variables que resultan significativas para estimar la media de consumo son rain, AC, temp_outside y speed. Lo cual coincide con el resultado de la matriz de correlaciones, son las variables que aparecen mas correlacionadas con la variable consumo"
+    "### CONSUME COST"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### CONSUME COST"
+    "- We'll enrich the data with the price of the gas"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1551,7 +1317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1560,7 +1326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1569,7 +1335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -1594,52 +1360,48 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>cost_per_distance</th>\n",
-       "      <th>distance</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>gas_type_SP98</th>\n",
        "      <th></th>\n",
-       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>1.396125</td>\n",
-       "      <td>21.095541</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1.305041</td>\n",
-       "      <td>18.866667</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "               cost_per_distance   distance\n",
-       "gas_type_SP98                              \n",
-       "0                       1.396125  21.095541\n",
-       "1                       1.305041  18.866667"
+       "               cost_per_distance\n",
+       "gas_type_SP98                   \n",
+       "0                       1.396125\n",
+       "1                       1.305041"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "price_gr = consume_data.groupby('gas_type_SP98').agg({'cost_per_distance': 'mean', 'distance': 'mean'})\n",
+    "price_gr = consume_data.groupby('gas_type_SP98').agg({'cost_per_distance': 'mean'})\n",
     "price_gr"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "- From the above data frame we can see that the cars with ethanol gas-type, have a higher cost consumption, even if the price of ethanol is lower."
+   ]
   }
  ],
  "metadata": {