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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "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>specials</th>\n",
+       "      <th>gas_type</th>\n",
+       "      <th>AC</th>\n",
+       "      <th>rain</th>\n",
+       "      <th>sun</th>\n",
+       "      <th>refill liters</th>\n",
+       "      <th>refill gas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>28</td>\n",
+       "      <td>5</td>\n",
+       "      <td>26</td>\n",
+       "      <td>21,5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45</td>\n",
+       "      <td>E10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12</td>\n",
+       "      <td>4,2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>21,5</td>\n",
+       "      <td>13</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>NaN</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>12,9</td>\n",
+       "      <td>3,9</td>\n",
+       "      <td>36</td>\n",
+       "      <td>21,5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>18,5</td>\n",
+       "      <td>4,5</td>\n",
+       "      <td>46</td>\n",
+       "      <td>21,5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  distance consume  speed temp_inside  temp_outside specials gas_type  AC  \\\n",
+       "0       28       5     26        21,5            12      NaN      E10   0   \n",
+       "1       12     4,2     30        21,5            13      NaN      E10   0   \n",
+       "2     11,2     5,5     38        21,5            15      NaN      E10   0   \n",
+       "3     12,9     3,9     36        21,5            14      NaN      E10   0   \n",
+       "4     18,5     4,5     46        21,5            15      NaN      E10   0   \n",
+       "\n",
+       "   rain  sun refill liters refill gas  \n",
+       "0     0    0            45        E10  \n",
+       "1     0    0           NaN        NaN  \n",
+       "2     0    0           NaN        NaN  \n",
+       "3     0    0           NaN        NaN  \n",
+       "4     0    0           NaN        NaN  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Read csv\n",
+    "df=pd.read_csv('/Users/mikel/Documents/Projects/reto_data/measurements.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Drop null columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "temp_inside       12\n",
+       "specials         295\n",
+       "refill liters    375\n",
+       "refill gas       375\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nan_cols=df.isna().sum()\n",
+    "\n",
+    "nan_cols[nan_cols>0]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=df.drop(columns=['specials', 'refill liters', 'refill gas'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Change Dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "distance        object\n",
+       "consume         object\n",
+       "speed            int64\n",
+       "temp_inside     object\n",
+       "temp_outside     int64\n",
+       "gas_type        object\n",
+       "AC               int64\n",
+       "rain             int64\n",
+       "sun              int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# I had some trouble converting data to float, so I replaced , with .\n",
+    "df['distance']=df['distance'].str.replace(',','.')\n",
+    "df['consume']=df['consume'].str.replace(',','.')\n",
+    "df['temp_inside']=df['temp_inside'].str.replace(',','.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['distance']=df['distance'].astype('float')\n",
+    "df['consume']=df['consume'].astype('float')\n",
+    "df['temp_inside']=df['temp_inside'].astype('float')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "distance        float64\n",
+       "consume         float64\n",
+       "speed             int64\n",
+       "temp_inside     float64\n",
+       "temp_outside      int64\n",
+       "gas_type         object\n",
+       "AC                int64\n",
+       "rain              int64\n",
+       "sun               int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Correlation matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lets see how the vairables are related"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Select only numeric data in this first approach\n",
+    "df_numeric=df.select_dtypes(include=['int64', 'float64'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1152x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(16, 6))\n",
+    "heatmap = sns.heatmap(df_numeric.corr(), vmin=-1, vmax=1, annot=True, cmap='BrBG')\n",
+    "heatmap.set_title('Correlation Heatmap', fontdict={'fontsize':18}, pad=12);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Insights:\n",
+    "1. Speed and distance are positively correlated\n",
+    "2. Consume and tem_outside are negatively correlated, if the temperature is high the consume is lower.\n",
+    "3. If its rainy, the consume is higher, when there is sun, lower."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Gas type analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.gas_type.value_counts().plot(kind='bar');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas_type</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>E10</th>\n",
+       "      <td>21.096250</td>\n",
+       "      <td>4.931250</td>\n",
+       "      <td>43.506250</td>\n",
+       "      <td>21.917197</td>\n",
+       "      <td>10.11875</td>\n",
+       "      <td>0.043750</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.075000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SP98</th>\n",
+       "      <td>18.639912</td>\n",
+       "      <td>4.899123</td>\n",
+       "      <td>40.820175</td>\n",
+       "      <td>21.938356</td>\n",
+       "      <td>12.22807</td>\n",
+       "      <td>0.100877</td>\n",
+       "      <td>0.140351</td>\n",
+       "      <td>0.087719</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           distance   consume      speed  temp_inside  temp_outside        AC  \\\n",
+       "gas_type                                                                        \n",
+       "E10       21.096250  4.931250  43.506250    21.917197      10.11875  0.043750   \n",
+       "SP98      18.639912  4.899123  40.820175    21.938356      12.22807  0.100877   \n",
+       "\n",
+       "              rain       sun  \n",
+       "gas_type                      \n",
+       "E10       0.100000  0.075000  \n",
+       "SP98      0.140351  0.087719  "
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#We can see that the consume in E10 is bigger with the data we have.\n",
+    "df.groupby(['gas_type']).mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Correlation matrix per fuel tipe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets go deeper with this analysis, we will plot the correlation matrix again and see if there are notorious differences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_E10=df[df.gas_type == 'E10']\n",
+    "df_SP98=df[df.gas_type == 'SP98']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1152x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(16, 6))\n",
+    "heatmap = sns.heatmap(df_E10.corr(), vmin=-1, vmax=1, annot=True, cmap='BrBG')\n",
+    "heatmap.set_title('Correlation Heatmap E10', fontdict={'fontsize':18}, pad=12);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1152x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(16, 6))\n",
+    "heatmap = sns.heatmap(df_SP98.corr(), vmin=-1, vmax=1, annot=True, cmap='BrBG')\n",
+    "heatmap.set_title('Correlation Heatmap SP98', fontdict={'fontsize':18}, pad=12);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Insights:\n",
+    "1. Fuel SP98 is more correlated to the temp_inside thant E10\n",
+    "2. AC is higher correlated to consume in SP98 than in E10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.gas_type.value_counts().plot(kind='bar');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Predict fuel consumption"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I read the file again so I don't miss any column because I am biased about their impact."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2=pd.read_excel('/Users/mikel/Documents/Projects/reto_data/measurements2.xlsx')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Column 2 (consumption) will be the dependent variable\n",
+    "x = df2.iloc[:,[0,2,3,4,5,6,7,8,9]]\n",
+    "y = df2.iloc[:,1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "temp_inside     12\n",
+       "specials       295\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Check null columns\n",
+    "nan_cols=x.isna().sum()\n",
+    "\n",
+    "nan_cols[nan_cols>0]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Fill null values with ' ' in this case\n",
+    "x.loc[:,['specials']] = x.loc[:,['specials']].fillna(' ')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fill null values with mean\n",
+    "x['temp_inside']=x['temp_inside'].fillna(x['temp_inside'].mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Series([], dtype: int64)"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Now we do not have nulls\n",
+    "nan_cols=x.isna().sum()\n",
+    "\n",
+    "nan_cols[nan_cols>0]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sklearn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First of all, do get_dummies to encode specials and gas_type columns\n",
+    "encoder = LabelEncoder()\n",
+    "x = pd.get_dummies(x, prefix=['specials', 'gas_type'], drop_first=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Train test split\n",
+    "x_train,x_test,y_train,y_test = train_test_split(x,y, random_state=9, train_size = 0.8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Noramlize values\n",
+    "scale_x = StandardScaler()\n",
+    "x_train.iloc[:,0:4] = scale_x.fit_transform(x_train.iloc[:,0:4])\n",
+    "x_test.iloc[:,0:4] = scale_x.fit_transform(x_test.iloc[:,0:4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Linear regression & predict\n",
+    "lin_reg = LinearRegression()\n",
+    "lin_reg.fit(x_train, y_train)\n",
+    "y_pred = lin_reg.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9720417139513281"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#set mean squared error \n",
+    "rmse = mean_squared_error(y_test, y_pred)\n",
+    "rmse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#predict by fuel type\n",
+    "sp98_y = lin_reg.predict(x_test[x_test['gas_type_SP98']==1])\n",
+    "E10_y = lin_reg.predict(x_test[x_test['gas_type_SP98']==0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.809541721770504"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sp98_y.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.139547010896096"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "E10_y.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.3300052891255918\n"
+     ]
+    }
+   ],
+   "source": [
+    "Change=(E10_y.mean()-sp98_y.mean())\n",
+    "print(Change)\n",
+    "#E10 consumptions are higher than sp98 by 0.33 per 100km"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Feature importance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will make an overview of the weight every variable has in the fuel consumption"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 648x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "coefs = pd.DataFrame(\n",
+    "   lin_reg.coef_,\n",
+    "   columns=['Coefficients'], index=x_train.columns\n",
+    ")\n",
+    "\n",
+    "coefs.plot(kind='barh', figsize=(9, 7))\n",
+    "plt.title('Ridge model')\n",
+    "plt.axvline(x=0, color='.5');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Final thoughts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Without taking fuel's price into account, E10 consumptions are higher than using sp98. So fuel tipe is a relevant variable to consider.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "e134e05457d34029b6460cd73bbf1ed73f339b5b6d98c95be70b69eba114fe95"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.8.5 64-bit ('base': conda)",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/measurements.csv b/measurements.csv
new file mode 100644
index 0000000..f9a71ce
--- /dev/null
+++ b/measurements.csv
@@ -0,0 +1,389 @@
+distance,consume,speed,temp_inside,temp_outside,specials,gas_type,AC,rain,sun,refill liters,refill gas
+28,5,26,"21,5",12,,E10,0,0,0,45,E10
+12,"4,2",30,"21,5",13,,E10,0,0,0,,
+"11,2","5,5",38,"21,5",15,,E10,0,0,0,,
+"12,9","3,9",36,"21,5",14,,E10,0,0,0,,
+"18,5","4,5",46,"21,5",15,,E10,0,0,0,,
+"8,3","6,4",50,"21,5",10,,E10,0,0,0,,
+"7,8","4,4",43,"21,5",11,,E10,0,0,0,,
+"12,3",5,40,"21,5",6,,E10,0,0,0,,
+"4,9","6,4",26,"21,5",4,,E10,0,0,0,,
+"11,9","5,3",30,"21,5",9,,E10,0,0,0,,
+"12,4","5,6",42,"21,5",4,,E10,0,0,0,,
+"11,8","4,6",38,"21,5",0,,E10,0,0,0,,
+"12,3","5,9",59,"21,5",10,,E10,0,0,0,,
+"24,7","5,1",58,"21,5",12,,E10,0,0,0,,
+"12,4","4,7",46,"21,5",11,,E10,0,0,0,,
+"17,3","5,1",24,"21,5",5,,E10,0,0,0,,
+"33,4","5,6",36,"21,5",3,,E10,0,0,0,,
+"11,8","5,1",32,"21,5",3,,E10,0,0,0,,
+"25,9","4,9",39,"21,5",8,,E10,0,0,0,,
+"11,8","4,7",40,"21,5",4,,E10,0,0,0,,
+"25,3","5,5",32,"21,5",3,,E10,0,0,0,,
+"14,2","5,9",38,"21,5",1,,E10,0,0,0,,
+"17,9","5,7",37,"21,5",1,,E10,0,0,0,,
+"11,8","4,7",36,"21,5",1,,E10,0,0,0,,
+"12,3","5,9",62,"21,5",6,,E10,0,0,0,,
+"12,4","4,1",57,"21,5",9,,E10,0,0,0,,
+"18,4","5,7",21,"22,5",2,,E10,0,0,0,,
+"18,4","5,8",28,"21,5",3,,E10,0,0,0,,
+"18,3","5,5",29,"21,5",1,,E10,0,0,0,,
+"18,4","5,7",35,"21,5",4,,E10,0,0,0,,
+"12,3","5,3",51,"21,5",11,,E10,0,0,0,,
+"11,8",5,29,"21,5",10,,E10,0,0,0,,
+"12,3","5,6",58,"21,5",8,,E10,0,0,0,,
+"32,6","4,8",40,"21,5",7,,E10,0,0,0,,
+19,"4,3",36,"21,5",7,,E10,0,0,0,,
+"12,1","5,7",36,"21,5",8,,E10,0,0,0,,
+20,"5,2",37,"21,5",4,,E10,0,0,0,,
+"4,9","7,4",26,"21,5",6,,E10,0,0,0,,
+"11,8","4,8",23,"21,5",9,,E10,0,0,0,,
+"12,3","6,5",58,"21,5",7,,E10,0,0,0,,
+"12,4",4,55,"21,5",7,AC rain,E10,1,1,0,,
+"4,5",5,29,"21,5",7,AC,E10,1,0,0,,
+"11,7","4,6",37,"21,5",6,,E10,0,0,0,,
+"10,2","4,3",44,"21,5",7,,E10,0,0,0,,
+"5,4","3,3",32,"21,5",7,,SP98,0,0,0,"37,6",SP98
+2,"9,9",21,"21,5",4,,SP98,0,0,0,,
+"14,2","5,1",32,"21,5",7,,SP98,0,0,0,,
+16,"4,9",25,"21,5",8,,SP98,0,0,0,,
+"11,8","4,9",25,"21,5",11,,SP98,0,0,0,,
+"27,3","4,7",36,"21,5",10,,SP98,0,0,0,,
+"11,8","5,1",29,"21,5",5,rain,SP98,0,1,0,,
+"10,6","4,9",63,"21,5",6,,SP98,0,0,0,,
+"11,6","4,2",61,"21,5",7,,SP98,0,0,0,,
+"13,1","6,1",46,"21,5",6,rain,SP98,0,1,0,,
+"6,1","6,4",28,"21,5",8,,SP98,0,0,0,,
+"153,5","4,9",82,"21,5",3,rain,SP98,0,1,0,,
+"11,8","4,9",24,"21,5",7,,SP98,0,0,0,,
+"12,3","6,2",61,"21,5",8,,SP98,0,0,0,,
+"12,4","4,2",57,"21,5",8,,SP98,0,0,0,,
+"12,3","5,7",52,"21,5",8,rain,SP98,0,1,0,,
+"2,9","7,9",18,"21,5",9,AC rain,SP98,1,1,0,,
+"2,1","12,2",23,"21,5",9,,SP98,1,1,0,,
+"9,8","5,8",24,"21,5",6,,SP98,0,0,0,,
+"6,8","5,3",26,"21,5",6,,SP98,0,0,0,,
+"12,4","5,4",24,"21,5",7,,SP98,0,0,0,,
+"12,4","4,7",57,"21,5",8,,SP98,0,0,0,,
+"11,8","4,6",41,"21,5",6,,SP98,0,0,0,,
+"12,3","5,4",55,"21,5",8,,SP98,0,0,0,,
+14,"4,3",40,"21,5",8,,SP98,0,0,0,,
+"11,8","4,5",37,"21,5",4,AC,SP98,1,0,0,,
+"12,3","5,2",55,"21,5",12,,SP98,0,0,0,,
+2,"6,2",20,"21,5",10,,SP98,0,0,0,,
+"13,9","5,1",29,"21,5",6,,SP98,0,0,0,,
+"9,7","3,9",42,"21,5",8,,SP98,0,0,0,,
+"11,6",5,25,"21,5",6,,SP98,0,0,0,,
+"14,2","5,4",38,"21,5",7,,SP98,0,0,0,,
+"11,8","4,5",39,"21,5",6,,SP98,0,0,0,,
+"24,8","5,1",50,"21,5",9,,SP98,0,0,0,,
+"12,4","4,7",56,"21,5",7,,SP98,0,0,0,,
+"34,8",4,28,20,4,,SP98,0,0,0,,
+"14,2","5,4",36,20,6,,SP98,0,0,0,,
+"5,2","4,5",39,20,10,,SP98,0,0,0,,
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diff --git a/measurements2.xlsx b/measurements2.xlsx
new file mode 100644
index 0000000..8688d01
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