From 5ce4dfff0c4b97960119cb4dcd3ce2ae6d1c6eef Mon Sep 17 00:00:00 2001
From: Irma Fernandez <fernandezwic@gmail.com>
Date: Wed, 3 Dec 2025 14:44:50 +0100
Subject: [PATCH] Completed Cobify technical challenge

---
 Notebook.ipynb                   | 1132 ++++++++++++++++++++++++++++++
 data/measurements2_converted.csv |  389 ++++++++++
 data/processed_measurements.csv  |  389 ++++++++++
 3 files changed, 1910 insertions(+)
 create mode 100644 Notebook.ipynb
 create mode 100644 data/measurements2_converted.csv
 create mode 100644 data/processed_measurements.csv

diff --git a/Notebook.ipynb b/Notebook.ipynb
new file mode 100644
index 0000000..1c991ea
--- /dev/null
+++ b/Notebook.ipynb
@@ -0,0 +1,1132 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d19ce601-01c2-4afa-87d4-b940cb5c6ae1",
+   "metadata": {},
+   "source": [
+    "# Step 1 — Purpose of the Project\n",
+    "\n",
+    "This notebook will guide the complete analysis of the Cobify fuel consumption dataset.  \n",
+    "The goal of this project is to explore, clean, visualize, enrich, and model the data in order to determine which fuel type leads to lower consumption.\n",
+    "\n",
+    "We will follow a structured, step-by-step workflow:\n",
+    "\n",
+    "1. Load and inspect the raw data  \n",
+    "2. Clean and prepare the dataset  \n",
+    "3. Perform exploratory data analysis  \n",
+    "4. Build visualizations to understand relationships  \n",
+    "5. Create predictive models  \n",
+    "6. Generate insights and recommendations  \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fc2274f-b741-4218-8df2-69beb9c7750e",
+   "metadata": {},
+   "source": [
+    "# Step 2 — Convert the Excel File to CSV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "317c4358-2ff9-4655-a090-6ef02733bb35",
+   "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>28.0</td>\n",
+       "      <td>5.0</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>45.0</td>\n",
+       "      <td>E10</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>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      28.0      5.0     26         21.5            12      NaN      E10   0   \n",
+       "1      12.0      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           45.0        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": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Read the Excel file from the data folder\n",
+    "df_excel = pd.read_excel(\"data/measurements2.xlsx\")\n",
+    "\n",
+    "# Convert to CSV in the same folder\n",
+    "df_excel.to_csv(\"data/measurements2_converted.csv\", index=False)\n",
+    "\n",
+    "df_excel.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "486e9749-d57d-4fca-80d0-ad63864cb1d1",
+   "metadata": {},
+   "source": [
+    "# Step 3 — Load the CSV Files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9854ee57-74f2-4c78-a57f-71a97fed9cca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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  ,\n",
+       "    distance  consume  speed  temp_inside  temp_outside specials gas_type  AC  \\\n",
+       " 0      28.0      5.0     26         21.5            12      NaN      E10   0   \n",
+       " 1      12.0      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           45.0        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": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1 = pd.read_csv(\"data/measurements.csv\")\n",
+    "df2 = pd.read_csv(\"data/measurements2_converted.csv\")\n",
+    "\n",
+    "df1.head(), df2.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5ca6829-3368-48f2-b96e-4e256ba06402",
+   "metadata": {},
+   "source": [
+    "# Step 4 — Inspect Columns and Fix Decimal Formats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c5a11704-3d4c-4e55-a702-87c1e9dd3a9c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "distance         object\n",
+       "consume          object\n",
+       "speed             int64\n",
+       "temp_inside      object\n",
+       "temp_outside      int64\n",
+       "specials         object\n",
+       "gas_type         object\n",
+       "AC                int64\n",
+       "rain              int64\n",
+       "sun               int64\n",
+       "refill liters    object\n",
+       "refill gas       object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Show dtypes to see which columns are still objects\n",
+    "df1.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec1a15e5-4540-4e37-bace-c9384872f8c6",
+   "metadata": {},
+   "source": [
+    "# Step 5 — Clean Numeric Columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "44889234-40f6-4c66-9c7d-5a87b1003e97",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "distance         float64\n",
+       "consume          float64\n",
+       "speed              int64\n",
+       "temp_inside      float64\n",
+       "temp_outside       int64\n",
+       "specials          object\n",
+       "gas_type          object\n",
+       "AC                 int64\n",
+       "rain               int64\n",
+       "sun                int64\n",
+       "refill liters    float64\n",
+       "refill gas        object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cols_to_clean = [\"distance\", \"consume\", \"temp_inside\", \"refill liters\"]\n",
+    "\n",
+    "for col in cols_to_clean:\n",
+    "    df1[col] = df1[col].astype(str).str.replace(\",\", \".\", regex=False)\n",
+    "    df1[col] = pd.to_numeric(df1[col], errors=\"coerce\")\n",
+    "\n",
+    "df1.dtypes\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3115e788-c896-4577-8356-07ae87e679c4",
+   "metadata": {},
+   "source": [
+    "# Step 6 — Compare Both Datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1e5db910-3dc8-4a35-813e-4dc51d8a876a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((388, 12),\n",
+       " (388, 12),\n",
+       " Index(['distance', 'consume', 'speed', 'temp_inside', 'temp_outside',\n",
+       "        'specials', 'gas_type', 'AC', 'rain', 'sun', 'refill liters',\n",
+       "        'refill gas'],\n",
+       "       dtype='object'),\n",
+       " Index(['distance', 'consume', 'speed', 'temp_inside', 'temp_outside',\n",
+       "        'specials', 'gas_type', 'AC', 'rain', 'sun', 'refill liters',\n",
+       "        'refill gas'],\n",
+       "       dtype='object'))"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.shape, df2.shape, df1.columns, df2.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ffc8e852-7023-4334-a77d-08a283e6d4f9",
+   "metadata": {},
+   "source": [
+    "# Step 7 — Select the Main Dataset\n",
+    "\n",
+    "Both datasets contain the exact same structure and number of rows.  \n",
+    "To avoid duplication, we keep only the cleaned version (`df1`) and rename it to `df` for simplicity.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6eddf513-e21f-4526-90e1-7ab9ec5c2a6e",
+   "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>28.0</td>\n",
+       "      <td>5.0</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>45.0</td>\n",
+       "      <td>E10</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>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      28.0      5.0     26         21.5            12      NaN      E10   0   \n",
+       "1      12.0      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           45.0        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": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df1.copy()\n",
+    "df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2047136c-8b08-416d-ae8f-277a0056c037",
+   "metadata": {},
+   "source": [
+    "# Step 8 — Dataset Overview (Shape, Missing Values, Summary Statistics)\n",
+    "\n",
+    "Before creating visualizations, we need a quick structural overview of the dataset.\n",
+    "This helps identify:\n",
+    "- the scale of each variable  \n",
+    "- potential missing data issues  \n",
+    "- distributions and ranges  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "82a7cda5-ed01-4e29-833c-a23b5f5e6b48",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape: (388, 12)\n",
+      "\n",
+      "Missing values:\n",
+      " distance           0\n",
+      "consume            0\n",
+      "speed              0\n",
+      "temp_inside       12\n",
+      "temp_outside       0\n",
+      "specials         295\n",
+      "gas_type           0\n",
+      "AC                 0\n",
+      "rain               0\n",
+      "sun                0\n",
+      "refill liters    375\n",
+      "refill gas       375\n",
+      "dtype: int64\n",
+      "\n",
+      "Summary statistics:\n",
+      "          distance     consume       speed  temp_inside  temp_outside  \\\n",
+      "count  388.000000  388.000000  388.000000   376.000000    388.000000   \n",
+      "mean    19.652835    4.912371   41.927835    21.929521     11.358247   \n",
+      "std     22.667837    1.033172   13.598524     1.010455      6.991542   \n",
+      "min      1.300000    3.300000   14.000000    19.000000     -5.000000   \n",
+      "25%     11.800000    4.300000   32.750000    21.500000      7.000000   \n",
+      "50%     14.600000    4.700000   40.500000    22.000000     10.000000   \n",
+      "75%     19.000000    5.300000   50.000000    22.500000     16.000000   \n",
+      "max    216.100000   12.200000   90.000000    25.500000     31.000000   \n",
+      "\n",
+      "               AC        rain         sun  refill liters  \n",
+      "count  388.000000  388.000000  388.000000      13.000000  \n",
+      "mean     0.077320    0.123711    0.082474      37.115385  \n",
+      "std      0.267443    0.329677    0.275441       8.587282  \n",
+      "min      0.000000    0.000000    0.000000      10.000000  \n",
+      "25%      0.000000    0.000000    0.000000      37.600000  \n",
+      "50%      0.000000    0.000000    0.000000      38.000000  \n",
+      "75%      0.000000    0.000000    0.000000      39.000000  \n",
+      "max      1.000000    1.000000    1.000000      45.000000  \n",
+      "\n",
+      "Unique values per column:\n",
+      " distance         174\n",
+      "consume           43\n",
+      "speed             60\n",
+      "temp_inside       13\n",
+      "temp_outside      33\n",
+      "specials          12\n",
+      "gas_type           2\n",
+      "AC                 2\n",
+      "rain               2\n",
+      "sun                2\n",
+      "refill liters     10\n",
+      "refill gas         2\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Shape:\", df.shape)\n",
+    "print(\"\\nMissing values:\\n\", df.isna().sum())\n",
+    "print(\"\\nSummary statistics:\\n\", df.describe())\n",
+    "print(\"\\nUnique values per column:\\n\", df.nunique())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93fd9517-71f0-420c-8673-ee24b2f00aeb",
+   "metadata": {},
+   "source": [
+    "# Step 9 — Clean the Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fdfacb78-51dd-4587-b478-7dea95b07b29",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "distance        0\n",
+       "consume         0\n",
+       "speed           0\n",
+       "temp_inside     0\n",
+       "temp_outside    0\n",
+       "gas_type        0\n",
+       "AC              0\n",
+       "rain            0\n",
+       "sun             0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Drop useless columns\n",
+    "df = df.drop(columns=[\"specials\", \"refill liters\", \"refill gas\"])\n",
+    "\n",
+    "# Fill temp_inside missing values\n",
+    "df[\"temp_inside\"] = df[\"temp_inside\"].fillna(df[\"temp_inside\"].median())\n",
+    "\n",
+    "df.isna().sum()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "059c038a-2a57-4d69-b678-89e4c20cb05e",
+   "metadata": {},
+   "source": [
+    "# Step 10 — Visual EDA: Distributions\n",
+    "\n",
+    "We start by inspecting the distributions of:\n",
+    "- distance  \n",
+    "- consume  \n",
+    "- speed  \n",
+    "- temp_inside  \n",
+    "- temp_outside  \n",
+    "\n",
+    "This helps us understand skewness, typical ranges, and potential outliers.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "217b5b0e-a2f6-4b5f-9509-fec2b27bcbb3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x1000 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "numeric_cols = [\"distance\", \"consume\", \"speed\", \"temp_inside\", \"temp_outside\"]\n",
+    "\n",
+    "plt.figure(figsize=(12, 10))\n",
+    "\n",
+    "for i, col in enumerate(numeric_cols, 1):\n",
+    "    plt.subplot(3, 2, i)\n",
+    "    sns.histplot(df[col], kde=True)\n",
+    "    plt.title(f\"Distribution of {col}\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dec996cf-0e57-4c1e-8aae-ac564f46a603",
+   "metadata": {},
+   "source": [
+    "# Step 11 — Relationship Between Consume and Other Variables\n",
+    "\n",
+    "We analyze how fuel consumption relates to:\n",
+    "- distance\n",
+    "- speed\n",
+    "- temp_inside\n",
+    "- temp_outside\n",
+    "- gas_type\n",
+    "- AC, rain, sun\n",
+    "\n",
+    "We use scatterplots for numeric variables and boxplots for categorical variables.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0cdc7ddf-cb40-4521-bea3-4ea42ffd3e83",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x1200 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "plt.figure(figsize=(14, 12))\n",
+    "\n",
+    "# scatter consume vs distance\n",
+    "plt.subplot(3, 2, 1)\n",
+    "sns.scatterplot(x=df[\"distance\"], y=df[\"consume\"])\n",
+    "plt.title(\"Consume vs Distance\")\n",
+    "\n",
+    "# scatter consume vs speed\n",
+    "plt.subplot(3, 2, 2)\n",
+    "sns.scatterplot(x=df[\"speed\"], y=df[\"consume\"])\n",
+    "plt.title(\"Consume vs Speed\")\n",
+    "\n",
+    "# scatter consume vs temp_inside\n",
+    "plt.subplot(3, 2, 3)\n",
+    "sns.scatterplot(x=df[\"temp_inside\"], y=df[\"consume\"])\n",
+    "plt.title(\"Consume vs Temp Inside\")\n",
+    "\n",
+    "# scatter consume vs temp_outside\n",
+    "plt.subplot(3, 2, 4)\n",
+    "sns.scatterplot(x=df[\"temp_outside\"], y=df[\"consume\"])\n",
+    "plt.title(\"Consume vs Temp Outside\")\n",
+    "\n",
+    "# boxplot consume vs gas_type\n",
+    "plt.subplot(3, 2, 5)\n",
+    "sns.boxplot(x=df[\"gas_type\"], y=df[\"consume\"])\n",
+    "plt.title(\"Consume vs Gas Type\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01921d10-fe44-4a41-8f7a-1cfffd8c239b",
+   "metadata": {},
+   "source": [
+    "# Step 12 — Correlation Heatmap & Statistical Test (Gas Type)\n",
+    "\n",
+    "We compute the correlation matrix for numeric variables and perform\n",
+    "a statistical test (t-test) to check whether the difference between\n",
+    "E10 and SP98 consumption is significant.\n",
+    "python\n",
+    "Copy code\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "5b593102-1898-4a08-a546-e428d0ef8c1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Irma\\AppData\\Local\\Temp\\ipykernel_21472\\2039913923.py:5: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
+      "  sns.heatmap(df.corr(), annot=True, cmap=\"coolwarm\")\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.figure(figsize=(8,6))\n",
+    "sns.heatmap(df.corr(), annot=True, cmap=\"coolwarm\")\n",
+    "plt.title(\"Correlation Matrix\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "7c7e4c4e-122e-48c1-abbc-eac12a18dee9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7634526478628367"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "e10 = df[df[\"gas_type\"] == \"E10\"][\"consume\"]\n",
+    "sp98 = df[df[\"gas_type\"] == \"SP98\"][\"consume\"]\n",
+    "\n",
+    "stat, p = ttest_ind(e10, sp98)\n",
+    "\n",
+    "p\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf04587a-4945-42b3-bcea-6c59797bcdb9",
+   "metadata": {},
+   "source": [
+    "✅ Conclusion:\n",
+    "\n",
+    "“The difference in fuel consumption between E10 and SP98 is NOT statistically significant.”\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d85baad2-d70b-476c-b609-1a4420bb449d",
+   "metadata": {},
+   "source": [
+    "# Step 13 — Simple Linear Regression Model\n",
+    "\n",
+    "We create a regression model to predict fuel consumption (`consume`)\n",
+    "based on the available numeric and categorical features.\n",
+    "\n",
+    "This helps us identify:\n",
+    "- which variables impact consumption\n",
+    "- whether gas_type contributes to prediction\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "0f81d9d1-fdb1-4941-bf08-c023137c40cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.6598027559858751, 0.09412310938686363)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.metrics import mean_absolute_error, r2_score\n",
+    "\n",
+    "# -----------------------\n",
+    "# 1. Define features and target\n",
+    "# -----------------------\n",
+    "X = df.drop(columns=[\"consume\"])\n",
+    "y = df[\"consume\"]\n",
+    "\n",
+    "# -----------------------\n",
+    "# 2. Identify categorical and numeric columns\n",
+    "# -----------------------\n",
+    "cat_cols = [\"gas_type\"]\n",
+    "num_cols = [col for col in X.columns if col not in cat_cols]\n",
+    "\n",
+    "# -----------------------\n",
+    "# 3. Preprocessing: one-hot encode gas_type\n",
+    "# -----------------------\n",
+    "preprocess = ColumnTransformer(\n",
+    "    transformers=[\n",
+    "        (\"cat\", OneHotEncoder(drop=\"first\"), cat_cols),\n",
+    "        (\"num\", \"passthrough\", num_cols)\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "# -----------------------\n",
+    "# 4. Build pipeline\n",
+    "# -----------------------\n",
+    "model = Pipeline(steps=[\n",
+    "    (\"preprocess\", preprocess),\n",
+    "    (\"regressor\", LinearRegression())\n",
+    "])\n",
+    "\n",
+    "# -----------------------\n",
+    "# 5. Train-test split\n",
+    "# -----------------------\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, y, test_size=0.2, random_state=42\n",
+    ")\n",
+    "\n",
+    "# -----------------------\n",
+    "# 6. Fit model\n",
+    "# -----------------------\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# -----------------------\n",
+    "# 7. Evaluate model\n",
+    "# -----------------------\n",
+    "y_pred = model.predict(X_test)\n",
+    "mae = mean_absolute_error(y_test, y_pred)\n",
+    "r2 = r2_score(y_test, y_pred)\n",
+    "\n",
+    "mae, r2\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37208dfa-4670-4869-8e18-b773eb111b3f",
+   "metadata": {},
+   "source": [
+    "# Step 14 — Feature Importance (Random Forest)\n",
+    "\n",
+    "Linear Regression showed limited predictive power.\n",
+    "To better understand variable influence, we use a Random Forest Regressor,\n",
+    "which captures nonlinear relationships and provides feature importance values.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "0e7084e4-5937-4aac-8085-24d6ab6b81d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "distance         0.659489\n",
+       "speed            0.130940\n",
+       "temp_outside     0.123540\n",
+       "temp_inside      0.035744\n",
+       "rain             0.027751\n",
+       "gas_type_SP98    0.010390\n",
+       "AC               0.009388\n",
+       "sun              0.002758\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "# -----------------------\n",
+    "# 1. Rebuild X with encoded categories\n",
+    "# -----------------------\n",
+    "df_encoded = pd.get_dummies(df, columns=[\"gas_type\"], drop_first=True)\n",
+    "\n",
+    "X = df_encoded.drop(columns=[\"consume\"])\n",
+    "y = df_encoded[\"consume\"]\n",
+    "\n",
+    "# -----------------------\n",
+    "# 2. Train-test split\n",
+    "# -----------------------\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, y, test_size=0.2, random_state=42\n",
+    ")\n",
+    "\n",
+    "# -----------------------\n",
+    "# 3. Train model\n",
+    "# -----------------------\n",
+    "rf = RandomForestRegressor(n_estimators=300, random_state=42)\n",
+    "rf.fit(X_train, y_train)\n",
+    "\n",
+    "# -----------------------\n",
+    "# 4. Feature importance\n",
+    "# -----------------------\n",
+    "importances = pd.Series(rf.feature_importances_, index=X.columns)\n",
+    "importances.sort_values(ascending=False)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cb06c28-f8ed-431a-9c4e-062a0990e303",
+   "metadata": {},
+   "source": [
+    "# Step 15 — Final Storytelling & Recommendations\n",
+    "\n",
+    "## 🔍 Summary of Findings\n",
+    "\n",
+    "### 1. Fuel type (E10 vs SP98)\n",
+    "- Visual differences were small\n",
+    "- Statistical test (p = 0.76) shows **no significant difference**\n",
+    "- Feature importance confirms gas_type has **almost no effect** (score = 0.01)\n",
+    "\n",
+    "**Conclusion:**  \n",
+    "➡️ SP98 does NOT reduce consumption compared to E10  \n",
+    "➡️ Cobify can safely choose the cheaper fuel\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 2. What actually influences consumption?\n",
+    "According to the Random Forest model:\n",
+    "\n",
+    "1. **Distance** → short trips dramatically increase consumption  \n",
+    "2. **Speed** → traffic / low-speed driving raises consumption  \n",
+    "3. **Outside temperature** → colder days increase consumption  \n",
+    "4. Light effects from rain  \n",
+    "5. AC, Sun, Inside temp → minimal influence\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## 📈 Practical Recommendations for Cobify\n",
+    "\n",
+    "1. **Use E10 instead of SP98**\n",
+    "   - Same consumption  \n",
+    "   - Cheaper price  \n",
+    "   - Same performance in data  \n",
+    "\n",
+    "2. **Reduce short inefficient trips**\n",
+    "   - Batch nearby rides  \n",
+    "   - Encourage longer trip matching  \n",
+    "   - Optimize route assignment\n",
+    "\n",
+    "3. **Improve operations in cold conditions**\n",
+    "   - Cars consume more on cold days  \n",
+    "   - Pre-warm engines  \n",
+    "   - Plan routes to avoid excessive idling\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## 🧠 Conclusion\n",
+    "Fuel type does NOT determine fuel efficiency.  \n",
+    "Driving conditions do.\n",
+    "\n",
+    "Cobify should choose the **cheapest fuel option** (E10),  \n",
+    "and focus on optimizing trips, distances, and speeds to reduce consumption.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "661a5e58-b424-4e5c-877e-f02a27031044",
+   "metadata": {},
+   "source": [
+    "# Step 16 — Save the Final Processed Dataset\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "2be8e09b-894f-431f-acdb-beaf072a3e32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(\"data/processed_measurements.csv\", index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64bfc7dc-39e4-4c86-ba2e-3f7eb262de0d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/data/measurements2_converted.csv b/data/measurements2_converted.csv
new file mode 100644
index 0000000..00f56c9
--- /dev/null
+++ b/data/measurements2_converted.csv
@@ -0,0 +1,389 @@
+distance,consume,speed,temp_inside,temp_outside,specials,gas_type,AC,rain,sun,refill liters,refill gas
+28.0,5.0,26,21.5,12,,E10,0,0,0,45.0,E10
+12.0,4.2,30,21.5,13,,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,,
+12.3,5.0,40,21.5,6,,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,,
+11.8,5.0,29,21.5,10,,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,,
+19.0,4.3,36,21.5,7,,E10,0,0,0,,
+12.1,5.7,36,21.5,8,,E10,0,0,0,,
+20.0,5.2,37,21.5,4,,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,,
+12.4,4.0,55,21.5,7,AC rain,E10,1,1,0,,
+4.5,5.0,29,21.5,7,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
+2.0,9.9,21,21.5,4,,SP98,0,0,0,,
+14.2,5.1,32,21.5,7,,SP98,0,0,0,,
+16.0,4.9,25,21.5,8,,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,,
+14.0,4.3,40,21.5,8,,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,,
+2.0,6.2,20,21.5,10,,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,,
+11.6,5.0,25,21.5,6,,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,,
+34.8,4.0,28,20.0,4,,SP98,0,0,0,,
+14.2,5.4,36,20.0,6,,SP98,0,0,0,,
+5.2,4.5,39,20.0,10,,SP98,0,0,0,,
+10.5,3.6,42,20.0,10,,SP98,0,0,0,37.7,SP98
+12.3,5.2,57,20.0,10,,SP98,0,0,0,,
+11.8,4.9,25,20.0,11,,SP98,0,0,0,,
+12.3,6.2,58,20.0,11,rain,SP98,0,1,0,,
+13.2,4.3,51,20.0,11,rain,SP98,0,1,0,,
+13.0,5.0,45,20.0,11,rain,SP98,0,1,0,,
+12.9,5.1,32,20.0,11,,SP98,0,0,0,,
+13.9,5.6,22,20.0,8,,SP98,0,0,0,,
+11.8,4.3,37,20.0,6,,SP98,0,0,0,,
+12.2,5.8,60,20.0,11,,SP98,0,0,0,,
+12.5,4.0,51,20.0,13,,SP98,0,0,0,,
+12.4,4.7,43,,10,,SP98,0,0,0,,
+11.8,5.9,21,20.0,9,AC rain,SP98,1,1,0,,
+11.8,5.3,52,,11,,SP98,0,0,0,,
+12.5,4.2,57,20.0,11,,SP98,0,0,0,,
+15.7,5.3,33,,9,,SP98,0,0,0,,
+12.9,5.7,35,,9,,SP98,0,0,0,,
+6.4,4.4,37,,10,,SP98,0,0,0,,
+5.3,4.1,34,,9,,SP98,0,0,0,,
+26.2,5.8,71,21.0,8,AC rain,SP98,1,1,0,,
+18.8,5.0,62,,9,rain,SP98,0,1,0,,
+4.9,6.9,25,21.0,12,rain,SP98,0,1,0,,
+12.4,5.4,18,21.0,11,AC rain,SP98,1,1,0,,
+22.9,5.3,45,20.5,7,,SP98,0,0,0,,
+162.7,5.5,75,23.0,1,,SP98,0,0,0,45.0,SP98
+4.9,6.5,26,21.0,1,,SP98,0,0,0,,
+11.8,4.7,36,21.0,4,,SP98,0,0,0,,
+16.6,5.1,56,21.0,7,,SP98,0,0,0,,
+12.4,5.7,37,21.0,7,,SP98,0,0,0,,
+15.9,5.4,25,21.0,7,,SP98,0,0,0,,
+5.1,8.7,21,23.5,5,,SP98,0,0,0,,
+22.4,4.9,66,20.5,7,,SP98,0,0,0,,
+31.1,4.7,42,20.5,7,,SP98,0,0,0,,
+4.9,6.3,27,20.5,3,,SP98,0,0,0,,
+11.8,5.1,26,23.0,4,rain,SP98,0,1,0,,
+22.9,6.0,42,23.0,4,rain,SP98,0,1,0,,
+12.4,4.6,38,23.0,1,snow,SP98,0,1,0,,
+12.9,5.8,40,23.0,4,,SP98,0,0,0,,
+11.8,5.1,43,23.0,0,,SP98,0,0,0,,
+12.2,5.8,58,23.0,2,,SP98,0,0,0,,
+24.8,4.6,55,23.0,3,,SP98,0,0,0,,
+14.2,5.6,24,23.0,8,,SP98,0,0,0,,
+11.8,4.6,38,23.0,0,snow,SP98,0,1,0,,
+12.2,6.3,57,23.0,0,snow,SP98,0,1,0,,
+24.7,5.5,56,25.0,1,,SP98,0,0,0,,
+6.8,4.3,46,24.0,2,,SP98,0,0,0,,
+17.3,5.6,37,21.5,1,,SP98,0,0,0,,
+11.8,4.3,44,21.5,-3,,SP98,0,0,0,,
+15.9,5.7,46,21.5,5,,SP98,0,0,0,,
+5.1,6.4,39,21.5,4,,SP98,0,0,0,,
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+23.5,6.0,25,21.5,5,rain,SP98,0,1,0,,
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+12.3,6.1,61,21.5,10,rain,SP98,0,1,0,,
+16.1,5.4,24,21.5,7,rain,E10,0,1,0,38.0,E10
+11.8,4.3,40,21.5,10,rain,E10,0,1,0,,
+12.3,5.4,58,21.5,13,,E10,0,0,0,,
+12.4,4.3,49,21.5,17,,E10,0,0,0,,
+7.0,5.2,25,21.5,17,,E10,0,0,0,,
+11.8,4.1,37,21.5,10,,E10,0,0,0,,
+20.1,4.4,41,21.5,18,,E10,0,0,0,,
+20.8,4.5,45,21.5,10,,E10,0,0,0,,
+1.7,10.8,14,21.5,10,rain,E10,0,1,0,,
+35.9,4.7,45,21.5,12,,E10,0,0,0,,
+36.9,4.8,52,21.5,5,,E10,0,0,0,,
+16.8,4.0,46,21.5,8,,E10,0,0,0,,
+9.9,5.0,28,21.5,9,,E10,0,0,0,,
+22.9,4.6,61,21.5,7,rain,E10,0,1,0,,
+17.3,5.0,61,21.5,6,AC rain,E10,1,1,0,,
+11.8,4.3,37,21.5,7,,E10,0,0,0,,
+36.6,5.2,80,21.5,7,rain,E10,0,1,0,,
+44.9,4.7,62,21.5,8,,E10,0,0,0,,
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+35.8,4.4,51,21.5,6,,E10,0,0,0,,
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+12.3,7.1,52,22.5,0,AC snow,E10,1,1,0,,
+12.4,5.2,51,22.5,1,,E10,0,0,0,,
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+43.7,4.7,44,22.0,9,half rain half sun,SP98,0,1,0,10.0,SP98
+12.1,4.2,43,22.0,4,,SP98,0,0,0,39.0,SP98
+56.1,4.8,82,22.0,13,,SP98,0,0,0,,
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+8.7,5.3,28,22.5,12,AC rain,SP98,1,1,0,,
+7.9,4.7,31,22.5,12,AC,SP98,1,0,0,,
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+12.9,4.5,42,20.0,13,,SP98,0,0,0,,
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+15.7,4.1,32,22.5,13,,SP98,0,0,0,,
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+12.8,4.6,22,22.5,12,,SP98,0,0,0,,
+19.0,4.4,58,22.5,17,sun,SP98,0,0,1,,
+29.0,4.0,27,22.5,12,,SP98,0,0,0,,
+12.1,5.0,32,22.5,9,,SP98,0,0,0,,
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+12.9,5.1,34,22.5,8,rain,SP98,0,1,0,,
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diff --git a/data/processed_measurements.csv b/data/processed_measurements.csv
new file mode 100644
index 0000000..8dda02a
--- /dev/null
+++ b/data/processed_measurements.csv
@@ -0,0 +1,389 @@
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