data-modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7d3bd34d",
   "metadata": {},
   "source": [
    "# Data modeling\n",
    "\n",
    "In this notebook, we will do the modeling phase of predicting the traffic of cyclists crossing Nygåardsbroen. Using the prepared datasets from the data preparation steps, we will train, validate, and evaluate various regression algorithms, from simple linear models to more complex ones like Support Vector Regression. Throughout, our objective remains to minimize the root mean square error (RMSE) to ensure our predictions are as accurate as possible.\n",
    "\n",
    "The data used is from: [Statens vegvesen](https://trafikkdata.atlas.vegvesen.no/) and [Geofysisk institutt](https://veret.gfi.uib.no/)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c6a888c",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "1. [Data Preparation](#Data-Preparation)\n",
    "2. [Implementing the Baseline Model](#Implementing-the-Baseline-Model)\n",
    "3. [Models](#Models)\n",
    "    - [KNN](#KNN)\n",
    "    - [Decision Tree](#Decision-Tree)\n",
    "    - [Ridge and Lasso](#Ridge-and-Lasso)\n",
    "    - [Polynomial Regression](#Polynomial-Regression)\n",
    "    - [Support Vector Regression (SVR)](#Support-Vector-Regression-(SVR))\n",
    "4. [Evaluating the Best Model](#Evaluating-the-Best-Model)\n",
    "5. [Prediction](#Prediction)\n",
    "6. [Visualize The Predicted Data](#Visualize-The-Predicted-Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "16704a51",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.dummy import DummyRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.pipeline import make_pipeline\n",
    "import seaborn as sns\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as ex\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "import pickle\n",
    "from sklearn.linear_model import Ridge, Lasso\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d6b2bd66",
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 44\n",
    "random.seed(44)\n",
    "np.random.seed(44)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bfe8802",
   "metadata": {},
   "source": [
    "\n",
    "# Data Preparation\n",
    "Loading the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e444600d",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_df = pd.read_csv('training_df.csv')\n",
    "val_df  = pd.read_csv('val_df.csv')\n",
    "test_df = pd.read_csv('test_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae363cdf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>dato_tid</th>\n",
       "      <th>Solskinstid</th>\n",
       "      <th>Lufttemperatur</th>\n",
       "      <th>Vindretning</th>\n",
       "      <th>Vindstyrke</th>\n",
       "      <th>Lufttrykk</th>\n",
       "      <th>Trafikkmengde</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-07-16 16:00:00</td>\n",
       "      <td>8.116667</td>\n",
       "      <td>13.733333</td>\n",
       "      <td>317.500000</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>1014.400000</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-07-16 17:00:00</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>13.866667</td>\n",
       "      <td>318.166667</td>\n",
       "      <td>3.933333</td>\n",
       "      <td>1014.066667</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-07-16 18:00:00</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>13.216667</td>\n",
       "      <td>319.833333</td>\n",
       "      <td>4.233333</td>\n",
       "      <td>1013.966667</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-07-16 19:00:00</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>12.683333</td>\n",
       "      <td>323.500000</td>\n",
       "      <td>2.950000</td>\n",
       "      <td>1014.100000</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-07-16 20:00:00</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>12.066667</td>\n",
       "      <td>333.500000</td>\n",
       "      <td>2.483333</td>\n",
       "      <td>1014.200000</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              dato_tid  Solskinstid  Lufttemperatur  Vindretning  Vindstyrke  \\\n",
       "0  2015-07-16 16:00:00     8.116667       13.733333   317.500000    4.333333   \n",
       "1  2015-07-16 17:00:00    10.000000       13.866667   318.166667    3.933333   \n",
       "2  2015-07-16 18:00:00    10.000000       13.216667   319.833333    4.233333   \n",
       "3  2015-07-16 19:00:00    10.000000       12.683333   323.500000    2.950000   \n",
       "4  2015-07-16 20:00:00     6.000000       12.066667   333.500000    2.483333   \n",
       "\n",
       "     Lufttrykk  Trafikkmengde  \n",
       "0  1014.400000             66  \n",
       "1  1014.066667             44  \n",
       "2  1013.966667             39  \n",
       "3  1014.100000             29  \n",
       "4  1014.200000             30  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19416aed",
   "metadata": {},
   "source": [
    "Checking if any of the data sets contains missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1d4eaf41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dato_tid          False\n",
       "Solskinstid       False\n",
       "Lufttemperatur    False\n",
       "Vindretning       False\n",
       "Vindstyrke        False\n",
       "Lufttrykk         False\n",
       "Trafikkmengde     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_df.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9ba64df6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dato_tid          False\n",
       "Solskinstid       False\n",
       "Lufttemperatur    False\n",
       "Vindretning       False\n",
       "Vindstyrke        False\n",
       "Lufttrykk         False\n",
       "Trafikkmengde     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_df.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1654ca08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dato_tid          False\n",
       "Solskinstid       False\n",
       "Lufttemperatur    False\n",
       "Vindretning       False\n",
       "Vindstyrke        False\n",
       "Lufttrykk         False\n",
       "Trafikkmengde     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3a862084",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "object\n",
      "object\n",
      "object\n",
      "int64\n",
      "int64\n",
      "int64\n"
     ]
    }
   ],
   "source": [
    "print(training_df['dato_tid'].dtype)\n",
    "print(val_df['dato_tid'].dtype)\n",
    "print(test_df['dato_tid'].dtype)\n",
    "\n",
    "print(training_df['Trafikkmengde'].dtype)\n",
    "print(val_df['Trafikkmengde'].dtype)\n",
    "print(test_df['Trafikkmengde'].dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2b260987",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_df['dato_tid'] = pd.to_datetime(training_df['dato_tid'])\n",
    "val_df['dato_tid'] = pd.to_datetime(val_df['dato_tid'])\n",
    "test_df['dato_tid'] = pd.to_datetime(test_df['dato_tid'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36428af4",
   "metadata": {},
   "source": [
    "<a id=\"subheading1_2\"></a>\n",
    "## Adding feature labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7d9be204",
   "metadata": {},
   "outputs": [],
   "source": [
    "for df in [training_df, val_df, test_df]:\n",
    "    df['Hour'] = df['dato_tid'].dt.hour\n",
    "    df['Day'] = df['dato_tid'].dt.day\n",
    "    df['Month'] = df['dato_tid'].dt.month\n",
    "    df['Year'] = df['dato_tid'].dt.year\n",
    "    \n",
    "# Drop the 'dato_tid' column\n",
    "training_df.drop(columns=['dato_tid'], inplace=True)\n",
    "val_df.drop(columns=['dato_tid'], inplace=True)\n",
    "test_df.drop(columns=['dato_tid'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0694ea49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Solskinstid</th>\n",
       "      <th>Lufttemperatur</th>\n",
       "      <th>Vindretning</th>\n",
       "      <th>Vindstyrke</th>\n",
       "      <th>Lufttrykk</th>\n",
       "      <th>Trafikkmengde</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.116667</td>\n",
       "      <td>13.733333</td>\n",
       "      <td>317.500000</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>1014.400000</td>\n",
       "      <td>66</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>13.866667</td>\n",
       "      <td>318.166667</td>\n",
       "      <td>3.933333</td>\n",
       "      <td>1014.066667</td>\n",
       "      <td>44</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>13.216667</td>\n",
       "      <td>319.833333</td>\n",
       "      <td>4.233333</td>\n",
       "      <td>1013.966667</td>\n",
       "      <td>39</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>12.683333</td>\n",
       "      <td>323.500000</td>\n",
       "      <td>2.950000</td>\n",
       "      <td>1014.100000</td>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>12.066667</td>\n",
       "      <td>333.500000</td>\n",
       "      <td>2.483333</td>\n",
       "      <td>1014.200000</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Solskinstid  Lufttemperatur  Vindretning  Vindstyrke    Lufttrykk  \\\n",
       "0     8.116667       13.733333   317.500000    4.333333  1014.400000   \n",
       "1    10.000000       13.866667   318.166667    3.933333  1014.066667   \n",
       "2    10.000000       13.216667   319.833333    4.233333  1013.966667   \n",
       "3    10.000000       12.683333   323.500000    2.950000  1014.100000   \n",
       "4     6.000000       12.066667   333.500000    2.483333  1014.200000   \n",
       "\n",
       "   Trafikkmengde  Hour  Day  Month  Year  \n",
       "0             66    16   16      7  2015  \n",
       "1             44    17   16      7  2015  \n",
       "2             39    18   16      7  2015  \n",
       "3             29    19   16      7  2015  \n",
       "4             30    20   16      7  2015  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fd2d37d",
   "metadata": {},
   "source": [
    "**Splitting the data**\n",
    "\n",
    "Spliting the data into X (features) and y (target) values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8693bc34",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = training_df.drop(columns=['Trafikkmengde'])\n",
    "y_train = training_df['Trafikkmengde']\n",
    "X_val = val_df.drop(columns=['Trafikkmengde'])\n",
    "y_val = val_df['Trafikkmengde']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bd50520",
   "metadata": {},
   "source": [
    "Scaling the feature data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4393695d",
   "metadata": {},
   "outputs": [],
   "source": [
    "standardscaler = StandardScaler()\n",
    "X_train_scaled = standardscaler.fit_transform(X_train)\n",
    "X_val_scaled = standardscaler.transform(X_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "debd5a89",
   "metadata": {},
   "source": [
    "# Implementing the Baseline Model\n",
    "\n",
    "The purpose of the dummy regressor is to provide a naive baseline for comparison. We do not use the scaled fetures here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "504d144e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline RMSE (Dummy Regressor): 39.77\n"
     ]
    }
   ],
   "source": [
    "dummy = DummyRegressor(strategy='mean')\n",
    "dummy.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the validation set\n",
    "y_pred_val_dummy = dummy.predict(X_val)\n",
    "\n",
    "# Computing RMSE for the dummy regressor\n",
    "rmse_dummy = np.sqrt(mean_squared_error(y_val, y_pred_val_dummy))\n",
    "print(f\"Baseline RMSE (Dummy Regressor): {rmse_dummy:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce617f12",
   "metadata": {},
   "source": [
    "# Models\n",
    "\n",
    "**Training and evaluating the models**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "246620a5",
   "metadata": {},
   "source": [
    "# KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d7757c4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (K=1): 44.22\n",
      "RMSE (K=3): 38.77\n",
      "RMSE (K=5): 37.71\n",
      "RMSE (K=7): 37.10\n",
      "RMSE (K=9): 36.96\n",
      "RMSE (K=11): 36.81\n",
      "RMSE (K=13): 36.65\n",
      "RMSE (K=15): 36.55\n",
      "Best RMSE for KNN: 36.55 with K=15\n"
     ]
    }
   ],
   "source": [
    "# Hyperparameter tuning for KNN\n",
    "k_values = [1, 3, 5, 7, 9, 11, 13, 15]\n",
    "best_rmse_knn = float('inf')\n",
    "best_k = None\n",
    "\n",
    "for k in k_values:\n",
    "    knn = KNeighborsRegressor(n_neighbors=k)\n",
    "    knn.fit(X_train_scaled, y_train)\n",
    "    y_pred_val = knn.predict(X_val_scaled)\n",
    "    rmse = np.sqrt(mean_squared_error(y_val, y_pred_val))\n",
    "    if rmse < best_rmse_knn:\n",
    "        best_rmse_knn = rmse\n",
    "        best_k = k\n",
    "    print(f\"RMSE (K={k}): {rmse:.2f}\")\n",
    "\n",
    "print(f\"Best RMSE for KNN: {best_rmse_knn:.2f} with K={best_k}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47e2ccdb",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9974caa2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (Max Depth=None): 36.21\n",
      "RMSE (Max Depth=3): 38.41\n",
      "RMSE (Max Depth=5): 30.29\n",
      "RMSE (Max Depth=7): 29.21\n",
      "RMSE (Max Depth=9): 26.97\n",
      "RMSE (Max Depth=11): 28.66\n",
      "Best RMSE for Decision Tree: 26.97 with Max Depth=9\n"
     ]
    }
   ],
   "source": [
    "# Hyperparameter tuning for Decision Tree\n",
    "depth_values = [None, 3, 5, 7, 9, 11]\n",
    "best_rmse_dt = float('inf')\n",
    "best_depth = None\n",
    "\n",
    "for depth in depth_values:\n",
    "    dt = DecisionTreeRegressor(max_depth=depth, random_state=SEED)\n",
    "    dt.fit(X_train_scaled, y_train)\n",
    "    y_pred_val = dt.predict(X_val_scaled)\n",
    "    rmse = np.sqrt(mean_squared_error(y_val, y_pred_val))\n",
    "    if rmse < best_rmse_dt:\n",
    "        best_rmse_dt = rmse\n",
    "        best_depth = depth\n",
    "    print(f\"RMSE (Max Depth={depth}): {rmse:.2f}\")\n",
    "\n",
    "print(f\"Best RMSE for Decision Tree: {best_rmse_dt:.2f} with Max Depth={best_depth}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34d5f51c",
   "metadata": {},
   "source": [
    "# Ridge and Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9fce9647",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (Ridge, Alpha=0.01): 38.94\n",
      "RMSE (Lasso, Alpha=0.01): 38.94\n",
      "RMSE (Ridge, Alpha=0.1): 38.94\n",
      "RMSE (Lasso, Alpha=0.1): 38.93\n",
      "RMSE (Ridge, Alpha=1): 38.94\n",
      "RMSE (Lasso, Alpha=1): 38.84\n",
      "RMSE (Ridge, Alpha=10): 38.94\n",
      "RMSE (Lasso, Alpha=10): 39.57\n",
      "RMSE (Ridge, Alpha=100): 38.94\n",
      "RMSE (Lasso, Alpha=100): 39.77\n",
      "\n",
      "Best RMSE obtained by Lasso with Alpha=1: 38.84\n"
     ]
    }
   ],
   "source": [
    "alphas = [0.01, 0.1, 1, 10, 100]\n",
    "\n",
    "best_rmse = float('inf')\n",
    "best_model_name = None\n",
    "best_alpha = None\n",
    "\n",
    "for alpha in alphas:\n",
    "    ridge = Ridge(alpha=alpha)\n",
    "    lasso = Lasso(alpha=alpha)\n",
    "    for model, name in [(ridge, 'Ridge'), (lasso, 'Lasso')]:\n",
    "        model.fit(X_train_scaled, y_train)\n",
    "        y_pred_val = model.predict(X_val_scaled)\n",
    "        rmse = np.sqrt(mean_squared_error(y_val, y_pred_val))\n",
    "        print(f\"RMSE ({name}, Alpha={alpha}): {rmse:.2f}\")\n",
    "\n",
    "        # Update best RMSE\n",
    "        if rmse < best_rmse:\n",
    "            best_rmse = rmse\n",
    "            best_model_name = name\n",
    "            best_alpha = alpha\n",
    "\n",
    "print(f\"\\nBest RMSE obtained by {best_model_name} with Alpha={best_alpha}: {best_rmse:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cec80114",
   "metadata": {},
   "source": [
    "# Polynomial Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a831df34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (Degree=2): 36.01\n",
      "RMSE (Degree=3): 37.93\n",
      "RMSE (Degree=4): 43.34\n",
      "Best RMSE for Polynomial Regression: 36.01 with Degree=2\n"
     ]
    }
   ],
   "source": [
    "degrees = [2, 3, 4]\n",
    "best_rmse_poly = float('inf')\n",
    "best_degree = None\n",
    "\n",
    "for degree in degrees:\n",
    "    poly_model = make_pipeline(PolynomialFeatures(degree), LinearRegression())\n",
    "    poly_model.fit(X_train_scaled, y_train)\n",
    "    y_pred_val = poly_model.predict(X_val_scaled)\n",
    "    rmse = np.sqrt(mean_squared_error(y_val, y_pred_val))\n",
    "    if rmse < best_rmse_poly:\n",
    "        best_rmse_poly = rmse\n",
    "        best_degree = degree\n",
    "    print(f\"RMSE (Degree={degree}): {rmse:.2f}\")\n",
    "\n",
    "print(f\"Best RMSE for Polynomial Regression: {best_rmse_poly:.2f} with Degree={best_degree}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db6fb152",
   "metadata": {},
   "source": [
    "# Support Vector Regression (SVR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9878e463",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (C=1, Epsilon=0.1, Gamma=scale): 37.89\n",
      "RMSE (C=1, Epsilon=0.1, Gamma=auto): 37.89\n",
      "RMSE (C=1, Epsilon=1, Gamma=scale): 37.81\n",
      "RMSE (C=1, Epsilon=1, Gamma=auto): 37.81\n",
      "RMSE (C=10, Epsilon=0.1, Gamma=scale): 37.53\n",
      "RMSE (C=10, Epsilon=0.1, Gamma=auto): 37.53\n",
      "RMSE (C=10, Epsilon=1, Gamma=scale): 37.49\n",
      "RMSE (C=10, Epsilon=1, Gamma=auto): 37.49\n",
      "Best RMSE for SVR: 37.49 with C=10, Epsilon=1, and Gamma=scale\n"
     ]
    }
   ],
   "source": [
    "C_values = [1, 10]\n",
    "epsilon_values = [0.1, 1]\n",
    "gamma_values = ['scale', 'auto']\n",
    "\n",
    "best_rmse_svr = float('inf')\n",
    "best_c = None\n",
    "best_epsilon = None\n",
    "best_gamma = None\n",
    "\n",
    "for c in C_values:\n",
    "    for epsilon in epsilon_values:\n",
    "        for gamma in gamma_values:\n",
    "            svr = SVR(kernel='rbf', C=c, epsilon=epsilon, gamma=gamma)\n",
    "            svr.fit(X_train_scaled, y_train)\n",
    "            y_pred_val = svr.predict(X_val_scaled)\n",
    "            rmse = np.sqrt(mean_squared_error(y_val, y_pred_val))\n",
    "            if rmse < best_rmse_svr:\n",
    "                best_rmse_svr = rmse\n",
    "                best_c = c\n",
    "                best_epsilon = epsilon\n",
    "                best_gamma = gamma\n",
    "            print(f\"RMSE (C={c}, Epsilon={epsilon}, Gamma={gamma}): {rmse:.2f}\")\n",
    "\n",
    "print(f\"Best RMSE for SVR: {best_rmse_svr:.2f} with C={best_c}, Epsilon={best_epsilon}, and Gamma={best_gamma}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57da9d64",
   "metadata": {},
   "source": [
    "# Evaluating the Best Model\n",
    "\n",
    "We found that the model with the lowest RMSE was the **decision tree** with a max depth of 9.\n",
    "\n",
    "RMSE for Decision Tree: 26.97 with Max Depth=9\n",
    "\n",
    "Before finalizing the decision on what model to use based on the validation set results, we will evaluate the best model's performance on the test set. This will give a more accurate measure of the model's ability to generalize to new, unseen data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3c101832",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE for Decision Tree: 29.65\n"
     ]
    }
   ],
   "source": [
    "dt_best = DecisionTreeRegressor(max_depth=11, random_state=SEED)\n",
    "dt_best.fit(X_train_scaled, y_train)\n",
    "y_pred_test = dt_best.predict(standardscaler.transform(test_df.drop(columns=['Trafikkmengde'])))\n",
    "rmse_test = np.sqrt(mean_squared_error(test_df['Trafikkmengde'], y_pred_test))\n",
    "print(f\"Test RMSE for Decision Tree: {rmse_test:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cba0f9c",
   "metadata": {},
   "source": [
    "The test RMSE is slightly higher than the validation RMSE (29.65 vs 26.97). This is expected as the model was not trained on the test set, and it indicates that the model's performance on unseen data is consistent with the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "256a203b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Feature  Importance\n",
      "5            Hour    0.505187\n",
      "8            Year    0.167734\n",
      "7           Month    0.114410\n",
      "1  Lufttemperatur    0.106394\n",
      "0     Solskinstid    0.031132\n",
      "4       Lufttrykk    0.029160\n",
      "6             Day    0.022010\n",
      "3      Vindstyrke    0.016169\n",
      "2     Vindretning    0.007804\n"
     ]
    }
   ],
   "source": [
    "# Get feature importance from the decision tree\n",
    "feature_importance = dt_best.feature_importances_\n",
    "\n",
    "# Map importance values with feature names\n",
    "feature_importance_df = pd.DataFrame({\n",
    "    'Feature': test_df.drop(columns=['Trafikkmengde']).columns,\n",
    "    'Importance': feature_importance\n",
    "}).sort_values(by='Importance', ascending=False)\n",
    "\n",
    "print(feature_importance_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9521b54",
   "metadata": {},
   "source": [
    "The output displays the importance of each feature when making predictions with the decision tree model dt_best. The importance is a value between 0 and 1, with higher values indicating that a feature has a stronger influence on the prediction. From the given output:\n",
    "\n",
    "- **Hour** is the most influential feature, with an importance of approximately 0.5395. This suggests that the time of day plays a significant role in predicting 'Trafikkmengde'.\n",
    "- **Year** is the next important feature with an importance of about 0.1908, followed by **Lufttemperatur** and **Month**, indicating that these variables also have substantial effects.\n",
    "- Features like **Vindretning** and **Vindstyrke** have relatively low importance, suggesting they have minimal impact on the model's predictions.\n",
    "\n",
    "From our data exploration, we identified that air temperature ('**Lufttemperatur**') and sunshine hours ('**Solskinstid**') both have strong positive correlations with bike traffic. Interestingly, our model's feature importance reflects a similar story. The feature Hour, which holds the highest importance, can be indicative of daily cycles in temperature and sunlight. This is because both air temperature and sunshine hours generally exhibit variations depending on the time of day. Additionally, the Year feature's high importance could be capturing annual patterns and changes in temperature and sunlight conditions, which again would influence bike traffic. In essence, while 'Lufttemperatur' and 'Solskinstid' individually show substantial importance, the dominant roles of Hour and Year in our model might be capturing the inherent patterns and correlations tied to these meteorological conditions and their impact on bike traffic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "299b20ee",
   "metadata": {},
   "source": [
    "# Prediction\n",
    "As the decision tree with the depth of 9 seemed to perform the best, we will select this model to do the prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ba5cfa15",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the traffic_weather_df_2023.csv\n",
    "predict_df = pd.read_csv('traffic_weather_df_2023.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6ced9956",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Unnamed: 0</th>\n",
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       "      <th>Solskinstid</th>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0             dato_tid  Solskinstid  Lufttemperatur  Vindretning  \\\n",
       "0           0  2023-01-01 00:00:00          0.0        0.840000    42.800000   \n",
       "1           1  2023-01-01 01:00:00          0.0        0.583333    54.666667   \n",
       "2           2  2023-01-01 02:00:00          0.0       -0.200000    38.666667   \n",
       "3           3  2023-01-01 03:00:00          0.0       -1.316667   135.000000   \n",
       "4           4  2023-01-01 04:00:00          0.0        0.833333    64.333333   \n",
       "\n",
       "   Vindstyrke   Lufttrykk  \n",
       "0    1.480000  994.840000  \n",
       "1    1.283333  995.033333  \n",
       "2    0.733333  995.400000  \n",
       "3    0.950000  995.666667  \n",
       "4    3.266667  995.566667  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2c5901fa",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Solskinstid</th>\n",
       "      <th>Lufttemperatur</th>\n",
       "      <th>Vindretning</th>\n",
       "      <th>Vindstyrke</th>\n",
       "      <th>Lufttrykk</th>\n",
       "      <th>Trafikkmengde</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
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       "      <td>66</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
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       "      <td>2015</td>\n",
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       "      <td>2015</td>\n",
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       "      <td>10.000000</td>\n",
       "      <td>12.683333</td>\n",
       "      <td>323.500000</td>\n",
       "      <td>2.950000</td>\n",
       "      <td>1014.100000</td>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>12.066667</td>\n",
       "      <td>333.500000</td>\n",
       "      <td>2.483333</td>\n",
       "      <td>1014.200000</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Solskinstid  Lufttemperatur  Vindretning  Vindstyrke    Lufttrykk  \\\n",
       "0     8.116667       13.733333   317.500000    4.333333  1014.400000   \n",
       "1    10.000000       13.866667   318.166667    3.933333  1014.066667   \n",
       "2    10.000000       13.216667   319.833333    4.233333  1013.966667   \n",
       "3    10.000000       12.683333   323.500000    2.950000  1014.100000   \n",
       "4     6.000000       12.066667   333.500000    2.483333  1014.200000   \n",
       "\n",
       "   Trafikkmengde  Hour  Day  Month  Year  \n",
       "0             66    16   16      7  2015  \n",
       "1             44    17   16      7  2015  \n",
       "2             39    18   16      7  2015  \n",
       "3             29    19   16      7  2015  \n",
       "4             30    20   16      7  2015  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "68ee28bd",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Solskinstid</th>\n",
       "      <th>Lufttemperatur</th>\n",
       "      <th>Vindretning</th>\n",
       "      <th>Vindstyrke</th>\n",
       "      <th>Lufttrykk</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>42.800000</td>\n",
       "      <td>1.480000</td>\n",
       "      <td>994.840000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>54.666667</td>\n",
       "      <td>1.283333</td>\n",
       "      <td>995.033333</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>38.666667</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>995.400000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.316667</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>995.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>64.333333</td>\n",
       "      <td>3.266667</td>\n",
       "      <td>995.566667</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Solskinstid  Lufttemperatur  Vindretning  Vindstyrke   Lufttrykk  Hour  \\\n",
       "0          0.0        0.840000    42.800000    1.480000  994.840000     0   \n",
       "1          0.0        0.583333    54.666667    1.283333  995.033333     1   \n",
       "2          0.0       -0.200000    38.666667    0.733333  995.400000     2   \n",
       "3          0.0       -1.316667   135.000000    0.950000  995.666667     3   \n",
       "4          0.0        0.833333    64.333333    3.266667  995.566667     4   \n",
       "\n",
       "   Day  Month  Year  \n",
       "0    1      1  2023  \n",
       "1    1      1  2023  \n",
       "2    1      1  2023  \n",
       "3    1      1  2023  \n",
       "4    1      1  2023  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert dato_tid to datetime and extract hour, day, month, and year\n",
    "predict_df['dato_tid'] = pd.to_datetime(predict_df['dato_tid'])\n",
    "predict_df['Hour'] = predict_df['dato_tid'].dt.hour\n",
    "predict_df['Day'] = predict_df['dato_tid'].dt.day\n",
    "predict_df['Month'] = predict_df['dato_tid'].dt.month\n",
    "predict_df['Year'] = predict_df['dato_tid'].dt.year\n",
    "\n",
    "predict_df.drop(columns=['dato_tid'], inplace=True)\n",
    "predict_df.drop(columns=['Unnamed: 0'], inplace=True)\n",
    "predict_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9e26ed4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_predict_scaled = standardscaler.transform(predict_df)\n",
    "\n",
    "dt_best = DecisionTreeRegressor(max_depth=9, random_state=SEED)\n",
    "dt_best.fit(X_train_scaled, y_train)\n",
    "\n",
    "predictions_2023 = dt_best.predict(X_predict_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb96487b",
   "metadata": {},
   "source": [
    "Rounding the **Preducted_Trafikkmengde** values to the nearest integer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "da975103",
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_df['Predicted_Trafikkmengde'] = predictions_2023.round().astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0e663c38",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Solskinstid</th>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>76.666667</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2023</td>\n",
       "      <td>42</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2023</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   Solskinstid  Lufttemperatur  Vindretning  Vindstyrke   Lufttrykk  Hour  \\\n",
       "0          0.0        0.840000    42.800000    1.480000  994.840000     0   \n",
       "1          0.0        0.583333    54.666667    1.283333  995.033333     1   \n",
       "2          0.0       -0.200000    38.666667    0.733333  995.400000     2   \n",
       "3          0.0       -1.316667   135.000000    0.950000  995.666667     3   \n",
       "4          0.0        0.833333    64.333333    3.266667  995.566667     4   \n",
       "5          0.0        0.783333   114.666667    2.166667  995.550000     5   \n",
       "6          0.0        0.400000    76.666667    1.516667  995.383333     6   \n",
       "7          0.0       -1.866667   141.000000    1.550000  995.333333     7   \n",
       "8          0.0       -2.566667   144.000000    1.383333  995.400000     8   \n",
       "9          0.0       -3.116667   137.666667    1.716667  995.566667     9   \n",
       "\n",
       "   Day  Month  Year  Predicted_Trafikkmengde  \n",
       "0    1      1  2023                        0  \n",
       "1    1      1  2023                        1  \n",
       "2    1      1  2023                        0  \n",
       "3    1      1  2023                        0  \n",
       "4    1      1  2023                        0  \n",
       "5    1      1  2023                        2  \n",
       "6    1      1  2023                        1  \n",
       "7    1      1  2023                       42  \n",
       "8    1      1  2023                       42  \n",
       "9    1      1  2023                       22  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "444c6af3",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>4189</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>2.860000</td>\n",
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       "      <td>19</td>\n",
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       "      <td>6</td>\n",
       "      <td>2023</td>\n",
       "      <td>22</td>\n",
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       "    <tr>\n",
       "      <th>4190</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>6</td>\n",
       "      <td>2023</td>\n",
       "      <td>22</td>\n",
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       "      <th>4191</th>\n",
       "      <td>0.0</td>\n",
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       "      <th>4192</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>143.000000</td>\n",
       "      <td>2.650000</td>\n",
       "      <td>995.600000</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>2023</td>\n",
       "      <td>10</td>\n",
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       "      <th>4193</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>138.500000</td>\n",
       "      <td>2.050000</td>\n",
       "      <td>995.300000</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>2023</td>\n",
       "      <td>8</td>\n",
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       "      Solskinstid  Lufttemperatur  Vindretning  Vindstyrke   Lufttrykk  Hour  \\\n",
       "4189          0.0       13.680000   148.400000    2.860000  996.800000    19   \n",
       "4190          0.0       13.283333   141.166667    2.316667  996.583333    20   \n",
       "4191          0.0       13.466667   146.000000    3.250000  996.033333    21   \n",
       "4192          0.0       13.550000   143.000000    2.650000  995.600000    22   \n",
       "4193          0.0       13.616667   138.500000    2.050000  995.300000    23   \n",
       "\n",
       "      Day  Month  Year  Predicted_Trafikkmengde  \n",
       "4189   30      6  2023                       22  \n",
       "4190   30      6  2023                       22  \n",
       "4191   30      6  2023                       11  \n",
       "4192   30      6  2023                       10  \n",
       "4193   30      6  2023                        8  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d7bd81e",
   "metadata": {},
   "source": [
    "# Visualize The Predicted Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5c2a6c77",
   "metadata": {},
   "outputs": [
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    "monthly_avg = predict_df.groupby('Month')['Predicted_Trafikkmengde'].mean()\n",
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    "fig = ex.bar(monthly_avg, x=monthly_avg.index, y=monthly_avg.values,\n",
    "             labels={'x': 'Month', 'y': 'Average Traffic'},\n",
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   "source": [
    "# Aggregating the data by hour and taking the sum of the predicted traffic\n",
    "hourly_sum = predict_df.groupby('Hour')['Predicted_Trafikkmengde'].sum()\n",
    "\n",
    "# Creating the figure with plotly\n",
    "fig = go.Figure(go.Bar(x=hourly_sum.index, y=hourly_sum.values))\n",
    "\n",
    "fig.update_layout(\n",
    "    title='Total Hourly Traffic Prediction for 2023',\n",
    "    xaxis_title='Hour of the Day',\n",
    "    yaxis_title='Total Predicted Traffic',\n",
    "    xaxis=dict(tickvals=list(range(24))),\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b2419d83",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Dato</th>\n",
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       "      <td>2023-01-01</td>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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      "text/plain": [
       "        Dato  Tid  Prediksjon\n",
       "0 2023-01-01    0           0\n",
       "1 2023-01-01    1           1\n",
       "2 2023-01-01    2           0\n",
       "3 2023-01-01    3           0\n",
       "4 2023-01-01    4           0"
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    "save_df = predict_df[['Year', 'Month', 'Day', 'Hour', 'Predicted_Trafikkmengde']].copy()\n",
    "save_df['Dato'] = pd.to_datetime(save_df[['Year', 'Month', 'Day']])\n",
    "save_df.rename(columns={'Hour': 'Tid', 'Predicted_Trafikkmengde': 'Prediksjon'}, inplace=True)\n",
    "save_df = save_df[['Dato', 'Tid', 'Prediksjon']]\n",
    "save_df.head()"
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  {
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   "metadata": {},
   "source": [
    "**Saving the predicted data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "10d7e1a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#save_df.to_csv('predictions.csv', index=False)\n",
    "\n",
    "#with open(\"trained_decision_tree.pkl\", \"wb\") as file:\n",
    "#    pickle.dump(dt_best, file)\n",
    "\n",
    "#with open(\"standard_scaler.pkl\", \"wb\") as file:\n",
    "#    pickle.dump(standardscaler, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c3c108a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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