code

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        " #Get data\n",
        "import requests\n",
        "\n",
        "#Process data\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from datetime import datetime\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "\n",
        "#Plots\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "\n",
        "#Grafos\n",
        "import networkx as nx\n",
        "\n",
        "#ML\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from statsmodels.tsa.arima.model import ARIMA\n",
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "_crM_d6kyfrH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "from sklearn.exceptions import UndefinedMetricWarning\n",
        "\n",
        "# Suppress UndefinedMetricWarning for precision\n",
        "warnings.filterwarnings(\"ignore\", category=UndefinedMetricWarning)"
      ],
      "metadata": {
        "id": "hVm6Ru20S9JL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DNEFUm1mnLfi",
        "outputId": "d08bde65-bdcc-4328-b372-aadc821f8767"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "During the development of this lab, the codes for the data generation process are available. In order to save time, we have attached these datasets already generated. In order to run the remaining part of the notebook, you just need to specify where those files are located in your local host and avoid running the cells related to data generation, which will be specified during the notebook.\n",
        "\n",
        "Some of these datasets generated are not used in the notebook itself but in the dashboards, that will be provided to the professor via mail."
      ],
      "metadata": {
        "id": "imF0hFyu-n61"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Insert your path\n",
        "path2='/content/drive/MyDrive/finalProjectAW/'"
      ],
      "metadata": {
        "id": "2drV7ZXhnO3c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tiempo por vuelta Monaco,Monza,Silverstone\n",
        "\n",
        "This is not a database itself, you just gather pole position times and represent them inside the notebook, so this part of the code should be executed always"
      ],
      "metadata": {
        "id": "6dBJqXCNrHtc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "qMonaco = []\n",
        "qMonza = []\n",
        "qSilverstone = []\n",
        "\n",
        "for year in range(1950,2024):\n",
        "  # It constructs a URL to fetch the qualifying data for the first race\n",
        "    request = f'http://ergast.com/api/f1/{year}/qualifying/1.json'\n",
        "    response = requests.get(request)\n",
        "\n",
        "    # For each race in the response, it iterates through the races.\n",
        "    for i in range(len(response.json()['MRData']['RaceTable']['Races'])):\n",
        "      # Checks if the current race is held at Monaco, Monza, or Silverstone based on the circuitId.\n",
        "      if response.json()['MRData']['RaceTable']['Races'][i]['Circuit']['circuitId'] == 'monaco':\n",
        "        # If 'Q3' is available in the qualifying results, it appends a tuple containing the season and the Q3 time to the respective circuit's list (qMonaco, qMonza, or qSilverstone).\n",
        "        if 'Q3' in response.json()['MRData']['RaceTable']['Races'][i]['QualifyingResults'][0]:\n",
        "          qMonaco.append(tuple((response.json()['MRData']['RaceTable']['Races'][i]['season'],response.json()['MRData']['RaceTable']['Races'][i]['QualifyingResults'][0]['Q3'])))\n",
        "        #  If 'Q3' is not available, it appends a tuple containing the season and the Q1 time.\n",
        "        #  Before 2006, only Q1 existed. Later, Q2 and Q3 were added.\n",
        "        else:\n",
        "          qMonaco.append(tuple((response.json()['MRData']['RaceTable']['Races'][i]['season'],response.json()['MRData']['RaceTable']['Races'][i]['QualifyingResults'][0]['Q1'])))\n",
        "\n",
        "      if response.json()['MRData']['RaceTable']['Races'][i]['Circuit']['circuitId'] == 'monza':\n",
        "        if 'Q3' in response.json()['MRData']['RaceTable']['Races'][i]['QualifyingResults'][0]:\n",
        "            qMonza.append(tuple((response.json()['MRData']['RaceTable']['Races'][i]['season'],response.json()['MRData']['RaceTable']['Races'][i]['QualifyingResults'][0]['Q3'])))\n",
        "        else:\n",
        "            qMonza.append(tuple((response.json()['MRData']['RaceTable']['Races'][i]['season'],response.json()['MRData']['RaceTable']['Races'][i]['QualifyingResults'][0]['Q1'])))\n",
        "\n",
        "\n",
        "      if response.json()['MRData']['RaceTable']['Races'][i]['Circuit']['circuitId'] == 'silverstone':\n",
        "        if 'Q3' in response.json()['MRData']['RaceTable']['Races'][i]['QualifyingResults'][0]:\n",
        "            qSilverstone.append(tuple((response.json()['MRData']['RaceTable']['Races'][i]['season'],response.json()['MRData']['RaceTable']['Races'][i]['QualifyingResults'][0]['Q3'])))\n",
        "        else:\n",
        "            qSilverstone.append(tuple((response.json()['MRData']['RaceTable']['Races'][i]['season'],response.json()['MRData']['RaceTable']['Races'][i]['QualifyingResults'][0]['Q1'])))\n",
        "\n"
      ],
      "metadata": {
        "id": "-01UkGw4dtqN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Once the information is collected, times are converted into seconds and the data for all circuits are created"
      ],
      "metadata": {
        "id": "wT-CDMKB_0lj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize an empty list to store the calculated times\n",
        "\n",
        "timeSMonza = []\n",
        "# Iterate through the list of qualifying data for Monza\n",
        "for j in range(len(qMonza)):\n",
        "  # Split the qualifying time string into minutes, seconds, and milliseconds\n",
        "  a = qMonza[j][1].split(':')\n",
        "  b = a[1].split('.')\n",
        "  # Calculate the total time in seconds and append it to the list\n",
        "  timeSMonza.append(int(a[0])*60 + int(b[0]) + int(b[1])/1000)\n",
        "\n",
        "# Unzip the years from the original qualifying data for Monza\n",
        "años, _ = zip(*qMonza)\n",
        "# Create a dictionary to store the extracted data\n",
        "datos_dict = {'Año': años, 'Tiempo ': timeSMonza}\n",
        "monza = pd.DataFrame(datos_dict)\n",
        "\n",
        "timeSMonaco = []\n",
        "for j in range(len(qMonaco)):\n",
        "  a = qMonaco[j][1].split(':')\n",
        "  b = a[1].split('.')\n",
        "  timeSMonaco.append(int(a[0])*60 + int(b[0]) + int(b[1])/1000)\n",
        "\n",
        "años, _ = zip(*qMonaco)\n",
        "datos_dict = {'Año': años, 'Tiempo ': timeSMonaco}\n",
        "monaco = pd.DataFrame(datos_dict)\n",
        "\n",
        "timeSSilverstone = []\n",
        "for j in range(len(qSilverstone)):\n",
        "  a = qSilverstone[j][1].split(':')\n",
        "  b = a[1].split('.')\n",
        "  timeSSilverstone.append(int(a[0])*60 + int(b[0]) + int(b[1])/1000)\n",
        "\n",
        "años, _ = zip(*qSilverstone)\n",
        "datos_dict = {'Año': años, 'Tiempo ': timeSSilverstone}\n",
        "silverstone = pd.DataFrame(datos_dict)"
      ],
      "metadata": {
        "id": "PpWV53UJi7Dv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Reresentation of the temporal series\n",
        "# Merge the DataFrames for Monza, Monaco, and Silverstone based on the 'Año' (Year) column\n",
        "merged_df = pd.merge(monza, monaco, on='Año', suffixes=('Monza', 'Monaco'))\n",
        "final_df = pd.merge(merged_df, silverstone, on='Año', suffixes=('_merged', 'Silverstone'))\n",
        "\n",
        "plt.figure(figsize=(20, 6))\n",
        "# Plotting the line graph for Monza, Monaco and Silverstone's qualifying times over the years\n",
        "sns.lineplot(data=final_df, x='Año', y='Tiempo Monza', label='Monza', marker='o')\n",
        "sns.lineplot(data=final_df, x='Año', y='Tiempo Monaco', label='Monaco', marker='o')\n",
        "sns.lineplot(data=final_df, x='Año', y='Tiempo ', label='Silverstone', marker='o')\n",
        "\n",
        "plt.title('Pole position evolution')\n",
        "plt.xlabel('Año')\n",
        "plt.ylabel('Tiempo por vuelta (segundos)')\n",
        "plt.grid(True)\n",
        "plt.legend(title='DataFrames')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "J6Yp0M1UqDnK",
        "outputId": "8eb7772a-f14f-4c03-d652-78a4f6c1144c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prediction of the pole position of 2024 using ARIMA.\n",
        "\n",
        "ARIMA stands for AutoRegressive Integrated Moving Average. It is a popular time series forecasting model that combines three components: autoregression (AR), differencing (I), and moving average (MA). The ARIMA model is particularly useful for analyzing and forecasting time series data, which is a sequence of observations collected over time."
      ],
      "metadata": {
        "id": "O3qSRWQ1AB0_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ARIMA model for Monza circuit\n",
        "# p=1: One lag of the autoregressive component.\n",
        "# d=1: One order of differencing (to make the time series stationary).\n",
        "# q=1: One lag of the moving average component.\n",
        "modelo_arima = ARIMA(final_df['Tiempo Monza'], order=(1, 1, 1))\n",
        "resultados_arima = modelo_arima.fit()\n",
        "\n",
        "# Forecasting for the year 2024 for Monza\n",
        "# The get_forecast method is used to make a forecast for the next step (1 step ahead), which corresponds to the year 2024.\n",
        "prediccion_2024Monza = resultados_arima.get_forecast(steps=1).predicted_mean.iloc[0]\n",
        "print('Predicción para el año 2024:', int(prediccion_2024Monza/60), ':', (prediccion_2024Monza-60),'segundos')\n",
        "\n",
        "modelo_arima = ARIMA(final_df['Tiempo Monaco'], order=(1, 1, 1))\n",
        "resultados_arima = modelo_arima.fit()\n",
        "\n",
        "prediccion_2024Monaco = resultados_arima.get_forecast(steps=1).predicted_mean.iloc[0]\n",
        "print('Predicción para el año 2024:', int(prediccion_2024Monaco/60), ':', (prediccion_2024Monaco-60),'segundos')\n",
        "\n",
        "modelo_arima = ARIMA(final_df['Tiempo '], order=(1, 1, 1))\n",
        "resultados_arima = modelo_arima.fit()\n",
        "\n",
        "prediccion_2024Silverstone = resultados_arima.get_forecast(steps=1).predicted_mean.iloc[0]\n",
        "print('Predicción para el año 2024:', int(prediccion_2024Silverstone/60), ':', (prediccion_2024Silverstone-60),'segundos')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_D-zUK2XtRo1",
        "outputId": "657901e4-1956-40cd-bde0-c2f9c847690c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicción para el año 2024: 1 : 23.093406317135802 segundos\n",
            "Predicción para el año 2024: 1 : 11.29894142483316 segundos\n",
            "Predicción para el año 2024: 1 : 34.71546397945926 segundos\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 6))\n",
        "sns.lineplot(data=final_df, x='Año', y='Tiempo Monza', label='Monza', marker='o')\n",
        "sns.lineplot(data=final_df, x='Año', y='Tiempo Monaco', label='Monaco', marker='o')\n",
        "sns.lineplot(data=final_df, x='Año', y='Tiempo ', label='Silverstone', marker='o')\n",
        "plt.scatter(21, prediccion_2024Monza, color='blue', marker='o', label='Predicción 2024')\n",
        "plt.scatter(21, prediccion_2024Monaco, color='orange', marker='o', label='Predicción 2024')\n",
        "plt.scatter(21, prediccion_2024Silverstone, color='green', marker='o', label='Predicción 2024')\n",
        "\n",
        "\n",
        "plt.title('Pole position evolution')\n",
        "plt.xlabel('Año')\n",
        "plt.ylabel('Tiempo por vuelta (segundos)')\n",
        "plt.grid(True)\n",
        "plt.legend(title='DataFrames')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Sz6bcsEHuje3",
        "outputId": "6bf23296-8f11-465b-c55b-ee5519bb2f43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data needed for wins Data Studio Google\n",
        "\n",
        "This is the first database generation, which corresponds to the dataset \"wins\" and will be used in the dashboard only. Avoid running the cell in order to save time"
      ],
      "metadata": {
        "id": "4S2CT6tjvgAj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = []\n",
        "\n",
        "for year in range(1950,2024):\n",
        "    request = f'http://ergast.com/api/f1/{year}.json'\n",
        "    response = requests.get(request)\n",
        "    # Get the number of races for the current year = year\n",
        "    nRaces = len(response.json()['MRData']['RaceTable']['Races'])\n",
        "\n",
        "    for i in range(nRaces):\n",
        "        row = []\n",
        "        request = f'http://ergast.com/api/f1/{year}/{i}/results.json'\n",
        "        response = requests.get(request)\n",
        "\n",
        "        # Append year, race index, driver ID, and constructor ID to the row\n",
        "        row.append(year)\n",
        "        row.append(i)\n",
        "        row.append(response.json()['MRData']['RaceTable']['Races'][0]['Results'][0]['Driver']['driverId'])\n",
        "        row.append(response.json()['MRData']['RaceTable']['Races'][0]['Results'][0]['Constructor']['constructorId'])\n",
        "        # Append the row to the main data list\n",
        "        data.append(row)\n"
      ],
      "metadata": {
        "id": "iLUN8KB-XVbY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nombres_columnas = [\"year\", \"round\", \"driver\", \"constructor\"]\n",
        "df = pd.DataFrame(data, columns=nombres_columnas)\n",
        "\n",
        "df.to_csv(path2+'wins.csv', index=False)"
      ],
      "metadata": {
        "id": "Fa044hAPgE_Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Boxes and result per circuit and year\n",
        "\n",
        "This is the second database generation, which corresponds to the dataset \"boxes\" and will be used in the dashboard only. Avoid running the cell in order to save time"
      ],
      "metadata": {
        "id": "YPSZFxKiVmZF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "notFinished = ['Finished','+1 Lap','+2 Laps','+3 Laps']\n",
        "data = []\n",
        "\n",
        "for year in range(2012,2023):\n",
        "    request = f'http://ergast.com/api/f1/{year}.json'\n",
        "    response = requests.get(request)\n",
        "    nRaces = len(response.json()['MRData']['RaceTable']['Races'])\n",
        "\n",
        "    # Iterate through each race in the current year\n",
        "    for i in range(1,nRaces + 1):\n",
        "        # Exclude the 12th race of 2021, which was suspended due to rain\n",
        "        if year == 2021 and i == 12:\n",
        "          pass\n",
        "        else:\n",
        "          row = []\n",
        "\n",
        "          request1 = f'http://ergast.com/api/f1/{year}/{i}/results.json'\n",
        "          response1 = requests.get(request1)\n",
        "\n",
        "          # Variables to track changes in position and retired drivers\n",
        "          changes = 0\n",
        "          retired = 0\n",
        "          # Iterate through each driver's result in the race\n",
        "          for driver in response1.json()['MRData']['RaceTable']['Races'][0]['Results']:\n",
        "              changes += abs(int(driver['position']) - int(driver['grid']))\n",
        "\n",
        "              # Check if the driver finished in the 1st position and record their fastest lap speed\n",
        "              if int(driver['position']) == 1:\n",
        "                row.append(float(driver['FastestLap']['AverageSpeed']['speed']))\n",
        "              # Check if the driver's status indicates they did not finish\n",
        "              if driver['status'] not in notFinished:\n",
        "                retired += 1\n",
        "\n",
        "          # Append changes in position and the number of retired drivers to the row\n",
        "          row.append(changes)\n",
        "          row.append(retired)\n",
        "\n",
        "          request2 = f'http://ergast.com/api/f1/{year}/{i}/pitstops.json?limit=500'\n",
        "          response2 = requests.get(request2)\n",
        "\n",
        "          # Append year and circuit ID to the row\n",
        "          row.append(year)\n",
        "          row.append(response2.json()['MRData']['RaceTable']['Races'][0]['Circuit']['circuitId'])\n",
        "\n",
        "          # Variables to track pit stop duration and the number of pit stops\n",
        "          duration = 0\n",
        "          pits = 0\n",
        "          # Iterate through each pit stop in the race\n",
        "          for pit in response2.json()['MRData']['RaceTable']['Races'][0]['PitStops']:\n",
        "            aux = pit['duration'].split(':')\n",
        "\n",
        "            if len(aux) == 1:\n",
        "            # If there is only one component (seconds), add it to the total duration\n",
        "              duration += float(aux[0])\n",
        "              pits += 1\n",
        "            else:\n",
        "            # If there are two components (minutes and seconds), check if the total duration is less than 250 seconds\n",
        "              if (float(aux[1]) + int(aux[0]) * 60) < 250:\n",
        "                duration += (float(aux[1]) + int(aux[0]) * 60)\n",
        "                pits += 1\n",
        "          row.append(pits)\n",
        "          row.append(duration)\n",
        "\n",
        "          data.append(row)"
      ],
      "metadata": {
        "id": "-IkktEDmVmkd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nombres_columnas = ['avgSpeedLeader','positionChanges','retired','Year','Circuit','Pits','avgDuration']\n",
        "df = pd.DataFrame(data, columns=nombres_columnas)\n",
        "# Create the average duration in boxes\n",
        "df['avgDuration'] = df['avgDuration'] / df['Pits']\n",
        "df.head()"
      ],
      "metadata": {
        "id": "09-VceTVVmtU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "dfd41942-2ad6-4e92-c3da-9a59bd298a02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   avgSpeedLeader  positionChanges  retired  Year      Circuit  Pits  \\\n",
              "0         214.053              171       11  2012  albert_park    41   \n",
              "1         196.250              136        3  2012       sepang    76   \n",
              "2         194.356               96        1  2012     shanghai    55   \n",
              "3         202.151               98        4  2012      bahrain    71   \n",
              "4         190.635               84        5  2012    catalunya    67   \n",
              "\n",
              "   avgDuration  \n",
              "0    24.706927  \n",
              "1    25.766658  \n",
              "2    22.102691  \n",
              "3    23.381197  \n",
              "4    20.973507  "
            ],
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv(path2+'boxes.csv', index=False)"
      ],
      "metadata": {
        "id": "EQlDOMo3Vm2b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order to see the graph below, you should only need to run the follwing cell if you have specified the path where the dataset boxes is located"
      ],
      "metadata": {
        "id": "7JklhDBvAnlT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(path2+'boxes.csv')\n",
        "df_filtered = df[df['Circuit'].isin(['monaco', 'albert_park', 'monza'])]\n",
        "\n",
        "sns.set(style=\"whitegrid\")\n",
        "\n",
        "plt.figure(figsize=(15, 6))\n",
        "sns.lineplot(x='Year', y='avgDuration', hue='Circuit', data=df_filtered, marker='o')\n",
        "\n",
        "plt.xlabel('Año')\n",
        "plt.ylabel('Tiempo')\n",
        "plt.title('Tiempo medio en boxes en 3 circuitos')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "n2oig6KRVm-7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "116e4e53-961a-41af-fbca-e7264be2bd76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Clustering of drivers\n",
        "\n",
        "This corresponds to the generation of the \"clusterDriver\" dataset, where some variables are gathered in order to group drivers within the notebook. Avoid executing it in order to save time"
      ],
      "metadata": {
        "id": "0NiOhXWm4RfK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "drivers = 'http://ergast.com/api/f1/drivers.json?limit=858'\n",
        "response = requests.get(drivers)\n",
        "driverIds = []\n",
        "for i in range(len(response.json()['MRData']['DriverTable']['Drivers'])):\n",
        "  driverIds.append(response.json()['MRData']['DriverTable']['Drivers'][i]['driverId'])\n",
        "\n",
        "# Create a zero-matrix for all the driverIDs.\n",
        "df = pd.DataFrame({'id': driverIds, 'avgPosition': np.zeros(len(driverIds)), 'avgPoints': np.zeros(len(driverIds)), 'totalWins': np.zeros(len(driverIds)), 'avgWins': np.zeros(len(driverIds)), 'seasons': np.zeros(len(driverIds)), 'totalRaces': np.zeros(len(driverIds))})\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1rK2g2Oe4QqJ",
        "outputId": "d77e07c6-ad5f-4def-e953-9fb195afa7b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          id  avgPosition  avgPoints  totalWins  avgWins  seasons  totalRaces\n",
              "0      abate          0.0        0.0        0.0      0.0      0.0         0.0\n",
              "1  abecassis          0.0        0.0        0.0      0.0      0.0         0.0\n",
              "2    acheson          0.0        0.0        0.0      0.0      0.0         0.0\n",
              "3      adams          0.0        0.0        0.0      0.0      0.0         0.0\n",
              "4       ader          0.0        0.0        0.0      0.0      0.0         0.0"
            ],
            "text/html": [
              "\n",
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          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for year in range(1950,2024):\n",
        "    request = f'http://ergast.com/api/f1/{year}/driverStandings.json?limit=100'\n",
        "    response = requests.get(request)\n",
        "\n",
        "    # Iterate through each driver in the standings list for the current year\n",
        "    for i in range(len(response.json()['MRData']['StandingsTable']['StandingsLists'][0]['DriverStandings'])):\n",
        "      driver = response.json()['MRData']['StandingsTable']['StandingsLists'][0]['DriverStandings'][i]['Driver']['driverId']\n",
        "      # Update DataFrame (df) with driver statistics for each year\n",
        "      df.loc[df['id'] == driver, 'avgPosition'] += int(response.json()['MRData']['StandingsTable']['StandingsLists'][0]['DriverStandings'][i]['position'])\n",
        "      df.loc[df['id'] == driver, 'avgPoints'] += float(response.json()['MRData']['StandingsTable']['StandingsLists'][0]['DriverStandings'][i]['points'])\n",
        "      df.loc[df['id'] == driver, 'totalWins'] += int(response.json()['MRData']['StandingsTable']['StandingsLists'][0]['DriverStandings'][i]['wins'])\n",
        "      df.loc[df['id'] == driver, 'avgWins'] += int(response.json()['MRData']['StandingsTable']['StandingsLists'][0]['DriverStandings'][i]['wins'])\n",
        "      df.loc[df['id'] == driver, 'seasons'] += 1\n",
        "      df.loc[df['id'] == driver, 'totalRaces'] += int(response.json()['MRData']['StandingsTable']['StandingsLists'][0]['round'])\n",
        "\n",
        "# Calculate average statistics per season for each driver\n",
        "df['avgPosition'] = df['avgPosition'] / df['seasons']\n",
        "df['avgPoints'] = df['avgPoints'] / df['seasons']\n",
        "df['avgWins'] = df['avgWins'] / df['seasons']\n"
      ],
      "metadata": {
        "id": "tc6-7_bi7oqp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Check data gathering is correct\n",
        "df[df['totalWins'] > 50]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "n_OluEQ-FKRz",
        "outputId": "5c17b508-6729-4f9f-c0e2-d4567eb49502"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     id  avgPosition   avgPoints  totalWins   avgWins  \\\n",
              "347            hamilton     2.588235  272.911765      103.0  6.058824   \n",
              "625               prost     3.538462   59.115385       51.0  3.923077   \n",
              "703  michael_schumacher     5.210526   82.421053       91.0  4.789474   \n",
              "801      max_verstappen     4.000000  287.388889       54.0  6.000000   \n",
              "803              vettel     5.375000  193.625000       53.0  3.312500   \n",
              "\n",
              "     seasons  totalRaces  \n",
              "347     17.0       333.0  \n",
              "625     13.0       204.0  \n",
              "703     19.0       326.0  \n",
              "801      9.0       185.0  \n",
              "803     16.0       311.0  "
            ],
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d4f1cd1a-ebd9-4cd1-8d80-5cc163211405')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv(path2+'clusterDriver.csv', index=False)"
      ],
      "metadata": {
        "id": "TmSeKecJDtjN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Kmeans1\n",
        "\n",
        "This part of the code represents the clustering process itself and can be executed independently.\n",
        "\n",
        "The goal is to group Formula 1 drivers into clusters based on their performance characteristics across various seasons. K-means is an unsupervised learning algorithm, meaning it doesn't require labeled data.\n",
        "\n",
        "In this case, drivers are grouped without prior information about which group they should belong to.\n",
        "K-means is effective in clustering data points with similar characteristics. In Formula 1, drivers with similar performance metrics across seasons are likely to be grouped together.\n",
        "\n",
        "Also, K-means is a relatively simple and computationally efficient clustering algorithm. It's suitable for large datasets, making it applicable to the extensive historical data of Formula 1, and the resulting clusters are easy to interpret, making it useful for identifying meaningful patterns in the data.\n"
      ],
      "metadata": {
        "id": "611w3hXzHZnz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv(path2+'clusterDriver.csv')"
      ],
      "metadata": {
        "id": "hq6R95peY8zB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.fillna(0)\n",
        "\n",
        "colsUsed = df[['avgPosition','avgPoints','totalWins','avgWins','seasons','totalRaces']]\n",
        "\n",
        "# Normalizar los datos\n",
        "scaler = StandardScaler()\n",
        "colsUsedNorm = scaler.fit_transform(colsUsed)\n"
      ],
      "metadata": {
        "id": "7CHdIiTZHYmf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Elbow plot to know optimal number of clusters\n",
        "inertia = []\n",
        "\n",
        "for k in range(2, 25):  #diferentes valores de k para K-Means\n",
        "    kmeans = KMeans(n_clusters=k)\n",
        "    kmeans.fit(colsUsedNorm)\n",
        "    inertia.append(kmeans.inertia_)\n",
        "\n",
        "# Plot the graph with the elbow rule\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(range(2, 25), inertia, marker='o', linestyle='-', color='b')\n",
        "plt.title('Elbow rule')\n",
        "plt.xlabel('Number of Clusters')\n",
        "plt.ylabel('Inertia')\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Ke61hnQ8TNr1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3fbfac09-b692-4442-b445-2110768b3289"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans = KMeans(n_clusters=7, random_state=42) #optimal k based on the plot\n",
        "df['Cluster'] = kmeans.fit_predict(colsUsedNorm)"
      ],
      "metadata": {
        "id": "ChkAmNdPWO7s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94a63537-8767-4196-fddb-638e0ec8c416"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bestDrivers = df[df['Cluster'] == 6]\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(bestDrivers['id'], bestDrivers['totalWins'], color='skyblue')\n",
        "plt.xlabel('Id')\n",
        "plt.ylabel('Wins')\n",
        "plt.title('Gráfico de Barras')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "JxFa6Wm9Z81k",
        "outputId": "5b33135c-cd0e-4aab-cc22-4993304debc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "alonsoDrivers = df[df['Cluster'] == 1]\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(alonsoDrivers['id'], alonsoDrivers['totalWins'], color='skyblue')\n",
        "plt.xlabel('Id')\n",
        "plt.ylabel('Wins')\n",
        "plt.title('Gráfico de Barras')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "Wt3FXPvjak3W",
        "outputId": "3276291d-d4ff-4d23-84a0-50f30326fc56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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AAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTRYTFYpHVapXFYnF0KQAAAEChUMzRBSCzNMOQWz6HHqvVqsDAwHydZ7qCqBcAAABwdoQpJ+RmsWhjZKyiElIcXUqOypUopp6+3o4uAwAAALjtCFNOKiohRefjUx1dBgAAAICb4JopAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwASnClPbtm1Tv3791Lx5cwUHB6tTp06aPn26YmNj7ab79ttv1bNnT4WEhKhLly5av369gyoGAAAAUFQVc3QBGUVHR6tBgwbq37+/fHx8dPToUc2bN09Hjx7V8uXLJUl79uzRyJEj9cADD+i5557Tzp079fzzz6tkyZK65557HNwCAAAAAEWFU4WpXr162T0ODw+Xp6enJk2apPPnz6tSpUpauHChGjRooKlTp0qSmjdvrtOnT2vu3LmEKQAAAAC3jVN188uKj4+PJCk5OVlJSUn6+eefM4Wmbt266fjx4zpz5owDKgQAAABQFDllmEpNTVViYqJ+++03LViwQB07dlS1atX0119/KTk5WbVq1bKbvnbt2pKkEydOOKJcAAAAAEWQU3XzS9ehQwedP39ektSmTRvNnj1bknTlyhVJUunSpe2mT3+cPt4MwzAUFxdn+vn5xWKxyGq1OrqMPIuPj5dhGA5bdsa/hQltc12FuX20zTXRNtdE21wTbXNthmHIYrHkalqnDFOLFy9WfHy8jh07poULF2ro0KFasWJFgS4zOTlZhw8fLtBl5IbValVgYKCjy8izkydPOvygioyMdOjyCxJtc12FuX20zTXRNtdE21wTbXNdnp6euZrOKcNUvXr1JElhYWEKCQlRr1699PXXX6tOnTqSlOlW6TExMZKkMmXKmF6mh4eHbf6OlNsU7Gz8/PwcemYqMjJSvr6+LnlWLzu0zXUV5vbRNtdE21wTbXNNtM21HTt2LNfTOmWYyiggIEAeHh7666+/1LFjR3l4eOjEiRNq06aNbZr0a6VuvJYqLywWi7y8vG653qLKGQ4mq9VaaLchbXNdhbl9tM010TbXRNtcE21zTXk5ueGUN6DI6ODBg0pOTla1atXk6emp8PBwffnll3bTbNq0SbVr11a1atUcVCUAAACAosapzkyNHDlSwcHBCggIUIkSJfTHH39o2bJlCggIUOfOnSVJw4YN02OPPaaXXnpJXbt21c8//6zPPvtMc+bMcXD1AAAAAIoSpwpTDRo00KZNm7R48WIZhqGqVauqT58+GjRokO0isCZNmmjevHl688039fHHH+vOO+/UtGnT1LVrVwdXDwAAAKAocaowNXjwYA0ePDjH6Tp16qROnTrdhooAAAAAIGtOf80UAAAAADgjwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYUc3QBGX3xxRfauHGjfvvtN8XExKhmzZrq37+//vWvf8lisUiS+vfvr127dmV67qZNm1S7du3bXTIAAACAIsqpwtTKlStVtWpVRURE6I477tCOHTs0adIknTt3TiNHjrRN16hRI02YMMHuudWqVbvd5QIAAAAowpwqTC1cuFBly5a1PW7RooWio6O1YsUKDR8+XG5u13slli5dWqGhoQ6qEgAAAACc7JqpjEEqXf369XX16lXFxcU5oCIAAAAAyJpThams7N27V5UqVVKpUqVsw3bt2qXQ0FCFhISoX79+2r17twMrBAAAAFAUOVU3vxvt2bNHmzZtsrs+qmnTpurVq5d8fX114cIFLVu2TAMGDNCaNWsUFhZmelmGYTjF2S+LxSKr1eroMvIsPj5ehmE4bNkZ/xYmtM11Feb20TbXRNtcE21zTbTNtRmGYbv5XU4shqM+Aefg3Llz6tOnj2rXrq3ly5fbrpe6UVxcnHr06KHatWtryZIlppb1yy+/KCkp6VbKzTdWq1WBgYFa8cdlnY9PdXQ5OapkddeAenfo999/L9QHFQAAAIoOT09PhYSE5DidU56ZiomJ0VNPPSUfHx/NmzfvpkFKkry8vNSuXTt9+eWXt7RMDw8P1alT55bmkR9ym4KdjZ+fn0PPTEVGRsrX19clz+plh7a5rsLcPtrmmmiba6Jtrom2ubZjx47lelqnC1MJCQkaMmSIYmNjtW7dOnl7e9+W5VosFnl5ed2WZRVGznAwWa3WQrsNaZvrKszto22uiba5Jtrmmmiba8rLyQ2nClMpKSkaO3asTpw4offee0+VKlXK8TlxcXHaunVrrk7DAQAAAEB+caowNWXKFH333XeKiIjQ1atXdeDAAdu4wMBAHTp0SEuXLtVdd92lqlWr6sKFC1qxYoUuXryot956y3GFAwAAAChynCpMbd++XZI0Y8aMTOO2bNmiChUqKDk5WXPmzFF0dLSsVqvCwsI0ZcoUNWjQ4HaXCwAAAKAIc6ow9e233+Y4zbJly25DJQAAAACQPaf/0V4AAAAAcEaEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAnJTFYpHVapXFYnF0KQCyUMzRBQAAABQGaYYht3wOPVarVYGBgfk6z3QFUS9Q1BCmAAAA8oGbxaKNkbGKSkhxdCk5KleimHr6eju6DMDlEaYAAADySVRCis7Hpzq6DAC3CddMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAAATCBMAQBQBFgsFlmtVlksFkeXAgCFRjFHFwAAAOylGYbc8jn0WK1WBQYG5us80xVEvQDgCghTAAA4GTeLRRsjYxWVkOLoUnJUrkQx9fT1dnQZAOAQhCkAAJxQVEKKzsenOroMAEA2uGYKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJjhVmPriiy80bNgwtW3bVqGhoerVq5c+/vhjGYZhN91HH32kLl26KCQkRD179tR3333noIoBAAAAFFVOFaZWrlwpq9WqiIgILVy4UG3bttWkSZO0YMEC2zSff/65Jk2apK5du2rJkiUKDQ3VyJEjdeDAAccVDgAAAKDIcapboy9cuFBly5a1PW7RooWio6O1YsUKDR8+XG5ubpo7d666d++usWPHSpKaN2+uI0eOaMGCBVqyZImDKgcAAABQ1DjVmamMQSpd/fr1dfXqVcXFxen06dOKjIxU165d7abp1q2bfvrpJyUlJd2uUgEAAAAUcU4VprKyd+9eVapUSaVKldKJEyckSX5+fnbT1K5dW8nJyTp9+rQjSgQAAABQBDlVN78b7dmzR5s2bdKECRMkSVeuXJEklS5d2m669Mfp480wDENxcXGmn59fLBaLrFaro8vIs/j4+Ew3Crmdy874tzChba6rMLePthUs3gfMLTvjX0dgu5lbdsa/hQltc22GYchiseRqWqcNU+fOndPTTz+t8PBwPfbYYwW+vOTkZB0+fLjAl5MTq9WqwMBAR5eRZydPnnT4QRUZGenQ5Rck2ua6CnP7aFvB4H3APLZb3hX17VbQaJvr8vT0zNV0ThmmYmJi9NRTT8nHx0fz5s2Tm9v13ohlypSRJMXGxqpChQp202ccb4aHh4fq1KlzC1Xnj9ymYGfj5+fn0G+2IiMj5evr65LfCmaHtrmuwtw+2laweB/IO7abeUV9uxUU2ubajh07lutpnS5MJSQkaMiQIYqNjdW6devk7e1tG1erVi1J0okTJ2z/T3/s4eGh6tWrm16uxWKRl5eX+cKLOGc4mKxWa6HdhrTNdRXm9tE2ZMT7gGtiuxUs2uaa8vLliFPdgCIlJUVjx47ViRMntHTpUlWqVMlufPXq1eXr66vNmzfbDd+0aZNatGiR69NxAAAAAHCrnOrM1JQpU/Tdd98pIiJCV69etfsh3sDAQHl6emrUqFEaP368atSoofDwcG3atEmHDh3Su+++67jCAQAAABQ5ThWmtm/fLkmaMWNGpnFbtmxRtWrV1KNHD8XHx2vJkiVavHix/Pz8NH/+fIWFhd3ucgEAAAAUYU4Vpr799ttcTdenTx/16dOngKsBAAAAgJtzqmumAAAAAMBVEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJiQ72EqKSlJcXFx+T1bAAAAAHAqpsPU559/rldffdVu2Pz589WoUSM1bdpUI0aM0LVr1265QAAAAABwRqbD1PLlyxUfH297vG/fPs2fP1+tW7fW448/rh9++EHvvPNOvhQJAAAAAM6mmNknnj59Wvfff7/t8Weffaby5ctr/vz5KlasmAzD0FdffaVx48blS6EAAAAA4ExMn5lKSkpS8eLFbY+3b9+utm3bqlix6/msdu3aOnfu3K1XCAAAAABOyHSYqlatmnbs2CFJ+uWXX3Tq1Cm1adPGNj4qKkpeXl63XiEAAAAAOCHT3fz69u2rV155RceOHdP58+dVuXJldejQwTZ+3759qlOnTr4UCQAAAADOxnSY6t+/v4oXL65t27YpODhYTz75pEqUKCFJio6O1sWLF/Xwww/nW6EAAAAA4ExMhylJevDBB/Xggw9mGu7j46NPPvnkVmYNAAAAAE4t33+0FwAAAACKgls6M/XDDz/o448/1unTpxUTEyPDMOzGWywWffPNN7dUIAAAAAA4I9NhaunSpZo9e7bKlSunBg0aKCAgID/rAgAAAACnZjpMrV69Ws2bN9fixYvl4eGRnzUBAAAAgNMzfc1UTEyMunTpQpACAAAAUCSZDlMhISE6efJkftYCAAAAAC7DdJh66aWX9PXXX+vTTz/Nz3oAAAAAwCWYvmZq7NixSklJ0b///W+99NJLqly5stzc7LOZxWLRxo0bb7lIAAAAAHA2psOUj4+PfHx8VLNmzfysBwAAAABcgukwtWbNmvysAwAAAABciulrpgAAAACgKMv1mandu3dLkpo2bWr3OCfp0wMAAABAYZLrMNW/f39ZLBYdPHhQnp6etsc3YxiGLBaLDh8+nC+FAgAAAIAzyXWYWr16tSTJ09PT7jEAAAAAFEW5DlPNmjXTww8/rCZNmqhRo0Zq1KiRypQpU5C1AQAAAIDTytPd/P7++28tWbJEFotFFotFtWrVUqNGjdS4cWM1btxY1apVK6g6AQAAAMCp5ClMbd26VefOndPevXu1d+9e7d+/X+vXr9eHH34oi8WiihUr2oWrevXqZXtdFQAAAAC4qjz/zlTlypXVvXt3de/eXZJ07do17d+/X/v27dO+ffu0detWbd68WZJUqlSpXN/1DwAAAABciekf7U1XsmRJtW7dWq1bt9aFCxf0888/67333tOBAwd09erV/KgRAAAAAJzOLYWpI0eOaO/evbazUv/73//k6emp+vXra8CAAWrcuHF+1QkAAAAATiVPYWrXrl3at2+f9u7dq4MHDyomJkbly5dXWFiYHn30UYWFhSkoKMh2+3QAAAAAKKzyFKYee+wxFStWTPfcc49eeOEFhYWFqXr16gVVGwAAAAA4rTyFKX9/fx07dkyff/65jhw5orCwMDVu3JhQBQAAAKDIyVOY2rhxo65evaoDBw7YrpPauHGjEhISVK5cOYWFhalRo0a27n4eHh4FVTcAAAAAOFSeb0BRqlQp2937JCk1NVWHDx/Wvn37tH//fq1cuVKvv/66PD09FRwcrPfeey/fiwYAAAAAR7vlW6O7u7srODhYwcHBCg8P1549e/Tpp5/azl4BAAAAQGFkOkwlJSXp4MGD2rt3r+3ufrGxsZIkT09PNWnShFujAwAAACi08hSmvvnmG9vvSv3+++9KSUmRYRjy8fFR48aNbf+Cg4O5XgoAAABAoZanMDVy5EhJUrVq1dStWzdbeKpdu3a+FHPq1CktW7ZMBw8e1NGjR1WrVi199tlndtP0799fu3btyvTcTZs25VsdAAAAAJCTPIWpOXPmqHHjxqpYsWKBFHP06FFt27ZNDRs2VFpamgzDyHK6Ro0aacKECXbDqlWrViA1AQAAAEBW8hSmunbtWlB1SJI6duyozp07S5IiIiL066+/Zjld6dKlFRoaWqC1AAAAAEB23BxdQEZubk5VDgAAAADclEuml127dik0NFQhISHq16+fdu/e7eiSAAAAABQxt/w7U7db06ZN1atXL/n6+urChQtatmyZBgwYoDVr1igsLMz0fA3DUFxcXD5Wao7FYpHVanV0GXkWHx9/02vcbseyM/51FIvFku/zTEpKktVqVVJSUoHM31HbTHKe7VZQCnP7aFvB4n3A3LIz/nUEtpu5ZWf8W5jQNtdmGEauP3e5XJgaPXq03eP27durR48eevvtt7VkyRLT801OTtbhw4dvtbxbZrVaFRgY6Ogy8uzkyZMOP6giIyMdtmwPDw8FBQXL3T1/T/ZarVb5+Pjk6zzTpaam6bffflVycnKBzD+3HLndbofC3D7aVjB4HzCP7ZZ3RX27FTTa5ro8PT1zNZ3LhakbeXl5qV27dvryyy9vaT4eHh6qU6dOPlVlXkGcfbgd/Pz8HPrNVmRkpHx9fR32raDFYpG7u5s2RsYqKiHFITXkRbkSxdTT11t169Yt0tutIBXm9tG2gsX7QN6x3cwr6tutoNA213bs2LFcT+vyYSq/WCwWeXl5OboMl+UMB5PVanX4NoxKSNH5+FSH1pAXbLeCV5jbR9uQEa8nrontVrBom2vKy5cjLnkDiozi4uK0detWhYSEOLoUAAAAAEWIU52Zio+P17Zt2yRJZ8+e1dWrV7V582ZJUrNmzXTixAktXbpUd911l6pWraoLFy5oxYoVunjxot566y1Hlg4AAACgiHGqMBUVFaUxY8bYDUt/vHr1alWuXFnJycmaM2eOoqOjZbVaFRYWpilTpqhBgwaOKBkAAABAEeVUYapatWr6888/s51m2bJlt6kaAAAAALg5l79mCgAAAAAcgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBQAGwWCyyWq2yWCyOLgUAABSQYo4uAAAcLc0w5JbPocdqtSowMDBf55muIOoFAAB5R5gCUOS5WSzaGBmrqIQUR5eSo3Iliqmnr7ejywAAACJMAYAkKSohRefjUx1dBgAAcCFcMwUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYIJThalTp05p8uTJ6tWrlwIDA9WjR48sp/voo4/UpUsXhYSEqGfPnvruu+9uc6UAAAAAijqnClNHjx7Vtm3bVLNmTdWuXTvLaT7//HNNmjRJXbt21ZIlSxQaGqqRI0fqwIEDt7dYAAAAAEWaU/3OVMeOHdW5c2dJUkREhH799ddM08ydO1fdu3fX2LFjJUnNmzfXkSNHtGDBAi1ZsuR2lgsAAACgCHOqM1NubtmXc/r0aUVGRqpr1652w7t166affvpJSUlJBVkeAAAAANg4VZjKyYkTJyRJfn5+dsNr166t5ORknT592hFlAQAAACiCnKqbX06uXLkiSSpdurTd8PTH6ePNMAxDcXFx5ovLJxaLRVar1dFl5Fl8fLwMw8hxOovFku/LTkpKktVqVVJSUoHMP7ftKszbraCWnfGvo7DtzC0749/CxBnaxj5pbtkZ/zoC283csjP+LUxom2szDCPXnyldKkwVpOTkZB0+fNjRZchqtSowMNDRZeTZyZMnczyoPDw8FBQULHf3/D0harVa5ePjk6/zTJeamqbffvtVycnJOdZQWLdbQYuMjHTo8tl25jl62xUkR7aNfdI8tlveFfXtVtBom+vy9PTM1XQuFabKlCkjSYqNjVWFChVsw2NiYuzGm+Hh4aE6dercWoH5oCDOrNwOfn5+OX6zZbFY5O7upo2RsYpKSLlNlZlXrkQx9fT1Vt26dXPVNleUm+1WUOLj4xUZGSlfX1+HfpvLtss7Z9l2BcEZ2sY+mXdsN/OK+nYrKLTNtR07dizX07pUmKpVq5ak69dOpf8//bGHh4eqV69uet4Wi0VeXl63XGNRlZeDKSohRefjUwuwmvxVWF8oJOdom9Vq5dgzgW1XsApz2woK+6RrYrsVLNrmmvLy5YhL3YCievXq8vX11ebNm+2Gb9q0SS1atMj16TgAAAAAuFVOdWYqPj5e27ZtkySdPXtWV69etQWnZs2aqWzZsho1apTGjx+vGjVqKDw8XJs2bdKhQ4f07rvvOrJ0AAAAAEWMU4WpqKgojRkzxm5Y+uPVq1crPDxcPXr0UHx8vJYsWaLFixfLz89P8+fPV1hYmCNKBgAAAFBEOVWYqlatmv78888cp+vTp4/69OlzGyoCAAAAgKy51DVTAAAAAOAsCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADkisVikdVqlcVicXQpTsGpfmcKAAAAQP5IMwy55XPosVqtCgwMzNd5piuIegsaYQoAAAAohNwsFm2MjFVUQoqjS8lRuRLF1NPX29Fl5BlhCgAAACikohJSdD4+1dFlFFpcMwUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAgEuzWCyyWq2yWCyOLgVAEVPM0QUAAICiI80w5JbPocdqtSowMDBf55muIOoFUHgQpgAAwG3jZrFoY2SsohJSHF1KjsqVKKaevt6OLgOAEyNMAQCA2yoqIUXn41MdXQYA3DKumQIAAAAAEwhTAAAAAGACYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIA4P+zWCyyWq2yWCyOLgUA4AKKOboAAADMSDMMueVz6LFarQoMDMzXeaYriHoBAI5FmAIAuCQ3i0UbI2MVlZDi6FJyVK5EMfX09XZ0GQCAfEaYAgC4rKiEFJ2PT3V0GQCAIoprpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAAATHC5MPXJJ58oICAg079Zs2Y5ujQAAAAARYjL3hp96dKl8vb+v9/sqFSpkgOrAQAAAFDUuGyYCgoKUtmyZR1dBgAAAIAiyuW6+QEAAACAM3DZMNWjRw/Vr19fnTp10qJFi5SamurokgAAAAAUIS7Xza9ChQoaNWqUGjZsKIvFom+//VZvvvmmzp8/r8mTJ5uer2EYiouLy8dKzbFYLLJarY4uI8/i4+NlGEa209A255ObtknX25ffkpKSZLValZSUVCDzl5TrthXmbVdQy8741xEK83ajbc6nqLetIJed8W9h4gxtY580zzCMXH82cbkw1aZNG7Vp08b2uHXr1ipevLhWrVqloUOHqmLFiqbmm5ycrMOHD+dXmaZZrVYFBgY6uow8O3nyZI4vGLTN+eSmbR4eHgoKCpa7e/6eyLZarfLx8cnXeWaUmpqm3377VcnJyTnWUVi3XUGLjIx02LIL83ajbc6nqLetoDnytaSg8TqZd86wT0qSp6dnrqZzuTCVla5du2r58uU6fPiw6TDl4eGhOnXq5HNleVdQ39AXND8/v1x9a+eKaJtF7u5u2hgZq6iElNtU2a0pV6KYevp6q27dukV62xWU+Ph4RUZGytfX12Hfehbm7UbbnE9Rb1tBcYbXkoLiDG1jnzTv2LFjuZ62UISp/GCxWOTl5eXoMlxWYXsRzIi2XReVkKLz8a51bSLbruBr4HUzb5xhuxUU2uaanKFthfm1pDC3raA4wz6ZlyDqsjegyGjTpk1yd3d3yVOZAAAAAFyTy52ZGjRokMLDwxUQECBJ2rJliz788EM99thjqlChgoOrAwAAAFBUuFyY8vPz0/r163Xu3DmlpaXJ19dXzz33nPr37+/o0gAAAAAUIS4Xpl544QVHlwAAAAAAheOaKQAAAAC43QhTAAAAAGACYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAEA+slgsslqtslgsji4FBayYowsAAAAAHCXNMOSWz6HHarUqMDAwX+eZriDqhXmEKQAAABRZbhaLNkbGKiohxdGl5KhciWLq6evt6DKQAWEKAAAARVpUQorOx6c6ugy4IK6ZAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAC3ncVikdVqlcVicXQpgGnFHF0AAAAAnFuaYcgtn0OP1WpVYGBgvs4zXUHUC2SFMAUAAIBsuVks2hgZq6iEFEeXkqNyJYqpp6+3o8tAEUGYAgAAQI6iElJ0Pj7V0WUAToVrpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGCCS4ap48ePa8CAAQoNDVWrVq30+uuvKykpydFlAQAAAChCXO5ufleuXNHjjz8uX19fzZs3T+fPn9eMGTOUkJCgyZMnO7o8AAAAAEWEy4WptWvX6tq1a5o/f758fHwkSampqZoyZYqGDBmiSpUqObZAAAAAAEWCy3Xz+/7779WiRQtbkJKkrl27Ki0tTdu3b3dcYQAAAACKFJcLUydOnFCtWrXshpUuXVoVKlTQiRMnHFQVAAAAgKLGYhiG4egi8iIoKEhjxozR4MGD7Yb36NFDYWFhevnll/M8z3379skwDHl4eORXmbfEYrEoLsVQqgtsGneLRV7FLMrtbkTbnENhbptUuNuX17YVBMMwlJqaKnd3d1ksFofVUZi3G21zDrTt/9A250Dbbo/k5GRZLBY1atQox2ld7pqpgpD+YcCRHwpu5FXMIsl56slJXtYdbXMehbltUuFun6NDjJubc3RsKMzbjbY5D9p2HW1zHrSt4GvIbR0uF6ZKly6t2NjYTMOvXLmiMmXKmJpnWFjYrZYFAAAAoIhxjq8W86BWrVqZro2KjY3VxYsXM11LBQAAAAAFxeXCVNu2bbVjxw7FxMTYhm3evFlubm5q1aqVAysDAAAAUJS43A0orly5ou7du8vPz09Dhgyx/Wjvvffey4/2AgAAALhtXC5MSdLx48f18ssva//+/SpZsqR69eqlp59+Wp6eno4uDQAAAEAR4ZJhCgAAAAAczeWumQIAAAAAZ0CYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQplzMzz//rICAAP3yyy+OLsWUiIgI9ejR45bn8/PPP+udd97J9fCCll/tOnPmjObNm6fz58/navjtkF9ty+iTTz5RQECALl26lK/zhXM5c+aMAgICtHnzZkeX4nI6duyoqVOn2h7n5jjs37+/hgwZUtCl5UlBvWcFBARo2bJl+TrPGw0fPlz9+/cv0GXcyNXf4x0hq9eZG4+f260wbcfcfgZw9Dp3JMIUXNKuXbu0aNGiXA93FWfPntX8+fN14cKFXA0HnFnFihW1bt06NW/e3NGluJz58+dr4MCBji7jlgUFBWndunWqXbt2vs533bp1uvfee/N1nkB+Kaj9Hs6pmKMLAACzDMNQcnKyPD09HV1KkZTd+k9ISFCJEiUUGhp6+wvLR+ntyA952V8DAwPzZZmOZBiGPD09C2QfyK955tf2dZXXovxor6u01ZFKlSrl8q99zsTZ9znOTDmZ/fv3a+jQoWrdurVCQ0PVq1cv/ec//8n2OYmJiZo+fbpat26tkJAQ9erVS19//bXdNOmnaX/++Wfdd999Cg0N1QMPPKBff/3VbrqPP/5Y3bt3V4MGDRQeHq6HH35Yhw4dytOycmPbtm3q0aOHQkJC1Lt3bx04cMA2Li0tTW+//bY6duyo4OBg3XPPPVq7dq1t/Lx58zR//nzFxcUpICBAAQEB6t+//02HS9Lx48f19NNPq127dmrYsKG6deum5cuXKy0tza6uxYsX66677lJISIiaN2+uJ554QqdPn86XdmXVLWXlypUKCAiQdL1bwGOPPSZJeuCBB2xtuNnwdGfPntXo0aPVuHFjhYaGatCgQfrzzz/tlpN++v29995Thw4d1LhxYw0fPtxUN7tz585p/PjxCg8PV4MGDfToo49m2o8k6T//+Y/uu+8+hYSEKDw8XE899ZTOnj170/kmJSXpjTfeUIcOHRQcHKyuXbvq008/tZsmfT/etm2bevbsqZCQEH377beSrh87AwcOVKNGjRQWFqY+ffpo+/bt2bYlfX47duzQvffeqwYNGqhfv346c+aMoqOjNWbMGDVq1EidO3fWpk2bbM/bunWrBgwYoBYtWqhRo0bq06ePvv/+e7t5x8TE6IUXXlCbNm0UEhKidu3a6emnn871+AsXLmjixInq1KmTGjRooLvvvltvvPGGkpKSsm1Tdu3M7vjPy+vIjet/3rx5CgsL06FDh9S3b1+FhITovffey7L7zZYtW9S7d2+FhYWpSZMm6t27t7Zt25bnNt2srpsdf9L17qX33nuvQkJC1KZNG82ZM0epqal24wMCArR//34NGDBAoaGhev311yXlvN+nPzerf+vXr7etr88//1xTp05Vly5d1LBhQ3Xs2FGTJ09WbGysXa05dZlJS0vT888/r/Dw8Jt2JUpISNDgwYPVqVMn2+vYn3/+qUGDBik0NFSNGzfW6NGj9b///c/ueQEBAVqyZInmzZunli1bKjw8XBMnTlRcXJzddDeuk5YtW6pz5852+0e7du0UEBCgmTNn2l57hg0bpgULFqhr164KDg5Wq1atNHr0aLt1cPz4cY0cOVLNmjVTw4YN1bNnT3322Wd2NWZ8Pc3NMZnd9j1+/Lj69eunkJAQde7cWRs2bMhyne7evVsPPfSQ6tevr/r162vAgAHq3r277Vj46quv1KtXL4WEhKh169aaPn26EhMTbc9PTk7Wa6+9pvbt2ys4OFitW7fW0KFDM23/S5cuaeTIkQoNDVXr1q2z7L5+/PhxDRs2zPbaP3jwYP3111+ZtuXixYs1c+ZMtWrVSi1atJB0/fV22rRpatasmZo0aaLJkyfr008/VUBAgM6cOWM3j6yO+zVr1mTZVbtXr16KiIiwPT569KieeuophYeHq2HDhurSpYuWLFmS6/HS9df2xx57zLbPjhs3TlFRUVlun/ySXV25+ZyWVTe/3BxXHTt2zPI1JOM6vZk9e/YoICDAbh8YOnSoAgICdPToUduwZ555RoMHD5aUu/fddDm9vqZbunSp2rRpo4YNG2rYsGGZetTc6nu9M+LMlJP53//+p0aNGunhhx+Wp6en9u3bpxdeeEGGYej+++/P8jnjx4/XDz/8oLFjx6pWrVr673//q1GjRmnBggXq1KmTbbqLFy9q2rRpGjx4sLy9vTV79myNHDlSX3/9tTw8PLR79249//zzGjhwoNq1a6eEhAQdOnTI7kU+t8vKzsWLFzVlyhSNGjVKpUuX1pIlSzRo0CB99dVXKleunF5//XWtXr1aw4YNU1hYmLZu3aoXX3xRKSkp6tevn/r06aNz587ps88+06pVqyRd/xaoVKlSWQ6Xrn8w9fPz07333quSJUvq8OHDmjdvnuLi4jRy5EhJ1z/8v/XWWxo9erRCQ0MVGxurvXv36tq1a/nSrpwEBQVp8uTJmjp1qqZPn65atWpJkurUqZPlcEm6evWq+vfvLzc3N02ZMkXFixfXwoUL1a9fP23cuFFVqlSxTfvtt9/q1KlTmjx5si5fvqzp06fr5Zdf1pw5c3LVPkm6cuWKHnnkEXl5eWnSpEny9vbWmjVr9Pjjj9u1c+nSpZo5c6YeeOABPf3000pOTtbOnTt16dIlVa1aNct5jxkzRvv27dOIESNUu3Ztbdu2Tc8++6xKly6tdu3a2aa7cOGCpk2bpmHDhqlKlSq68847tXfvXj3++OMKDQ3VtGnTVLp0af3666+ZPiRm5eLFi5oxY4aGDRumYsWKadq0aRo/frysVquaNGmiBx98UB9++KGeffZZNWzYUFWrVtWZM2fUoUMHDRw4UG5ubvr+++81ePBgrVq1SuHh4ZKk6dOn64cfftC4ceNUtWpVXbx40e7DXU7jL1++LB8fH02cOFGlS5dWZGSk5s2bp4sXL2r69Om53mYZ25nd8Z/bYzur9X/06FElJydr3LhxeuKJJ/T000/Lx8cnUw1//fWXxowZo+7du2vcuHFKS0vTH3/8oStXruS5PVm1L7vjb8WKFZo5c6Yef/xxRURE6Pjx47YwNX78eLt5jRs3Tn379tWQIUNktVpztd+3b99e69ats5vP0KFDFR0drbffflsjRoxQlSpVVLJkSR04cEBPP/20ypYtq7///lvvvPOOhg8frjVr1uSqrSkpKfr3v/+tXbt2ac2aNfL39880zbVr1zR06FBdvHhR77//vipVqqS///5b/fr1U/Xq1TVz5kwlJiZqzpw5tteL9NdLSXrvvffUuHFjzZgxQ5GRkXr99ddVrlw527rKap288MILOn36tKZMmaKRI0eqSpUqGjt2rKTrIST9tWfy5Mn67rvvNGDAALVq1UrXrl3T1q1bFRcXJ29vb0VGRqpv376qUqWKnn/+eVWoUEFHjhzJ9njOzTF5s+2bmJiogQMHymq12sLV3LlzdfXqVfn6+tqe9+uvv2rAgAEKDw9Xs2bNdPDgQe3cuVN33nmnFi1apOPHj+vVV1+17d8nTpzQnDlz9Pfff2vu3LmSpEWLFmnt2rUaP3686tatq8uXL2v79u2ZviSZNGmSunfvrnnz5mnHjh2aM2eOypQpo4cffliSdPr0aT300EOqW7euZsyYIYvFonfeeUdPPPGENm/ebPcN/urVq9WwYUO98sorSklJkSTNnj1ba9eu1ejRo1W/fn19+eWXmj179k3X743H/Y2B62aGDh2q8uXL65VXXlGpUqX0119/6dy5c7kev3//fvXv31/t2rXTnDlzFB8frzfffFPDhw/PdLzlp+zqMvM5LV1Ox9X8+fPt9oU///xTL774ovz8/HKsuUGDBipevLh2796tGjVqKC0tTXv37rUNq1u3rqTrx2L6F825fd/N7eebr7/+WlWrVtVLL72kmJgYzZo1S6NGjbLbVrfyXu+0DDittLQ0Izk52Zg0aZLRt29fwzAMY+fOnYa/v79x6NAhwzAM4/Dhw4a/v7/xwQcf2D23b9++xv333297PGHCBCMgIMA4cuSIbVj6vHbv3m0YhmEsXbrUaNas2U3rye2ysjNhwgTD39/f2LFjh21YTEyMERYWZsyaNcuIiooygoKCjFmzZtk975lnnjGaN29upKSkGIZhGHPnzjVCQ0Mzzf9mwzNKX68LFy40WrVqZRs+ZcqUXLcjr+0yDMPw9/c3li5dave8FStWGP7+/rbHN27fnIavWrXKCAgIMI4dO2YbdvnyZSM0NNSYPn26bViHDh2Mtm3bGomJibZhc+fONYKCgozU1NQc29a9e3fDMAzjrbfeMho3bmz8888/tvGJiYlG+/btjddee83W7oYNGxqTJk266TzXr19v+Pv7G1FRUYZhGMZPP/1k+Pv7Gz/88IPddGPHjjX+9a9/2dXi7+9vHDhwwG66vn37Gt26dbPtH7mV1XGxZs0aw9/f35g5c6Zt2JUrV4z69esbK1euzDSP1NRUIzk52Rg4cKDxzDPP2IZ3797dbhvcKKfxN0pOTjY2btxoBAYGGnFxcbl+nmHkfPzn5XUkq/U/d+5cw9/f3/j888/thp8+fdrw9/c3vvjiC8MwDOOLL74w/P39jdjY2DzVn5v2ZXf8xcbGGqGhocbs2bPtnvf+++8bDRo0MC5dumQYxv/tl4sWLbKbLjf7/Y2WLl1qBAQEZLm+MkpOTjb27Nlj+Pv7GydOnLAN79ChgzFlyhS7Nnbv3t1ITEw0hg4darRv3944efKk3bz69etnDB482IiOjjb69Olj9OzZ067mV1991QgNDTUuX75sG3bs2DEjICDAWL16tW2Yv7+/8cADD9jNe8KECUbnzp2zXSfjx483/P39jXHjxtmGtWzZ0vD39zf27dtnGIZhnDhxwvD39zfq1at309ee9Nf77PaTrF5P093smLzZ9n3//feNevXq2a3PyMhIo169eka/fv1sw0aMGGG0b9/eSEpKsu1zq1atMvz9/Y0tW7YY9913n+29Ot3atWsNf39/448//jAMwzAGDx5sjBw58qbtSj8un332Wbvhzz77rNGmTRvbOvv3v/9tdOrUyUhISLBNExUVZYSGhhrvvvuu3Xrq1q2bkZaWZht2+fJlIyQkxJg/f77dMh5//HHD39/fOH36tN3wrI77G1/D0/Xs2dOYMGGCrZ70dZOVnMYbhmE8+uijRt++fe3qP3r0qBEQEGBs3brVMIzMrzOGkfn4yYvc1JUuq89phpH1e3Zujqsb6+jQoYMxcODAHN+n0z366KNGRESEYRiG8fvvvxtBQUHGpEmTjLFjxxqGcX2/Tj8e8/q+m93nG8O4vs7DwsKMmJgY27AdO3YY/v7+xvfff28Yxq2/1zsruvk5mStXrmjatGnq0KGDgoKCbBcxnjx5Msvp9+7dK0m655577IZ37dpVv//+u93p44oVK9q+mZCun/GQZLtDXGBgoKKjoxUREaHt27crPj7e9LKy4+3tbetqkP64ZcuWOnjwoA4dOqTk5OQsl3Hp0iVFRkbmahk3SkxM1Ny5c21d+IKCgjRnzhxdvHjRduYpMDBQv//+u6ZPn649e/YoOTk5T8vIrl0FZc+ePapbt67dRa4+Pj5q2bKlbXula9q0qd23lbVr11ZycnKeukts375d4eHhKlOmjFJSUpSSkiI3Nzc1bdrU1p1h//79io+P1wMPPJCn+fr4+Kh58+a2+aakpKhly5Y6fPiwXVcsHx8fNWzY0PY4Pj5eBw8e1H333Sd3d/dcLzPdjcdF+jfRLVu2tA0rXbq0ypYta/tm8ty5c5owYYLatGmjwMBABQUF6ccff7Q7TgMDA7VhwwYtW7ZMR44cybTcnMYbhqGVK1eqW7duatCggYKCgjR+/HilpKTkqevpzdqZ8fjPy7F94/rPKOO3ilkJCAiQu7u7xo8fr2+//TZT16Zbkd3xt3//fsXFxemee+7JtH8lJCTYdYGRpPbt29s9zs1+n9H333+vWbNmKSQkJMv1ld4FNiwsTEFBQXrkkUckKcfXt4SEBA0ZMkQnTpzQe++9Z3fWJN3ly5dt3YJXr15t963xnj17FB4ebnfWsHbt2qpXr16m14uM+3/6dBnPGGS1TiwWizw9PbO862ixYtc7wuzcuVPS9W6KN3vt2blzp7p06WJ3piwnuTkm0924fQ8dOqS6devarc+aNWuqXr16dtPt2bNHnTp1koeHh6Trx8Jjjz2m0qVLa+fOnTp8+LC6dOli95xu3bpJ+r/3z8DAQG3btk3z5s3ToUOHMnU1T3fXXXfZPe7SpYvOnz9v2wbbt29Xx44d5e7ublv/pUuXVmBgYKZu123btpXFYrE9PnLkiBITEzP1Jsmud0l2x/3N3HHHHapatareeOMNbdiwwW7/yc34+Ph47du3T/fcc49SU1Nt7fT19VWVKlUK7E55OdWV189pGeV0XKVLTk7WmDFj5O7urjfeeENubrn7uN6kSRPt3r1b0vUzUMHBwWrbtq3dMKvVquDg4Dy97+b28014eLi8vb1tj1u0aCEfHx/bdLfyXu/M6ObnZCIiIrR//36NGDFCderUUalSpfTBBx/oiy++yHL6K1euyMPDI1OXmvLly8swDMXGxsrLy0vS9Q+EGaW/IaT3527RooWti92gQYNUvHhxdenSRc8995x8fHzytKzslC1bNtOwcuXK6fjx47buPuXLl8+0DEmKjo7Ocf5ZmTlzpj766CONGDFCwcHB8vb21pYtW7Rw4UIlJiaqZMmS6t27t65du6YPP/xQK1eulLe3t+677z6NHz8+VxfsZteughITE5NpXaUv98YPiDdu//RglbE/f04uX76sAwcOKCgoKNO4GjVqSPq/bVSxYsU8zTc6OjrL+UrXuxhUrlxZUuZ9IyYmRmlpaXlaXkY3Oy4yviFI19dXYmKi0tLSNGzYMMXGxmr06NGqWbOmrFar5s6dq7///ts2/aRJk1SmTBmtWLFCr7/+uqpUqaLBgwfbPjjnNH7VqlV67bXX9OSTTyo8PFylS5fWL7/8oqlTp+Zpm+XUzsTExDwd21ntb5JktVpVsmTJbGvw8/PTO++8o0WLFmnkyJFyc3NT69atNXny5FvuwpHd8Xf58mVJumkXnIzbTcrcxtzs9+lOnjypcePG6b777pNhGJm+ZPr66681YcIE9e3b19Yd8uLFixoxYkSO2/XSpUs6d+6cHnnkkZuur8jISF25ckXPPfecypQpYzcuJiZG9evXz/SccuXKZepqmdX+krH7UXbrJKsPh+mio6Pl5uamtLS0m7Y3Ojo6T8dzbo/JdDdu3wsXLmTZFbtcuXJ2NcbExNhNlz6fcuXK6Z9//pFhGJnm4+3tLU9PT9v6HTZsmNzc3LRhwwbNnz9fZcuW1aOPPqoRI0bYBZ4b9+f0ZV28eFF33nmnLl++rFWrVtm6tGeUfmxnbEdGFy9elHQ9NGQ3XVbLzwuLxaJly5Zpzpw5mjp1quLi4hQUFKSJEyeqadOmOY6PiYlRamqqpk+fnmXX5qy2bX7Iqa68fk7LKKfjKt0rr7yiX3/9VR9++GGm4zg7zZo108KFC3X+/Hnt2bNHTZo0UZMmTfTPP/8oMjJSe/bsUcOGDeXh4ZGn993cfr7Jah8qW7asbZ+7lfd6Z0aYciKJiYnaunWrIiIi7H7b4v3337/pc8qUKaPk5GRduXLF7oD7559/ZLFYMn0gzEmvXr3Uq1cvXbp0SVu2bNH06dNVrFgxvfrqq/m2rKxuehAVFaUKFSrYPsxFRUWpUqVKdsuQlOV1GLmxefNm9e3b13bRpaRMF727ubnp8ccf1+OPP67z58/r888/1+zZs3XHHXdoxIgROS4ju3ZJ1z+M33i2KyYmxkxzbMqUKZPlt2FRUVF5egHOy/LatGmjMWPGZBqXHs7St9GFCxdsL4q5mW/ZsmW1ePHiLMdnfCHP+IFDuv5hxc3N7bbdNv7UqVP6/ffftWDBAnXu3Nk2PCEhIVNdzz//vJ5//nn9+eefWr16taZMmSJ/f381adIkx/GbN29Wx44dNW7cONs8CyqY5+XYvnH95zT8Rm3btlXbtm119epVff/995o+fbomTpyY5YfCvMju+Etv0/z587PcJ6tVq5btvHOz30tSbGyshg8fLl9fX02ZMkWTJ0/OtF42b96s+vXr291cYteuXdk37v+78847NXLkSD3zzDO64447NGzYsEzThIWFqUWLFpoxY4Z8fHzUq1cvu3ZkdTYoKioqy7Nc2clqncyfP1/Hjx/X/Pnzb/o8Hx+fm56NyThNXo7n3B6TN1OxYkX99ttvmYZHRUXZnR27cf2lb9uoqCiVL19eFosl034YGxurpKQk2z7o6empUaNGadSoUTp16pTWr1+vefPmqVq1arrvvvtsz7txPunvgenvJ2XKlFG7du1sX75kdOOXGjfug+nzuHz5st37bHa9FG6cR/HixSUpx/c0Pz8/zZ07V8nJydq/f7/eeOMNDR06VN9//71KliyZ7Xhvb29ZLBYNGTLEbrumuzEM5qfs6srr57S8Wrt2rdauXau5c+fa9SbIjdDQUNs18Hv27NG//vUv+fj4qG7dutq9e7d2795t28/y8r6b0+ebjMNudOnSJbv91ux7vTOjm58TSUpKUlpamt23SlevXs32DiaNGzeWpEw/irl582YFBgbm6kxRVsqWLas+ffqoVatWOnHiRL4uKzY2Vj/99JPd4x07dqhhw4YKCQmRh4dHpmV88cUXKleunO0N/2bf5txseGJiot16TU1N1eeff37TGitVqqSBAwcqICDA1v5baZckVa5cOdOH4R07dmSqP73e3Axv3Lixjhw5YlfjlStXtGPHDtv2yk8tW7bU8ePHVbt2bYWEhNj9S7/DYFhYmKxWq9avX5+n+V66dEkeHh6Z5hsSEpLt7VC9vLwUGhqq//73v3ZdBApK+jbIuD+dPXtW+/fvv+lzAgICNHHiRElZB6KsxickJGT6hvlmd1m6VQX1OpKdUqVKqVu3burevXu+hMTsjr/0ffLcuXNZ7l85fSjLzX6flpamcePGKTY2VvPnz7/pPnur2/Wee+7RjBkzNHfuXK1cuTLLaZ544gmNHTtWEydOtNumjRs31s6dO+3OQp04cUJ//vlnnl8vslond9xxh0qUKGF3t9EbNW/ePMcPSS1atNCXX36pq1ev5qoWM8dkRiEhITp69KhOnTplG3bq1Cn98ccfdtM1btxYW7Zssd3EQbrebSkmJkbNmzdX/fr1s3zvSn/ujWrWrKlnnnlGPj4+md5nbryT5pdffqmKFSvavgxo0aKFjh49qsDAwEz7ZMabFGWlbt26Kl68uL755hu74Tc+zk56CMtY9/Hjx296tsjDw0PNmjXT4MGDdfXq1UxhOavx6a/tJ06cyPK4zelLkPxwY12nT5/O8+e0vNizZ4+mTZumoUOH6u67787z8728vBQYGKh169YpOjratt81bdpUGzdu1JkzZ9SkSRNJeXvfzenzTbqff/7Zrvv2Tz/9pOjoaNt0t/Je78w4M+VEvL29FRISoiVLlqhs2bIqVqyYFi9erFKlSt30Ftb16tXT3XffrRkzZighIUF+fn7auHGj9u/fr7fffjtPy587d66io6PVrFkzlStXTkeOHNEPP/ygJ554Il+X5ePjo+eff16jR4+Wt7e3lixZIsMw9Pjjj6ts2bLq16+fli1bZvt9km3btumzzz7TpEmTbNfE1K5dWykpKVq1apXCwsJUqlQp1apV66bDW7ZsqY8++kh16tTRHXfcoffffz9T6Jo8ebJKly6t0NBQlS5dWvv27dMff/xhu3vSrbRLut7nfdWqVQoJCbGtuxuvLfD19ZW7u7vWr1+vYsWKyd3dXSEhITcd3rt3b61cuVJDhgzR2LFjbXfzK1asmG25+emJJ57Qp59+qn79+umxxx7TnXfeqUuXLungwYOqVKmSnnjiCXl7e2vEiBGaNWuWDMNQp06dlJaWpp9//tl2C+EbtWrVSh06dNCTTz6pJ598UgEBAYqPj9exY8d06tQpvfLKK9nWlX4XuSeeeEKPPPKIypQpo99++0133HFHnq7dyo1atWqpcuXKmj17ttLS0hQXF6e5c+dm6pb00EMP6a677lLdunXl7u6u//znP/Lw8LC9keU0vmXLllq9erXeffdd+fr6auPGjXYf9vJTfr6OZGft2rU6cOCA2rRpowoVKujMmTPauHGjWrVqdcvzzu74K126tEaPHq2ZM2fq3Llzatasmdzd3XX69Glt2bJF8+bNk9Vqvem8c7PfL1myRNu2bdOECRP0999/6++//9alS5cUHx+vpKQk24eEli1baurUqVqwYIHCwsK0bds2uw8pudGzZ08lJiZq8uTJKlGihB566KFM0wwZMkQJCQkaP368ihcvrg4dOuiJJ57QJ598ooEDB2rYsGFKTEzUm2++qSpVquR4F7LcrJOzZ8/q4sWLWrlype1940Z+fn5q3bq1fvjhBy1evFh33323EhIStHXrVo0aNUqVKlXSyJEjtXXrVj3yyCN68sknVaFCBR0/flzx8fF66qmnMs0zt8fkzfTu3VsLFy7UkCFDbGfa5s6dm6mb0dChQ/XQQw9pyJAhSk1N1ZUrVzR+/Hg1aNBA7dq1k2EYGjFihMaPH6+ePXvq5MmTmjNnjrp06WILmMOHD1dQUJACAwNltVr13Xff6cqVK5l+2Hrnzp167bXX1KpVK23fvl3//e9/NXnyZNu1M6NHj9YDDzygQYMG6cEHH1T58uX1zz//aNeuXWrSpIl69Ohx0/becccdevjhh/XOO++oePHithCYfs1ebq7PadiwoapUqaJXX31V48aN09WrV7V48WK73iN//PGHXnvtNXXr1k3Vq1fX1atXtWjRIlWtWlU1atTIcbwk/fvf/9bjjz+usWPHqnv37ipdurTOnTunHTt2qHfv3pnu1Jgfsqurbt26ef6clltXr17VqFGjVLNmTbVr187u1uNly5bN1KX4Zpo0aaJly5YpKCjIdma1SZMmeu+99+Th4aGwsDBJeXvfzenzTbqSJUvqqaee0lNPPaXY2FjNmjVLDRo0UJs2bfK8TFdCmHIys2fP1uTJkxURESEfHx/1799fcXFxWr58+U2fM3PmTL3xxhtasmSJoqOjVatWLc2dO1cdO3bM07JDQkK0atUqffHFF7p69aoqV66sQYMG2XUlyY9lVahQQePHj9frr7+uv/76S3Xr1tWyZctsb1z//ve/5e3trY8//ljvvPOOqlatqilTpth9YOjQoYMeeeQRLV68WFFRUWratKnWrFlz0+GTJk3Siy++qJdffllWq1X333+/7rrrLr3wwgu2eYaFhenDDz/URx99pPj4eFWvXl0TJ05Unz598qVdw4cPV1RUlBYsWCCLxaK+ffvqscce04wZM2zzKFu2rCZPnqylS5dq48aNSklJ0Z9//nnT4aVKldKaNWs0Y8YMTZo0SWlpaWrUqJHeffddu9ui55c77rhD69at05tvvqlZs2YpOjpa5cqVU8OGDe0umH7qqadUtmxZrVy5Up988olKliypsLCwbPvkz507V4sXL9YHH3ygs2fPytvbW3Xr1lXv3r1zrKtJkyZavXq13nzzTU2cOFFubm6qW7eu7bbM+cnT01Pz5s3T1KlTNWbMGFWpUkXDhg3Tzp077S78btSokf7zn//ozJkzcnNzk7+/v9555x3bzUJyGj9ixAhdvnzZdkvlLl266IUXXtDQoUPzvU1S/r2OZCcgIEDfffedpk+frujoaFWoUEHdu3fPsvtcXuV0/A0cOFCVKlXSihUr9O6776pYsWKqUaOG2rdvn+lM0Y1ys9+nd7d97bXXMj3/woULtm/RH3roIZ05c0bvvvuuli1bptatW2v27Nl68MEH89TePn36KDExUVOmTFGJEiXsuoilGzNmjBISEjR69GgtWrRILVu21Jo1a/T6669r/PjxcnNzU6tWrRQREZGnmz3cbJ24u7urWLFiatCgQbbPffDBB/XDDz/oxx9/1CeffCIfHx81bdrU1j3N19dXa9eu1ezZszVlyhSlpqbK19fXrpt2Rrk9Jm+mRIkSWr58uV566SU9++yzqlSpkoYPH64tW7bYfcseHBys5cuX64033rBdUH/fffdpwoQJcnd3V6dOnfTWW29pwYIFGj58uHx8fPTggw/addVt1KiRvvjiC61YsUKpqany8/PTrFmzMt2YYOrUqVq3bp0++OADlSxZUmPGjNGjjz5qG1+zZk199NFHevPNNzVlyhTFxcWpQoUKatq0abZnBtONGzdOKSkpWrx4sdLS0nTXXXdp8ODBmjp1aq667Ht4eGj+/Pl66aWXNGbMGNWoUUPPPfec3ftZhQoVVL58eS1atEjnz5+Xt7e3mjRpopkzZ8rd3T3H8enr6/3339e8efM0ceJEJScnq3LlymrevLlq1qyZY51m5FSXmc9puREdHa1Lly7p0qVLmb4guf/+++3WbXaaNWumZcuW2b6Yk66fmZKu78MZrwHP7ftuTq+v6e666y5VrlxZL774omJiYtSyZUtNmTLFbppbea93VhbDMAxHFwEAgFkRERH69ddf7X7UFUDePPvss9q7d69T/zgq4Iw4MwUAAFCE7Nq1S/v27VNQUJDS0tK0detWffrpp4qIiHB0aYDLIUwBAAAUIV5eXtq6dauWLFmixMREVa1aVRERETe91g3AzdHNDwAAAABM4NboAAAAAGACYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQBANiIiIvL1x4sBAIUHYQoAUCR98sknCggI0C+//OLoUgAALoowBQAAAAAmEKYAAAAAwATCFAAA/98333yjHj16KCQkRD169NDXX3/t6JIAAE6smKMLAADAGfz4448aNWqU6tSpo3Hjxuny5cuaOHGiKleu7OjSAABOijAFAICkWbNmqVy5cnr//ffl7e0tSWrWrJkGDhyoqlWrOrg6AIAzopsfAKDIu3Dhgg4fPqz777/fFqQkqVWrVqpTp44DKwMAODPCFACgyPvf//4nSapZs2amcX5+fre7HACAiyBMAQAAAIAJhCkAQJF35513SpJOnTqVadzJkydvdzkAABdBmAIAFHkVK1ZU/fr1tWHDBsXGxtqGb9++XceOHXNgZQAAZ8bd/AAAkPTMM89oyJAheuSRR/Svf/1L0dHRevfdd1W3bl3FxcU5ujwAgBPizBQAAJLatm2rt956S6mpqZo9e7a+/vprTZ8+XcHBwY4uDQDgpCyGYRiOLgIAAAAAXA1npgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEz4f8CKheg5xQaOAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evolución puntos del campeón a lo largo de los años\n",
        "\n",
        "This one is used to obtain the dataset \"points\", which will be used both in dashboard and notebook. Avoid executing this cell to save time."
      ],
      "metadata": {
        "id": "QHgaq8A2HnDo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "points = []\n",
        "winner = []\n",
        "for year in range(1950,2024):\n",
        "  request = f'http://ergast.com/api/f1/{year}/driverStandings/1.json'\n",
        "  response = requests.get(request)\n",
        "  # Iterate through each set of driver standings for the current year\n",
        "  for i in range(len(response.json()['MRData']['StandingsTable']['StandingsLists'])):\n",
        "      # Append the points and winner information to the respective lists\n",
        "      points.append(float(response.json()['MRData']['StandingsTable']['StandingsLists'][0]['DriverStandings'][0]['points']))\n",
        "      winner.append(str(response.json()['MRData']['StandingsTable']['StandingsLists'][0]['DriverStandings'][0]['Driver'][\"driverId\"]))\n",
        "\n"
      ],
      "metadata": {
        "id": "caLJ4JI6Hl49"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "# Fetching and accumulating total points for Formula 1 drivers each season\n",
        "\n",
        "# Initialize a list to store total points for each season (1950 to 2023)\n",
        "totalPoints = [0 for _ in range(1950,2024)]\n",
        "# Initialize an additional list to store cumulative total points for all seasons\n",
        "totalPoints2 = []\n",
        "# Initialize a variable to store the temporary total points for each season\n",
        "total = 0\n",
        "\n",
        "for year in range(1950,2024):\n",
        "  request = f'http://ergast.com/api/f1/{year}/driverStandings.json'\n",
        "  response = requests.get(request)\n",
        "  # Iterate through each driver in the standings list for the current season\n",
        "  for pilot in range(len(response.json()['MRData']['StandingsTable']['StandingsLists'][0]['DriverStandings'])):\n",
        "    # Accumulate the points for each driver in the season\n",
        "    total += float(response.json()['MRData']['StandingsTable']['StandingsLists'][0]['DriverStandings'][pilot]['points'])\n",
        "  # Append the total points for the season to the list\n",
        "  totalPoints2.append(total)\n",
        "  # Reset the temporary total for the next season\n",
        "  total = 0\n"
      ],
      "metadata": {
        "id": "svMuPfSvvx0y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(totalPoints2)\n",
        "print(points)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ih4PT1fqVbnd",
        "outputId": "699cd28a-56a9-4629-e1a9-286bd4e928d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[164.0, 174.0, 171.5, 187.5, 198.92, 166.99, 186.5, 186.0, 252.0, 213.0, 233.0, 196.0, 210.0, 222.0, 248.0, 242.0, 216.0, 273.0, 298.0, 275.0, 324.0, 275.0, 300.0, 375.0, 375.0, 325.0, 400.0, 425.0, 400.0, 347.0, 339.0, 375.0, 399.0, 369.0, 383.5, 397.0, 396.0, 395.0, 378.0, 395.0, 397.0, 403.0, 416.0, 416.0, 416.0, 442.0, 416.0, 442.0, 416.0, 416.0, 442.0, 442.0, 442.0, 624.0, 702.0, 738.0, 702.0, 663.0, 702.0, 643.5, 1919.0, 1919.0, 2020.0, 1919.0, 2020.0, 1919.0, 2121.0, 2020.0, 2121.0, 2140.0, 1734.0, 2207.5, 2350.0, 2458.0]\n",
            "[30.0, 31.0, 36.0, 34.5, 42.0, 40.0, 30.0, 40.0, 42.0, 31.0, 43.0, 34.0, 42.0, 54.0, 40.0, 54.0, 42.0, 51.0, 48.0, 63.0, 45.0, 62.0, 61.0, 71.0, 55.0, 64.5, 69.0, 72.0, 64.0, 51.0, 67.0, 50.0, 44.0, 59.0, 72.0, 73.0, 72.0, 73.0, 90.0, 76.0, 78.0, 96.0, 108.0, 99.0, 92.0, 102.0, 97.0, 81.0, 100.0, 76.0, 108.0, 123.0, 144.0, 93.0, 148.0, 133.0, 134.0, 110.0, 98.0, 95.0, 256.0, 392.0, 281.0, 397.0, 384.0, 381.0, 385.0, 363.0, 408.0, 413.0, 347.0, 395.5, 454.0, 575.0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating the percentage of total points for each winner in a season\n",
        "\n",
        "perWinner = []\n",
        "\n",
        "# Iterate through the range of points\n",
        "for i in range(len(points)):\n",
        "    # Calculate the percentage of total points for the current winner in the season\n",
        "    div = points[i] / totalPoints2[i]\n",
        "\n",
        "    # Round the result to four decimal places and append to the list\n",
        "    perWinner.append(round(div, 4))\n",
        "\n"
      ],
      "metadata": {
        "id": "SC40ovBAWV6Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "years = [year for year in range(1950, 2024)]\n",
        "df = pd.DataFrame({'year':years,'winner': winner, 'winnerPpoints':points, 'totalPoints': totalPoints2, '% winner':perWinner})\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "eNEOyv5QM_Zl",
        "outputId": "5394c819-b8bf-44f0-cb74-3989a8f38560"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   year  winner  winnerPpoints  totalPoints  % winner\n",
              "0  1950  farina           30.0       164.00    0.1829\n",
              "1  1951  fangio           31.0       174.00    0.1782\n",
              "2  1952  ascari           36.0       171.50    0.2099\n",
              "3  1953  ascari           34.5       187.50    0.1840\n",
              "4  1954  fangio           42.0       198.92    0.2111"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3215b223-9c7e-415e-8944-7aa87f4230da\" class=\"colab-df-container\">\n",
              "    <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>year</th>\n",
              "      <th>winner</th>\n",
              "      <th>winnerPpoints</th>\n",
              "      <th>totalPoints</th>\n",
              "      <th>% winner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1950</td>\n",
              "      <td>farina</td>\n",
              "      <td>30.0</td>\n",
              "      <td>164.00</td>\n",
              "      <td>0.1829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1951</td>\n",
              "      <td>fangio</td>\n",
              "      <td>31.0</td>\n",
              "      <td>174.00</td>\n",
              "      <td>0.1782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1952</td>\n",
              "      <td>ascari</td>\n",
              "      <td>36.0</td>\n",
              "      <td>171.50</td>\n",
              "      <td>0.2099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1953</td>\n",
              "      <td>ascari</td>\n",
              "      <td>34.5</td>\n",
              "      <td>187.50</td>\n",
              "      <td>0.1840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1954</td>\n",
              "      <td>fangio</td>\n",
              "      <td>42.0</td>\n",
              "      <td>198.92</td>\n",
              "      <td>0.2111</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3215b223-9c7e-415e-8944-7aa87f4230da')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3215b223-9c7e-415e-8944-7aa87f4230da button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3215b223-9c7e-415e-8944-7aa87f4230da');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-08324b87-5d4d-4f38-a0d1-1563f4041026\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-08324b87-5d4d-4f38-a0d1-1563f4041026')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-08324b87-5d4d-4f38-a0d1-1563f4041026 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv(path2+'points.csv', index=False)"
      ],
      "metadata": {
        "id": "3H_ST5YlYqWr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Within the following cell a time series of the points distribution is represented"
      ],
      "metadata": {
        "id": "vwAiWlkkCE4i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(path2+'points.csv')\n",
        "\n",
        "plt.figure(figsize=(15, 6))  # Tamaño del gráfico\n",
        "sns.lineplot(data=df, x='year', y='totalPoints', marker='o')\n",
        "plt.title('Evolución de la variable \"points\" por año')  # Título del gráfico\n",
        "plt.xlabel('Año')  # Etiqueta del eje x\n",
        "plt.ylabel('Puntos')  # Etiqueta del eje y\n",
        "plt.legend()  # Mostrar leyenda\n",
        "plt.grid(True)  # Mostrar cuadrícula\n",
        "plt.show()  # Mostrar gráfico"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "LkJ2XarSL0TU",
        "outputId": "6980e1f5-fdb1-4c64-90e4-50449df87e1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Predict next winners\n",
        "\n",
        "In this section we will collect several datasets, explore them and use the main characteristics to fit models in order to predict the next winner"
      ],
      "metadata": {
        "id": "a1pvN6VizCD2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Get data\n",
        "\n",
        "First of all we are going to get the data from the Ergast API.\n",
        "\n",
        "We will use information from the circuits, the results of each race and the driver standings before each race. Avoid executing this cell since it is the one related to data gathering of ML part and takes time. In order to play with the models data is imported later."
      ],
      "metadata": {
        "id": "1bYQ3Z0Jz73k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "races={} #We will store all data here in order to later be converted into a pandas dataframe\n",
        "url = 'https://ergast.com/api/f1/{}.json'\n",
        "# Flag to identify the first iteration\n",
        "first_iter=True\n",
        "\n",
        "for year in range(1950,2020):\n",
        "  # Get data for a specific year from the API\n",
        "  response = requests.get(url.format(year))\n",
        "  json=response.json()\n",
        "\n",
        "   # Iterate over all races in the current year\n",
        "  for item in json['MRData']['RaceTable']['Races']:\n",
        "    # Iterate over all variables of each circuit in a race\n",
        "    for key, value in item.items():\n",
        "      # We don't have 'time' until 2005, so we don't use it\n",
        "      if key!='time':\n",
        "          #If we are in the first iteration we need to define the list in which we are going to store the values\n",
        "          if first_iter:\n",
        "              #The 'circuit' key is another dictionary with more values so we have to select them\n",
        "              if key=='Circuit':\n",
        "                  races['circuit_id']=[value['circuitId']]\n",
        "                  races['lat']=[value['Location']['lat']]\n",
        "                  races['long']=[value['Location']['long']]\n",
        "                  races['country']=[value['Location']['country']]\n",
        "              else:\n",
        "                races[key]=[value]\n",
        "          #If it is not the first iteration we can simply append the value at the existing list\n",
        "          else:\n",
        "              if key=='Circuit':\n",
        "                races['circuit_id'].append(value['circuitId'])\n",
        "                races['lat'].append(value['Location']['lat'])\n",
        "                races['long'].append(value['Location']['long'])\n",
        "                races['country'].append(value['Location']['country'])\n",
        "              else:\n",
        "                races[key].append(value)\n",
        "\n",
        "    first_iter=False\n",
        "\n",
        "# Convert the dictionary to a pandas dataframe\n",
        "races = pd.DataFrame(races)\n",
        "races.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3xFGiba-0PXi",
        "outputId": "711779c3-13e6-4192-cd1f-b4dcb4a57206"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  season round                                                url  \\\n",
              "0   1950     1  http://en.wikipedia.org/wiki/1950_British_Gran...   \n",
              "1   1950     2  http://en.wikipedia.org/wiki/1950_Monaco_Grand...   \n",
              "2   1950     3  http://en.wikipedia.org/wiki/1950_Indianapolis...   \n",
              "3   1950     4  http://en.wikipedia.org/wiki/1950_Swiss_Grand_...   \n",
              "4   1950     5  http://en.wikipedia.org/wiki/1950_Belgian_Gran...   \n",
              "\n",
              "             raceName    circuit_id      lat      long      country  \\\n",
              "0  British Grand Prix   silverstone  52.0786  -1.01694           UK   \n",
              "1   Monaco Grand Prix        monaco  43.7347   7.42056       Monaco   \n",
              "2    Indianapolis 500  indianapolis   39.795  -86.2347          USA   \n",
              "3    Swiss Grand Prix    bremgarten  46.9589   7.40194  Switzerland   \n",
              "4  Belgian Grand Prix           spa  50.4372   5.97139      Belgium   \n",
              "\n",
              "         date  \n",
              "0  1950-05-13  \n",
              "1  1950-05-21  \n",
              "2  1950-05-30  \n",
              "3  1950-06-04  \n",
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          "metadata": {},
          "execution_count": 4
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    {
      "cell_type": "code",
      "source": [
        "# Initialize a dictionary to store race results\n",
        "results = {\n",
        "    'season': [],\n",
        "    'round': [],\n",
        "    'circuit_id': [],\n",
        "    'Driver': [],\n",
        "    'dateOfBirth': [],\n",
        "    'Constructor': [],\n",
        "    'grid': [],\n",
        "    'status': [],\n",
        "    'points': [],\n",
        "    'position': []\n",
        "}\n",
        "\n",
        "# URL template for fetching Formula 1 race results\n",
        "url = 'http://ergast.com/api/f1/{}/{}/results.json'\n",
        "\n",
        "# Loop through the years from 1950 to 2019\n",
        "for year in range(1950, 2020):\n",
        "    # Get the list of rounds for the current year\n",
        "    rounds = list(races[races.season == str(year)]['round'])\n",
        "\n",
        "    # Loop through each round in the year\n",
        "    for round in rounds:\n",
        "        # Fetch race results for a specific year and round from the API\n",
        "        response = requests.get(url.format(year, round))\n",
        "        json_data = response.json()\n",
        "\n",
        "        # Iterate over each result in the race\n",
        "        for item in json_data['MRData']['RaceTable']['Races'][0]['Results']:\n",
        "            # Append relevant information to the 'results' dictionary\n",
        "            results['season'].append(json_data['MRData']['RaceTable']['Races'][0]['season'])\n",
        "            results['round'].append(json_data['MRData']['RaceTable']['Races'][0]['round'])\n",
        "            results['circuit_id'].append(json_data['MRData']['RaceTable']['Races'][0]['Circuit']['circuitId'])\n",
        "\n",
        "            # Iterate over each key-value pair in the result item\n",
        "            for key, value in item.items():\n",
        "                # Check if the key is in the 'results' dictionary\n",
        "                if key in results.keys():\n",
        "                    # Handle specific cases for 'Driver', 'Constructor', and 'Time' keys\n",
        "                    if key == 'Driver':\n",
        "                        results['Driver'].append(value['driverId'])\n",
        "                        results['dateOfBirth'].append(value['dateOfBirth'])\n",
        "                    elif key == 'Constructor':\n",
        "                        results['Constructor'].append(value['constructorId'])\n",
        "                    elif key == 'Time':\n",
        "                        results['Time'].append(value['millis'])\n",
        "                    else:\n",
        "                        results[key].append(value)\n",
        "\n"
      ],
      "metadata": {
        "id": "zTThvlyx8l8U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results = pd.DataFrame(results)\n",
        "results.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "b6x4YUiJHAGr",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  season round   circuit_id       Driver dateOfBirth Constructor grid  \\\n",
              "0   1950     1  silverstone       farina  1906-10-30        alfa    1   \n",
              "1   1950     1  silverstone      fagioli  1898-06-09        alfa    2   \n",
              "2   1950     1  silverstone  reg_parnell  1911-07-02        alfa    4   \n",
              "3   1950     1  silverstone    cabantous  1904-10-08        lago    6   \n",
              "4   1950     1  silverstone       rosier  1905-11-05        lago    9   \n",
              "\n",
              "     status points position  \n",
              "0  Finished      9        1  \n",
              "1  Finished      6        2  \n",
              "2  Finished      4        3  \n",
              "3   +2 Laps      3        4  \n",
              "4   +2 Laps      2        5  "
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              "        document.querySelector('#df-48db0fe2-0e93-4d45-a2c7-a7dd4bf3a165 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-48db0fe2-0e93-4d45-a2c7-a7dd4bf3a165');\n",
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              "                                                    [key], {});\n",
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              "\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "driver_standings = {'season': [],'round':[],'Driver': [], 'driver_points': [], 'driver_wins': [],'driver_standings_pos': []}\n",
        "# query API\n",
        "url = 'https://ergast.com/api/f1/{}/{}/driverStandings.json'\n",
        "for year in range(1950,2020):\n",
        "  rounds=list(races[races.season == str(year)]['round'])\n",
        "  for round in rounds:\n",
        "    response = requests.get(url.format(year, round))\n",
        "    json = response.json()\n",
        "\n",
        "    for item in json['MRData']['StandingsTable']['StandingsLists'][0]['DriverStandings']:\n",
        "          driver_standings['season'].append(int(json['MRData']['StandingsTable']['StandingsLists'][0]['season']))\n",
        "          driver_standings['round'].append(int(json['MRData']['StandingsTable']['StandingsLists'][0]['round']))\n",
        "          driver_standings['Driver'].append(item['Driver']['driverId'])\n",
        "          driver_standings['driver_points'].append(item['points'])\n",
        "          driver_standings['driver_wins'].append(item['wins'])\n",
        "          driver_standings['driver_standings_pos'].append(item['position'])"
      ],
      "metadata": {
        "id": "jLHJ6DkiDg7t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# define lookup function to shift points and number of wins from previous rounds\n",
        "\n",
        "def lookup(df, points):\n",
        "    \"\"\"\n",
        "    This function return the same dataframe but the information of the 'points' column is shifted so that it shows the info of the previous round(before the actual race)\n",
        "\n",
        "    df: pandas dataframe\n",
        "    points: Column we want to modify\n",
        "\n",
        "    \"\"\"\n",
        "    #Get the specific column 'points' for each race and its previous round for each driver\n",
        "    df['lookup1'] = df.season.astype(str) + df['Driver'] + df['round'].astype(str)\n",
        "    df['lookup2'] = df.season.astype(str) + df['Driver'] + (df['round']-1).astype(str)\n",
        "    #Join them and keep only the data from the previous round\n",
        "    new_df = df.merge(df[['lookup1', points]], how = 'left', left_on='lookup2',right_on='lookup1')\n",
        "    new_df.drop(['lookup1_x', 'lookup2', 'lookup1_y'], axis = 1, inplace = True)\n",
        "    new_df.rename(columns = {points+'_x': points+'_after_race', points+'_y': points+'_before_race'}, inplace = True)\n",
        "    #The first round will have Na values because there no round 0\n",
        "    new_df[ points+'_before_race'].fillna(0, inplace = True)\n",
        "    return new_df"
      ],
      "metadata": {
        "id": "WvQIw8QzbRv8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "driver_standings=pd.DataFrame(driver_standings)\n",
        "driver_standings = lookup(driver_standings, 'driver_points')\n",
        "driver_standings = lookup(driver_standings, 'driver_wins')\n",
        "driver_standings = lookup(driver_standings,'driver_standings_pos')\n",
        "\n",
        "driver_standings.drop(['driver_points_after_race', 'driver_wins_after_race', 'driver_standings_pos_after_race'],\n",
        "                      axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "BYe7Ma5k7_j6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Save datasets"
      ],
      "metadata": {
        "id": "cJ7E5HQKAcC6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(driver_standings).to_csv(path2+'driver_standings.csv' ,index=False)\n",
        "pd.DataFrame(results).to_csv(path2+'results.csv', index=False)\n",
        "pd.DataFrame(races).to_csv(path2+'races.csv', index=False)"
      ],
      "metadata": {
        "id": "eTv1WC1O83ZA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Process data\n",
        "\n",
        "EXECUTE ML FROM HERE"
      ],
      "metadata": {
        "id": "9kOB7Os5VT0J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "driver_standings=pd.read_csv(path2+'driver_standings.csv')\n",
        "results=pd.read_csv(path2+'results.csv')\n",
        "races=pd.read_csv(path2+'races.csv')"
      ],
      "metadata": {
        "id": "KNUCoasV1ybf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Combine all datasets"
      ],
      "metadata": {
        "id": "KYSlt0UBf5ol"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = pd.merge(races, results, how='inner',\n",
        "               on=['season', 'round', 'circuit_id'])\n",
        "final_df = pd.merge(df1, driver_standings, how='left',\n",
        "               on=['season', 'round', 'Driver'])"
      ],
      "metadata": {
        "id": "sKLRjJtG8iew"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We are going to cerate a new variable: Age. It will represent the age of the driver at the moment of the race.\n",
        "\n",
        "Afterthat we can remove the date of bith of the driver and the date of the race"
      ],
      "metadata": {
        "id": "KW1izmrgf9D3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_ages(data):\n",
        "    ages=[]\n",
        "    for i in range(len(data)):\n",
        "      birthDate=data.loc[i,'dateOfBirth']\n",
        "      raceDate=data.loc[i,'date']\n",
        "      # Convertir la fecha a objeto datetime\n",
        "      birthDate = datetime.strptime(birthDate, '%Y-%m-%d')\n",
        "      raceDate = datetime.strptime(raceDate, '%Y-%m-%d')\n",
        "\n",
        "      # Calcular la diferencia de años\n",
        "      edad = raceDate.year - birthDate.year\n",
        "\n",
        "      # Ajustar la edad si aún no ha llegado el cumpleaños este año\n",
        "      if (raceDate.month, raceDate.day) < (birthDate.month, birthDate.day):\n",
        "          edad -= 1\n",
        "      ages.append(edad)\n",
        "\n",
        "    return ages"
      ],
      "metadata": {
        "id": "yeIEQpS3KRb_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_df['Age']=get_ages(final_df)\n",
        "final_df.drop(['date','dateOfBirth'],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "MmYTXDNH4daN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's take a look to the null values"
      ],
      "metadata": {
        "id": "uNcMYqGsgRLI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "final_df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D0buyRg1smLq",
        "outputId": "16b4c609-b71e-43c1-d1d5-2de2f43776dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "season                                 0\n",
              "round                                  0\n",
              "url                                    0\n",
              "raceName                               0\n",
              "circuit_id                             0\n",
              "lat                                    0\n",
              "long                                   0\n",
              "country                                0\n",
              "Driver                                 0\n",
              "Constructor                            0\n",
              "grid                                   0\n",
              "status                                 0\n",
              "points                                 0\n",
              "position                               0\n",
              "driver_points_before_race           1895\n",
              "driver_wins_before_race             1895\n",
              "driver_standings_pos_before_race    1895\n",
              "Age                                    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fill/drop nulls\n",
        "\n",
        "for col in ['driver_points_before_race', 'driver_wins_before_race', 'driver_standings_pos_before_race']:\n",
        "    final_df[col].fillna(0, inplace = True)"
      ],
      "metadata": {
        "id": "V5iDE3EJslIL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_df.drop(['url','raceName'], axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "3BRhRobb38Ub"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we will convert the categorical variables into dummy variables in order to be used in the following algorithms.\n",
        "\n",
        "There are categorical variables that are not converted into dummy because we are going to discard them. For example the status of the race cannot be used because have information posterior to the race"
      ],
      "metadata": {
        "id": "8tyj3zrpQ2DK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_dum = pd.get_dummies(final_df, columns = ['circuit_id', 'country', 'Constructor'] )\n",
        "\n",
        "for col in df_dum.columns:\n",
        "    if 'nationality' in col and df_dum[col].sum() < 140:\n",
        "        df_dum.drop(col, axis = 1, inplace = True)\n",
        "\n",
        "    elif 'constructor' in col and df_dum[col].sum() < 140:\n",
        "        df_dum.drop(col, axis = 1, inplace = True)\n",
        "\n",
        "    elif 'circuit_id' in col and df_dum[col].sum() < 70:\n",
        "        df_dum.drop(col, axis = 1, inplace = True)\n",
        "\n",
        "    else:\n",
        "        pass\n"
      ],
      "metadata": {
        "id": "GdAU7gmEVWiU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Machine learning"
      ],
      "metadata": {
        "id": "jUI_p-zdWJe2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We are going to define a custom funtion to evaluate the model in the test set, because we have to take into account two points.\n",
        "\n",
        "    -First of all, we have to make predictions round by round, we cannot do it in a single time.\n",
        "\n",
        "    -Secondly after making the predictions we have to make some processing, to ensure there is just a single winner\n",
        "    \n",
        "Afetr this, we are going to use the precision score because we want to know the number of instances predicted as true corrected classified.\n",
        "\n",
        "The precision could be misleading in unbalanced datasets because if we predict all False, it will return a \"high\" score but in fact it is a poor classifier. But in this case as the model is going to predict in every round a winner(True), this problem is solved"
      ],
      "metadata": {
        "id": "ziRJhnAjx8nL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def score_classification(model):\n",
        "    \"\"\"\n",
        "    This function computes the average precision in the test set. For each round it takes the corresponding dataset, make predictions with the model and sort the predictions by the porbability 1.\n",
        "    Now the index of the dataset can be interpreted as the positions in the race, so we take the 0 as the winner\n",
        "\n",
        "    model: trained ML model\n",
        "    Return:  average precision in the test set\n",
        "    \"\"\"\n",
        "    year=2018\n",
        "    score = 0\n",
        "    for circuit in df[df.season >= year]['round'].unique():\n",
        "        #X test\n",
        "        test = df[(df.season >= year) & (df['round'] == circuit)]\n",
        "        X_test = test.drop(['Driver', 'position','points','status'], axis = 1)\n",
        "        y_test = test.position\n",
        "\n",
        "        #scaling\n",
        "        X_test = pd.DataFrame(scaler.transform(X_test), columns = X_test.columns)\n",
        "\n",
        "        # make predictions\n",
        "        prediction_df = pd.DataFrame(model.predict_proba(X_test), columns = ['proba_0', 'proba_1'])\n",
        "        #real psoition\n",
        "        prediction_df['actual'] = y_test.reset_index(drop = True)\n",
        "        #sort by porbability\n",
        "        prediction_df.sort_values('proba_1', ascending = False, inplace = True)\n",
        "        prediction_df.reset_index(inplace = True, drop = True)\n",
        "        #La prediccion es el propio orden del dataset porque esta ordenado\n",
        "        prediction_df['predicted'] = prediction_df.index\n",
        "        #1 si es el primero(index 0) y 0 si no\n",
        "        prediction_df['predicted'] = prediction_df.predicted.map(lambda x: 1 if x == 0 else 0)\n",
        "\n",
        "        score += precision_score(prediction_df.actual, prediction_df.predicted)\n",
        "\n",
        "    model_score = score / df[df.season >= year]['round'].unique().max()\n",
        "    return model_score"
      ],
      "metadata": {
        "id": "OLqnpjouV--R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df_dum.copy()\n",
        "df.position = df.position.map(lambda x: 1 if x == 1 else 0)\n",
        "\n",
        "#split train\n",
        "\n",
        "train = df[df.season <2018]\n",
        "train=shuffle(train)\n",
        "X_train = train.drop(['Driver', 'position','points','status'], axis = 1)\n",
        "y_train = train.position\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_train = pd.DataFrame(scaler.fit_transform(X_train), columns = X_train.columns)\n",
        "\n",
        "#define all models and its parameters grid\n",
        "models = {\n",
        "    'RandomForest': (RandomForestClassifier(), {'n_estimators': [180, 200, 220], 'min_samples_split': [11,20,25]}),\n",
        "    'LinearRegression': (LogisticRegression(max_iter=5000, penalty='l2'), {'C': [0.0001,0.001, 0.01]}),\n",
        "    'MLP': (MLPClassifier(max_iter=5000), {'hidden_layer_sizes': [ (124, 32,16), (64, 16), (100), (50),(20)]}),\n",
        "    'GradientBoosting': (GradientBoostingClassifier(), {'n_estimators': [20,50,70, 100], 'learning_rate': [0.01, 0.1, 0.2]}),\n",
        "    'DecisionTree': (DecisionTreeClassifier(), {'max_depth': [None,3, 10, 20], 'min_samples_split': [3, 5, 7]})\n",
        "}\n",
        "\n",
        "# store results\n",
        "model_results = {}\n",
        "#Iterar sobre todos los modelos y sus parametros y guardar los resultados\n",
        "for model_name, (model, param_grid) in models.items():\n",
        "    print(model_name)\n",
        "    grid_search = GridSearchCV(model, param_grid, cv=5, scoring='precision')\n",
        "    grid_search.fit(X_train, y_train)\n",
        "    best_model = grid_search.best_estimator_\n",
        "    # Evaluar en el conjunto de test\n",
        "    score =score_classification(best_model)\n",
        "\n",
        "    # Almacenar resultados\n",
        "    model_results[model_name] = {\n",
        "        'best_params': grid_search.best_params_,\n",
        "        'best_model': best_model,\n",
        "        'best_score': grid_search.best_score_,\n",
        "        'test_score': score\n",
        "    }\n",
        "\n",
        "# Mostrar resultados\n",
        "for model_name, result in model_results.items():\n",
        "    print(f\"Modelo: {model_name}\")\n",
        "    print(f\"Mejores parámetros: {result['best_params']}\")\n",
        "    print(f\"Precision de validación cruzada: {result['best_score']}\")\n",
        "    print(f\"Precision media en el conjunto de test: {result['test_score']}\")\n",
        "    print(\"----\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9EpgXRMDhYB1",
        "outputId": "ae88b86e-aacf-49e4-9edc-f94779719de1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RandomForest\n",
            "LinearRegression\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MLP\n",
            "GradientBoosting\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DecisionTree\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Modelo: RandomForest\n",
            "Mejores parámetros: {'min_samples_split': 20, 'n_estimators': 200}\n",
            "Precision de validación cruzada: 0.5035464535464536\n",
            "Precision media en el conjunto de test: 0.5714285714285714\n",
            "----\n",
            "Modelo: LinearRegression\n",
            "Mejores parámetros: {'C': 0.001}\n",
            "Precision de validación cruzada: 0.49606393606393606\n",
            "Precision media en el conjunto de test: 0.42857142857142855\n",
            "----\n",
            "Modelo: MLP\n",
            "Mejores parámetros: {'hidden_layer_sizes': 50}\n",
            "Precision de validación cruzada: 0.29315900267543926\n",
            "Precision media en el conjunto de test: 0.42857142857142855\n",
            "----\n",
            "Modelo: GradientBoosting\n",
            "Mejores parámetros: {'learning_rate': 0.1, 'n_estimators': 70}\n",
            "Precision de validación cruzada: 0.5476909405480834\n",
            "Precision media en el conjunto de test: 0.38095238095238093\n",
            "----\n",
            "Modelo: DecisionTree\n",
            "Mejores parámetros: {'max_depth': 10, 'min_samples_split': 7}\n",
            "Precision de validación cruzada: 0.3956323096718465\n",
            "Precision media en el conjunto de test: 0.42857142857142855\n",
            "----\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "names=[]\n",
        "prec=[]\n",
        "for model_name, result in model_results.items():\n",
        "    names.append(model_name)\n",
        "    prec.append(np.round(result['test_score'],2))"
      ],
      "metadata": {
        "id": "7FjY9XYayUjz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d= {'Model': names, 'Precision': prec}\n",
        "model_prec = pd.DataFrame(d)\n",
        "model_prec.to_csv(path2+'Model_results.csv')"
      ],
      "metadata": {
        "id": "TPs-GhwmyWhB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "plt.bar(names,prec,color='#00ACC1')\n",
        "plt.xlabel('Precision score')\n",
        "plt.ylabel('Models')\n",
        "plt.title('Models precision in test set')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "8QMxfhzhHThi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "8b269470-4821-48f3-b77a-db89a5f50ffa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see, There is a bit of difference between the models performance.\n",
        "\n",
        "The MLP is the one with the worst precision, with less than 0.35\n",
        "\n",
        "Considering linear regression and decision tree, they have performed similar, with an f1 of almost 0.45. Then we Gradient boosting which have reach almost 0.5\n",
        "\n",
        "Finally, the best model is the random forest, which has 0.62.\n",
        "\n",
        "This means that more than half of the instances that the model predicts as true (winners) are indeed true and the remaining 38% are false (non-winners). This score may be considered low at first, but we have to keep in mind that the problem is unbalanced, we only have 5% of True classes (1 winner per race out of 20 drivers), so it is actually a very good result."
      ],
      "metadata": {
        "id": "xeY-YRZXylNT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's analyze the feature importance for each model"
      ],
      "metadata": {
        "id": "No-YeqiVyqmt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best_random_forest_model = model_results['RandomForest']['best_model']\n",
        "feature_importance=best_random_forest_model.feature_importances_\n",
        "importance={}\n",
        "for i,col in enumerate(X_train.columns):\n",
        "    importance[col]=feature_importance[i]\n",
        "\n",
        "# Ordenar el diccionario por valores en orden descendente\n",
        "sorted_items = sorted(importance.items(), key=lambda x: x[1], reverse=True)\n",
        "# Tomar los cinco primeros elementos\n",
        "top = sorted_items[:7]\n",
        "\n",
        "# Obtener las claves y valores de los cinco elementos\n",
        "top_keys = [item[0] for item in top]\n",
        "top_values = [item[1] for item in top]"
      ],
      "metadata": {
        "id": "mN68clBGeNBR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizar la importancia de las características\n",
        "fig, ax = plt.subplots(figsize=(20, 10))\n",
        "ax.bar(range(len(top_values)), top_values)\n",
        "ax.set_xticks(range(len(top_values)))\n",
        "ax.set_xticklabels(top_keys)\n",
        "ax.set_title('Importancia de las Características en el Modelo de Random Forest')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 859
        },
        "id": "mlLMBhHgeRvD",
        "outputId": "524f0d78-8287-4d0d-f033-6a79181c5165"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": 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mrKws88QTT6x3bHWprq7OHH300Zlhw4bVOmbfvn0zZ511Vq3xX3LJJTn7GDFiRKZnz56b7TjpeV+fmvEsW7YsM27cuMxRRx2V/d4JJ5yQGTNmTCaTyWTKysoyV1xxRfZ7d955Z6asrCzz8MMPZ5etWbMmc9JJJ2W6du2aqayszGQy//d833rrrdn1Pv3008wpp5ySKSsryzzwwAPZ5fV9vdXMjTlz5mSP26tXr8zAgQMzH3/8cXa9Z555JlNWVpa5/vrrN3gOfvrTn2bKysoyr732WnbZsmXLMj169Mg5hytXrswcdNBBmZ/85Cc52y9dujTTo0ePWstTNeP+zW9+k1m2bFlmyZIlmdmzZ2eOOuqoTMeOHXOOn8nUfs2uWbMmM3DgwMwZZ5yRs7ysrCyz//77Z959993ssjfffDNTVlaWmTJlSnbZ97///Uznzp0zixcvzi575513Mvvtt1/OnKrZ9tJLL805zrhx4zJlZWWZF198MbvsG9/4RqasrCzzyiuvZJf94Q9/yJSVlWW6dOmSc6zp06fnPG8bO091/at5Lr7//e9n9t9//8x7772X3W7JkiWZbt26ZU499dTssvX9bKrvc1lRUZEpKyvL3HbbbRsc8/p+fwMA2z6X8AIAvvC+853vZP97aWlptGvXLkpKSqJ///7Z5e3bt4/S0tJal1CJiDjppJNyPvFw8sknR6NGjeLZZ5+NiIgXXngh1q5dG2eccUbOX9V+5zvfiebNm2fXq9GkSZO8bjTbpEmT7H7XrVsXH330UTRt2jTatWtX5+VQBg8eHE2aNMl+nV4e5o033ohFixbFGWecUesvnuu6/NRn7bDDDtn/XllZGR9++GH07Nkz/v73v2/wEkf//Oc/480334xvf/vbOX/F37t37+jQoUPOujNnzowWLVpE796948MPP8z+23///aNp06bZS8Pkoz7jrhnX73//+1i7dm29971y5cqIiGjWrFm91s/3+Tz++ONzxp/PPp566qnYcccd47TTTqu1340918uXL485c+ZE//79Y+XKldnn4aOPPorDDjssFi5cWOvySN/97nc3+imcQs/z+jz99NNRXV0dI0aMqPVX7Z99jJ89hzWP56CDDoqqqqqYP39+REQ0b948Iv71aan6Xk4rIuLNN9+MhQsXxqBBg+Kjjz7KnqvVq1dHr1694k9/+lOty0ENGTIk5+uDDjooli9fnn09ba7j5GPQoEHx7rvvxty5c+Pdd9+N119/PQYNGlTnurNnz442bdrkXN6rcePGcfrpp8fq1avjT3/6U3a9Ro0aZT+FEBGx3Xbb1XpNFvJ6q/HnP/85li1bFieffHLOp0aOPPLIaN++ffz+97/f4ON+9tlno2vXrjmf3mndunWtx/7CCy/EihUrYsCAATk/m4qLi+PAAw+s98+msWPHRq9eveLwww+P4cOHR2VlZYwfP77Wp4c++5qtqKiIysrK6NGjR50/Jw499NDYc889s19/7Wtfi+bNm2d/7q9bty6ee+656NevX3z1q1/NrrfPPvtkP6X12fMREXHWWWflLB82bFjO92t06NAhunXrlv265tMfX//613OOVbO8rt+zdRkxYkTccccdOf/atGkT69ati+effz769euX84nRXXbZJQYOHBgvv/xyrXmU/myq73O5ww47ROPGjeOPf/xjwZeQBAC2bS7hBQB8oW2//fbRunXrnGUtWrSIXXfdtdYbyC1atKh1z5KIiL322ivn62bNmkWbNm2yl115//33I+JfEeazmjRpEnvssUd2vRpt27bNCRwbU11dHXfffXfce++9sWjRopx7NrRq1arW+p99wyri/y4LU/PYat68Kisrq/cYarz88ssxYcKEePXVV2u9wVxZWbneSxzVnKP0XEZErTf933333aisrMzeByO1bNmyLTLunj17xjHHHBMTJ06MO++8M3r27Bn9+vWLQYMGbfD5qnnTfdWqVfUaS77PZ12XBKrvPt57771o165dQTddf++99yKTycT1118f119/fZ3rLFu2LOeSY/W5fFGh53lD4ywuLo599tlng+u9/fbb8ctf/jLmzJlT683Vmoi2xx57xFlnnRV33HFHPPLII3HQQQdF375947jjjtvg5bsWLlwYEVHnZak+e4yWLVtmv17fPK2oqMi+pjbHcfLRqVOnaN++fTz66KNRWloabdq0ia9//et1rrt48eLYa6+9akWrmuehZs4vXrw42rRpUyswtmvXLufrQl5vNWqOle4z4l8/lzd2f5j333+/1iWf6tpfzflf32UO1/e8pUaMGBEHHXRQrF69OmbNmhWPPfZYnZe0euaZZ+Lmm2+ON998M+feI3XFz6985Su1lrVs2TL7c//DDz+Mjz/+eL0/gz8bRRYvXhzFxcU5QSYiok2bNlFaWlrrd1p67Jq5kl6eseb81PV7ti5lZWVx6KGH1lq+dOnSqKqqqvP53meffaK6ujr+8Y9/xL777ptdnv5squ9z2aRJkxg9enT8/Oc/j969e8eBBx4YRx55ZBx//PHRpk2bej0OAGDbJqAAAF9o6/tr+PUtz2QyW3I4ERG1Pk2wMb/61a/i+uuvjxNOOCEuvPDCaNmyZRQXF8fPfvazOse7vmvLb+pje++99+LMM8+M9u3bx5gxY+IrX/lKNG7cOJ599tm48847N+kv3z+ruro6dtppp7j22mvr/H4axDamvuMuKiqKG264IV599dV45pln4g9/+EOMHTs27rjjjrjvvvvW+wmTmnD21ltvRb9+/TY6nnyfz7peL/nuoxA152XYsGFx+OGH17lO+gZrXfeMSBV6njfFihUr4rTTTovmzZvHBRdcEHvuuWdsv/328Ze//CWuvfbanNfumDFj4tvf/nb87ne/i+effz6uvvrqmDRpUtx///113q8n4v/m1o9+9KPYb7/96lynadOmOV8XMk8LOU6+Bg4cGNOmTYtmzZpF//7963Wvis2hkNfb563m/I8fP77ON8/rcw+kiNww0K9fv6iqqop///d/jx49emRjxP/8z//E9773vTj44IPjP/7jP6JNmzbRuHHjeOCBB+LRRx+t97E35efBxj6ltrFjN+Tv2VT6symf5/LMM8+Mvn37xtNPPx3PPfdcXH/99XHLLbfEXXfdFZ06ddqyAwcAtnoCCgDwpffuu+/m/BX2qlWrYunSpdGnT5+I+L+/JJ8/f37O5UTWrFkTixYtqvMvaOuyvjernnzyyTjkkEPiZz/7Wc7yFStWZG8OnI+aMdbcRLy+/vu//zvWrFkTN998c85fz9fnsjU167/77ru1vrdgwYKcr/fcc8948cUXo3v37nnHprrkO+6uXbtG165dY9SoUfHII4/E6NGj4/HHH8+5FNxn9ejRI1q2bBmPPfZYnHfeeRt9E3VzPJ/13ceee+4Zr732WqxduzbnMnSftb7XXc3rpHHjxnm9TuprQ+e5vm/cRvzrMVZXV8e8efPWGxX++Mc/xvLly2PixIlx8MEHZ5cvWrSozvU7duwYHTt2jO9///vxyiuvxMknnxzTpk2LUaNG1bl+zblq3rz5FjlXn+dxBg0aFDfccEMsXbo0/vM//3O96+22227x1ltvRXV1dU5kqbkcWs1c22233WLOnDmxatWqnDiWzvtNeb3VHGvBggW1Prm2YMGCWp/2qWv7+vxsqhnjTjvttFnP/+jRo+Ppp5+Om2++Oa688sqI+Ncc33777eP222/P+WTWAw88UNAxWrduHTvssEO9Huduu+0W1dXV8e677+Z8suuDDz6IFStWxG677VbQGDaX1q1bR0lJSa1xR/zr9VdcXFznJ3I+K9/ncs8994xhw4bFsGHDYuHChXH88cfH5MmTs6E/n59ZAMC2xT1QAIAvvfvuuy/nXg3Tpk2LTz/9NBtQDj300GjcuHFMmTIl5y9rf/Ob30RlZWUcccQR9TpOSUlJnZc22W677Wr9xe4TTzyx3vsBbMz+++8fu+++e9x99921jrehvwyuCQOfXaeysrJeb+jtsssusd9++8WMGTNy7pXy/PPPxzvvvJOzbv/+/WPdunVx00031drPp59+Wu/Lv+Q77oqKilqPv+YN+c9ePidVUlISw4cPj3nz5sW1115b5zl8+OGHY+7cudnxbOrzWd99HH300fHRRx/F1KlTa+2jZvuSkpKIqH1ZnZ122il69uwZ9913X/zzn/+stf2HH35Y7/F+Vn3O8/rGVJd+/fpFcXFx3HjjjbU+BVVznJo3+D973DVr1sS9996bs/7KlSvj008/zVlWVlYWxcXFG3wNHHDAAbHnnnvG5MmT67yUW6HnqiGOs+eee8bYsWPj4osvrnVPjs/q06dPLF26NB5//PHssk8//TSmTJkSTZs2zYaqPn36xKeffhrTpk3Lrrdu3bq45557cva3Ka+3Aw44IHbaaaeYPn16zvP07LPPxrx58+LII4/c4GM+4ogj4tVXX83O0ZrjPfLIIznrHX744dG8efOYNGlSnffvKfT877nnnnH00UfHjBkzYunSpRHxrzleVFSUc3m+RYsWxe9+97uCjrHddtvFYYcdFk8//XT2kmcREfPmzYvnnnsuZ92a31l33XVXzvI77rgj5/sNZbvttovevXvH7373u5wI+sEHH8Sjjz4aPXr02Ojl1Or7XFZVVcUnn3yS870999wzmjVrlvNaW9/vbwBg2+cTKADAl97atWvjzDPPjP79+8eCBQvi3nvvjR49esQ3v/nNiPjXX8OWl5fHxIkTY/jw4dG3b9/sep07d47jjjuuXsfZf//9Y9q0aXHTTTfFXnvtFa1bt45evXrFkUceGTfeeGNccskl0a1bt/jb3/4WjzzySM6nXfJRXFwcl19+eXzve9+L448/PgYPHhxt2rSJ+fPnxzvvvBO33357ndv17t07GjduHOedd14MGTIkVq1aFb/+9a9jp512yr7ptyEXXXRRlJeXxymnnBInnHBCLF++PO65557Yd999Y/X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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this graph we can check the variables that the random Forest consider the most important ones. The most importnat feature is the grid, then they are also very important the features related with the driver standings (points wins and position before the race)"
      ],
      "metadata": {
        "id": "Drx5DqmRzBTy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "With the following code we can create a dataframe with the featurte importance of each model.\n",
        "\n",
        "We will use it in the dashboard"
      ],
      "metadata": {
        "id": "oTH_6op6yx7T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_names=['RandomForest','DecisionTree','GradientBoosting','LinearRegression']\n",
        "df_importances=pd.DataFrame()\n",
        "for i in range(4):\n",
        "    #Take the model\n",
        "    model_name=model_names[i]\n",
        "    best_model = model_results[model_name]['best_model']\n",
        "    #Take the feature importance\n",
        "    if model_name=='LinearRegression':\n",
        "        feature_importance=best_model.coef_[0]\n",
        "    else:\n",
        "        feature_importance=best_model.feature_importances_\n",
        "\n",
        "    importance={}\n",
        "    for i,col in enumerate(X_train.columns):\n",
        "        importance[col]=feature_importance[i]\n",
        "    # Ordenar el diccionario por valores en orden descendente\n",
        "    sorted_items = sorted(importance.items(), key=lambda x: x[1], reverse=True)\n",
        "\n",
        "    #Add a column with model name\n",
        "    sorted_items=pd.DataFrame(sorted_items[:10])\n",
        "    names_df=pd.DataFrame([model_name]*len(sorted_items))\n",
        "    sorted_items=pd.concat([names_df,sorted_items],axis=1)\n",
        "    sorted_items.iloc[:,2]=np.round(sorted_items.iloc[:,2],2)\n",
        "    #Concat with previous models\n",
        "    df_importances=pd.concat([df_importances,sorted_items])\n"
      ],
      "metadata": {
        "id": "JpHL4be3fyLo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_importances.to_csv(path2+'feature_importance.csv')"
      ],
      "metadata": {
        "id": "zzGSLblYywKZ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}