notebooks/Modulo6/Tarea_DS_8_ES.ipynb

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      "source": [
        "<a href=\"https://www.bigdatauniversity.com\"><img src=\"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\" width=\"400\" align=\"center\"></a>\n",
        "\n",
        "<h1 align=\"center\"><font size=\"5\">Clasificación con Python</font></h1>\n"
      ]
    },
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      "source": [
        "En este cuaderno trataremos de practicar todos los algoritmos de clasificación aprendidos durante el curso.\n",
        "\n",
        "Cargaremos un conjunto de datos mediante la librería Pandas, aplicaremos los siguientes algoritmos y encontraremos el mejor para este conjunto en específico con métodos precisos de evaluación.\n",
        "\n",
        "Cargemos primero las librerías necesarias:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 136,
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      "source": [
        "import itertools\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.ticker import NullFormatter\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.ticker as ticker\n",
        "from sklearn import preprocessing\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
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      },
      "source": [
        "### Acerca del conjunto de datos\n"
      ]
    },
    {
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      "source": [
        "Este conjunto de datos trata sobre el desempeño de diversos equipos de baloncesto. El conjunto **cbb.csv** incluye información sobre el desempeño de 354 equipos a lo largo de cinco temporadas. Contiene los siguientes campos de información:\n",
        "\n",
        "| Campo      | Descripción                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |\n",
        "| ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n",
        "| TEAM       | El equipo escolar de baloncesto de División I                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |\n",
        "| CONF       | Conferencia Deportiva en la que participa la escuela (A10 = Atlantic 10, ACC = Atlantic Coast Conference, AE = America East, Amer = American, ASun = ASUN, B10 = Big Ten, B12 = Big 12, BE = Big East, BSky = Big Sky, BSth = Big South, BW = Big West, CAA = Colonial Athletic Association, CUSA = Conference USA, Horz = Horizon League, Ivy = Ivy League, MAAC = Metro Atlantic Athletic Conference, MAC = Mid-American Conference, MEAC = Mid-Eastern Athletic Conference, MVC = Missouri Valley Conference, MWC = Mountain West, NEC = Northeast Conference, OVC = Ohio Valley Conference, P12 = Pac-12, Pat = Patriot League, SB = Sun Belt, SC = Southern Conference, SEC = South Eastern Conference, Slnd = Southland Conference, Sum = Summit League, SWAC = Southwestern Athletic Conference, WAC = Western Athletic Conference, WCC = West Coast Conference) |\n",
        "| G          | Número de partidos jugados                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |\n",
        "| W          | Número de partidos ganados                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |\n",
        "| ADJOE      | Adjusted Offensive Efficiency / Eficiencia Ofensiva (Una estimación de la eficiencia ofensiva (puntos anotados por cada 100 posesiones) que un equipo tendría respecto al promedio defensivo de la División I)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |\n",
        "| ADJDE      | Adjusted Defensive Efficiency / Eficiencia Defensiva (Una estimación de la eficiencia defensiva (puntos permitidos por cada 100 posesiones) que un equipo tendría respecto al promedio ofensivo de la División I)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |\n",
        "| BARTHAG    | Power Rating / Clasificación (La oportunidad de superar el promedio de un equipo de la División I)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |\n",
        "| EFG_O      | Porcentaje de Efectividad de Tiro                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |\n",
        "| EFG_D      | Porcentaje de Efectividad Permitido                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |\n",
        "| TOR        | Porcentaje de Rotación Permitido (Clasificación de Rotación)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |\n",
        "| TORD       | Porcentaje de Rotación Ejecutado (Steal Rate)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |\n",
        "| ORB        | Porcentaje de Rebote Ofensivo                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |\n",
        "| DRB        | Porcentaje de Rebote Defensivo                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |\n",
        "| FTR        | Tasa de Tiro Libre (Que tan a menudo el equipo realiza tiros libres)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |\n",
        "| FTRD       | Tasa de Tiro Libre Permitido                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |\n",
        "| 2P_O       | Porcentaje de Tiros de Dos Puntos                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |\n",
        "| 2P_D       | Porcentaje de Tiros de Dos Puntos Permitidos                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |\n",
        "| 3P_O       | Porcentaje de Tiros de Tres Puntos                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |\n",
        "| 3P_D       | Porcentaje de Tiros de Tres Puntos Permitidos                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |\n",
        "| ADJ_T      | Tempo Ajustado (Estimación del tempo (posesiones por cada 40 minutos) que un equipo tendría respecto aquel equipo que quisiera juagar con el tempo promedio de la División I)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |\n",
        "| WAB        | Victorias sobre Burbuja (La burbuja se refiere al corte entre lograr el NCAA March Madness Tournament y no)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |\n",
        "| POSTSEASON | Ronda donde el equipo fue eliminado o donde finalizó su temporada (R68 = Primeros Cuatro, R64 = Ronda de 64, R32 = Ronda de 32, S16 = Sweet Sixteen, E8 = Elite Eight, F4 = Ultimos Cuatro, 2ND = Subcampeón, Champion = Ganador del NCAA March Madness Tournament de ese año)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |\n",
        "| SEED       | Semilla del NCAA March Madness Tournament                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |\n",
        "| YEAR       | Temporada                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |\n"
      ]
    },
    {
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        "id": "gyW9cEbOMWXi"
      },
      "source": [
        "### Cargar datos desde el archivo CSV\n"
      ]
    },
    {
      "cell_type": "markdown",
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      },
      "source": [
        "Cargemo el conjunto de datos [NB Necesita dar el enlace hacia el archivo csv]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 137,
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          "height": 261
        },
        "id": "_A8HCTJUMWXj",
        "outputId": "3c39c96b-0438-493a-c779-3ab97b9dea6b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
              "<style scoped>\n",
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              "    }\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>TEAM</th>\n",
              "      <th>CONF</th>\n",
              "      <th>G</th>\n",
              "      <th>W</th>\n",
              "      <th>ADJOE</th>\n",
              "      <th>ADJDE</th>\n",
              "      <th>BARTHAG</th>\n",
              "      <th>EFG_O</th>\n",
              "      <th>EFG_D</th>\n",
              "      <th>TOR</th>\n",
              "      <th>TORD</th>\n",
              "      <th>ORB</th>\n",
              "      <th>DRB</th>\n",
              "      <th>FTR</th>\n",
              "      <th>FTRD</th>\n",
              "      <th>2P_O</th>\n",
              "      <th>2P_D</th>\n",
              "      <th>3P_O</th>\n",
              "      <th>3P_D</th>\n",
              "      <th>ADJ_T</th>\n",
              "      <th>WAB</th>\n",
              "      <th>POSTSEASON</th>\n",
              "      <th>SEED</th>\n",
              "      <th>YEAR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>North Carolina</td>\n",
              "      <td>ACC</td>\n",
              "      <td>40</td>\n",
              "      <td>33</td>\n",
              "      <td>123.3</td>\n",
              "      <td>94.9</td>\n",
              "      <td>0.9531</td>\n",
              "      <td>52.6</td>\n",
              "      <td>48.1</td>\n",
              "      <td>15.4</td>\n",
              "      <td>18.2</td>\n",
              "      <td>40.7</td>\n",
              "      <td>30.0</td>\n",
              "      <td>32.3</td>\n",
              "      <td>30.4</td>\n",
              "      <td>53.9</td>\n",
              "      <td>44.6</td>\n",
              "      <td>32.7</td>\n",
              "      <td>36.2</td>\n",
              "      <td>71.7</td>\n",
              "      <td>8.6</td>\n",
              "      <td>2ND</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Villanova</td>\n",
              "      <td>BE</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>123.1</td>\n",
              "      <td>90.9</td>\n",
              "      <td>0.9703</td>\n",
              "      <td>56.1</td>\n",
              "      <td>46.7</td>\n",
              "      <td>16.3</td>\n",
              "      <td>20.6</td>\n",
              "      <td>28.2</td>\n",
              "      <td>29.4</td>\n",
              "      <td>34.1</td>\n",
              "      <td>30.0</td>\n",
              "      <td>57.4</td>\n",
              "      <td>44.1</td>\n",
              "      <td>36.2</td>\n",
              "      <td>33.9</td>\n",
              "      <td>66.7</td>\n",
              "      <td>8.9</td>\n",
              "      <td>Champions</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Notre Dame</td>\n",
              "      <td>ACC</td>\n",
              "      <td>36</td>\n",
              "      <td>24</td>\n",
              "      <td>118.3</td>\n",
              "      <td>103.3</td>\n",
              "      <td>0.8269</td>\n",
              "      <td>54.0</td>\n",
              "      <td>49.5</td>\n",
              "      <td>15.3</td>\n",
              "      <td>14.8</td>\n",
              "      <td>32.7</td>\n",
              "      <td>32.1</td>\n",
              "      <td>32.9</td>\n",
              "      <td>26.0</td>\n",
              "      <td>52.9</td>\n",
              "      <td>46.5</td>\n",
              "      <td>37.4</td>\n",
              "      <td>36.9</td>\n",
              "      <td>65.5</td>\n",
              "      <td>2.3</td>\n",
              "      <td>E8</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Virginia</td>\n",
              "      <td>ACC</td>\n",
              "      <td>37</td>\n",
              "      <td>29</td>\n",
              "      <td>119.9</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.9600</td>\n",
              "      <td>54.8</td>\n",
              "      <td>48.4</td>\n",
              "      <td>15.1</td>\n",
              "      <td>18.8</td>\n",
              "      <td>29.9</td>\n",
              "      <td>25.2</td>\n",
              "      <td>32.1</td>\n",
              "      <td>33.4</td>\n",
              "      <td>52.6</td>\n",
              "      <td>46.3</td>\n",
              "      <td>40.3</td>\n",
              "      <td>34.7</td>\n",
              "      <td>61.9</td>\n",
              "      <td>8.6</td>\n",
              "      <td>E8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kansas</td>\n",
              "      <td>B12</td>\n",
              "      <td>37</td>\n",
              "      <td>32</td>\n",
              "      <td>120.9</td>\n",
              "      <td>90.4</td>\n",
              "      <td>0.9662</td>\n",
              "      <td>55.7</td>\n",
              "      <td>45.1</td>\n",
              "      <td>17.8</td>\n",
              "      <td>18.5</td>\n",
              "      <td>32.2</td>\n",
              "      <td>27.9</td>\n",
              "      <td>38.6</td>\n",
              "      <td>37.3</td>\n",
              "      <td>52.7</td>\n",
              "      <td>43.4</td>\n",
              "      <td>41.3</td>\n",
              "      <td>32.5</td>\n",
              "      <td>70.1</td>\n",
              "      <td>11.6</td>\n",
              "      <td>E8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2016</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             TEAM CONF   G   W  ADJOE  ...  ADJ_T   WAB  POSTSEASON  SEED  YEAR\n",
              "0  North Carolina  ACC  40  33  123.3  ...   71.7   8.6         2ND   1.0  2016\n",
              "1       Villanova   BE  40  35  123.1  ...   66.7   8.9   Champions   2.0  2016\n",
              "2      Notre Dame  ACC  36  24  118.3  ...   65.5   2.3          E8   6.0  2016\n",
              "3        Virginia  ACC  37  29  119.9  ...   61.9   8.6          E8   1.0  2016\n",
              "4          Kansas  B12  37  32  120.9  ...   70.1  11.6          E8   1.0  2016\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ],
      "source": [
        "df = pd.read_csv('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0120ENv3/Dataset/ML0101EN_EDX_skill_up/cbb.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 138,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k1O-vAJzMWXk",
        "outputId": "d24e13ff-9464-47f1-ee90-7384efb28af4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1406, 24)"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mHmpXqCZMWXk"
      },
      "source": [
        "## Añadir Columna\n",
        "\n",
        "A continuación añadiremos una columna que mostrará \"true\" si las victorias sobre la burbuja superan las 7 y \"false\" si no. Nombraremos esta columna Win Index o \"windex\" para abreviar.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 139,
      "metadata": {
        "id": "EhvNwtLzMWXk"
      },
      "outputs": [],
      "source": [
        "df['windex'] = np.where(df.WAB > 7, 'True', 'False')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "hfTuAC9DMWXk"
      },
      "source": [
        "# Visualización de los Datos y pre procesamiento\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "B91c32AyMWXk"
      },
      "source": [
        "Después filtraremos los equipos dentro del conjunto de datos que lograrón un \"Sweet Sixteen\", \"Elite Eight\" y \"Final Four\". Generaremos además un nuevo dataframe que mantendra los valores de la nueva columna.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 140,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "2DDwyUzTMWXk",
        "outputId": "9e66bedb-ec6c-466f-89fc-bfdf9aaeee78"
      },
      "outputs": [
        {
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          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TEAM</th>\n",
              "      <th>CONF</th>\n",
              "      <th>G</th>\n",
              "      <th>W</th>\n",
              "      <th>ADJOE</th>\n",
              "      <th>ADJDE</th>\n",
              "      <th>BARTHAG</th>\n",
              "      <th>EFG_O</th>\n",
              "      <th>EFG_D</th>\n",
              "      <th>TOR</th>\n",
              "      <th>TORD</th>\n",
              "      <th>ORB</th>\n",
              "      <th>DRB</th>\n",
              "      <th>FTR</th>\n",
              "      <th>FTRD</th>\n",
              "      <th>2P_O</th>\n",
              "      <th>2P_D</th>\n",
              "      <th>3P_O</th>\n",
              "      <th>3P_D</th>\n",
              "      <th>ADJ_T</th>\n",
              "      <th>WAB</th>\n",
              "      <th>POSTSEASON</th>\n",
              "      <th>SEED</th>\n",
              "      <th>YEAR</th>\n",
              "      <th>windex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Notre Dame</td>\n",
              "      <td>ACC</td>\n",
              "      <td>36</td>\n",
              "      <td>24</td>\n",
              "      <td>118.3</td>\n",
              "      <td>103.3</td>\n",
              "      <td>0.8269</td>\n",
              "      <td>54.0</td>\n",
              "      <td>49.5</td>\n",
              "      <td>15.3</td>\n",
              "      <td>14.8</td>\n",
              "      <td>32.7</td>\n",
              "      <td>32.1</td>\n",
              "      <td>32.9</td>\n",
              "      <td>26.0</td>\n",
              "      <td>52.9</td>\n",
              "      <td>46.5</td>\n",
              "      <td>37.4</td>\n",
              "      <td>36.9</td>\n",
              "      <td>65.5</td>\n",
              "      <td>2.3</td>\n",
              "      <td>E8</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2016</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Virginia</td>\n",
              "      <td>ACC</td>\n",
              "      <td>37</td>\n",
              "      <td>29</td>\n",
              "      <td>119.9</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.9600</td>\n",
              "      <td>54.8</td>\n",
              "      <td>48.4</td>\n",
              "      <td>15.1</td>\n",
              "      <td>18.8</td>\n",
              "      <td>29.9</td>\n",
              "      <td>25.2</td>\n",
              "      <td>32.1</td>\n",
              "      <td>33.4</td>\n",
              "      <td>52.6</td>\n",
              "      <td>46.3</td>\n",
              "      <td>40.3</td>\n",
              "      <td>34.7</td>\n",
              "      <td>61.9</td>\n",
              "      <td>8.6</td>\n",
              "      <td>E8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2016</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kansas</td>\n",
              "      <td>B12</td>\n",
              "      <td>37</td>\n",
              "      <td>32</td>\n",
              "      <td>120.9</td>\n",
              "      <td>90.4</td>\n",
              "      <td>0.9662</td>\n",
              "      <td>55.7</td>\n",
              "      <td>45.1</td>\n",
              "      <td>17.8</td>\n",
              "      <td>18.5</td>\n",
              "      <td>32.2</td>\n",
              "      <td>27.9</td>\n",
              "      <td>38.6</td>\n",
              "      <td>37.3</td>\n",
              "      <td>52.7</td>\n",
              "      <td>43.4</td>\n",
              "      <td>41.3</td>\n",
              "      <td>32.5</td>\n",
              "      <td>70.1</td>\n",
              "      <td>11.6</td>\n",
              "      <td>E8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2016</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Oregon</td>\n",
              "      <td>P12</td>\n",
              "      <td>37</td>\n",
              "      <td>30</td>\n",
              "      <td>118.4</td>\n",
              "      <td>96.2</td>\n",
              "      <td>0.9163</td>\n",
              "      <td>52.3</td>\n",
              "      <td>48.9</td>\n",
              "      <td>16.1</td>\n",
              "      <td>20.2</td>\n",
              "      <td>34.1</td>\n",
              "      <td>30.5</td>\n",
              "      <td>40.3</td>\n",
              "      <td>32.0</td>\n",
              "      <td>52.6</td>\n",
              "      <td>46.1</td>\n",
              "      <td>34.4</td>\n",
              "      <td>36.2</td>\n",
              "      <td>69.0</td>\n",
              "      <td>6.7</td>\n",
              "      <td>E8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2016</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Syracuse</td>\n",
              "      <td>ACC</td>\n",
              "      <td>37</td>\n",
              "      <td>23</td>\n",
              "      <td>111.9</td>\n",
              "      <td>93.6</td>\n",
              "      <td>0.8857</td>\n",
              "      <td>50.0</td>\n",
              "      <td>47.3</td>\n",
              "      <td>18.1</td>\n",
              "      <td>20.4</td>\n",
              "      <td>33.5</td>\n",
              "      <td>35.3</td>\n",
              "      <td>35.4</td>\n",
              "      <td>28.0</td>\n",
              "      <td>47.2</td>\n",
              "      <td>48.1</td>\n",
              "      <td>36.0</td>\n",
              "      <td>30.7</td>\n",
              "      <td>65.5</td>\n",
              "      <td>-0.3</td>\n",
              "      <td>F4</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2016</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         TEAM CONF   G   W  ADJOE  ...   WAB  POSTSEASON  SEED  YEAR  windex\n",
              "2  Notre Dame  ACC  36  24  118.3  ...   2.3          E8   6.0  2016   False\n",
              "3    Virginia  ACC  37  29  119.9  ...   8.6          E8   1.0  2016    True\n",
              "4      Kansas  B12  37  32  120.9  ...  11.6          E8   1.0  2016    True\n",
              "5      Oregon  P12  37  30  118.4  ...   6.7          E8   1.0  2016   False\n",
              "6    Syracuse  ACC  37  23  111.9  ...  -0.3          F4  10.0  2016   False\n",
              "\n",
              "[5 rows x 25 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ],
      "source": [
        "df1 = df.loc[df['POSTSEASON'].str.contains('F4|S16|E8', na=False)]\n",
        "df1.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 141,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "crLJYU1-MWXl",
        "outputId": "2866317e-e26a-4dfb-880d-855d2f5d7a7d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S16    32\n",
              "E8     16\n",
              "F4      8\n",
              "Name: POSTSEASON, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ],
      "source": [
        "df1['POSTSEASON'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "Fud9hFA6MWXl"
      },
      "source": [
        "32 equipos alcanzarón a llegar a Sweet Sixteen, 16 a Elite Eight y 8 a Final Four a lo largo de 5 temporadas.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZItTMM7kMWXl"
      },
      "source": [
        "Grafiquemos algunas columnas para entender mejor la información:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 142,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NyhNIanZMWXl",
        "outputId": "919274dd-87e4-4228-e318-3e047bd9f598"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: conda: command not found\n"
          ]
        }
      ],
      "source": [
        "# observación: la instalación de seaborn podría tomar algunos minutos.\n",
        "!conda install -c anaconda seaborn -y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 143,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "fUndU0_WMWXl",
        "outputId": "8c5a574e-34ef-469f-dde0-b355f3143e83"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BbN2yLbfjVaM8Z/oeWjOAl3e8mtvxGvfdlz+tXZvb8fo6n6zyVPJM9zWDano8OWj74/VEyYVNUh3wE+DTEfFi+/cjYg4wB6C5uTl6FFU3lGPG2kqeAbca5T1DbyXN+NteX+eTVZ6pX7grt2MtunxabscC2LFtR0X9f1lSWZQ0iEJRuzkiftqjHs3MzMqolLsiBfwXsCIi/rP8IZmZmXVfKWds7wI+DBwraVm25HfOaWZmlqNOv2OLiAeAyv3W0szMrIifPGJmZklxYTMzs6S4sJmZWVJc2MzMLCkubGZmlhQXNjMzS4oLm5mZJcWFzczMkuLCZmZmSXFhMzOzpLiwmZlZUlzYzMwsKV2aQTtPg2sHse2V7X3Vfa+rGVRT0TPgVrpKm6HX0lLfWE/bmra+DsNy0meFbdsr2ytqxtVyq7QZZvsb//1ZObWtafPPV0L8sdXMzJLiwmZmZklxYTMzs6S4sJmZWVJc2MzMLCkubGZmlhQXNjMzS4oLm5mZJcWFzczMkuLCZmZmSXFhMzOzpLiwmZlZUlzYzMwsKSUVNknTJK2UtFrSZ8sdlJmZWXd1WtgkDQC+DpwATALOkDSp3IGZmZl1RylnbO8EVkfEkxGxFfgBcHJ5wzIzM+seRcTuG0inAdMi4pzs9YeBqRFxQbt25wLnZi8PAlbmH26PjALW93UQvcxjrizrI2JaKQ2dTxWpGscMlTvuXeZTbjNoR8QcYE5ex8ubpMUR0dzXcfQmj7n/cj5VnmocM/TPcZdyKfJZYL+i1w3ZNjMzs4pTSmH7LXCApAmSBgOnA78ob1hmZmbd0+mlyIjYLukC4G5gAHBTRDxW9sjyV7GXdcrIY7Zyqca/52ocM/TDcXd684iZmVl/4iePmJlZUlzYzMwsKf2+sHX2uC9JjZLuk/SIpN9Jmp5tHy/pZUnLsuWbvR9995Qw5nGS7snGu0BSQ9F7Z0p6IlvO7N3Ie6aH43616N/aNz/thnOqOnIq6XyKiH67ULiZ5Y/AW4DBwHJgUrs2c4DzsvVJwNPZ+njg0b4eQ5nG/GPgzGz9WOB72fqbgCezP0dk6yP6ekzlHnf2enNfj6E/LM6p6sip1POpv5+xlfK4rwCGZ+t7AW29GF85lDLmScC92fp9Re+/F5gfEX+JiBeA+UBJT8KoAD0Zt5XOOVUdOZV0PvX3wlYPrCl63ZptKzYL+JCkVuBO4MKi9yZkl1Pul3RUWSPNTyljXg6ckq3PAPaUNLLEfStVT8YNUCtpsaSHJb2/vKH2a86p6sippPOpvxe2UpwBzIuIBmA68D1JNcBaoDEi3g5cDNwiafhujtOfXAIcI+kR4BgKT4p5tW9D6hW7G/e4KDwW6J+Br0h6ax/FmALnVHXkVL/Np9yeFdlHSnnc19lklwYi4iFJtcCoiHgeeCXbvkTSH4EDgcVlj7pnOh1zRLSRfdKSVAecGhEbJT0LtLTbd0E5g81Rt8edvfds9ueTkhYAb6fwHYO9nnOqOnIq7Xzq6y/5erJQKMxPAhP42xegk9u1+R/grGx9IoXvAwSMBgZk299C4R/1TX09ppzGPAqoydavBC7P1t8EPEXhS+4R2XrFjzmHcY8AhhS1eYJ2X5R76dLfs3Oqn+dU6vnU5wHk8A80HVhF4dPC57JtlwPvy9YnAb/J/uGWAcdn208FHsu2LQVO6uux5Djm07IftlXA3J0/hNl7/wKszpaP9PVYemPcwJHA77Ofgd8DZ/f1WCp5cU5VR06lnE9+pJaZmSWlGm4eMTOzKuLCZmZmSXFhMzOzpLiwmZlZUlzYzMwsKS5sFaDoSdnLJS2VdGS79z8taYukvYq2tUjalO33B0nXSDqk6Inbf5H0VLb+v9mT1x9td9xZki4pej1Q0jpJs9u1q5P0DUl/zOJbIumj5fr7MOsJ55O5sFWGlyOiKSIOBS4D/qPd+2cAv+Vvz23baWFENFH4rf8TgeHZcZqAXwCfyV4fV2Ic/0Dhd1Y+IElF2+cCLwAHRMRhFJ468aYujM+sNzmfqpwLW+UZTuGHHoDsGWx1wOcpJOQbRMTLFH4ptqcPXz0D+CrwDHBEUf/vBD4fETuy/tZFxFU97MusNzifqlB/f1ZkKoZKWgbUAmMozH200+kUppRYCBwk6c0R8efinSWNAA4Aft1JP2/N+tlpX+Ca7Bi1wHHAx4C9KSTlg8BkYPnOJDTrB5xPVc5nbJVh56WTt1G4LPHdoksXZwA/yBLhJ8AHivY7StJyCs/kuzsinuuknz/uvLSSXV4pnuH4ROC+7NPqT4D3SxrQ/gCSPpd9z9Df5+CydDmfqpwLW4WJiIcoPFh0tKRDKHxynC/paQqfNosvnyzMvkeYDJwtqakHXZ8BHJf1swQYSeGT7uPAodm0JETElVkSpzIdiSXM+VSdXNgqjKS3UZi2fQOF5JgVEeOzZSwwVtK44n0i4ilgNvBv3exzOHAUhbm0xkfEeOATwBkRsZrCtCNX7PzEmV1m0a6OZ1YpnE/VyYWtMgzdeVsx8EPgzIh4lcInytvatb0t297eN4GjJY3vRv8zgHsj4pWibT8HTpI0BDiHwifO1ZIWA/OBS7vRj1lvcD5VOT/d38zMkuIzNjMzS4oLm5mZJcWFzczMkuLCZmZmSXFhMzOzpLiwmZlZUlzYzMwsKf8P6pA578CH4cQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1296x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "bins = np.linspace(df1.BARTHAG.min(), df1.BARTHAG.max(), 10)\n",
        "g = sns.FacetGrid(df1, col=\"windex\", hue=\"POSTSEASON\", palette=\"Set1\", col_wrap=6)\n",
        "g.map(plt.hist, 'BARTHAG', bins=bins, ec=\"k\")\n",
        "\n",
        "g.axes[-1].legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 144,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "GRDF8Et5MWXl",
        "outputId": "0f4aa4e0-219e-407c-c74e-a69b36a60562"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "bins = np.linspace(df1.ADJOE.min(), df1.ADJOE.max(), 10)\n",
        "g = sns.FacetGrid(df1, col=\"windex\", hue=\"POSTSEASON\", palette=\"Set1\", col_wrap=2)\n",
        "g.map(plt.hist, 'ADJOE', bins=bins, ec=\"k\")\n",
        "\n",
        "g.axes[-1].legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "a4rgkEC6MWXm"
      },
      "source": [
        "# Pre procesamiento: selección/extracción de características\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "SM71loQwMWXm"
      },
      "source": [
        "### Veamos como se grafica el valor Adjusted Defense Efficiency ( Eficiencia Defensiva)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 145,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "NESBxmx7MWXm",
        "outputId": "e2b776cb-e762-4bc8-a06c-a41214e1a5a1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "bins = np.linspace(df1.ADJDE.min(), df1.ADJDE.max(), 10)\n",
        "g = sns.FacetGrid(df1, col=\"windex\", hue=\"POSTSEASON\", palette=\"Set1\", col_wrap=2)\n",
        "g.map(plt.hist, 'ADJDE', bins=bins, ec=\"k\")\n",
        "g.axes[-1].legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "rSvAPeCwMWXm"
      },
      "source": [
        "Observamos que estos datos no impactan en la habilidad de un equipo de poder alcanzar Final Four.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "ZZxYj8LoMWXm"
      },
      "source": [
        "## Convertir características categóricas en valores numéricos\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "n8TUrIx-MWXm"
      },
      "source": [
        "Veamos la post temporada: \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 146,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "__pMzEqHMWXn",
        "outputId": "ef35e70e-90cb-4079-8371-7b994a742650"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "windex  POSTSEASON\n",
              "False   S16           0.605263\n",
              "        E8            0.263158\n",
              "        F4            0.131579\n",
              "True    S16           0.500000\n",
              "        E8            0.333333\n",
              "        F4            0.166667\n",
              "Name: POSTSEASON, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ],
      "source": [
        "df1.groupby(['windex'])['POSTSEASON'].value_counts(normalize=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "6tDjKTLZMWXn"
      },
      "source": [
        "El 13% de los equipos con 6 o menos victorias sobre la burbuja llegaron a Final Four mientra que el 17% con 7 o mas lo hicierón.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "DRX_saGWMWXn"
      },
      "source": [
        "Transformemos las victorias sobre la burbuja (winindex) abajo de 7 hasta 0 y arriba de 7 hasta 1:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 147,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "GAhKg00XMWXn",
        "outputId": "39f677d0-071f-4014-e298-1a07f8fe025d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/series.py:4582: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  method=method,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TEAM</th>\n",
              "      <th>CONF</th>\n",
              "      <th>G</th>\n",
              "      <th>W</th>\n",
              "      <th>ADJOE</th>\n",
              "      <th>ADJDE</th>\n",
              "      <th>BARTHAG</th>\n",
              "      <th>EFG_O</th>\n",
              "      <th>EFG_D</th>\n",
              "      <th>TOR</th>\n",
              "      <th>TORD</th>\n",
              "      <th>ORB</th>\n",
              "      <th>DRB</th>\n",
              "      <th>FTR</th>\n",
              "      <th>FTRD</th>\n",
              "      <th>2P_O</th>\n",
              "      <th>2P_D</th>\n",
              "      <th>3P_O</th>\n",
              "      <th>3P_D</th>\n",
              "      <th>ADJ_T</th>\n",
              "      <th>WAB</th>\n",
              "      <th>POSTSEASON</th>\n",
              "      <th>SEED</th>\n",
              "      <th>YEAR</th>\n",
              "      <th>windex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Notre Dame</td>\n",
              "      <td>ACC</td>\n",
              "      <td>36</td>\n",
              "      <td>24</td>\n",
              "      <td>118.3</td>\n",
              "      <td>103.3</td>\n",
              "      <td>0.8269</td>\n",
              "      <td>54.0</td>\n",
              "      <td>49.5</td>\n",
              "      <td>15.3</td>\n",
              "      <td>14.8</td>\n",
              "      <td>32.7</td>\n",
              "      <td>32.1</td>\n",
              "      <td>32.9</td>\n",
              "      <td>26.0</td>\n",
              "      <td>52.9</td>\n",
              "      <td>46.5</td>\n",
              "      <td>37.4</td>\n",
              "      <td>36.9</td>\n",
              "      <td>65.5</td>\n",
              "      <td>2.3</td>\n",
              "      <td>E8</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2016</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Virginia</td>\n",
              "      <td>ACC</td>\n",
              "      <td>37</td>\n",
              "      <td>29</td>\n",
              "      <td>119.9</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.9600</td>\n",
              "      <td>54.8</td>\n",
              "      <td>48.4</td>\n",
              "      <td>15.1</td>\n",
              "      <td>18.8</td>\n",
              "      <td>29.9</td>\n",
              "      <td>25.2</td>\n",
              "      <td>32.1</td>\n",
              "      <td>33.4</td>\n",
              "      <td>52.6</td>\n",
              "      <td>46.3</td>\n",
              "      <td>40.3</td>\n",
              "      <td>34.7</td>\n",
              "      <td>61.9</td>\n",
              "      <td>8.6</td>\n",
              "      <td>E8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kansas</td>\n",
              "      <td>B12</td>\n",
              "      <td>37</td>\n",
              "      <td>32</td>\n",
              "      <td>120.9</td>\n",
              "      <td>90.4</td>\n",
              "      <td>0.9662</td>\n",
              "      <td>55.7</td>\n",
              "      <td>45.1</td>\n",
              "      <td>17.8</td>\n",
              "      <td>18.5</td>\n",
              "      <td>32.2</td>\n",
              "      <td>27.9</td>\n",
              "      <td>38.6</td>\n",
              "      <td>37.3</td>\n",
              "      <td>52.7</td>\n",
              "      <td>43.4</td>\n",
              "      <td>41.3</td>\n",
              "      <td>32.5</td>\n",
              "      <td>70.1</td>\n",
              "      <td>11.6</td>\n",
              "      <td>E8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Oregon</td>\n",
              "      <td>P12</td>\n",
              "      <td>37</td>\n",
              "      <td>30</td>\n",
              "      <td>118.4</td>\n",
              "      <td>96.2</td>\n",
              "      <td>0.9163</td>\n",
              "      <td>52.3</td>\n",
              "      <td>48.9</td>\n",
              "      <td>16.1</td>\n",
              "      <td>20.2</td>\n",
              "      <td>34.1</td>\n",
              "      <td>30.5</td>\n",
              "      <td>40.3</td>\n",
              "      <td>32.0</td>\n",
              "      <td>52.6</td>\n",
              "      <td>46.1</td>\n",
              "      <td>34.4</td>\n",
              "      <td>36.2</td>\n",
              "      <td>69.0</td>\n",
              "      <td>6.7</td>\n",
              "      <td>E8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2016</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Syracuse</td>\n",
              "      <td>ACC</td>\n",
              "      <td>37</td>\n",
              "      <td>23</td>\n",
              "      <td>111.9</td>\n",
              "      <td>93.6</td>\n",
              "      <td>0.8857</td>\n",
              "      <td>50.0</td>\n",
              "      <td>47.3</td>\n",
              "      <td>18.1</td>\n",
              "      <td>20.4</td>\n",
              "      <td>33.5</td>\n",
              "      <td>35.3</td>\n",
              "      <td>35.4</td>\n",
              "      <td>28.0</td>\n",
              "      <td>47.2</td>\n",
              "      <td>48.1</td>\n",
              "      <td>36.0</td>\n",
              "      <td>30.7</td>\n",
              "      <td>65.5</td>\n",
              "      <td>-0.3</td>\n",
              "      <td>F4</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2016</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         TEAM CONF   G   W  ADJOE  ...   WAB  POSTSEASON  SEED  YEAR  windex\n",
              "2  Notre Dame  ACC  36  24  118.3  ...   2.3          E8   6.0  2016       0\n",
              "3    Virginia  ACC  37  29  119.9  ...   8.6          E8   1.0  2016       1\n",
              "4      Kansas  B12  37  32  120.9  ...  11.6          E8   1.0  2016       1\n",
              "5      Oregon  P12  37  30  118.4  ...   6.7          E8   1.0  2016       0\n",
              "6    Syracuse  ACC  37  23  111.9  ...  -0.3          F4  10.0  2016       0\n",
              "\n",
              "[5 rows x 25 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 147
        }
      ],
      "source": [
        "df1['windex'].replace(to_replace=['False','True'], value=[0,1],inplace=True)\n",
        "df1.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "YuNAjz2UMWXn"
      },
      "source": [
        "### Selección de característica\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "A0tvYyF-MWXn"
      },
      "source": [
        "Definamos los conjuntos de características, X:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 148,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "MpgRCLnaMWXn",
        "outputId": "75595217-9cc7-4076-b218-2fed7cd69452"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>G</th>\n",
              "      <th>W</th>\n",
              "      <th>ADJOE</th>\n",
              "      <th>ADJDE</th>\n",
              "      <th>BARTHAG</th>\n",
              "      <th>EFG_O</th>\n",
              "      <th>EFG_D</th>\n",
              "      <th>TOR</th>\n",
              "      <th>TORD</th>\n",
              "      <th>ORB</th>\n",
              "      <th>DRB</th>\n",
              "      <th>FTR</th>\n",
              "      <th>FTRD</th>\n",
              "      <th>2P_O</th>\n",
              "      <th>2P_D</th>\n",
              "      <th>3P_O</th>\n",
              "      <th>3P_D</th>\n",
              "      <th>ADJ_T</th>\n",
              "      <th>WAB</th>\n",
              "      <th>SEED</th>\n",
              "      <th>windex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>36</td>\n",
              "      <td>24</td>\n",
              "      <td>118.3</td>\n",
              "      <td>103.3</td>\n",
              "      <td>0.8269</td>\n",
              "      <td>54.0</td>\n",
              "      <td>49.5</td>\n",
              "      <td>15.3</td>\n",
              "      <td>14.8</td>\n",
              "      <td>32.7</td>\n",
              "      <td>32.1</td>\n",
              "      <td>32.9</td>\n",
              "      <td>26.0</td>\n",
              "      <td>52.9</td>\n",
              "      <td>46.5</td>\n",
              "      <td>37.4</td>\n",
              "      <td>36.9</td>\n",
              "      <td>65.5</td>\n",
              "      <td>2.3</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>37</td>\n",
              "      <td>29</td>\n",
              "      <td>119.9</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.9600</td>\n",
              "      <td>54.8</td>\n",
              "      <td>48.4</td>\n",
              "      <td>15.1</td>\n",
              "      <td>18.8</td>\n",
              "      <td>29.9</td>\n",
              "      <td>25.2</td>\n",
              "      <td>32.1</td>\n",
              "      <td>33.4</td>\n",
              "      <td>52.6</td>\n",
              "      <td>46.3</td>\n",
              "      <td>40.3</td>\n",
              "      <td>34.7</td>\n",
              "      <td>61.9</td>\n",
              "      <td>8.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>37</td>\n",
              "      <td>32</td>\n",
              "      <td>120.9</td>\n",
              "      <td>90.4</td>\n",
              "      <td>0.9662</td>\n",
              "      <td>55.7</td>\n",
              "      <td>45.1</td>\n",
              "      <td>17.8</td>\n",
              "      <td>18.5</td>\n",
              "      <td>32.2</td>\n",
              "      <td>27.9</td>\n",
              "      <td>38.6</td>\n",
              "      <td>37.3</td>\n",
              "      <td>52.7</td>\n",
              "      <td>43.4</td>\n",
              "      <td>41.3</td>\n",
              "      <td>32.5</td>\n",
              "      <td>70.1</td>\n",
              "      <td>11.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>37</td>\n",
              "      <td>30</td>\n",
              "      <td>118.4</td>\n",
              "      <td>96.2</td>\n",
              "      <td>0.9163</td>\n",
              "      <td>52.3</td>\n",
              "      <td>48.9</td>\n",
              "      <td>16.1</td>\n",
              "      <td>20.2</td>\n",
              "      <td>34.1</td>\n",
              "      <td>30.5</td>\n",
              "      <td>40.3</td>\n",
              "      <td>32.0</td>\n",
              "      <td>52.6</td>\n",
              "      <td>46.1</td>\n",
              "      <td>34.4</td>\n",
              "      <td>36.2</td>\n",
              "      <td>69.0</td>\n",
              "      <td>6.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>37</td>\n",
              "      <td>23</td>\n",
              "      <td>111.9</td>\n",
              "      <td>93.6</td>\n",
              "      <td>0.8857</td>\n",
              "      <td>50.0</td>\n",
              "      <td>47.3</td>\n",
              "      <td>18.1</td>\n",
              "      <td>20.4</td>\n",
              "      <td>33.5</td>\n",
              "      <td>35.3</td>\n",
              "      <td>35.4</td>\n",
              "      <td>28.0</td>\n",
              "      <td>47.2</td>\n",
              "      <td>48.1</td>\n",
              "      <td>36.0</td>\n",
              "      <td>30.7</td>\n",
              "      <td>65.5</td>\n",
              "      <td>-0.3</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    G   W  ADJOE  ADJDE  BARTHAG  EFG_O  ...  3P_O  3P_D  ADJ_T   WAB  SEED  windex\n",
              "2  36  24  118.3  103.3   0.8269   54.0  ...  37.4  36.9   65.5   2.3   6.0       0\n",
              "3  37  29  119.9   91.0   0.9600   54.8  ...  40.3  34.7   61.9   8.6   1.0       1\n",
              "4  37  32  120.9   90.4   0.9662   55.7  ...  41.3  32.5   70.1  11.6   1.0       1\n",
              "5  37  30  118.4   96.2   0.9163   52.3  ...  34.4  36.2   69.0   6.7   1.0       0\n",
              "6  37  23  111.9   93.6   0.8857   50.0  ...  36.0  30.7   65.5  -0.3  10.0       0\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ],
      "source": [
        "X = df1[['G', 'W', 'ADJOE', 'ADJDE', 'BARTHAG', 'EFG_O', 'EFG_D',\n",
        "       'TOR', 'TORD', 'ORB', 'DRB', 'FTR', 'FTRD', '2P_O', '2P_D', '3P_O',\n",
        "       '3P_D', 'ADJ_T', 'WAB', 'SEED', 'windex']]\n",
        "X[0:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "fZQXDz70MWXn"
      },
      "source": [
        "¿Cuales son nuestras etiquetas? Rondas donde el equipo fue eliminado o donde finalizó su temporada (R68 = Primeros Cuatro, R64 = Ronda de 64, R32 = Ronda de 32, S16 = Sweet Sixteen, E8 = Elite Eight, F4 = Ultimos Cuatro, 2ND = Subcampeón, Champion = Ganador del NCAA March Madness Tournament de ese año)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 149,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ppy2xxt5MWXn",
        "outputId": "5c7c7e57-f91a-4db1-cd7a-534f0bec5c60"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['E8', 'E8', 'E8', 'E8', 'F4'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ],
      "source": [
        "y = df1['POSTSEASON'].values\n",
        "y[0:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "WQhy5ChPMWXo"
      },
      "source": [
        "## Datos Normalizados\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "yp074hbzMWXo"
      },
      "source": [
        "La estandarización de los datos le da a estos una media de cero y varianza con valor de la unidad (tecnicamente debería hacerse despues de la división entre grupos de prueba y entrenamiento)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 150,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z8ytM0bqMWXo",
        "outputId": "4442f6be-c9c0-4480-dd88-4b3f2a4f11dc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.43331874, -1.26140173,  0.28034482,  2.74329908, -2.45717765,\n",
              "         0.10027963,  0.94171924, -1.16188145, -1.71391372,  0.12750511,\n",
              "         1.33368704, -0.4942211 , -0.87998988,  0.02784185,  0.00307239,\n",
              "         0.22576157,  1.59744386, -1.12106011, -1.0448016 ,  0.49716104,\n",
              "        -0.6882472 ],\n",
              "       [ 0.40343468,  0.35874728,  0.64758014, -0.90102957,  1.127076  ,\n",
              "         0.39390887,  0.38123706, -1.29466791, -0.03522254, -0.62979797,\n",
              "        -1.31585883, -0.68542235,  0.55458056, -0.07167795, -0.0829545 ,\n",
              "         1.32677295,  0.65081046, -2.369021  ,  0.98050611, -1.14054592,\n",
              "         1.45296631],\n",
              "       [ 0.40343468,  1.33083669,  0.87710222, -1.0788017 ,  1.29403598,\n",
              "         0.72424177, -1.30020946,  0.49794919, -0.16112438, -0.00772758,\n",
              "        -0.27908001,  0.86808783,  1.31063795, -0.03850468, -1.33034432,\n",
              "         1.70643205, -0.29582294,  0.47355659,  1.94493836, -1.14054592,\n",
              "         1.45296631],\n",
              "       [ 0.40343468,  0.68277708,  0.30329703,  0.63966222, -0.04972253,\n",
              "        -0.52368251,  0.63600169, -0.63073565,  0.55231938,  0.50615665,\n",
              "         0.71929959,  1.2743905 ,  0.28317534, -0.07167795, -0.16898138,\n",
              "        -0.91321572,  1.29624232,  0.0922352 ,  0.36969903, -1.14054592,\n",
              "        -0.6882472 ],\n",
              "       [ 0.40343468, -1.58543153, -1.18859646, -0.13068368, -0.87375079,\n",
              "        -1.36786658, -0.17924511,  0.69712887,  0.63625394,  0.34387742,\n",
              "         2.56246194,  0.10328282, -0.49226814, -1.8630343 ,  0.69128747,\n",
              "        -0.30576117, -1.07034117, -1.12106011, -1.88064288,  1.80732661,\n",
              "        -0.6882472 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ],
      "source": [
        "X= preprocessing.StandardScaler().fit(X).transform(X)\n",
        "X[0:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "PRHT1qBIMWXo"
      },
      "source": [
        "## Entrenamiento y Validación\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "snb2a8DLMWXo"
      },
      "source": [
        "Dividir el conjunto entre datos de entrenamiento y validación.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 151,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y59GPcwPMWXo",
        "outputId": "6f8392e5-ac57-4acc-dc0f-297a019432d7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set: (44, 21) (44,)\n",
            "Validation set: (12, 21) (12,)\n"
          ]
        }
      ],
      "source": [
        "# Dividir X entre entrenamiento y prueba para encontrar el mejor valor de k\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=4)\n",
        "print ('Train set:', X_train.shape,  y_train.shape)\n",
        "print ('Validation set:', X_val.shape,  y_val.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "yIy0xDvRMWXp"
      },
      "source": [
        "# Clasificación\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "fQDWNFNoMWXp"
      },
      "source": [
        "Ahora es su turno, utilice el conjunto de entrenamiento para construir un modelo preciso. Despues use el conjunto de validación para reportar la precisión del modelo. \n",
        "Deberá utilizar el siguiente algoritmo:\n",
        "\n",
        "-   K Nearest Neighbor(KNN)\n",
        "-   Arbol de Decisión\n",
        "-   Maquina de Soporte Vectorial (SVM) \n",
        "-   Regresión Logística\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9AR7uR5VMWXp"
      },
      "source": [
        "# K Nearest Neighbor(KNN)\n",
        "\n",
        "<b>Pregunta  1 </b> Construya un modelo KNN donde k sea igual a cinco, encuentre la precisión de los datos de validación (X_val y y_val)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3K655zOnMWXp"
      },
      "source": [
        "Puede utilizar <code> accuracy_score</cdoe>\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 152,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nl1hFcEmMWXp",
        "outputId": "fbc52b39-aa5e-413e-c696-ad0085956f89"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Entrenar el set de Certeza:  0.6363636363636364\n",
            "Probar el set de Certeza:  0.6666666666666666\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "\n",
        "k = 5\n",
        "neigh = KNeighborsClassifier(n_neighbors=k).fit(X_train, y_train)\n",
        "yhat = neigh.predict(X_val)\n",
        "print(\"Entrenar el set de Certeza: \", accuracy_score(y_train, neigh.predict(X_train)))\n",
        "print(\"Probar el set de Certeza: \", accuracy_score(y_val, yhat))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pjeNMhDiMWXp"
      },
      "source": [
        "<b>Pregunta  2</b> Determine e imprima la precisión para los primeros 15 valores de k en los datos de validación:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 153,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MMxjH2zsMWXq",
        "outputId": "29d9fc80-1847-4255-80dc-6145a84c5870"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "precision entrenamiento: [1.0, 0.6363636363636364, 0.6363636363636364, 0.6363636363636364, 0.6363636363636364, 0.5909090909090909, 0.5681818181818182, 0.5909090909090909, 0.5454545454545454, 0.6136363636363636, 0.5909090909090909, 0.6136363636363636, 0.6363636363636364, 0.6136363636363636, 0.5909090909090909]\n",
            "precision prediccion: [0.3333333333333333, 0.3333333333333333, 0.5, 0.5833333333333334, 0.6666666666666666, 0.5833333333333334, 0.5833333333333334, 0.6666666666666666, 0.5833333333333334, 0.5833333333333334, 0.5833333333333334, 0.5, 0.5833333333333334, 0.5833333333333334, 0.5833333333333334]\n"
          ]
        }
      ],
      "source": [
        "# En los datos de validación solamente tenemos 12 muestras. \n",
        "# Por esto, no podremos mostrar 15.\n",
        "\n",
        "train_accuracy = []\n",
        "test_accuracy = []\n",
        "Ks = 16\n",
        "\n",
        "for k in range(1, Ks):\n",
        "  knn = KNeighborsClassifier(n_neighbors=k).fit(X_train, y_train)\n",
        "  yhat = knn.predict(X_val)\n",
        "  train_accuracy.append(accuracy_score(y_train, knn.predict(X_train)))\n",
        "  test_accuracy.append(accuracy_score(y_val, yhat))\n",
        "\n",
        "print(\"precision entrenamiento:\", train_accuracy)\n",
        "print(\"precision prediccion:\", test_accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(1, Ks), test_accuracy, 'g')\n",
        "plt.legend(('Certeza ', 'irrelevant'))\n",
        "plt.ylabel('Certeza ')\n",
        "plt.xlabel('Número de Vecinos (K)')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "ka4U4CYCS8Ev",
        "outputId": "a4b19c0f-4623-4dad-bfba-70574fa8f961"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R9NrtzzaMWXq"
      },
      "source": [
        "# Arbol de Decisión\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g20T6-2jMWXq"
      },
      "source": [
        "Las siguientes líneas de código se ajustan en <code>DecisionTreeClassifier</code>:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 155,
      "metadata": {
        "id": "7X1tYtE-MWXq"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9lASuiEmMWXq"
      },
      "source": [
        "<b>Pregunta  3</b> Determine el valor mínimo para el parámetro <code>max_depth</code> que mejora los resultados\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 156,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xR9Hb0gQMWXq",
        "outputId": "bbcaefeb-3293-471b-8f7c-397bde5ed004"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy: 0.6666666666666666, depth: 1\n"
          ]
        }
      ],
      "source": [
        "best_accuracy_score = -1\n",
        "best_depth = 0\n",
        "\n",
        "for depth in range(1, 100):\n",
        "  decisionTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = depth)\n",
        "  decisionTree.fit(X_train, y_train)\n",
        "  predTree = decisionTree.predict(X_val)\n",
        "  # print(predTree[0:5])\n",
        "  # print(y_val[0:5])\n",
        "  score = accuracy_score(y_val, predTree)\n",
        "  if best_accuracy_score < score:\n",
        "    best_accuracy_score = score\n",
        "    best_depth = depth\n",
        "  # print(f\"Precisión del Arbol de Decisión({depth}): {score}\")\n",
        "\n",
        "print(f\"accuracy: {best_accuracy_score}, depth: {best_depth}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "  decisionTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 1)\n",
        "  decisionTree.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4wOB8Ugua3K6",
        "outputId": "c4ff6a5b-6e43-412f-ea9f-309259e55485"
      },
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', max_depth=1)"
            ]
          },
          "metadata": {},
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6WmBMxWuMWXq"
      },
      "source": [
        "# Maquina de Soporte Vectorial (SVM)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CH1PDicqMWXq"
      },
      "source": [
        "<b>Pregunta  4</b> Entrene el modelo Maquina de Soporte Vectorial y determine la precisión de los datos de validación de cada nucleo. Encuentre el nucleo ((linear, poly, rbf, sigmoid) que otorge la mejor puntuación para los datos y entrene un SVM con el.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 158,
      "metadata": {
        "id": "wPQlvFYDMWXr"
      },
      "outputs": [],
      "source": [
        "from sklearn import svm\n",
        "from sklearn.metrics import classification_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 159,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nfxcNjRDMWXr",
        "outputId": "ababc131-638e-406e-f638-171b47119bd3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "======linear======\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          E8       0.14      0.50      0.22         2\n",
            "          F4       0.00      0.00      0.00         2\n",
            "         S16       0.40      0.25      0.31         8\n",
            "\n",
            "    accuracy                           0.25        12\n",
            "   macro avg       0.18      0.25      0.18        12\n",
            "weighted avg       0.29      0.25      0.24        12\n",
            "\n",
            "======rbf======\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          E8       0.00      0.00      0.00         2\n",
            "          F4       0.00      0.00      0.00         2\n",
            "         S16       0.64      0.88      0.74         8\n",
            "\n",
            "    accuracy                           0.58        12\n",
            "   macro avg       0.21      0.29      0.25        12\n",
            "weighted avg       0.42      0.58      0.49        12\n",
            "\n",
            "======poly======\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          E8       0.00      0.00      0.00         2\n",
            "          F4       0.00      0.00      0.00         2\n",
            "         S16       0.67      1.00      0.80         8\n",
            "\n",
            "    accuracy                           0.67        12\n",
            "   macro avg       0.22      0.33      0.27        12\n",
            "weighted avg       0.44      0.67      0.53        12\n",
            "\n",
            "======sigmoid======\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          E8       0.33      0.50      0.40         2\n",
            "          F4       0.00      0.00      0.00         2\n",
            "         S16       0.67      0.75      0.71         8\n",
            "\n",
            "    accuracy                           0.58        12\n",
            "   macro avg       0.33      0.42      0.37        12\n",
            "weighted avg       0.50      0.58      0.54        12\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ],
      "source": [
        "kernels = ['linear', 'rbf', 'poly', 'sigmoid']\n",
        "\n",
        "for kernel in kernels:\n",
        "  clf = svm.SVC(kernel=kernel)\n",
        "  clf.fit(X_train, y_train)\n",
        "  yhat = clf.predict(X_val)\n",
        "  print(f\"======{kernel}======\")\n",
        "  print(classification_report(y_val, yhat))\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# hemos visto que el nucleo sigmoid nos da los mejores resultados  \n",
        "clf = svm.SVC(kernel='sigmoid')\n",
        "clf.fit(X_train, y_train)\n",
        "yhat = clf.predict(X_val)"
      ],
      "metadata": {
        "id": "P1VVOfopYZpz"
      },
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3bU688xUMWXr"
      },
      "source": [
        "# Regresión Logística\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h2_IsWixMWXr"
      },
      "source": [
        "<b>Pregunta 5</b> Entrene un modelo de regresión logística y determine la precisión de los datos de validación (set C=0.01)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 161,
      "metadata": {
        "id": "F37ZatPIMWXr"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 162,
      "metadata": {
        "id": "eWnnONTSMWXr"
      },
      "outputs": [],
      "source": [
        "LR = LogisticRegression(C=0.01, solver='liblinear').fit(X_train,y_train)\n",
        "yhat = LR.predict(X_val)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.metrics import jaccard_similarity_score\n",
        "# jaccard_similarity_score(y_test, yhat)"
      ],
      "metadata": {
        "id": "IxjMV-wYZGE_"
      },
      "execution_count": 163,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tlw4N3JKMWXr"
      },
      "source": [
        "# Evaluación del Modelo con el Conjunto de Prueba\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 164,
      "metadata": {
        "id": "wqZ28CbqMWXr"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import f1_score\n",
        "# para f1_score establezca el promedio del parámetro en 'micro'\n",
        "from sklearn.metrics import log_loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 165,
      "metadata": {
        "id": "ZVmdk80iMWXr"
      },
      "outputs": [],
      "source": [
        "def jaccard_index(predictions, true):\n",
        "    if (len(predictions) == len(true)):\n",
        "        intersect = 0;\n",
        "        for x,y in zip(predictions, true):\n",
        "            if (x == y):\n",
        "                intersect += 1\n",
        "        return intersect / (len(predictions) + len(true) - intersect)\n",
        "    else:\n",
        "        return -1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "omXYTddBMWXr"
      },
      "source": [
        "<b>Pregunta  5</b> Calcule la puntuación de F1 y de la medida de Jaccard para cada modelo de arriba. Utilice el hiper parámetro de mejor desempeño para los datos de validación. **para f1_score establezca el promedio del parámetro en 'micro'.**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "YE1NILWMMWXs"
      },
      "source": [
        "### Cargar el conjunto de prueba para la evaluación\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 166,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "U4rpYgkaMWXs",
        "outputId": "774cef77-7ba9-440f-fe23-c07d4c0b680d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TEAM</th>\n",
              "      <th>CONF</th>\n",
              "      <th>G</th>\n",
              "      <th>W</th>\n",
              "      <th>ADJOE</th>\n",
              "      <th>ADJDE</th>\n",
              "      <th>BARTHAG</th>\n",
              "      <th>EFG_O</th>\n",
              "      <th>EFG_D</th>\n",
              "      <th>TOR</th>\n",
              "      <th>TORD</th>\n",
              "      <th>ORB</th>\n",
              "      <th>DRB</th>\n",
              "      <th>FTR</th>\n",
              "      <th>FTRD</th>\n",
              "      <th>2P_O</th>\n",
              "      <th>2P_D</th>\n",
              "      <th>3P_O</th>\n",
              "      <th>3P_D</th>\n",
              "      <th>ADJ_T</th>\n",
              "      <th>WAB</th>\n",
              "      <th>POSTSEASON</th>\n",
              "      <th>SEED</th>\n",
              "      <th>YEAR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>North Carolina</td>\n",
              "      <td>ACC</td>\n",
              "      <td>40</td>\n",
              "      <td>33</td>\n",
              "      <td>123.3</td>\n",
              "      <td>94.9</td>\n",
              "      <td>0.9531</td>\n",
              "      <td>52.6</td>\n",
              "      <td>48.1</td>\n",
              "      <td>15.4</td>\n",
              "      <td>18.2</td>\n",
              "      <td>40.7</td>\n",
              "      <td>30.0</td>\n",
              "      <td>32.3</td>\n",
              "      <td>30.4</td>\n",
              "      <td>53.9</td>\n",
              "      <td>44.6</td>\n",
              "      <td>32.7</td>\n",
              "      <td>36.2</td>\n",
              "      <td>71.7</td>\n",
              "      <td>8.6</td>\n",
              "      <td>2ND</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Villanova</td>\n",
              "      <td>BE</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>123.1</td>\n",
              "      <td>90.9</td>\n",
              "      <td>0.9703</td>\n",
              "      <td>56.1</td>\n",
              "      <td>46.7</td>\n",
              "      <td>16.3</td>\n",
              "      <td>20.6</td>\n",
              "      <td>28.2</td>\n",
              "      <td>29.4</td>\n",
              "      <td>34.1</td>\n",
              "      <td>30.0</td>\n",
              "      <td>57.4</td>\n",
              "      <td>44.1</td>\n",
              "      <td>36.2</td>\n",
              "      <td>33.9</td>\n",
              "      <td>66.7</td>\n",
              "      <td>8.9</td>\n",
              "      <td>Champions</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Notre Dame</td>\n",
              "      <td>ACC</td>\n",
              "      <td>36</td>\n",
              "      <td>24</td>\n",
              "      <td>118.3</td>\n",
              "      <td>103.3</td>\n",
              "      <td>0.8269</td>\n",
              "      <td>54.0</td>\n",
              "      <td>49.5</td>\n",
              "      <td>15.3</td>\n",
              "      <td>14.8</td>\n",
              "      <td>32.7</td>\n",
              "      <td>32.1</td>\n",
              "      <td>32.9</td>\n",
              "      <td>26.0</td>\n",
              "      <td>52.9</td>\n",
              "      <td>46.5</td>\n",
              "      <td>37.4</td>\n",
              "      <td>36.9</td>\n",
              "      <td>65.5</td>\n",
              "      <td>2.3</td>\n",
              "      <td>E8</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Virginia</td>\n",
              "      <td>ACC</td>\n",
              "      <td>37</td>\n",
              "      <td>29</td>\n",
              "      <td>119.9</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.9600</td>\n",
              "      <td>54.8</td>\n",
              "      <td>48.4</td>\n",
              "      <td>15.1</td>\n",
              "      <td>18.8</td>\n",
              "      <td>29.9</td>\n",
              "      <td>25.2</td>\n",
              "      <td>32.1</td>\n",
              "      <td>33.4</td>\n",
              "      <td>52.6</td>\n",
              "      <td>46.3</td>\n",
              "      <td>40.3</td>\n",
              "      <td>34.7</td>\n",
              "      <td>61.9</td>\n",
              "      <td>8.6</td>\n",
              "      <td>E8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kansas</td>\n",
              "      <td>B12</td>\n",
              "      <td>37</td>\n",
              "      <td>32</td>\n",
              "      <td>120.9</td>\n",
              "      <td>90.4</td>\n",
              "      <td>0.9662</td>\n",
              "      <td>55.7</td>\n",
              "      <td>45.1</td>\n",
              "      <td>17.8</td>\n",
              "      <td>18.5</td>\n",
              "      <td>32.2</td>\n",
              "      <td>27.9</td>\n",
              "      <td>38.6</td>\n",
              "      <td>37.3</td>\n",
              "      <td>52.7</td>\n",
              "      <td>43.4</td>\n",
              "      <td>41.3</td>\n",
              "      <td>32.5</td>\n",
              "      <td>70.1</td>\n",
              "      <td>11.6</td>\n",
              "      <td>E8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2016</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             TEAM CONF   G   W  ADJOE  ...  ADJ_T   WAB  POSTSEASON  SEED  YEAR\n",
              "0  North Carolina  ACC  40  33  123.3  ...   71.7   8.6         2ND   1.0  2016\n",
              "1       Villanova   BE  40  35  123.1  ...   66.7   8.9   Champions   2.0  2016\n",
              "2      Notre Dame  ACC  36  24  118.3  ...   65.5   2.3          E8   6.0  2016\n",
              "3        Virginia  ACC  37  29  119.9  ...   61.9   8.6          E8   1.0  2016\n",
              "4          Kansas  B12  37  32  120.9  ...   70.1  11.6          E8   1.0  2016\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ],
      "source": [
        "test_df = pd.read_csv('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0120ENv3/Dataset/ML0101EN_EDX_skill_up/basketball_train.csv',error_bad_lines=False)\n",
        "test_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 167,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IdIQSo8FMWXs",
        "outputId": "20b968d8-e658-4857-a275-14aa653fd89e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/series.py:4582: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  method=method,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       [ 3.63958290e-01,  6.11862762e-01,  3.60227129e-01,\n",
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              "       [ 3.63958290e-01, -1.38688893e+00, -1.12575060e+00,\n",
              "         3.92401673e-04, -9.03545224e-01, -1.13094639e+00,\n",
              "         1.09073363e-02,  7.34168378e-01,  5.61328631e-01,\n",
              "         2.28823098e-01,  2.52408203e+00, -5.07336709e-02,\n",
              "        -5.87592258e-01, -1.62650023e+00,  7.67424763e-01,\n",
              "        -2.40566627e-01, -1.00142717e+00, -8.36649260e-01,\n",
              "        -1.81525154e+00,  1.82698619e+00, -6.77003200e-01]])"
            ]
          },
          "metadata": {},
          "execution_count": 167
        }
      ],
      "source": [
        "test_df['windex'] = np.where(test_df.WAB > 7, 'True', 'False')\n",
        "test_df1 = test_df[test_df['POSTSEASON'].str.contains('F4|S16|E8', na=False)]\n",
        "test_Feature = test_df1[['G', 'W', 'ADJOE', 'ADJDE', 'BARTHAG', 'EFG_O', 'EFG_D',\n",
        "       'TOR', 'TORD', 'ORB', 'DRB', 'FTR', 'FTRD', '2P_O', '2P_D', '3P_O',\n",
        "       '3P_D', 'ADJ_T', 'WAB', 'SEED', 'windex']]\n",
        "test_Feature['windex'].replace(to_replace=['False','True'], value=[0,1],inplace=True)\n",
        "test_X=test_Feature\n",
        "test_X= preprocessing.StandardScaler().fit(test_X).transform(test_X)\n",
        "test_X[0:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 168,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vekBJ4ljMWXs",
        "outputId": "224df652-754c-4e0b-f629-6f160d4c1b0f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['E8', 'E8', 'E8', 'E8', 'F4'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ],
      "source": [
        "test_y = test_df1['POSTSEASON'].values\n",
        "test_y[0:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mJGecgHJMWXs"
      },
      "source": [
        "KNN\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 169,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ccz-_rRwMWXs",
        "outputId": "9bceb70f-1b1f-4273-f1d4-5d95b498afe6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy_score: 0.6285714285714286\n",
            "jacard_index: 0.4583333333333333\n",
            "f1_score 0.6285714285714286\n"
          ]
        }
      ],
      "source": [
        "knn_y_hat = neigh.predict(test_X)\n",
        "print(\"accuracy_score:\", accuracy_score(test_y, knn_y_hat))\n",
        "print(\"jacard_index:\", jaccard_index(knn_y_hat, test_y))\n",
        "print(\"f1_score\", f1_score(test_y, knn_y_hat, average='micro'))\n",
        "# print(log_loss(test_y, knn_y_hat))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dPNk4NPRMWXs"
      },
      "source": [
        "Arbol de Decisión\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 170,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "70rtt45dMWXs",
        "outputId": "03fd2f9d-0fef-4006-a7cf-c08bb10462ed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy_score: 0.6428571428571429\n",
            "jacard_index: 0.47368421052631576\n",
            "f1_score: 0.6428571428571429\n"
          ]
        }
      ],
      "source": [
        "decision_tree_y_hat = decisionTree.predict(test_X)\n",
        "print(\"accuracy_score:\", accuracy_score(test_y, decision_tree_y_hat))\n",
        "print(\"jacard_index:\", jaccard_index(decision_tree_y_hat, test_y))\n",
        "print(\"f1_score:\", f1_score(test_y, decision_tree_y_hat, average='micro'))\n",
        "# print(log_loss(test_y, decision_tree_y_hat))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EOQXwlYMMWXs"
      },
      "source": [
        "SVM\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 171,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PJ8SA0gVMWXt",
        "outputId": "ee864d85-543c-4948-ea72-67e4a662677a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy_score: 0.6\n",
            "jacard_index: 0.42857142857142855\n",
            "f1_score: 0.6\n"
          ]
        }
      ],
      "source": [
        "svm_y_hat = clf.predict(test_X)\n",
        "print(\"accuracy_score:\", accuracy_score(test_y, svm_y_hat))\n",
        "print(\"jacard_index:\", jaccard_index(svm_y_hat, test_y))\n",
        "print(\"f1_score:\",f1_score(test_y, svm_y_hat, average='micro'))\n",
        "# print(log_loss(test_y, svm_y_hat))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hYs5SAZ2MWXt"
      },
      "source": [
        "Regresión Logistica\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 172,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "meELpudPMWXt",
        "outputId": "aefd19db-2327-44b7-d6bd-d75b6e65c69a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy_score: 0.6857142857142857\n",
            "jacard_index: 0.5217391304347826\n",
            "f1_score: 0.6857142857142857\n"
          ]
        }
      ],
      "source": [
        "lr_y_hat = LR.predict(test_X)\n",
        "print(\"accuracy_score:\", accuracy_score(test_y, lr_y_hat))\n",
        "print(\"jacard_index:\", jaccard_index(lr_y_hat, test_y))\n",
        "print(\"f1_score:\",f1_score(test_y, lr_y_hat, average='micro'))\n",
        "# print(log_loss(test_y, lr_y_hat))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nI1hNn_IMWXt"
      },
      "source": [
        "# Reporte\n",
        "\n",
        "Deberá reportar la precisión del modelo utilizando distintas métricas de evaluación:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2mZ3lDmZMWXt"
      },
      "source": [
        "| Algoritmo           | Precisión | Jaccard  | F1       | LogLoss |\n",
        "| ------------------- | --------- | -------- | -------- | ------- |\n",
        "| KNN                 | 0.628571  | 0.458333 | 0.628571 | NA      |\n",
        "| Arbol de Decisión   | 0.642857  | 0.473684 | 0.642857 | NA      |\n",
        "| SVM                 | 0.685714  | 0.521739 | 0.685714 | NA      |\n",
        "| Regresión Logística | 0.685714  | 0.521739 | 0.685714 | 1.03719 |\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XO4FP3NCMWXt"
      },
      "source": [
        "Algo a tener en cuenta cuando se crean modelos para predecir los resultados en torneos de baloncesto o deportes en general es su dificultad debido a la gran cantidad de factores que influyen en el juego. Incluso en deportes de apuestas, una precisión del 55% o mas se considera buena debido a que indica ganancias.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "n-W6DnNnMWXt"
      },
      "source": [
        "<h2>¿Quiere aprender mas?</h2>\n",
        "\n",
        "IBM SPSS Modeler es una plataforma para el análisis que contiene muchos algoritmos de aprendizaje automático (machine learning). Ha sido diseñado con el fin de ofrecer predicciones inteligentes a las decisiones hechas por personas, grupos, sistemas - con la compañia como un todo. Una prueba gratuita esta disponible a traves de este curso desde aqui: <a href=\"http://cocl.us/ML0101EN-SPSSModeler\">SPSS Modeler</a>\n",
        "\n",
        "Además, puede utilizar Watson Studio para ejecutar estos cuardernos mas rápido y con conjuntos de datos mas grandes. Watson Studio es la solución líder de IBM en la nube para científicos de datos, contruido por científicos de datos. Con los cuadernos de Jupyter, RStudio, Apache Spark y librerías pre instaladas en la nube, Watson Studio permite a los científicos de datos colaborar en sus proyectos sin necesidad de instalar nada. Unase a la gran comunidad de usuarios de Watson Studio hoy con un cuenta gratuita en <a href=\"https://cocl.us/ML0101EN_DSX\">Watson Studio</a>\n",
        "\n",
        "<h3>Gracias por terminar esta lección</h3>\n",
        "\n",
        "<h4>Autores: <a href=\"https://www.linkedin.com/in/azim-hirjani-691a07179/\">Azim Hirjani</a> y <a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\">Joseph Santarcangelo</a></h4>\n",
        "\n",
        "<p><a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a> es un Cientifico de Datos en IBM que posee un Doctorado en Ingeniería Electrica. Su investigación se enfoca en el uso de Aprendizaje Automático, Procesamiento de Señales y Visión Artificial para determinar el impacto de los videos en la cognición. Joseph ha trabajado para IBM desde el término de su doctorado.\n",
        "</p>\n",
        "\n",
        "<hr>\n",
        "\n",
        "<p>Copyright &copy; 2018 <a href=\"https://cocl.us/DX0108EN_CC\">Cognitive Class</a>. This notebook and its source code are released under the terms of the <a href=\"https://bigdatauniversity.com/mit-license/\">MIT License</a>.</p>\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 172,
      "metadata": {
        "id": "JJ5VXzPAMWXt"
      },
      "outputs": [],
      "source": [
        ""
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