Entregable_Parte_2_general.ipynb

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      "name": "Entregable_Parte_2_general.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zO4bRoxr2Apy"
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      "source": [
        "**Diplomatura en Ciencia de Datos, Aprendizaje Automático y sus Aplicaciones**\n",
        "\n",
        "**Exploración y Curación de Datos**\n",
        "\n",
        "*Edición 2021*\n",
        "\n",
        "----\n",
        "\n",
        "# Trabajo práctico entregable - parte 2\n",
        "\n",
        "\n",
        "En el ejercicio 1 de la parte 1 del entregable seleccionaron las filas y columnas relevantes al problema de predicción de precios de una propiedad. Además de ello, tuvieron que reducir el número de valores posibles para las variables categóricas utilizando información de dominio.\n",
        "\n",
        "En el ejercicio 2 de la parte 1 del entregable imputaron los valores faltantes de las columnas `Suburb` y las columnas obtenidas a partir del conjunto de datos `airbnb`.\n",
        "\n",
        "En esta notebook, **se utilizará resultado de dichas operaciones.**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4udjxjk1EtVU"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "sns.set_context('talk')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "_qeFN3GnEvMk",
        "outputId": "70f540f1-e999-4f78-a2f0-4374fb0a24b1"
      },
      "source": [
        "# Acá deberían leer el conjunto de datos que ya tienen.\n",
        "url = 'https://drive.google.com/file/d/10X-tkgrUkqV2gPRq6X8hr-PR0iDz-sJp/view?usp=sharing'\n",
        "path = 'https://drive.google.com/uc?export=download&id='+url.split('/')[-2]\n",
        "melb_df = pd.read_csv(path)\n",
        "melb_df[:3]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Unnamed: 0</th>\n",
              "      <th>Rooms</th>\n",
              "      <th>Type</th>\n",
              "      <th>Price</th>\n",
              "      <th>Date</th>\n",
              "      <th>Distance</th>\n",
              "      <th>Postcode</th>\n",
              "      <th>Bedroom2</th>\n",
              "      <th>Bathroom</th>\n",
              "      <th>Car</th>\n",
              "      <th>Landsize</th>\n",
              "      <th>BuildingArea</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>CouncilArea</th>\n",
              "      <th>Regionname</th>\n",
              "      <th>Propertycount</th>\n",
              "      <th>Suburb</th>\n",
              "      <th>PrecioPromedioAirbnb</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>h</td>\n",
              "      <td>1480000.0</td>\n",
              "      <td>2016-03-12</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>202.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>130.624031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>h</td>\n",
              "      <td>1035000.0</td>\n",
              "      <td>2016-04-02</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>156.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>130.624031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>h</td>\n",
              "      <td>1465000.0</td>\n",
              "      <td>2017-04-03</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>134.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>130.624031</td>\n",
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            "text/plain": [
              "   Unnamed: 0  Rooms Type  ...  Propertycount      Suburb  PrecioPromedioAirbnb\n",
              "0           0      2    h  ...         4019.0  Abbotsford            130.624031\n",
              "1           1      2    h  ...         4019.0  Abbotsford            130.624031\n",
              "2           2      3    h  ...         4019.0  Abbotsford            130.624031\n",
              "\n",
              "[3 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
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      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "2VmkNPlH5K4i"
      },
      "source": [
        "melb_df.drop('Unnamed: 0', axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s-mixICN22kA"
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      "source": [
        "## Ejercicio 1: Encoding\n",
        "\n",
        "1. Seleccionar todas las filas y columnas del conjunto de datos obtenido en la parte 1 del entregable, **excepto** `BuildingArea` y `YearBuilt`, que volveremos a imputar más adelante.\n",
        "\n",
        "2. Aplicar una codificación One-hot encoding a cada fila, tanto para variables numéricas como categóricas. Si lo consideran necesario, pueden volver a reducir el número de categorías únicas.\n",
        "\n",
        "Algunas opciones:\n",
        "  1. Utilizar `OneHotEncoder` junto con el parámetro `categories` para las variables categóricas y luego usar `numpy.hstack` para concatenar el resultado con las variables numéricas. \n",
        "  2. `DictVectorizer` con algunos pasos de pre-proceso previo.\n",
        "\n",
        "Recordar también que el atributo `pandas.DataFrame.values` permite acceder a la matriz de numpy subyacente a un DataFrame.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e9t4Q_yY7Qch",
        "outputId": "3a47ebb5-b800-45e5-af51-c5d4853b7bac"
      },
      "source": [
        "melb_df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Rooms', 'Type', 'Price', 'Date', 'Distance', 'Postcode', 'Bedroom2',\n",
              "       'Bathroom', 'Car', 'Landsize', 'BuildingArea', 'YearBuilt',\n",
              "       'CouncilArea', 'Regionname', 'Propertycount', 'Suburb',\n",
              "       'PrecioPromedioAirbnb'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-SpxY1uFD608"
      },
      "source": [
        "Separamos las columnas categoricas a las cuales vamos a hacer onehotencoder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K_Qk43aLIl4d"
      },
      "source": [
        "categorical_cols = ['Type', 'CouncilArea', 'Suburb', 'Regionname'] #columnas para pasar a onehotencoder\n",
        "numerical_cols = ['Rooms', 'Bedroom2', 'Bathroom', 'Car', 'Price', 'PrecioPromedioAirbnb', 'Distance', 'Propertycount']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dF4S6SLiEVlg"
      },
      "source": [
        "Aplicamos one-hot encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XVlCjdoW5fKb",
        "outputId": "2528cc45-ce38-41c5-da67-05626dba3b0f"
      },
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "encoder = OneHotEncoder(sparse=False)\n",
        "encoder.fit(melb_df[categorical_cols])\n",
        "# We can inspect the categories found by the encoder\n",
        "#encoder.categories_\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OneHotEncoder(categories='auto', drop=None, dtype=<class 'numpy.float64'>,\n",
              "              handle_unknown='error', sparse=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ws78QT32-JCN",
        "outputId": "b24c504b-06a6-41a5-82c6-fbcad4fcb421"
      },
      "source": [
        "encoded_types = encoder.transform(melb_df[categorical_cols])\n",
        "encoded_types[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0., ..., 0., 0., 0.],\n",
              "       [1., 0., 0., ..., 0., 0., 0.],\n",
              "       [1., 0., 0., ..., 0., 0., 0.],\n",
              "       ...,\n",
              "       [1., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 1., ..., 0., 0., 0.],\n",
              "       [1., 0., 0., ..., 0., 0., 0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DQ_0Fx2E_aAI"
      },
      "source": [
        "encoded_df = pd.DataFrame(data=encoded_types, columns=encoder.get_feature_names(categorical_cols))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "cv6pIx4BDeRO",
        "outputId": "c959ba59-a25e-4660-d896-7b7e911f7ff2"
      },
      "source": [
        "encoded_df.head(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Type_h</th>\n",
              "      <th>Type_t</th>\n",
              "      <th>Type_u</th>\n",
              "      <th>CouncilArea_Abbotsford</th>\n",
              "      <th>CouncilArea_Airport West</th>\n",
              "      <th>CouncilArea_Alphington</th>\n",
              "      <th>CouncilArea_Ascot Vale</th>\n",
              "      <th>CouncilArea_Ashburton</th>\n",
              "      <th>CouncilArea_Ashwood</th>\n",
              "      <th>CouncilArea_Balaclava</th>\n",
              "      <th>CouncilArea_Banyule</th>\n",
              "      <th>CouncilArea_Bayside</th>\n",
              "      <th>CouncilArea_Bayswater</th>\n",
              "      <th>CouncilArea_Bayswater North</th>\n",
              "      <th>CouncilArea_Bellfield</th>\n",
              "      <th>CouncilArea_Bentleigh East</th>\n",
              "      <th>CouncilArea_Blackburn</th>\n",
              "      <th>CouncilArea_Blackburn North</th>\n",
              "      <th>CouncilArea_Blackburn South</th>\n",
              "      <th>CouncilArea_Boroondara</th>\n",
              "      <th>CouncilArea_Briar Hill</th>\n",
              "      <th>CouncilArea_Brighton</th>\n",
              "      <th>CouncilArea_Brighton East</th>\n",
              "      <th>CouncilArea_Brimbank</th>\n",
              "      <th>CouncilArea_Brooklyn</th>\n",
              "      <th>CouncilArea_Brunswick</th>\n",
              "      <th>CouncilArea_Brunswick East</th>\n",
              "      <th>CouncilArea_Brunswick West</th>\n",
              "      <th>CouncilArea_Bulleen</th>\n",
              "      <th>CouncilArea_Bundoora</th>\n",
              "      <th>CouncilArea_Burnside</th>\n",
              "      <th>CouncilArea_Burnside Heights</th>\n",
              "      <th>CouncilArea_Burwood</th>\n",
              "      <th>CouncilArea_Burwood East</th>\n",
              "      <th>CouncilArea_Camberwell</th>\n",
              "      <th>CouncilArea_Cardinia</th>\n",
              "      <th>CouncilArea_Carlton</th>\n",
              "      <th>CouncilArea_Carlton North</th>\n",
              "      <th>CouncilArea_Carnegie</th>\n",
              "      <th>CouncilArea_Caroline Springs</th>\n",
              "      <th>...</th>\n",
              "      <th>Suburb_Templestowe Lower</th>\n",
              "      <th>Suburb_The Basin</th>\n",
              "      <th>Suburb_Thomastown</th>\n",
              "      <th>Suburb_Thornbury</th>\n",
              "      <th>Suburb_Toorak</th>\n",
              "      <th>Suburb_Travancore</th>\n",
              "      <th>Suburb_Truganina</th>\n",
              "      <th>Suburb_Tullamarine</th>\n",
              "      <th>Suburb_Upwey</th>\n",
              "      <th>Suburb_Vermont</th>\n",
              "      <th>Suburb_Vermont South</th>\n",
              "      <th>Suburb_Viewbank</th>\n",
              "      <th>Suburb_Wantirna</th>\n",
              "      <th>Suburb_Wantirna South</th>\n",
              "      <th>Suburb_Warrandyte</th>\n",
              "      <th>Suburb_Waterways</th>\n",
              "      <th>Suburb_Watsonia</th>\n",
              "      <th>Suburb_Watsonia North</th>\n",
              "      <th>Suburb_Werribee</th>\n",
              "      <th>Suburb_West Footscray</th>\n",
              "      <th>Suburb_West Melbourne</th>\n",
              "      <th>Suburb_Westmeadows</th>\n",
              "      <th>Suburb_Wheelers Hill</th>\n",
              "      <th>Suburb_Whittlesea</th>\n",
              "      <th>Suburb_Williams Landing</th>\n",
              "      <th>Suburb_Williamstown</th>\n",
              "      <th>Suburb_Williamstown North</th>\n",
              "      <th>Suburb_Windsor</th>\n",
              "      <th>Suburb_Wollert</th>\n",
              "      <th>Suburb_Wonga Park</th>\n",
              "      <th>Suburb_Wyndham Vale</th>\n",
              "      <th>Suburb_Yallambie</th>\n",
              "      <th>Suburb_Yarra Glen</th>\n",
              "      <th>Suburb_Yarraville</th>\n",
              "      <th>Regionname_Eastern Metropolitan</th>\n",
              "      <th>Regionname_Northern Metropolitan</th>\n",
              "      <th>Regionname_South-Eastern Metropolitan</th>\n",
              "      <th>Regionname_Southern Metropolitan</th>\n",
              "      <th>Regionname_Victoria</th>\n",
              "      <th>Regionname_Western Metropolitan</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 472 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Type_h  Type_t  ...  Regionname_Victoria  Regionname_Western Metropolitan\n",
              "0     1.0     0.0  ...                  0.0                              0.0\n",
              "1     1.0     0.0  ...                  0.0                              0.0\n",
              "\n",
              "[2 rows x 472 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "knS6RNReyG98",
        "outputId": "cea122a1-74c7-4521-f4fe-f687be623da6"
      },
      "source": [
        "encoded_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13441, 472)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UV7PXc5mDX7P"
      },
      "source": [
        "#melb_df_enc = pd.concat([encoded_df, melb_df[numerical_cols]], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ismngxPcfoWb"
      },
      "source": [
        "## Ejercicio 2: Imputación por KNN\n",
        "\n",
        "En el teórico se presentó el método `IterativeImputer` para imputar valores faltantes en variables numéricas. Sin embargo, los ejemplos presentados sólo utilizaban algunas variables numéricas presentes en el conjunto de datos. En este ejercicio, utilizaremos la matriz de datos codificada para imputar datos faltantes de manera más precisa.\n",
        "\n",
        "1. Agregue a la matriz obtenida en el punto anterior las columnas `YearBuilt` y `BuildingArea`.\n",
        "2. Aplique una instancia de `IterativeImputer` con un estimador `KNeighborsRegressor` para imputar los valores de las variables. ¿Es necesario estandarizar o escalar los datos previamente?\n",
        "3. Realice un gráfico mostrando la distribución de cada variable antes de ser imputada, y con ambos métodos de imputación."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NoKH4llnH1OL"
      },
      "source": [
        "## Escalamos las variables numericas para que esten en un mismo rango"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qKpZSP5oZOm-"
      },
      "source": [
        "Consideramos necesario escalar las variables ya que KNN requiere escalado de datos porque usa la distancia euclidiana entre dos puntos de datos para encontrar vecinos más cercanos. La distancia euclidiana es sensible a las magnitudes. Las características con magnitudes altas pesarán más que las características con magnitudes bajas. KNN tampoco es adecuado para datos de grandes dimensiones. Para obtener mejores resultados, se recomienda encarecidamente normalizar los datos en la misma escala. Generalmente, el rango de normalización considerado entre 0 y 1. KNN no es adecuado para datos de grandes dimensiones. En tales casos, la dimensión debe reducirse para mejorar el rendimiento. Además, manejar los valores perdidos nos ayudará a mejorar los resultados. \n",
        "\n",
        "*Fuente: https://acortar.link/h7ybe*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YqObSG4Nak2X"
      },
      "source": [
        "numerical_cols =['Rooms', 'Bedroom2', 'Bathroom', 'Car', 'PrecioPromedioAirbnb', 'Distance', 'Propertycount']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gklhytbGEa_L"
      },
      "source": [
        "df_melb_numeric= melb_df[numerical_cols]\n",
        "df_melb_numerical = pd.concat([df_melb_numeric, melb_df[['YearBuilt', 'BuildingArea']]], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TAmoCfMTlUtY"
      },
      "source": [
        "from sklearn import preprocessing\n",
        "\n",
        "scaler = preprocessing.StandardScaler().fit(df_melb_numerical[['Rooms', 'Bedroom2', 'Bathroom', 'Car', 'PrecioPromedioAirbnb', 'Distance', 'Propertycount','YearBuilt','BuildingArea']])\n",
        "df_melb_numerical_scaled = scaler.transform(df_melb_numerical[['Rooms', 'Bedroom2', 'Bathroom', 'Car', 'PrecioPromedioAirbnb', 'Distance', 'Propertycount','YearBuilt','BuildingArea']])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sp3i_VTbKTqp"
      },
      "source": [
        "df_melb_numerical_scaled = pd.DataFrame(df_melb_numerical_scaled, columns=['Rooms', 'Bedroom2', 'Bathroom', 'Car', 'PrecioPromedioAirbnb', 'Distance', 'Propertycount','YearBuilt','BuildingArea'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "XQfWui70Kj8W",
        "outputId": "db5aaaa6-4414-488c-c903-f3dfcf21edee"
      },
      "source": [
        "df_melb_numerical_scaled.head()"
      ],
      "execution_count": null,
      "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",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rooms</th>\n",
              "      <th>Bedroom2</th>\n",
              "      <th>Bathroom</th>\n",
              "      <th>Car</th>\n",
              "      <th>PrecioPromedioAirbnb</th>\n",
              "      <th>Distance</th>\n",
              "      <th>Propertycount</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>BuildingArea</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.987651</td>\n",
              "      <td>-0.975218</td>\n",
              "      <td>-0.790416</td>\n",
              "      <td>-0.645986</td>\n",
              "      <td>-0.009309</td>\n",
              "      <td>-1.322411</td>\n",
              "      <td>-0.785303</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.987651</td>\n",
              "      <td>-0.975218</td>\n",
              "      <td>-0.790416</td>\n",
              "      <td>-1.726191</td>\n",
              "      <td>-0.009309</td>\n",
              "      <td>-1.322411</td>\n",
              "      <td>-0.785303</td>\n",
              "      <td>-1.797693</td>\n",
              "      <td>-0.515686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.072400</td>\n",
              "      <td>0.097881</td>\n",
              "      <td>0.694210</td>\n",
              "      <td>-1.726191</td>\n",
              "      <td>-0.009309</td>\n",
              "      <td>-1.322411</td>\n",
              "      <td>-0.785303</td>\n",
              "      <td>-1.797693</td>\n",
              "      <td>0.035098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.072400</td>\n",
              "      <td>0.097881</td>\n",
              "      <td>0.694210</td>\n",
              "      <td>-0.645986</td>\n",
              "      <td>-0.009309</td>\n",
              "      <td>-1.322411</td>\n",
              "      <td>-0.785303</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.132450</td>\n",
              "      <td>0.097881</td>\n",
              "      <td>-0.790416</td>\n",
              "      <td>0.434220</td>\n",
              "      <td>-0.009309</td>\n",
              "      <td>-1.322411</td>\n",
              "      <td>-0.785303</td>\n",
              "      <td>1.355655</td>\n",
              "      <td>-0.026962</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Rooms  Bedroom2  Bathroom  ...  Propertycount  YearBuilt  BuildingArea\n",
              "0 -0.987651 -0.975218 -0.790416  ...      -0.785303        NaN           NaN\n",
              "1 -0.987651 -0.975218 -0.790416  ...      -0.785303  -1.797693     -0.515686\n",
              "2  0.072400  0.097881  0.694210  ...      -0.785303  -1.797693      0.035098\n",
              "3  0.072400  0.097881  0.694210  ...      -0.785303        NaN           NaN\n",
              "4  1.132450  0.097881 -0.790416  ...      -0.785303   1.355655     -0.026962\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hIGLIdCNGMS2",
        "outputId": "7297f9cb-4fdd-4261-ca86-d7b1a810abbb"
      },
      "source": [
        "df_melb_numerical_scaled.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Rooms                      0\n",
              "Bedroom2                   0\n",
              "Bathroom                   0\n",
              "Car                        0\n",
              "PrecioPromedioAirbnb       0\n",
              "Distance                   0\n",
              "Propertycount              0\n",
              "YearBuilt               5307\n",
              "BuildingArea            6378\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G4ClSr_JapCw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c42f66d-5995-43d7-f5e0-4334a182ff5f"
      },
      "source": [
        "from sklearn.experimental import enable_iterative_imputer\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.impute import IterativeImputer\n",
        "\n",
        "melb_data_mice = df_melb_numerical_scaled.copy(deep=True)\n",
        "\n",
        "mice_imputer = IterativeImputer(random_state=0, estimator=KNeighborsRegressor())\n",
        "melb_data_mice[['YearBuilt','BuildingArea']] = mice_imputer.fit_transform(\n",
        "    melb_data_mice[['YearBuilt', 'BuildingArea']])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/impute/_iterative.py:638: ConvergenceWarning: [IterativeImputer] Early stopping criterion not reached.\n",
            "  \" reached.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "jK1p0P1ObtgK",
        "outputId": "d6f00859-776d-4374-834e-3114cce84727"
      },
      "source": [
        "melb_data_mice"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rooms</th>\n",
              "      <th>Bedroom2</th>\n",
              "      <th>Bathroom</th>\n",
              "      <th>Car</th>\n",
              "      <th>PrecioPromedioAirbnb</th>\n",
              "      <th>Distance</th>\n",
              "      <th>Propertycount</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>BuildingArea</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>-0.987651</td>\n",
              "      <td>-0.975218</td>\n",
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              "      <td>-0.645986</td>\n",
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              "      <td>-0.785303</td>\n",
              "      <td>0.885419</td>\n",
              "      <td>0.216624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.987651</td>\n",
              "      <td>-0.975218</td>\n",
              "      <td>-0.790416</td>\n",
              "      <td>-1.726191</td>\n",
              "      <td>-0.009309</td>\n",
              "      <td>-1.322411</td>\n",
              "      <td>-0.785303</td>\n",
              "      <td>-1.797693</td>\n",
              "      <td>-0.515686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.072400</td>\n",
              "      <td>0.097881</td>\n",
              "      <td>0.694210</td>\n",
              "      <td>-1.726191</td>\n",
              "      <td>-0.009309</td>\n",
              "      <td>-1.322411</td>\n",
              "      <td>-0.785303</td>\n",
              "      <td>-1.797693</td>\n",
              "      <td>0.035098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.072400</td>\n",
              "      <td>0.097881</td>\n",
              "      <td>0.694210</td>\n",
              "      <td>-0.645986</td>\n",
              "      <td>-0.009309</td>\n",
              "      <td>-1.322411</td>\n",
              "      <td>-0.785303</td>\n",
              "      <td>0.885419</td>\n",
              "      <td>0.216624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.132450</td>\n",
              "      <td>0.097881</td>\n",
              "      <td>-0.790416</td>\n",
              "      <td>0.434220</td>\n",
              "      <td>-0.009309</td>\n",
              "      <td>-1.322411</td>\n",
              "      <td>-0.785303</td>\n",
              "      <td>1.355655</td>\n",
              "      <td>-0.026962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13436</th>\n",
              "      <td>1.132450</td>\n",
              "      <td>1.170980</td>\n",
              "      <td>0.694210</td>\n",
              "      <td>0.434220</td>\n",
              "      <td>-0.111827</td>\n",
              "      <td>1.142129</td>\n",
              "      <td>-0.015194</td>\n",
              "      <td>0.442844</td>\n",
              "      <td>0.134394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13437</th>\n",
              "      <td>0.072400</td>\n",
              "      <td>0.097881</td>\n",
              "      <td>0.694210</td>\n",
              "      <td>0.434220</td>\n",
              "      <td>0.930322</td>\n",
              "      <td>-0.576106</td>\n",
              "      <td>-0.246250</td>\n",
              "      <td>0.830097</td>\n",
              "      <td>-0.096780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13438</th>\n",
              "      <td>0.072400</td>\n",
              "      <td>0.097881</td>\n",
              "      <td>0.694210</td>\n",
              "      <td>2.594631</td>\n",
              "      <td>0.930322</td>\n",
              "      <td>-0.576106</td>\n",
              "      <td>-0.246250</td>\n",
              "      <td>0.885419</td>\n",
              "      <td>0.216624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13439</th>\n",
              "      <td>1.132450</td>\n",
              "      <td>1.170980</td>\n",
              "      <td>-0.790416</td>\n",
              "      <td>3.674836</td>\n",
              "      <td>0.930322</td>\n",
              "      <td>-0.576106</td>\n",
              "      <td>-0.246250</td>\n",
              "      <td>-1.244474</td>\n",
              "      <td>0.089401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13440</th>\n",
              "      <td>1.132450</td>\n",
              "      <td>1.170980</td>\n",
              "      <td>-0.790416</td>\n",
              "      <td>-0.645986</td>\n",
              "      <td>0.068162</td>\n",
              "      <td>-0.662886</td>\n",
              "      <td>-0.209034</td>\n",
              "      <td>-1.244474</td>\n",
              "      <td>-0.259688</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13441 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Rooms  Bedroom2  Bathroom  ...  Propertycount  YearBuilt  BuildingArea\n",
              "0     -0.987651 -0.975218 -0.790416  ...      -0.785303   0.885419      0.216624\n",
              "1     -0.987651 -0.975218 -0.790416  ...      -0.785303  -1.797693     -0.515686\n",
              "2      0.072400  0.097881  0.694210  ...      -0.785303  -1.797693      0.035098\n",
              "3      0.072400  0.097881  0.694210  ...      -0.785303   0.885419      0.216624\n",
              "4      1.132450  0.097881 -0.790416  ...      -0.785303   1.355655     -0.026962\n",
              "...         ...       ...       ...  ...            ...        ...           ...\n",
              "13436  1.132450  1.170980  0.694210  ...      -0.015194   0.442844      0.134394\n",
              "13437  0.072400  0.097881  0.694210  ...      -0.246250   0.830097     -0.096780\n",
              "13438  0.072400  0.097881  0.694210  ...      -0.246250   0.885419      0.216624\n",
              "13439  1.132450  1.170980 -0.790416  ...      -0.246250  -1.244474      0.089401\n",
              "13440  1.132450  1.170980 -0.790416  ...      -0.209034  -1.244474     -0.259688\n",
              "\n",
              "[13441 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PMK1ktqYQTJK"
      },
      "source": [
        "## Variable YearBuilt original sin NaN\n",
        "melb_year_build_orig = melb_df.YearBuilt.dropna().to_frame()\n",
        "melb_year_build_orig['No Imputation'] = 'Original'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lwx0SjCddIs1"
      },
      "source": [
        "## Variable BuildingArea original sin NaN\n",
        "melb_build_area_orig = melb_df.BuildingArea.dropna().to_frame()\n",
        "melb_build_area_orig['No Imputation'] = 'Original'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uEnt-duYG9_A"
      },
      "source": [
        "## Variable BuildingArea luego de escalar e imputar\n",
        "mice_build_area_esc = melb_data_mice.BuildingArea.to_frame()\n",
        "mice_build_area_esc['Imputation'] = 'KNN over BuildingArea'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P0pwDLyYdOpC"
      },
      "source": [
        "## Variable YearBuilt luego de escalar e imputar\n",
        "mice_year_built_esc = melb_data_mice.YearBuilt.to_frame()\n",
        "mice_year_built_esc['Imputation'] = 'KNN over YearBuilt'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "JgYf6u7vI124",
        "outputId": "06ec50fd-c0fe-43cf-c5b5-0331f4d2bbae"
      },
      "source": [
        "fig, ax = plt.subplots(1,2, figsize=(20,6))\n",
        "#plt.xlim(-1, 1)\n",
        "sns.kdeplot(data=melb_year_build_orig, x='YearBuilt', hue='No Imputation', ax=ax[0], )\n",
        "sns.kdeplot(data=melb_build_area_orig, x='BuildingArea', hue='No Imputation', ax=ax[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcbb44cb6d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "IunNREzNehL8",
        "outputId": "529aafb8-be8f-476a-8dba-fa674047c601"
      },
      "source": [
        "fig, ax = plt.subplots(1,2, figsize=(20,6))\n",
        "plt.xlim(-5, 5)\n",
        "sns.kdeplot(data=mice_year_built_esc, x='YearBuilt', hue='Imputation', ax=ax[0])\n",
        "sns.kdeplot(data=mice_build_area_esc, x='BuildingArea', hue='Imputation', ax=ax[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcbb4a12310>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YqEZrrSMfPD2"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "azOeYZ3PF3uW",
        "outputId": "4d9e9bf6-0683-4103-e733-be8a2ceb5f26"
      },
      "source": [
        "melb_data_mice.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Rooms                   0\n",
              "Bedroom2                0\n",
              "Bathroom                0\n",
              "Car                     0\n",
              "PrecioPromedioAirbnb    0\n",
              "Distance                0\n",
              "Propertycount           0\n",
              "YearBuilt               0\n",
              "BuildingArea            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yCiXTUA4LG5a"
      },
      "source": [
        "melb_df_final = pd.concat([encoded_df,melb_data_mice], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w4g1c58T9-Mw"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q7SiSez_amzJ"
      },
      "source": [
        "https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NBN7-5OIxjJW"
      },
      "source": [
        "## Ejercicio 3: Reducción de dimensionalidad.\n",
        "\n",
        "Utilizando la matriz obtenida en el ejercicio anterior:\n",
        "1. Aplique `PCA` para obtener $n$ componentes principales de la matriz, donde `n = min(20, X.shape[0])`. ¿Es necesario estandarizar o escalar los datos?\n",
        "2. Grafique la varianza capturada por los primeros $n$ componentes principales, para cada $n$.\n",
        "3. En base al gráfico, seleccione las primeras $m$ columnas de la matriz transformada para agregar como nuevas características al conjunto de datos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xsZ2P_h9hjg3"
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pca = PCA().fit_transform(melb_df_final)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9luNDJxPTa7S",
        "outputId": "4ab49f61-9468-44f7-9046-beaca46411a6"
      },
      "source": [
        "pca.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13441, 481)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "81aQzO6sk2Y6"
      },
      "source": [
        "ex_variance=np.var(pca,axis=0)\n",
        "ex_variance_ratio = ex_variance/np.sum(ex_variance)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DO0Be0q2m7Kk"
      },
      "source": [
        "https://etav.github.io/python/scikit_pca.html"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RS05nLZmNLpi"
      },
      "source": [
        "Antes de aplicar PCA es necesario estandarizar siempre ya que el analisis de componentes principales es un método de reduccion de variables que identifica aquellas direcciones en las que la varianza es mayor. Como la varianza de una variable se mide en su misma escala elevada al cuadrado, si antes de calcular las componentes no se estandarizan todas las variables para que tengan media 0 y desviación estándar 1, aquellas variables cuya escala sea mayor dominarán al resto. De ahí que sea recomendable estandarizar siempre los datos. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KeaGQV49myZu"
      },
      "source": [
        "#### Paso a paso"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mwEkPbT0mwHu"
      },
      "source": [
        "covar_matrix = PCA(n_components = 481)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sTPVnA21mx7O"
      },
      "source": [
        "covar_matrix.fit(melb_df_final)\n",
        "variance = covar_matrix.explained_variance_ratio_ "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gm40ycRenER3"
      },
      "source": [
        "var=np.cumsum(np.round(covar_matrix.explained_variance_ratio_, decimals=3)*100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "hfoh_I-znN36",
        "outputId": "809625fa-6eb7-4265-a53e-d206013b5491"
      },
      "source": [
        "plt.ylabel('% Variance Explained')\n",
        "plt.xlabel('# of Features')\n",
        "plt.title('PCA Analysis')\n",
        "plt.ylim(30,100.5)\n",
        "plt.style.context('seaborn-whitegrid')\n",
        "\n",
        "\n",
        "plt.plot(var)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fcbb4a35d10>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S3RcZQDJzuE6"
      },
      "source": [
        "Elegimos las primeras 88 columnas, ya que contienen el 97% de la varianza total, luego alcanza el plateau."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PlVS-IbZwkl8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "outputId": "2626339b-1093-46d2-d4c6-84e88e6999d5"
      },
      "source": [
        "processed_melb_df = pd.DataFrame(data=pca[:,:87])\n",
        "processed_melb_df.head()"
      ],
      "execution_count": null,
      "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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
              "      <th>35</th>\n",
              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>...</th>\n",
              "      <th>47</th>\n",
              "      <th>48</th>\n",
              "      <th>49</th>\n",
              "      <th>50</th>\n",
              "      <th>51</th>\n",
              "      <th>52</th>\n",
              "      <th>53</th>\n",
              "      <th>54</th>\n",
              "      <th>55</th>\n",
              "      <th>56</th>\n",
              "      <th>57</th>\n",
              "      <th>58</th>\n",
              "      <th>59</th>\n",
              "      <th>60</th>\n",
              "      <th>61</th>\n",
              "      <th>62</th>\n",
              "      <th>63</th>\n",
              "      <th>64</th>\n",
              "      <th>65</th>\n",
              "      <th>66</th>\n",
              "      <th>67</th>\n",
              "      <th>68</th>\n",
              "      <th>69</th>\n",
              "      <th>70</th>\n",
              "      <th>71</th>\n",
              "      <th>72</th>\n",
              "      <th>73</th>\n",
              "      <th>74</th>\n",
              "      <th>75</th>\n",
              "      <th>76</th>\n",
              "      <th>77</th>\n",
              "      <th>78</th>\n",
              "      <th>79</th>\n",
              "      <th>80</th>\n",
              "      <th>81</th>\n",
              "      <th>82</th>\n",
              "      <th>83</th>\n",
              "      <th>84</th>\n",
              "      <th>85</th>\n",
              "      <th>86</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.821557</td>\n",
              "      <td>-0.444794</td>\n",
              "      <td>-0.685471</td>\n",
              "      <td>0.490321</td>\n",
              "      <td>-0.565186</td>\n",
              "      <td>0.267297</td>\n",
              "      <td>0.845927</td>\n",
              "      <td>-0.443765</td>\n",
              "      <td>0.707117</td>\n",
              "      <td>-0.559314</td>\n",
              "      <td>-0.606248</td>\n",
              "      <td>-0.001820</td>\n",
              "      <td>-0.110282</td>\n",
              "      <td>-0.088054</td>\n",
              "      <td>0.311657</td>\n",
              "      <td>-0.006170</td>\n",
              "      <td>0.529354</td>\n",
              "      <td>0.449718</td>\n",
              "      <td>0.061464</td>\n",
              "      <td>-0.020577</td>\n",
              "      <td>-0.018113</td>\n",
              "      <td>-0.245175</td>\n",
              "      <td>-0.065491</td>\n",
              "      <td>0.109852</td>\n",
              "      <td>0.028493</td>\n",
              "      <td>0.003698</td>\n",
              "      <td>0.009033</td>\n",
              "      <td>-0.013122</td>\n",
              "      <td>-0.012992</td>\n",
              "      <td>-0.078789</td>\n",
              "      <td>-0.046889</td>\n",
              "      <td>-0.079875</td>\n",
              "      <td>0.016385</td>\n",
              "      <td>0.007456</td>\n",
              "      <td>0.037522</td>\n",
              "      <td>-0.096864</td>\n",
              "      <td>0.024623</td>\n",
              "      <td>0.061762</td>\n",
              "      <td>-0.003236</td>\n",
              "      <td>-0.077995</td>\n",
              "      <td>...</td>\n",
              "      <td>0.030399</td>\n",
              "      <td>-0.018889</td>\n",
              "      <td>0.014332</td>\n",
              "      <td>0.017434</td>\n",
              "      <td>-0.035035</td>\n",
              "      <td>-0.042253</td>\n",
              "      <td>-0.076321</td>\n",
              "      <td>0.013822</td>\n",
              "      <td>0.000899</td>\n",
              "      <td>-0.024239</td>\n",
              "      <td>0.014811</td>\n",
              "      <td>-0.005650</td>\n",
              "      <td>0.017172</td>\n",
              "      <td>0.040803</td>\n",
              "      <td>0.028580</td>\n",
              "      <td>-0.042655</td>\n",
              "      <td>-0.004464</td>\n",
              "      <td>0.005151</td>\n",
              "      <td>0.003280</td>\n",
              "      <td>-0.007525</td>\n",
              "      <td>-0.004415</td>\n",
              "      <td>-0.005908</td>\n",
              "      <td>0.009493</td>\n",
              "      <td>0.004292</td>\n",
              "      <td>0.005303</td>\n",
              "      <td>0.006336</td>\n",
              "      <td>0.002871</td>\n",
              "      <td>-0.002694</td>\n",
              "      <td>-0.003434</td>\n",
              "      <td>-0.001745</td>\n",
              "      <td>-0.012142</td>\n",
              "      <td>0.031185</td>\n",
              "      <td>-0.166599</td>\n",
              "      <td>0.035932</td>\n",
              "      <td>-0.174104</td>\n",
              "      <td>0.095717</td>\n",
              "      <td>-0.039459</td>\n",
              "      <td>0.009610</td>\n",
              "      <td>0.021972</td>\n",
              "      <td>0.001422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-2.367708</td>\n",
              "      <td>-0.170635</td>\n",
              "      <td>-2.349807</td>\n",
              "      <td>-1.027666</td>\n",
              "      <td>0.692563</td>\n",
              "      <td>-0.330422</td>\n",
              "      <td>-0.202039</td>\n",
              "      <td>0.045194</td>\n",
              "      <td>0.872314</td>\n",
              "      <td>-0.487898</td>\n",
              "      <td>-0.268634</td>\n",
              "      <td>-0.031850</td>\n",
              "      <td>0.007391</td>\n",
              "      <td>-0.037367</td>\n",
              "      <td>0.340731</td>\n",
              "      <td>-0.005894</td>\n",
              "      <td>0.513153</td>\n",
              "      <td>0.420405</td>\n",
              "      <td>0.022955</td>\n",
              "      <td>0.025493</td>\n",
              "      <td>0.021166</td>\n",
              "      <td>-0.232884</td>\n",
              "      <td>-0.073189</td>\n",
              "      <td>0.089073</td>\n",
              "      <td>0.016194</td>\n",
              "      <td>-0.001879</td>\n",
              "      <td>0.004405</td>\n",
              "      <td>0.011738</td>\n",
              "      <td>-0.001697</td>\n",
              "      <td>-0.093749</td>\n",
              "      <td>-0.052979</td>\n",
              "      <td>-0.062777</td>\n",
              "      <td>0.001123</td>\n",
              "      <td>-0.005289</td>\n",
              "      <td>0.033737</td>\n",
              "      <td>-0.107093</td>\n",
              "      <td>0.029299</td>\n",
              "      <td>0.068475</td>\n",
              "      <td>0.000383</td>\n",
              "      <td>-0.055401</td>\n",
              "      <td>...</td>\n",
              "      <td>0.029718</td>\n",
              "      <td>-0.019990</td>\n",
              "      <td>0.007939</td>\n",
              "      <td>0.013723</td>\n",
              "      <td>-0.046991</td>\n",
              "      <td>-0.042463</td>\n",
              "      <td>-0.058624</td>\n",
              "      <td>0.019942</td>\n",
              "      <td>0.008936</td>\n",
              "      <td>-0.013734</td>\n",
              "      <td>0.002594</td>\n",
              "      <td>-0.005346</td>\n",
              "      <td>0.004509</td>\n",
              "      <td>0.035715</td>\n",
              "      <td>0.029952</td>\n",
              "      <td>-0.041142</td>\n",
              "      <td>-0.002356</td>\n",
              "      <td>0.011297</td>\n",
              "      <td>0.014991</td>\n",
              "      <td>-0.000059</td>\n",
              "      <td>-0.009221</td>\n",
              "      <td>-0.007458</td>\n",
              "      <td>0.008983</td>\n",
              "      <td>-0.000557</td>\n",
              "      <td>-0.002572</td>\n",
              "      <td>0.019789</td>\n",
              "      <td>-0.001969</td>\n",
              "      <td>-0.003720</td>\n",
              "      <td>-0.008081</td>\n",
              "      <td>0.007292</td>\n",
              "      <td>-0.022465</td>\n",
              "      <td>0.033121</td>\n",
              "      <td>-0.158644</td>\n",
              "      <td>0.037699</td>\n",
              "      <td>-0.177623</td>\n",
              "      <td>0.090517</td>\n",
              "      <td>-0.036183</td>\n",
              "      <td>0.004916</td>\n",
              "      <td>0.026813</td>\n",
              "      <td>0.003310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.540339</td>\n",
              "      <td>0.333028</td>\n",
              "      <td>-2.876554</td>\n",
              "      <td>-0.845228</td>\n",
              "      <td>0.026232</td>\n",
              "      <td>-0.817699</td>\n",
              "      <td>-0.375888</td>\n",
              "      <td>0.319769</td>\n",
              "      <td>0.986357</td>\n",
              "      <td>-0.482600</td>\n",
              "      <td>-0.031682</td>\n",
              "      <td>-0.089086</td>\n",
              "      <td>-0.101005</td>\n",
              "      <td>-0.075786</td>\n",
              "      <td>0.333632</td>\n",
              "      <td>-0.053555</td>\n",
              "      <td>0.497043</td>\n",
              "      <td>0.394309</td>\n",
              "      <td>0.018919</td>\n",
              "      <td>0.040317</td>\n",
              "      <td>0.005835</td>\n",
              "      <td>-0.255254</td>\n",
              "      <td>-0.052494</td>\n",
              "      <td>0.068648</td>\n",
              "      <td>0.010445</td>\n",
              "      <td>0.010053</td>\n",
              "      <td>0.013986</td>\n",
              "      <td>0.011134</td>\n",
              "      <td>0.000690</td>\n",
              "      <td>-0.095652</td>\n",
              "      <td>-0.052284</td>\n",
              "      <td>-0.058487</td>\n",
              "      <td>0.001224</td>\n",
              "      <td>-0.007694</td>\n",
              "      <td>0.031911</td>\n",
              "      <td>-0.112678</td>\n",
              "      <td>0.030119</td>\n",
              "      <td>0.069399</td>\n",
              "      <td>0.009846</td>\n",
              "      <td>-0.054739</td>\n",
              "      <td>...</td>\n",
              "      <td>0.027915</td>\n",
              "      <td>-0.019978</td>\n",
              "      <td>0.011017</td>\n",
              "      <td>0.012615</td>\n",
              "      <td>-0.044613</td>\n",
              "      <td>-0.043295</td>\n",
              "      <td>-0.057153</td>\n",
              "      <td>0.017395</td>\n",
              "      <td>0.006289</td>\n",
              "      <td>-0.010666</td>\n",
              "      <td>0.001681</td>\n",
              "      <td>-0.002561</td>\n",
              "      <td>0.004695</td>\n",
              "      <td>0.037969</td>\n",
              "      <td>0.024985</td>\n",
              "      <td>-0.044963</td>\n",
              "      <td>-0.002429</td>\n",
              "      <td>0.004487</td>\n",
              "      <td>0.012996</td>\n",
              "      <td>-0.001187</td>\n",
              "      <td>-0.010549</td>\n",
              "      <td>-0.006403</td>\n",
              "      <td>0.012051</td>\n",
              "      <td>0.002757</td>\n",
              "      <td>0.000129</td>\n",
              "      <td>0.018713</td>\n",
              "      <td>-0.000809</td>\n",
              "      <td>0.001587</td>\n",
              "      <td>-0.011989</td>\n",
              "      <td>0.006455</td>\n",
              "      <td>-0.023591</td>\n",
              "      <td>0.033443</td>\n",
              "      <td>-0.158451</td>\n",
              "      <td>0.039309</td>\n",
              "      <td>-0.180916</td>\n",
              "      <td>0.087495</td>\n",
              "      <td>-0.035056</td>\n",
              "      <td>0.002370</td>\n",
              "      <td>0.023752</td>\n",
              "      <td>0.003177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.081844</td>\n",
              "      <td>0.029136</td>\n",
              "      <td>-1.198634</td>\n",
              "      <td>0.615810</td>\n",
              "      <td>-1.155626</td>\n",
              "      <td>-0.182378</td>\n",
              "      <td>0.272119</td>\n",
              "      <td>-0.516228</td>\n",
              "      <td>0.790814</td>\n",
              "      <td>-0.555517</td>\n",
              "      <td>-0.419058</td>\n",
              "      <td>-0.046011</td>\n",
              "      <td>-0.224869</td>\n",
              "      <td>-0.123175</td>\n",
              "      <td>0.305492</td>\n",
              "      <td>-0.047259</td>\n",
              "      <td>0.514028</td>\n",
              "      <td>0.423466</td>\n",
              "      <td>0.053038</td>\n",
              "      <td>-0.005788</td>\n",
              "      <td>-0.030579</td>\n",
              "      <td>-0.267640</td>\n",
              "      <td>-0.047306</td>\n",
              "      <td>0.092616</td>\n",
              "      <td>0.023511</td>\n",
              "      <td>0.015760</td>\n",
              "      <td>0.014481</td>\n",
              "      <td>-0.014915</td>\n",
              "      <td>-0.009026</td>\n",
              "      <td>-0.080203</td>\n",
              "      <td>-0.046407</td>\n",
              "      <td>-0.077322</td>\n",
              "      <td>0.016086</td>\n",
              "      <td>0.005661</td>\n",
              "      <td>0.036390</td>\n",
              "      <td>-0.102679</td>\n",
              "      <td>0.026786</td>\n",
              "      <td>0.064069</td>\n",
              "      <td>0.005781</td>\n",
              "      <td>-0.077550</td>\n",
              "      <td>...</td>\n",
              "      <td>0.028516</td>\n",
              "      <td>-0.017821</td>\n",
              "      <td>0.017536</td>\n",
              "      <td>0.017278</td>\n",
              "      <td>-0.033485</td>\n",
              "      <td>-0.042915</td>\n",
              "      <td>-0.075142</td>\n",
              "      <td>0.010295</td>\n",
              "      <td>-0.001316</td>\n",
              "      <td>-0.022166</td>\n",
              "      <td>0.013025</td>\n",
              "      <td>-0.003325</td>\n",
              "      <td>0.017338</td>\n",
              "      <td>0.042712</td>\n",
              "      <td>0.024653</td>\n",
              "      <td>-0.046281</td>\n",
              "      <td>-0.004629</td>\n",
              "      <td>-0.001337</td>\n",
              "      <td>0.002257</td>\n",
              "      <td>-0.008389</td>\n",
              "      <td>-0.006867</td>\n",
              "      <td>-0.003732</td>\n",
              "      <td>0.012942</td>\n",
              "      <td>0.007998</td>\n",
              "      <td>0.008570</td>\n",
              "      <td>0.006186</td>\n",
              "      <td>0.003898</td>\n",
              "      <td>0.002213</td>\n",
              "      <td>-0.006684</td>\n",
              "      <td>-0.002174</td>\n",
              "      <td>-0.012397</td>\n",
              "      <td>0.031043</td>\n",
              "      <td>-0.166080</td>\n",
              "      <td>0.037115</td>\n",
              "      <td>-0.175514</td>\n",
              "      <td>0.091250</td>\n",
              "      <td>-0.035370</td>\n",
              "      <td>0.006888</td>\n",
              "      <td>0.021822</td>\n",
              "      <td>0.001609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.219040</td>\n",
              "      <td>-0.241701</td>\n",
              "      <td>-0.592575</td>\n",
              "      <td>0.350476</td>\n",
              "      <td>-0.674304</td>\n",
              "      <td>0.755907</td>\n",
              "      <td>1.098144</td>\n",
              "      <td>-1.606551</td>\n",
              "      <td>0.089675</td>\n",
              "      <td>-0.672967</td>\n",
              "      <td>-0.160040</td>\n",
              "      <td>-0.047620</td>\n",
              "      <td>-0.162459</td>\n",
              "      <td>-0.143207</td>\n",
              "      <td>0.341875</td>\n",
              "      <td>-0.034336</td>\n",
              "      <td>0.531674</td>\n",
              "      <td>0.485352</td>\n",
              "      <td>0.081958</td>\n",
              "      <td>-0.076242</td>\n",
              "      <td>-0.045255</td>\n",
              "      <td>-0.121497</td>\n",
              "      <td>0.027853</td>\n",
              "      <td>0.264938</td>\n",
              "      <td>0.754534</td>\n",
              "      <td>0.036549</td>\n",
              "      <td>-0.046706</td>\n",
              "      <td>-0.036739</td>\n",
              "      <td>-0.028472</td>\n",
              "      <td>-0.063664</td>\n",
              "      <td>-0.041732</td>\n",
              "      <td>-0.104343</td>\n",
              "      <td>0.036622</td>\n",
              "      <td>0.019665</td>\n",
              "      <td>0.043713</td>\n",
              "      <td>-0.093638</td>\n",
              "      <td>0.020031</td>\n",
              "      <td>0.059508</td>\n",
              "      <td>0.011794</td>\n",
              "      <td>-0.084221</td>\n",
              "      <td>...</td>\n",
              "      <td>0.033053</td>\n",
              "      <td>-0.021173</td>\n",
              "      <td>0.028705</td>\n",
              "      <td>0.013707</td>\n",
              "      <td>-0.037429</td>\n",
              "      <td>-0.036700</td>\n",
              "      <td>-0.083109</td>\n",
              "      <td>0.010221</td>\n",
              "      <td>0.003761</td>\n",
              "      <td>-0.026134</td>\n",
              "      <td>0.018739</td>\n",
              "      <td>-0.012242</td>\n",
              "      <td>0.020411</td>\n",
              "      <td>0.037365</td>\n",
              "      <td>0.022606</td>\n",
              "      <td>-0.045518</td>\n",
              "      <td>-0.009883</td>\n",
              "      <td>0.004112</td>\n",
              "      <td>0.000745</td>\n",
              "      <td>-0.015943</td>\n",
              "      <td>-0.004617</td>\n",
              "      <td>-0.005360</td>\n",
              "      <td>0.014369</td>\n",
              "      <td>0.001168</td>\n",
              "      <td>0.007844</td>\n",
              "      <td>0.002986</td>\n",
              "      <td>0.002520</td>\n",
              "      <td>-0.003909</td>\n",
              "      <td>0.003186</td>\n",
              "      <td>-0.007228</td>\n",
              "      <td>-0.000496</td>\n",
              "      <td>0.024465</td>\n",
              "      <td>-0.168768</td>\n",
              "      <td>0.033933</td>\n",
              "      <td>-0.172148</td>\n",
              "      <td>0.100928</td>\n",
              "      <td>-0.037109</td>\n",
              "      <td>0.012288</td>\n",
              "      <td>0.025391</td>\n",
              "      <td>0.002370</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 87 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         0         1         2   ...        84        85        86\n",
              "0 -1.821557 -0.444794 -0.685471  ...  0.009610  0.021972  0.001422\n",
              "1 -2.367708 -0.170635 -2.349807  ...  0.004916  0.026813  0.003310\n",
              "2 -0.540339  0.333028 -2.876554  ...  0.002370  0.023752  0.003177\n",
              "3 -0.081844  0.029136 -1.198634  ...  0.006888  0.021822  0.001609\n",
              "4  0.219040 -0.241701 -0.592575  ...  0.012288  0.025391  0.002370\n",
              "\n",
              "[5 rows x 87 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oFOMPtXqVwRS"
      },
      "source": [
        "processed_melb_df.to_csv(\"melb_pca.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WrZTYmG_ZyDy"
      },
      "source": [
        "## Ejercicio 4: Composición del resultado\n",
        "\n",
        "Transformar nuevamente el conjunto de datos procesado en un `pandas.DataFrame` y guardarlo en un archivo.\n",
        "\n",
        "Para eso, será necesario recordar el nombre original de cada columna de la matriz, en el orden correcto. Tener en cuenta:\n",
        "1. El método `OneHotEncoder.get_feature_names` o el atributo `OneHotEncoder.categories_` permiten obtener una lista con los valores de la categoría que le corresponde a cada índice de la matriz.\n",
        "2. Ninguno de los métodos aplicados intercambia de lugar las columnas o las filas de la matriz."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DfchYPgTxvQ4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "outputId": "5ae9cbd0-a28d-4f17-8bc7-0232953eb1bf"
      },
      "source": [
        "## Small example\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "## If we process our data with the following steps:\n",
        "categorical_cols = ['Type', 'Regionname']\n",
        "numerical_cols = ['Rooms', 'Distance']\n",
        "new_columns = []\n",
        "\n",
        "# Step 1: encode categorical columns\n",
        "encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
        "X_cat = encoder.fit_transform(melb_df[categorical_cols])\n",
        "for col, col_values in zip(categorical_cols, encoder.categories_):\n",
        "  for col_value in col_values:\n",
        "    new_columns.append('{}={}'.format(col, col_value))\n",
        "print(\"Matrix has shape {}, with columns: {}\".format(X_cat.shape, new_columns))\n",
        "\n",
        "# Step 2: Append the numerical columns\n",
        "import numpy\n",
        "import pandas\n",
        "X = numpy.hstack([X_cat, melb_df[numerical_cols].values])\n",
        "new_columns.extend(numerical_cols)\n",
        "print(\"Matrix has shape {}, with columns: {}\".format(X_cat.shape, new_columns))\n",
        "\n",
        "# Step 3: Append some new features, like PCA\n",
        "pca = PCA(n_components=2)\n",
        "pca_dummy_features = pca.fit_transform(X)\n",
        "X_pca = numpy.hstack([X, pca_dummy_features])\n",
        "new_columns.extend(['pca1', 'pca2'])\n",
        "\n",
        "## Re-build dataframe\n",
        "processed_melb_df = pandas.DataFrame(data=X_pca, columns=new_columns)\n",
        "processed_melb_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Matrix has shape (13441, 9), with columns: ['Type=h', 'Type=t', 'Type=u', 'Regionname=Eastern Metropolitan', 'Regionname=Northern Metropolitan', 'Regionname=South-Eastern Metropolitan', 'Regionname=Southern Metropolitan', 'Regionname=Victoria', 'Regionname=Western Metropolitan']\n",
            "Matrix has shape (13441, 9), with columns: ['Type=h', 'Type=t', 'Type=u', 'Regionname=Eastern Metropolitan', 'Regionname=Northern Metropolitan', 'Regionname=South-Eastern Metropolitan', 'Regionname=Southern Metropolitan', 'Regionname=Victoria', 'Regionname=Western Metropolitan', 'Rooms', 'Distance']\n"
          ],
          "name": "stdout"
        },
        {
          "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>Type=h</th>\n",
              "      <th>Type=t</th>\n",
              "      <th>Type=u</th>\n",
              "      <th>Regionname=Eastern Metropolitan</th>\n",
              "      <th>Regionname=Northern Metropolitan</th>\n",
              "      <th>Regionname=South-Eastern Metropolitan</th>\n",
              "      <th>Regionname=Southern Metropolitan</th>\n",
              "      <th>Regionname=Victoria</th>\n",
              "      <th>Regionname=Western Metropolitan</th>\n",
              "      <th>Rooms</th>\n",
              "      <th>Distance</th>\n",
              "      <th>pca1</th>\n",
              "      <th>pca2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>-7.650841</td>\n",
              "      <td>-0.266237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>-7.650841</td>\n",
              "      <td>-0.266237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>-7.600617</td>\n",
              "      <td>0.640855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>-7.600617</td>\n",
              "      <td>0.640855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>-7.550392</td>\n",
              "      <td>1.547948</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Type=h  Type=t  Type=u  ...  Distance      pca1      pca2\n",
              "0     1.0     0.0     0.0  ...       2.5 -7.650841 -0.266237\n",
              "1     1.0     0.0     0.0  ...       2.5 -7.650841 -0.266237\n",
              "2     1.0     0.0     0.0  ...       2.5 -7.600617  0.640855\n",
              "3     1.0     0.0     0.0  ...       2.5 -7.600617  0.640855\n",
              "4     1.0     0.0     0.0  ...       2.5 -7.550392  1.547948\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NRnJGWXFlctj"
      },
      "source": [
        "melb_df_final.to_csv(\"melb_df_final\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mVBLFc8PhRtW"
      },
      "source": [
        "## Ejercicio 5: Documentación\n",
        "\n",
        "En un documento `.pdf` o `.md` realizar un reporte de las operaciones que realizaron para obtener el conjunto de datos final. Se debe incluir:\n",
        "  1. Criterios de exclusión (o inclusión) de filas\n",
        "  2. Interpretación de las columnas presentes\n",
        "  2. Todas las transofrmaciones realizadas\n",
        "\n",
        "Este documento es de uso técnico exclusivamente, y su objetivo es permitir que otres desarrolladores puedan reproducir los mismos pasos y obtener el mismo resultado. Debe ser detallado pero consiso. Por ejemplo:\n",
        "\n",
        "```\n",
        "  ## Criterios de exclusión de ejemplos\n",
        "  1. Se eliminan ejemplos donde el año de construcción es previo a 1900\n",
        "\n",
        "  ## Características seleccionadas\n",
        "  ### Características categóricas\n",
        "  1. Type: tipo de propiedad. 3 valores posibles\n",
        "  2. ...\n",
        "  Todas las características categóricas fueron codificadas con un\n",
        "  método OneHotEncoding utilizando como máximo sus 30 valores más \n",
        "  frecuentes.\n",
        "  \n",
        "  ### Características numéricas\n",
        "  1. Rooms: Cantidad de habitaciones\n",
        "  2. Distance: Distancia al centro de la ciudad.\n",
        "  3. airbnb_mean_price: Se agrega el precio promedio diario de \n",
        "     publicaciones de la plataforma AirBnB en el mismo código \n",
        "     postal. [Link al repositorio con datos externos].\n",
        "\n",
        "  ### Transformaciones:\n",
        "  1. Todas las características numéricas fueron estandarizadas.\n",
        "  2. La columna `Suburb` fue imputada utilizando el método ...\n",
        "  3. Las columnas `YearBuilt` y ... fueron imputadas utilizando el \n",
        "     algoritmo ...\n",
        "  4. ...\n",
        "\n",
        "  ### Datos aumentados\n",
        "  1. Se agregan las 5 primeras columnas obtenidas a través del\n",
        "     método de PCA, aplicado sobre el conjunto de datos\n",
        "     totalmente procesado.\n",
        "```\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bq7nuPg2HIYx"
      },
      "source": [
        ""
      ]
    }
  ]
}