Asteroids_Credit_Card_Approval__Reliance_Stock_RiosAlexanderDaniel_CoderHouse.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/aletbm/Hazardous_Asteroids_Predictions--CoderHouse/blob/main/Asteroids_Credit_Card_Approval__Reliance_Stock_RiosAlexanderDaniel_CoderHouse.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ☄️ NASA JPL Asteroid Dataset\n",
        "\n",
        "<img src=\"https://media.ambito.com/p/da6a05c9b05cb3047a4a7f5451d7a098/adjuntos/239/imagenes/040/635/0040635192/asteroidesjpg.jpg\">\n",
        "\n",
        "## 📣 Acerca del dataset\n",
        "\n",
        "Este dataset fue elaborado por el Investigador en Astronomia y Astrofisica Mir Sakhawat Hossain, este dataset es mantenido oficialmente por el Laboratorio de Propulsion a Chorro (JPL por sus siglas en ingles) del Instituto Tecnologico de California la cual es una organizacion que se encuentra bajo la supervision de la NASA. Este dataset contiene varios tipos de datos relacionados con asteroides.\n",
        "\n",
        "Este dataset puede ser utilizado en proyectos de Machine Learning para tareas tanto de clasificacion como de regresion.\n",
        "\n",
        "### Definicion de columnas:\n",
        "\n",
        "+ **id**: ID interno\n",
        "+ **spkid**: ID principal\n",
        "+ **fullname**: Designacion/Nombre completo del objeto\n",
        "+ **pdes**: Designación primaria de objeto\n",
        "+ **name**: Nombre del objeto en la Union astronomica Internacional\n",
        "+ **prefix**: Prefijo del cometa\n",
        "+ **neo**: Objeto cercano a la tierra (Near-Earth Object) (Y/N)\n",
        "+ **pha**: Asteroide potencialmente peligroso (Potencial Hazardous Asteroid) (Y/N)\n",
        "+ **H**: Parámetro de magnitud absoluta\n",
        "+ **diameter**: Diametro del objeto (equivalente a una esfera) (km)\n",
        "+ **albedo**: Albedo geométrico\n",
        "+ **diameter_sigma**: Incertidumbre 1-sigma en el diámetro del objeto (km)\n",
        "+ **orbit_id**: ID de solución de órbita\n",
        "+ **epoch**: Época de osculación en forma de día juliano (TBD)\n",
        "+ **epoch_mjd**: Época de osculación en forma de día juliana modificada (TBD)\n",
        "+ **epoch_cal**: Época de osculación en forma de fecha/hora del calendario (TBD)\n",
        "+ **equinox**: Equinoccio de marco de referencia\n",
        "+ **e**: Excentricidad\n",
        "+ **a**: Semieje mayor (au)\n",
        "+ **q**: Distancia del perihelio (au)\n",
        "+ **i**: Inclinación. Angulo con respecto al plano eclíptico x-y (deg)\n",
        "+ **om**: Longitud del nodo ascendente (deg)\n",
        "+ **w**: Argumento del perihelio (deg)\n",
        "+ **ma**: Anomalía media (deg)\n",
        "+ **ad**: Distancia afelio (au)\n",
        "+ **n**: Noción media (deg/d)\n",
        "+ **tp**: Tiempo de paso del perihelio (TBD)\n",
        "+ **tp_cal**: Tiempo de paso del perihelio en forma de fecha/hora del calendario (TBD)\n",
        "+ **per**: Período orbital sideral (d)\n",
        "+ **per_y**: Período orbital sideral (years)\n",
        "+ **moid**: Distancia de intersección de la órbita mínima de la Tierra (au)\n",
        "+ **moid_ld**: Distancia de intersección de la órbita mínima de la Tierra (LD)\n",
        "+ **sigma_e**: Excentricidad (incertidumbre 1-sigma)\n",
        "+ **sigma_a**: Semieje mayor (incertidumbre 1-sigma) (au)\n",
        "+ **sigma_q**: Distancia del perihelio (incertidumbre 1-sigma) (au)\n",
        "+ **sigma_i**: Inclinación. Angulo con respecto al plano eclíptico x-y (incertidumbre 1-sigma) (deg)\n",
        "+ **sigma_om**: Longitud del nodo ascendente (incertidumbre 1-sigma) (deg)\n",
        "+ **sigma_w**: Argumento del perihelio (incertidumbre 1-sigma) (deg)\n",
        "+ **sigma_ma**: Anomalía media (incertidumbre 1-sigma) (deg)\n",
        "+ **sigma_ad**: Distancia afelio (incertidumbre 1-sigma) (au)\n",
        "+ **sigma_n**: Noción media (incertidumbre 1-sigma) (deg/d)\n",
        "+ **sigma_tp**: Tiempo de paso del perihelio (incertidumbre 1-sigma) (TBD)\n",
        "+ **sigma_per**: Período orbital sideral (incertidumbre 1-sigma) (d)\n",
        "+ **class**: Clasificación de órbita\n",
        "+ **rms**: RMS de ajuste de órbita normalizado (arcsec)\n",
        "\n",
        "## 👁️ Echando un vistazo al dataset"
      ],
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      "source": [
        "df_asteroids = pd.read_csv(\"/content/drive/MyDrive/Coder/Data Science/asteroid_dataset.csv\", engine='c', low_memory=False, index_col=\"id\")\n",
        "df_asteroids.head()"
      ],
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              "            spkid       full_name pdes     name prefix neo pha     H  \\\n",
              "id                                                                     \n",
              "a0000001  2000001         1 Ceres    1    Ceres    NaN   N   N  3.40   \n",
              "a0000002  2000002        2 Pallas    2   Pallas    NaN   N   N  4.20   \n",
              "a0000003  2000003          3 Juno    3     Juno    NaN   N   N  5.33   \n",
              "a0000004  2000004         4 Vesta    4    Vesta    NaN   N   N  3.00   \n",
              "a0000005  2000005       5 Astraea    5  Astraea    NaN   N   N  6.90   \n",
              "\n",
              "          diameter  albedo  ...       sigma_i      sigma_om       sigma_w  \\\n",
              "id                          ...                                             \n",
              "a0000001   939.400  0.0900  ...  4.608900e-09  6.168800e-08  6.624800e-08   \n",
              "a0000002   545.000  0.1010  ...  3.469400e-06  6.272400e-06  9.128200e-06   \n",
              "a0000003   246.596  0.2140  ...  3.223100e-06  1.664600e-05  1.772100e-05   \n",
              "a0000004   525.400  0.4228  ...  2.170600e-07  3.880800e-07  1.789300e-07   \n",
              "a0000005   106.699  0.2740  ...  2.740800e-06  2.894900e-05  2.984200e-05   \n",
              "\n",
              "              sigma_ma      sigma_ad       sigma_n      sigma_tp  \\\n",
              "id                                                                 \n",
              "a0000001  7.820700e-09  1.111300e-11  1.196500e-12  3.782900e-08   \n",
              "a0000002  8.859100e-06  4.961300e-09  4.653600e-10  4.078700e-05   \n",
              "a0000003  8.110400e-06  4.363900e-09  4.413400e-10  3.528800e-05   \n",
              "a0000004  1.206800e-06  1.648600e-09  2.612500e-10  4.103700e-06   \n",
              "a0000005  8.303800e-06  4.729000e-09  5.522700e-10  3.474300e-05   \n",
              "\n",
              "             sigma_per  class      rms  \n",
              "id                                      \n",
              "a0000001  9.415900e-09    MBA  0.43301  \n",
              "a0000002  3.680700e-06    MBA  0.35936  \n",
              "a0000003  3.107200e-06    MBA  0.33848  \n",
              "a0000004  1.274900e-06    MBA  0.39980  \n",
              "a0000005  3.490500e-06    MBA  0.52191  \n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'El dataset tiene {df_asteroids.shape[0]} registros y {df_asteroids.shape[1]} features.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RmenEsof4074",
        "outputId": "b6a8bbee-4489-4781-dd6f-0ea06da5b68c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "El dataset tiene 958524 registros y 44 features.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_asteroids.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nlxErSjUaDks",
        "outputId": "fa44d16d-a0e8-4f24-c517-0744aa086521"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 958524 entries, a0000001 to bT3S2678\n",
            "Data columns (total 44 columns):\n",
            " #   Column          Non-Null Count   Dtype  \n",
            "---  ------          --------------   -----  \n",
            " 0   spkid           958524 non-null  int64  \n",
            " 1   full_name       958524 non-null  object \n",
            " 2   pdes            958524 non-null  object \n",
            " 3   name            22064 non-null   object \n",
            " 4   prefix          18 non-null      object \n",
            " 5   neo             958520 non-null  object \n",
            " 6   pha             938603 non-null  object \n",
            " 7   H               952261 non-null  float64\n",
            " 8   diameter        136209 non-null  float64\n",
            " 9   albedo          135103 non-null  float64\n",
            " 10  diameter_sigma  136081 non-null  float64\n",
            " 11  orbit_id        958524 non-null  object \n",
            " 12  epoch           958524 non-null  float64\n",
            " 13  epoch_mjd       958524 non-null  int64  \n",
            " 14  epoch_cal       958524 non-null  float64\n",
            " 15  equinox         958524 non-null  object \n",
            " 16  e               958524 non-null  float64\n",
            " 17  a               958524 non-null  float64\n",
            " 18  q               958524 non-null  float64\n",
            " 19  i               958524 non-null  float64\n",
            " 20  om              958524 non-null  float64\n",
            " 21  w               958524 non-null  float64\n",
            " 22  ma              958523 non-null  float64\n",
            " 23  ad              958520 non-null  float64\n",
            " 24  n               958524 non-null  float64\n",
            " 25  tp              958524 non-null  float64\n",
            " 26  tp_cal          958524 non-null  float64\n",
            " 27  per             958520 non-null  float64\n",
            " 28  per_y           958523 non-null  float64\n",
            " 29  moid            938603 non-null  float64\n",
            " 30  moid_ld         958397 non-null  float64\n",
            " 31  sigma_e         938602 non-null  float64\n",
            " 32  sigma_a         938602 non-null  float64\n",
            " 33  sigma_q         938602 non-null  float64\n",
            " 34  sigma_i         938602 non-null  float64\n",
            " 35  sigma_om        938602 non-null  float64\n",
            " 36  sigma_w         938602 non-null  float64\n",
            " 37  sigma_ma        938602 non-null  float64\n",
            " 38  sigma_ad        938598 non-null  float64\n",
            " 39  sigma_n         938602 non-null  float64\n",
            " 40  sigma_tp        938602 non-null  float64\n",
            " 41  sigma_per       938598 non-null  float64\n",
            " 42  class           958524 non-null  object \n",
            " 43  rms             958522 non-null  float64\n",
            "dtypes: float64(33), int64(2), object(9)\n",
            "memory usage: 329.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analisis de regresión:\n",
        "\n",
        "Como analisis de regresion planteo predecir el diametro de un posible asteroide en base a variables tales como su **excentricidad**, su **magnitud absoluta**, su **albedo geometrico** y si es o no **potencialmente peligroso**.\n",
        "\n",
        "Analisis de clasificación:\n",
        "\n",
        "Como analisis de clasificacion planteo diferencia aquellos **asteroides potencialmente peligrosos** de lo que no lo son para la Tierra, apoyandonos en variables tales como **distancia del perihelio y afelio**, el **periodo orbital sideral**, la **distancia de intersección de la órbita mínima de la Tierra** o la **longitud del nodo ascendente**.\n",
        "\n"
      ],
      "metadata": {
        "id": "WO9GSrhTwJxe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 💳 Credit Card Approval Prediction\n",
        "\n",
        "<img src=\"https://www.forbes.com/advisor/wp-content/uploads/2022/07/credit_cards.jpeg-1-1-900x510.jpg\">\n",
        "\n",
        "## 📣 Acerca del dataset\n",
        "\n",
        "Las puntuacion de tarjetas de crédito son un método común de control de riesgos en la industria financiera. Utiliza la información personal y los datos presentados por los solicitantes de tarjetas de crédito para predecir la probabilidad de futuros incumplimientos y préstamos de tarjetas de crédito. El banco puede decidir si emite una tarjeta de crédito al solicitante. Los puntajes de crédito pueden cuantificar objetivamente la magnitud del riesgo.\n",
        "\n",
        "Este dataset puede ser utlizado en proyectos de Machine Learning para tareas de clasificacion entre cliente **bueno** o **malo**.\n",
        "\n",
        "### Definicion de columnas:\n",
        "\n",
        "El archivo *application_record.csv* contiene información personal de los clientes, que podría usar como características para predecir:\n",
        "\n",
        "+ **ID**:\tNumero de cliente\n",
        "+ **CODE_GENDER**:\tGenero\n",
        "+ **FLAG_OWN_CAR**:\tPosee auto\n",
        "+ **FLAG_OWN_REALTY**: Posee propiedad inmobiliaria\n",
        "+ **CNT_CHILDREN**: Cantidad de hijos\n",
        "+ **AMT_INCOME_TOTAL**: Sueldo anual\n",
        "+ **NAME_INCOME_TYPE**: Categoria de ingresos\n",
        "+ **NAME_EDUCATION_TYPE**: Nivel de education\n",
        "+ **NAME_FAMILY_STATUS**: Estado marital\n",
        "+ **NAME_HOUSING_TYPE**: Modo de vida\n",
        "+ **DAYS_BIRTH**: Cumpleaños. Cuenta hacia atrás desde el día actual (0), -1 significa ayer\n",
        "+ **DAYS_EMPLOYED**: Fecha de inicio del empleo. Contar hacia atrás desde el día actual (0). Si es positivo, significa la persona actualmente desempleada.\n",
        "+ **FLAG_MOBIL**: Posee telefono movil\n",
        "+ **FLAG_WORK_PHONE**: Posee telefono movil laboral\n",
        "+ **FLAG_PHONE**: Posee telefono\n",
        "+ **FLAG_EMAIL**: Posee email\n",
        "+ **OCCUPATION_TYPE**: Ocupación\n",
        "+ **CNT_FAM_MEMBERS**: Tamaño familiar\n",
        "\n",
        "El archivo *credit_record.csv* registra los comportamientos de los usuarios de la tarjeta de crédito.:\n",
        "\n",
        "+ **ID**: Numero de cliente\n",
        "+ **MONTHS_BALANCE**: Mes récord.\tEl mes de los datos extraídos es el punto de partida, al revés, 0 es el mes actual, -1 es el mes anterior, etc.\n",
        "+ **STATUS**: Estado.\t0: 1-29 días de atraso 1: 30-59 días de atraso 2: 60-89 días de atraso 3: 90-119 días de atraso 4: 120-149 días de atraso 5: Deudas atrasadas o incobrables, canceladas por más de 150 días C: pagado ese mes X: No hay préstamo para el mes\n",
        "\n",
        "## 👁️ Echando un vistazo al dataset"
      ],
      "metadata": {
        "id": "U6LcVlRby7Kc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_user_info = pd.read_csv(\"/content/drive/MyDrive/Coder/Data Science/Credit Card Approval Prediction/application_record.csv\", engine='c', index_col=\"ID\")\n",
        "df_user_info.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "rBViYQDj1EnS",
        "outputId": "a396116e-6031-4720-bdbf-733a5e9438fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN  \\\n",
              "ID                                                               \n",
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              "         AMT_INCOME_TOTAL      NAME_INCOME_TYPE  \\\n",
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              "                   NAME_EDUCATION_TYPE    NAME_FAMILY_STATUS  \\\n",
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              "ID                                                                  \n",
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              "</style>\n",
              "\n",
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              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
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              "  </style>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d193e1d3-c68b-43d0-b909-633ca5ace431');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_user_info.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iOHKaRgy3z5U",
        "outputId": "92f29231-7974-42ba-954f-b010e6dd255e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 438557 entries, 5008804 to 6842885\n",
            "Data columns (total 17 columns):\n",
            " #   Column               Non-Null Count   Dtype  \n",
            "---  ------               --------------   -----  \n",
            " 0   CODE_GENDER          438557 non-null  object \n",
            " 1   FLAG_OWN_CAR         438557 non-null  object \n",
            " 2   FLAG_OWN_REALTY      438557 non-null  object \n",
            " 3   CNT_CHILDREN         438557 non-null  int64  \n",
            " 4   AMT_INCOME_TOTAL     438557 non-null  float64\n",
            " 5   NAME_INCOME_TYPE     438557 non-null  object \n",
            " 6   NAME_EDUCATION_TYPE  438557 non-null  object \n",
            " 7   NAME_FAMILY_STATUS   438557 non-null  object \n",
            " 8   NAME_HOUSING_TYPE    438557 non-null  object \n",
            " 9   DAYS_BIRTH           438557 non-null  int64  \n",
            " 10  DAYS_EMPLOYED        438557 non-null  int64  \n",
            " 11  FLAG_MOBIL           438557 non-null  int64  \n",
            " 12  FLAG_WORK_PHONE      438557 non-null  int64  \n",
            " 13  FLAG_PHONE           438557 non-null  int64  \n",
            " 14  FLAG_EMAIL           438557 non-null  int64  \n",
            " 15  OCCUPATION_TYPE      304354 non-null  object \n",
            " 16  CNT_FAM_MEMBERS      438557 non-null  float64\n",
            "dtypes: float64(2), int64(7), object(8)\n",
            "memory usage: 60.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_credit_record = pd.read_csv(\"/content/drive/MyDrive/Coder/Data Science/Credit Card Approval Prediction/credit_record.csv\", engine='c', index_col=\"ID\")\n",
        "df_credit_record.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "2crlLbJq4RlW",
        "outputId": "971dfac5-8f95-429e-9608-ebcbd1a9cdce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         MONTHS_BALANCE STATUS\n",
              "ID                            \n",
              "5001711               0      X\n",
              "5001711              -1      0\n",
              "5001711              -2      0\n",
              "5001711              -3      0\n",
              "5001712               0      C"
            ],
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              "      <th>MONTHS_BALANCE</th>\n",
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              "      <th>5001712</th>\n",
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              "      <td>C</td>\n",
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              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
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              "\n",
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              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
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              "\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7f377174-d603-440b-856e-634ee8176e5a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7f377174-d603-440b-856e-634ee8176e5a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_credit_record.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G8VhT_ak4j8x",
        "outputId": "9d2464d7-8e14-4df4-a959-bbcf573b0952"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1048575 entries, 5001711 to 5150487\n",
            "Data columns (total 2 columns):\n",
            " #   Column          Non-Null Count    Dtype \n",
            "---  ------          --------------    ----- \n",
            " 0   MONTHS_BALANCE  1048575 non-null  int64 \n",
            " 1   STATUS          1048575 non-null  object\n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 24.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'El dataset tiene {df_user_info.shape[0]} registros y {df_user_info.shape[1] + df_user_info.shape[1]} features.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xd55-aKe5Rl7",
        "outputId": "3854b2ad-1706-40c6-f149-92a3ae503b20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "El dataset tiene 438557 registros y 34 features.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 💼 Reliance Stock Price Dataset\n",
        "\n",
        "<img src=\"https://www.nextinbeautymag.com/uploads/s1/17/29/relaince-industries-1.jpeg\">\n",
        "\n",
        "## 📣 Acerca del dataset\n",
        "\n",
        "Un conjunto de datos que contiene los precios de las acciones de Reliance desde 1996 hasta 2020. Contiene el volumen, la facturación, los precios de apertura, máximos, mínimos y de cierre de las acciones.\n",
        "\n",
        "Este conjunto de datos está destinado a usarse para predecir los precios de las acciones en el futuro.\n",
        "\n",
        "### Definicion de columnas\n",
        "\n",
        "+ **Date**: Fecha\n",
        "+ **Symbol**: Reliance\n",
        "+ **Series**: Equidad\n",
        "+ **Prev Close**: Precio de cierre del día anterior\n",
        "+ **Open**: Precio de apertura en un día\n",
        "+ **High**: Precio más alto alcanzado en un día\n",
        "+ **Low**: Precio más bajo en un día\n",
        "+ **Last**: Precio que se calculó por última vez\n",
        "+ **Close**: Precio de cierre en un día\n",
        "+ **VWAP**: Precio medio ponderado por volumen\n",
        "+ **Volume**: Número de existencias\n",
        "+ **Turnover**: Gasto de reemplazar a las personas que dejan una organización durante un período específico\n",
        "+ **Trades**: Compras y ventas\n",
        "+ **Deliverable Volume**: Volumen de entrega\n",
        "+ **%Deliverble**: Porcentaje de entrega\n",
        "\n",
        "## 👁️ Echando un vistazo al dataset\n"
      ],
      "metadata": {
        "id": "Y79faIB86o34"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_reliance = pd.read_csv(\"/content/drive/MyDrive/Coder/Data Science/reliance_stock_price_dataset.csv\", engine='c')\n",
        "df_reliance.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "WM9k1gKC7fFo",
        "outputId": "407f5762-ce55-4d48-ec57-fe5d134fe9c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date    Symbol Series  Prev Close    Open    High     Low  Last  \\\n",
              "0  01-01-1996  RELIANCE     EQ      204.65  205.00  206.10  203.65   NaN   \n",
              "1  02-01-1996  RELIANCE     EQ      205.75  205.25  206.25  202.65   NaN   \n",
              "2  03-01-1996  RELIANCE     EQ      204.15  207.50  216.95  205.25   NaN   \n",
              "3  04-01-1996  RELIANCE     EQ      205.70  203.75  204.40  201.05   NaN   \n",
              "4  05-01-1996  RELIANCE     EQ      203.80  203.00  203.00  200.65   NaN   \n",
              "\n",
              "    Close    VWAP   Volume      Turnover  Trades  Deliverable Volume  \\\n",
              "0  205.75  205.26  3717450  7.630000e+13     NaN                 NaN   \n",
              "1  204.15  204.13  6024650  1.230000e+14     NaN                 NaN   \n",
              "2  205.70  207.04  7473500  1.550000e+14     NaN                 NaN   \n",
              "3  203.80  202.47  7744000  1.570000e+14     NaN                 NaN   \n",
              "4  202.40  202.05  5952000  1.200000e+14     NaN                 NaN   \n",
              "\n",
              "   %Deliverble  \n",
              "0          NaN  \n",
              "1          NaN  \n",
              "2          NaN  \n",
              "3          NaN  \n",
              "4          NaN  "
            ],
            "text/html": [
              "\n",
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              "      <th>Date</th>\n",
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              "      <th>Series</th>\n",
              "      <th>Prev Close</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
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              "      <td>RELIANCE</td>\n",
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              "      <td>205.00</td>\n",
              "      <td>206.10</td>\n",
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              "      <td>205.75</td>\n",
              "      <td>205.26</td>\n",
              "      <td>3717450</td>\n",
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              "      <td>6024650</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>03-01-1996</td>\n",
              "      <td>RELIANCE</td>\n",
              "      <td>EQ</td>\n",
              "      <td>204.15</td>\n",
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              "      <td>216.95</td>\n",
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              "      <th>3</th>\n",
              "      <td>04-01-1996</td>\n",
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              "      <td>205.70</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>4</th>\n",
              "      <td>05-01-1996</td>\n",
              "      <td>RELIANCE</td>\n",
              "      <td>EQ</td>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "              title=\"Suggest charts.\"\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
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              "  quickchartButtonEl.style.display =\n",
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              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5406d873-6652-43a2-ac22-c0de7b02ce54 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5406d873-6652-43a2-ac22-c0de7b02ce54');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_reliance.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y9EIRdhE7qxE",
        "outputId": "cf9f8a0a-46fd-45b0-b88a-47f17e24e9c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6205 entries, 0 to 6204\n",
            "Data columns (total 15 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   Date                6205 non-null   object \n",
            " 1   Symbol              6205 non-null   object \n",
            " 2   Series              6205 non-null   object \n",
            " 3   Prev Close          6205 non-null   float64\n",
            " 4   Open                6205 non-null   float64\n",
            " 5   High                6205 non-null   float64\n",
            " 6   Low                 6205 non-null   float64\n",
            " 7   Last                5657 non-null   float64\n",
            " 8   Close               6205 non-null   float64\n",
            " 9   VWAP                6205 non-null   float64\n",
            " 10  Volume              6205 non-null   int64  \n",
            " 11  Turnover            6205 non-null   float64\n",
            " 12  Trades              2356 non-null   float64\n",
            " 13  Deliverable Volume  4693 non-null   float64\n",
            " 14  %Deliverble         4693 non-null   float64\n",
            "dtypes: float64(11), int64(1), object(3)\n",
            "memory usage: 727.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'El dataset tiene {df_reliance.shape[0]} registros y {df_reliance.shape[1]} features.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wwV24SJa_Xcm",
        "outputId": "7eaae81c-e1c3-4bd4-cf34-01293817e2c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "El dataset tiene 6205 registros y 15 features.\n"
          ]
        }
      ]
    }
  ]
}