Semana1.ipynb

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      "name": "Semana1.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/CharlesPrado23/Notebooks/blob/main/Semana1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EDN4Vi3TkMNh"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B9D2n17jkMNv"
      },
      "source": [
        "#### Data de expiração do cartão, Limite do Cartão, \n",
        "#### a) Lista de estabelecimentos mais utilizados pelo cliente.\n",
        "#### b) Valor médio das compras.\n",
        "#### c) Tempo de registro.\n",
        "#### d) Status do CPF.\n",
        "#### e) Número de casos de chargeback.\n",
        "#### f) Valor de pico nos últimos 12 meses.\n",
        "#### g) Indicador de uso anterior na Internet.\n",
        "#### h) Indicador de uso anterior internacional.\n",
        "#### i) Operaçao na Internet \n",
        "#### j) Compra nacional ou internacional\n",
        "#### k) Fraudes já ocorridas no estabelecimento\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ic9VfXijkMNw"
      },
      "source": [
        "obj = {'Regiao de Origem do Cartão':['RJ','SP','MG','PE'],'Local de uso do cartão':['RJ','SP','MG','PE'],'Expiração do cartão':['04/24','05/25','06/26','07/27'],'Limite do Cartão':[5.000,1.000,10000,2000],\n",
        "       'Valor médio das compras':[500,1000,4000,2500], }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "source": [
        "obj"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Regiao de Origem do Cartão': ['RJ', 'SP', 'MG', 'PE'],\n",
              " 'Local de uso do cartão': ['RJ', 'SP', 'MG', 'PE'],\n",
              " 'Expiração do cartão': ['04/24', '05/25', '06/26', '07/27'],\n",
              " 'Limite do Cartão': [5.0, 1.0, 10000, 2000],\n",
              " 'Valor médio das compras': [500, 1000, 4000, 2500]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
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        "outputId": "88bce149-94e4-45aa-8d7b-c77c3810468a"
      },
      "source": [
        "df = pd.DataFrame(data=obj)\n",
        "df"
      ],
      "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>Regiao de Origem do Cartão</th>\n",
              "      <th>Local de uso do cartão</th>\n",
              "      <th>Expiração do cartão</th>\n",
              "      <th>Limite do Cartão</th>\n",
              "      <th>Valor médio das compras</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RJ</td>\n",
              "      <td>RJ</td>\n",
              "      <td>04/24</td>\n",
              "      <td>5.0</td>\n",
              "      <td>500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SP</td>\n",
              "      <td>SP</td>\n",
              "      <td>05/25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MG</td>\n",
              "      <td>MG</td>\n",
              "      <td>06/26</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>4000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>PE</td>\n",
              "      <td>PE</td>\n",
              "      <td>07/27</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>2500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Regiao de Origem do Cartão Local de uso do cartão Expiração do cartão  \\\n",
              "0                         RJ                     RJ               04/24   \n",
              "1                         SP                     SP               05/25   \n",
              "2                         MG                     MG               06/26   \n",
              "3                         PE                     PE               07/27   \n",
              "\n",
              "   Limite do Cartão  Valor médio das compras  \n",
              "0               5.0                      500  \n",
              "1               1.0                     1000  \n",
              "2           10000.0                     4000  \n",
              "3            2000.0                     2500  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
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    {
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        "id": "MoDMZXvNkMN0",
        "outputId": "12ef014d-7a7b-4bf5-d475-53fdaecc7cdf"
      },
      "source": [
        "df = pd.read_csv('creditcard.csv')\n",
        "df.head()"
      ],
      "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>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>...</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
              "      <td>2.536347</td>\n",
              "      <td>1.378155</td>\n",
              "      <td>-0.338321</td>\n",
              "      <td>0.462388</td>\n",
              "      <td>0.239599</td>\n",
              "      <td>0.098698</td>\n",
              "      <td>0.363787</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.018307</td>\n",
              "      <td>0.277838</td>\n",
              "      <td>-0.110474</td>\n",
              "      <td>0.066928</td>\n",
              "      <td>0.128539</td>\n",
              "      <td>-0.189115</td>\n",
              "      <td>0.133558</td>\n",
              "      <td>-0.021053</td>\n",
              "      <td>149.62</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.191857</td>\n",
              "      <td>0.266151</td>\n",
              "      <td>0.166480</td>\n",
              "      <td>0.448154</td>\n",
              "      <td>0.060018</td>\n",
              "      <td>-0.082361</td>\n",
              "      <td>-0.078803</td>\n",
              "      <td>0.085102</td>\n",
              "      <td>-0.255425</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.225775</td>\n",
              "      <td>-0.638672</td>\n",
              "      <td>0.101288</td>\n",
              "      <td>-0.339846</td>\n",
              "      <td>0.167170</td>\n",
              "      <td>0.125895</td>\n",
              "      <td>-0.008983</td>\n",
              "      <td>0.014724</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.358354</td>\n",
              "      <td>-1.340163</td>\n",
              "      <td>1.773209</td>\n",
              "      <td>0.379780</td>\n",
              "      <td>-0.503198</td>\n",
              "      <td>1.800499</td>\n",
              "      <td>0.791461</td>\n",
              "      <td>0.247676</td>\n",
              "      <td>-1.514654</td>\n",
              "      <td>...</td>\n",
              "      <td>0.247998</td>\n",
              "      <td>0.771679</td>\n",
              "      <td>0.909412</td>\n",
              "      <td>-0.689281</td>\n",
              "      <td>-0.327642</td>\n",
              "      <td>-0.139097</td>\n",
              "      <td>-0.055353</td>\n",
              "      <td>-0.059752</td>\n",
              "      <td>378.66</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.966272</td>\n",
              "      <td>-0.185226</td>\n",
              "      <td>1.792993</td>\n",
              "      <td>-0.863291</td>\n",
              "      <td>-0.010309</td>\n",
              "      <td>1.247203</td>\n",
              "      <td>0.237609</td>\n",
              "      <td>0.377436</td>\n",
              "      <td>-1.387024</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.108300</td>\n",
              "      <td>0.005274</td>\n",
              "      <td>-0.190321</td>\n",
              "      <td>-1.175575</td>\n",
              "      <td>0.647376</td>\n",
              "      <td>-0.221929</td>\n",
              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>123.50</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-1.158233</td>\n",
              "      <td>0.877737</td>\n",
              "      <td>1.548718</td>\n",
              "      <td>0.403034</td>\n",
              "      <td>-0.407193</td>\n",
              "      <td>0.095921</td>\n",
              "      <td>0.592941</td>\n",
              "      <td>-0.270533</td>\n",
              "      <td>0.817739</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.009431</td>\n",
              "      <td>0.798278</td>\n",
              "      <td>-0.137458</td>\n",
              "      <td>0.141267</td>\n",
              "      <td>-0.206010</td>\n",
              "      <td>0.502292</td>\n",
              "      <td>0.219422</td>\n",
              "      <td>0.215153</td>\n",
              "      <td>69.99</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
              "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
              "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
              "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
              "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
              "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
              "\n",
              "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
              "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
              "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
              "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
              "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
              "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
              "\n",
              "        V26       V27       V28  Amount  Class  \n",
              "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
              "1  0.125895 -0.008983  0.014724    2.69      0  \n",
              "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
              "3 -0.221929  0.062723  0.061458  123.50      0  \n",
              "4  0.502292  0.219422  0.215153   69.99      0  \n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
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    {
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      "metadata": {
        "id": "AKhimOI8kMN1",
        "outputId": "28d0318b-294c-408b-cb4d-2f6013e1567e"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
              "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
              "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
              "       'Class'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3U93dtUskMN2",
        "outputId": "7a832343-4d5e-438d-b831-ce51c63741f6"
      },
      "source": [
        "df_groupByClass = df.groupby(['Class'])['Class'].count()\n",
        "df_groupByClass"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Class\n",
              "0    284315\n",
              "1       492\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-rlLEwckMN3",
        "outputId": "96bdcbfc-3104-4282-f719-c81e916d782d"
      },
      "source": [
        "df_groupByClass.T.plot.bar(rot=0);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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": "code",
      "metadata": {
        "id": "anRdY6xokMN4",
        "outputId": "dde03a42-1c61-4d8e-b4b8-d8720d0520ad"
      },
      "source": [
        "plt.figure(figsize=(12,10))\n",
        "sns.heatmap(df.corr().round(3))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6RY6W_sckMN5"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}