Bank_customers_prediction.ipynb

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      "name": "Bank customers prediction.ipynb",
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      "source": [
        "# Importing necessary libraries\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bank = pd.read_csv(\"Bank customers.csv\")\n",
        "bank.head(5)"
      ],
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            "text/plain": [
              "   CLIENTNUM     Attrition_Flag  Customer_Age Gender  Dependent_count  \\\n",
              "0  768805383  Existing Customer            45      M                3   \n",
              "1  818770008  Existing Customer            49      F                5   \n",
              "2  713982108  Existing Customer            51      M                3   \n",
              "3  769911858  Existing Customer            40      F                4   \n",
              "4  709106358  Existing Customer            40      M                3   \n",
              "\n",
              "  Education_Level Marital_Status Income_Category Card_Category  \\\n",
              "0     High School        Married     $60K - $80K          Blue   \n",
              "1        Graduate         Single  Less than $40K          Blue   \n",
              "2        Graduate        Married    $80K - $120K          Blue   \n",
              "3     High School        Unknown  Less than $40K          Blue   \n",
              "4      Uneducated        Married     $60K - $80K          Blue   \n",
              "\n",
              "   Months_on_book  ...  Months_Inactive_12_mon  Contacts_Count_12_mon  \\\n",
              "0              39  ...                       1                      3   \n",
              "1              44  ...                       1                      2   \n",
              "2              36  ...                       1                      0   \n",
              "3              34  ...                       4                      1   \n",
              "4              21  ...                       1                      0   \n",
              "\n",
              "   Credit_Limit  Total_Revolving_Bal  Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  \\\n",
              "0       12691.0                  777          11914.0                 1.335   \n",
              "1        8256.0                  864           7392.0                 1.541   \n",
              "2        3418.0                    0           3418.0                 2.594   \n",
              "3        3313.0                 2517            796.0                 1.405   \n",
              "4        4716.0                    0           4716.0                 2.175   \n",
              "\n",
              "   Total_Trans_Amt  Total_Trans_Ct  Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio  \n",
              "0             1144              42                1.625                  0.061  \n",
              "1             1291              33                3.714                  0.105  \n",
              "2             1887              20                2.333                  0.000  \n",
              "3             1171              20                2.333                  0.760  \n",
              "4              816              28                2.500                  0.000  \n",
              "\n",
              "[5 rows x 21 columns]"
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              "      <th>CLIENTNUM</th>\n",
              "      <th>Attrition_Flag</th>\n",
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              "      <th>Gender</th>\n",
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              "      <th>Income_Category</th>\n",
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              "      <th>Months_on_book</th>\n",
              "      <th>...</th>\n",
              "      <th>Months_Inactive_12_mon</th>\n",
              "      <th>Contacts_Count_12_mon</th>\n",
              "      <th>Credit_Limit</th>\n",
              "      <th>Total_Revolving_Bal</th>\n",
              "      <th>Avg_Open_To_Buy</th>\n",
              "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
              "      <th>Total_Trans_Amt</th>\n",
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              "      <td>818770008</td>\n",
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              "      <td>Existing Customer</td>\n",
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              "      <td>M</td>\n",
              "      <td>3</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Married</td>\n",
              "      <td>$80K - $120K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>36</td>\n",
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              "      <td>3418.0</td>\n",
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              "      <td>Uneducated</td>\n",
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              "      <td>$60K - $80K</td>\n",
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        "bank.shape"
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              "(10127, 21)"
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      "source": [
        "bank.isnull().sum()"
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      "execution_count": 4,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CLIENTNUM                   0\n",
              "Attrition_Flag              0\n",
              "Customer_Age                0\n",
              "Gender                      0\n",
              "Dependent_count             0\n",
              "Education_Level             0\n",
              "Marital_Status              0\n",
              "Income_Category             0\n",
              "Card_Category               0\n",
              "Months_on_book              0\n",
              "Total_Relationship_Count    0\n",
              "Months_Inactive_12_mon      0\n",
              "Contacts_Count_12_mon       0\n",
              "Credit_Limit                0\n",
              "Total_Revolving_Bal         0\n",
              "Avg_Open_To_Buy             0\n",
              "Total_Amt_Chng_Q4_Q1        0\n",
              "Total_Trans_Amt             0\n",
              "Total_Trans_Ct              0\n",
              "Total_Ct_Chng_Q4_Q1         0\n",
              "Avg_Utilization_Ratio       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
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      "cell_type": "code",
      "source": [
        "# So we will be needing to extract data from income_category columns and other categorical columns, since as you can see the data \n",
        "# is in string format and the range is giving, not an exact value which can be feeded into the model directly."
      ],
      "metadata": {
        "id": "p17VCYXALrAz"
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      "execution_count": 5,
      "outputs": []
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      "source": [
        "# Understanding about some columns first.\n",
        "\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "sns.boxplot(bank['Customer_Age'])"
      ],
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            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
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            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f156cb3c4d0>"
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          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# So most of the customers are somewhere near 45 age, mean being 45 with some outliers that are depicted by the dots on the right\n",
        "# hand side at age 70 and maybe 75"
      ],
      "metadata": {
        "id": "bdIhbG7RLzZm"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bank[['Gender','Credit_Limit']].groupby('Gender').agg(['mean','count'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "a7V1UaNkL3xN",
        "outputId": "1682337e-48d0-4fc4-e4ff-961621a6e9d1"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Credit_Limit      \n",
              "                mean count\n",
              "Gender                    \n",
              "F        5023.854274  5358\n",
              "M       12685.674963  4769"
            ],
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              "\n",
              "  <div id=\"df-54fcfd9c-b99b-4899-8fc8-f1cde8452b54\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "      <th colspan=\"2\" halign=\"left\">Credit_Limit</th>\n",
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              "    <tr>\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>F</th>\n",
              "      <td>5023.854274</td>\n",
              "      <td>5358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td>12685.674963</td>\n",
              "      <td>4769</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-54fcfd9c-b99b-4899-8fc8-f1cde8452b54')\"\n",
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              "\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-54fcfd9c-b99b-4899-8fc8-f1cde8452b54 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-54fcfd9c-b99b-4899-8fc8-f1cde8452b54');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bank[['Gender','Avg_Utilization_Ratio']].groupby('Gender').agg(['mean','count'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "AffloNGzL8yf",
        "outputId": "9c7e7b99-5070-43cc-af9a-56c4d1de13fd"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Avg_Utilization_Ratio      \n",
              "                        mean count\n",
              "Gender                            \n",
              "F                   0.341957  5358\n",
              "M                   0.199548  4769"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1c5b295c-9ef1-4e63-8a95-d8e6e8158bc8\">\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>F</th>\n",
              "      <td>0.341957</td>\n",
              "      <td>5358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td>0.199548</td>\n",
              "      <td>4769</td>\n",
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              "  </tbody>\n",
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              "                                                     [key], {});\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Average Utilization Ratio: it's how much you currently owe divided by your credit limit. It is generally expressed as a percent. \n",
        "# For example, if you have a total of $10,000 in credit available on two credit cards, and a balance of $5,000 on one, your credit \n",
        "# utilization rate is 50% — you're using half of the total credit you have available. A low credit utilization rate shows you're\n",
        "# using less of your available credit."
      ],
      "metadata": {
        "id": "oOD-L39zL_pU"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bank_cards = bank.groupby(\"Card_Category\")\n",
        "bank_cards['Customer_Age'].max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Icn6D-seMFGk",
        "outputId": "c3cd98be-fe5e-4226-a8c1-685f05f625e2"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Card_Category\n",
              "Blue        73\n",
              "Gold        63\n",
              "Platinum    56\n",
              "Silver      65\n",
              "Name: Customer_Age, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bank_cards['Customer_Age'].min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nz89rrg9MIZY",
        "outputId": "cb69fd5c-85cd-4cff-a37d-37fef81b9f9e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Card_Category\n",
              "Blue        26\n",
              "Gold        29\n",
              "Platinum    39\n",
              "Silver      26\n",
              "Name: Customer_Age, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bank.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fpzkbFhsMK2l",
        "outputId": "bb10309a-df50-42e2-d839-72401dc82d17"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['CLIENTNUM', 'Attrition_Flag', 'Customer_Age', 'Gender',\n",
              "       'Dependent_count', 'Education_Level', 'Marital_Status',\n",
              "       'Income_Category', 'Card_Category', 'Months_on_book',\n",
              "       'Total_Relationship_Count', 'Months_Inactive_12_mon',\n",
              "       'Contacts_Count_12_mon', 'Credit_Limit', 'Total_Revolving_Bal',\n",
              "       'Avg_Open_To_Buy', 'Total_Amt_Chng_Q4_Q1', 'Total_Trans_Amt',\n",
              "       'Total_Trans_Ct', 'Total_Ct_Chng_Q4_Q1', 'Avg_Utilization_Ratio'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bank_cards['Avg_Utilization_Ratio'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mXkvuS86MNX3",
        "outputId": "a4e0b683-3559-4f5c-bd32-6d26d4055c74"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Card_Category\n",
              "Blue        0.290859\n",
              "Gold        0.057103\n",
              "Platinum    0.043650\n",
              "Silver      0.057310\n",
              "Name: Avg_Utilization_Ratio, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bank_marital = bank.groupby(\"Marital_Status\")"
      ],
      "metadata": {
        "id": "5XK1OG6vMQ1e"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Now lets move forward and see if we have categorical data in our dataset.\n",
        "\n",
        "bank.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "l8lYBItVMU5s",
        "outputId": "7e807d33-af78-4bb3-9c55-51b5a05a258e"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CLIENTNUM     Attrition_Flag  Customer_Age Gender  Dependent_count  \\\n",
              "0  768805383  Existing Customer            45      M                3   \n",
              "1  818770008  Existing Customer            49      F                5   \n",
              "2  713982108  Existing Customer            51      M                3   \n",
              "\n",
              "  Education_Level Marital_Status Income_Category Card_Category  \\\n",
              "0     High School        Married     $60K - $80K          Blue   \n",
              "1        Graduate         Single  Less than $40K          Blue   \n",
              "2        Graduate        Married    $80K - $120K          Blue   \n",
              "\n",
              "   Months_on_book  ...  Months_Inactive_12_mon  Contacts_Count_12_mon  \\\n",
              "0              39  ...                       1                      3   \n",
              "1              44  ...                       1                      2   \n",
              "2              36  ...                       1                      0   \n",
              "\n",
              "   Credit_Limit  Total_Revolving_Bal  Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  \\\n",
              "0       12691.0                  777          11914.0                 1.335   \n",
              "1        8256.0                  864           7392.0                 1.541   \n",
              "2        3418.0                    0           3418.0                 2.594   \n",
              "\n",
              "   Total_Trans_Amt  Total_Trans_Ct  Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio  \n",
              "0             1144              42                1.625                  0.061  \n",
              "1             1291              33                3.714                  0.105  \n",
              "2             1887              20                2.333                  0.000  \n",
              "\n",
              "[3 rows x 21 columns]"
            ],
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              "\n",
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              "      <td>818770008</td>\n",
              "      <td>Existing Customer</td>\n",
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              "      <td>F</td>\n",
              "      <td>5</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Single</td>\n",
              "      <td>Less than $40K</td>\n",
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              "      <td>1291</td>\n",
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              "    <tr>\n",
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              "      <td>713982108</td>\n",
              "      <td>Existing Customer</td>\n",
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              "      <td>3</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Married</td>\n",
              "      <td>$80K - $120K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>36</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3418.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3418.0</td>\n",
              "      <td>2.594</td>\n",
              "      <td>1887</td>\n",
              "      <td>20</td>\n",
              "      <td>2.333</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 21 columns</p>\n",
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              "          const element = document.querySelector('#df-4118180b-e369-4eaa-a95f-086935283c02');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bank['Attrition_Flag'].value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nvYAOH4IMYGl",
        "outputId": "4f9485cd-5de8-4197-8684-2e98bd02504d"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Existing Customer    8500\n",
              "Attrited Customer    1627\n",
              "Name: Attrition_Flag, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Note here that Attrition Flag has a value Attrited Customer, meaning this particular customer has already closed his/her\n",
        "# account and is not associated with the bank as of now.\n",
        "# Now for predicting the card classes, we can remove the people/customers who have attrited or keep it as it is, its your choice."
      ],
      "metadata": {
        "id": "WYhI0nxDMbN0"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bank['Gender'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C8fmIooHMdRd",
        "outputId": "db2e2bed-6159-4e5e-fa4f-c45e4e824050"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "F    5358\n",
              "M    4769\n",
              "Name: Gender, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bank['Education_Level'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IVYnR4cpMfEI",
        "outputId": "3669a18a-9681-40fe-a47a-86f2dd94c779"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Graduate         3128\n",
              "High School      2013\n",
              "Unknown          1519\n",
              "Uneducated       1487\n",
              "College          1013\n",
              "Post-Graduate     516\n",
              "Doctorate         451\n",
              "Name: Education_Level, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bank['Marital_Status'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DPVBDtwuMhaM",
        "outputId": "bee9277a-a746-46d8-8ebb-ac50b3ba65a6"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Married     4687\n",
              "Single      3943\n",
              "Unknown      749\n",
              "Divorced     748\n",
              "Name: Marital_Status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def ref1(x):\n",
        "  if x == 'M':\n",
        "    return 1\n",
        "  else:\n",
        "    return 0\n",
        "\n",
        "bank['Gender'] = bank['Gender'].map(ref1)"
      ],
      "metadata": {
        "id": "rr4ItYLwMlDl"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def ref2(x):\n",
        "  if x == 'Existing Customer':\n",
        "    return 1\n",
        "  else:\n",
        "    return 0\n",
        "\n",
        "bank['Attrition_Flag'] = bank['Attrition_Flag'].map(ref2)"
      ],
      "metadata": {
        "id": "xeaQmQhHMoTD"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Converting Categorical data into numerical data.\n",
        "\n",
        "y = bank['Card_Category']\n",
        "X = bank.copy()\n",
        "X.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "TPPFueowMrv1",
        "outputId": "09afede4-002c-44dd-c45d-b671fed539b7"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CLIENTNUM  Attrition_Flag  Customer_Age  Gender  Dependent_count  \\\n",
              "0  768805383               1            45       1                3   \n",
              "1  818770008               1            49       0                5   \n",
              "2  713982108               1            51       1                3   \n",
              "\n",
              "  Education_Level Marital_Status Income_Category Card_Category  \\\n",
              "0     High School        Married     $60K - $80K          Blue   \n",
              "1        Graduate         Single  Less than $40K          Blue   \n",
              "2        Graduate        Married    $80K - $120K          Blue   \n",
              "\n",
              "   Months_on_book  ...  Months_Inactive_12_mon  Contacts_Count_12_mon  \\\n",
              "0              39  ...                       1                      3   \n",
              "1              44  ...                       1                      2   \n",
              "2              36  ...                       1                      0   \n",
              "\n",
              "   Credit_Limit  Total_Revolving_Bal  Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  \\\n",
              "0       12691.0                  777          11914.0                 1.335   \n",
              "1        8256.0                  864           7392.0                 1.541   \n",
              "2        3418.0                    0           3418.0                 2.594   \n",
              "\n",
              "   Total_Trans_Amt  Total_Trans_Ct  Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio  \n",
              "0             1144              42                1.625                  0.061  \n",
              "1             1291              33                3.714                  0.105  \n",
              "2             1887              20                2.333                  0.000  \n",
              "\n",
              "[3 rows x 21 columns]"
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              "      <td>$80K - $120K</td>\n",
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              "      <td>0</td>\n",
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              "      <td>2.594</td>\n",
              "      <td>1887</td>\n",
              "      <td>20</td>\n",
              "      <td>2.333</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bdb7dc36-50a6-4490-b6c7-dd89fae755bd')\"\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bdb7dc36-50a6-4490-b6c7-dd89fae755bd');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extracting data from the Income_Category column\n",
        "\n",
        "X['Income_Category'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z2VZGUN6MwDl",
        "outputId": "fae276c9-89e8-4c9b-e30f-c32a4d6112be"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Less than $40K    3561\n",
              "$40K - $60K       1790\n",
              "$80K - $120K      1535\n",
              "$60K - $80K       1402\n",
              "Unknown           1112\n",
              "$120K +            727\n",
              "Name: Income_Category, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "def label_encoded(feat):\n",
        "    le = LabelEncoder()\n",
        "    le.fit(feat)\n",
        "    print(feat.name,le.classes_)\n",
        "    return le.transform(feat)"
      ],
      "metadata": {
        "id": "e7-1CdQ8M73D"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X['Income_Category'] = label_encoded(X['Income_Category'])\n",
        "X['Education_Level'] = label_encoded(X['Education_Level'])\n",
        "X['Marital_Status'] = label_encoded(X['Marital_Status'])\n",
        "\n",
        "X.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "id": "xdQfC2H-NC37",
        "outputId": "bc49eed1-c5ca-42e3-e99d-decc371bba0e"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Income_Category ['$120K +' '$40K - $60K' '$60K - $80K' '$80K - $120K' 'Less than $40K'\n",
            " 'Unknown']\n",
            "Education_Level ['College' 'Doctorate' 'Graduate' 'High School' 'Post-Graduate'\n",
            " 'Uneducated' 'Unknown']\n",
            "Marital_Status ['Divorced' 'Married' 'Single' 'Unknown']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CLIENTNUM  Attrition_Flag  Customer_Age  Gender  Dependent_count  \\\n",
              "0  768805383               1            45       1                3   \n",
              "1  818770008               1            49       0                5   \n",
              "2  713982108               1            51       1                3   \n",
              "\n",
              "   Education_Level  Marital_Status  Income_Category Card_Category  \\\n",
              "0                3               1                2          Blue   \n",
              "1                2               2                4          Blue   \n",
              "2                2               1                3          Blue   \n",
              "\n",
              "   Months_on_book  ...  Months_Inactive_12_mon  Contacts_Count_12_mon  \\\n",
              "0              39  ...                       1                      3   \n",
              "1              44  ...                       1                      2   \n",
              "2              36  ...                       1                      0   \n",
              "\n",
              "   Credit_Limit  Total_Revolving_Bal  Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  \\\n",
              "0       12691.0                  777          11914.0                 1.335   \n",
              "1        8256.0                  864           7392.0                 1.541   \n",
              "2        3418.0                    0           3418.0                 2.594   \n",
              "\n",
              "   Total_Trans_Amt  Total_Trans_Ct  Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio  \n",
              "0             1144              42                1.625                  0.061  \n",
              "1             1291              33                3.714                  0.105  \n",
              "2             1887              20                2.333                  0.000  \n",
              "\n",
              "[3 rows x 21 columns]"
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5e2f5466-a57c-4c13-b809-a358c585ae3d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-5e2f5466-a57c-4c13-b809-a358c585ae3d 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-5e2f5466-a57c-4c13-b809-a358c585ae3d');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "Z6sXBbtZNGUl",
        "outputId": "53cb7934-9af9-46ec-d716-94c40a24e693"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          CLIENTNUM  Attrition_Flag  Customer_Age        Gender  \\\n",
              "count  1.012700e+04    10127.000000  10127.000000  10127.000000   \n",
              "mean   7.391776e+08        0.839340     46.325960      0.470919   \n",
              "std    3.690378e+07        0.367235      8.016814      0.499178   \n",
              "min    7.080821e+08        0.000000     26.000000      0.000000   \n",
              "25%    7.130368e+08        1.000000     41.000000      0.000000   \n",
              "50%    7.179264e+08        1.000000     46.000000      0.000000   \n",
              "75%    7.731435e+08        1.000000     52.000000      1.000000   \n",
              "max    8.283431e+08        1.000000     73.000000      1.000000   \n",
              "\n",
              "       Dependent_count  Education_Level  Marital_Status  Income_Category  \\\n",
              "count     10127.000000     10127.000000    10127.000000     10127.000000   \n",
              "mean          2.346203         3.096574        1.463415         2.863928   \n",
              "std           1.298908         1.834812        0.737808         1.504700   \n",
              "min           0.000000         0.000000        0.000000         0.000000   \n",
              "25%           1.000000         2.000000        1.000000         2.000000   \n",
              "50%           2.000000         3.000000        1.000000         3.000000   \n",
              "75%           3.000000         5.000000        2.000000         4.000000   \n",
              "max           5.000000         6.000000        3.000000         5.000000   \n",
              "\n",
              "       Months_on_book  Total_Relationship_Count  Months_Inactive_12_mon  \\\n",
              "count    10127.000000              10127.000000            10127.000000   \n",
              "mean        35.928409                  3.812580                2.341167   \n",
              "std          7.986416                  1.554408                1.010622   \n",
              "min         13.000000                  1.000000                0.000000   \n",
              "25%         31.000000                  3.000000                2.000000   \n",
              "50%         36.000000                  4.000000                2.000000   \n",
              "75%         40.000000                  5.000000                3.000000   \n",
              "max         56.000000                  6.000000                6.000000   \n",
              "\n",
              "       Contacts_Count_12_mon  Credit_Limit  Total_Revolving_Bal  \\\n",
              "count           10127.000000  10127.000000         10127.000000   \n",
              "mean                2.455317   8631.953698          1162.814061   \n",
              "std                 1.106225   9088.776650           814.987335   \n",
              "min                 0.000000   1438.300000             0.000000   \n",
              "25%                 2.000000   2555.000000           359.000000   \n",
              "50%                 2.000000   4549.000000          1276.000000   \n",
              "75%                 3.000000  11067.500000          1784.000000   \n",
              "max                 6.000000  34516.000000          2517.000000   \n",
              "\n",
              "       Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  Total_Trans_Amt  Total_Trans_Ct  \\\n",
              "count     10127.000000          10127.000000     10127.000000    10127.000000   \n",
              "mean       7469.139637              0.759941      4404.086304       64.858695   \n",
              "std        9090.685324              0.219207      3397.129254       23.472570   \n",
              "min           3.000000              0.000000       510.000000       10.000000   \n",
              "25%        1324.500000              0.631000      2155.500000       45.000000   \n",
              "50%        3474.000000              0.736000      3899.000000       67.000000   \n",
              "75%        9859.000000              0.859000      4741.000000       81.000000   \n",
              "max       34516.000000              3.397000     18484.000000      139.000000   \n",
              "\n",
              "       Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio  \n",
              "count         10127.000000           10127.000000  \n",
              "mean              0.712222               0.274894  \n",
              "std               0.238086               0.275691  \n",
              "min               0.000000               0.000000  \n",
              "25%               0.582000               0.023000  \n",
              "50%               0.702000               0.176000  \n",
              "75%               0.818000               0.503000  \n",
              "max               3.714000               0.999000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-46b392b4-5b1a-4f43-98f1-646506e2550e\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CLIENTNUM</th>\n",
              "      <th>Attrition_Flag</th>\n",
              "      <th>Customer_Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Dependent_count</th>\n",
              "      <th>Education_Level</th>\n",
              "      <th>Marital_Status</th>\n",
              "      <th>Income_Category</th>\n",
              "      <th>Months_on_book</th>\n",
              "      <th>Total_Relationship_Count</th>\n",
              "      <th>Months_Inactive_12_mon</th>\n",
              "      <th>Contacts_Count_12_mon</th>\n",
              "      <th>Credit_Limit</th>\n",
              "      <th>Total_Revolving_Bal</th>\n",
              "      <th>Avg_Open_To_Buy</th>\n",
              "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
              "      <th>Total_Trans_Amt</th>\n",
              "      <th>Total_Trans_Ct</th>\n",
              "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
              "      <th>Avg_Utilization_Ratio</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
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              "      <td>10127.000000</td>\n",
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              "      <td>10127.000000</td>\n",
              "      <td>10127.000000</td>\n",
              "      <td>10127.000000</td>\n",
              "      <td>10127.000000</td>\n",
              "      <td>10127.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>7.391776e+08</td>\n",
              "      <td>0.839340</td>\n",
              "      <td>46.325960</td>\n",
              "      <td>0.470919</td>\n",
              "      <td>2.346203</td>\n",
              "      <td>3.096574</td>\n",
              "      <td>1.463415</td>\n",
              "      <td>2.863928</td>\n",
              "      <td>35.928409</td>\n",
              "      <td>3.812580</td>\n",
              "      <td>2.341167</td>\n",
              "      <td>2.455317</td>\n",
              "      <td>8631.953698</td>\n",
              "      <td>1162.814061</td>\n",
              "      <td>7469.139637</td>\n",
              "      <td>0.759941</td>\n",
              "      <td>4404.086304</td>\n",
              "      <td>64.858695</td>\n",
              "      <td>0.712222</td>\n",
              "      <td>0.274894</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.690378e+07</td>\n",
              "      <td>0.367235</td>\n",
              "      <td>8.016814</td>\n",
              "      <td>0.499178</td>\n",
              "      <td>1.298908</td>\n",
              "      <td>1.834812</td>\n",
              "      <td>0.737808</td>\n",
              "      <td>1.504700</td>\n",
              "      <td>7.986416</td>\n",
              "      <td>1.554408</td>\n",
              "      <td>1.010622</td>\n",
              "      <td>1.106225</td>\n",
              "      <td>9088.776650</td>\n",
              "      <td>814.987335</td>\n",
              "      <td>9090.685324</td>\n",
              "      <td>0.219207</td>\n",
              "      <td>3397.129254</td>\n",
              "      <td>23.472570</td>\n",
              "      <td>0.238086</td>\n",
              "      <td>0.275691</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>7.080821e+08</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1438.300000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>510.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.130368e+08</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2555.000000</td>\n",
              "      <td>359.000000</td>\n",
              "      <td>1324.500000</td>\n",
              "      <td>0.631000</td>\n",
              "      <td>2155.500000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>0.582000</td>\n",
              "      <td>0.023000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.179264e+08</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
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              "      <td>1276.000000</td>\n",
              "      <td>3474.000000</td>\n",
              "      <td>0.736000</td>\n",
              "      <td>3899.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>0.702000</td>\n",
              "      <td>0.176000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
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              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>11067.500000</td>\n",
              "      <td>1784.000000</td>\n",
              "      <td>9859.000000</td>\n",
              "      <td>0.859000</td>\n",
              "      <td>4741.000000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>0.818000</td>\n",
              "      <td>0.503000</td>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>6.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
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              "      <td>3.397000</td>\n",
              "      <td>18484.000000</td>\n",
              "      <td>139.000000</td>\n",
              "      <td>3.714000</td>\n",
              "      <td>0.999000</td>\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-46b392b4-5b1a-4f43-98f1-646506e2550e 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-46b392b4-5b1a-4f43-98f1-646506e2550e');\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": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = X.drop(['CLIENTNUM', 'Card_Category'], axis = 1)"
      ],
      "metadata": {
        "id": "bcI5ikQtNJPg"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ygyKvA1cNLhd",
        "outputId": "6b3618ba-39d3-4779-9d27-d5698a7c38d3"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10127, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA(n_components = 7)\n",
        "pca2 = PCA(n_components = 10)\n",
        "pca_fit = pca.fit_transform(X)\n",
        "pca_fit2 = pca2.fit_transform(X)"
      ],
      "metadata": {
        "id": "Qfw-9dPlNOc9"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "mhgv-SkNNQhN"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# You can use both of the reduced dataset to see how much accuracy or predictive power of the model is affected.\n",
        "\n",
        "Xtrain, Xtest, ytrain, ytest = train_test_split(pca_fit2, y, test_size=0.2, random_state=5000)"
      ],
      "metadata": {
        "id": "jlyELmTTNbsN"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "random_model = RandomForestClassifier(n_estimators=3000, n_jobs = -1)"
      ],
      "metadata": {
        "id": "2QqsGVRpN3HN"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Fit\n",
        "random_model.fit(Xtrain, ytrain)\n",
        "\n",
        "y_pred = random_model.predict(Xtest)\n",
        "\n",
        "#Checking the accuracy\n",
        "random_model_accuracy = round(random_model.score(Xtrain, ytrain)*100,2)\n",
        "print(round(random_model_accuracy, 2), '%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U6WqlB_DOA8f",
        "outputId": "4cc61998-2688-4a4f-d79d-938c89b84211"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "100.0 %\n"
          ]
        }
      ]
    }
  ]
}