Group1_EDA.ipynb

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      "source": [
        "# Import dependencies\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from google.colab import files\n",
        "import io\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Upload csv file\n",
        "uploaded = files.upload()"
      ],
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          "height": 73
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              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
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              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
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              "  return _uploadFilesContinue(outputId);\n",
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              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
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              "\n",
              "/**\n",
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              "    li.appendChild(percent);\n",
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              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
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              "\n",
              "scope.google = scope.google || {};\n",
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving all_data.csv to all_data.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create DataFrame from csv file\n",
        "df = pd.read_csv(io.BytesIO(uploaded['all_data.csv']))\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "SYyECNUjqC7k",
        "outputId": "c2eb470f-7987-41da-b1c3-79533bda50a6"
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      "execution_count": 3,
      "outputs": [
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          "data": {
            "text/plain": [
              "   clientnum                                     attrition_flag  customer_age  \\\n",
              "0  768805383  Existing Customer                             ...            45   \n",
              "1  818770008  Existing Customer                             ...            49   \n",
              "2  818770008  Existing Customer                             ...            49   \n",
              "3  818770008  Existing Customer                             ...            49   \n",
              "4  713982108  Existing Customer                             ...            51   \n",
              "\n",
              "                                              gender  dependent_count  \\\n",
              "0  M                                             ...                3   \n",
              "1  F                                             ...                5   \n",
              "2  F                                             ...                5   \n",
              "3  F                                             ...                5   \n",
              "4  M                                             ...                3   \n",
              "\n",
              "                                     education_level  \\\n",
              "0  High School                                   ...   \n",
              "1  Graduate                                      ...   \n",
              "2  Graduate                                      ...   \n",
              "3  Graduate                                      ...   \n",
              "4  Graduate                                      ...   \n",
              "\n",
              "                                      marital_status  \\\n",
              "0  Married                                       ...   \n",
              "1  Single                                        ...   \n",
              "2  Single                                        ...   \n",
              "3  Single                                        ...   \n",
              "4  Married                                       ...   \n",
              "\n",
              "                                     income_category  \\\n",
              "0  $60K - $80K                                   ...   \n",
              "1  Less than $40K                                ...   \n",
              "2  Less than $40K                                ...   \n",
              "3  Less than $40K                                ...   \n",
              "4  $80K - $120K                                  ...   \n",
              "\n",
              "                                       card_category  months_on_book  ...  \\\n",
              "0  Blue                                          ...              39  ...   \n",
              "1  Blue                                          ...              44  ...   \n",
              "2  Blue                                          ...              44  ...   \n",
              "3  Blue                                          ...              44  ...   \n",
              "4  Blue                                          ...              36  ...   \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        8256.0                  864           7392.0                 1.541   \n",
              "3        8256.0                  864           7392.0                 1.541   \n",
              "4        3418.0                    0           3418.0                 2.594   \n",
              "\n",
              "   total_trans_amt  total_trans_ct  total_ct_chng_q4_q1  \\\n",
              "0             1144              42                1.625   \n",
              "1             1291              33                3.714   \n",
              "2             1291              33                3.714   \n",
              "3             1291              33                3.714   \n",
              "4             1887              20                2.333   \n",
              "\n",
              "   avg_utilization_ratio  Weekly_median_earnings  Yearly_median_earnings  \n",
              "0                  0.061                   809.0                 42068.0  \n",
              "1                  0.105                   963.0                 50076.0  \n",
              "2                  0.105                  1334.0                 69368.0  \n",
              "3                  0.105                  1924.0                100048.0  \n",
              "4                  0.000                   963.0                 50076.0  \n",
              "\n",
              "[5 rows x 23 columns]"
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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",
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              "      <th>income_category</th>\n",
              "      <th>card_category</th>\n",
              "      <th>months_on_book</th>\n",
              "      <th>...</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",
              "      <th>Weekly_median_earnings</th>\n",
              "      <th>Yearly_median_earnings</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>768805383</td>\n",
              "      <td>Existing Customer                             ...</td>\n",
              "      <td>45</td>\n",
              "      <td>M                                             ...</td>\n",
              "      <td>3</td>\n",
              "      <td>High School                                   ...</td>\n",
              "      <td>Married                                       ...</td>\n",
              "      <td>$60K - $80K                                   ...</td>\n",
              "      <td>Blue                                          ...</td>\n",
              "      <td>39</td>\n",
              "      <td>...</td>\n",
              "      <td>12691.0</td>\n",
              "      <td>777</td>\n",
              "      <td>11914.0</td>\n",
              "      <td>1.335</td>\n",
              "      <td>1144</td>\n",
              "      <td>42</td>\n",
              "      <td>1.625</td>\n",
              "      <td>0.061</td>\n",
              "      <td>809.0</td>\n",
              "      <td>42068.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>818770008</td>\n",
              "      <td>Existing Customer                             ...</td>\n",
              "      <td>49</td>\n",
              "      <td>F                                             ...</td>\n",
              "      <td>5</td>\n",
              "      <td>Graduate                                      ...</td>\n",
              "      <td>Single                                        ...</td>\n",
              "      <td>Less than $40K                                ...</td>\n",
              "      <td>Blue                                          ...</td>\n",
              "      <td>44</td>\n",
              "      <td>...</td>\n",
              "      <td>8256.0</td>\n",
              "      <td>864</td>\n",
              "      <td>7392.0</td>\n",
              "      <td>1.541</td>\n",
              "      <td>1291</td>\n",
              "      <td>33</td>\n",
              "      <td>3.714</td>\n",
              "      <td>0.105</td>\n",
              "      <td>963.0</td>\n",
              "      <td>50076.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>818770008</td>\n",
              "      <td>Existing Customer                             ...</td>\n",
              "      <td>49</td>\n",
              "      <td>F                                             ...</td>\n",
              "      <td>5</td>\n",
              "      <td>Graduate                                      ...</td>\n",
              "      <td>Single                                        ...</td>\n",
              "      <td>Less than $40K                                ...</td>\n",
              "      <td>Blue                                          ...</td>\n",
              "      <td>44</td>\n",
              "      <td>...</td>\n",
              "      <td>8256.0</td>\n",
              "      <td>864</td>\n",
              "      <td>7392.0</td>\n",
              "      <td>1.541</td>\n",
              "      <td>1291</td>\n",
              "      <td>33</td>\n",
              "      <td>3.714</td>\n",
              "      <td>0.105</td>\n",
              "      <td>1334.0</td>\n",
              "      <td>69368.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>818770008</td>\n",
              "      <td>Existing Customer                             ...</td>\n",
              "      <td>49</td>\n",
              "      <td>F                                             ...</td>\n",
              "      <td>5</td>\n",
              "      <td>Graduate                                      ...</td>\n",
              "      <td>Single                                        ...</td>\n",
              "      <td>Less than $40K                                ...</td>\n",
              "      <td>Blue                                          ...</td>\n",
              "      <td>44</td>\n",
              "      <td>...</td>\n",
              "      <td>8256.0</td>\n",
              "      <td>864</td>\n",
              "      <td>7392.0</td>\n",
              "      <td>1.541</td>\n",
              "      <td>1291</td>\n",
              "      <td>33</td>\n",
              "      <td>3.714</td>\n",
              "      <td>0.105</td>\n",
              "      <td>1924.0</td>\n",
              "      <td>100048.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>713982108</td>\n",
              "      <td>Existing Customer                             ...</td>\n",
              "      <td>51</td>\n",
              "      <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",
              "      <td>...</td>\n",
              "      <td>3418.0</td>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4744add1-8a74-4673-bed2-d12297204d5e');\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",
              "  "
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          },
          "metadata": {},
          "execution_count": 3
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
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          "height": 386
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        "id": "QEMLrk6sxGpA",
        "outputId": "36401059-7719-4be5-9e1a-cf878dc8eb15"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   clientnum                                     attrition_flag  customer_age  \\\n",
              "0  768805383  Existing Customer                             ...            45   \n",
              "1  818770008  Existing Customer                             ...            49   \n",
              "2  818770008  Existing Customer                             ...            49   \n",
              "3  818770008  Existing Customer                             ...            49   \n",
              "4  713982108  Existing Customer                             ...            51   \n",
              "\n",
              "                                              gender  dependent_count  \\\n",
              "0  M                                             ...                3   \n",
              "1  F                                             ...                5   \n",
              "2  F                                             ...                5   \n",
              "3  F                                             ...                5   \n",
              "4  M                                             ...                3   \n",
              "\n",
              "                                     education_level  \\\n",
              "0  High School                                   ...   \n",
              "1  Graduate                                      ...   \n",
              "2  Graduate                                      ...   \n",
              "3  Graduate                                      ...   \n",
              "4  Graduate                                      ...   \n",
              "\n",
              "                                      marital_status  \\\n",
              "0  Married                                       ...   \n",
              "1  Single                                        ...   \n",
              "2  Single                                        ...   \n",
              "3  Single                                        ...   \n",
              "4  Married                                       ...   \n",
              "\n",
              "                                     income_category  \\\n",
              "0  $60K - $80K                                   ...   \n",
              "1  Less than $40K                                ...   \n",
              "2  Less than $40K                                ...   \n",
              "3  Less than $40K                                ...   \n",
              "4  $80K - $120K                                  ...   \n",
              "\n",
              "                                       card_category  months_on_book  ...  \\\n",
              "0  Blue                                          ...              39  ...   \n",
              "1  Blue                                          ...              44  ...   \n",
              "2  Blue                                          ...              44  ...   \n",
              "3  Blue                                          ...              44  ...   \n",
              "4  Blue                                          ...              36  ...   \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        8256.0                  864           7392.0                 1.541   \n",
              "3        8256.0                  864           7392.0                 1.541   \n",
              "4        3418.0                    0           3418.0                 2.594   \n",
              "\n",
              "   total_trans_amt  total_trans_ct  total_ct_chng_q4_q1  \\\n",
              "0             1144              42                1.625   \n",
              "1             1291              33                3.714   \n",
              "2             1291              33                3.714   \n",
              "3             1291              33                3.714   \n",
              "4             1887              20                2.333   \n",
              "\n",
              "   avg_utilization_ratio  Weekly_median_earnings  Yearly_median_earnings  \n",
              "0                  0.061                   809.0                 42068.0  \n",
              "1                  0.105                   963.0                 50076.0  \n",
              "2                  0.105                  1334.0                 69368.0  \n",
              "3                  0.105                  1924.0                100048.0  \n",
              "4                  0.000                   963.0                 50076.0  \n",
              "\n",
              "[5 rows x 23 columns]"
            ],
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              "      <th></th>\n",
              "      <th>clientnum</th>\n",
              "      <th>attrition_flag</th>\n",
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              "      <th>gender</th>\n",
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              "      <th>months_on_book</th>\n",
              "      <th>...</th>\n",
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              "      <th>total_amt_chng_q4_q1</th>\n",
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              "      <th>total_trans_ct</th>\n",
              "      <th>total_ct_chng_q4_q1</th>\n",
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              "      <th>Yearly_median_earnings</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>768805383</td>\n",
              "      <td>Existing Customer                             ...</td>\n",
              "      <td>45</td>\n",
              "      <td>M                                             ...</td>\n",
              "      <td>3</td>\n",
              "      <td>High School                                   ...</td>\n",
              "      <td>Married                                       ...</td>\n",
              "      <td>$60K - $80K                                   ...</td>\n",
              "      <td>Blue                                          ...</td>\n",
              "      <td>39</td>\n",
              "      <td>...</td>\n",
              "      <td>12691.0</td>\n",
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              "      <td>1.335</td>\n",
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              "      <td>42068.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>818770008</td>\n",
              "      <td>Existing Customer                             ...</td>\n",
              "      <td>49</td>\n",
              "      <td>F                                             ...</td>\n",
              "      <td>5</td>\n",
              "      <td>Graduate                                      ...</td>\n",
              "      <td>Single                                        ...</td>\n",
              "      <td>Less than $40K                                ...</td>\n",
              "      <td>Blue                                          ...</td>\n",
              "      <td>44</td>\n",
              "      <td>...</td>\n",
              "      <td>8256.0</td>\n",
              "      <td>864</td>\n",
              "      <td>7392.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>818770008</td>\n",
              "      <td>Existing Customer                             ...</td>\n",
              "      <td>49</td>\n",
              "      <td>F                                             ...</td>\n",
              "      <td>5</td>\n",
              "      <td>Graduate                                      ...</td>\n",
              "      <td>Single                                        ...</td>\n",
              "      <td>Less than $40K                                ...</td>\n",
              "      <td>Blue                                          ...</td>\n",
              "      <td>44</td>\n",
              "      <td>...</td>\n",
              "      <td>8256.0</td>\n",
              "      <td>864</td>\n",
              "      <td>7392.0</td>\n",
              "      <td>1.541</td>\n",
              "      <td>1291</td>\n",
              "      <td>33</td>\n",
              "      <td>3.714</td>\n",
              "      <td>0.105</td>\n",
              "      <td>1334.0</td>\n",
              "      <td>69368.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>818770008</td>\n",
              "      <td>Existing Customer                             ...</td>\n",
              "      <td>49</td>\n",
              "      <td>F                                             ...</td>\n",
              "      <td>5</td>\n",
              "      <td>Graduate                                      ...</td>\n",
              "      <td>Single                                        ...</td>\n",
              "      <td>Less than $40K                                ...</td>\n",
              "      <td>Blue                                          ...</td>\n",
              "      <td>44</td>\n",
              "      <td>...</td>\n",
              "      <td>8256.0</td>\n",
              "      <td>864</td>\n",
              "      <td>7392.0</td>\n",
              "      <td>1.541</td>\n",
              "      <td>1291</td>\n",
              "      <td>33</td>\n",
              "      <td>3.714</td>\n",
              "      <td>0.105</td>\n",
              "      <td>1924.0</td>\n",
              "      <td>100048.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>713982108</td>\n",
              "      <td>Existing Customer                             ...</td>\n",
              "      <td>51</td>\n",
              "      <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",
              "      <td>...</td>\n",
              "      <td>3418.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3418.0</td>\n",
              "      <td>2.594</td>\n",
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              "      <td>0.000</td>\n",
              "      <td>963.0</td>\n",
              "      <td>50076.0</td>\n",
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              "  </tbody>\n",
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              "<p>5 rows × 23 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bdc71e79-5a29-462a-9552-7276fd1f7862')\"\n",
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              "                                                     [key], {});\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# See the number of rows and columns of the DataFrame\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "acPEePlLwG6o",
        "outputId": "feec06ec-6730-4923-ac79-acf0234e6ee9"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(17902, 23)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# See column data types\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DZJ47oT3xTyb",
        "outputId": "1830ba29-ee2d-4111-dfb8-df874dd11ee5"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 17902 entries, 0 to 17901\n",
            "Data columns (total 23 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   clientnum                 17902 non-null  int64  \n",
            " 1   attrition_flag            17902 non-null  object \n",
            " 2   customer_age              17902 non-null  int64  \n",
            " 3   gender                    17902 non-null  object \n",
            " 4   dependent_count           17902 non-null  int64  \n",
            " 5   education_level           17902 non-null  object \n",
            " 6   marital_status            17902 non-null  object \n",
            " 7   income_category           17902 non-null  object \n",
            " 8   card_category             17902 non-null  object \n",
            " 9   months_on_book            17902 non-null  int64  \n",
            " 10  total_relationship_count  17902 non-null  int64  \n",
            " 11  months_inactive_12_mon    17902 non-null  int64  \n",
            " 12  contacts_count_12_mon     17902 non-null  int64  \n",
            " 13  credit_limit              17902 non-null  float64\n",
            " 14  total_revolving_bal       17902 non-null  int64  \n",
            " 15  avg_open_to_buy           17902 non-null  float64\n",
            " 16  total_amt_chng_q4_q1      17902 non-null  float64\n",
            " 17  total_trans_amt           17902 non-null  int64  \n",
            " 18  total_trans_ct            17902 non-null  int64  \n",
            " 19  total_ct_chng_q4_q1       17902 non-null  float64\n",
            " 20  avg_utilization_ratio     17902 non-null  float64\n",
            " 21  Weekly_median_earnings    16889 non-null  float64\n",
            " 22  Yearly_median_earnings    16889 non-null  float64\n",
            "dtypes: float64(7), int64(10), object(6)\n",
            "memory usage: 3.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check if there are any missing values in our dataset or not\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q9EKuxIqxeEH",
        "outputId": "18a60a11-79a1-4d39-f6dc-96e8bfd02699"
      },
      "execution_count": 7,
      "outputs": [
        {
          "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",
              "Weekly_median_earnings      1013\n",
              "Yearly_median_earnings      1013\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# See a quick summary of the dataset\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "lCEIiYLRwSY6",
        "outputId": "2016de10-e3d8-46f7-c4bd-db805609d458"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          clientnum  customer_age  dependent_count  months_on_book  \\\n",
              "count  1.790200e+04  17902.000000     17902.000000    17902.000000   \n",
              "mean   7.391687e+08     46.333817         2.347950       35.937325   \n",
              "std    3.682304e+07      8.019978         1.300793        8.005709   \n",
              "min    7.080821e+08     26.000000         0.000000       13.000000   \n",
              "25%    7.130819e+08     41.000000         1.000000       31.000000   \n",
              "50%    7.179563e+08     46.000000         2.000000       36.000000   \n",
              "75%    7.726618e+08     52.000000         3.000000       40.000000   \n",
              "max    8.283431e+08     73.000000         5.000000       56.000000   \n",
              "\n",
              "       total_relationship_count  months_inactive_12_mon  \\\n",
              "count              17902.000000            17902.000000   \n",
              "mean                   3.815719                2.341750   \n",
              "std                    1.553742                1.013433   \n",
              "min                    1.000000                0.000000   \n",
              "25%                    3.000000                2.000000   \n",
              "50%                    4.000000                2.000000   \n",
              "75%                    5.000000                3.000000   \n",
              "max                    6.000000                6.000000   \n",
              "\n",
              "       contacts_count_12_mon  credit_limit  total_revolving_bal  \\\n",
              "count           17902.000000  17902.000000         17902.000000   \n",
              "mean                2.457044   8597.048659          1162.461792   \n",
              "std                 1.106134   9062.723313           813.460928   \n",
              "min                 0.000000   1438.300000             0.000000   \n",
              "25%                 2.000000   2555.000000           425.000000   \n",
              "50%                 2.000000   4562.000000          1272.000000   \n",
              "75%                 3.000000  10958.250000          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     17902.000000          17902.000000     17902.000000    17902.000000   \n",
              "mean       7434.586867              0.759167      4410.475087       64.857725   \n",
              "std        9068.589870              0.218396      3400.544523       23.527074   \n",
              "min           3.000000              0.000000       510.000000       10.000000   \n",
              "25%        1305.000000              0.631000      2149.250000       45.000000   \n",
              "50%        3478.500000              0.736000      3895.000000       67.000000   \n",
              "75%        9783.750000              0.859000      4748.000000       81.000000   \n",
              "max       34516.000000              3.397000     18484.000000      139.000000   \n",
              "\n",
              "       total_ct_chng_q4_q1  avg_utilization_ratio  Weekly_median_earnings  \\\n",
              "count         17902.000000           17902.000000            16889.000000   \n",
              "mean              0.711732               0.276015             1207.782462   \n",
              "std               0.238504               0.276341              431.427418   \n",
              "min               0.000000               0.000000              626.000000   \n",
              "25%               0.581000               0.024000              899.000000   \n",
              "50%               0.702000               0.177000             1057.000000   \n",
              "75%               0.818000               0.505000             1334.000000   \n",
              "max               3.714000               0.999000             1924.000000   \n",
              "\n",
              "       Yearly_median_earnings  \n",
              "count            16889.000000  \n",
              "mean             62804.688022  \n",
              "std              22434.225760  \n",
              "min              32552.000000  \n",
              "25%              46748.000000  \n",
              "50%              54964.000000  \n",
              "75%              69368.000000  \n",
              "max             100048.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6b41c5c8-0948-4a17-b282-cf61cef93c73\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>clientnum</th>\n",
              "      <th>customer_age</th>\n",
              "      <th>dependent_count</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",
              "      <th>Weekly_median_earnings</th>\n",
              "      <th>Yearly_median_earnings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.790200e+04</td>\n",
              "      <td>17902.000000</td>\n",
              "      <td>17902.000000</td>\n",
              "      <td>17902.000000</td>\n",
              "      <td>17902.000000</td>\n",
              "      <td>17902.000000</td>\n",
              "      <td>17902.000000</td>\n",
              "      <td>17902.000000</td>\n",
              "      <td>17902.000000</td>\n",
              "      <td>17902.000000</td>\n",
              "      <td>17902.000000</td>\n",
              "      <td>17902.000000</td>\n",
              "      <td>17902.000000</td>\n",
              "      <td>17902.000000</td>\n",
              "      <td>17902.000000</td>\n",
              "      <td>16889.000000</td>\n",
              "      <td>16889.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>7.391687e+08</td>\n",
              "      <td>46.333817</td>\n",
              "      <td>2.347950</td>\n",
              "      <td>35.937325</td>\n",
              "      <td>3.815719</td>\n",
              "      <td>2.341750</td>\n",
              "      <td>2.457044</td>\n",
              "      <td>8597.048659</td>\n",
              "      <td>1162.461792</td>\n",
              "      <td>7434.586867</td>\n",
              "      <td>0.759167</td>\n",
              "      <td>4410.475087</td>\n",
              "      <td>64.857725</td>\n",
              "      <td>0.711732</td>\n",
              "      <td>0.276015</td>\n",
              "      <td>1207.782462</td>\n",
              "      <td>62804.688022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.682304e+07</td>\n",
              "      <td>8.019978</td>\n",
              "      <td>1.300793</td>\n",
              "      <td>8.005709</td>\n",
              "      <td>1.553742</td>\n",
              "      <td>1.013433</td>\n",
              "      <td>1.106134</td>\n",
              "      <td>9062.723313</td>\n",
              "      <td>813.460928</td>\n",
              "      <td>9068.589870</td>\n",
              "      <td>0.218396</td>\n",
              "      <td>3400.544523</td>\n",
              "      <td>23.527074</td>\n",
              "      <td>0.238504</td>\n",
              "      <td>0.276341</td>\n",
              "      <td>431.427418</td>\n",
              "      <td>22434.225760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>7.080821e+08</td>\n",
              "      <td>26.000000</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",
              "      <td>626.000000</td>\n",
              "      <td>32552.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.130819e+08</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>1.000000</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>425.000000</td>\n",
              "      <td>1305.000000</td>\n",
              "      <td>0.631000</td>\n",
              "      <td>2149.250000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>0.581000</td>\n",
              "      <td>0.024000</td>\n",
              "      <td>899.000000</td>\n",
              "      <td>46748.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.179563e+08</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4562.000000</td>\n",
              "      <td>1272.000000</td>\n",
              "      <td>3478.500000</td>\n",
              "      <td>0.736000</td>\n",
              "      <td>3895.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>0.702000</td>\n",
              "      <td>0.177000</td>\n",
              "      <td>1057.000000</td>\n",
              "      <td>54964.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.726618e+08</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>10958.250000</td>\n",
              "      <td>1784.000000</td>\n",
              "      <td>9783.750000</td>\n",
              "      <td>0.859000</td>\n",
              "      <td>4748.000000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>0.818000</td>\n",
              "      <td>0.505000</td>\n",
              "      <td>1334.000000</td>\n",
              "      <td>69368.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>8.283431e+08</td>\n",
              "      <td>73.000000</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",
              "      <td>34516.000000</td>\n",
              "      <td>2517.000000</td>\n",
              "      <td>34516.000000</td>\n",
              "      <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",
              "      <td>1924.000000</td>\n",
              "      <td>100048.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Determine the number of unique values in each column.\n",
        "df.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "COLClORoPdpH",
        "outputId": "684159dc-5984-4de6-bce6-dddf9087b926"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "clientnum                   10127\n",
              "attrition_flag                  2\n",
              "customer_age                   45\n",
              "gender                          2\n",
              "dependent_count                 6\n",
              "education_level                 7\n",
              "marital_status                  4\n",
              "income_category                 6\n",
              "card_category                   4\n",
              "months_on_book                 44\n",
              "total_relationship_count        6\n",
              "months_inactive_12_mon          7\n",
              "contacts_count_12_mon           7\n",
              "credit_limit                 6205\n",
              "total_revolving_bal          1974\n",
              "avg_open_to_buy              6813\n",
              "total_amt_chng_q4_q1         1158\n",
              "total_trans_amt              5033\n",
              "total_trans_ct                126\n",
              "total_ct_chng_q4_q1           830\n",
              "avg_utilization_ratio         964\n",
              "Weekly_median_earnings          9\n",
              "Yearly_median_earnings          9\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check duplicated values\n",
        "print(df.duplicated(\"clientnum\").sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LNlbgnlFvoXK",
        "outputId": "5d510c1f-5b30-432a-f06c-cfce1a037933"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7775\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop duplicated values\n",
        "df_cleaned = df.drop_duplicates(\"clientnum\")\n",
        "df_cleaned"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "qtnuDlHYyIct",
        "outputId": "1de54a8a-824b-4ff4-e6ed-167bd47a884d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       clientnum                                     attrition_flag  \\\n",
              "0      768805383  Existing Customer                             ...   \n",
              "1      818770008  Existing Customer                             ...   \n",
              "4      713982108  Existing Customer                             ...   \n",
              "7      769911858  Existing Customer                             ...   \n",
              "8      709106358  Existing Customer                             ...   \n",
              "...          ...                                                ...   \n",
              "17890  772366833  Existing Customer                             ...   \n",
              "17893  710638233  Attrited Customer                             ...   \n",
              "17895  716506083  Attrited Customer                             ...   \n",
              "17896  717406983  Attrited Customer                             ...   \n",
              "17899  714337233  Attrited Customer                             ...   \n",
              "\n",
              "       customer_age                                             gender  \\\n",
              "0                45  M                                             ...   \n",
              "1                49  F                                             ...   \n",
              "4                51  M                                             ...   \n",
              "7                40  F                                             ...   \n",
              "8                40  M                                             ...   \n",
              "...             ...                                                ...   \n",
              "17890            50  M                                             ...   \n",
              "17893            41  M                                             ...   \n",
              "17895            44  F                                             ...   \n",
              "17896            30  M                                             ...   \n",
              "17899            43  F                                             ...   \n",
              "\n",
              "       dependent_count                                    education_level  \\\n",
              "0                    3  High School                                   ...   \n",
              "1                    5  Graduate                                      ...   \n",
              "4                    3  Graduate                                      ...   \n",
              "7                    4  High School                                   ...   \n",
              "8                    3  Uneducated                                    ...   \n",
              "...                ...                                                ...   \n",
              "17890                2  Graduate                                      ...   \n",
              "17893                2  Unknown                                       ...   \n",
              "17895                1  High School                                   ...   \n",
              "17896                2  Graduate                                      ...   \n",
              "17899                2  Graduate                                      ...   \n",
              "\n",
              "                                          marital_status  \\\n",
              "0      Married                                       ...   \n",
              "1      Single                                        ...   \n",
              "4      Married                                       ...   \n",
              "7      Unknown                                       ...   \n",
              "8      Married                                       ...   \n",
              "...                                                  ...   \n",
              "17890  Single                                        ...   \n",
              "17893  Divorced                                      ...   \n",
              "17895  Married                                       ...   \n",
              "17896  Unknown                                       ...   \n",
              "17899  Married                                       ...   \n",
              "\n",
              "                                         income_category  \\\n",
              "0      $60K - $80K                                   ...   \n",
              "1      Less than $40K                                ...   \n",
              "4      $80K - $120K                                  ...   \n",
              "7      Less than $40K                                ...   \n",
              "8      $60K - $80K                                   ...   \n",
              "...                                                  ...   \n",
              "17890  $40K - $60K                                   ...   \n",
              "17893  $40K - $60K                                   ...   \n",
              "17895  Less than $40K                                ...   \n",
              "17896  $40K - $60K                                   ...   \n",
              "17899  Less than $40K                                ...   \n",
              "\n",
              "                                           card_category  months_on_book  ...  \\\n",
              "0      Blue                                          ...              39  ...   \n",
              "1      Blue                                          ...              44  ...   \n",
              "4      Blue                                          ...              36  ...   \n",
              "7      Blue                                          ...              34  ...   \n",
              "8      Blue                                          ...              21  ...   \n",
              "...                                                  ...             ...  ...   \n",
              "17890  Blue                                          ...              40  ...   \n",
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              "       total_ct_chng_q4_q1  avg_utilization_ratio  Weekly_median_earnings  \\\n",
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              "      <td>$80K - $120K                                  ...</td>\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the total number of existing and attrited customers\n",
        "Attrition_Flag = df_cleaned.attrition_flag.value_counts()\n",
        "Attrition_Flag_df = pd.DataFrame(data=Attrition_Flag)\n",
        "Attrition_Flag_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "GJB03knDQSa_",
        "outputId": "19ef9dde-0757-43b2-a349-870a933e2e82"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                    attrition_flag\n",
              "Existing Customer                                             8500\n",
              "Attrited Customer                                             1627"
            ],
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              "      <th>Existing Customer</th>\n",
              "      <td>8500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Attrited Customer</th>\n",
              "      <td>1627</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e01873ac-cd2e-4f10-a7cf-7413fc39dcaa')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      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-e01873ac-cd2e-4f10-a7cf-7413fc39dcaa 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-e01873ac-cd2e-4f10-a7cf-7413fc39dcaa');\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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Attrition visualization\n",
        "Attrition_Flag_df.plot.bar()\n",
        "plt.xticks(rotation=1)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "oLvOX27NTlGT",
        "outputId": "7241e4f7-6409-4043-d847-8464d7876493"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Unique values for customer ages\n",
        "Customer_Age = df_cleaned[\"customer_age\"].unique()\n",
        "Customer_Age.sort()\n",
        "Customer_Age "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rThB7qvrzHbZ",
        "outputId": "668bb715-da07-4bef-ff88-49c595c35cab"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,\n",
              "       43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,\n",
              "       60, 61, 62, 63, 64, 65, 66, 67, 68, 70, 73])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Total number of customer per age \n",
        "Customer_Age_count = df_cleaned.customer_age.value_counts()\n",
        "Customer_Age_count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FrDcdEwbYAno",
        "outputId": "4cabf2f4-cbd8-4d7b-cef9-36c45c90061a"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "44    500\n",
              "49    495\n",
              "46    490\n",
              "45    486\n",
              "47    479\n",
              "43    473\n",
              "48    472\n",
              "50    452\n",
              "42    426\n",
              "51    398\n",
              "53    387\n",
              "41    379\n",
              "52    376\n",
              "40    361\n",
              "39    333\n",
              "54    307\n",
              "38    303\n",
              "55    279\n",
              "56    262\n",
              "37    260\n",
              "57    223\n",
              "36    221\n",
              "35    184\n",
              "59    157\n",
              "58    157\n",
              "34    146\n",
              "33    127\n",
              "60    127\n",
              "32    106\n",
              "65    101\n",
              "61     93\n",
              "62     93\n",
              "31     91\n",
              "26     78\n",
              "30     70\n",
              "63     65\n",
              "29     56\n",
              "64     43\n",
              "27     32\n",
              "28     29\n",
              "67      4\n",
              "66      2\n",
              "68      2\n",
              "70      1\n",
              "73      1\n",
              "Name: customer_age, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Customer count by gender\n",
        "Gender_count = df_cleaned.gender.value_counts()\n",
        "print(Gender_count)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qFjmNOIqpcZP",
        "outputId": "5a700659-a3a3-470c-8bda-206cf2ade8ef"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F                                                     5358\n",
            "M                                                     4769\n",
            "Name: gender, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Gender_count.plot.pie(labels=(\"F\", \"M\"), autopct='%1.1f%%')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "1PY35BE3Z4kn",
        "outputId": "3ab68492-c292-434b-ba1c-4bede4842216"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Dependent_count = df_cleaned.dependent_count.value_counts()\n",
        "Dependent_count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B_HSQ7kzD6TA",
        "outputId": "9d31011a-f252-49c9-dd90-b2cba0735e8c"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    2732\n",
              "2    2655\n",
              "1    1838\n",
              "4    1574\n",
              "0     904\n",
              "5     424\n",
              "Name: dependent_count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Education_Level_count = df_cleaned.education_level.value_counts()\n",
        "Education_Level_count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a6W8Fnia0fWQ",
        "outputId": "33539a9c-ddc4-4845-db1c-386151ba73cd"
      },
      "execution_count": 19,
      "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": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Education_Level_count.plot.pie(labels=(\"Graduate\", \"High School\", \"Unknown\", \"Uneducated\", \"College\", \"Post-Graduate\", \"Doctorate\"), \n",
        "                               autopct='%1.1f%%', title=\"Education Level\", label=\"\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "NrwWbCVCa677",
        "outputId": "f808800a-1fc4-4be6-e840-b4ca1fa2bfb8"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Marital_Status_count = df_cleaned.marital_status.value_counts()\n",
        "Marital_Status_count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_j4Gxqrd0u7m",
        "outputId": "36dc3ebc-20e8-4ab2-f644-264663ce06d0"
      },
      "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": [
        "Marital_Status_count.plot.pie(labels=(\"Married\", \"Single\", \"Unknown\", \"Divorced\"), label=\"\", autopct='%1.1f%%', title=\"Marital Status\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "w3ta6jJnbd3Z",
        "outputId": "4085c087-f8ae-4a6b-9c53-616ec3446dab"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Income_Category_count = df_cleaned.income_category.value_counts()\n",
        "Income_Category_count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hd8IG9IT1HxC",
        "outputId": "39e715cd-ebe9-4277-ee70-5c000de0d865"
      },
      "execution_count": 23,
      "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": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Income_Category_count.plot.pie(labels=(\"Less than $40K\", \"$40K - $60K\", \"$80K - $120K\", \"$60K - $80K\", \"Unknown\", \"120K +\"), label=\"\", autopct='%1.1f%%', title=\"Income Category\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "KzNRogBScHcM",
        "outputId": "e608fd69-1ac5-4a5f-90c7-4cfc52c6a63f"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# What are the credit card categories and how many customers are in each category?\n",
        "Card_Category_count = df_cleaned.card_category.value_counts()\n",
        "Card_Category_count"
      ],
      "metadata": {
        "id": "s2j7o6jx1Wqs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "45c75e4f-fd6e-4c98-af93-f3370d04cc40"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Blue                                                  9436\n",
              "Silver                                                 555\n",
              "Gold                                                   116\n",
              "Platinum                                                20\n",
              "Name: card_category, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Card_Category_count.plot.pie(labels=(\"Blue\", \"Silver\", \"Gold\", \"Platinum\"), label=\"\", autopct='%1.1f%%', title=\"Card Category\", figsize=(10,12))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605
        },
        "id": "DavEkMTLcrW-",
        "outputId": "c71ea084-155b-4d49-f3d4-b0c5047bbd92"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x864 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# What is the credit limit for each credit card category?\n",
        "credit_limit = df_cleaned.groupby(\"card_category\").mean()\n",
        "print(credit_limit[\"credit_limit\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FH7PkS7z46CZ",
        "outputId": "9dfb9aa5-7d1f-49b5-a2ae-e6913db0fa53"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "card_category\n",
            "Blue                                                   7363.780002\n",
            "Gold                                                  28416.370690\n",
            "Platinum                                              30283.450000\n",
            "Silver                                                25277.836036\n",
            "Name: credit_limit, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Total number of existing and attrited customers for each card category\n",
        "existing_customers = df_cleaned.groupby([\"card_category\", \"attrition_flag\"]).count()\n",
        "print(existing_customers[\"clientnum\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l1EAz089AL4F",
        "outputId": "9b07365f-8e85-4eaf-cb2e-aea7d586fee5"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "card_category                                       attrition_flag                                    \n",
            "Blue                                                Attrited Customer                                     1519\n",
            "                                                    Existing Customer                                     7917\n",
            "Gold                                                Attrited Customer                                       21\n",
            "                                                    Existing Customer                                       95\n",
            "Platinum                                            Attrited Customer                                        5\n",
            "                                                    Existing Customer                                       15\n",
            "Silver                                              Attrited Customer                                       82\n",
            "                                                    Existing Customer                                      473\n",
            "Name: clientnum, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# What is the average number of months on book for each card category?\n",
        "months_on_book_avg = df_cleaned.groupby([\"card_category\", \"attrition_flag\"]).mean()\n",
        "print(months_on_book_avg[\"months_on_book\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "utVzJn5wBOrQ",
        "outputId": "f7cf0517-c80a-499f-d80b-ab271ab44b1f"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "card_category                                       attrition_flag                                    \n",
            "Blue                                                Attrited Customer                                     36.200132\n",
            "                                                    Existing Customer                                     35.914740\n",
            "Gold                                                Attrited Customer                                     33.904762\n",
            "                                                    Existing Customer                                     35.884211\n",
            "Platinum                                            Attrited Customer                                     36.000000\n",
            "                                                    Existing Customer                                     36.333333\n",
            "Silver                                              Attrited Customer                                     36.365854\n",
            "                                                    Existing Customer                                     35.293869\n",
            "Name: months_on_book, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Months_on_book_count = df_cleaned.months_on_book.value_counts()\n",
        "Months_on_book_count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hi2Kl1ljEw6l",
        "outputId": "c423600d-f83a-4e87-8733-43c46a8d6d67"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "36    2463\n",
              "37     358\n",
              "34     353\n",
              "38     347\n",
              "39     341\n",
              "40     333\n",
              "31     318\n",
              "35     317\n",
              "33     305\n",
              "30     300\n",
              "41     297\n",
              "32     289\n",
              "28     275\n",
              "43     273\n",
              "42     271\n",
              "29     241\n",
              "44     230\n",
              "45     227\n",
              "27     206\n",
              "46     197\n",
              "26     186\n",
              "47     171\n",
              "25     165\n",
              "48     162\n",
              "24     160\n",
              "49     141\n",
              "23     116\n",
              "22     105\n",
              "56     103\n",
              "50      96\n",
              "21      83\n",
              "51      80\n",
              "53      78\n",
              "20      74\n",
              "13      70\n",
              "19      63\n",
              "52      62\n",
              "18      58\n",
              "54      53\n",
              "55      42\n",
              "17      39\n",
              "15      34\n",
              "16      29\n",
              "14      16\n",
              "Name: months_on_book, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Total_Relationship_Count = df_cleaned.total_relationship_count.value_counts()\n",
        "Total_Relationship_Count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SajiwfriE7LJ",
        "outputId": "780fec22-3731-41e7-f7be-cd62589f1652"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    2305\n",
              "4    1912\n",
              "5    1891\n",
              "6    1866\n",
              "2    1243\n",
              "1     910\n",
              "Name: total_relationship_count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Months_Inactive_12_mon = df_cleaned.months_inactive_12_mon.value_counts()\n",
        "Months_Inactive_12_mon"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VoVD0wEDEwY4",
        "outputId": "fe86d2c1-b0bb-4ba2-de38-c8dbc10e7237"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    3846\n",
              "2    3282\n",
              "1    2233\n",
              "4     435\n",
              "5     178\n",
              "6     124\n",
              "0      29\n",
              "Name: months_inactive_12_mon, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Contacts_Count_12_mon = df_cleaned.contacts_count_12_mon.value_counts()\n",
        "Contacts_Count_12_mon"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cMrA96DvE7g5",
        "outputId": "68a69967-f238-4e88-dc00-60cb93a71272"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    3380\n",
              "2    3227\n",
              "1    1499\n",
              "4    1392\n",
              "0     399\n",
              "5     176\n",
              "6      54\n",
              "Name: contacts_count_12_mon, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Total_Trans_Ct = df_cleaned[\"total_trans_ct\"].unique()\n",
        "Total_Trans_Ct.sort()\n",
        "Total_Trans_Ct"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ciOs-QtJJCMZ",
        "outputId": "652eeb87-4cb5-4b03-f5cd-6b68401081e7"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 10,  11,  12,  13,  14,  15,  16,  17,  18,  19,  20,  21,  22,\n",
              "        23,  24,  25,  26,  27,  28,  29,  30,  31,  32,  33,  34,  35,\n",
              "        36,  37,  38,  39,  40,  41,  42,  43,  44,  45,  46,  47,  48,\n",
              "        49,  50,  51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,\n",
              "        62,  63,  64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,\n",
              "        75,  76,  77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,\n",
              "        88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100,\n",
              "       101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,\n",
              "       114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,\n",
              "       127, 128, 129, 130, 131, 132, 134, 138, 139])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Total_Ct_Chng_Q4_Q1 = df_cleaned[\"total_ct_chng_q4_q1\"].unique()\n",
        "Total_Ct_Chng_Q4_Q1.sort()\n",
        "Total_Ct_Chng_Q4_Q1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x7Tfe1umJCjm",
        "outputId": "532f138f-8157-4667-bc59-5eb5ccbbcf3f"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.   , 0.028, 0.029, 0.038, 0.053, 0.059, 0.062, 0.074, 0.077,\n",
              "       0.091, 0.097, 0.103, 0.105, 0.107, 0.111, 0.119, 0.125, 0.13 ,\n",
              "       0.133, 0.143, 0.147, 0.15 , 0.154, 0.161, 0.162, 0.167, 0.171,\n",
              "       0.172, 0.174, 0.176, 0.179, 0.182, 0.185, 0.188, 0.189, 0.19 ,\n",
              "       0.192, 0.194, 0.2  , 0.206, 0.207, 0.208, 0.211, 0.212, 0.214,\n",
              "       0.217, 0.222, 0.226, 0.227, 0.231, 0.233, 0.235, 0.238, 0.24 ,\n",
              "       0.241, 0.242, 0.244, 0.25 , 0.256, 0.257, 0.259, 0.261, 0.262,\n",
              "       0.263, 0.265, 0.267, 0.269, 0.273, 0.275, 0.276, 0.278, 0.28 ,\n",
              "       0.281, 0.282, 0.286, 0.29 , 0.292, 0.293, 0.294, 0.295, 0.296,\n",
              "       0.297, 0.3  , 0.303, 0.304, 0.306, 0.308, 0.31 , 0.312, 0.314,\n",
              "       0.316, 0.317, 0.318, 0.32 , 0.321, 0.323, 0.324, 0.327, 0.333,\n",
              "       0.341, 0.343, 0.344, 0.345, 0.346, 0.347, 0.348, 0.349, 0.35 ,\n",
              "       0.351, 0.353, 0.355, 0.357, 0.359, 0.36 , 0.361, 0.362, 0.364,\n",
              "       0.367, 0.368, 0.37 , 0.371, 0.372, 0.375, 0.378, 0.379, 0.381,\n",
              "       0.382, 0.385, 0.387, 0.388, 0.389, 0.391, 0.393, 0.394, 0.395,\n",
              "       0.396, 0.4  , 0.405, 0.406, 0.407, 0.408, 0.409, 0.41 , 0.411,\n",
              "       0.412, 0.414, 0.415, 0.417, 0.419, 0.421, 0.422, 0.423, 0.424,\n",
              "       0.425, 0.426, 0.429, 0.431, 0.432, 0.433, 0.434, 0.435, 0.436,\n",
              "       0.438, 0.439, 0.44 , 0.441, 0.442, 0.444, 0.447, 0.448, 0.449,\n",
              "       0.45 , 0.451, 0.452, 0.453, 0.455, 0.456, 0.457, 0.458, 0.459,\n",
              "       0.46 , 0.462, 0.463, 0.464, 0.465, 0.467, 0.468, 0.469, 0.471,\n",
              "       0.472, 0.473, 0.474, 0.475, 0.476, 0.477, 0.478, 0.479, 0.48 ,\n",
              "       0.481, 0.483, 0.484, 0.485, 0.486, 0.487, 0.488, 0.489, 0.49 ,\n",
              "       0.491, 0.492, 0.5  , 0.508, 0.509, 0.51 , 0.511, 0.512, 0.513,\n",
              "       0.514, 0.515, 0.516, 0.517, 0.518, 0.519, 0.52 , 0.521, 0.522,\n",
              "       0.523, 0.524, 0.525, 0.526, 0.527, 0.528, 0.529, 0.531, 0.532,\n",
              "       0.533, 0.534, 0.535, 0.536, 0.537, 0.538, 0.54 , 0.541, 0.542,\n",
              "       0.543, 0.544, 0.545, 0.547, 0.548, 0.549, 0.55 , 0.551, 0.552,\n",
              "       0.553, 0.554, 0.556, 0.557, 0.558, 0.559, 0.56 , 0.561, 0.562,\n",
              "       0.564, 0.565, 0.566, 0.567, 0.568, 0.569, 0.571, 0.573, 0.574,\n",
              "       0.575, 0.576, 0.577, 0.578, 0.579, 0.58 , 0.581, 0.582, 0.583,\n",
              "       0.585, 0.586, 0.587, 0.588, 0.589, 0.59 , 0.591, 0.592, 0.593,\n",
              "       0.594, 0.595, 0.596, 0.597, 0.6  , 0.603, 0.604, 0.605, 0.606,\n",
              "       0.607, 0.608, 0.609, 0.61 , 0.611, 0.612, 0.613, 0.614, 0.615,\n",
              "       0.616, 0.617, 0.618, 0.619, 0.62 , 0.621, 0.622, 0.623, 0.625,\n",
              "       0.627, 0.628, 0.629, 0.63 , 0.631, 0.632, 0.633, 0.634, 0.635,\n",
              "       0.636, 0.638, 0.639, 0.64 , 0.641, 0.642, 0.643, 0.644, 0.645,\n",
              "       0.646, 0.647, 0.648, 0.649, 0.65 , 0.651, 0.652, 0.653, 0.654,\n",
              "       0.655, 0.656, 0.657, 0.658, 0.659, 0.66 , 0.661, 0.662, 0.667,\n",
              "       0.671, 0.672, 0.673, 0.674, 0.675, 0.676, 0.677, 0.678, 0.679,\n",
              "       0.68 , 0.681, 0.682, 0.683, 0.684, 0.685, 0.686, 0.687, 0.688,\n",
              "       0.689, 0.69 , 0.691, 0.692, 0.693, 0.694, 0.695, 0.696, 0.697,\n",
              "       0.698, 0.699, 0.7  , 0.702, 0.703, 0.704, 0.705, 0.706, 0.707,\n",
              "       0.708, 0.709, 0.71 , 0.711, 0.712, 0.714, 0.716, 0.717, 0.718,\n",
              "       0.719, 0.72 , 0.721, 0.722, 0.723, 0.724, 0.725, 0.726, 0.727,\n",
              "       0.729, 0.73 , 0.731, 0.732, 0.733, 0.734, 0.735, 0.736, 0.737,\n",
              "       0.738, 0.739, 0.74 , 0.741, 0.742, 0.743, 0.744, 0.745, 0.746,\n",
              "       0.75 , 0.753, 0.754, 0.755, 0.756, 0.757, 0.758, 0.759, 0.76 ,\n",
              "       0.761, 0.762, 0.763, 0.764, 0.765, 0.766, 0.767, 0.768, 0.769,\n",
              "       0.77 , 0.771, 0.772, 0.773, 0.774, 0.775, 0.776, 0.778, 0.779,\n",
              "       0.78 , 0.781, 0.782, 0.783, 0.784, 0.785, 0.786, 0.787, 0.788,\n",
              "       0.789, 0.79 , 0.791, 0.792, 0.793, 0.794, 0.795, 0.796, 0.797,\n",
              "       0.8  , 0.803, 0.804, 0.805, 0.806, 0.807, 0.808, 0.809, 0.81 ,\n",
              "       0.811, 0.812, 0.814, 0.815, 0.816, 0.817, 0.818, 0.819, 0.82 ,\n",
              "       0.821, 0.822, 0.823, 0.824, 0.825, 0.826, 0.827, 0.828, 0.829,\n",
              "       0.83 , 0.831, 0.833, 0.836, 0.837, 0.838, 0.839, 0.84 , 0.841,\n",
              "       0.842, 0.843, 0.844, 0.846, 0.847, 0.848, 0.849, 0.85 , 0.851,\n",
              "       0.852, 0.853, 0.854, 0.855, 0.857, 0.859, 0.86 , 0.861, 0.862,\n",
              "       0.863, 0.864, 0.865, 0.866, 0.867, 0.868, 0.869, 0.87 , 0.871,\n",
              "       0.872, 0.873, 0.875, 0.878, 0.879, 0.88 , 0.881, 0.882, 0.884,\n",
              "       0.885, 0.886, 0.887, 0.889, 0.891, 0.892, 0.893, 0.894, 0.895,\n",
              "       0.896, 0.897, 0.898, 0.9  , 0.902, 0.903, 0.905, 0.906, 0.907,\n",
              "       0.909, 0.911, 0.912, 0.913, 0.914, 0.915, 0.917, 0.918, 0.919,\n",
              "       0.92 , 0.921, 0.922, 0.923, 0.925, 0.926, 0.927, 0.929, 0.93 ,\n",
              "       0.931, 0.932, 0.933, 0.934, 0.935, 0.936, 0.938, 0.939, 0.941,\n",
              "       0.943, 0.944, 0.946, 0.947, 0.948, 0.949, 0.95 , 0.951, 0.952,\n",
              "       0.953, 0.955, 0.956, 0.957, 0.958, 0.96 , 0.962, 0.963, 0.964,\n",
              "       0.966, 0.967, 0.968, 0.969, 0.97 , 0.971, 0.972, 0.973, 0.974,\n",
              "       0.975, 0.976, 0.977, 0.978, 0.98 , 0.981, 1.   , 1.018, 1.022,\n",
              "       1.023, 1.024, 1.025, 1.026, 1.027, 1.028, 1.029, 1.03 , 1.031,\n",
              "       1.032, 1.033, 1.034, 1.036, 1.037, 1.038, 1.04 , 1.042, 1.043,\n",
              "       1.045, 1.048, 1.049, 1.05 , 1.051, 1.053, 1.054, 1.056, 1.057,\n",
              "       1.059, 1.06 , 1.061, 1.062, 1.064, 1.065, 1.067, 1.069, 1.071,\n",
              "       1.073, 1.074, 1.075, 1.077, 1.079, 1.08 , 1.081, 1.083, 1.086,\n",
              "       1.087, 1.088, 1.091, 1.093, 1.094, 1.095, 1.097, 1.1  , 1.103,\n",
              "       1.105, 1.107, 1.108, 1.111, 1.114, 1.115, 1.118, 1.12 , 1.121,\n",
              "       1.122, 1.125, 1.128, 1.13 , 1.133, 1.135, 1.136, 1.138, 1.139,\n",
              "       1.143, 1.147, 1.148, 1.15 , 1.152, 1.154, 1.156, 1.158, 1.16 ,\n",
              "       1.161, 1.162, 1.167, 1.171, 1.172, 1.174, 1.176, 1.179, 1.182,\n",
              "       1.188, 1.189, 1.194, 1.2  , 1.206, 1.207, 1.208, 1.211, 1.212,\n",
              "       1.214, 1.217, 1.219, 1.222, 1.226, 1.227, 1.229, 1.231, 1.235,\n",
              "       1.24 , 1.25 , 1.258, 1.263, 1.273, 1.278, 1.286, 1.29 , 1.292,\n",
              "       1.294, 1.3  , 1.303, 1.308, 1.31 , 1.312, 1.316, 1.318, 1.32 ,\n",
              "       1.321, 1.323, 1.333, 1.346, 1.35 , 1.357, 1.36 , 1.364, 1.367,\n",
              "       1.37 , 1.375, 1.381, 1.382, 1.385, 1.391, 1.4  , 1.412, 1.417,\n",
              "       1.419, 1.421, 1.429, 1.438, 1.444, 1.45 , 1.455, 1.458, 1.462,\n",
              "       1.467, 1.471, 1.474, 1.476, 1.478, 1.48 , 1.5  , 1.522, 1.533,\n",
              "       1.538, 1.545, 1.571, 1.579, 1.583, 1.6  , 1.609, 1.615, 1.625,\n",
              "       1.636, 1.647, 1.65 , 1.667, 1.684, 1.7  , 1.722, 1.75 , 1.778,\n",
              "       1.783, 1.8  , 1.833, 1.857, 1.875, 1.882, 1.909, 1.923, 2.   ,\n",
              "       2.083, 2.1  , 2.167, 2.182, 2.2  , 2.222, 2.273, 2.286, 2.333,\n",
              "       2.4  , 2.429, 2.5  , 2.571, 2.75 , 2.875, 3.   , 3.25 , 3.5  ,\n",
              "       3.571, 3.714])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Avg_Utilization_Ratio = df_cleaned[\"avg_utilization_ratio\"].unique()\n",
        "Avg_Utilization_Ratio.sort()\n",
        "Avg_Utilization_Ratio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDVhSphXJC5L",
        "outputId": "ff57d886-73d0-439c-a4c4-bbbed92bafc1"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.   , 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01 , 0.011,\n",
              "       0.012, 0.013, 0.014, 0.015, 0.016, 0.017, 0.018, 0.019, 0.02 ,\n",
              "       0.021, 0.022, 0.023, 0.024, 0.025, 0.026, 0.027, 0.028, 0.029,\n",
              "       0.03 , 0.031, 0.032, 0.033, 0.034, 0.035, 0.036, 0.037, 0.038,\n",
              "       0.039, 0.04 , 0.041, 0.042, 0.043, 0.044, 0.045, 0.046, 0.047,\n",
              "       0.048, 0.049, 0.05 , 0.051, 0.052, 0.053, 0.054, 0.055, 0.056,\n",
              "       0.057, 0.058, 0.059, 0.06 , 0.061, 0.062, 0.063, 0.064, 0.065,\n",
              "       0.066, 0.067, 0.068, 0.069, 0.07 , 0.071, 0.072, 0.073, 0.074,\n",
              "       0.075, 0.076, 0.077, 0.078, 0.079, 0.08 , 0.081, 0.082, 0.083,\n",
              "       0.084, 0.085, 0.086, 0.087, 0.088, 0.089, 0.09 , 0.091, 0.092,\n",
              "       0.093, 0.094, 0.095, 0.096, 0.097, 0.098, 0.099, 0.1  , 0.101,\n",
              "       0.102, 0.103, 0.104, 0.105, 0.106, 0.107, 0.108, 0.109, 0.11 ,\n",
              "       0.111, 0.112, 0.113, 0.114, 0.115, 0.116, 0.117, 0.118, 0.119,\n",
              "       0.12 , 0.121, 0.122, 0.123, 0.124, 0.125, 0.126, 0.127, 0.128,\n",
              "       0.129, 0.13 , 0.131, 0.132, 0.133, 0.134, 0.135, 0.136, 0.137,\n",
              "       0.138, 0.139, 0.14 , 0.141, 0.142, 0.143, 0.144, 0.145, 0.146,\n",
              "       0.147, 0.148, 0.149, 0.15 , 0.151, 0.152, 0.153, 0.154, 0.155,\n",
              "       0.156, 0.157, 0.158, 0.159, 0.16 , 0.161, 0.162, 0.163, 0.164,\n",
              "       0.165, 0.166, 0.167, 0.168, 0.169, 0.17 , 0.171, 0.172, 0.173,\n",
              "       0.174, 0.175, 0.176, 0.177, 0.178, 0.179, 0.18 , 0.181, 0.182,\n",
              "       0.183, 0.184, 0.185, 0.186, 0.187, 0.188, 0.189, 0.19 , 0.191,\n",
              "       0.192, 0.193, 0.194, 0.195, 0.196, 0.197, 0.198, 0.199, 0.2  ,\n",
              "       0.201, 0.202, 0.203, 0.204, 0.205, 0.206, 0.207, 0.208, 0.209,\n",
              "       0.21 , 0.211, 0.212, 0.213, 0.214, 0.215, 0.216, 0.217, 0.218,\n",
              "       0.219, 0.22 , 0.221, 0.222, 0.223, 0.224, 0.225, 0.226, 0.227,\n",
              "       0.228, 0.229, 0.23 , 0.231, 0.232, 0.233, 0.234, 0.235, 0.236,\n",
              "       0.237, 0.238, 0.239, 0.24 , 0.241, 0.242, 0.243, 0.244, 0.245,\n",
              "       0.246, 0.247, 0.248, 0.249, 0.25 , 0.251, 0.252, 0.253, 0.254,\n",
              "       0.255, 0.256, 0.257, 0.258, 0.259, 0.26 , 0.261, 0.262, 0.263,\n",
              "       0.264, 0.265, 0.266, 0.267, 0.268, 0.269, 0.27 , 0.271, 0.272,\n",
              "       0.273, 0.274, 0.275, 0.276, 0.277, 0.278, 0.279, 0.28 , 0.281,\n",
              "       0.282, 0.283, 0.284, 0.285, 0.286, 0.287, 0.288, 0.289, 0.29 ,\n",
              "       0.291, 0.292, 0.293, 0.294, 0.295, 0.296, 0.297, 0.298, 0.299,\n",
              "       0.3  , 0.301, 0.302, 0.303, 0.304, 0.305, 0.306, 0.307, 0.308,\n",
              "       0.309, 0.31 , 0.311, 0.312, 0.313, 0.314, 0.315, 0.316, 0.317,\n",
              "       0.318, 0.319, 0.32 , 0.321, 0.322, 0.323, 0.324, 0.325, 0.326,\n",
              "       0.327, 0.328, 0.329, 0.33 , 0.331, 0.332, 0.333, 0.334, 0.335,\n",
              "       0.336, 0.337, 0.338, 0.339, 0.34 , 0.341, 0.342, 0.343, 0.344,\n",
              "       0.345, 0.346, 0.347, 0.348, 0.349, 0.35 , 0.351, 0.352, 0.353,\n",
              "       0.354, 0.355, 0.356, 0.357, 0.358, 0.359, 0.36 , 0.361, 0.362,\n",
              "       0.363, 0.364, 0.365, 0.366, 0.367, 0.368, 0.369, 0.37 , 0.371,\n",
              "       0.372, 0.373, 0.374, 0.375, 0.376, 0.377, 0.378, 0.379, 0.38 ,\n",
              "       0.381, 0.382, 0.383, 0.384, 0.385, 0.386, 0.387, 0.388, 0.389,\n",
              "       0.39 , 0.391, 0.392, 0.393, 0.394, 0.395, 0.396, 0.397, 0.398,\n",
              "       0.399, 0.4  , 0.401, 0.402, 0.403, 0.404, 0.405, 0.406, 0.407,\n",
              "       0.408, 0.409, 0.41 , 0.411, 0.412, 0.413, 0.414, 0.415, 0.416,\n",
              "       0.417, 0.418, 0.419, 0.42 , 0.421, 0.422, 0.423, 0.424, 0.425,\n",
              "       0.426, 0.427, 0.428, 0.429, 0.43 , 0.431, 0.432, 0.433, 0.434,\n",
              "       0.435, 0.436, 0.437, 0.438, 0.439, 0.44 , 0.441, 0.442, 0.443,\n",
              "       0.444, 0.445, 0.446, 0.447, 0.448, 0.449, 0.45 , 0.451, 0.452,\n",
              "       0.453, 0.454, 0.455, 0.456, 0.457, 0.458, 0.459, 0.46 , 0.461,\n",
              "       0.462, 0.463, 0.464, 0.465, 0.466, 0.467, 0.468, 0.469, 0.47 ,\n",
              "       0.471, 0.472, 0.473, 0.474, 0.475, 0.476, 0.477, 0.478, 0.479,\n",
              "       0.48 , 0.481, 0.482, 0.483, 0.484, 0.485, 0.486, 0.487, 0.488,\n",
              "       0.489, 0.49 , 0.491, 0.492, 0.493, 0.494, 0.495, 0.496, 0.497,\n",
              "       0.498, 0.499, 0.5  , 0.501, 0.502, 0.503, 0.504, 0.505, 0.506,\n",
              "       0.507, 0.508, 0.509, 0.51 , 0.511, 0.512, 0.513, 0.514, 0.515,\n",
              "       0.516, 0.517, 0.518, 0.519, 0.52 , 0.521, 0.522, 0.523, 0.524,\n",
              "       0.525, 0.526, 0.527, 0.528, 0.529, 0.53 , 0.531, 0.532, 0.533,\n",
              "       0.534, 0.535, 0.536, 0.537, 0.538, 0.539, 0.54 , 0.541, 0.542,\n",
              "       0.543, 0.544, 0.545, 0.546, 0.547, 0.548, 0.549, 0.55 , 0.551,\n",
              "       0.552, 0.553, 0.554, 0.555, 0.556, 0.557, 0.558, 0.559, 0.56 ,\n",
              "       0.561, 0.562, 0.563, 0.564, 0.565, 0.566, 0.567, 0.568, 0.569,\n",
              "       0.57 , 0.571, 0.572, 0.573, 0.574, 0.575, 0.576, 0.577, 0.578,\n",
              "       0.579, 0.58 , 0.581, 0.582, 0.583, 0.584, 0.585, 0.586, 0.587,\n",
              "       0.588, 0.589, 0.59 , 0.591, 0.592, 0.593, 0.594, 0.595, 0.596,\n",
              "       0.597, 0.598, 0.599, 0.6  , 0.601, 0.602, 0.603, 0.604, 0.605,\n",
              "       0.606, 0.607, 0.608, 0.609, 0.61 , 0.611, 0.612, 0.613, 0.614,\n",
              "       0.615, 0.616, 0.617, 0.618, 0.619, 0.62 , 0.621, 0.622, 0.623,\n",
              "       0.624, 0.625, 0.626, 0.627, 0.628, 0.629, 0.63 , 0.631, 0.632,\n",
              "       0.633, 0.634, 0.635, 0.636, 0.637, 0.638, 0.639, 0.64 , 0.641,\n",
              "       0.642, 0.643, 0.644, 0.645, 0.646, 0.647, 0.648, 0.649, 0.65 ,\n",
              "       0.651, 0.652, 0.653, 0.654, 0.655, 0.656, 0.657, 0.658, 0.659,\n",
              "       0.66 , 0.661, 0.662, 0.663, 0.664, 0.665, 0.666, 0.667, 0.668,\n",
              "       0.669, 0.67 , 0.671, 0.672, 0.673, 0.674, 0.675, 0.676, 0.677,\n",
              "       0.678, 0.679, 0.68 , 0.681, 0.682, 0.683, 0.684, 0.685, 0.686,\n",
              "       0.687, 0.688, 0.689, 0.69 , 0.691, 0.692, 0.693, 0.694, 0.695,\n",
              "       0.696, 0.697, 0.698, 0.699, 0.7  , 0.701, 0.702, 0.703, 0.704,\n",
              "       0.705, 0.706, 0.707, 0.708, 0.709, 0.71 , 0.711, 0.712, 0.713,\n",
              "       0.714, 0.715, 0.716, 0.717, 0.718, 0.719, 0.72 , 0.721, 0.722,\n",
              "       0.723, 0.724, 0.725, 0.726, 0.727, 0.728, 0.729, 0.73 , 0.731,\n",
              "       0.732, 0.733, 0.734, 0.735, 0.736, 0.737, 0.738, 0.739, 0.74 ,\n",
              "       0.741, 0.742, 0.743, 0.744, 0.745, 0.746, 0.747, 0.748, 0.749,\n",
              "       0.75 , 0.751, 0.752, 0.753, 0.754, 0.755, 0.756, 0.757, 0.758,\n",
              "       0.759, 0.76 , 0.761, 0.762, 0.763, 0.764, 0.765, 0.766, 0.767,\n",
              "       0.768, 0.769, 0.77 , 0.771, 0.772, 0.773, 0.774, 0.775, 0.776,\n",
              "       0.777, 0.778, 0.779, 0.78 , 0.781, 0.782, 0.783, 0.784, 0.785,\n",
              "       0.786, 0.787, 0.788, 0.789, 0.79 , 0.791, 0.792, 0.793, 0.794,\n",
              "       0.795, 0.796, 0.797, 0.798, 0.799, 0.8  , 0.801, 0.802, 0.803,\n",
              "       0.804, 0.805, 0.806, 0.807, 0.808, 0.809, 0.81 , 0.811, 0.812,\n",
              "       0.813, 0.814, 0.815, 0.816, 0.817, 0.818, 0.819, 0.82 , 0.821,\n",
              "       0.822, 0.823, 0.824, 0.825, 0.826, 0.827, 0.828, 0.829, 0.83 ,\n",
              "       0.831, 0.832, 0.833, 0.834, 0.835, 0.836, 0.837, 0.839, 0.84 ,\n",
              "       0.841, 0.842, 0.843, 0.844, 0.845, 0.846, 0.847, 0.848, 0.849,\n",
              "       0.85 , 0.851, 0.852, 0.853, 0.854, 0.855, 0.856, 0.857, 0.858,\n",
              "       0.859, 0.86 , 0.861, 0.862, 0.863, 0.864, 0.865, 0.866, 0.867,\n",
              "       0.868, 0.869, 0.87 , 0.871, 0.872, 0.873, 0.874, 0.875, 0.876,\n",
              "       0.877, 0.878, 0.879, 0.88 , 0.881, 0.882, 0.883, 0.884, 0.885,\n",
              "       0.886, 0.887, 0.889, 0.891, 0.893, 0.894, 0.895, 0.896, 0.897,\n",
              "       0.898, 0.899, 0.9  , 0.901, 0.902, 0.903, 0.904, 0.905, 0.906,\n",
              "       0.907, 0.909, 0.91 , 0.911, 0.912, 0.913, 0.914, 0.915, 0.916,\n",
              "       0.917, 0.918, 0.919, 0.92 , 0.921, 0.922, 0.924, 0.925, 0.926,\n",
              "       0.927, 0.928, 0.929, 0.93 , 0.931, 0.932, 0.934, 0.935, 0.938,\n",
              "       0.94 , 0.941, 0.942, 0.943, 0.944, 0.945, 0.948, 0.949, 0.951,\n",
              "       0.952, 0.953, 0.954, 0.955, 0.956, 0.958, 0.959, 0.96 , 0.962,\n",
              "       0.963, 0.964, 0.968, 0.971, 0.972, 0.973, 0.976, 0.977, 0.978,\n",
              "       0.983, 0.984, 0.985, 0.987, 0.988, 0.99 , 0.992, 0.994, 0.995,\n",
              "       0.999])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Average and exsiting attrited customer age per card category\n",
        "customers_age = df_cleaned.groupby([\"card_category\", \"attrition_flag\"]).mean()\n",
        "print(customers_age[\"customer_age\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aTafJuTkELlx",
        "outputId": "7b0f964c-083d-483e-e6e3-fc9b98e3830f"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "card_category                                       attrition_flag                                    \n",
            "Blue                                                Attrited Customer                                     46.662278\n",
            "                                                    Existing Customer                                     46.316787\n",
            "Gold                                                Attrited Customer                                     43.857143\n",
            "                                                    Existing Customer                                     45.789474\n",
            "Platinum                                            Attrited Customer                                     49.400000\n",
            "                                                    Existing Customer                                     46.866667\n",
            "Silver                                              Attrited Customer                                     47.158537\n",
            "                                                    Existing Customer                                     45.422833\n",
            "Name: customer_age, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Attrited and existing customer gender per card category\n",
        "customers_gender = df_cleaned.groupby([\"card_category\", \"attrition_flag\", \"gender\"]).count()\n",
        "print(customers_gender[\"clientnum\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XOBZ3h3YHHkI",
        "outputId": "ebe255d4-ec85-46b8-81be-f939ed37373f"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "card_category                                       attrition_flag                                      gender                                            \n",
            "Blue                                                Attrited Customer                                   F                                                      890\n",
            "                                                                                                        M                                                      629\n",
            "                                                    Existing Customer                                   F                                                     4211\n",
            "                                                                                                        M                                                     3706\n",
            "Gold                                                Attrited Customer                                   F                                                        8\n",
            "                                                                                                        M                                                       13\n",
            "                                                    Existing Customer                                   F                                                       30\n",
            "                                                                                                        M                                                       65\n",
            "Platinum                                            Attrited Customer                                   F                                                        4\n",
            "                                                                                                        M                                                        1\n",
            "                                                    Existing Customer                                   F                                                        5\n",
            "                                                                                                        M                                                       10\n",
            "Silver                                              Attrited Customer                                   F                                                       28\n",
            "                                                                                                        M                                                       54\n",
            "                                                    Existing Customer                                   F                                                      182\n",
            "                                                                                                        M                                                      291\n",
            "Name: clientnum, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Attrited and existing customer dependent_count per card category\n",
        "customers_dependents = df_cleaned.groupby([\"card_category\", \"attrition_flag\", \"dependent_count\"]).count()\n",
        "print(customers_dependents[\"clientnum\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "orpWXC7rJuZS",
        "outputId": "7db66ca9-51d5-4e95-bb95-e6cd0ba17b2e"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "card_category                                       attrition_flag                                      dependent_count\n",
            "Blue                                                Attrited Customer                                   0                   126\n",
            "                                                                                                        1                   256\n",
            "                                                                                                        2                   385\n",
            "                                                                                                        3                   456\n",
            "                                                                                                        4                   238\n",
            "                                                                                                        5                    58\n",
            "                                                    Existing Customer                                   0                   713\n",
            "                                                                                                        1                  1483\n",
            "                                                                                                        2                  2098\n",
            "                                                                                                        3                  2090\n",
            "                                                                                                        4                  1203\n",
            "                                                                                                        5                   330\n",
            "Gold                                                Attrited Customer                                   0                     1\n",
            "                                                                                                        1                     3\n",
            "                                                                                                        2                     7\n",
            "                                                                                                        3                     4\n",
            "                                                                                                        4                     5\n",
            "                                                                                                        5                     1\n",
            "                                                    Existing Customer                                   0                     7\n",
            "                                                                                                        1                    11\n",
            "                                                                                                        2                    21\n",
            "                                                                                                        3                    26\n",
            "                                                                                                        4                    25\n",
            "                                                                                                        5                     5\n",
            "Platinum                                            Attrited Customer                                   0                     1\n",
            "                                                                                                        2                     2\n",
            "                                                                                                        3                     1\n",
            "                                                                                                        4                     1\n",
            "                                                    Existing Customer                                   0                     1\n",
            "                                                                                                        1                     1\n",
            "                                                                                                        2                     4\n",
            "                                                                                                        3                     7\n",
            "                                                                                                        4                     2\n",
            "Silver                                              Attrited Customer                                   0                     7\n",
            "                                                                                                        1                    10\n",
            "                                                                                                        2                    23\n",
            "                                                                                                        3                    21\n",
            "                                                                                                        4                    16\n",
            "                                                                                                        5                     5\n",
            "                                                    Existing Customer                                   0                    48\n",
            "                                                                                                        1                    74\n",
            "                                                                                                        2                   115\n",
            "                                                                                                        3                   127\n",
            "                                                                                                        4                    84\n",
            "                                                                                                        5                    25\n",
            "Name: clientnum, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Attrited and existing customer education level per card category\n",
        "customers_education = df_cleaned.groupby([\"card_category\", \"attrition_flag\", \"education_level\"]).count()\n",
        "print(customers_education[\"clientnum\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IWXFvH_HKOBH",
        "outputId": "de4af240-3125-4e64-9aa2-614a80881a0c"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "card_category                                       attrition_flag                                      education_level                                   \n",
            "Blue                                                Attrited Customer                                   College                                                143\n",
            "                                                                                                        Doctorate                                               91\n",
            "                                                                                                        Graduate                                               450\n",
            "                                                                                                        High School                                            287\n",
            "                                                                                                        Post-Graduate                                           83\n",
            "                                                                                                        Uneducated                                             222\n",
            "                                                                                                        Unknown                                                243\n",
            "                                                    Existing Customer                                   College                                                797\n",
            "                                                                                                        Doctorate                                              331\n",
            "                                                                                                        Graduate                                              2449\n",
            "                                                                                                        High School                                           1601\n",
            "                                                                                                        Post-Graduate                                          393\n",
            "                                                                                                        Uneducated                                            1169\n",
            "                                                                                                        Unknown                                               1177\n",
            "Gold                                                Attrited Customer                                   College                                                  1\n",
            "                                                                                                        Doctorate                                                1\n",
            "                                                                                                        Graduate                                                 6\n",
            "                                                                                                        High School                                              4\n",
            "                                                                                                        Post-Graduate                                            1\n",
            "                                                                                                        Uneducated                                               4\n",
            "                                                                                                        Unknown                                                  4\n",
            "                                                    Existing Customer                                   College                                                 14\n",
            "                                                                                                        Doctorate                                                4\n",
            "                                                                                                        Graduate                                                30\n",
            "                                                                                                        High School                                             22\n",
            "                                                                                                        Post-Graduate                                            4\n",
            "                                                                                                        Uneducated                                               7\n",
            "                                                                                                        Unknown                                                 14\n",
            "Platinum                                            Attrited Customer                                   Doctorate                                                1\n",
            "                                                                                                        Graduate                                                 3\n",
            "                                                                                                        Post-Graduate                                            1\n",
            "                                                    Existing Customer                                   Doctorate                                                1\n",
            "                                                                                                        Graduate                                                 5\n",
            "                                                                                                        High School                                              1\n",
            "                                                                                                        Post-Graduate                                            2\n",
            "                                                                                                        Uneducated                                               3\n",
            "                                                                                                        Unknown                                                  3\n",
            "Silver                                              Attrited Customer                                   College                                                 10\n",
            "                                                                                                        Doctorate                                                2\n",
            "                                                                                                        Graduate                                                28\n",
            "                                                                                                        High School                                             15\n",
            "                                                                                                        Post-Graduate                                            7\n",
            "                                                                                                        Uneducated                                              11\n",
            "                                                                                                        Unknown                                                  9\n",
            "                                                    Existing Customer                                   College                                                 48\n",
            "                                                                                                        Doctorate                                               20\n",
            "                                                                                                        Graduate                                               157\n",
            "                                                                                                        High School                                             83\n",
            "                                                                                                        Post-Graduate                                           25\n",
            "                                                                                                        Uneducated                                              71\n",
            "                                                                                                        Unknown                                                 69\n",
            "Name: clientnum, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Attrited and existing customer marital status per card category\n",
        "customers_status = df_cleaned.groupby([\"card_category\", \"attrition_flag\", \"marital_status\"]).count()\n",
        "print(customers_status[\"clientnum\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "egiHSqqdKhwV",
        "outputId": "c5e83223-b366-4273-b677-9db7b0c7440e"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "card_category                                       attrition_flag                                      marital_status                                    \n",
            "Blue                                                Attrited Customer                                   Divorced                                               114\n",
            "                                                                                                        Married                                                674\n",
            "                                                                                                        Single                                                 611\n",
            "                                                                                                        Unknown                                                120\n",
            "                                                    Existing Customer                                   Divorced                                               582\n",
            "                                                                                                        Married                                               3759\n",
            "                                                                                                        Single                                                3013\n",
            "                                                                                                        Unknown                                                563\n",
            "Gold                                                Attrited Customer                                   Married                                                  6\n",
            "                                                                                                        Single                                                  11\n",
            "                                                                                                        Unknown                                                  4\n",
            "                                                    Existing Customer                                   Divorced                                                 5\n",
            "                                                                                                        Married                                                 35\n",
            "                                                                                                        Single                                                  47\n",
            "                                                                                                        Unknown                                                  8\n",
            "Platinum                                            Attrited Customer                                   Married                                                  2\n",
            "                                                                                                        Single                                                   3\n",
            "                                                    Existing Customer                                   Divorced                                                 1\n",
            "                                                                                                        Married                                                  5\n",
            "                                                                                                        Single                                                   7\n",
            "                                                                                                        Unknown                                                  2\n",
            "Silver                                              Attrited Customer                                   Divorced                                                 7\n",
            "                                                                                                        Married                                                 27\n",
            "                                                                                                        Single                                                  43\n",
            "                                                                                                        Unknown                                                  5\n",
            "                                                    Existing Customer                                   Divorced                                                39\n",
            "                                                                                                        Married                                                179\n",
            "                                                                                                        Single                                                 208\n",
            "                                                                                                        Unknown                                                 47\n",
            "Name: clientnum, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Attrited and existing customer income per card category\n",
        "customers_income = df_cleaned.groupby([\"card_category\", \"attrition_flag\", \"income_category\"]).count()\n",
        "print(customers_income[\"clientnum\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P9sJn5QLKzDw",
        "outputId": "ee164f0b-b1f0-422e-c279-11d000f02f11"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "card_category                                       attrition_flag                                      income_category                                   \n",
            "Blue                                                Attrited Customer                                   $120K +                                                113\n",
            "                                                                                                        $40K - $60K                                            257\n",
            "                                                                                                        $60K - $80K                                            172\n",
            "                                                                                                        $80K - $120K                                           215\n",
            "                                                                                                        Less than $40K                                         586\n",
            "                                                                                                        Unknown                                                176\n",
            "                                                    Existing Customer                                   $120K +                                                532\n",
            "                                                                                                        $40K - $60K                                           1418\n",
            "                                                                                                        $60K - $80K                                           1101\n",
            "                                                                                                        $80K - $120K                                          1180\n",
            "                                                                                                        Less than $40K                                        2817\n",
            "                                                                                                        Unknown                                                869\n",
            "Gold                                                Attrited Customer                                   $120K +                                                  2\n",
            "                                                                                                        $40K - $60K                                              2\n",
            "                                                                                                        $60K - $80K                                              6\n",
            "                                                                                                        $80K - $120K                                             5\n",
            "                                                                                                        Less than $40K                                           4\n",
            "                                                                                                        Unknown                                                  2\n",
            "                                                    Existing Customer                                   $120K +                                                 16\n",
            "                                                                                                        $40K - $60K                                             13\n",
            "                                                                                                        $60K - $80K                                             23\n",
            "                                                                                                        $80K - $120K                                            16\n",
            "                                                                                                        Less than $40K                                          20\n",
            "                                                                                                        Unknown                                                  7\n",
            "Platinum                                            Attrited Customer                                   $40K - $60K                                              1\n",
            "                                                                                                        Less than $40K                                           2\n",
            "                                                                                                        Unknown                                                  2\n",
            "                                                    Existing Customer                                   $120K +                                                  4\n",
            "                                                                                                        $60K - $80K                                              4\n",
            "                                                                                                        $80K - $120K                                             2\n",
            "                                                                                                        Less than $40K                                           2\n",
            "                                                                                                        Unknown                                                  3\n",
            "Silver                                              Attrited Customer                                   $120K +                                                 11\n",
            "                                                                                                        $40K - $60K                                             11\n",
            "                                                                                                        $60K - $80K                                             11\n",
            "                                                                                                        $80K - $120K                                            22\n",
            "                                                                                                        Less than $40K                                          20\n",
            "                                                                                                        Unknown                                                  7\n",
            "                                                    Existing Customer                                   $120K +                                                 49\n",
            "                                                                                                        $40K - $60K                                             88\n",
            "                                                                                                        $60K - $80K                                             85\n",
            "                                                                                                        $80K - $120K                                            95\n",
            "                                                                                                        Less than $40K                                         110\n",
            "                                                                                                        Unknown                                                 46\n",
            "Name: clientnum, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Total and average transaction count by card category\n",
        "transaction_count = df_cleaned.groupby(\"card_category\").sum()\n",
        "print(transaction_count[\"total_trans_amt\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oPzU_4k2LcUD",
        "outputId": "c12d8458-e3b3-440f-93e5-b315830203d2"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "card_category\n",
            "Blue                                                  39870938\n",
            "Gold                                                    891531\n",
            "Platinum                                                179995\n",
            "Silver                                                 3657718\n",
            "Name: total_trans_amt, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "transaction_avg = df_cleaned.groupby(\"card_category\").mean()\n",
        "print(transaction_avg[\"total_trans_amt\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2td8KalFMyaH",
        "outputId": "fe6e6b15-9acf-42a3-9463-b5e0e85caafc"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "card_category\n",
            "Blue                                                  4225.406740\n",
            "Gold                                                  7685.612069\n",
            "Platinum                                              8999.750000\n",
            "Silver                                                6590.482883\n",
            "Name: total_trans_amt, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Total revolving balance for each credit card category of attrited and existing customers\n",
        "total_revolving_balance = df_cleaned.groupby([\"card_category\", \"attrition_flag\"])[\"total_revolving_bal\"].sum()\n",
        "print(total_revolving_balance)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZ9Rk4j8rgUs",
        "outputId": "634793b6-a475-4293-a8f3-eaaffd05ac3f"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "card_category                                       attrition_flag                                    \n",
            "Blue                                                Attrited Customer                                     1016265\n",
            "                                                    Existing Customer                                     9908851\n",
            "Gold                                                Attrited Customer                                       21567\n",
            "                                                    Existing Customer                                      134374\n",
            "Platinum                                            Attrited Customer                                        1317\n",
            "                                                    Existing Customer                                       24042\n",
            "Silver                                              Attrited Customer                                       55534\n",
            "                                                    Existing Customer                                      613868\n",
            "Name: total_revolving_bal, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Total revolving balance for each defined customer age segment (months on book)\n",
        "total_revolving_balance_age = df_cleaned.groupby([\"months_on_book\"])[\"total_revolving_bal\"].sum().sort_index()\n",
        "print(total_revolving_balance_age)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "COGqNn8OvInh",
        "outputId": "f89e3d6a-1b8b-4939-cd84-66a636e35be7"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "months_on_book\n",
            "13      83272\n",
            "14      17983\n",
            "15      32211\n",
            "16      33558\n",
            "17      49569\n",
            "18      69699\n",
            "19      73856\n",
            "20      80724\n",
            "21      93409\n",
            "22     113811\n",
            "23     131741\n",
            "24     195395\n",
            "25     193021\n",
            "26     226431\n",
            "27     238408\n",
            "28     302919\n",
            "29     306155\n",
            "30     348573\n",
            "31     370254\n",
            "32     334923\n",
            "33     349896\n",
            "34     410439\n",
            "35     371936\n",
            "36    2837274\n",
            "37     401043\n",
            "38     395211\n",
            "39     411423\n",
            "40     379066\n",
            "41     349959\n",
            "42     317263\n",
            "43     322362\n",
            "44     273616\n",
            "45     248845\n",
            "46     227283\n",
            "47     215208\n",
            "48     195665\n",
            "49     159871\n",
            "50     100714\n",
            "51      73240\n",
            "52      77426\n",
            "53     111972\n",
            "54      71092\n",
            "55      55800\n",
            "56     123302\n",
            "Name: total_revolving_bal, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cleaned.hist(figsize=(20, 30))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "z1N2OI6_RhL1",
        "outputId": "007766b4-1224-41a4-e86c-8a697e9b42f5"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'clientnum'}>,\n",
              "        <Axes: title={'center': 'customer_age'}>,\n",
              "        <Axes: title={'center': 'dependent_count'}>,\n",
              "        <Axes: title={'center': 'months_on_book'}>],\n",
              "       [<Axes: title={'center': 'total_relationship_count'}>,\n",
              "        <Axes: title={'center': 'months_inactive_12_mon'}>,\n",
              "        <Axes: title={'center': 'contacts_count_12_mon'}>,\n",
              "        <Axes: title={'center': 'credit_limit'}>],\n",
              "       [<Axes: title={'center': 'total_revolving_bal'}>,\n",
              "        <Axes: title={'center': 'avg_open_to_buy'}>,\n",
              "        <Axes: title={'center': 'total_amt_chng_q4_q1'}>,\n",
              "        <Axes: title={'center': 'total_trans_amt'}>],\n",
              "       [<Axes: title={'center': 'total_trans_ct'}>,\n",
              "        <Axes: title={'center': 'total_ct_chng_q4_q1'}>,\n",
              "        <Axes: title={'center': 'avg_utilization_ratio'}>,\n",
              "        <Axes: title={'center': 'Weekly_median_earnings'}>],\n",
              "       [<Axes: title={'center': 'Yearly_median_earnings'}>, <Axes: >,\n",
              "        <Axes: >, <Axes: >]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x2160 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Export cleaned DataFrame to csv to create a database\n",
        "# df_cleaned.to_csv('all_data_cleaned.csv', index=None) \n",
        "# files.download('all_data_cleaned.csv')"
      ],
      "metadata": {
        "id": "ZPqNOra5R8hM"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pi7ryM_ZHlpH"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}