case_study_churn_data.ipynb

{
  "cells": [
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      "cell_type": "code",
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      "metadata": {
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      "source": [
        "# Import necessary libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import io\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "import matplotlib.ticker as mtick\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import accuracy_score, auc, confusion_matrix, roc_auc_score, roc_curve, recall_score\n",
        "from sklearn.model_selection import (train_test_split, GridSearchCV, RandomizedSearchCV)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "height": 74
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              "      Upload widget is only available when the cell has been executed in the\n",
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              "      <script>// Copyright 2017 Google LLC\n",
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              "// 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",
              " */\n",
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              "\n",
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              "\n",
              "scope.google = scope.google || {};\n",
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              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
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              "})(self);\n",
              "</script> "
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          "output_type": "stream",
          "text": [
            "Saving bank_customer_churn_dataset.csv to bank_customer_churn_dataset.csv\n"
          ]
        }
      ],
      "source": [
        "# Upload data file in the drive\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8TrKg2t5v6LY"
      },
      "outputs": [],
      "source": [
        "# Store dataset in a Pandas Dataframe\n",
        "df = pd.read_csv(io.BytesIO(uploaded['bank_customer_churn_dataset.csv']))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_v9qUWNm1ol1",
        "outputId": "830d9ea7-1e11-4fa9-9230-d5b59ef45eab"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 12 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   customer_id       10000 non-null  int64  \n",
            " 1   credit_score      10000 non-null  int64  \n",
            " 2   country           10000 non-null  object \n",
            " 3   gender            10000 non-null  object \n",
            " 4   age               10000 non-null  int64  \n",
            " 5   tenure            10000 non-null  int64  \n",
            " 6   balance           10000 non-null  float64\n",
            " 7   products_number   10000 non-null  int64  \n",
            " 8   credit_card       10000 non-null  int64  \n",
            " 9   active_member     10000 non-null  int64  \n",
            " 10  estimated_salary  10000 non-null  float64\n",
            " 11  churn             10000 non-null  int64  \n",
            "dtypes: float64(2), int64(8), object(2)\n",
            "memory usage: 937.6+ KB\n"
          ]
        }
      ],
      "source": [
        "# Descriptive statistics of dataframe\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "KthJNQu7MOI-",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_id</th>\n",
              "      <th>credit_score</th>\n",
              "      <th>age</th>\n",
              "      <th>tenure</th>\n",
              "      <th>balance</th>\n",
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              "      <th>churn</th>\n",
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              "  </thead>\n",
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              "      <th>count</th>\n",
              "      <td>1.000000e+04</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
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              "      <td>650.528800</td>\n",
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              "      <th>std</th>\n",
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              "      <th>min</th>\n",
              "      <td>1.556570e+07</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>11.580000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
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              "      <td>3.000000</td>\n",
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              "      <td>51002.110000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.569074e+07</td>\n",
              "      <td>652.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>97198.540000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>100193.915000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.575323e+07</td>\n",
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              "      <td>44.000000</td>\n",
              "      <td>7.000000</td>\n",
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              "      <td>2.000000</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
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              "      <td>10.000000</td>\n",
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              "      <td>199992.480000</td>\n",
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            "text/plain": [
              "        customer_id  credit_score           age        tenure        balance  \\\n",
              "count  1.000000e+04  10000.000000  10000.000000  10000.000000   10000.000000   \n",
              "mean   1.569094e+07    650.528800     38.921800      5.012800   76485.889288   \n",
              "std    7.193619e+04     96.653299     10.487806      2.892174   62397.405202   \n",
              "min    1.556570e+07    350.000000     18.000000      0.000000       0.000000   \n",
              "25%    1.562853e+07    584.000000     32.000000      3.000000       0.000000   \n",
              "50%    1.569074e+07    652.000000     37.000000      5.000000   97198.540000   \n",
              "75%    1.575323e+07    718.000000     44.000000      7.000000  127644.240000   \n",
              "max    1.581569e+07    850.000000     92.000000     10.000000  250898.090000   \n",
              "\n",
              "       products_number  credit_card  active_member  estimated_salary  \\\n",
              "count     10000.000000  10000.00000   10000.000000      10000.000000   \n",
              "mean          1.530200      0.70550       0.515100     100090.239881   \n",
              "std           0.581654      0.45584       0.499797      57510.492818   \n",
              "min           1.000000      0.00000       0.000000         11.580000   \n",
              "25%           1.000000      0.00000       0.000000      51002.110000   \n",
              "50%           1.000000      1.00000       1.000000     100193.915000   \n",
              "75%           2.000000      1.00000       1.000000     149388.247500   \n",
              "max           4.000000      1.00000       1.000000     199992.480000   \n",
              "\n",
              "              churn  \n",
              "count  10000.000000  \n",
              "mean       0.203700  \n",
              "std        0.402769  \n",
              "min        0.000000  \n",
              "25%        0.000000  \n",
              "50%        0.000000  \n",
              "75%        0.000000  \n",
              "max        1.000000  "
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Descriptive statistics\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ScgQTafM36U",
        "outputId": "78b5b3d2-3c20-4042-b33d-097c9dbc18f0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "customer_id         False\n",
              "credit_score        False\n",
              "country             False\n",
              "gender              False\n",
              "age                 False\n",
              "tenure              False\n",
              "balance             False\n",
              "products_number     False\n",
              "credit_card         False\n",
              "active_member       False\n",
              "estimated_salary    False\n",
              "churn               False\n",
              "dtype: bool"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Check any missing value\n",
        "df.isna().any()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "cIkMLOJaB9mG",
        "outputId": "07984001-bd4f-4713-db8f-64249648ae5f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(df.credit_score, facecolor='g')\n",
        "plt.xlabel('Credit Scores')\n",
        "plt.ylabel('Customers Number')\n",
        "plt.title('Histogram of Credit Scores')\n",
        "plt.grid(True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nMuhfIwC3VQG"
      },
      "source": [
        "The \"best\" customers mean who's credit score is highest, that is according to the definition of credit score, higher credit score is the best, which depends on credit behavior, such as how likely they are to pay a loan back on time."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CELmBYwJ2DYF",
        "outputId": "534847f2-8a35-43ba-8e45-9eb01c0f4ff4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "850\n"
          ]
        }
      ],
      "source": [
        "# Find highest credit_score customers\n",
        "best_customers = df[(df['credit_score'] == max(df.credit_score))]\n",
        "print(max(df.credit_score))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ivuCcYDAAjS_",
        "outputId": "3e27b144-b808-46fd-fa7a-646eaffa81b4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "4       15737888\n",
              "38      15717426\n",
              "180     15716334\n",
              "200     15604482\n",
              "223     15733247\n",
              "          ...   \n",
              "9581    15709256\n",
              "9646    15603111\n",
              "9688    15730579\n",
              "9931    15647800\n",
              "9949    15798615\n",
              "Name: customer_id, Length: 233, dtype: int64"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "best_customers['customer_id']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "FTt_RVpjE_7Y",
        "outputId": "eb09486b-76a4-42c5-d645-9401411edf1b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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PJa3dizLNzKwPyhwpnE9KDI8BL5GuRoqI2KGrF0XE1ZImlYxjP2BORLwELJK0ENgVuK7k683MrB+USQpnAx8D7mT1OYW++Jykw4CbgS9FxDPAlsD1NcssydNeR9IMYAbA2LFjaWlp6YeQ6qu1tXVQxt0Xw7HOE9afwGnbnFb3chu1nYfjezwU61wmKTwREXP7qbwzgZNIJ6pPAmYCH+/JCiJiFqnXVpqamqK5ubmfQquflpYWBmPcfTEc6zxz9kyOua/+f+eJQ6LuZcLwfI+HYp3LJIVbJf0SuJzUfAT07pLUiFjWNizpLOCKPPoIMLFm0Ql5mpmZ1VGZpDCClAzeWzOtV5ekShoXEW036DkAWJCH5wK/lHQ6MB6YArS/25uZmVWszP0UjuzNiiXNBpqBMZKWAF8HmiXtSEoqDwGfymXcJeki4G7SrT4/GxGv9qZcMzPrvTJ/XvsZ+VactSKiy3MBEXFIB5PP7mL5U4BTuovHzMyqU6b56Iqa4Q1IzT6PVhOOmZk1Upnmo1/XjudmoWsri8jMzBqmzD+a25sCbNHfgZiZWeOVOafQdl+FNo8Bx1YWkZmZNUyZ5iPfV8HMbJjo6s5rb+rqhRHxj/4Px8zMGqmrI4Xfsvp2nG0C2Jx0TsG9mJqZDTFd3Y7zbbXjucfTY4G9gG9WGpWZmTVEt1cfSZoi6Vzg98B8YGpE/KDqwMzMrP66OqewPfA10o1vvg0c5a4nzMyGtq7OKdwOLCadW9gV2FVafXohIo6uNjQzM6u3rpJCj+5zYGZmg19XJ5rPq2cgZmbWeL3p5sLMzIYoJwUzMys4KZiZWaHM/xS+LWmUpHUlzZP0hKRD6xGcmZnVV5kjhfdGxHLgA6RbaE4GvlxlUGZm1hhlksK6+fn9wMUR8VyF8ZiZWQOVuR3nXEn3Ai8An5G0OfBitWGZmVkjdHmkIGkt4HJgd6ApIlYBzwP71SE2MzOrsy6TQkS8BvwoIp5u6/coIlZGxGN1ic7MzOqqzDmFeZI+pNqOj8zMbEgqkxQ+BVwMvCxpuaQVkpZXHJeZmTWA79FsZmaFMn9ek6RDJf1nHp8oadfqQzMzs3or03z0Y2A34F/zeCvwo8oiMjOzhinzP4V3RsROkm4FiIhnJK1XcVxmZtYAZY4UVklaGwiA/Oe11yqNyszMGqJMUjgDuBTYQtIpwLXANyuNyszMGqLM1Ue/kDQf2BMQsH9E3FN5ZGZmVndlzikALAOuycuPkLRTRNxSXVhmZtYI3SYFSScBRwAPkM8r5Of3VBeWmZk1QpkjhYOArSPi5aqDMTOzxipzonkBMLrqQMzMrPHKHCl8C7hV0gLgpbaJEfEvlUVlZmYNUSYpnAf8F3AnPfh/gqRzSLfwfDwits/TNgUuBCaRbu15UP4znIDvA/uQ7tdwhE9km5nVX5nmo+cj4oyIuCoi/tL2KPG6c4G92007DpgXEVOAeXkc4H3AlPyYAZxZKnozM+tXZZLCNZK+JWk3STu1Pbp7UURcDTzdbvJ+pCMP8vP+NdPPj+R6YLSkcSXrYGZm/aRM89E78vO0mmm9vSR1bEQszcOPAWPz8JbA4prlluRpS2lH0gzS0QRjx46lpaWlF2E0Vmtr66CMuy+GY50nrD+B07Y5re7lNmo7D8f3eCjWucw/mqdXUXBEhKTofsnXvW4WMAugqakpmpub+zu0yrW0tDAY4+6L4VjnmbNncsx9x9S93Dikx1+rfjEc3+OhWOcy91PYRNLpkm7Oj5mSNullecvamoXy8+N5+iPAxJrlJuRpZmZWR2XOKZwDrCD9ie0gYDnws16WNxc4PA8fDlxWM/2wfEOfacBzNc1MZmZWJ2XOKWwdER+qGT9R0m3dvUjSbKAZGCNpCfB14FTgIklHAQ+TkgzA70iXoy4kXZJ6ZOkamJlZvymTFF6QtEdEXAsg6V3AC929KCIO6WTWnh0sG8BnS8RiZmYVKpMUPg2cX3Me4RlWNwGZmdkQUiYpLI+It0saBRARyyW9ueK4zMysAcqcaP41pGQQEcvztF9VF5KZmTVKp0cKkrYFtgM2kfTBmlmjgA2qDszMzOqvq+ajt5I6tBsN7FszfQXwySqDMjOzxug0KUTEZcBlknaLiOvqGJOZmTVImXMKB0gaJWldSfMkPSHp0MojMzOzuiuTFN6bTzB/gHQPhMnAl6sMyszMGqNMUlg3P78fuDginqswHjMza6Ay/1O4XNK9pH8xf0bS5sCL1YZlZmaN0O2RQkQcB+wONEXEKmAl6aY4ZmY2xHR7pCDpsJrh2lnnVxGQmZk1Tpnmo11qhjcgdWh3C04KZmZDTpk7r32+dlzSaGBOZRGZmVnDlLn6qL2VgDvEMzMbgsqcU7gcaLvp61rAVOCiKoMyM7PGKHNO4bSa4VeAhyNiSUXxmJlZA3XVS+pkYGxE/KXd9HdJWj8iHqg8OjMzq6uuzil8D1jewfTleZ6ZmQ0xXSWFsRFxZ/uJedqkyiIyM7OG6SopjO5i3oj+DsTMzBqvq6Rws6TX3UxH0ieA+dWFZGZmjdLV1UdfBC6V9FFWJ4EmYD3ggKoDMzOz+uvqzmvLgN0lTQe2z5N/GxF/rktkZmZWd2W6ubgKuKoOsZiZWYP1ppsLMzMbopwUzMys4KRgZmYFJwUzMys4KZiZWcFJwczMCk4KZmZWcFIwM7OCk4KZmRWcFMzMrFDmdpz9TtJDwArgVeCViGiStClwIeleDQ8BB0XEM42Iz8xsuGrkkcL0iNgxIpry+HHAvIiYAszL42ZmVkcDqfloP+C8PHwesH8DYzEzG5YUEfUvVFoEPAME8JOImCXp2YgYnecLeKZtvN1rZwAzAMaOHbvznDlz6hh5/2htbWXkyJGNDqOuhmOdlz29jCUvLal7uTuP27nuZcLwfI8Ha52nT58+v6aVZg0NOacA7BERj0jaAvijpHtrZ0ZESOowW0XELGAWQFNTUzQ3N1cebH9raWlhMMbdF8OxzjNnz+SY+46pe7lxSP139GB4vsdDsc4NaT6KiEfy8+PApcCuwDJJ4wDy8+ONiM3MbDire1KQtJGkjduGgfcCC4C5wOF5scOBy+odm5nZcNeI5qOxpHs/t5X/y4j4b0k3ARdJOgp4GDioAbGZmQ1rdU8KEfEg8PYOpj8F7FnveMzMbLWBdEmqmZk1mJOCmZkVnBTMzKzgpGBmZgUnBTMzKzgpmJlZwUnBzMwKTgpmZlZwUjAzs4KTgpmZFZwUzMys4KRgZmYFJwUzMys4KZiZWcFJwczMCk4KZmZWcFIwM7OCk4KZmRWcFMzMrOCkYGZmBScFMzMrOCmYmVnBScHMzApOCmZmVnBSMDOzgpOCmZkVnBTMzKzgpGBmZgUnBTMzKzgpmJlZwUnBzMwKTgpmZlZwUjAzs4KTgpmZFZwUzMys4KRgZmaFAZcUJO0t6e+SFko6rtHxmJkNJwMqKUhaG/gR8D5gKnCIpKmNjcrMbPgYUEkB2BVYGBEPRsTLwBxgvwbHZGY2bKzT6ADa2RJYXDO+BHhn7QKSZgAz8mirpL/XKbb+NAZ4stFB1JnrXCc6QfUuso3f48Fjq85mDLSk0K2ImAXManQcfSHp5ohoanQc9eQ6D33Drb4wNOs80JqPHgEm1oxPyNPMzKwOBlpSuAmYIunNktYDDgbmNjgmM7NhY0A1H0XEK5I+B/wBWBs4JyLuanBYVRjUzV+95DoPfcOtvjAE66yIaHQMZmY2QAy05iMzM2sgJwUzMys4KfSRpH+XdJekBZJmS9qgZt4ZklprxreSNE/SHZJaJE3oZJ3rSZol6T5J90r6UD3qUlZFdT5E0p15uf+WNKYedSmrozpLOlfSIkm35ceOeVnl7bAw12enTta5c67zwrx8w/5g0JH+rrOkDSX9Nn+m75J0av1r1bUq3ueadc+VtKA+NemDiPCjlw/Sn+0WASPy+EXAEXm4CbgAaK1Z/mLg8Dz8HuCCTtZ7InByHl4LGNPoulZZZ9IFD4+31RP4NnBCo+vaXZ2Bc4EDO1h+H+D3gIBpwA2drPfGPF95+fc1uq5V1hnYEJieh9cDrhnqda5Z9oPAL4EFja5ndw8fKfTdOsAISeuQPvSP5j6cvgN8pd2yU4E/5+Gr6LwLj48D3wKIiNciYqD9Y7K/66z82CjvLY8CHq0i8D54XZ27WHY/4PxIrgdGSxpXu0AeHxUR10f61Tgf2L+i2HurX+scEc9HxFV5+GXgFtJ/kQaSfq0zgKSRwP8FTq4i4P7mpNAHEfEIcBrwD2Ap8FxEXAl8DpgbEUvbveR20h4DwAHAxpI2q11A0ug8eJKkWyRdLGlsZZXooSrqHBGrgM8Ad5K+hFOBsyurRA91UWeAU3LTwXclrZ+nddRdy5btVrtlnt7VMg1TUZ0L+XO+LzCv34PvpQrrfBIwE3i+msj7l5NCH0h6A2lv4c3AeNKe7mHAh4EfdPCSY4B3S7oVeDfp39qvtltmHdLe098iYifgOtIHdUCoos6S1iUlhXfkdd4BHF9VHXqqkzofSopxW2AXYFPg2IYF2c+qrHPeC58NnBERD/Zb0H1URZ3z+YetI+LS/o+4Gk4KfbMXsCginsh7u5eQzgdMBhZKegjYUNJCgIh4NCI+GBHvAL6Wpz3bbp1PkfYoLsnjFwNdnsCqsyrqvGOe/kBuSrkI2L0utSmnozrvHhFLc9PBS8DPSL38QrnuWh5hzaaTgdalSxV1bjMLuD8ivldR7L1VRZ13A5ry9+JaYBtJLRXWoc+cFPrmH8C0fFWFgD2B0yPijRExKSImAc9HxGQASWMktW3z44Fz2q8w/yheDjTnSXsCd1dbjR7p9zqTvkhTJW2ex/8ZuKfSWvRMR3W+p639OE/bH2i7smQucFi+OmUaqRlijWa1PL5c0rT8+sOAy+pUnzL6vc75dScDmwBfrEcleqiK9/nMiBifvxd7APdFRHN9qtNL9TqjPVQfpL3ke0kflAuA9dvNr70S50DgfuA+4Ke1ywK31QxvBVxNakaZB7yp0fWsQ50/TUoEd5CS4maNrmd3dSadQL8zT/s5MDIvK9LNoh7I85s6qXNTfu0DwA/JPQwMlEd/15m0Jx35fb4tPz7R6HpW/T7XTJvEILj6yN1cmJlZwc1HZmZWcFIwM7OCk4KZmRWcFMzMrOCkYGZmBScFG7IkvVHSHEkPSJov6XeStunD+s6VdGAe/qmkqXn4q1285uNa3fvrAkmd9XdlNiD4klQbkvIfjf4GnBcR/z9PezupE7prapZbJyJeKbnOc4ErIuJX7aa3RsTIDpafAPwF2Ckinssdo20eEYv6UK/S8Zr1ho8UbKiaDqxqSwgAEXF7RFwjqVnSNZLmAndLWlvSdyTdlPfoPwVFf/k/lPR3SX8Ctmhbl9K9IZqU7gkwQqmf/V+0i2ELYAXQmstvbUsIkiZL+pOk25U6Ptw6l/edfERxp6SP5GXLxjtO0tU5lgWS/ndVG9eGrnUaHYBZRbYH5ncxfydg+4hYJGkGqYuCXZR6wPyrpCtJHfS9ldRr61hSdyNrdNMREcdJ+lxE7NhBGbcDy4BFkuYBl0TE5XneL4BTI+JSpZsUrUXqTXZH4O3AGOAmSVf3IN4PAn+IiFOUujLfsPzmMkucFGy4urGmGee9wA5t5wtIffNMAf4JmB0Rr5LuGfHnDtbTqYh4VdLepN419wS+K2lnUjfKW0buOTMiXgSQtEdNecsk/SW/dnnJeG8CzlHqdfY3EXFbT+I1AycFG7ruIvW71JmVNcMCPh8Rf6hdQNI+fQ0i0km7G4EbJf2R1MvmzF6sqtt4AST9E/B+4FxJp0fE+b0oy4Yxn1OwoerPwPq5qQUASTt00s7+B+AzeQ8bSdtI2ojUKeFHchv+ONJ5io6santtLUnjteZ9e3cEHo6IFcASSfvn5daXtCHp9pRt5W1OOlK5sWy8krYClkXEWaTOBwdSl+s2SPhIwYakiAhJBwDfk3Qs8CLwEKnL5vZ3x/opqQfLW/JVS0+Quki+lHRf6btJ3Spf10lxs4A7JN0SER+tmb4ucJqk8bn8J0i9wQJ8DPiJpG8Aq0g3KbqU1P/+7aTeRL8SEY9J2rZkvM3AlyWtIp3cPqzrrWT2er4k1USenIAAAAAvSURBVMzMCm4+MjOzgpOCmZkVnBTMzKzgpGBmZgUnBTMzKzgpmJlZwUnBzMwK/wOMG7fyPicYeQAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(best_customers.credit_score, facecolor='g')\n",
        "plt.xlabel('Credit Scores')\n",
        "plt.ylabel('Customers Number')\n",
        "plt.title('Histogram of Best Customer Credit Scores')\n",
        "plt.grid(True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yUhuPSjd0GPN"
      },
      "outputs": [],
      "source": [
        "# Define a function for histrogram\n",
        "def hist(feature):\n",
        "    group_df = df.groupby([feature, 'churn']).size().reset_index()\n",
        "    group_df = group_df.rename(columns = {0: 'Count'})\n",
        "    group_df['churn'] = group_df['churn'].map({0 : 'No', 1: 'Yes'})\n",
        "    fig = px.histogram(group_df, x = feature, y = 'Count', color = 'churn', \n",
        "                       marginal = 'box', \n",
        "                       title = f'Churn frequency to {feature} distribution', \n",
        "                       color_discrete_sequence = [\"blue\", \"pink\"])\n",
        "    print(group_df)\n",
        "    fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 792
        },
        "id": "ztLKChAiKAAk",
        "outputId": "5c16c000-6782-49fa-c32f-887cf5755f1f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     credit_score churn  Count\n",
            "0             350   Yes      5\n",
            "1             351   Yes      1\n",
            "2             358   Yes      1\n",
            "3             359   Yes      1\n",
            "4             363   Yes      1\n",
            "..            ...   ...    ...\n",
            "857           848    No      5\n",
            "858           849    No      6\n",
            "859           849   Yes      2\n",
            "860           850    No    190\n",
            "861           850   Yes     43\n",
            "\n",
            "[862 rows x 3 columns]\n"
          ]
        },
        {
          "data": {
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      "source": [
        "hist('credit_score')"
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          "text": [
            "     age churn  Count\n",
            "0     18    No     20\n",
            "1     18   Yes      2\n",
            "2     19    No     26\n",
            "3     19   Yes      1\n",
            "4     20    No     38\n",
            "..   ...   ...    ...\n",
            "123   84    No      1\n",
            "124   84   Yes      1\n",
            "125   85    No      1\n",
            "126   88    No      1\n",
            "127   92    No      2\n",
            "\n",
            "[128 rows x 3 columns]\n"
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      "source": [
        "hist('age')"
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      "source": [
        "The context of comparison is different depending on separate variables. age group (40-49) is more critical as well as credit score 600 to 700."
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          "text": [
            "     age churn  Count\n",
            "0     18    No     20\n",
            "1     18   Yes      2\n",
            "2     19    No     26\n",
            "3     19   Yes      1\n",
            "4     20    No     38\n",
            "..   ...   ...    ...\n",
            "123   84    No      1\n",
            "124   84   Yes      1\n",
            "125   85    No      1\n",
            "126   88    No      1\n",
            "127   92    No      2\n",
            "\n",
            "[128 rows x 3 columns]\n"
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      ],
      "source": [
        "hist('age')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "28iBbc5YdTsQ"
      },
      "source": [
        "From the above plot, we can see that age group (40-49) is more likely to churn."
      ]
    },
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      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "V9zazpO4KqvY",
        "outputId": "49bbc985-5d8f-4bd5-ea20-097eb1c88d9a"
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          "data": {
            "text/plain": [
              "0    7963\n",
              "1    2037\n",
              "Name: churn, dtype: int64"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Frequency of Churn\n",
        "df['churn'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "46isDPcsYNx0"
      },
      "outputs": [],
      "source": [
        "# Define Pie diagram function\n",
        "def pie(parameter):\n",
        "    class_names = ['No','Yes']\n",
        "    target_instance = df[parameter].value_counts().to_frame()\n",
        "    target_instance = target_instance.reset_index()\n",
        "    target_instance = target_instance.rename(columns = {'index': 'Category'})\n",
        "    fig = px.pie(target_instance, values = parameter, names = ('No','Yes'), \n",
        "                 color_discrete_sequence=[\"blue\", \"pink\"], \n",
        "                 title = f'Distribution of {parameter}')\n",
        "    fig.show()"
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
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        "id": "AvdUT9pXjBeG",
        "outputId": "5ed548e0-f2da-4ea7-c3f7-a8edfeea781d"
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    {
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      "execution_count": null,
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          "text": [
            "   country churn  Count\n",
            "0   France    No   4204\n",
            "1   France   Yes    810\n",
            "2  Germany    No   1695\n",
            "3  Germany   Yes    814\n",
            "4    Spain    No   2064\n",
            "5    Spain   Yes    413\n"
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "hist('country')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bzYK-NODbxM-"
      },
      "source": [
        "According to the above histrogram, Germany is the country from which the churners most likely to be."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VDsOifbCKSl7"
      },
      "outputs": [],
      "source": [
        "# Define a function to calculate relationship\n",
        "def relationship(variable):\n",
        "    correlations = df.corrwith(variable)\n",
        "    correlations = correlations[correlations != 1]\n",
        "    positive_correlations = correlations[correlations > 0].sort_values(ascending = False)\n",
        "    negative_correlations = correlations[correlations < 0].sort_values(ascending = False)\n",
        "    print('Most Positive Correlations:', positive_correlations)\n",
        "    print('Most Negative Correlations:', negative_correlations)\n",
        "\n",
        "\n",
        "    correlations.plot.bar(figsize = (14, 6), fontsize = 15, \n",
        "                      color = 'orange', rot = 45, grid = True)\n",
        "    plt.title('Correlation with Churn', horizontalalignment = \"center\", \n",
        "          fontstyle = \"normal\", fontsize = \"14\", fontfamily = \"sans-serif\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 653
        },
        "id": "TKbf41aQo9_e",
        "outputId": "ca27545c-cfe0-4148-d258-16f8fe347caf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Most Positive Correlations: age                 0.285323\n",
            "balance             0.118533\n",
            "estimated_salary    0.012097\n",
            "dtype: float64\n",
            "Most Negative Correlations: customer_id       -0.006248\n",
            "credit_card       -0.007138\n",
            "tenure            -0.014001\n",
            "credit_score      -0.027094\n",
            "products_number   -0.047820\n",
            "active_member     -0.156128\n",
            "dtype: float64\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "relationship(df.churn)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KYBd2bVpnF9u"
      },
      "source": [
        "From the correlation plot, we can easily conclude, that there have positive relationship between churn and income."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 792
        },
        "id": "DzjbrXObKSq8",
        "outputId": "dfc75ffe-3356-4734-8ec6-262568f61516"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     credit_score churn  Count\n",
            "0             350   Yes      5\n",
            "1             351   Yes      1\n",
            "2             358   Yes      1\n",
            "3             359   Yes      1\n",
            "4             363   Yes      1\n",
            "..            ...   ...    ...\n",
            "857           848    No      5\n",
            "858           849    No      6\n",
            "859           849   Yes      2\n",
            "860           850    No    190\n",
            "861           850   Yes     43\n",
            "\n",
            "[862 rows x 3 columns]\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<html>\n",
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      "source": [
        "hist('credit_score')"
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      "execution_count": null,
      "metadata": {
        "id": "-b53bINzUyMD"
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        "def bar(feature, df = df):\n",
        "    # Groupby the categorical feature\n",
        "    temp_df = df.groupby([feature, 'churn']).size().reset_index()\n",
        "    temp_df = temp_df.rename(columns={0:'Count'})\n",
        "    # Calculate the value counts of each distribution and it's corresponding Percentages\n",
        "    value_counts_df = df[feature].value_counts().to_frame().reset_index()\n",
        "    categories = [cat[1][0] for cat in value_counts_df.iterrows()]\n",
        "    # Calculate the value counts of each distribution and it's corresponding Percentages\n",
        "    num_list = [num[1][1] for num in value_counts_df.iterrows()]\n",
        "    div_list = [element / sum(num_list) for element in num_list]\n",
        "    percentage = [round(element * 100, 1) for element in div_list]\n",
        "    # Define string formatting for graph annotation\n",
        "    # Numeric section\n",
        "    def num_format(list_instance):\n",
        "        formatted_str = ''\n",
        "        for index,num in enumerate(list_instance):\n",
        "            if index < len(list_instance) - 2:\n",
        "                formatted_str = formatted_str+f'{num}%, ' #append to empty string(formatted_str)\n",
        "            elif index == len(list_instance) - 2:\n",
        "                formatted_str = formatted_str+f'{num}% & '\n",
        "            else:\n",
        "                formatted_str = formatted_str+f'{num}%'\n",
        "        return formatted_str\n",
        "    #Categorical section\n",
        "    def str_format(list_instance):\n",
        "        formatted_str = ''\n",
        "        for index, cat in enumerate(list_instance):\n",
        "            if index < len(list_instance) - 2:\n",
        "                formatted_str = formatted_str+f'{cat}, '\n",
        "            elif index == len(list_instance) - 2:\n",
        "                formatted_str = formatted_str+f'{cat} & '\n",
        "            else:\n",
        "                formatted_str = formatted_str+f'{cat}'\n",
        "        return formatted_str\n",
        "    # Running the formatting functions\n",
        "    num_str = num_format(percentage)\n",
        "    cat_str = str_format(categories)\n",
        "    # Setting graph framework\n",
        "    fig = px.bar(temp_df, x = feature, y = 'Count', color = 'churn', title = f'Churn rate by {feature}', \n",
        "                 barmode = \"group\", color_discrete_sequence = [\"pink\",\"red\"])\n",
        "    fig.add_annotation(\n",
        "                text = f'Value count of distribution of {cat_str} are<br>{num_str} percentage respectively.',\n",
        "                align = 'left',\n",
        "                showarrow = False,\n",
        "                xref = 'paper',\n",
        "                yref = 'paper',\n",
        "                x = 1.1,\n",
        "                y = 1.1,\n",
        "                bordercolor = 'black',\n",
        "                borderwidth = 0.5)\n",
        "    #fig.update_layout(\n",
        "    #    # Margin space for the annotations on the right\n",
        "    #    margin = dict(r = 700),\n",
        "    #)\n",
        "     \n",
        "    return fig.show() "
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        "id": "Chs2BlWKVRmT",
        "outputId": "e315f848-cff5-4e16-fc41-5f91d8858853"
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        {
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          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ]
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      "source": [
        "# Calculating churn percentages\n",
        "churn_rate = df[[\"churn\", \"customer_id\"]]\n",
        "churn_rate[\"churn_label\"] = pd.Series(np.where((churn_rate[\"churn\"] == 0), \"No\", \"Yes\"))\n",
        "sectors = churn_rate.groupby(\"churn_label\")\n",
        "churn_rate = pd.DataFrame(sectors[\"customer_id\"].count())\n",
        "churn_rate[\"Churn Rate\"] = (churn_rate[\"customer_id\"]/ sum(churn_rate[\"customer_id\"]))*100\n"
      ]
    },
    {
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            "text/plain": [
              "             customer_id  Churn Rate\n",
              "churn_label                         \n",
              "No                  7963       79.63\n",
              "Yes                 2037       20.37"
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          },
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",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "ax = churn_rate[[\"Churn Rate\"]].plot.bar(title = 'Overall Churn Rate', \n",
        "                                         legend = True, table = False, \n",
        "                                         grid = False, subplots = False, \n",
        "                                         figsize =(12, 7), color = '#ec838a', \n",
        "                                         fontsize = 15, stacked = False, ylim =(0,100))\n",
        "plt.ylabel('Proportion of Customers', horizontalalignment = \"center\", \n",
        "           fontstyle = \"normal\", fontsize = \"large\", fontfamily = \"sans-serif\")\n",
        "plt.xlabel('Churn', horizontalalignment = \"center\", fontstyle = \"normal\", \n",
        "           fontsize = \"large\", fontfamily = \"sans-serif\")\n",
        "plt.title('Overall Churn Rate \\n', horizontalalignment = \"center\", \n",
        "          fontstyle = \"normal\", fontsize = \"22\", fontfamily = \"sans-serif\")\n",
        "plt.legend(loc = 'upper right', fontsize = \"medium\")\n",
        "plt.xticks(rotation = 0, horizontalalignment = \"center\")\n",
        "plt.yticks(rotation=0, horizontalalignment = \"right\")\n",
        "ax.yaxis.set_major_formatter(mtick.PercentFormatter())\n",
        "x_labels = np.array(churn_rate[[\"customer_id\"]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IRO_ddiOpws8"
      },
      "source": [
        "From the above distribution plot, we can conclude that highest credit score is more likely to churn."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nK_ELjUiKStu"
      },
      "outputs": [],
      "source": [
        "### What are the most influential factors on the churn?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        },
        "id": "d4LpjEqQKdL-",
        "outputId": "eb459eaf-58b3-4271-8cda-4e240770810a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Most Positive Correlations: age                 0.285323\n",
            "balance             0.118533\n",
            "estimated_salary    0.012097\n",
            "dtype: float64\n",
            "Most Negative Correlations: customer_id       -0.006248\n",
            "credit_card       -0.007138\n",
            "tenure            -0.014001\n",
            "credit_score      -0.027094\n",
            "products_number   -0.047820\n",
            "active_member     -0.156128\n",
            "dtype: float64\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "relationship(df.churn)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "t6oVJ5Owq618",
        "outputId": "1c49e762-2a92-49f5-c83f-17619fb6dcee"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Correlation between variables\n",
        "corr = df.corr()\n",
        "fig = px.imshow(corr, width = 600, height = 600)\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ssum4I8JrBCn"
      },
      "source": [
        "The most influencial factors on the churn are age, balance and estimated_salary."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cgzhvs4Lum8A"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "lQXuE18RwRU-",
        "outputId": "1ab62279-c05b-4065-9636-c1e3c164c64f"
      },
      "outputs": [
        {
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              "      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-fa7c2dc0-17c7-417f-a4c3-01989757b254 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-fa7c2dc0-17c7-417f-a4c3-01989757b254');\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",
              "  "
            ],
            "text/plain": [
              "   customer_id  credit_score country  gender  age  tenure    balance  \\\n",
              "0     15634602           619  France  Female   42       2       0.00   \n",
              "1     15647311           608   Spain  Female   41       1   83807.86   \n",
              "2     15619304           502  France  Female   42       8  159660.80   \n",
              "3     15701354           699  France  Female   39       1       0.00   \n",
              "4     15737888           850   Spain  Female   43       2  125510.82   \n",
              "\n",
              "   products_number  credit_card  active_member  estimated_salary  churn  \n",
              "0                1            1              1         101348.88      1  \n",
              "1                1            0              1         112542.58      0  \n",
              "2                3            1              0         113931.57      1  \n",
              "3                2            0              0          93826.63      0  \n",
              "4                1            1              1          79084.10      0  "
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DDfXECYywcfz"
      },
      "outputs": [],
      "source": [
        "df_c = df\n",
        "df_c['country'] = df_c['country'].map({'France' : 0, 'Germany' : 1, 'Spain' : 2})\n",
        "df_c['gender'] = df_c['gender'].map({'Male' : 0, 'Female' : 1})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9VUEJv2PuPqf"
      },
      "outputs": [],
      "source": [
        "# Prepared data for predictive model\n",
        "Y = df['churn']\n",
        "# Get categorical dataframe\n",
        "cat_data_churn = df[[\"country\", \"gender\", \"credit_card\", \"active_member\"]]\n",
        "X = df.drop(labels = [\"churn\", \"country\", \"gender\", \"credit_card\", \"active_member\"], axis = 1)\n",
        "# Get labels for modelling\n",
        "cols = X.columns\n",
        "indx = X.index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_C20fIcfuPuI",
        "outputId": "a0d7db7b-8856-43de-d7db-0c27b17a21e9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(10000, 11) (10000,)\n"
          ]
        }
      ],
      "source": [
        "# Transform dataset then concat\n",
        "transformer = RobustScaler().fit(X)\n",
        "X = transformer.transform(X)\n",
        "X = pd.DataFrame(X, columns = cols, index = indx)\n",
        "X = pd.concat([X, cat_data_churn], axis = 1)\n",
        "print(X.shape, Y.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2rv6Fi1YvraU",
        "outputId": "88c5b65b-0a78-4c51-b55c-6e85f278c38a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      customer_id  credit_score       age  tenure   balance  products_number  \\\n",
            "0       -0.450149     -0.246269  0.416667   -0.75 -0.761480              0.0   \n",
            "1       -0.348236     -0.328358  0.333333   -1.00 -0.104906              0.0   \n",
            "2       -0.572822     -1.119403  0.416667    0.75  0.489346              2.0   \n",
            "3        0.085129      0.350746  0.166667   -1.00 -0.761480              1.0   \n",
            "4        0.378091      1.477612  0.500000   -0.75  0.221806              0.0   \n",
            "...           ...           ...       ...     ...       ...              ...   \n",
            "9995    -0.677669      0.888060  0.166667    0.00 -0.761480              1.0   \n",
            "9996    -0.969051     -1.014925 -0.166667    1.25 -0.312031              0.0   \n",
            "9997    -0.851654      0.425373 -0.083333    0.50 -0.761480              0.0   \n",
            "9998    -0.067222      0.895522  0.416667   -0.50 -0.173319              1.0   \n",
            "9999    -0.500531      1.044776 -0.750000   -0.25  0.258094              0.0   \n",
            "\n",
            "      estimated_salary  country  gender  credit_card  active_member  \n",
            "0             0.011739        0       1            1              1  \n",
            "1             0.125512        2       1            0              1  \n",
            "2             0.139630        0       1            1              0  \n",
            "3            -0.064717        0       1            0              0  \n",
            "4            -0.214561        2       1            1              1  \n",
            "...                ...      ...     ...          ...            ...  \n",
            "9995         -0.039876        0       0            1              0  \n",
            "9996          0.015306        0       0            1              1  \n",
            "9997         -0.590615        0       1            0              1  \n",
            "9998         -0.074252        1       0            1              0  \n",
            "9999         -0.630202        0       1            1              0  \n",
            "\n",
            "[10000 rows x 11 columns]\n"
          ]
        }
      ],
      "source": [
        "print(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mxPQzDltuPwE",
        "outputId": "9e6bde74-b62d-47a8-9101-e642c8b7db3f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(8000, 11) (2000, 11) (8000,) (2000,)\n"
          ]
        }
      ],
      "source": [
        "# Devide training and test datasets\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 40)\n",
        "print(X_train.shape, X_test.shape, Y_train.shape, Y_test.shape)\n",
        "train_identity = X_train['customer_id']\n",
        "X_train = X_train.drop(columns = ['customer_id'])\n",
        "test_identity = X_test['customer_id']\n",
        "X_test = X_test.drop(columns = ['customer_id'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xlx6rcuCuPx1",
        "outputId": "ffa63ea6-d6a1-4c33-ed90-b20ffe20a5b3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
              "             param_grid={'n_neighbors': array([ 5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,\n",
              "       22, 23, 24, 25]),\n",
              "                         'weights': ['uniform', 'distance']})"
            ]
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# KNeighbors Classifier\n",
        "knn = KNeighborsClassifier()\n",
        "# Set hyperparameter grid for tuning\n",
        "knn_param_grid = {'n_neighbors' : np.arange(5, 26),\n",
        "                  'weights' : ['uniform', 'distance']}\n",
        "# Tune hyperparameters\n",
        "knn_cv = GridSearchCV(knn, param_grid = knn_param_grid, cv = 5)\n",
        "# Fit knn to training data\n",
        "knn_cv.fit(X_train, Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xnw-lnuH2-7I",
        "outputId": "fb685cf7-ae0b-49d4-a11c-4fa8b51c36aa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Tuned KNN Parameters: {'n_neighbors': 10, 'weights': 'distance'}\n",
            "Best KNN Training Score:0.8404999999999999\n",
            "KNN Test Performance: 0.842\n",
            "KNN AUROC: 0.8154578885622937\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.95      0.91      1616\n",
            "           1       0.65      0.39      0.49       384\n",
            "\n",
            "    accuracy                           0.84      2000\n",
            "   macro avg       0.76      0.67      0.70      2000\n",
            "weighted avg       0.83      0.84      0.83      2000\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Get best hyperparameters\n",
        "print(\"Tuned KNN Parameters: {}\".format(knn_cv.best_params_))\n",
        "print(\"Best KNN Training Score:{}\".format(knn_cv.best_score_))\n",
        "\n",
        "# Predict knn on test data\n",
        "print(\"KNN Test Performance: {}\".format(knn_cv.score(X_test, Y_test)))\n",
        "# Obtain model performance metrics\n",
        "knn_pred_prob = knn_cv.predict_proba(X_test)[:, 1]\n",
        "knn_auroc = roc_auc_score(Y_test, knn_pred_prob)\n",
        "print(\"KNN AUROC: {}\".format(knn_auroc))\n",
        "knn_y_pred = knn_cv.predict(X_test)\n",
        "print(classification_report(Y_test, knn_y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t502JcbB2--2"
      },
      "outputs": [],
      "source": [
        "### Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JA4Pv-IN2_A3",
        "outputId": "e4a831b6-2991-4930-b5c4-b904aa20c0f5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=LogisticRegression(random_state=30),\n",
              "             param_grid={'C': [0.0001, 0.001, 0.01, 0.05, 0.1]})"
            ]
          },
          "execution_count": 74,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#  Classifier instantiation\n",
        "lr = LogisticRegression(random_state = 30)\n",
        "# tune by setting up hyperparameter grid\n",
        "lr_param_grid = {'C' : [0.0001, 0.001, 0.01, 0.05, 0.1] }\n",
        "# Tune hyperparamters\n",
        "lr_cv = GridSearchCV(lr, param_grid = lr_param_grid, cv = 5)\n",
        "# Fit lr to training data\n",
        "lr_cv.fit(X_train, Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2VF8X1Gu5fK2",
        "outputId": "3fd45ea4-c398-48c3-9d58-d265e8448c87"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Tuned LR Parameters: {'C': 0.1}\n",
            "Best LR Training Score:0.80475\n"
          ]
        }
      ],
      "source": [
        "# Find best hyperparameters\n",
        "print(\"Tuned LR Parameters: {}\".format(lr_cv.best_params_))\n",
        "print(\"Best LR Training Score:{}\".format(lr_cv.best_score_))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-K8reetv5fUj",
        "outputId": "201c3855-0333-42fb-ab7b-27aa1e3155be"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "LR Test Performance: 0.8185\n",
            "LR AUROC: 0.7684338258044554\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.97      0.90      1616\n",
            "           1       0.59      0.18      0.28       384\n",
            "\n",
            "    accuracy                           0.82      2000\n",
            "   macro avg       0.71      0.57      0.59      2000\n",
            "weighted avg       0.79      0.82      0.78      2000\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Predict lr on test data\n",
        "print(\"LR Test Performance: {}\".format(lr_cv.score(X_test, Y_test)))\n",
        "# model performance metrics\n",
        "lr_pred_prob = lr_cv.predict_proba(X_test)[:,1]\n",
        "lr_auroc = roc_auc_score(Y_test, lr_pred_prob)\n",
        "print(\"LR AUROC: {}\".format(lr_auroc))\n",
        "lr_y_pred = lr_cv.predict(X_test)\n",
        "print(classification_report(Y_test, lr_y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "eJkV-uKr6hhU",
        "outputId": "8c74effe-15ea-4829-9379-8e188c93e5cb"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Compare model\n",
        "knn_fpr, knn_tpr, knn_thresh = roc_curve(Y_test, knn_pred_prob)\n",
        "plt.plot(knn_fpr,knn_tpr,label = \"KNN: auc =\"+str(round(knn_auroc, 3)),\n",
        "         color = 'blue')\n",
        "lr_fpr, lr_tpr, lr_thresh = roc_curve(Y_test, lr_pred_prob)\n",
        "plt.plot(lr_fpr, lr_tpr,label = \"LR: auc=\"+str(round(lr_auroc, 3)),\n",
        "         color = 'red')\n",
        "\n",
        "plt.plot([0, 1], [0, 1], color = 'gray', lw = 1, linestyle = '--', \n",
        "         label = 'Random Guess')\n",
        "\n",
        "plt.legend(loc = 'best', frameon = True, facecolor = 'lightgray')\n",
        "plt.title('ROC Curve for Classification Models')\n",
        "plt.xlabel('False Positive Rate (1 - specificity)')\n",
        "plt.ylabel('True Positive Rate (sensitivity)')\n",
        "plt.text(0.85, 0.75, 'threshold = 0', fontsize = 12)\n",
        "plt.arrow(0.85, 0.8, 0.14, 0.18, head_width = 0.06)\n",
        "plt.text(0.05, 0, 'threshold = 1', fontsize = 12)\n",
        "plt.arrow(0.05, 0, -0.03, 0, head_width = 0.06)\n",
        "plt.savefig('Plot-ROC_4models.png', dpi = 1200)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sHoTSr4otVtr"
      },
      "source": [
        "From the above ROC curve, it can be concluded, that KNN have performed better comparing with Logistic Regression."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZjgU-ycQHsLX"
      },
      "outputs": [],
      "source": [
        "# Depending on correlation, need to do further analysis on age, balance and income.\n",
        "# Need to focus more in the age group.\n",
        "# Need to take more care for the customer who have less or highest credit score."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.6rc1 (v3.8.6rc1:08bd63da6e, Sep  7 2020, 16:14:12) \n[Clang 6.0 (clang-600.0.57)]"
    },
    "vscode": {
      "interpreter": {
        "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}