Bank_Customer_Churn_prediction.ipynb

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    "colab": {
      "name": "Bank Customer Churn prediction.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP8plMzwum+QYK4/6MGbRKo"
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        },
        "id": "YWPSOXuveznT",
        "outputId": "45f92a55-d827-4290-8173-a24bdafbbc1c"
      },
      "outputs": [
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (1.5.12)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.64.0)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle) (2022.6.15)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle) (6.1.2)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (3.0.4)\n"
          ]
        }
      ],
      "source": [
        "! pip install kaggle"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! mkdir ~/.kaggle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OelyBxLfgABT",
        "outputId": "e0583d85-a887-4446-a0e8-75888e58a19b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! cp kaggle.json ~/.kaggle/"
      ],
      "metadata": {
        "id": "K18inRsXgD2-"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "282-AOMSgGIc"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d barelydedicated/bank-customer-churn-modeling"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lv89oK91gJia",
        "outputId": "ba11b706-a89d-4a77-dbcd-316e444bdda9"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading bank-customer-churn-modeling.zip to /content\n",
            "\r  0% 0.00/262k [00:00<?, ?B/s]\n",
            "\r100% 262k/262k [00:00<00:00, 75.0MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! unzip /content/bank-customer-churn-modeling.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e69tQQ-GhNC3",
        "outputId": "a59572fc-87ff-4191-855f-3acfe91cdf02"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/bank-customer-churn-modeling.zip\n",
            "  inflating: Churn_Modelling.csv     \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "#Load the data\n",
        "df=pd.read_csv('/content/Churn_Modelling.csv')\n",
        "df.sample(5)"
      ],
      "metadata": {
        "colab": {
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          "height": 250
        },
        "id": "SUmWqDELhj2-",
        "outputId": "fa55aa53-6b41-47d7-b885-8f856402cd06"
      },
      "execution_count": 11,
      "outputs": [
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              "      RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
              "7270       7271    15706268     Smith          697   Germany    Male   51   \n",
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              "(10000, 14)"
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          "execution_count": 12
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              "RowNumber            int64\n",
              "CustomerId           int64\n",
              "Surname             object\n",
              "CreditScore          int64\n",
              "Geography           object\n",
              "Gender              object\n",
              "Age                  int64\n",
              "Tenure               int64\n",
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      "execution_count": 15,
      "outputs": []
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              "CreditScore        0\n",
              "Geography          0\n",
              "Gender             0\n",
              "Age                0\n",
              "Tenure             0\n",
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      "execution_count": 17,
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              "      CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
              "1             608     Spain  Female   41       1   83807.86              1   \n",
              "3             699    France  Female   39       1       0.00              2   \n",
              "4             850     Spain  Female   43       2  125510.82              1   \n",
              "6             822    France    Male   50       7       0.00              2   \n",
              "8             501    France    Male   44       4  142051.07              2   \n",
              "...           ...       ...     ...  ...     ...        ...            ...   \n",
              "9993          644    France    Male   28       7  155060.41              1   \n",
              "9994          800    France  Female   29       2       0.00              2   \n",
              "9995          771    France    Male   39       5       0.00              2   \n",
              "9996          516    France    Male   35      10   57369.61              1   \n",
              "9999          792    France  Female   28       4  130142.79              1   \n",
              "\n",
              "      HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
              "1             0               1        112542.58       0  \n",
              "3             0               0         93826.63       0  \n",
              "4             1               1         79084.10       0  \n",
              "6             1               1         10062.80       0  \n",
              "8             0               1         74940.50       0  \n",
              "...         ...             ...              ...     ...  \n",
              "9993          1               0         29179.52       0  \n",
              "9994          0               0        167773.55       0  \n",
              "9995          1               0         96270.64       0  \n",
              "9996          1               1        101699.77       0  \n",
              "9999          1               0         38190.78       0  \n",
              "\n",
              "[7963 rows x 11 columns]"
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              "      <td>608</td>\n",
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              "      <td>Female</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>83807.86</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>112542.58</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>699</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>93826.63</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>850</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>43</td>\n",
              "      <td>2</td>\n",
              "      <td>125510.82</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>79084.10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>822</td>\n",
              "      <td>France</td>\n",
              "      <td>Male</td>\n",
              "      <td>50</td>\n",
              "      <td>7</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10062.80</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>501</td>\n",
              "      <td>France</td>\n",
              "      <td>Male</td>\n",
              "      <td>44</td>\n",
              "      <td>4</td>\n",
              "      <td>142051.07</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>74940.50</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9993</th>\n",
              "      <td>644</td>\n",
              "      <td>France</td>\n",
              "      <td>Male</td>\n",
              "      <td>28</td>\n",
              "      <td>7</td>\n",
              "      <td>155060.41</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>29179.52</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9994</th>\n",
              "      <td>800</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>29</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>167773.55</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>771</td>\n",
              "      <td>France</td>\n",
              "      <td>Male</td>\n",
              "      <td>39</td>\n",
              "      <td>5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>96270.64</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>516</td>\n",
              "      <td>France</td>\n",
              "      <td>Male</td>\n",
              "      <td>35</td>\n",
              "      <td>10</td>\n",
              "      <td>57369.61</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>101699.77</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>792</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>28</td>\n",
              "      <td>4</td>\n",
              "      <td>130142.79</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38190.78</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7963 rows × 11 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-24870399-4518-4951-b61d-12b3c37af121')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df.Exited==1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ApOHSPj8jhcN",
        "outputId": "55a3e60b-5e58-40e8-86cb-e5a397a3da97"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
              "0             619    France  Female   42       2       0.00              1   \n",
              "2             502    France  Female   42       8  159660.80              3   \n",
              "5             645     Spain    Male   44       8  113755.78              2   \n",
              "7             376   Germany  Female   29       4  115046.74              4   \n",
              "16            653   Germany    Male   58       1  132602.88              1   \n",
              "...           ...       ...     ...  ...     ...        ...            ...   \n",
              "9981          498   Germany    Male   42       3  152039.70              1   \n",
              "9982          655   Germany  Female   46       7  137145.12              1   \n",
              "9991          597    France  Female   53       4   88381.21              1   \n",
              "9997          709    France  Female   36       7       0.00              1   \n",
              "9998          772   Germany    Male   42       3   75075.31              2   \n",
              "\n",
              "      HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
              "0             1               1        101348.88       1  \n",
              "2             1               0        113931.57       1  \n",
              "5             1               0        149756.71       1  \n",
              "7             1               0        119346.88       1  \n",
              "16            1               0          5097.67       1  \n",
              "...         ...             ...              ...     ...  \n",
              "9981          1               1         53445.17       1  \n",
              "9982          1               0        115146.40       1  \n",
              "9991          1               0         69384.71       1  \n",
              "9997          0               1         42085.58       1  \n",
              "9998          1               0         92888.52       1  \n",
              "\n",
              "[2037 rows x 11 columns]"
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              "<p>2037 rows × 11 columns</p>\n",
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              "      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-632d0a0e-863d-4ad5-bdb1-fefa32d4798d 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-632d0a0e-863d-4ad5-bdb1-fefa32d4798d');\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": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data Visualization**"
      ],
      "metadata": {
        "id": "URnni4Ypjn2s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "tenure_churn_0 = df[df.Exited==0].Tenure\n",
        "tenure_churn_1 = df[df.Exited==1].Tenure\n",
        "\n",
        "plt.xlabel(\"Tenure\")\n",
        "plt.ylabel(\"Number Of Customers\")\n",
        "plt.title(\"Customer Churn Prediction Visualiztion\")\n",
        "\n",
        "plt.hist([tenure_churn_1, tenure_churn_0], rwidth=0.95, color=['green','red'],label=['Exited=Yes','Exited=No'])\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "0KNEAiXCjl4-",
        "outputId": "e0ae284d-9141-4e2c-e31f-6780c6065ad3"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py:3208: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  return asarray(a).size\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fc640192610>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "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": [
        "balance_churn_0 = df[df.Exited==0].Balance\n",
        "balance_churn_1 = df[df.Exited==1].Balance\n",
        "\n",
        "plt.xlabel(\"Balance\")\n",
        "plt.ylabel(\"Number Of Customers\")\n",
        "plt.title(\"Customer Churn Prediction Visualiztion\")\n",
        "\n",
        "plt.hist([balance_churn_1, balance_churn_0], rwidth=0.95, color=['green','red'],label=['Exited=Yes','Exited=No'])\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "j2hr9yI2kRtW",
        "outputId": "d75906b2-c238-4ecf-f6ff-e55a59dce35b"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py:3208: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  return asarray(a).size\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fc63fb36710>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "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": [
        "salary_churn_0 = df[df.Exited==0].EstimatedSalary\n",
        "salary_churn_1 = df[df.Exited==1].EstimatedSalary\n",
        "\n",
        "plt.xlabel(\"Estimated Salary\")\n",
        "plt.ylabel(\"Number Of Customers\")\n",
        "plt.title(\"Customer Churn Prediction Visualiztion\")\n",
        "\n",
        "plt.hist([salary_churn_1, salary_churn_0], rwidth=0.95, color=['green','red'],label=['Exited=Yes','Exited=No'])\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "U21fwy-Kkl7j",
        "outputId": "285249d4-87c4-4c2e-c7e5-ee3a017c4560"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py:3208: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  return asarray(a).size\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fc63fa68d90>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "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": [
        "def print_unique_col_values(df):\n",
        "       for column in df:\n",
        "            if df[column].dtypes=='object':\n",
        "                print(f'{column}: {df[column].unique()}') "
      ],
      "metadata": {
        "id": "q21eiHgZk7XC"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print_unique_col_values(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rKUR2tY7lBFs",
        "outputId": "f092457c-2b31-4188-ee4d-0c2c97e1c827"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Geography: ['France' 'Spain' 'Germany']\n",
            "Gender: ['Female' 'Male']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Gender'].replace({'Female':1,'Male':0},inplace=True)"
      ],
      "metadata": {
        "id": "oxm0-lxRlF5m"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print_unique_col_values(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z_GbWifUlNjk",
        "outputId": "d53e13f6-bb8b-41b7-ee24-768b9051f285"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Geography: ['France' 'Spain' 'Germany']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Using One Hot Encoding for categorical column Geography\n",
        "df1=pd.get_dummies(data=df,columns=['Geography'])\n",
        "df1.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rTuMbXpAlR8_",
        "outputId": "feca7713-54d1-4645-d1e0-dda780cfcbd5"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['CreditScore', 'Gender', 'Age', 'Tenure', 'Balance', 'NumOfProducts',\n",
              "       'HasCrCard', 'IsActiveMember', 'EstimatedSalary', 'Exited',\n",
              "       'Geography_France', 'Geography_Germany', 'Geography_Spain'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def check_age_category(x):\n",
        "  if(x>50):\n",
        "    return 1\n",
        "  else:\n",
        "    return 0"
      ],
      "metadata": {
        "id": "0tcauvmemotr"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.Age=df1.Age.apply(check_age_category)"
      ],
      "metadata": {
        "id": "rMWXPIEUm9LV"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.Age.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2egNvL6-nh7z",
        "outputId": "8b66c9b1-eaa4-467d-fddf-bd70b1ec7dee"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g3iY8_Jombg5",
        "outputId": "6f17f7d7-8463-41a2-b9ef-d18c24b9411c"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CreditScore            int64\n",
              "Gender                 int64\n",
              "Age                    int64\n",
              "Tenure                 int64\n",
              "Balance              float64\n",
              "NumOfProducts          int64\n",
              "HasCrCard              int64\n",
              "IsActiveMember         int64\n",
              "EstimatedSalary      float64\n",
              "Exited                 int64\n",
              "Geography_France       uint8\n",
              "Geography_Germany      uint8\n",
              "Geography_Spain        uint8\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cols_to_scale=['Tenure','Balance','EstimatedSalary','CreditScore']\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler=MinMaxScaler()\n",
        "df1[cols_to_scale]=scaler.fit_transform(df1[cols_to_scale])"
      ],
      "metadata": {
        "id": "V7vjeYZ4mf7G"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for col in df1:\n",
        "    print(f'{col}: {df1[col].unique()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kuTp6bURoOTu",
        "outputId": "a1c783b3-22cc-41f6-f989-6615f514b0ae"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CreditScore: [0.538 0.516 0.304 0.698 1.    0.59  0.944 0.052 0.302 0.668 0.356 0.294\n",
            " 0.252 0.398 0.57  0.532 0.606 0.474 0.752 0.764 0.572 0.32  0.638 0.992\n",
            " 0.454 0.812 0.442 0.448 0.122 0.482 0.366 0.406 0.34  0.744 0.25  0.28\n",
            " 0.908 0.464 0.244 0.23  0.412 0.968 0.62  0.852 0.958 0.574 0.4   0.696\n",
            " 0.47  0.876 0.61  0.502 0.612 0.75  0.322 0.528 0.784 0.674 0.41  0.506\n",
            " 0.802 0.462 0.77  0.622 0.65  0.776 0.926 0.614 0.508 0.338 0.628 0.656\n",
            " 0.814 0.132 0.63  0.854 0.386 0.312 0.286 0.604 0.8   0.758 0.592 0.594\n",
            " 0.916 0.348 0.838 0.76  0.33  0.846 0.928 0.72  0.126 0.546 0.64  0.544\n",
            " 0.87  0.51  0.258 0.67  0.376 0.424 0.742 0.556 0.636 0.956 0.648 0.55\n",
            " 0.164 0.84  0.816 0.89  0.672 0.878 0.478 0.222 0.468 0.458 0.626 0.664\n",
            " 0.886 0.682 0.27  0.6   0.808 0.37  0.732 0.378 0.712 0.472 0.562 0.734\n",
            " 0.9   0.666 0.708 0.53  0.634 0.268 0.26  0.456 0.324 0.512 0.494 0.856\n",
            " 0.328 0.35  0.73  0.46  0.914 0.342 0.818 0.332 0.722 0.536 0.586 0.642\n",
            " 0.678 0.54  0.652 0.444 0.69  0.484 0.434 0.688 0.394 0.488 0.646 0.52\n",
            " 0.834 0.826 0.724 0.706 0.624 0.618 0.346 0.844 0.39  0.568 0.778 0.842\n",
            " 0.662 0.388 0.692 0.832 0.754 0.686 0.414 0.362 0.296 0.602 0.882 0.766\n",
            " 0.922 0.714 0.728 0.864 0.85  0.898 0.504 0.788 0.476 0.794 0.466 0.554\n",
            " 0.762 0.558 0.176 0.584 0.912 0.248 0.418 0.158 0.66  0.798 0.768 0.588\n",
            " 0.552 0.598 0.91  0.736 0.98  0.56  0.608 0.824 0.436 0.526 0.344 0.774\n",
            " 0.596 0.186 0.58  0.38  0.22  0.486 0.902 0.522 0.904 0.79  0.266 0.68\n",
            " 0.284 0.718 0.71  0.42  0.804 0.702 0.374 0.274 0.492 0.704 0.272 0.748\n",
            " 0.396 0.228 0.88  0.368 0.796 0.288 0.48  0.236 0.318 0.936 0.932 0.372\n",
            " 0.806 0.848 0.542 0.438 0.616 0.896 0.582 0.384 0.684 0.578 0.83  0.44\n",
            " 0.576 0.498 0.564 0.858 0.354 0.428 0.966 0.308 0.984 0.316 0.134 0.496\n",
            " 0.782 0.514 0.822 0.996 0.392 0.178 0.81  0.82  0.352 0.726 0.7   0.632\n",
            " 0.432 0.29  0.676 0.524 0.254 0.154 0.978 0.938 0.74  0.218 0.306 0.548\n",
            " 0.358 0.426 0.264 0.892 0.19  0.792 0.872 0.408 0.644 0.874 0.298 0.988\n",
            " 0.2   0.93  0.976 0.906 0.772 0.566 0.5   0.658 0.334 0.884 0.786 0.276\n",
            " 0.142 0.982 0.716 0.314 0.31  0.212 0.17  0.422 0.336 0.43  0.756 0.868\n",
            " 0.404 0.518 0.828 0.694 0.746 0.402 0.188 0.738 0.292 0.382 0.96  0.924\n",
            " 0.654 0.14  0.49  0.534 0.918 0.3   0.952 0.168 0.326 0.256 0.894 0.026\n",
            " 0.098 0.226 0.86  0.204 0.45  0.974 0.888 0.948 0.156 0.946 0.862 0.998\n",
            " 0.278 0.162 0.214 0.836 0.962 0.018 0.94  0.446 0.452 0.416 0.934 0.198\n",
            " 0.18  0.13  0.942 0.36  0.    0.192 0.15  0.78  0.262 0.866 0.016 0.99\n",
            " 0.202 0.216 0.238 0.146 0.108 0.972 0.246 0.97  0.232 0.282 0.002 0.954\n",
            " 0.986 0.03  0.364 0.128 0.206 0.242 0.102 0.92  0.964 0.24  0.194 0.144\n",
            " 0.95  0.16  0.172 0.152 0.116 0.994 0.136 0.174 0.12  0.208 0.114 0.21\n",
            " 0.224 0.072 0.11  0.066 0.09  0.234 0.166 0.184 0.148 0.196 0.182 0.034\n",
            " 0.124 0.064 0.046 0.138]\n",
            "Gender: [1 0]\n",
            "Age: [0 1]\n",
            "Tenure: [0.2 0.1 0.8 0.7 0.4 0.6 0.3 1.  0.5 0.9 0. ]\n",
            "Balance: [0.         0.33403148 0.63635718 ... 0.22865702 0.29922631 0.51870777]\n",
            "NumOfProducts: [1 3 2 4]\n",
            "HasCrCard: [1 0]\n",
            "IsActiveMember: [1 0]\n",
            "EstimatedSalary: [0.50673489 0.56270874 0.56965435 ... 0.21039009 0.46442905 0.19091423]\n",
            "Exited: [1 0]\n",
            "Geography_France: [1 0]\n",
            "Geography_Germany: [0 1]\n",
            "Geography_Spain: [0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Splitting train and test data\n",
        "X=df1.drop('Exited',axis='columns')\n",
        "y=df1['Exited']\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,Y_train,Y_test=train_test_split(X,y,test_size=0.2,random_state=10)"
      ],
      "metadata": {
        "id": "HRrj3LIUofD2"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iawxaOlqo6WJ",
        "outputId": "6cc3a83e-3496-46fa-9591-6731958b0062"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8000, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B9oHnQSOo8w3",
        "outputId": "24561e62-ee25-42d4-872c-a800bda9453e"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2000, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Building a ANN model in tendorflow/keras\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "model=keras.Sequential([\n",
        "                        keras.layers.Dense(12,input_shape=(12,),activation='relu'),\n",
        "                        keras.layers.Dense(7,activation='relu'),\n",
        "                        keras.layers.Dense(1,activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "model.fit(X_train,Y_train,epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GMjgweSmo-_X",
        "outputId": "c55d3530-3cd5-40e9-a6df-746c279a3081"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "250/250 [==============================] - 2s 4ms/step - loss: 0.5699 - accuracy: 0.7011\n",
            "Epoch 2/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.4568 - accuracy: 0.8075\n",
            "Epoch 3/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.4453 - accuracy: 0.8184\n",
            "Epoch 4/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.4387 - accuracy: 0.8226\n",
            "Epoch 5/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.4334 - accuracy: 0.8245\n",
            "Epoch 6/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.4259 - accuracy: 0.8234\n",
            "Epoch 7/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.4182 - accuracy: 0.8255\n",
            "Epoch 8/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.4101 - accuracy: 0.8314\n",
            "Epoch 9/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3999 - accuracy: 0.8342\n",
            "Epoch 10/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3913 - accuracy: 0.8381\n",
            "Epoch 11/100\n",
            "250/250 [==============================] - 1s 4ms/step - loss: 0.3848 - accuracy: 0.8409\n",
            "Epoch 12/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3807 - accuracy: 0.8419\n",
            "Epoch 13/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3770 - accuracy: 0.8439\n",
            "Epoch 14/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3754 - accuracy: 0.8435\n",
            "Epoch 15/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3740 - accuracy: 0.8447\n",
            "Epoch 16/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3723 - accuracy: 0.8449\n",
            "Epoch 17/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3709 - accuracy: 0.8447\n",
            "Epoch 18/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3703 - accuracy: 0.8455\n",
            "Epoch 19/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3696 - accuracy: 0.8460\n",
            "Epoch 20/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3688 - accuracy: 0.8456\n",
            "Epoch 21/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3677 - accuracy: 0.8459\n",
            "Epoch 22/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3676 - accuracy: 0.8444\n",
            "Epoch 23/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3671 - accuracy: 0.8450\n",
            "Epoch 24/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3667 - accuracy: 0.8461\n",
            "Epoch 25/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3668 - accuracy: 0.8471\n",
            "Epoch 26/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3661 - accuracy: 0.8459\n",
            "Epoch 27/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3657 - accuracy: 0.8464\n",
            "Epoch 28/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3652 - accuracy: 0.8464\n",
            "Epoch 29/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3653 - accuracy: 0.8470\n",
            "Epoch 30/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3650 - accuracy: 0.8459\n",
            "Epoch 31/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3640 - accuracy: 0.8474\n",
            "Epoch 32/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3640 - accuracy: 0.8469\n",
            "Epoch 33/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3643 - accuracy: 0.8465\n",
            "Epoch 34/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3639 - accuracy: 0.8465\n",
            "Epoch 35/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3643 - accuracy: 0.8460\n",
            "Epoch 36/100\n",
            "250/250 [==============================] - 1s 4ms/step - loss: 0.3628 - accuracy: 0.8490\n",
            "Epoch 37/100\n",
            "250/250 [==============================] - 1s 4ms/step - loss: 0.3629 - accuracy: 0.8462\n",
            "Epoch 38/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3624 - accuracy: 0.8464\n",
            "Epoch 39/100\n",
            "250/250 [==============================] - 1s 4ms/step - loss: 0.3622 - accuracy: 0.8465\n",
            "Epoch 40/100\n",
            "250/250 [==============================] - 2s 7ms/step - loss: 0.3624 - accuracy: 0.8471\n",
            "Epoch 41/100\n",
            "250/250 [==============================] - 1s 4ms/step - loss: 0.3624 - accuracy: 0.8469\n",
            "Epoch 42/100\n",
            "250/250 [==============================] - 1s 4ms/step - loss: 0.3620 - accuracy: 0.8456\n",
            "Epoch 43/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3621 - accuracy: 0.8470\n",
            "Epoch 44/100\n",
            "250/250 [==============================] - 1s 5ms/step - loss: 0.3618 - accuracy: 0.8468\n",
            "Epoch 45/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3619 - accuracy: 0.8454\n",
            "Epoch 46/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3609 - accuracy: 0.8446\n",
            "Epoch 47/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3612 - accuracy: 0.8459\n",
            "Epoch 48/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3605 - accuracy: 0.8462\n",
            "Epoch 49/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3606 - accuracy: 0.8469\n",
            "Epoch 50/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3603 - accuracy: 0.8457\n",
            "Epoch 51/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3605 - accuracy: 0.8471\n",
            "Epoch 52/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3594 - accuracy: 0.8480\n",
            "Epoch 53/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3598 - accuracy: 0.8464\n",
            "Epoch 54/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3596 - accuracy: 0.8459\n",
            "Epoch 55/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3590 - accuracy: 0.8459\n",
            "Epoch 56/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3585 - accuracy: 0.8453\n",
            "Epoch 57/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3589 - accuracy: 0.8459\n",
            "Epoch 58/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3582 - accuracy: 0.8454\n",
            "Epoch 59/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3585 - accuracy: 0.8464\n",
            "Epoch 60/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3591 - accuracy: 0.8475\n",
            "Epoch 61/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3578 - accuracy: 0.8480\n",
            "Epoch 62/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3589 - accuracy: 0.8487\n",
            "Epoch 63/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3576 - accuracy: 0.8461\n",
            "Epoch 64/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3587 - accuracy: 0.8456\n",
            "Epoch 65/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3580 - accuracy: 0.8464\n",
            "Epoch 66/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3581 - accuracy: 0.8487\n",
            "Epoch 67/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3573 - accuracy: 0.8465\n",
            "Epoch 68/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3575 - accuracy: 0.8459\n",
            "Epoch 69/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3581 - accuracy: 0.8471\n",
            "Epoch 70/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3578 - accuracy: 0.8465\n",
            "Epoch 71/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3577 - accuracy: 0.8478\n",
            "Epoch 72/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3574 - accuracy: 0.8490\n",
            "Epoch 73/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3579 - accuracy: 0.8478\n",
            "Epoch 74/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3577 - accuracy: 0.8460\n",
            "Epoch 75/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3576 - accuracy: 0.8459\n",
            "Epoch 76/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3578 - accuracy: 0.8466\n",
            "Epoch 77/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3574 - accuracy: 0.8471\n",
            "Epoch 78/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3569 - accuracy: 0.8469\n",
            "Epoch 79/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3568 - accuracy: 0.8469\n",
            "Epoch 80/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3575 - accuracy: 0.8466\n",
            "Epoch 81/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3575 - accuracy: 0.8474\n",
            "Epoch 82/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3568 - accuracy: 0.8484\n",
            "Epoch 83/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3571 - accuracy: 0.8466\n",
            "Epoch 84/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3570 - accuracy: 0.8485\n",
            "Epoch 85/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3565 - accuracy: 0.8481\n",
            "Epoch 86/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3566 - accuracy: 0.8490\n",
            "Epoch 87/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3574 - accuracy: 0.8468\n",
            "Epoch 88/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3566 - accuracy: 0.8482\n",
            "Epoch 89/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3558 - accuracy: 0.8478\n",
            "Epoch 90/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3566 - accuracy: 0.8493\n",
            "Epoch 91/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3562 - accuracy: 0.8482\n",
            "Epoch 92/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3563 - accuracy: 0.8466\n",
            "Epoch 93/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3561 - accuracy: 0.8465\n",
            "Epoch 94/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3566 - accuracy: 0.8454\n",
            "Epoch 95/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3554 - accuracy: 0.8482\n",
            "Epoch 96/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3565 - accuracy: 0.8480\n",
            "Epoch 97/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3560 - accuracy: 0.8487\n",
            "Epoch 98/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3556 - accuracy: 0.8493\n",
            "Epoch 99/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3561 - accuracy: 0.8468\n",
            "Epoch 100/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3560 - accuracy: 0.8466\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fc5c4b1f7d0>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test,Y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K52d87HhqMPa",
        "outputId": "711d4e1f-8445-4dc3-b0b1-eea281ece705"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 0s 3ms/step - loss: 0.3871 - accuracy: 0.8380\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.387067973613739, 0.8379999995231628]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yp=model.predict(X_test)"
      ],
      "metadata": {
        "id": "7lDfw62CqQLq"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = []\n",
        "for element in yp:\n",
        "    if element > 0.5:\n",
        "        y_pred.append(1)\n",
        "    else:\n",
        "        y_pred.append(0)"
      ],
      "metadata": {
        "id": "Q_lend7Dql-u"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VDvLG9Ltqo1y",
        "outputId": "823492d9-9f84-4061-c4a7-b37ccd2637ee"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 0, 0, 1, 0, 0, 0, 0, 0]"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_test[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "545EIOoHqrAY",
        "outputId": "61abca50-8ea8-49dc-c36a-3e882c9ee4d3"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "937     0\n",
              "9355    0\n",
              "2293    1\n",
              "192     0\n",
              "8675    0\n",
              "53      1\n",
              "9596    0\n",
              "265     0\n",
              "984     0\n",
              "9793    0\n",
              "Name: Exited, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(classification_report(Y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ScsCFTRlqwsd",
        "outputId": "d8288a86-2284-46ae-bda4-f7a49155125d"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.97      0.90      1578\n",
            "           1       0.76      0.34      0.47       422\n",
            "\n",
            "    accuracy                           0.84      2000\n",
            "   macro avg       0.80      0.65      0.69      2000\n",
            "weighted avg       0.83      0.84      0.81      2000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sn\n",
        "cm = tf.math.confusion_matrix(labels=Y_test,predictions=y_pred)\n",
        "\n",
        "plt.figure(figsize = (10,7))\n",
        "sn.heatmap(cm, annot=True, fmt='d')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "fbmDTOL2rFFW",
        "outputId": "9347406a-3318-4d41-a748-ebfe21f1c685"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Truth')"
            ]
          },
          "metadata": {},
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FObIwuOQrPS9",
        "outputId": "627ef165-3af7-47b1-ec3b-3cd3f4d53e4c"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2000,)"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Accuracy**"
      ],
      "metadata": {
        "id": "o2LLXv9JrVJF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "round((1534+142)/(1534+142+280+44),2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fwHeH1u7rUbm",
        "outputId": "a35f816a-bd64-4ae1-fee2-15d1d72676b1"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.84"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Precision for 0 class i.e. Precision for bank customers who did not churn**"
      ],
      "metadata": {
        "id": "fsgBH6bprgjS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "round((1534)/(1534+280),2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QPJmwXQEro3Q",
        "outputId": "299d2d25-72ad-4847-ad2b-b3759c5463f1"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.85"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Precision for 1 class i.e Precision for bank customers who actually churned**"
      ],
      "metadata": {
        "id": "PdpyPgIorvLF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "round((142)/(142+44),2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z9UROEyhr2m9",
        "outputId": "1b565ed8-d7ef-4ba7-b6d4-7af5119bbc61"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.76"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Recall for 0 class**"
      ],
      "metadata": {
        "id": "EvhnEKGXr8bT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "round((1534)/(1534+44),2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "in9Hz4Xer-9o",
        "outputId": "c026a73d-bef6-4651-e32a-2d9b31d6b5d4"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.97"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Recall for 1 class**"
      ],
      "metadata": {
        "id": "roM3yf_LsC3d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "round((142)/(280+142),2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BA1D8QO1sExi",
        "outputId": "44e3641c-313c-482a-9231-6049c3e34f34"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.34"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**F1- score for 0 class**"
      ],
      "metadata": {
        "id": "VZD6PLJUsLU5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "round(2*(0.85*0.97)/(0.85+0.92),2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CuY9SgXAsOLn",
        "outputId": "3f02bf00-c49a-4548-a450-66f0eb492db8"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.93"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**F1- score for 1 class**"
      ],
      "metadata": {
        "id": "1x97J9dysY_Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "round(2*(0.76*0.34)/(0.76+0.34),2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dpMaBlcwsb5s",
        "outputId": "bbbf0d2f-37f6-4e4d-d2cf-cbd437445d02"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.47"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    }
  ]
}