Customer_Churn_.ipynb

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        "Customer Churn"
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      "metadata": {
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      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "%matplotlib inline\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd /content/drive/MyDrive/Colab Notebooks"
      ],
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      "execution_count": 10,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Colab Notebooks\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe=pd.read_csv(\"customer_churn.csv\")"
      ],
      "metadata": {
        "id": "yNVD5V6B14D4"
      },
      "execution_count": 11,
      "outputs": []
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      "execution_count": 12,
      "outputs": [
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          "data": {
            "text/plain": [
              "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
              "3252  1536-YHDOE    Male              0     Yes        Yes      17   \n",
              "426   2806-MLNTI    Male              1     Yes         No      52   \n",
              "2477  1212-GLHMD    Male              0      No         No       3   \n",
              "4545  6145-NNPNO  Female              0      No         No       1   \n",
              "3043  3677-IYRBF  Female              1      No         No       2   \n",
              "\n",
              "     PhoneService     MultipleLines InternetService OnlineSecurity  ...  \\\n",
              "3252          Yes               Yes             DSL            Yes  ...   \n",
              "426           Yes               Yes     Fiber optic             No  ...   \n",
              "2477          Yes                No     Fiber optic            Yes  ...   \n",
              "4545          Yes                No             DSL             No  ...   \n",
              "3043           No  No phone service             DSL             No  ...   \n",
              "\n",
              "     DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
              "3252               No         Yes          No              No        One year   \n",
              "426               Yes          No         Yes             Yes        One year   \n",
              "2477               No          No          No             Yes  Month-to-month   \n",
              "4545               No          No          No              No  Month-to-month   \n",
              "3043              Yes          No          No              No  Month-to-month   \n",
              "\n",
              "     PaperlessBilling              PaymentMethod MonthlyCharges  TotalCharges  \\\n",
              "3252              Yes               Mailed check          62.10       1096.65   \n",
              "426               Yes  Bank transfer (automatic)         105.10        5376.4   \n",
              "2477              Yes               Mailed check          86.05        244.85   \n",
              "4545               No  Bank transfer (automatic)          44.15         44.15   \n",
              "3043              Yes           Electronic check          30.40         78.65   \n",
              "\n",
              "     Churn  \n",
              "3252    No  \n",
              "426     No  \n",
              "2477    No  \n",
              "4545    No  \n",
              "3043   Yes  \n",
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              "      <td>Fiber optic</td>\n",
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              "      <td>No</td>\n",
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              "      <td>244.85</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "# DATA EXPLORATION\n",
        "dataframe.drop('customerID',axis='columns',inplace=True)"
      ],
      "metadata": {
        "id": "ZFapnjoV54Vv"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe.dtypes\n",
        "#We'll observe customerID is gone"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "id": "dHQP2vc85NZF",
        "outputId": "b72af9a4-53d8-4401-b933-0004ea0fd335"
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      "execution_count": 14,
      "outputs": [
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          "data": {
            "text/plain": [
              "gender               object\n",
              "SeniorCitizen         int64\n",
              "Partner              object\n",
              "Dependents           object\n",
              "tenure                int64\n",
              "PhoneService         object\n",
              "MultipleLines        object\n",
              "InternetService      object\n",
              "OnlineSecurity       object\n",
              "OnlineBackup         object\n",
              "DeviceProtection     object\n",
              "TechSupport          object\n",
              "StreamingTV          object\n",
              "StreamingMovies      object\n",
              "Contract             object\n",
              "PaperlessBilling     object\n",
              "PaymentMethod        object\n",
              "MonthlyCharges      float64\n",
              "TotalCharges         object\n",
              "Churn                object\n",
              "dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>gender</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Partner</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dependents</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tenure</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PhoneService</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MultipleLines</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InternetService</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OnlineBackup</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DeviceProtection</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TechSupport</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>StreamingTV</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>StreamingMovies</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Contract</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PaymentMethod</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TotalCharges</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Churn</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#We observed monthly charges are float value and total charge is object\n",
        "dataframe.TotalCharges.values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bGgHGdVG5bNq",
        "outputId": "62e7edb1-29dc-48f9-d682-135c13b079db"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['29.85', '1889.5', '108.15', ..., '346.45', '306.6', '6844.5'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe.MonthlyCharges.values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3z8bJBTbAwcf",
        "outputId": "535c1e68-c574-4a1c-aad9-0e1a4e180e9d"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 29.85,  56.95,  53.85, ...,  29.6 ,  74.4 , 105.65])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.to_numeric(dataframe.TotalCharges)   # to convert string to integer values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "dEZC5bI_Fbzt",
        "outputId": "e2799c51-356f-4516-be6b-ece58e757188"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Unable to parse string \" \" at position 488",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32mlib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.maybe_convert_numeric\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Unable to parse string \" \"",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-abe804b35bf9>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTotalCharges\u001b[0m\u001b[0;34m)\u001b[0m   \u001b[0;31m# to convert string to integer values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/tools/numeric.py\u001b[0m in \u001b[0;36mto_numeric\u001b[0;34m(arg, errors, downcast, dtype_backend)\u001b[0m\n\u001b[1;32m    220\u001b[0m         \u001b[0mcoerce_numeric\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m             values, new_mask = lib.maybe_convert_numeric(  # type: ignore[call-overload]  # noqa: E501\n\u001b[0m\u001b[1;32m    223\u001b[0m                 \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m                 \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mlib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.maybe_convert_numeric\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Unable to parse string \" \" at position 488"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Error is coming because at some places it is encountering spaces. Let's solve that."
      ],
      "metadata": {
        "id": "bpdLr2R_GQpm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.to_numeric(dataframe.TotalCharges,errors= 'coerce')  #ignores spaces"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "pnlJ9Mz9F9Rj",
        "outputId": "abefd7dd-f0a4-44b1-ed7e-0005d2cd2a79"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         29.85\n",
              "1       1889.50\n",
              "2        108.15\n",
              "3       1840.75\n",
              "4        151.65\n",
              "         ...   \n",
              "7038    1990.50\n",
              "7039    7362.90\n",
              "7040     346.45\n",
              "7041     306.60\n",
              "7042    6844.50\n",
              "Name: TotalCharges, Length: 7043, dtype: float64"
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TotalCharges</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>29.85</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1889.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>108.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1840.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>151.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7038</th>\n",
              "      <td>1990.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7039</th>\n",
              "      <td>7362.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7040</th>\n",
              "      <td>346.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7041</th>\n",
              "      <td>306.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7042</th>\n",
              "      <td>6844.50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7043 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe[pd.to_numeric(dataframe.TotalCharges,errors= 'coerce').isnull()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "id": "WH1PtBLSGq-z",
        "outputId": "df542f7a-7f1f-4a16-df59-6713f60511d8"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
              "488   Female              0     Yes        Yes       0           No   \n",
              "753     Male              0      No        Yes       0          Yes   \n",
              "936   Female              0     Yes        Yes       0          Yes   \n",
              "1082    Male              0     Yes        Yes       0          Yes   \n",
              "1340  Female              0     Yes        Yes       0           No   \n",
              "3331    Male              0     Yes        Yes       0          Yes   \n",
              "3826    Male              0     Yes        Yes       0          Yes   \n",
              "4380  Female              0     Yes        Yes       0          Yes   \n",
              "5218    Male              0     Yes        Yes       0          Yes   \n",
              "6670  Female              0     Yes        Yes       0          Yes   \n",
              "6754    Male              0      No        Yes       0          Yes   \n",
              "\n",
              "         MultipleLines InternetService       OnlineSecurity  \\\n",
              "488   No phone service             DSL                  Yes   \n",
              "753                 No              No  No internet service   \n",
              "936                 No             DSL                  Yes   \n",
              "1082               Yes              No  No internet service   \n",
              "1340  No phone service             DSL                  Yes   \n",
              "3331                No              No  No internet service   \n",
              "3826               Yes              No  No internet service   \n",
              "4380                No              No  No internet service   \n",
              "5218                No              No  No internet service   \n",
              "6670               Yes             DSL                   No   \n",
              "6754               Yes             DSL                  Yes   \n",
              "\n",
              "             OnlineBackup     DeviceProtection          TechSupport  \\\n",
              "488                    No                  Yes                  Yes   \n",
              "753   No internet service  No internet service  No internet service   \n",
              "936                   Yes                  Yes                   No   \n",
              "1082  No internet service  No internet service  No internet service   \n",
              "1340                  Yes                  Yes                  Yes   \n",
              "3331  No internet service  No internet service  No internet service   \n",
              "3826  No internet service  No internet service  No internet service   \n",
              "4380  No internet service  No internet service  No internet service   \n",
              "5218  No internet service  No internet service  No internet service   \n",
              "6670                  Yes                  Yes                  Yes   \n",
              "6754                  Yes                   No                  Yes   \n",
              "\n",
              "              StreamingTV      StreamingMovies  Contract PaperlessBilling  \\\n",
              "488                   Yes                   No  Two year              Yes   \n",
              "753   No internet service  No internet service  Two year               No   \n",
              "936                   Yes                  Yes  Two year               No   \n",
              "1082  No internet service  No internet service  Two year               No   \n",
              "1340                  Yes                   No  Two year               No   \n",
              "3331  No internet service  No internet service  Two year               No   \n",
              "3826  No internet service  No internet service  Two year               No   \n",
              "4380  No internet service  No internet service  Two year               No   \n",
              "5218  No internet service  No internet service  One year              Yes   \n",
              "6670                  Yes                   No  Two year               No   \n",
              "6754                   No                   No  Two year              Yes   \n",
              "\n",
              "                  PaymentMethod  MonthlyCharges TotalCharges Churn  \n",
              "488   Bank transfer (automatic)           52.55                 No  \n",
              "753                Mailed check           20.25                 No  \n",
              "936                Mailed check           80.85                 No  \n",
              "1082               Mailed check           25.75                 No  \n",
              "1340    Credit card (automatic)           56.05                 No  \n",
              "3331               Mailed check           19.85                 No  \n",
              "3826               Mailed check           25.35                 No  \n",
              "4380               Mailed check           20.00                 No  \n",
              "5218               Mailed check           19.70                 No  \n",
              "6670               Mailed check           73.35                 No  \n",
              "6754  Bank transfer (automatic)           61.90                 No  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
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              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
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              "      <th>OnlineBackup</th>\n",
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              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>488</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
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              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Two year</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>52.55</td>\n",
              "      <td></td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>753</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>20.25</td>\n",
              "      <td></td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>936</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>80.85</td>\n",
              "      <td></td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1082</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>25.75</td>\n",
              "      <td></td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1340</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>Credit card (automatic)</td>\n",
              "      <td>56.05</td>\n",
              "      <td></td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3331</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>19.85</td>\n",
              "      <td></td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3826</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>25.35</td>\n",
              "      <td></td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4380</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>20.00</td>\n",
              "      <td></td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5218</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>One year</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>19.70</td>\n",
              "      <td></td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6670</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>DSL</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>73.35</td>\n",
              "      <td></td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6754</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Two year</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>61.90</td>\n",
              "      <td></td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"dataframe[pd\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SeniorCitizen\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Partner\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tenure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PhoneService\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MultipleLines\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"No phone service\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"InternetService\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OnlineSecurity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OnlineBackup\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DeviceProtection\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TechSupport\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StreamingTV\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StreamingMovies\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Contract\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"One year\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PaperlessBilling\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PaymentMethod\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Bank transfer (automatic)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MonthlyCharges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.83148414101892,\n        \"min\": 19.7,\n        \"max\": 80.85,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          19.85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalCharges\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \" \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Churn\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe.iloc[488]      #integer location"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "id": "UVqYoUEnG1Wy",
        "outputId": "bd1c5690-8a21-4986-f7ad-2878f56bd132"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gender                                 Female\n",
              "SeniorCitizen                               0\n",
              "Partner                                   Yes\n",
              "Dependents                                Yes\n",
              "tenure                                      0\n",
              "PhoneService                               No\n",
              "MultipleLines                No phone service\n",
              "InternetService                           DSL\n",
              "OnlineSecurity                            Yes\n",
              "OnlineBackup                               No\n",
              "DeviceProtection                          Yes\n",
              "TechSupport                               Yes\n",
              "StreamingTV                               Yes\n",
              "StreamingMovies                            No\n",
              "Contract                             Two year\n",
              "PaperlessBilling                          Yes\n",
              "PaymentMethod       Bank transfer (automatic)\n",
              "MonthlyCharges                          52.55\n",
              "TotalCharges                                 \n",
              "Churn                                      No\n",
              "Name: 488, dtype: object"
            ],
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              "      <th>488</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>gender</th>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Partner</th>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dependents</th>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tenure</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PhoneService</th>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MultipleLines</th>\n",
              "      <td>No phone service</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InternetService</th>\n",
              "      <td>DSL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OnlineBackup</th>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DeviceProtection</th>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TechSupport</th>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>StreamingTV</th>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>StreamingMovies</th>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Contract</th>\n",
              "      <td>Two year</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PaymentMethod</th>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <td>52.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TotalCharges</th>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Churn</th>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#let's drop the dataset where totalcharges is null\n",
        "df1=dataframe[dataframe.TotalCharges!=' ']\n",
        "df1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z5_ldu6oG_Lw",
        "outputId": "b29d36a6-ddd0-4c8b-b7d9-6c3286a0d9bf"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7032, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.TotalCharges=pd.to_numeric(df1.TotalCharges)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d2gZvGzrHsE-",
        "outputId": "283dbc44-e464-490c-afb4-80c24ce4d845"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-22-01816c9a1a9f>:1: SettingWithCopyWarning: \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",
            "  df1.TotalCharges=pd.to_numeric(df1.TotalCharges)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.TotalCharges.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0jMktbY1IZ77",
        "outputId": "ebdb414c-d65a-435a-c1cd-453ebb9bc605"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('float64')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tenure_churn_no=df1[df1.Churn=='No'].tenure\n",
        "tenure_churn_yes=df1[df1.Churn=='Yes'].tenure\n",
        "\n",
        "plt.xlabel(\"Tenure\")\n",
        "plt.ylabel(\"Number Of Customer\")\n",
        "plt.title(\"Customer Churn Prediction Visualiztion\")\n",
        "plt.hist([tenure_churn_yes,tenure_churn_no ] , color=['blue','red'] , label=['Churn=Yes','Churn=No'])\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "Rce8t94NIeA3",
        "outputId": "9a2b770d-dd05-410d-f8fb-109a276b0a27"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7a5ae1d5a770>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tenure_churn_no=df1[df1.Churn=='No'].MonthlyCharges\n",
        "tenure_churn_yes=df1[df1.Churn=='Yes'].MonthlyCharges\n",
        "\n",
        "plt.xlabel(\"MonthlyCharges\")\n",
        "plt.ylabel(\"Number Of Customer\")\n",
        "plt.title(\"Customer Churn Prediction Visualiztion\")\n",
        "plt.hist([tenure_churn_yes,tenure_churn_no ] , color=['blue','red'] , label=['Churn=Yes','Churn=No'])\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "Z02EpTjAI7dO",
        "outputId": "c5d3bb62-fce2-4753-d08a-7b4b3f499653"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7a5adfbb3e80>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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GggULYGlpiQ4dOmi+oeUIDAyEXC7HV199haKiojLzX+bydE3GVRP16tVDTk4OTp48KU27desWNm3a9MK16lr//v1x48YN/Pjjj2XmPXjwQDrsURqeo6OjVdqoc8fjzp07w8rKCrNnz8bDhw9V5j2+Z+Bpn8EnOTs7o0WLFvjpp59UQunp06exa9eu5+7BeRHBwcHIzMzExx9/jKKiouceOgMeXe33OAMDAzRr1gwApN+90sD4+O9xXl5emVtelO5ZeXy8CgsLy/y+q2vmzJnYuXMn1q5d+9TDupX550EVi3uKqFKoV68e1qxZg/fffx9eXl4qd17++++/pUvhSw0dOhRz5szB0KFD4ePjg/j4eJw/f16lz/v376N27dp499130bx5c1haWmL37t1ITEzE119/LbVr3bo11q1bh8jISLz22muwtLREjx49MHz4cPzwww8YMmQIkpKSUKdOHfz+++84dOgQvv32W62dGC6Xy7F48WIMGjQIrVq1QlBQEOzt7XH16lX8+eef8Pf3x/fff/9CfWs6ruoKCgrCpEmT0KdPH0REREi3EGjYsKF0D6jKbtCgQfjtt98wYsQI7Nu3D/7+/igpKcG5c+fw22+/YefOnfDx8UGLFi0wYMAALFq0CDk5OWjbti327NmDixcvPncdcrkc33zzDYYOHYrXXnsNAwcOhK2tLU6cOIH8/HwpADztM1ieefPmoWvXrvDz80NoaKh0Cbi1tbXWnvH1uH79+mHkyJH4448/4OrqqrK35mmGDh2KrKwsvPXWW6hduzauXLmCBQsWoEWLFtI5eZ07d4abmxtCQ0MxYcIEGBoaYsWKFdJnv1Tbtm1ha2uLkJAQREREQCaT4Zdffnmhw02nTp3CrFmz0L59e2RmZmLVqlUq80vPU6rMPw+qYPq78I2orPPnz4thw4aJOnXqCBMTE2FlZSX8/f3FggULVC69zc/PF6GhocLa2lpYWVmJ/v37i8zMTJXLbwsKCsSECRNE8+bNhZWVlbCwsBDNmzcXixYtUllnbm6uGDhwoLCxsREAVC7FzcjIEB9++KGoVauWMDExEd7e3mVuBVB6me68efNUpu/bt08AEOvXr1eZXnrJcWJiYpn2gYGBwtraWpiZmYl69eqJIUOGiKNHj0ptQkJChIWFhabDqva4Aij3Fgbu7u4iJCREZdquXbtE06ZNhYmJiWjUqJFYtWrVUy/JL6/PZ40DALFv375nblPpum7fvv3Mdh06dBBNmjQpd15hYaGYO3euaNKkiTA1NRW2traidevWYubMmSqXnD948EBERESImjVrCgsLC9GjRw9x7dq1516SX2rLli2ibdu2okaNGkIul4s2bdqItWvXSvOf9hl82u0ndu/eLfz9/aX+evToIc6cOaPW+Dytxmd57733BAAxceLEcuc/eSn877//Ljp37iwcHByEiYmJcHNzEx9//LG4deuWynJJSUnC19dXajN//vxy6zt06JB4/fXXRY0aNYSLi4uYOHGi2LlzZ5nPyfMuyS/9bD3tVaqy/zyo4siE4NldRERERDyniIiIiAgMRUREREQAGIqIiIiIADAUEREREQFgKCIiIiICwFBEREREBIA3b1SLUqnEzZs3YWVl9UJPtiYiIiLdE0Lg/v37cHFxUeuZkQxFarh582a1eTgkERHRq+batWuoXbv2c9sxFKmh9HEO165dg1wu13M1REREpA6FQgFXV1e1H8vEUKSG0kNmcrmcoYiIiKiKUffUF55oTURERASGIiIiIiIADEVEREREAHhOERERVVMlJSUoKirSdxlUwUxMTNS63F4dDEVERFStCCGQnp6O7OxsfZdCOmBgYAAPDw+YmJi8dF8MRUREVK2UBiIHBweYm5vzprvVWOnNlW/dugU3N7eX/lkzFBERUbVRUlIiBaKaNWvquxzSAXt7e9y8eRPFxcUwNjZ+qb54ojUREVUbpecQmZub67kS0pXSw2YlJSUv3RdDERERVTs8ZPbq0ObPmqGIiIiICAxFREREVYZMJsPmzZv1XUa1xVBERESvBJlMt68XkZ6ejlGjRqFu3bowNTWFq6srevTogT179mh3MCpYaGgovL29UVhYqDJ9+/btMDExwbFjx/RU2bMxFBEREVUCly9fRuvWrbF3717MmzcPp06dQmxsLDp27IiwsLAKW++TwUUbvvnmG9y/fx/Tp0+XpmVnZ2PYsGGYOnUqWrVqpfV1agNDERERUSUwcuRIyGQyHDlyBP369UPDhg3RpEkTREZG4p9//pHa3blzB3369IG5uTkaNGiALVu2SPNiYmJgY2Oj0u/mzZtVTkaeMWMGWrRogWXLlsHDwwNmZmYAHh2aW7Zs2VP71oRcLsfKlSvx9ddf4/DhwwCAMWPG4D//+Q+mTJmCa9euoX///rCxsYGdnR169eqFy5cvS8vv378fbdq0gYWFBWxsbODv748rV668UC2aYCgiIiLSs6ysLMTGxiIsLAwWFhZl5j8edGbOnIn+/fvj5MmT6NatG4KDg5GVlaXR+i5evIgNGzZg48aNSE5OVrtvS0vLZ75GjBghte3YsSNGjhyJkJAQrF+/Hr/99ht+/vlnCCEQGBgIKysr/PXXXzh06BAsLS3RpUsXFBYWori4GL1790aHDh1w8uRJJCQkYPjw4Tq5opA3b6xOtPmBEUJ7fRER0TNdvHgRQgh4eno+t+2QIUMwYMAAAMBXX32F6OhoHDlyBF26dFF7fYWFhfj5559hb2+vUd+PB6jyyOVylfezZ89GbGwsgoKC8PXXX8PT0xOrVq2CUqnEsmXLpKCzcuVK2NjYYP/+/fDx8UFOTg7eeecd1KtXDwDg5eWl9ra9DIYiIiIiPRMa/Ee0WbNm0r8tLCwgl8uRmZmp0frc3d3LBCJ1+q5fv75G66lRowbGjx+PsWPHYvTo0QCAEydO4OLFi7CyslJp+/DhQ1y6dAmdO3fGkCFDEBgYiLfffhsBAQHo378/nJ2dNVr3i+DhMyIiIj1r0KABZDIZzp0799y2Tz7KQiaTQalUAnj0cNQnA1bpXb4fV94huuf1DWh2+KyUkZERDA0Npb1Cubm5aN26NZKTk1Ve58+fx8CBAwE82nOUkJCAtm3bYt26dWjYsKHKeVUVhXuKiIiI9MzOzg6BgYFYuHAhIiIiyoSW7OzsMidQl8fe3h73799HXl6e1MfzDnlpQtPDZ+Vp1aoV1q1bBwcHh2e2b9myJVq2bIkpU6bAz88Pa9asweuvv65pyRrhniIiIqJKYOHChSgpKUGbNm2wYcMGXLhwAWfPnkV0dDT8/PzU6sPX1xfm5ub49NNPcenSJaxZswYxMTFaq7F+/frPfDk4ODy3j+DgYNSqVQu9evXCX3/9hbS0NOzfvx8RERG4fv060tLSMGXKFCQkJODKlSvYtWsXLly4oJPzihiKiIiIKoG6devi2LFj6NixI8aNG4emTZvi7bffxp49e7B48WK1+rCzs8OqVauwfft2eHt7Y+3atZgxY0bFFq4hc3NzxMfHw83NDX379oWXlxdCQ0Px8OFDyOVymJub49y5c9JtCYYPH46wsDB8/PHHFV6bTGhydtcrSqFQwNraGjk5OWrtGtQbXn1GRK+4hw8fIi0tTeX+O1S9Petnrun3N/cUEREREUHPoSg+Ph49evSAi4vLcx9yN2LECMhkMnz77bcq07OyshAcHAy5XA4bGxuEhoYiNzdXpc3JkyfRrl07mJmZwdXVFVFRURWwNURERFSV6TUU5eXloXnz5li4cOEz223atAn//PMPXFxcyswLDg5GSkoK4uLisG3bNsTHx2P48OHSfIVCgc6dO8Pd3R1JSUmYN28eZsyYgaVLl2p9e4iIiKjq0usl+V27dkXXrl2f2ebGjRsYNWoUdu7cie7du6vMO3v2LGJjY5GYmAgfHx8AwIIFC9CtWzf873//g4uLC1avXo3CwkKsWLECJiYmaNKkCZKTkzF//nyV8ERERESvtkp9TpFSqcSgQYMwYcIENGnSpMz8hIQE2NjYSIEIAAICAmBgYCA9gC4hIQHt27eHiYmJ1CYwMBCpqam4d+9euestKCiAQqFQeREREVH1VqlD0dy5c2FkZISIiIhy56enp5e5J4KRkRHs7OyQnp4utXF0dFRpU/q+tM2TZs+eDWtra+nl6ur6sptCRERElVylDUVJSUn47rvvEBMTo5Mn4z5uypQpyMnJkV7Xrl3T6fqJiIhI9yptKPrrr7+QmZkJNzc3GBkZwcjICFeuXMG4ceNQp04dAICTk1OZh+AVFxcjKysLTk5OUpuMjAyVNqXvS9s8ydTUFHK5XOVFRERE1VulDUWDBg3CyZMnVR4W5+LiggkTJmDnzp0AAD8/P2RnZyMpKUlabu/evVAqlfD19ZXaxMfHqzwQLy4uDo0aNYKtra1uN4qIiIgqLb2GotzcXCnwAEBaWhqSk5Nx9epV1KxZE02bNlV5GRsbw8nJCY0aNQIAeHl5oUuXLhg2bBiOHDmCQ4cOITw8HEFBQdLl+wMHDoSJiQlCQ0ORkpKCdevW4bvvvkNkZKS+NpuIiOiFPO+efvRy9BqKjh49Kj0FFwAiIyPRsmVLTJs2Te0+Vq9eDU9PT3Tq1AndunXDG2+8oXIPImtra+zatQtpaWlo3bo1xo0bh2nTpvFyfCKiV41MptvXC0hPT8eoUaNQt25dmJqawtXVFT169MCePXu0PBgVb8aMGZDJZBgxYoTK9OTkZMhkMly+fFk/hT2DXu9T9Oabb0KTR6+VN4B2dnZYs2bNM5dr1qwZ/vrrL03LIyIi0pnLly/D398fNjY2mDdvHry9vVFUVISdO3ciLCwM586dq5D1FhYWqty2RpvMzMywfPlyjBs3Dg0aNKiQdWhTpT2niIiI6FUycuRIyGQyHDlyRHpCfJMmTRAZGYl//vlHanfnzh306dMH5ubmaNCgAbZs2SLNi4mJgY2NjUq/mzdvVrmKe8aMGWjRogWWLVum8hBVmUyGZcuWPbXvF9GoUSN07NgRn3322TPbHThwAG3atIGpqSmcnZ0xefJkFBcXv9S6XwRDERERkZ5lZWUhNjYWYWFhsLCwKDP/8aAzc+ZM9O/fHydPnkS3bt0QHByMrKwsjdZ38eJFbNiwARs3bpTO61Wnb0tLy2e+njxUBgBz5szBhg0bcPTo0XJruXHjBrp164bXXnsNJ06cwOLFi7F8+XJ88cUXGm2TNuj18BkRERE9CilCCHh6ej637ZAhQzBgwAAAwFdffYXo6GgcOXIEXbp0UXt9hYWF+Pnnn2Fvb69R348HqPKUdwubVq1aoX///pg0aVK550YtWrQIrq6u+P777yGTyeDp6YmbN29i0qRJmDZtGgwMdLf/hqGIiIhIzzQ5v7ZZs2bSvy0sLCCXy8vcs+953N3dywQidfquX7++Rusp9cUXX8DLywu7du0q8ySKs2fPws/PT+UQn7+/P3Jzc3H9+nW4ubm90DpfBA+fERER6VmDBg0gk8nUOpna2NhY5b1MJoNSqQQAGBgYlAlYj9+nr1R5h+ie1zfwYofPAKBevXoYNmwYJk+erFEA1DXuKSIiItIzOzs7BAYGYuHChYiIiCgTWrKzs8ucQF0ee3t73L9/H3l5eVIfzzvkpYkXOXxWatq0aahXrx5+/fVXleleXl7YsGEDhBDS3qJDhw7BysoKtWvXfumaNcE9RURERJXAwoULUVJSgjZt2mDDhg24cOECzp49i+joaPj5+anVh6+vL8zNzfHpp5/i0qVLWLNmDWJiYrRWY/369Z/5evLQ2OMcHR0RGRmJ6OholekjR47EtWvXMGrUKJw7dw5//PEHpk+fjsjISJ2eTwQwFBEREVUKdevWxbFjx9CxY0eMGzcOTZs2xdtvv409e/Zg8eLFavVhZ2eHVatWYfv27fD29sbatWsxY8aMii1cA+PHj4elpaXKtP/85z/Yvn07jhw5gubNm2PEiBEIDQ3F559/rvP6ZKIyH9yrJBQKBaytrZGTk1O5Hw77gndQLRc/FkRUBT18+BBpaWkq99+h6u1ZP3NNv7+5p4iIiIgIDEVEREREABiKiIiIiAAwFBEREREBYCgiIqJqiNcQvTq0+bNmKCIiomqj9I7M+fn5eq6EdKWwsBAAYGho+NJ98Y7WRERUbRgaGsLGxkZ6Xpe5ubnKM7WoelEqlbh9+zbMzc1hZPTykYahiIiIqhUnJycA0PghqVQ1GRgYwM3NTSvhl6GIiIiqFZlMBmdnZzg4OJT7MFSqXkxMTLT2OBCGIiIiqpYMDQ21cp4JvTp4ojURERERGIqIiIiIADAUEREREQFgKCIiIiICwFBEREREBIChiIiIiAgAQxERERERAIYiIiIiIgAMRUREREQAGIqIiIiIADAUEREREQFgKCIiIiICwFBEREREBIChiIiIiAgAQxERERERAIYiIiIiIgAMRUREREQAGIqIiIiIADAUEREREQHQcyiKj49Hjx494OLiAplMhs2bN0vzioqKMGnSJHh7e8PCwgIuLi4YPHgwbt68qdJHVlYWgoODIZfLYWNjg9DQUOTm5qq0OXnyJNq1awczMzO4uroiKipKF5tHREREVYheQ1FeXh6aN2+OhQsXlpmXn5+PY8eOYerUqTh27Bg2btyI1NRU9OzZU6VdcHAwUlJSEBcXh23btiE+Ph7Dhw+X5isUCnTu3Bnu7u5ISkrCvHnzMGPGDCxdurTCt4+IiIiqDpkQQui7CACQyWTYtGkTevfu/dQ2iYmJaNOmDa5cuQI3NzecPXsWjRs3RmJiInx8fAAAsbGx6NatG65fvw4XFxcsXrwYn332GdLT02FiYgIAmDx5MjZv3oxz586pVZtCoYC1tTVycnIgl8tfelsrjEymvb4qx8eCiIjohWn6/V2lzinKycmBTCaDjY0NACAhIQE2NjZSIAKAgIAAGBgY4PDhw1Kb9u3bS4EIAAIDA5Gamop79+6Vu56CggIoFAqVFxEREVVvVSYUPXz4EJMmTcKAAQOktJeeng4HBweVdkZGRrCzs0N6errUxtHRUaVN6fvSNk+aPXs2rK2tpZerq6u2N4eIiIgqmSoRioqKitC/f38IIbB48eIKX9+UKVOQk5Mjva5du1bh6yQiIiL9MtJ3Ac9TGoiuXLmCvXv3qhwTdHJyQmZmpkr74uJiZGVlwcnJSWqTkZGh0qb0fWmbJ5mamsLU1FSbm0FERESVXKXeU1QaiC5cuIDdu3ejZs2aKvP9/PyQnZ2NpKQkadrevXuhVCrh6+srtYmPj0dRUZHUJi4uDo0aNYKtra1uNoSIiIgqPb2GotzcXCQnJyM5ORkAkJaWhuTkZFy9ehVFRUV49913cfToUaxevRolJSVIT09Heno6CgsLAQBeXl7o0qULhg0bhiNHjuDQoUMIDw9HUFAQXFxcAAADBw6EiYkJQkNDkZKSgnXr1uG7775DZGSkvjabiIiIKiG9XpK/f/9+dOzYscz0kJAQzJgxAx4eHuUut2/fPrz55psAHt28MTw8HFu3boWBgQH69euH6OhoWFpaSu1PnjyJsLAwJCYmolatWhg1ahQmTZqkdp28JJ+IiKjq0fT7u9Lcp6gyYygiIiKqeqr1fYqIiIiIKgpDEREREREYioiIiIgAMBQRERERAWAoIiIiIgLAUEREREQEgKGIiIiICABDEREREREAhiIiIiIiAAxFRERERAAYioiIiIgAMBQRERERAWAoIiIiIgLAUEREREQEgKGIiIiICABDEREREREAhiIiIiIiAAxFRERERAAYioiIiIgAMBQRERERAWAoIiIiIgLAUEREREQEgKGIiIiICABDEREREREAhiIiIiIiAAxFRERERAA0DEVFRUXo1KkTLly4UFH1EBEREemFRqHI2NgYJ0+erKhaiIiIiPRG48NnH3zwAZYvX14RtRARERHpjZGmCxQXF2PFihXYvXs3WrduDQsLC5X58+fP11pxRERERLqicSg6ffo0WrVqBQA4f/68yjyZTKadqoiIiIh0TONQtG/fvoqog4iIiEivXviS/IsXL2Lnzp148OABAEAIobWiiIiIiHRN41B09+5ddOrUCQ0bNkS3bt1w69YtAEBoaCjGjRun9QKJiIiIdEHjUDR27FgYGxvj6tWrMDc3l6a///77iI2N1WpxRERERLqi8TlFu3btws6dO1G7dm2V6Q0aNMCVK1e0VhgRERGRLmm8pygvL09lD1GprKwsmJqaaqUoIiIiIl3TOBS1a9cOP//8s/ReJpNBqVQiKioKHTt21Kiv+Ph49OjRAy4uLpDJZNi8ebPKfCEEpk2bBmdnZ9SoUQMBAQFlHjGSlZWF4OBgyOVy2NjYIDQ0FLm5uSptTp48iXbt2sHMzAyurq6IiorSbKOJiIio2tM4FEVFRWHp0qXo2rUrCgsLMXHiRDRt2hTx8fGYO3euRn3l5eWhefPmWLhw4VPXFR0djSVLluDw4cOwsLBAYGAgHj58KLUJDg5GSkoK4uLisG3bNsTHx2P48OHSfIVCgc6dO8Pd3R1JSUmYN28eZsyYgaVLl2q66URERFSNycQLXEufk5OD77//HidOnEBubi5atWqFsLAwODs7v3ghMhk2bdqE3r17A3i0l8jFxQXjxo3D+PHjpfU6OjoiJiYGQUFBOHv2LBo3bozExET4+PgAAGJjY9GtWzdcv34dLi4uWLx4MT777DOkp6fDxMQEADB58mRs3rwZ586dU6s2hUIBa2tr5OTkQC6Xv/A2Vjht3jyTt1ggIqIqTtPvb41PtAYAa2trfPbZZy+yqNrS0tKQnp6OgIAAlfX6+voiISEBQUFBSEhIgI2NjRSIACAgIAAGBgY4fPgw+vTpg4SEBLRv314KRAAQGBiIuXPn4t69e7C1tS2z7oKCAhQUFEjvFQpFBW0lERERVRYvFIoePnyIkydPIjMzE0qlUmVez549tVJYeno6AMDR0VFluqOjozQvPT0dDg4OKvONjIxgZ2en0sbDw6NMH6XzygtFs2fPxsyZM7WyHURERFQ1aByKYmNjMXjwYNy5c6fMPJlMhpKSEq0Upk9TpkxBZGSk9F6hUMDV1VWPFREREVFF0/hE61GjRuG9997DrVu3oFQqVV7aDEROTk4AgIyMDJXpGRkZ0jwnJydkZmaqzC8uLkZWVpZKm/L6eHwdTzI1NYVcLld5ERERUfWmcSjKyMhAZGRkmcNa2ubh4QEnJyfs2bNHmqZQKHD48GH4+fkBAPz8/JCdnY2kpCSpzd69e6FUKuHr6yu1iY+PR1FRkdQmLi4OjRo1KvfQGREREb2aNA5F7777Lvbv36+Vlefm5iI5ORnJyckAHp1cnZycjKtXr0Imk2HMmDH44osvsGXLFpw6dQqDBw+Gi4uLdIWal5cXunTpgmHDhuHIkSM4dOgQwsPDERQUBBcXFwDAwIEDYWJigtDQUKSkpGDdunX47rvvVA6PEREREWl8SX5+fj7ee+892Nvbw9vbG8bGxirzIyIi1O5r//795d7wMSQkBDExMRBCYPr06Vi6dCmys7PxxhtvYNGiRWjYsKHUNisrC+Hh4di6dSsMDAzQr18/REdHw9LSUmpz8uRJhIWFITExEbVq1cKoUaMwadIktevkJflERERVj6bf3xqHouXLl2PEiBEwMzNDzZo1IXvsi1gmk+Hff//VvOpKjqGIiIio6qnw+xR99tlnmDlzJiZPngwDA42PvhERERFVShqnmsLCQrz//vsMRERERFStaJxsQkJCsG7duoqohYiIiEhvND58VlJSgqioKOzcuRPNmjUrc6L1/PnztVYcERERka5oHIpOnTqFli1bAgBOnz6tMk+mzRN9iYiIiHRI41C0b9++iqiDiIiISK9e6mzp69ev4/r169qqhYiIiEhvNA5FSqUS//3vf2FtbQ13d3e4u7vDxsYGs2bNglKprIgaiYiIiCrcC92naPny5ZgzZw78/f0BAAcPHsSMGTPw8OFDfPnll1ovkoiIiKiiaXxHaxcXFyxZsgQ9e/ZUmf7HH39g5MiRuHHjhlYLrAx4R2siIqKqR9Pvb40Pn2VlZcHT07PMdE9PT2RlZWnaHREREVGloHEoat68Ob7//vsy07///ns0b95cK0URERER6ZrG5xRFRUWhe/fu2L17N/z8/AAACQkJuHbtGrZv3671AomIiIh0QeM9RR06dMD58+fRp08fZGdnIzs7G3379kVqairatWtXETUSERERVTiNT7S+evUqXF1dy7179dWrV+Hm5qa14ioLnmhNRERU9VT4idYeHh64fft2mel3796Fh4eHpt0RERERVQoahyIhRLl7iXJzc2FmZqaVooiIiIh0Te0TrSMjIwE8eujr1KlTYW5uLs0rKSnB4cOH0aJFC60XSERERKQLaoei48ePA3i0p+jUqVMwMTGR5pmYmKB58+YYP3689iskIiIi0gG1Q9G+ffsAAB9++CG+++67yn3CMREREZGGND6naOXKlSqBSKFQYPPmzTh37pxWCyMiIiLSJY1DUf/+/aU7Wj948AA+Pj7o378/vL29sWHDBq0XSERERKQLGoei+Ph46SaNmzZtghAC2dnZiI6OxhdffKH1AomIiIh0QeNQlJOTAzs7OwBAbGws+vXrB3Nzc3Tv3h0XLlzQeoFEREREuqBxKHJ1dUVCQgLy8vIQGxuLzp07AwDu3bvH+xQRERFRlaXxA2HHjBmD4OBgWFpawt3dHW+++SaAR4fVvL29tV0fERERkU5oHIpGjhyJNm3a4Nq1a3j77bdhYPBoZ1PdunV5ThERERFVWRo/EPZVxAfCEhERVT2afn9rvKfoo48+eub8FStWaNolERERkd5pHIru3bun8r6oqAinT59GdnY23nrrLa0VRkRERKRLGoeiTZs2lZmmVCrxySefoF69elopioiIiEjXNL4kv9xODAwQGRmJb775RhvdEREREemcVkIRAFy6dAnFxcXa6o6IiIhIpzQ+fBYZGanyXgiBW7du4c8//0RISIjWCiMiIiLSJY1D0fHjx1XeGxgYwN7eHl9//fVzr0wjIiIiqqw0DkX79u2riDqIiIiI9Ertc4oePHiALVu24P79+2XmKRQKbNmyBQUFBVotjoiIiEhX1A5FS5cuxXfffQcrK6sy8+RyOaKjo7Fs2TKtFldSUoKpU6fCw8MDNWrUQL169TBr1iw8fhNuIQSmTZsGZ2dn1KhRAwEBAbhw4YJKP1lZWQgODoZcLoeNjQ1CQ0ORm5ur1VqJiIioalM7FK1evRpjxox56vwxY8bgp59+0kZNkrlz52Lx4sX4/vvvcfbsWcydOxdRUVFYsGCB1CYqKgrR0dFYsmQJDh8+DAsLCwQGBuLhw4dSm+DgYKSkpCAuLg7btm1DfHw8hg8frtVaiYiIqGpT+9lntra2OHHiBNzc3Mqdf/XqVTRv3rzMHa9fxjvvvANHR0csX75cmtavXz/UqFEDq1atghACLi4uGDduHMaPHw8AyMnJgaOjI2JiYhAUFISzZ8+icePGSExMhI+PDwAgNjYW3bp1w/Xr1+Hi4vLcOvjsMyIioqpH0+9vtfcUFRcX4/bt20+df/v2ba3fp6ht27bYs2cPzp8/DwA4ceIEDh48iK5duwIA0tLSkJ6ejoCAAGkZa2tr+Pr6IiEhAQCQkJAAGxsbKRABQEBAAAwMDHD48GGt1ktERERVl9pXnzVp0gS7d+9G69aty52/a9cuNGnSRGuFAcDkyZOhUCjg6ekJQ0NDlJSU4Msvv0RwcDAAID09HQDg6Oiospyjo6M0Lz09HQ4ODirzjYyMYGdnJ7V5UkFBgcpJ4wqFQmvbRERERJWT2nuKPvroI8yaNQvbtm0rM2/r1q348ssvtX6fot9++w2rV6/GmjVrcOzYMfz000/43//+p/Vzl540e/ZsWFtbSy9XV9cKXR8RERHpn9p7ioYPH474+Hj07NkTnp6eaNSoEQDg3LlzOH/+PPr376/1k5cnTJiAyZMnIygoCADg7e2NK1euYPbs2QgJCYGTkxMAICMjA87OztJyGRkZaNGiBQDAyckJmZmZKv0WFxcjKytLWv5JU6ZMUblzt0KhYDAiIiKq5jR69tmqVavw66+/omHDhjh//jxSU1PRqFEjrF27FmvXrtV6cfn5+TAwUC3R0NAQSqUSAODh4QEnJyfs2bNHmq9QKHD48GH4+fkBAPz8/JCdnY2kpCSpzd69e6FUKuHr61vuek1NTSGXy1VeREREVL1pfEfr/v37o3///hVRSxk9evTAl19+CTc3NzRp0gTHjx/H/PnzpcN0MpkMY8aMwRdffIEGDRrAw8MDU6dOhYuLC3r37g0A8PLyQpcuXTBs2DAsWbIERUVFCA8PR1BQkFpXnhEREdGrQeNQpEsLFizA1KlTMXLkSGRmZsLFxQUff/wxpk2bJrWZOHEi8vLyMHz4cGRnZ+ONN95AbGwszMzMpDarV69GeHg4OnXqBAMDA/Tr1w/R0dH62CQiIiKqpNS+T9GrjPcpIiIiqnoq7D5FRERERNWZWqHo5MmT0snNRERERNWRWqGoZcuWuHPnDgCgbt26uHv3boUWRURERKRraoUiGxsbpKWlAQAuX77MvUZERERU7ah19Vm/fv3QoUMHODs7QyaTwcfHB4aGhuW2/ffff7VaIBEREZEuqBWKli5dir59++LixYuIiIjAsGHDYGVlVdG1EREREemM2vcp6tKlCwAgKSkJo0ePZigiIiKiakXjmzeuXLkS2dnZOHr0KACgfv36sLGx0XZdRERERDql0X2KLl++jO7du6NWrVrw9fWFr68vatWqhXfeeQeXL1+uoBKJiIiIKp7ae4quXbuG119/HcbGxpg1axa8vLwAAGfOnMHixYvh5+eHxMRE1K5du8KKJSIiIqooaj/mIzQ0FBcvXsTOnTtVnisGAA8ePECXLl3QoEEDLFu2rEIK1Sc+5oOIiKjq0fT7W+09RbGxsVi3bl2ZQAQANWrUwKxZsxAUFKRZtURERESVhNrnFN25cwd16tR56vy6desiKytLGzURERER6ZzaocjZ2Rlnzpx56vzTp0/DyclJK0URERER6Zraoah3794YP348bt++XWZeZmYmJk2ahN69e2uzNiIiIiKdUftE63v37sHX1xfp6en44IMP4OnpCSEEzp49izVr1sDJyQn//PMP7OzsKrpmneOJ1kRERFVPhZ1obWtri8OHD+PTTz/Fr7/+iuzsbACPHhY7cOBAfPXVV9UyEBEREdGrQe09RY8TQkiH0ezt7SHT5h6KSoh7ioiIiKqeCttT9DiZTAYHB4cXWZSIiIioUtLoMR9ERERE1RVDEREREREYioiIiIgAaBiKioqK0KlTJ1y4cKGi6iEiIiLSC41CkbGxMU6ePFlRtRARERHpjcaHzz744AMsX768ImohIiIi0huNL8kvLi7GihUrsHv3brRu3RoWFhYq8+fPn6+14oiIiIh0ReNQdPr0abRq1QoAcP78eZV51f0mjkRERFR9aRyK9u3bVxF1EBEREenVC1+Sf/HiRezcuRMPHjwA8OjRH0RERERVlcah6O7du+jUqRMaNmyIbt264datWwCA0NBQjBs3TusFEhEREemCxqFo7NixMDY2xtWrV2Fubi5Nf//99xEbG6vV4oiIiIh0ReNzinbt2oWdO3eidu3aKtMbNGiAK1euaK0wIiIiIl3SeE9RXl6eyh6iUllZWTA1NdVKUURERES6pnEoateuHX7++WfpvUwmg1KpRFRUFDp27KjV4oiIiIh0RePDZ1FRUejUqROOHj2KwsJCTJw4ESkpKcjKysKhQ4cqokYiIiKiCqfxnqKmTZvi/PnzeOONN9CrVy/k5eWhb9++OH78OOrVq1cRNRIRERFVOJngDYaeS6FQwNraGjk5OZDL5fou5+m0eUdxfiyIiKiK0/T7W+PDZwBw7949LF++HGfPngUANG7cGB9++CHs7OxepDsiIiIivdP48Fl8fDzq1KmD6Oho3Lt3D/fu3UN0dDQ8PDwQHx+v9QJv3LiBDz74ADVr1kSNGjXg7e2No0ePSvOFEJg2bRqcnZ1Ro0YNBAQE4MKFCyp9ZGVlITg4GHK5HDY2NggNDUVubq7WayUiIqKqS+NQFBYWhvfffx9paWnYuHEjNm7ciH///RdBQUEICwvTanH37t2Dv78/jI2NsWPHDpw5cwZff/01bG1tpTZRUVGIjo7GkiVLcPjwYVhYWCAwMBAPHz6U2gQHByMlJQVxcXHYtm0b4uPjMXz4cK3WSkRERFWbxucU1ahRA8nJyWjUqJHK9NTUVLRo0UJ6Fpo2TJ48GYcOHcJff/1V7nwhBFxcXDBu3DiMHz8eAJCTkwNHR0fExMQgKCgIZ8+eRePGjZGYmAgfHx8AQGxsLLp164br16/DxcXluXXwnCIiIqKqR9Pvb433FLVq1Uo6l+hxZ8+eRfPmzTXt7pm2bNkCHx8fvPfee3BwcEDLli3x448/SvPT0tKQnp6OgIAAaZq1tTV8fX2RkJAAAEhISICNjY0UiAAgICAABgYGOHz4cLnrLSgogEKhUHkRERFR9abWidYnT56U/h0REYHRo0fj4sWLeP311wEA//zzDxYuXIg5c+Zotbh///0XixcvRmRkJD799FMkJiYiIiICJiYmCAkJQXp6OgDA0dFRZTlHR0dpXnp6OhwcHFTmGxkZwc7OTmrzpNmzZ2PmzJla3RYiIiKq3NQKRS1atIBMJsPjR9omTpxYpt3AgQPx/vvva604pVIJHx8ffPXVVwCAli1b4vTp01iyZAlCQkK0tp4nTZkyBZGRkdJ7hUIBV1fXClsfEVGlw8Px9ApSKxSlpaVVdB3lcnZ2RuPGjVWmeXl5YcOGDQAAJycnAEBGRgacnZ2lNhkZGWjRooXUJjMzU6WP4uJiZGVlScs/ydTUlM9xIyIiesWoFYrc3d0ruo5y+fv7IzU1VWXa+fPnpXo8PDzg5OSEPXv2SCFIoVDg8OHD+OSTTwAAfn5+yM7ORlJSElq3bg0A2Lt3L5RKJXx9fXW3MURERFSpvdDNG2/evImDBw8iMzMTSqVSZV5ERIRWCgOAsWPHom3btvjqq6/Qv39/HDlyBEuXLsXSpUsBPHoY7ZgxY/DFF1+gQYMG8PDwwNSpU+Hi4oLevXsDeLRnqUuXLhg2bBiWLFmCoqIihIeHIygoSK0rz4iIiOjVoPEl+TExMfj4449hYmKCmjVrQvbYcWeZTIZ///1XqwVu27YNU6ZMwYULF+Dh4YHIyEgMGzZMmi+EwPTp07F06VJkZ2fjjTfewKJFi9CwYUOpTVZWFsLDw7F161YYGBigX79+iI6OhqWlpVo18JJ8Inrl8O8JVQOafn9rHIpcXV0xYsQITJkyBQYGGl/RXyUxFBHRK4d/T6gaqPD7FOXn5yMoKOiVCURERET0atA42YSGhmL9+vUVUQsRERGR3mh8+KykpATvvPMOHjx4AG9vbxgbG6vMnz9/vlYLrAx4+IyIXjn8e0LVgKbf3xpffTZ79mzs3LlTevbZkydaExEREVVFGoeir7/+GitWrMCQIUMqoBwiIiIi/dA4FJmamsLf378iaiEiIno5POxHL0HjE61Hjx6NBQsWVEQtRERERHqj8Z6iI0eOYO/evdi2bRuaNGlS5kTrjRs3aq04IqIqj3suiKoMjUORjY0N+vbtWxG1EBEREemNxqFo5cqVFVEHERERkV7xttREREREeIE9RR4eHs+8H5G2HwhLREREpAsah6IxY8aovC8qKsLx48cRGxuLCRMmaKsuIiIiIp3SOBSNHj263OkLFy7E0aNHX7ogIiIiIn3Q2jlFXbt2xYYNG7TVHREREZFOaS0U/f7777Czs9NWd0REREQ6pfHhs5YtW6qcaC2EQHp6Om7fvo1FixZptTgiIiIiXdE4FPXu3VvlvYGBAezt7fHmm2/C09NTW3URERER6ZRMCN43/nkUCgWsra2Rk5MDuVyu73Kejo8TIKp8qurvJevm38FqQNPvb968kYiIiAgaHD4zMDB45k0bAUAmk6G4uPiliyIiIiLSNbVD0aZNm546LyEhAdHR0VAqlVopiogqEA8vEFU+/L2sFNQORb169SozLTU1FZMnT8bWrVsRHByM//73v1otjoiIiEhXXuicops3b2LYsGHw9vZGcXExkpOT8dNPP8Hd3V3b9RERERHphEahKCcnB5MmTUL9+vWRkpKCPXv2YOvWrWjatGlF1UdERESkE2ofPouKisLcuXPh5OSEtWvXlns4jYiIiKiqUvs+RQYGBqhRowYCAgJgaGj41HYbN27UWnGVBe9TRNUKPye6VVXHm3Wz7mpA0+9vtfcUDR48+LmX5BMRERFVVWqHopiYmAosg4iIiEi/eEdrIiIiIjAUEREREQFgKCIiIiICwFBEREREBIChiIiIiAgAQxERERERAIYiIiIiIgAMRUREREQAqlgomjNnDmQyGcaMGSNNe/jwIcLCwlCzZk1YWlqiX79+yMjIUFnu6tWr6N69O8zNzeHg4IAJEyaguLhYx9UT0UuRybT3IiIqR5UJRYmJifjhhx/QrFkzleljx47F1q1bsX79ehw4cAA3b95E3759pfklJSXo3r07CgsL8ffff+Onn35CTEwMpk2bputNICIiokqsSoSi3NxcBAcH48cff4Stra00PScnB8uXL8f8+fPx1ltvoXXr1li5ciX+/vtv/PPPPwCAXbt24cyZM1i1ahVatGiBrl27YtasWVi4cCEKCwv1tUlERERUyVSJUBQWFobu3bsjICBAZXpSUhKKiopUpnt6esLNzQ0JCQkAgISEBHh7e8PR0VFqExgYCIVCgZSUFN1sABEREVV6aj8QVl9+/fVXHDt2DImJiWXmpaenw8TEBDY2NirTHR0dkZ6eLrV5PBCVzi+dV56CggIUFBRI7xUKxctsAhEREVUBlXpP0bVr1zB69GisXr0aZmZmOlvv7NmzYW1tLb1cXV11tm4iIiLSj0odipKSkpCZmYlWrVrByMgIRkZGOHDgAKKjo2FkZARHR0cUFhYiOztbZbmMjAw4OTkBAJycnMpcjVb6vrTNk6ZMmYKcnBzpde3aNe1vHBEREVUqlToUderUCadOnUJycrL08vHxQXBwsPRvY2Nj7NmzR1omNTUVV69ehZ+fHwDAz88Pp06dQmZmptQmLi4OcrkcjRs3Lne9pqamkMvlKi8iIiKq3ir1OUVWVlZo2rSpyjQLCwvUrFlTmh4aGorIyEjY2dlBLpdj1KhR8PPzw+uvvw4A6Ny5Mxo3boxBgwYhKioK6enp+PzzzxEWFgZTU1OdbxMRERFVTpU6FKnjm2++gYGBAfr164eCggIEBgZi0aJF0nxDQ0Ns27YNn3zyCfz8/GBhYYGQkBD897//1WPVREREVNnIhBBC30VUdgqFAtbW1sjJyanch9K0eadefiyqr6r6OWHdrFsdrJt/vx+j6fd3pT6niIiIiEhXGIqIiIiIwFBEREREBIChiIiIiAgAQxERERERgGpwST4REf0/bV3ExOuX6FXEUEREVA6GC6JXDw+fEREREYGhiIiIiAgAQxERERERAIYiIiIiIgAMRUREREQAGIqIiIiIAPCSfCIiqgR4CwSqDLiniIiIiAgMRUREREQAGIqIiIiIADAUEREREQFgKCIiIiICwFBEREREBIChiIiIiAgAQxERERERAIYiIiIiIgAMRUREREQAGIqIiIiIAPDZZ0RERC+Mz2yrXriniIiIiAgMRUREREQAGIqIiIiIADAUEREREQHgidZERET0orR1pjkACP2fbs5QRPSiqtkfAyKiVx0PnxERERGBoYiIiIgIAEMREREREQCGIiIiIiIADEVEREREABiKiIiIiABU8lA0e/ZsvPbaa7CysoKDgwN69+6N1NRUlTYPHz5EWFgYatasCUtLS/Tr1w8ZGRkqba5evYru3bvD3NwcDg4OmDBhAoqLi3W5KURERFTJVepQdODAAYSFheGff/5BXFwcioqK0LlzZ+Tl5Ultxo4di61bt2L9+vU4cOAAbt68ib59+0rzS0pK0L17dxQWFuLvv//GTz/9hJiYGEybNk0fm0RERESVlEyIqnPXuNu3b8PBwQEHDhxA+/btkZOTA3t7e6xZswbvvvsuAODcuXPw8vJCQkICXn/9dezYsQPvvPMObt68CUdHRwDAkiVLMGnSJNy+fRsmJibPXa9CoYC1tTVycnIgl8srdBtfCm8mqFtVdbxZt1p1a2t1AqybdauxKh3XrTWV/O+Jpt/flXpP0ZNycnIAAHZ2dgCApKQkFBUVISAgQGrj6ekJNzc3JCQkAAASEhLg7e0tBSIACAwMhEKhQEpKSrnrKSgogEKhUHkRERFR9VZlQpFSqcSYMWPg7++Ppk2bAgDS09NhYmICGxsblbaOjo5IT0+X2jweiErnl84rz+zZs2FtbS29XF1dtbw1REREVNlUmVAUFhaG06dP49dff63wdU2ZMgU5OTnS69q1axW+TiIiItKvKvFA2PDwcGzbtg3x8fGoXbu2NN3JyQmFhYXIzs5W2VuUkZEBJycnqc2RI0dU+iu9Oq20zZNMTU1hamqq5a0gIiKiyqxS7ykSQiA8PBybNm3C3r174eHhoTK/devWMDY2xp49e6RpqampuHr1Kvz8/AAAfn5+OHXqFDIzM6U2cXFxkMvlaNy4sW42hIiIiCq9Sr2nKCwsDGvWrMEff/wBKysr6Rwga2tr1KhRA9bW1ggNDUVkZCTs7Owgl8sxatQo+Pn54fXXXwcAdO7cGY0bN8agQYMQFRWF9PR0fP755wgLC+PeICIiIpJU6kvyZU+51G/lypUYMmQIgEc3bxw3bhzWrl2LgoICBAYGYtGiRSqHxq5cuYJPPvkE+/fvh4WFBUJCQjBnzhwYGamXCXlJPpWrqo43667Wl1qzbtZdnX8vNaXp93elDkWVBUMRlauqjjfrrtZfdqybdVfn30tNVev7FBERERFVFIYiIiIiIjAUEREREQFgKCIiIiICwFBEREREBKCS36eIiP6f9q5yISKi8jAUVQL8siMiItI/Hj4jIiIiAkMREREREQAePqPKoJLfEZWIiF4N3FNEREREBIYiIiIiIgAMRUREREQAeE4RvYJ4CwQiIioP9xQRERERgaGIiIiICABDEREREREAhiIiIiIiAAxFRERERAAYioiIiIgAMBQRERERAWAoIiIiIgLAUEREREQEgKGIiIiICABDEREREREAhiIiIiIiAAxFRERERAAYioiIiIgAAEb6LoCqLplMO/0I7XRDRET0UhiKiKhCMTwTUVXBw2dERERE4J4iIiKiVw734JaPe4qIiIiIwFBEREREBIChiIiIiAgAQxERERERAIYiIiIiIgCvWChauHAh6tSpAzMzM/j6+uLIkSP6LomIiIgqiVcmFK1btw6RkZGYPn06jh07hubNmyMwMBCZmZn6Lo2IiIgqgVcmFM2fPx/Dhg3Dhx9+iMaNG2PJkiUwNzfHihUr9F0aERERVQKvRCgqLCxEUlISAgICpGkGBgYICAhAQkKCHisjIiKiyuKVuKP1nTt3UFJSAkdHR5Xpjo6OOHfuXJn2BQUFKCgokN7n5OQAABQKRcUW+pK0Wp0Ot5V1g3Wrsyqtdsa6n7sqrXbGup+7Kq12xrr/v8tHfQqh3r23X4lQpKnZs2dj5syZZaa7urrqoRr1WWu1M6329uxVabUz1v3cVWm1M9b93FVptTPW/dxVabUz1v3cVWm1s4qr+/79+7BWo/9XIhTVqlULhoaGyMjIUJmekZEBJyenMu2nTJmCyMhI6b1SqURWVhZq1qwJmbYeGFMOhUIBV1dXXLt2DXK5vMLWQ49wvHWHY61bHG/d4njrjqZjLYTA/fv34eLiolb/r0QoMjExQevWrbFnzx707t0bwKOgs2fPHoSHh5dpb2pqClNTU5VpNjY2Oqj0Eblczl8sHeJ46w7HWrc43rrF8dYdTcZanT1EpV6JUAQAkZGRCAkJgY+PD9q0aYNvv/0WeXl5+PDDD/VdGhEREVUCr0woev/993H79m1MmzYN6enpaNGiBWJjY8ucfE1ERESvplcmFAFAeHh4uYfLKgtTU1NMnz69zKE7qhgcb93hWOsWx1u3ON66U9FjLRPqXqdGREREVI29EjdvJCIiInoehiIiIiIiMBQRERERAWAoIiIiIgLAUKQXs2fPxmuvvQYrKys4ODigd+/eSE1NVWnz8OFDhIWFoWbNmrC0tES/fv3K3JGbNDdnzhzIZDKMGTNGmsax1q4bN27ggw8+QM2aNVGjRg14e3vj6NGj0nwhBKZNmwZnZ2fUqFEDAQEBuHDhgh4rrppKSkowdepUeHh4oEaNGqhXrx5mzZql8ownjvWLi4+PR48ePeDi4gKZTIbNmzerzFdnbLOyshAcHAy5XA4bGxuEhoYiNzdXh1tRdTxrvIuKijBp0iR4e3vDwsICLi4uGDx4MG7evKnShzbGm6FIDw4cOICwsDD8888/iIuLQ1FRETp37oy8vDypzdixY7F161asX78eBw4cwM2bN9G3b189Vl31JSYm4ocffkCzZs1UpnOstefevXvw9/eHsbExduzYgTNnzuDrr7+Gra2t1CYqKgrR0dFYsmQJDh8+DAsLCwQGBuLhw4d6rLzqmTt3LhYvXozvv/8eZ8+exdy5cxEVFYUFCxZIbTjWLy4vLw/NmzfHwoULy52vztgGBwcjJSUFcXFx2LZtG+Lj4zF8+HBdbUKV8qzxzs/Px7FjxzB16lQcO3YMGzduRGpqKnr27KnSTivjLUjvMjMzBQBx4MABIYQQ2dnZwtjYWKxfv15qc/bsWQFAJCQk6KvMKu3+/fuiQYMGIi4uTnTo0EGMHj1aCMGx1rZJkyaJN95446nzlUqlcHJyEvPmzZOmZWdnC1NTU7F27VpdlFhtdO/eXXz00Ucq0/r27SuCg4OFEBxrbQIgNm3aJL1XZ2zPnDkjAIjExESpzY4dO4RMJhM3btzQWe1V0ZPjXZ4jR44IAOLKlStCCO2NN/cUVQI5OTkAADs7OwBAUlISioqKEBAQILXx9PSEm5sbEhIS9FJjVRcWFobu3burjCnAsda2LVu2wMfHB++99x4cHBzQsmVL/Pjjj9L8tLQ0pKenq4y3tbU1fH19Od4aatu2Lfbs2YPz588DAE6cOIGDBw+ia9euADjWFUmdsU1ISICNjQ18fHykNgEBATAwMMDhw4d1XnN1k5OTA5lMJj2XVFvj/Urd0boyUiqVGDNmDPz9/dG0aVMAQHp6OkxMTMo8hNbR0RHp6el6qLJq+/XXX3Hs2DEkJiaWmcex1q5///0XixcvRmRkJD799FMkJiYiIiICJiYmCAkJkcb0ycfrcLw1N3nyZCgUCnh6esLQ0BAlJSX48ssvERwcDAAc6wqkztimp6fDwcFBZb6RkRHs7Ow4/i/p4cOHmDRpEgYMGCA9FFZb481QpGdhYWE4ffo0Dh48qO9SqqVr165h9OjRiIuLg5mZmb7LqfaUSiV8fHzw1VdfAQBatmyJ06dPY8mSJQgJCdFzddXLb7/9htWrV2PNmjVo0qQJkpOTMWbMGLi4uHCsqdoqKipC//79IYTA4sWLtd4/D5/pUXh4OLZt24Z9+/ahdu3a0nQnJycUFhYiOztbpX1GRgacnJx0XGXVlpSUhMzMTLRq1QpGRkYwMjLCgQMHEB0dDSMjIzg6OnKstcjZ2RmNGzdWmebl5YWrV68CgDSmT17dx/HW3IQJEzB58mQEBQXB29sbgwYNwtixYzF79mwAHOuKpM7YOjk5ITMzU2V+cXExsrKyOP4vqDQQXblyBXFxcdJeIkB7481QpAdCCISHh2PTpk3Yu3cvPDw8VOa3bt0axsbG2LNnjzQtNTUVV69ehZ+fn67LrdI6deqEU6dOITk5WXr5+PggODhY+jfHWnv8/f3L3F7i/PnzcHd3BwB4eHjAyclJZbwVCgUOHz7M8dZQfn4+DAxU/4QbGhpCqVQC4FhXJHXG1s/PD9nZ2UhKSpLa7N27F0qlEr6+vjqvuaorDUQXLlzA7t27UbNmTZX5WhvvFzgxnF7SJ598IqytrcX+/fvFrVu3pFd+fr7UZsSIEcLNzU3s3btXHD16VPj5+Qk/Pz89Vl19PH71mRAca206cuSIMDIyEl9++aW4cOGCWL16tTA3NxerVq2S2syZM0fY2NiIP/74Q5w8eVL06tVLeHh4iAcPHuix8qonJCRE/Oc//xHbtm0TaWlpYuPGjaJWrVpi4sSJUhuO9Yu7f/++OH78uDh+/LgAIObPny+OHz8uXe2kzth26dJFtGzZUhw+fFgcPHhQNGjQQAwYMEBfm1SpPWu8CwsLRc+ePUXt2rVFcnKyyvdmQUGB1Ic2xpuhSA8AlPtauXKl1ObBgwdi5MiRwtbWVpibm4s+ffqIW7du6a/oauTJUMSx1q6tW7eKpk2bClNTU+Hp6SmWLl2qMl+pVIqpU6cKR0dHYWpqKjp16iRSU1P1VG3VpVAoxOjRo4Wbm5swMzMTdevWFZ999pnKlwTH+sXt27ev3L/TISEhQgj1xvbu3btiwIABwtLSUsjlcvHhhx+K+/fv62FrKr9njXdaWtpTvzf37dsn9aGN8ZYJ8djtT4mIiIheUTyniIiIiAgMRUREREQAGIqIiIiIADAUEREREQFgKCIiIiICwFBEREREBIChiIiIiAgAQxERVTEymQybN29+6vz9+/dDJpOVeZ7dy4qJiYGNjY1W+ySiyoWhiIjUMmTIEMhkMowYMaLMvLCwMMhkMgwZMkRr65sxYwZatGihtf6eZ9++fejWrRtq1qwJc3NzNG7cGOPGjcONGzd0VgMR6RdDERGpzdXVFb/++isePHggTXv48CHWrFkDNzc3PVb2cn744QcEBATAyckJGzZswJkzZ7BkyRLk5OTg66+/rtB1FxUVVWj/RKQ+hiIiUlurVq3g6uqKjRs3StM2btwINzc3tGzZUppWUFCAiIgIODg4wMzMDG+88QYSExOl+aWHuPbs2QMfHx+Ym5ujbdu2SE1NBfDoUNXMmTNx4sQJyGQyyGQyxMTESMvfuXMHffr0gbm5ORo0aIAtW7aUW29eXh7kcjl+//13lembN2+GhYUF7t+/j+vXryMiIgIRERFYsWIF3nzzTdSpUwft27fHsmXLMG3aNJVld+7cCS8vL1haWqJLly64deuWNC8xMRFvv/02atWqBWtra3To0AHHjh1TWV4mk2Hx4sXo2bMnLCws8OWXXwIAvvjiCzg4OMDKygpDhw7F5MmTy+wpW7ZsGby8vGBmZgZPT08sWrRImldYWIjw8HA4OzvDzMwM7u7umD17drnjQkRPodUnuhFRtRUSEiJ69eol5s+fLzp16iRN79Spk/jmm29Er169pIdlRkRECBcXF7F9+3aRkpIiQkJChK2trbh7964Q4v8f/ujr6yv2798vUlJSRLt27UTbtm2FEELk5+eLcePGiSZNmkhPw87PzxdCPHqgcu3atcWaNWvEhQsXREREhLC0tCzT971794QQQgwbNkx069ZNZVt69uwpBg8eLIQQYv78+QKAuHnz5jO3f+XKlcLY2FgEBASIxMREkZSUJLy8vMTAgQOlNnv27BG//PKLOHv2rDhz5owIDQ0Vjo6OQqFQSG0ACAcHB7FixQpx6dIlceXKFbFq1SphZmYmVqxYIVJTU8XMmTOFXC4XzZs3l5ZbtWqVcHZ2Fhs2bBD//vuv2LBhg7CzsxMxMTFCCCHmzZsnXF1dRXx8vLh8+bL466+/xJo1a9T62RLRIwxFRKSW0lCUmZkpTE1NxeXLl8Xly5eFmZmZuH37thSKcnNzhbGxsVi9erW0bGFhoXBxcRFRUVFCiP8PLrt375ba/PnnnwKAePDggRBCiOnTp6uEglIAxOeffy69z83NFQDEjh07VPouDUWHDx8WhoaGUujJyMgQRkZGYv/+/UIIIT755BMhl8ufu/0rV64UAMTFixelaQsXLhSOjo5PXaakpERYWVmJrVu3qtQ/ZswYlXa+vr4iLCxMZZq/v7/K9terV69MyJk1a5bw8/MTQggxatQo8dZbbwmlUvncbSGi8vHwGRFpxN7eHt27d0dMTAxWrlyJ7t27o1atWtL8S5cuoaioCP7+/tI0Y2NjtGnTBmfPnlXpq1mzZtK/nZ2dAQCZmZnPreHx5SwsLCCXy5+6XJs2bdCkSRP89NNPAIBVq1bB3d0d7du3BwAIISCTyZ67TgAwNzdHvXr1VGp+fL0ZGRkYNmwYGjRoAGtra8jlcuTm5uLq1asq/fj4+Ki8T01NRZs2bcrUXSovLw+XLl1CaGgoLC0tpdcXX3yBS5cuAXh0InxycjIaNWqEiIgI7Nq1S61tIqL/Z6TvAoio6vnoo48QHh4OAFi4cOEL92NsbCz9uzSYKJVKjZYrXfZZyw0dOhQLFy7E5MmTsXLlSnz44YfS+ho2bIicnBzcunVLCmaarFcIIb0PCQnB3bt38d1338Hd3R2mpqbw8/NDYWGhynIWFhbP3cbH5ebmAgB+/PFH+Pr6qswzNDQE8Oh8r7S0NOzYsQO7d+9G//79ERAQUOZ8KiJ6Ou4pIiKNdenSBYWFhSgqKkJgYKDKvHr16sHExASHDh2SphUVFSExMRGNGzdWex0mJiYoKSnRSr0ffPABrly5gujoaJw5cwYhISHSvHfffRcmJiaIiooqd1lN7nd06NAhREREoFu3bmjSpAlMTU1x586d5y7XqFEjlRPRAai8d3R0hIuLC/7991/Ur19f5eXh4SG1k8vleP/99/Hjjz9i3bp12LBhA7KystSun+hVxz1FRKQxQ0ND6VBY6Z6KUhYWFvjkk08wYcIE2NnZwc3NDVFRUcjPz0doaKja66hTpw7S0tKQnJyM2rVrw8rKCqampi9Ur62tLfr27YsJEyagc+fOqF27tjTP1dUV33zzDcLDw6FQKDB48GDUqVMH169fx88//wxLS0u1L8tv0KABfvnlF/j4+EChUGDChAmoUaPGc5cbNWoUhg0bBh8fH7Rt2xbr1q3DyZMnUbduXanNzJkzERERAWtra3Tp0gUFBQU4evQo7t27h8jISMyfPx/Ozs5o2bIlDAwMsH79ejg5OfGGk0Qa4J4iInohcrkccrm83Hlz5sxBv379MGjQILRq1QoXL17Ezp07YWtrq3b//fr1Q5cuXdCxY0fY29tj7dq1L1VvaGgoCgsL8dFHH5WZN3LkSOzatQs3btxAnz594OnpiaFDh0Iul2P8+PFqr2P58uW4d+8eWrVqhUGDBkm3JXie4OBgTJkyBePHj5cOgw0ZMgRmZmZSm6FDh2LZsmVYuXIlvL290aFDB8TExEh7iqysrBAVFQUfHx+89tpruHz5MrZv3w4DA/6ZJ1KXTDx+QJyIqJr65ZdfMHbsWNy8eRMmJib6Lue53n77bTg5OeGXX37RdylErwwePiOiai0/Px+3bt3CnDlz8PHHH1fKQJSfn48lS5YgMDAQhoaGWLt2LXbv3o24uDh9l0b0SuF+VSKq1qKiouDp6QknJydMmTJF3+WUSyaTYfv27Wjfvj1at26NrVu3YsOGDQgICNB3aUSvFB4+IyIiIgL3FBEREREBYCgiIiIiAsBQRERERASAoYiIiIgIAEMREREREQCGIiIiIiIADEVEREREABiKiIiIiAAwFBEREREBAP4PX+5eMa2DZK0AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def print_unique_col_values(dataframe):\n",
        "  for column in dataframe:\n",
        "    if dataframe[column].dtypes=='object':\n",
        "       print(f'{column} : {dataframe[column].unique()}')\n"
      ],
      "metadata": {
        "id": "SRJ3c9rgKQhV"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print_unique_col_values(df1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9UPIaWxVKsA4",
        "outputId": "a0005765-2a89-4bbf-c495-f0e8ebff6d1d"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gender : ['Female' 'Male']\n",
            "Partner : ['Yes' 'No']\n",
            "Dependents : ['No' 'Yes']\n",
            "PhoneService : ['No' 'Yes']\n",
            "MultipleLines : ['No phone service' 'No' 'Yes']\n",
            "InternetService : ['DSL' 'Fiber optic' 'No']\n",
            "OnlineSecurity : ['No' 'Yes' 'No internet service']\n",
            "OnlineBackup : ['Yes' 'No' 'No internet service']\n",
            "DeviceProtection : ['No' 'Yes' 'No internet service']\n",
            "TechSupport : ['No' 'Yes' 'No internet service']\n",
            "StreamingTV : ['No' 'Yes' 'No internet service']\n",
            "StreamingMovies : ['No' 'Yes' 'No internet service']\n",
            "Contract : ['Month-to-month' 'One year' 'Two year']\n",
            "PaperlessBilling : ['Yes' 'No']\n",
            "PaymentMethod : ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
            " 'Credit card (automatic)']\n",
            "Churn : ['No' 'Yes']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.replace('No internet service','No',inplace=True)\n",
        "df1.replace('No phone service','No',inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IbPXctOrLzKN",
        "outputId": "892d9945-dabc-4fab-893b-1077e533e420"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-31-104b877f3854>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\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",
            "  df1.replace('No internet service','No',inplace=True)\n",
            "<ipython-input-31-104b877f3854>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\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",
            "  df1.replace('No phone service','No',inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print_unique_col_values(df1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SIe1EPf4MA72",
        "outputId": "b0f83b47-fc19-4eaf-904a-534bc00450e8"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gender : ['Female' 'Male']\n",
            "Partner : ['Yes' 'No']\n",
            "Dependents : ['No' 'Yes']\n",
            "PhoneService : ['No' 'Yes']\n",
            "MultipleLines : ['No' 'Yes']\n",
            "InternetService : ['DSL' 'Fiber optic' 'No']\n",
            "OnlineSecurity : ['No' 'Yes']\n",
            "OnlineBackup : ['Yes' 'No']\n",
            "DeviceProtection : ['No' 'Yes']\n",
            "TechSupport : ['No' 'Yes']\n",
            "StreamingTV : ['No' 'Yes']\n",
            "StreamingMovies : ['No' 'Yes']\n",
            "Contract : ['Month-to-month' 'One year' 'Two year']\n",
            "PaperlessBilling : ['Yes' 'No']\n",
            "PaymentMethod : ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
            " 'Credit card (automatic)']\n",
            "Churn : ['No' 'Yes']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yes_no_columns=['Partner','Dependents','PhoneService','MultipleLines','OnlineSecurity','OnlineBackup',\n",
        "                'DeviceProtection','TechSupport','StreamingTV','StreamingMovies','PaperlessBilling','Churn']\n",
        "\n",
        "for col in yes_no_columns:\n",
        "  df1[col].replace({'Yes':1,'No':0},inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6s5YZk_zMOrV",
        "outputId": "81494884-14be-4ade-86a8-cdaa50b371d7"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-33-4c84d9789213>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\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",
            "  df1[col].replace({'Yes':1,'No':0},inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in df1:\n",
        "  print(f'{col} : {df1[col].unique()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DUx7wbrlMyF4",
        "outputId": "9c7c3551-023f-43f3-8a39-a2f8b61f771e"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gender : ['Female' 'Male']\n",
            "SeniorCitizen : [0 1]\n",
            "Partner : [1 0]\n",
            "Dependents : [0 1]\n",
            "tenure : [ 1 34  2 45  8 22 10 28 62 13 16 58 49 25 69 52 71 21 12 30 47 72 17 27\n",
            "  5 46 11 70 63 43 15 60 18 66  9  3 31 50 64 56  7 42 35 48 29 65 38 68\n",
            " 32 55 37 36 41  6  4 33 67 23 57 61 14 20 53 40 59 24 44 19 54 51 26 39]\n",
            "PhoneService : [0 1]\n",
            "MultipleLines : [0 1]\n",
            "InternetService : ['DSL' 'Fiber optic' 'No']\n",
            "OnlineSecurity : [0 1]\n",
            "OnlineBackup : [1 0]\n",
            "DeviceProtection : [0 1]\n",
            "TechSupport : [0 1]\n",
            "StreamingTV : [0 1]\n",
            "StreamingMovies : [0 1]\n",
            "Contract : ['Month-to-month' 'One year' 'Two year']\n",
            "PaperlessBilling : [1 0]\n",
            "PaymentMethod : ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
            " 'Credit card (automatic)']\n",
            "MonthlyCharges : [29.85 56.95 53.85 ... 63.1  44.2  78.7 ]\n",
            "TotalCharges : [  29.85 1889.5   108.15 ...  346.45  306.6  6844.5 ]\n",
            "Churn : [0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1['gender'].replace({'Female':1,'Male':0},inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l8ZBoPxdM-3W",
        "outputId": "a51c17f0-7bcf-4232-8f99-5f4da4e9a2a4"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-35-ba153b6b6960>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\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",
            "  df1['gender'].replace({'Female':1,'Male':0},inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1['gender'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ZgWHwBLNTe7",
        "outputId": "849ea177-90b6-451f-a0bd-be699013cc92"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2=pd.get_dummies(df1,columns=['InternetService','Contract','PaymentMethod'])\n",
        "df2.columns"
      ],
      "metadata": {
        "id": "A-n-stpDNWC5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9960f88d-15ff-4759-99fb-8ac9553659d8"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure',\n",
              "       'PhoneService', 'MultipleLines', 'OnlineSecurity', 'OnlineBackup',\n",
              "       'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies',\n",
              "       'PaperlessBilling', 'MonthlyCharges', 'TotalCharges', 'Churn',\n",
              "       'InternetService_DSL', 'InternetService_Fiber optic',\n",
              "       'InternetService_No', 'Contract_Month-to-month', 'Contract_One year',\n",
              "       'Contract_Two year', 'PaymentMethod_Bank transfer (automatic)',\n",
              "       'PaymentMethod_Credit card (automatic)',\n",
              "       'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.sample(4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        },
        "id": "KllFTDQmYj9Q",
        "outputId": "5550d321-ab30-48b2-d26d-51151fda96c4"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
              "5677       0              0        1           0      40             1   \n",
              "6406       1              0        0           0      39             0   \n",
              "3686       1              0        0           0      13             1   \n",
              "4563       1              0        0           0      10             1   \n",
              "\n",
              "      MultipleLines  OnlineSecurity  OnlineBackup  DeviceProtection  ...  \\\n",
              "5677              0               0             1                 1  ...   \n",
              "6406              0               0             0                 1  ...   \n",
              "3686              1               0             0                 0  ...   \n",
              "4563              1               0             1                 0  ...   \n",
              "\n",
              "      InternetService_DSL  InternetService_Fiber optic  InternetService_No  \\\n",
              "5677                False                         True               False   \n",
              "6406                 True                        False               False   \n",
              "3686                False                         True               False   \n",
              "4563                False                         True               False   \n",
              "\n",
              "      Contract_Month-to-month  Contract_One year  Contract_Two year  \\\n",
              "5677                    False               True              False   \n",
              "6406                     True              False              False   \n",
              "3686                     True              False              False   \n",
              "4563                     True              False              False   \n",
              "\n",
              "      PaymentMethod_Bank transfer (automatic)  \\\n",
              "5677                                    False   \n",
              "6406                                    False   \n",
              "3686                                    False   \n",
              "4563                                     True   \n",
              "\n",
              "      PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
              "5677                                  False                            True   \n",
              "6406                                   True                           False   \n",
              "3686                                  False                           False   \n",
              "4563                                  False                           False   \n",
              "\n",
              "      PaymentMethod_Mailed check  \n",
              "5677                       False  \n",
              "6406                       False  \n",
              "3686                        True  \n",
              "4563                       False  \n",
              "\n",
              "[4 rows x 27 columns]"
            ],
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              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>...</th>\n",
              "      <th>InternetService_DSL</th>\n",
              "      <th>InternetService_Fiber optic</th>\n",
              "      <th>InternetService_No</th>\n",
              "      <th>Contract_Month-to-month</th>\n",
              "      <th>Contract_One year</th>\n",
              "      <th>Contract_Two year</th>\n",
              "      <th>PaymentMethod_Bank transfer (automatic)</th>\n",
              "      <th>PaymentMethod_Credit card (automatic)</th>\n",
              "      <th>PaymentMethod_Electronic check</th>\n",
              "      <th>PaymentMethod_Mailed check</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5677</th>\n",
              "      <td>0</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>6406</th>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>39</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3686</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>4563</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4 rows × 27 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "        document.querySelector('#df-3afe641b-c5ba-42f7-b676-c6ad16c62230 button.colab-df-convert');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-3afe641b-c5ba-42f7-b676-c6ad16c62230');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "\n",
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              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
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              "    80% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e23cde5d-a501-4032-b11c-b47fd9bb90bf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 931
        },
        "id": "uUosg2tiZ_Vk",
        "outputId": "a85a4f9a-2786-470a-ce5e-af91d078273e"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gender                                       int64\n",
              "SeniorCitizen                                int64\n",
              "Partner                                      int64\n",
              "Dependents                                   int64\n",
              "tenure                                       int64\n",
              "PhoneService                                 int64\n",
              "MultipleLines                                int64\n",
              "OnlineSecurity                               int64\n",
              "OnlineBackup                                 int64\n",
              "DeviceProtection                             int64\n",
              "TechSupport                                  int64\n",
              "StreamingTV                                  int64\n",
              "StreamingMovies                              int64\n",
              "PaperlessBilling                             int64\n",
              "MonthlyCharges                             float64\n",
              "TotalCharges                               float64\n",
              "Churn                                        int64\n",
              "InternetService_DSL                           bool\n",
              "InternetService_Fiber optic                   bool\n",
              "InternetService_No                            bool\n",
              "Contract_Month-to-month                       bool\n",
              "Contract_One year                             bool\n",
              "Contract_Two year                             bool\n",
              "PaymentMethod_Bank transfer (automatic)       bool\n",
              "PaymentMethod_Credit card (automatic)         bool\n",
              "PaymentMethod_Electronic check                bool\n",
              "PaymentMethod_Mailed check                    bool\n",
              "dtype: object"
            ],
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>gender</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Partner</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dependents</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tenure</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PhoneService</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MultipleLines</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OnlineBackup</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DeviceProtection</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TechSupport</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>StreamingTV</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>StreamingMovies</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TotalCharges</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Churn</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InternetService_DSL</th>\n",
              "      <td>bool</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InternetService_Fiber optic</th>\n",
              "      <td>bool</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InternetService_No</th>\n",
              "      <td>bool</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Contract_Month-to-month</th>\n",
              "      <td>bool</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Contract_One year</th>\n",
              "      <td>bool</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Contract_Two year</th>\n",
              "      <td>bool</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PaymentMethod_Bank transfer (automatic)</th>\n",
              "      <td>bool</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PaymentMethod_Credit card (automatic)</th>\n",
              "      <td>bool</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PaymentMethod_Electronic check</th>\n",
              "      <td>bool</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PaymentMethod_Mailed check</th>\n",
              "      <td>bool</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cols_to_scale=['tenure','MonthlyCharges','TotalCharges']\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler=MinMaxScaler()\n",
        "df2[cols_to_scale]=scaler.fit_transform(df2[cols_to_scale])"
      ],
      "metadata": {
        "id": "5h7wwJyDaFdU"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2.sample(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "id": "0V3_KVpjc1SP",
        "outputId": "9721a6e5-f5c2-46ff-c4e0-e6b69e00adf9"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      gender  SeniorCitizen  Partner  Dependents    tenure  PhoneService  \\\n",
              "528        0              0        1           1  0.478873             1   \n",
              "18         1              0        1           1  0.126761             1   \n",
              "2020       0              0        1           0  0.718310             1   \n",
              "\n",
              "      MultipleLines  OnlineSecurity  OnlineBackup  DeviceProtection  ...  \\\n",
              "528               0               0             0                 0  ...   \n",
              "18                0               0             0                 1  ...   \n",
              "2020              0               1             1                 0  ...   \n",
              "\n",
              "      InternetService_DSL  InternetService_Fiber optic  InternetService_No  \\\n",
              "528                 False                        False                True   \n",
              "18                   True                        False               False   \n",
              "2020                 True                        False               False   \n",
              "\n",
              "      Contract_Month-to-month  Contract_One year  Contract_Two year  \\\n",
              "528                     False              False               True   \n",
              "18                       True              False              False   \n",
              "2020                    False               True              False   \n",
              "\n",
              "      PaymentMethod_Bank transfer (automatic)  \\\n",
              "528                                     False   \n",
              "18                                      False   \n",
              "2020                                    False   \n",
              "\n",
              "      PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
              "528                                   False                           False   \n",
              "18                                     True                           False   \n",
              "2020                                  False                           False   \n",
              "\n",
              "      PaymentMethod_Mailed check  \n",
              "528                         True  \n",
              "18                         False  \n",
              "2020                        True  \n",
              "\n",
              "[3 rows x 27 columns]"
            ],
            "text/html": [
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              "      <th>Contract_Two year</th>\n",
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              "      <th>528</th>\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7678bd22-d498-4361-867d-4c29e6d408f4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=df2.drop(columns='Churn')\n",
        "y=df2['Churn']"
      ],
      "metadata": {
        "id": "GZl6-pFtc3-B"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "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=5)"
      ],
      "metadata": {
        "id": "pm4HRxaTc_17"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape,x_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w9iNWaGedGyM",
        "outputId": "ba33a1ee-caf0-4c85-9832-de3c82d2553f"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((5625, 26), (1407, 26))"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TaaqeuICdL4I",
        "outputId": "5b127aeb-8d58-4f6a-e203-e5bd7413b56a"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1407, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "MedR7oEadPMt",
        "outputId": "b78a8ab1-26a0-48a4-b8a4-8d780b2d371d"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      gender  SeniorCitizen  Partner  Dependents    tenure  PhoneService  \\\n",
              "5664       1              1        0           0  0.126761             1   \n",
              "101        1              0        1           1  0.000000             1   \n",
              "2621       0              0        1           0  0.985915             1   \n",
              "392        1              1        0           0  0.014085             1   \n",
              "1327       0              0        1           0  0.816901             1   \n",
              "3607       1              0        0           0  0.169014             1   \n",
              "2773       0              0        1           0  0.323944             0   \n",
              "1936       1              0        1           0  0.704225             1   \n",
              "5387       0              0        0           0  0.042254             0   \n",
              "4331       0              0        0           0  0.985915             1   \n",
              "\n",
              "      MultipleLines  OnlineSecurity  OnlineBackup  DeviceProtection  ...  \\\n",
              "5664              0               0             0                 1  ...   \n",
              "101               0               0             0                 0  ...   \n",
              "2621              0               0             1                 1  ...   \n",
              "392               0               0             0                 0  ...   \n",
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              "2773              0               0             0                 1  ...   \n",
              "1936              0               1             1                 0  ...   \n",
              "5387              0               0             0                 0  ...   \n",
              "4331              1               0             0                 0  ...   \n",
              "\n",
              "      InternetService_DSL  InternetService_Fiber optic  InternetService_No  \\\n",
              "5664                False                         True               False   \n",
              "101                 False                        False                True   \n",
              "2621                 True                        False               False   \n",
              "392                  True                        False               False   \n",
              "1327                False                         True               False   \n",
              "3607                 True                        False               False   \n",
              "2773                 True                        False               False   \n",
              "1936                 True                        False               False   \n",
              "5387                 True                        False               False   \n",
              "4331                False                        False                True   \n",
              "\n",
              "      Contract_Month-to-month  Contract_One year  Contract_Two year  \\\n",
              "5664                     True              False              False   \n",
              "101                      True              False              False   \n",
              "2621                    False              False               True   \n",
              "392                      True              False              False   \n",
              "1327                    False               True              False   \n",
              "3607                    False               True              False   \n",
              "2773                     True              False              False   \n",
              "1936                    False               True              False   \n",
              "5387                     True              False              False   \n",
              "4331                    False              False               True   \n",
              "\n",
              "      PaymentMethod_Bank transfer (automatic)  \\\n",
              "5664                                    False   \n",
              "101                                     False   \n",
              "2621                                    False   \n",
              "392                                     False   \n",
              "1327                                     True   \n",
              "3607                                    False   \n",
              "2773                                    False   \n",
              "1936                                     True   \n",
              "5387                                    False   \n",
              "4331                                     True   \n",
              "\n",
              "      PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
              "5664                                   True                           False   \n",
              "101                                   False                            True   \n",
              "2621                                   True                           False   \n",
              "392                                   False                            True   \n",
              "1327                                  False                           False   \n",
              "3607                                  False                           False   \n",
              "2773                                  False                            True   \n",
              "1936                                  False                           False   \n",
              "5387                                  False                            True   \n",
              "4331                                  False                           False   \n",
              "\n",
              "      PaymentMethod_Mailed check  \n",
              "5664                       False  \n",
              "101                        False  \n",
              "2621                       False  \n",
              "392                        False  \n",
              "1327                       False  \n",
              "3607                        True  \n",
              "2773                       False  \n",
              "1936                       False  \n",
              "5387                       False  \n",
              "4331                       False  \n",
              "\n",
              "[10 rows x 26 columns]"
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 26 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4060b706-ec19-4522-a2d3-003d5a6ee0c6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-4060b706-ec19-4522-a2d3-003d5a6ee0c6 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-4060b706-ec19-4522-a2d3-003d5a6ee0c6');\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",
              "\n",
              "\n",
              "<div id=\"df-3006bb27-b92e-4d02-b09e-693698d88cf1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3006bb27-b92e-4d02-b09e-693698d88cf1')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3006bb27-b92e-4d02-b09e-693698d88cf1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(x_train.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y7WgvvXZdV6j",
        "outputId": "3fcb97c1-37cc-45d4-d71e-ce3a26fdb873"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "model=keras.Sequential([\n",
        "    keras.layers.Dense(26,input_shape=(26,),activation='relu'),\n",
        "    #keras.layers.Dense(15,activation='relu'),\n",
        "    keras.layers.Dense(1,activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(x_train,y_train,epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qn6tHHScdbgD",
        "outputId": "ced105dd-63cd-4a1f-ffb8-df2f8c310e60"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.5762 - loss: 0.6564\n",
            "Epoch 2/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7928 - loss: 0.4301\n",
            "Epoch 3/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8075 - loss: 0.4187\n",
            "Epoch 4/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8027 - loss: 0.4174\n",
            "Epoch 5/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7982 - loss: 0.4169\n",
            "Epoch 6/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8022 - loss: 0.4221\n",
            "Epoch 7/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8092 - loss: 0.4077\n",
            "Epoch 8/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8157 - loss: 0.4038\n",
            "Epoch 9/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8199 - loss: 0.3918\n",
            "Epoch 10/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8102 - loss: 0.4050\n",
            "Epoch 11/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8004 - loss: 0.4127\n",
            "Epoch 12/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8084 - loss: 0.4092\n",
            "Epoch 13/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8125 - loss: 0.3999\n",
            "Epoch 14/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8116 - loss: 0.3979\n",
            "Epoch 15/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8107 - loss: 0.4042\n",
            "Epoch 16/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8096 - loss: 0.4026\n",
            "Epoch 17/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8144 - loss: 0.3993\n",
            "Epoch 18/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8149 - loss: 0.4005\n",
            "Epoch 19/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8104 - loss: 0.4098\n",
            "Epoch 20/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8215 - loss: 0.3931\n",
            "Epoch 21/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8152 - loss: 0.4017\n",
            "Epoch 22/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8072 - loss: 0.4073\n",
            "Epoch 23/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8220 - loss: 0.3924\n",
            "Epoch 24/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8179 - loss: 0.3933\n",
            "Epoch 25/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8221 - loss: 0.3911\n",
            "Epoch 26/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8058 - loss: 0.4108\n",
            "Epoch 27/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8113 - loss: 0.4034\n",
            "Epoch 28/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8142 - loss: 0.4025\n",
            "Epoch 29/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8105 - loss: 0.3996\n",
            "Epoch 30/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8217 - loss: 0.3927\n",
            "Epoch 31/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8163 - loss: 0.3895\n",
            "Epoch 32/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8193 - loss: 0.3836\n",
            "Epoch 33/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8211 - loss: 0.3909\n",
            "Epoch 34/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8120 - loss: 0.3958\n",
            "Epoch 35/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8159 - loss: 0.4000\n",
            "Epoch 36/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8212 - loss: 0.3865\n",
            "Epoch 37/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8103 - loss: 0.4063\n",
            "Epoch 38/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8144 - loss: 0.3987\n",
            "Epoch 39/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8162 - loss: 0.3960\n",
            "Epoch 40/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8187 - loss: 0.3862\n",
            "Epoch 41/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8184 - loss: 0.3929\n",
            "Epoch 42/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8171 - loss: 0.3881\n",
            "Epoch 43/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8096 - loss: 0.4033\n",
            "Epoch 44/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8246 - loss: 0.3868\n",
            "Epoch 45/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8303 - loss: 0.3738\n",
            "Epoch 46/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8097 - loss: 0.4028\n",
            "Epoch 47/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8236 - loss: 0.3795\n",
            "Epoch 48/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8153 - loss: 0.3940\n",
            "Epoch 49/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8100 - loss: 0.4010\n",
            "Epoch 50/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8143 - loss: 0.3937\n",
            "Epoch 51/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8188 - loss: 0.3863\n",
            "Epoch 52/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8189 - loss: 0.3821\n",
            "Epoch 53/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8153 - loss: 0.3835\n",
            "Epoch 54/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8179 - loss: 0.3950\n",
            "Epoch 55/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8118 - loss: 0.3977\n",
            "Epoch 56/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8174 - loss: 0.3964\n",
            "Epoch 57/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8235 - loss: 0.3857\n",
            "Epoch 58/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8119 - loss: 0.4035\n",
            "Epoch 59/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8206 - loss: 0.3892\n",
            "Epoch 60/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8274 - loss: 0.3729\n",
            "Epoch 61/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8164 - loss: 0.3924\n",
            "Epoch 62/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8123 - loss: 0.3913\n",
            "Epoch 63/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8200 - loss: 0.3815\n",
            "Epoch 64/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8163 - loss: 0.3886\n",
            "Epoch 65/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8175 - loss: 0.3839\n",
            "Epoch 66/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8142 - loss: 0.3955\n",
            "Epoch 67/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8203 - loss: 0.3836\n",
            "Epoch 68/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8218 - loss: 0.3762\n",
            "Epoch 69/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8081 - loss: 0.3959\n",
            "Epoch 70/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8264 - loss: 0.3779\n",
            "Epoch 71/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8169 - loss: 0.3892\n",
            "Epoch 72/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8172 - loss: 0.3759\n",
            "Epoch 73/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8267 - loss: 0.3827\n",
            "Epoch 74/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8149 - loss: 0.3897\n",
            "Epoch 75/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8153 - loss: 0.3848\n",
            "Epoch 76/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8192 - loss: 0.3859\n",
            "Epoch 77/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8134 - loss: 0.3836\n",
            "Epoch 78/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8276 - loss: 0.3825\n",
            "Epoch 79/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8168 - loss: 0.3930\n",
            "Epoch 80/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8180 - loss: 0.3844\n",
            "Epoch 81/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8240 - loss: 0.3819\n",
            "Epoch 82/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8238 - loss: 0.3850\n",
            "Epoch 83/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8249 - loss: 0.3804\n",
            "Epoch 84/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8213 - loss: 0.3845\n",
            "Epoch 85/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8113 - loss: 0.3910\n",
            "Epoch 86/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8261 - loss: 0.3829\n",
            "Epoch 87/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8164 - loss: 0.3849\n",
            "Epoch 88/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8234 - loss: 0.3725\n",
            "Epoch 89/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8216 - loss: 0.3798\n",
            "Epoch 90/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8214 - loss: 0.3725\n",
            "Epoch 91/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8239 - loss: 0.3719\n",
            "Epoch 92/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8099 - loss: 0.3987\n",
            "Epoch 93/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8269 - loss: 0.3788\n",
            "Epoch 94/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8089 - loss: 0.3814\n",
            "Epoch 95/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8242 - loss: 0.3792\n",
            "Epoch 96/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8128 - loss: 0.3990\n",
            "Epoch 97/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8305 - loss: 0.3700\n",
            "Epoch 98/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8168 - loss: 0.3881\n",
            "Epoch 99/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8167 - loss: 0.3791\n",
            "Epoch 100/100\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8233 - loss: 0.3729\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7a5a7da4c7f0>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(x_test,y_test)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dHB8ET1Nd0tJ",
        "outputId": "0b7955ca-6fbd-4e43-ad8d-0fa88c222dff"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7910 - loss: 0.4459\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.45297133922576904, 0.783226728439331]"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yp=model.predict(x_test)\n",
        "yp[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ECeNWv69eBEm",
        "outputId": "0c0e60a0-4181-4cbf-9261-721fccee70f7"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.23088111],\n",
              "       [0.37323007],\n",
              "       [0.01669401],\n",
              "       [0.8317551 ],\n",
              "       [0.4391092 ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "Ts6MHY9SeNmq",
        "outputId": "97e7fc02-3bc6-4e9a-c919-e9ad20e9c0b3"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2660    0\n",
              "744     0\n",
              "5579    1\n",
              "64      1\n",
              "3287    1\n",
              "Name: Churn, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2660</th>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "  </tbody>\n",
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              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "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": "VM2RMOyReTsF"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JwaOqxOjeX1u",
        "outputId": "69adff0d-249a-4654-8fa2-21e49a95c1d1"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 0, 0, 1, 0, 1, 0, 1, 0, 0]"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix,classification_report\n",
        "print(classification_report(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Itqb8OFpeZ9g",
        "outputId": "bcb119ab-667c-4f2a-c0af-ae477004b469"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.89      0.85       999\n",
            "           1       0.66      0.52      0.58       408\n",
            "\n",
            "    accuracy                           0.78      1407\n",
            "   macro avg       0.74      0.70      0.72      1407\n",
            "weighted avg       0.77      0.78      0.77      1407\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "cm=tf.math.confusion_matrix(labels=y_test,predictions=y_pred)\n",
        "sns.heatmap(cm,annot=True,fmt='d')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "dMkfXUgAehJc",
        "outputId": "e4470861-e931-4571-b26c-7e74508af84a"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(50.722222222222214, 0.5, 'Truth')"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accuracy"
      ],
      "metadata": {
        "id": "jnLnCceGfsUf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "round((862+229)/(862+229+137+179),2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u4LcsQVZembd",
        "outputId": "2f956d14-ab57-464a-959d-dc8f0149119e"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.78"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Precision for 0 class i.e. Precision for customers who did not churn"
      ],
      "metadata": {
        "id": "kyOijDBffSHz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "round(862/(862+137),2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8aF-VCZEfOMk",
        "outputId": "87fe5371-11a4-4c34-f386-67f70232bf7a"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.86"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Precision for 1 class i.e. Precision for customers who actually churn"
      ],
      "metadata": {
        "id": "Qsh8q80Zfejk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "round(229/(229+179),2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yDaNMjcgfdI-",
        "outputId": "8640882f-b426-41c3-9ae4-5011630357c5"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.56"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Recall for 0 class"
      ],
      "metadata": {
        "id": "-9VbTd8OfuLD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "round(862/(862+179),2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZgYuSWmNfmS_",
        "outputId": "c5429f3c-270c-4e4f-94d3-6dae3ba5398c"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.83"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "round(229/(229+137),2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5MP_Ann8fx9y",
        "outputId": "f55722c2-58a3-4e5b-8c31-76aa59ce0fa0"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.63"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "F64xLcgxf20o"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}