1.M1-CreditCardChurn.ipynb

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        "df = pd.read_csv('/content/Churn_Modelling.csv')"
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              "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
              "0          1    15634602  Hargrave          619    France  Female   42   \n",
              "1          2    15647311      Hill          608     Spain  Female   41   \n",
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              "3          4    15701354      Boni          699    France  Female   39   \n",
              "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
              "\n",
              "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
              "0       2       0.00              1          1               1   \n",
              "1       1   83807.86              1          0               1   \n",
              "2       8  159660.80              3          1               0   \n",
              "3       1       0.00              2          0               0   \n",
              "4       2  125510.82              1          1               1   \n",
              "\n",
              "   EstimatedSalary  Exited  \n",
              "0        101348.88       1  \n",
              "1        112542.58       0  \n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 14 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   RowNumber        10000 non-null  int64  \n",
            " 1   CustomerId       10000 non-null  int64  \n",
            " 2   Surname          10000 non-null  object \n",
            " 3   CreditScore      10000 non-null  int64  \n",
            " 4   Geography        10000 non-null  object \n",
            " 5   Gender           10000 non-null  object \n",
            " 6   Age              10000 non-null  int64  \n",
            " 7   Tenure           10000 non-null  int64  \n",
            " 8   Balance          10000 non-null  float64\n",
            " 9   NumOfProducts    10000 non-null  int64  \n",
            " 10  HasCrCard        10000 non-null  int64  \n",
            " 11  IsActiveMember   10000 non-null  int64  \n",
            " 12  EstimatedSalary  10000 non-null  float64\n",
            " 13  Exited           10000 non-null  int64  \n",
            "dtypes: float64(2), int64(9), object(3)\n",
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              "France     5014\n",
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              "   CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
              "0          619    France  Female   42       2       0.00              1   \n",
              "1          608     Spain  Female   41       1   83807.86              1   \n",
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              "3          699    France  Female   39       1       0.00              2   \n",
              "4          850     Spain  Female   43       2  125510.82              1   \n",
              "\n",
              "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
              "0          1               1        101348.88       1  \n",
              "1          0               1        112542.58       0  \n",
              "2          1               0        113931.57       1  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#onehot encoding to transform data into 0 and 1\n",
        "df = pd.get_dummies(df, columns = ['Gender','Geography'], drop_first= True)"
      ],
      "metadata": {
        "id": "VTQ_kwGyj1UU"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(6)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
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        "id": "pOm_zE4Ul1aP",
        "outputId": "3ede7495-35ab-49e3-8a33-5ed6234ae316"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CreditScore  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
              "0          619   42       2       0.00              1          1   \n",
              "1          608   41       1   83807.86              1          0   \n",
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              "4          850   43       2  125510.82              1          1   \n",
              "5          645   44       8  113755.78              2          1   \n",
              "\n",
              "   IsActiveMember  EstimatedSalary  Exited  Gender_Male  Geography_Germany  \\\n",
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              "1               1        112542.58       0            0                  0   \n",
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              "4               1         79084.10       0            0                  0   \n",
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              "\n",
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              "0                0  \n",
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              "5                1  "
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              "      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-cd5d9c72-e771-4c00-aa6e-b85a7e781d83 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-cd5d9c72-e771-4c00-aa6e-b85a7e781d83');\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",
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              "  </div>\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ba1d8439-c6bf-4689-8e67-e05fd449837d')\"\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-ba1d8439-c6bf-4689-8e67-e05fd449837d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(columns=['Exited'])\n",
        "y = df['Exited'].values\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0)"
      ],
      "metadata": {
        "id": "UgvNbneHl4P9"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "X_train_trf = scaler.fit_transform(X_train)\n",
        "X_test_trf = scaler.transform(X_test)\n"
      ],
      "metadata": {
        "id": "yXGZNiFLnOR5"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense"
      ],
      "metadata": {
        "id": "G6AT64r7p04o"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Dense(11,activation='sigmoid',input_dim=11))\n",
        "model.add(Dense(11,activation='sigmoid'))\n",
        "model.add(Dense(1,activation='sigmoid'))"
      ],
      "metadata": {
        "id": "nZWhTZ9I-eha"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tk7fu8Q6-i2k",
        "outputId": "fbafcb16-c2b1-4160-aa8f-a5e9d08bb5bb"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 11)                132       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 11)                132       \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 12        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 276 (1.08 KB)\n",
            "Trainable params: 276 (1.08 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='Adam',loss='binary_crossentropy',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "dlGnEJew-mYo"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train,y_train,batch_size=50,epochs=100,verbose=1,validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZZU_piTV-prt",
        "outputId": "d207361c-9866-4331-b47c-dbd21ea99fb5"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "128/128 [==============================] - 3s 5ms/step - loss: 0.5266 - accuracy: 0.7958 - val_loss: 0.5096 - val_accuracy: 0.7969\n",
            "Epoch 2/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.5079 - accuracy: 0.7958 - val_loss: 0.5041 - val_accuracy: 0.7969\n",
            "Epoch 3/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.5046 - accuracy: 0.7958 - val_loss: 0.5006 - val_accuracy: 0.7969\n",
            "Epoch 4/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.5020 - accuracy: 0.7958 - val_loss: 0.4986 - val_accuracy: 0.7969\n",
            "Epoch 5/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.5010 - accuracy: 0.7958 - val_loss: 0.4971 - val_accuracy: 0.7969\n",
            "Epoch 6/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.5003 - accuracy: 0.7958 - val_loss: 0.4966 - val_accuracy: 0.7969\n",
            "Epoch 7/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4999 - accuracy: 0.7958 - val_loss: 0.4963 - val_accuracy: 0.7969\n",
            "Epoch 8/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4998 - accuracy: 0.7958 - val_loss: 0.4958 - val_accuracy: 0.7969\n",
            "Epoch 9/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4995 - accuracy: 0.7958 - val_loss: 0.4961 - val_accuracy: 0.7969\n",
            "Epoch 10/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4997 - accuracy: 0.7958 - val_loss: 0.4955 - val_accuracy: 0.7969\n",
            "Epoch 11/100\n",
            "128/128 [==============================] - 1s 4ms/step - loss: 0.4995 - accuracy: 0.7958 - val_loss: 0.4954 - val_accuracy: 0.7969\n",
            "Epoch 12/100\n",
            "128/128 [==============================] - 1s 4ms/step - loss: 0.4994 - accuracy: 0.7958 - val_loss: 0.4954 - val_accuracy: 0.7969\n",
            "Epoch 13/100\n",
            "128/128 [==============================] - 1s 5ms/step - loss: 0.4994 - accuracy: 0.7958 - val_loss: 0.4953 - val_accuracy: 0.7969\n",
            "Epoch 14/100\n",
            "128/128 [==============================] - 1s 4ms/step - loss: 0.4994 - accuracy: 0.7958 - val_loss: 0.4953 - val_accuracy: 0.7969\n",
            "Epoch 15/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4993 - accuracy: 0.7958 - val_loss: 0.4954 - val_accuracy: 0.7969\n",
            "Epoch 16/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4993 - accuracy: 0.7958 - val_loss: 0.4953 - val_accuracy: 0.7969\n",
            "Epoch 17/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4952 - val_accuracy: 0.7969\n",
            "Epoch 18/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4953 - val_accuracy: 0.7969\n",
            "Epoch 19/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4952 - val_accuracy: 0.7969\n",
            "Epoch 20/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4993 - accuracy: 0.7958 - val_loss: 0.4954 - val_accuracy: 0.7969\n",
            "Epoch 21/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4952 - val_accuracy: 0.7969\n",
            "Epoch 22/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4951 - val_accuracy: 0.7969\n",
            "Epoch 23/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4954 - val_accuracy: 0.7969\n",
            "Epoch 24/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4952 - val_accuracy: 0.7969\n",
            "Epoch 25/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4952 - val_accuracy: 0.7969\n",
            "Epoch 26/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4953 - val_accuracy: 0.7969\n",
            "Epoch 27/100\n",
            "128/128 [==============================] - 1s 4ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4952 - val_accuracy: 0.7969\n",
            "Epoch 28/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4952 - val_accuracy: 0.7969\n",
            "Epoch 29/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4951 - val_accuracy: 0.7969\n",
            "Epoch 30/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4951 - val_accuracy: 0.7969\n",
            "Epoch 31/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4951 - val_accuracy: 0.7969\n",
            "Epoch 32/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4951 - val_accuracy: 0.7969\n",
            "Epoch 33/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4950 - val_accuracy: 0.7969\n",
            "Epoch 34/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4952 - val_accuracy: 0.7969\n",
            "Epoch 35/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4998 - accuracy: 0.7958 - val_loss: 0.4955 - val_accuracy: 0.7969\n",
            "Epoch 36/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4996 - accuracy: 0.7958 - val_loss: 0.4957 - val_accuracy: 0.7969\n",
            "Epoch 37/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4997 - accuracy: 0.7958 - val_loss: 0.4953 - val_accuracy: 0.7969\n",
            "Epoch 38/100\n",
            "128/128 [==============================] - 0s 4ms/step - loss: 0.4997 - accuracy: 0.7958 - val_loss: 0.4954 - val_accuracy: 0.7969\n",
            "Epoch 39/100\n",
            "128/128 [==============================] - 1s 5ms/step - loss: 0.4995 - accuracy: 0.7958 - val_loss: 0.4952 - val_accuracy: 0.7969\n",
            "Epoch 40/100\n",
            "128/128 [==============================] - 1s 4ms/step - loss: 0.4993 - accuracy: 0.7958 - val_loss: 0.4952 - val_accuracy: 0.7969\n",
            "Epoch 41/100\n",
            "128/128 [==============================] - 1s 4ms/step - loss: 0.4993 - accuracy: 0.7958 - val_loss: 0.4953 - val_accuracy: 0.7969\n",
            "Epoch 42/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4993 - accuracy: 0.7958 - val_loss: 0.4951 - val_accuracy: 0.7969\n",
            "Epoch 43/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4993 - accuracy: 0.7958 - val_loss: 0.4951 - val_accuracy: 0.7969\n",
            "Epoch 44/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4993 - accuracy: 0.7958 - val_loss: 0.4951 - val_accuracy: 0.7969\n",
            "Epoch 45/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4951 - val_accuracy: 0.7969\n",
            "Epoch 46/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4951 - val_accuracy: 0.7969\n",
            "Epoch 47/100\n",
            "128/128 [==============================] - 1s 5ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4949 - val_accuracy: 0.7969\n",
            "Epoch 48/100\n",
            "128/128 [==============================] - 1s 5ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4950 - val_accuracy: 0.7969\n",
            "Epoch 49/100\n",
            "128/128 [==============================] - 0s 4ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4950 - val_accuracy: 0.7969\n",
            "Epoch 50/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4993 - accuracy: 0.7958 - val_loss: 0.4949 - val_accuracy: 0.7969\n",
            "Epoch 51/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4951 - val_accuracy: 0.7969\n",
            "Epoch 52/100\n",
            "128/128 [==============================] - 1s 4ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4950 - val_accuracy: 0.7969\n",
            "Epoch 53/100\n",
            "128/128 [==============================] - 1s 5ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4949 - val_accuracy: 0.7969\n",
            "Epoch 54/100\n",
            "128/128 [==============================] - 1s 5ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4948 - val_accuracy: 0.7969\n",
            "Epoch 55/100\n",
            "128/128 [==============================] - 1s 4ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4950 - val_accuracy: 0.7969\n",
            "Epoch 56/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4994 - accuracy: 0.7958 - val_loss: 0.4949 - val_accuracy: 0.7969\n",
            "Epoch 57/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4952 - val_accuracy: 0.7969\n",
            "Epoch 58/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4949 - val_accuracy: 0.7969\n",
            "Epoch 59/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4948 - val_accuracy: 0.7969\n",
            "Epoch 60/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4949 - val_accuracy: 0.7969\n",
            "Epoch 61/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4949 - val_accuracy: 0.7969\n",
            "Epoch 62/100\n",
            "128/128 [==============================] - 1s 4ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4948 - val_accuracy: 0.7969\n",
            "Epoch 63/100\n",
            "128/128 [==============================] - 1s 5ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4948 - val_accuracy: 0.7969\n",
            "Epoch 64/100\n",
            "128/128 [==============================] - 1s 4ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4947 - val_accuracy: 0.7969\n",
            "Epoch 65/100\n",
            "128/128 [==============================] - 1s 4ms/step - loss: 0.4989 - accuracy: 0.7958 - val_loss: 0.4948 - val_accuracy: 0.7969\n",
            "Epoch 66/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4947 - val_accuracy: 0.7969\n",
            "Epoch 67/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4951 - val_accuracy: 0.7969\n",
            "Epoch 68/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4947 - val_accuracy: 0.7969\n",
            "Epoch 69/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4989 - accuracy: 0.7958 - val_loss: 0.4948 - val_accuracy: 0.7969\n",
            "Epoch 70/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4989 - accuracy: 0.7958 - val_loss: 0.4947 - val_accuracy: 0.7969\n",
            "Epoch 71/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4989 - accuracy: 0.7958 - val_loss: 0.4947 - val_accuracy: 0.7969\n",
            "Epoch 72/100\n",
            "128/128 [==============================] - 0s 4ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4947 - val_accuracy: 0.7969\n",
            "Epoch 73/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4946 - val_accuracy: 0.7969\n",
            "Epoch 74/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4989 - accuracy: 0.7958 - val_loss: 0.4947 - val_accuracy: 0.7969\n",
            "Epoch 75/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4988 - accuracy: 0.7958 - val_loss: 0.4946 - val_accuracy: 0.7969\n",
            "Epoch 76/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4944 - val_accuracy: 0.7969\n",
            "Epoch 77/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4945 - val_accuracy: 0.7969\n",
            "Epoch 78/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4988 - accuracy: 0.7958 - val_loss: 0.4945 - val_accuracy: 0.7969\n",
            "Epoch 79/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4989 - accuracy: 0.7958 - val_loss: 0.4946 - val_accuracy: 0.7969\n",
            "Epoch 80/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4988 - accuracy: 0.7958 - val_loss: 0.4946 - val_accuracy: 0.7969\n",
            "Epoch 81/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4989 - accuracy: 0.7958 - val_loss: 0.4945 - val_accuracy: 0.7969\n",
            "Epoch 82/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4988 - accuracy: 0.7958 - val_loss: 0.4948 - val_accuracy: 0.7969\n",
            "Epoch 83/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4989 - accuracy: 0.7958 - val_loss: 0.4947 - val_accuracy: 0.7969\n",
            "Epoch 84/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4987 - accuracy: 0.7958 - val_loss: 0.4946 - val_accuracy: 0.7969\n",
            "Epoch 85/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4988 - accuracy: 0.7958 - val_loss: 0.4946 - val_accuracy: 0.7969\n",
            "Epoch 86/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4987 - accuracy: 0.7958 - val_loss: 0.4947 - val_accuracy: 0.7969\n",
            "Epoch 87/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4948 - val_accuracy: 0.7969\n",
            "Epoch 88/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4947 - val_accuracy: 0.7969\n",
            "Epoch 89/100\n",
            "128/128 [==============================] - 1s 5ms/step - loss: 0.4989 - accuracy: 0.7958 - val_loss: 0.4948 - val_accuracy: 0.7969\n",
            "Epoch 90/100\n",
            "128/128 [==============================] - 1s 9ms/step - loss: 0.4989 - accuracy: 0.7958 - val_loss: 0.4947 - val_accuracy: 0.7969\n",
            "Epoch 91/100\n",
            "128/128 [==============================] - 1s 8ms/step - loss: 0.4988 - accuracy: 0.7958 - val_loss: 0.4950 - val_accuracy: 0.7969\n",
            "Epoch 92/100\n",
            "128/128 [==============================] - 1s 4ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4946 - val_accuracy: 0.7969\n",
            "Epoch 93/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4989 - accuracy: 0.7958 - val_loss: 0.4945 - val_accuracy: 0.7969\n",
            "Epoch 94/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4988 - accuracy: 0.7958 - val_loss: 0.4946 - val_accuracy: 0.7969\n",
            "Epoch 95/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4987 - accuracy: 0.7958 - val_loss: 0.4945 - val_accuracy: 0.7969\n",
            "Epoch 96/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4989 - accuracy: 0.7958 - val_loss: 0.4945 - val_accuracy: 0.7969\n",
            "Epoch 97/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4988 - accuracy: 0.7958 - val_loss: 0.4945 - val_accuracy: 0.7969\n",
            "Epoch 98/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4945 - val_accuracy: 0.7969\n",
            "Epoch 99/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4989 - accuracy: 0.7958 - val_loss: 0.4945 - val_accuracy: 0.7969\n",
            "Epoch 100/100\n",
            "128/128 [==============================] - 0s 3ms/step - loss: 0.4988 - accuracy: 0.7958 - val_loss: 0.4944 - val_accuracy: 0.7969\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tDDd7YTx-tQ8",
        "outputId": "a3461a23-9335-4bf1-e44b-038c88c38875"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.24780539],\n",
              "       [0.24780539],\n",
              "       [0.24780539],\n",
              "       ...,\n",
              "       [0.24780539],\n",
              "       [0.23844786],\n",
              "       [0.24780539]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = y_pred.argmax(axis=-1)"
      ],
      "metadata": {
        "id": "s17UWcma-zoG"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Df818Ze-89a",
        "outputId": "2c0f48c8-905b-4fd8-d730-a956974b05f5"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7975"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "k1lk-Fl_--5v",
        "outputId": "1f7cc2d0-0a6d-4b87-bccd-813b351354fb"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7b8b839c2440>]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "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": [],
      "metadata": {
        "id": "oJG_B4me_Gv5"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}