American_Express.ipynb

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    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf"
      ],
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        "id": "mwYBtLXMgTGo"
      },
      "execution_count": 183,
      "outputs": []
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      "source": [
        "data_set = pd.read_csv('Dataset.csv')\n",
        "data_set"
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        "outputId": "217f85c9-5561-4bd4-bd5f-d19bbf7423eb"
      },
      "execution_count": 184,
      "outputs": [
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              "      Credit Score  Geography  Gender   Age  Customer Since  Current Account  \\\n",
              "0            553.0      Delhi  Female  45.0             4.0     0.000000e+00   \n",
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              "4            492.0      Delhi  Female  57.0             6.0     1.912682e+06   \n",
              "...            ...        ...     ...   ...             ...              ...   \n",
              "9924         627.0     Mumbai  Female  42.0             4.0     1.893594e+06   \n",
              "9925         600.0  Bengaluru  Female  51.0             0.0     9.031778e+05   \n",
              "9926         553.0      Delhi    Male  75.0             7.0     0.000000e+00   \n",
              "9927           NaN        NaN     NaN   NaN             NaN              NaN   \n",
              "9928           NaN        NaN     NaN   NaN             NaN              NaN   \n",
              "\n",
              "      Num of products  UPI Enabled  Estimated Yearly Income  Closed  \n",
              "0                 4.0          1.0                 274150.0     0.0  \n",
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              "9926              4.0          1.0                 180031.0     0.0  \n",
              "9927              NaN          NaN                      NaN     NaN  \n",
              "9928              NaN          NaN                      NaN     NaN  \n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data_set');\n",
              "      }\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_set",
              "summary": "{\n  \"name\": \"data_set\",\n  \"rows\": 9929,\n  \"fields\": [\n    {\n      \"column\": \"Credit Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.61008867126506,\n        \"min\": 285.0,\n        \"max\": 692.0,\n        \"num_unique_values\": 377,\n        \"samples\": [\n          384.0,\n          524.0,\n          516.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Geography\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Delhi\",\n          \"Bengaluru\",\n          \"Mumbai\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\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\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.100516872985775,\n        \"min\": 17.0,\n        \"max\": 137.0,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          30.0,\n          45.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer Since\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.3929866403882447,\n        \"min\": 0.0,\n        \"max\": 8.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          1.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Current Account\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 840798.8453397888,\n        \"min\": 0.0,\n        \"max\": 3985304.216,\n        \"num_unique_values\": 6326,\n        \"samples\": [\n          1752881.546,\n          1130187.429\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Num of products\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0815279761877101,\n        \"min\": 2.0,\n        \"max\": 7.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"UPI Enabled\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.456089956229226,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estimated Yearly Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 157578.7004893497,\n        \"min\": 32.0,\n        \"max\": 547947.0,\n        \"num_unique_values\": 9841,\n        \"samples\": [\n          324646.0,\n          500641.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Closed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.40320312170507216,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 184
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data_set.loc[:, ~data_set.columns.str.contains('^Unnamed')]\n",
        "\n",
        "data = data.dropna()\n",
        "data.to_csv('Cleaned_Data.csv', index=False)\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "opjNSpu4gWQa",
        "outputId": "baea4d81-36f9-42cd-ee73-789eb6408ffe"
      },
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Credit Score  Geography  Gender   Age  Customer Since  Current Account  \\\n",
              "0            553.0      Delhi  Female  45.0             4.0     0.000000e+00   \n",
              "1            447.0  Bengaluru    Male  31.0             7.0     0.000000e+00   \n",
              "2            501.0      Delhi  Female  32.0             2.0     0.000000e+00   \n",
              "3            428.0      Delhi    Male  51.0             3.0     0.000000e+00   \n",
              "4            492.0      Delhi  Female  57.0             6.0     1.912682e+06   \n",
              "...            ...        ...     ...   ...             ...              ...   \n",
              "9922         594.0  Bengaluru    Male  28.0             6.0     0.000000e+00   \n",
              "9923         557.0  Bengaluru    Male  59.0             3.0     8.050490e+05   \n",
              "9924         627.0     Mumbai  Female  42.0             4.0     1.893594e+06   \n",
              "9925         600.0  Bengaluru  Female  51.0             0.0     9.031778e+05   \n",
              "9926         553.0      Delhi    Male  75.0             7.0     0.000000e+00   \n",
              "\n",
              "      Num of products  UPI Enabled  Estimated Yearly Income  Closed  \n",
              "0                 4.0          1.0                 274150.0     0.0  \n",
              "1                 4.0          1.0                 519360.0     0.0  \n",
              "2                 4.0          1.0                 545501.0     0.0  \n",
              "3                 4.0          1.0                  86868.0     0.0  \n",
              "4                 2.0          1.0                 518680.0     0.0  \n",
              "...               ...          ...                      ...     ...  \n",
              "9922              4.0          1.0                 394810.0     0.0  \n",
              "9923              2.0          0.0                  58163.0     1.0  \n",
              "9924              4.0          0.0                 494067.0     0.0  \n",
              "9925              2.0          1.0                 109375.0     1.0  \n",
              "9926              4.0          1.0                 180031.0     0.0  \n",
              "\n",
              "[9927 rows x 10 columns]"
            ],
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              "      <th>Credit Score</th>\n",
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              "      <th>9922</th>\n",
              "      <td>594.0</td>\n",
              "      <td>Bengaluru</td>\n",
              "      <td>Male</td>\n",
              "      <td>28.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>4.0</td>\n",
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              "      <td>394810.0</td>\n",
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              "      <td>557.0</td>\n",
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              "      <td>Mumbai</td>\n",
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              "      <td>42.0</td>\n",
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              "      <td>1.893594e+06</td>\n",
              "      <td>4.0</td>\n",
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              "        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-fb95afbb-8ba6-40f6-b80a-386835b1534d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_2c4e46ae-d4f6-4ce5-9e85-116ced945dec\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_2c4e46ae-d4f6-4ce5-9e85-116ced945dec button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 9927,\n  \"fields\": [\n    {\n      \"column\": \"Credit Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.61008867126506,\n        \"min\": 285.0,\n        \"max\": 692.0,\n        \"num_unique_values\": 377,\n        \"samples\": [\n          384.0,\n          524.0,\n          516.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Geography\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Delhi\",\n          \"Bengaluru\",\n          \"Mumbai\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\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\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.100516872985775,\n        \"min\": 17.0,\n        \"max\": 137.0,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          30.0,\n          45.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer Since\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.3929866403882447,\n        \"min\": 0.0,\n        \"max\": 8.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          1.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Current Account\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 840798.8453397888,\n        \"min\": 0.0,\n        \"max\": 3985304.216,\n        \"num_unique_values\": 6326,\n        \"samples\": [\n          1752881.546,\n          1130187.429\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Num of products\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0815279761877101,\n        \"min\": 2.0,\n        \"max\": 7.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"UPI Enabled\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.456089956229226,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estimated Yearly Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 157578.7004893497,\n        \"min\": 32.0,\n        \"max\": 547947.0,\n        \"num_unique_values\": 9841,\n        \"samples\": [\n          324646.0,\n          500641.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Closed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.40320312170507216,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = data.iloc[:, 0:-1].values\n",
        "y = data.iloc[:, -1].values"
      ],
      "metadata": {
        "id": "_drqqMPjgcSO"
      },
      "execution_count": 186,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Tb5z7I3ge3Z",
        "outputId": "b6dc3270-d541-445e-a749-cbc10b46c896"
      },
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[553.0, 'Delhi', 'Female', ..., 4.0, 1.0, 274150.0],\n",
              "       [447.0, 'Bengaluru', 'Male', ..., 4.0, 1.0, 519360.0],\n",
              "       [501.0, 'Delhi', 'Female', ..., 4.0, 1.0, 545501.0],\n",
              "       ...,\n",
              "       [627.0, 'Mumbai', 'Female', ..., 4.0, 0.0, 494067.0],\n",
              "       [600.0, 'Bengaluru', 'Female', ..., 2.0, 1.0, 109375.0],\n",
              "       [553.0, 'Delhi', 'Male', ..., 4.0, 1.0, 180031.0]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 187
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QPiMez6bgflb",
        "outputId": "5c429ed3-2fb0-4311-b301-1a80d49a0495"
      },
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., ..., 0., 1., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 188
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "x[:, 2] = le.fit_transform(x[:, 2])\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4iPv4a9Kgh4S",
        "outputId": "7122e7dd-819c-4142-8fe4-43c6896aecdd"
      },
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[553.0, 'Delhi', 0, ..., 4.0, 1.0, 274150.0],\n",
              "       [447.0, 'Bengaluru', 1, ..., 4.0, 1.0, 519360.0],\n",
              "       [501.0, 'Delhi', 0, ..., 4.0, 1.0, 545501.0],\n",
              "       ...,\n",
              "       [627.0, 'Mumbai', 0, ..., 4.0, 0.0, 494067.0],\n",
              "       [600.0, 'Bengaluru', 0, ..., 2.0, 1.0, 109375.0],\n",
              "       [553.0, 'Delhi', 1, ..., 4.0, 1.0, 180031.0]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 189
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "ct = ColumnTransformer(transformers= [['encoder', OneHotEncoder(), [1]]], remainder='passthrough')\n",
        "x = np.array(ct.fit_transform(x))"
      ],
      "metadata": {
        "id": "waLgWK89gjox"
      },
      "execution_count": 190,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZGjPYmhVgmgq",
        "outputId": "d5313cf8-cd1e-45e4-95c4-b0c420324973"
      },
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.0, 1.0, 0.0, ..., 4.0, 1.0, 274150.0],\n",
              "       [1.0, 0.0, 0.0, ..., 4.0, 1.0, 519360.0],\n",
              "       [0.0, 1.0, 0.0, ..., 4.0, 1.0, 545501.0],\n",
              "       ...,\n",
              "       [0.0, 0.0, 1.0, ..., 4.0, 0.0, 494067.0],\n",
              "       [1.0, 0.0, 0.0, ..., 2.0, 1.0, 109375.0],\n",
              "       [0.0, 1.0, 0.0, ..., 4.0, 1.0, 180031.0]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 191
        }
      ]
    },
    {
      "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=42)"
      ],
      "metadata": {
        "id": "hbvaf0ZRgoLY"
      },
      "execution_count": 192,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "x_train = sc.fit_transform(x_train)\n",
        "x_test = sc.fit_transform(x_test)"
      ],
      "metadata": {
        "id": "eTgQ1HkHgp9H"
      },
      "execution_count": 193,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ann = tf.keras.models.Sequential()"
      ],
      "metadata": {
        "id": "D9-0sYW3lL1n"
      },
      "execution_count": 194,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ann.add(tf.keras.layers.Dense(units=6, activation='relu'))"
      ],
      "metadata": {
        "id": "a6QXpcKNnAEQ"
      },
      "execution_count": 195,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ann.add(tf.keras.layers.Dense(units=6, activation='relu'))"
      ],
      "metadata": {
        "id": "ucnB_l0onHI3"
      },
      "execution_count": 196,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ann.add(tf.keras.layers.Dense(units=6, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "tdD_mVvXnK4r"
      },
      "execution_count": 197,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ann.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "Zg9mw4pbnbyV"
      },
      "execution_count": 198,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ann.add(Dense(5, activation='softmax'))  # Example for 5 classes"
      ],
      "metadata": {
        "id": "UrNSy1KMnxXs"
      },
      "execution_count": 199,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "# Assuming y_train contains class indices (e.g., 0, 1, 2, 3, 4)\n",
        "y_train = to_categorical(y_train, num_classes=5)"
      ],
      "metadata": {
        "id": "h5uY_snYoOq-"
      },
      "execution_count": 200,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ann.add(Dense(1, activation='sigmoid'))  # Binary classification"
      ],
      "metadata": {
        "id": "Qw-QJom5oUbN"
      },
      "execution_count": 201,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ann.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "U8cyz2WaoXI6"
      },
      "execution_count": 202,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ann.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n"
      ],
      "metadata": {
        "id": "JwkJnjK-oaFm"
      },
      "execution_count": 203,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "# Define your model\n",
        "ann = Sequential()\n",
        "\n",
        "# Add layers\n",
        "ann.add(Dense(128, activation='relu', input_dim=x_train.shape[1]))\n",
        "ann.add(Dense(64, activation='relu'))\n",
        "ann.add(Dense(5, activation='softmax'))  # For multi-class classification (5 classes)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w4guTSoTogXE",
        "outputId": "8e14b45c-5060-46f3-eb05-8f7526722f49"
      },
      "execution_count": 204,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ann.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n"
      ],
      "metadata": {
        "id": "dvuVImU1on1h"
      },
      "execution_count": 205,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"y_train shape: {y_train.shape}\")  # Should be (7941, 5)\n",
        "print(f\"Model output shape: {ann.output_shape}\")  # Should be (None, 5)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gwTvkwsHpDv3",
        "outputId": "f0a8a7a0-37eb-4c32-8ed3-9f0ca9fa2951"
      },
      "execution_count": 206,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "y_train shape: (7941, 5)\n",
            "Model output shape: (None, 5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ann.fit(x_train, y_train, batch_size=32, epochs=120)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5kQcfXt0pFG8",
        "outputId": "d48feede-525b-4ef5-b981-73fa2f24b08f"
      },
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8187 - loss: 0.4123\n",
            "Epoch 2/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8318 - loss: 0.3844\n",
            "Epoch 3/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8499 - loss: 0.3510\n",
            "Epoch 4/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8469 - loss: 0.3602\n",
            "Epoch 5/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8536 - loss: 0.3471\n",
            "Epoch 6/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8582 - loss: 0.3345\n",
            "Epoch 7/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8494 - loss: 0.3471\n",
            "Epoch 8/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8600 - loss: 0.3350\n",
            "Epoch 9/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8550 - loss: 0.3406\n",
            "Epoch 10/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8592 - loss: 0.3348\n",
            "Epoch 11/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8704 - loss: 0.3237\n",
            "Epoch 12/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8629 - loss: 0.3253\n",
            "Epoch 13/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8586 - loss: 0.3349\n",
            "Epoch 14/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8623 - loss: 0.3249\n",
            "Epoch 15/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8691 - loss: 0.3184\n",
            "Epoch 16/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8603 - loss: 0.3275\n",
            "Epoch 17/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8721 - loss: 0.3098\n",
            "Epoch 18/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8659 - loss: 0.3159\n",
            "Epoch 19/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8696 - loss: 0.3103\n",
            "Epoch 20/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8727 - loss: 0.3002\n",
            "Epoch 21/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8643 - loss: 0.3114\n",
            "Epoch 22/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8621 - loss: 0.3173\n",
            "Epoch 23/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8697 - loss: 0.3067\n",
            "Epoch 24/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8703 - loss: 0.2969\n",
            "Epoch 25/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8759 - loss: 0.3039\n",
            "Epoch 26/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8739 - loss: 0.2991\n",
            "Epoch 27/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8726 - loss: 0.2966\n",
            "Epoch 28/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8728 - loss: 0.3001\n",
            "Epoch 29/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8720 - loss: 0.2906\n",
            "Epoch 30/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8737 - loss: 0.2968\n",
            "Epoch 31/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8785 - loss: 0.2831\n",
            "Epoch 32/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8858 - loss: 0.2745\n",
            "Epoch 33/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8814 - loss: 0.2783\n",
            "Epoch 34/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8872 - loss: 0.2755\n",
            "Epoch 35/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8818 - loss: 0.2782\n",
            "Epoch 36/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8800 - loss: 0.2782\n",
            "Epoch 37/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8812 - loss: 0.2751\n",
            "Epoch 38/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8778 - loss: 0.2843\n",
            "Epoch 39/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8910 - loss: 0.2706\n",
            "Epoch 40/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8788 - loss: 0.2795\n",
            "Epoch 41/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8850 - loss: 0.2751\n",
            "Epoch 42/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8853 - loss: 0.2694\n",
            "Epoch 43/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8885 - loss: 0.2668\n",
            "Epoch 44/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8862 - loss: 0.2738\n",
            "Epoch 45/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8884 - loss: 0.2677\n",
            "Epoch 46/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8886 - loss: 0.2609\n",
            "Epoch 47/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8911 - loss: 0.2646\n",
            "Epoch 48/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8890 - loss: 0.2714\n",
            "Epoch 49/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8942 - loss: 0.2591\n",
            "Epoch 50/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8881 - loss: 0.2602\n",
            "Epoch 51/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8871 - loss: 0.2658\n",
            "Epoch 52/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8910 - loss: 0.2531\n",
            "Epoch 53/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8994 - loss: 0.2444\n",
            "Epoch 54/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8925 - loss: 0.2558\n",
            "Epoch 55/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8944 - loss: 0.2558\n",
            "Epoch 56/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9005 - loss: 0.2428\n",
            "Epoch 57/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9009 - loss: 0.2327\n",
            "Epoch 58/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9003 - loss: 0.2379\n",
            "Epoch 59/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9015 - loss: 0.2364\n",
            "Epoch 60/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8889 - loss: 0.2510\n",
            "Epoch 61/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9003 - loss: 0.2388\n",
            "Epoch 62/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9035 - loss: 0.2240\n",
            "Epoch 63/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9003 - loss: 0.2336\n",
            "Epoch 64/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8982 - loss: 0.2346\n",
            "Epoch 65/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8995 - loss: 0.2373\n",
            "Epoch 66/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9055 - loss: 0.2326\n",
            "Epoch 67/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8997 - loss: 0.2350\n",
            "Epoch 68/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9095 - loss: 0.2184\n",
            "Epoch 69/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9057 - loss: 0.2258\n",
            "Epoch 70/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9011 - loss: 0.2264\n",
            "Epoch 71/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9072 - loss: 0.2237\n",
            "Epoch 72/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9085 - loss: 0.2213\n",
            "Epoch 73/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9123 - loss: 0.2209\n",
            "Epoch 74/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9064 - loss: 0.2213\n",
            "Epoch 75/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9059 - loss: 0.2162\n",
            "Epoch 76/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9110 - loss: 0.2165\n",
            "Epoch 77/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9160 - loss: 0.2064\n",
            "Epoch 78/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9072 - loss: 0.2217\n",
            "Epoch 79/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9138 - loss: 0.2135\n",
            "Epoch 80/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9132 - loss: 0.2072\n",
            "Epoch 81/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9165 - loss: 0.2047\n",
            "Epoch 82/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9133 - loss: 0.2161\n",
            "Epoch 83/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9159 - loss: 0.2104\n",
            "Epoch 84/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9173 - loss: 0.2019\n",
            "Epoch 85/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9233 - loss: 0.1989\n",
            "Epoch 86/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9196 - loss: 0.1922\n",
            "Epoch 87/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9160 - loss: 0.2031\n",
            "Epoch 88/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9158 - loss: 0.2007\n",
            "Epoch 89/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9222 - loss: 0.1890\n",
            "Epoch 90/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9215 - loss: 0.1916\n",
            "Epoch 91/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9189 - loss: 0.2002\n",
            "Epoch 92/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9207 - loss: 0.1888\n",
            "Epoch 93/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9190 - loss: 0.1966\n",
            "Epoch 94/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9217 - loss: 0.1857\n",
            "Epoch 95/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9255 - loss: 0.1817\n",
            "Epoch 96/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9237 - loss: 0.1877\n",
            "Epoch 97/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9318 - loss: 0.1798\n",
            "Epoch 98/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9264 - loss: 0.1822\n",
            "Epoch 99/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9273 - loss: 0.1804\n",
            "Epoch 100/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9254 - loss: 0.1881\n",
            "Epoch 101/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9313 - loss: 0.1795\n",
            "Epoch 102/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9247 - loss: 0.1747\n",
            "Epoch 103/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9244 - loss: 0.1848\n",
            "Epoch 104/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9306 - loss: 0.1691\n",
            "Epoch 105/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9284 - loss: 0.1801\n",
            "Epoch 106/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9252 - loss: 0.1747\n",
            "Epoch 107/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9219 - loss: 0.1846\n",
            "Epoch 108/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9255 - loss: 0.1765\n",
            "Epoch 109/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9285 - loss: 0.1724\n",
            "Epoch 110/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.9245 - loss: 0.1753\n",
            "Epoch 111/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.9291 - loss: 0.1734\n",
            "Epoch 112/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9325 - loss: 0.1644\n",
            "Epoch 113/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9322 - loss: 0.1631\n",
            "Epoch 114/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9309 - loss: 0.1653\n",
            "Epoch 115/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9369 - loss: 0.1578\n",
            "Epoch 116/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9359 - loss: 0.1608\n",
            "Epoch 117/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9312 - loss: 0.1652\n",
            "Epoch 118/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9405 - loss: 0.1523\n",
            "Epoch 119/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9363 - loss: 0.1605\n",
            "Epoch 120/120\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9384 - loss: 0.1514\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7ca69f6df820>"
            ]
          },
          "metadata": {},
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "y_test = to_categorical(y_test, num_classes=5)\n"
      ],
      "metadata": {
        "id": "KOfoDaJZqDIg"
      },
      "execution_count": 211,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_accuracy = ann.evaluate(x_test, y_test)\n",
        "print(f\"Test Loss: {test_loss}, Test Accuracy: {test_accuracy}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ea55jVTvqDtI",
        "outputId": "3ee2b257-2a5e-4d37-fe6b-2ccec6081160"
      },
      "execution_count": 215,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8318 - loss: 0.6030\n",
            "Test Loss: 0.6186280846595764, Test Accuracy: 0.8227593302726746\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"x_test shape: {x_test.shape}\")\n",
        "print(f\"y_test shape: {y_test.shape}\")  # Should be (num_samples, 5) for one-hot encoded labels\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dImmxYnyqUuq",
        "outputId": "3b9fc1ef-9ebb-4323-c7a3-c8c4d82e9c12"
      },
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_test shape: (1986, 11)\n",
            "y_test shape: (1986, 5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "import numpy as np\n",
        "\n",
        "y_pred = ann.predict(x_test)\n",
        "y_pred_classes = np.argmax(y_pred, axis=1)\n",
        "y_test_classes = np.argmax(y_test, axis=1)\n",
        "\n",
        "cm = confusion_matrix(y_test_classes, y_pred_classes)\n",
        "print(\"Confusion Matrix:\")\n",
        "print(cm)\n",
        "\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test_classes, y_pred_classes))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MhyoSr3vqXqF",
        "outputId": "c643700b-4472-4a7b-84b7-1cfef2ebf223"
      },
      "execution_count": 217,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
            "Confusion Matrix:\n",
            "[[1429  136]\n",
            " [ 216  205]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.91      0.89      1565\n",
            "           1       0.60      0.49      0.54       421\n",
            "\n",
            "    accuracy                           0.82      1986\n",
            "   macro avg       0.73      0.70      0.71      1986\n",
            "weighted avg       0.81      0.82      0.82      1986\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "history = ann.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=100, batch_size=32)\n",
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qtzu_Ys3rGRZ",
        "outputId": "2363a41e-a5fe-4082-ff5a-7227bb137b6d"
      },
      "execution_count": 218,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9335 - loss: 0.1632 - val_accuracy: 0.8288 - val_loss: 0.6296\n",
            "Epoch 2/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9377 - loss: 0.1499 - val_accuracy: 0.8172 - val_loss: 0.6203\n",
            "Epoch 3/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9372 - loss: 0.1565 - val_accuracy: 0.8157 - val_loss: 0.6153\n",
            "Epoch 4/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9357 - loss: 0.1573 - val_accuracy: 0.8283 - val_loss: 0.6144\n",
            "Epoch 5/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9409 - loss: 0.1477 - val_accuracy: 0.8187 - val_loss: 0.6417\n",
            "Epoch 6/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9407 - loss: 0.1502 - val_accuracy: 0.8162 - val_loss: 0.6294\n",
            "Epoch 7/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9450 - loss: 0.1458 - val_accuracy: 0.8152 - val_loss: 0.6414\n",
            "Epoch 8/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9441 - loss: 0.1461 - val_accuracy: 0.8137 - val_loss: 0.6311\n",
            "Epoch 9/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9358 - loss: 0.1530 - val_accuracy: 0.8308 - val_loss: 0.6519\n",
            "Epoch 10/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9419 - loss: 0.1426 - val_accuracy: 0.8082 - val_loss: 0.6366\n",
            "Epoch 11/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9440 - loss: 0.1390 - val_accuracy: 0.7966 - val_loss: 0.6564\n",
            "Epoch 12/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9435 - loss: 0.1423 - val_accuracy: 0.8147 - val_loss: 0.6522\n",
            "Epoch 13/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9440 - loss: 0.1442 - val_accuracy: 0.8228 - val_loss: 0.6645\n",
            "Epoch 14/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9419 - loss: 0.1425 - val_accuracy: 0.8167 - val_loss: 0.6548\n",
            "Epoch 15/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9457 - loss: 0.1384 - val_accuracy: 0.8122 - val_loss: 0.6730\n",
            "Epoch 16/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9410 - loss: 0.1439 - val_accuracy: 0.8263 - val_loss: 0.7050\n",
            "Epoch 17/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9446 - loss: 0.1355 - val_accuracy: 0.8207 - val_loss: 0.7020\n",
            "Epoch 18/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9514 - loss: 0.1262 - val_accuracy: 0.8031 - val_loss: 0.6806\n",
            "Epoch 19/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9484 - loss: 0.1356 - val_accuracy: 0.8107 - val_loss: 0.7029\n",
            "Epoch 20/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9416 - loss: 0.1385 - val_accuracy: 0.8127 - val_loss: 0.6921\n",
            "Epoch 21/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9522 - loss: 0.1252 - val_accuracy: 0.8167 - val_loss: 0.7110\n",
            "Epoch 22/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9472 - loss: 0.1330 - val_accuracy: 0.8122 - val_loss: 0.6877\n",
            "Epoch 23/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9512 - loss: 0.1270 - val_accuracy: 0.8147 - val_loss: 0.7084\n",
            "Epoch 24/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9474 - loss: 0.1327 - val_accuracy: 0.8061 - val_loss: 0.6990\n",
            "Epoch 25/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9537 - loss: 0.1229 - val_accuracy: 0.8031 - val_loss: 0.7167\n",
            "Epoch 26/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9526 - loss: 0.1204 - val_accuracy: 0.8162 - val_loss: 0.7223\n",
            "Epoch 27/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9472 - loss: 0.1235 - val_accuracy: 0.8212 - val_loss: 0.7341\n",
            "Epoch 28/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9492 - loss: 0.1276 - val_accuracy: 0.8051 - val_loss: 0.7239\n",
            "Epoch 29/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9495 - loss: 0.1275 - val_accuracy: 0.8041 - val_loss: 0.6974\n",
            "Epoch 30/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9458 - loss: 0.1337 - val_accuracy: 0.8228 - val_loss: 0.7292\n",
            "Epoch 31/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9514 - loss: 0.1253 - val_accuracy: 0.8137 - val_loss: 0.7471\n",
            "Epoch 32/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9430 - loss: 0.1382 - val_accuracy: 0.8097 - val_loss: 0.7197\n",
            "Epoch 33/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9551 - loss: 0.1160 - val_accuracy: 0.8167 - val_loss: 0.7343\n",
            "Epoch 34/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9453 - loss: 0.1369 - val_accuracy: 0.8167 - val_loss: 0.7487\n",
            "Epoch 35/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9532 - loss: 0.1198 - val_accuracy: 0.8001 - val_loss: 0.7373\n",
            "Epoch 36/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9567 - loss: 0.1200 - val_accuracy: 0.8202 - val_loss: 0.7814\n",
            "Epoch 37/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9534 - loss: 0.1165 - val_accuracy: 0.7991 - val_loss: 0.7605\n",
            "Epoch 38/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9520 - loss: 0.1183 - val_accuracy: 0.8127 - val_loss: 0.7811\n",
            "Epoch 39/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9516 - loss: 0.1184 - val_accuracy: 0.8197 - val_loss: 0.7904\n",
            "Epoch 40/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9515 - loss: 0.1205 - val_accuracy: 0.8026 - val_loss: 0.7768\n",
            "Epoch 41/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9578 - loss: 0.1118 - val_accuracy: 0.8066 - val_loss: 0.7891\n",
            "Epoch 42/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9536 - loss: 0.1157 - val_accuracy: 0.8051 - val_loss: 0.7933\n",
            "Epoch 43/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - accuracy: 0.9539 - loss: 0.1175 - val_accuracy: 0.8056 - val_loss: 0.7962\n",
            "Epoch 44/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9554 - loss: 0.1107 - val_accuracy: 0.8077 - val_loss: 0.7981\n",
            "Epoch 45/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.9521 - loss: 0.1145 - val_accuracy: 0.8107 - val_loss: 0.7996\n",
            "Epoch 46/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9552 - loss: 0.1159 - val_accuracy: 0.8061 - val_loss: 0.7960\n",
            "Epoch 47/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9529 - loss: 0.1172 - val_accuracy: 0.7986 - val_loss: 0.7900\n",
            "Epoch 48/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.9522 - loss: 0.1135 - val_accuracy: 0.8046 - val_loss: 0.8028\n",
            "Epoch 49/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9469 - loss: 0.1294 - val_accuracy: 0.8066 - val_loss: 0.8338\n",
            "Epoch 50/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9578 - loss: 0.1074 - val_accuracy: 0.8016 - val_loss: 0.8289\n",
            "Epoch 51/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9594 - loss: 0.1028 - val_accuracy: 0.8167 - val_loss: 0.8321\n",
            "Epoch 52/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9610 - loss: 0.1015 - val_accuracy: 0.8006 - val_loss: 0.8317\n",
            "Epoch 53/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9539 - loss: 0.1156 - val_accuracy: 0.8001 - val_loss: 0.8140\n",
            "Epoch 54/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9580 - loss: 0.1057 - val_accuracy: 0.8187 - val_loss: 0.8396\n",
            "Epoch 55/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9562 - loss: 0.1027 - val_accuracy: 0.8066 - val_loss: 0.8333\n",
            "Epoch 56/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9603 - loss: 0.0987 - val_accuracy: 0.8011 - val_loss: 0.8339\n",
            "Epoch 57/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9638 - loss: 0.0997 - val_accuracy: 0.8097 - val_loss: 0.8496\n",
            "Epoch 58/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9628 - loss: 0.0998 - val_accuracy: 0.7981 - val_loss: 0.8392\n",
            "Epoch 59/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9587 - loss: 0.1057 - val_accuracy: 0.8097 - val_loss: 0.8567\n",
            "Epoch 60/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9536 - loss: 0.1128 - val_accuracy: 0.8061 - val_loss: 0.8746\n",
            "Epoch 61/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9581 - loss: 0.1086 - val_accuracy: 0.7991 - val_loss: 0.8670\n",
            "Epoch 62/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9565 - loss: 0.1097 - val_accuracy: 0.8207 - val_loss: 0.8704\n",
            "Epoch 63/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9610 - loss: 0.0985 - val_accuracy: 0.8011 - val_loss: 0.8810\n",
            "Epoch 64/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9643 - loss: 0.0958 - val_accuracy: 0.8122 - val_loss: 0.8893\n",
            "Epoch 65/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9582 - loss: 0.1033 - val_accuracy: 0.8061 - val_loss: 0.8754\n",
            "Epoch 66/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9581 - loss: 0.1065 - val_accuracy: 0.8051 - val_loss: 0.8754\n",
            "Epoch 67/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9635 - loss: 0.0936 - val_accuracy: 0.8187 - val_loss: 0.9152\n",
            "Epoch 68/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9634 - loss: 0.0954 - val_accuracy: 0.8142 - val_loss: 0.9358\n",
            "Epoch 69/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9622 - loss: 0.0930 - val_accuracy: 0.8107 - val_loss: 0.8954\n",
            "Epoch 70/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9603 - loss: 0.0986 - val_accuracy: 0.8016 - val_loss: 0.9038\n",
            "Epoch 71/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9637 - loss: 0.0958 - val_accuracy: 0.8077 - val_loss: 0.9322\n",
            "Epoch 72/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9571 - loss: 0.1013 - val_accuracy: 0.8021 - val_loss: 0.9166\n",
            "Epoch 73/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9627 - loss: 0.0930 - val_accuracy: 0.8112 - val_loss: 0.8977\n",
            "Epoch 74/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9659 - loss: 0.0908 - val_accuracy: 0.8066 - val_loss: 0.8931\n",
            "Epoch 75/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9673 - loss: 0.0912 - val_accuracy: 0.7875 - val_loss: 0.9150\n",
            "Epoch 76/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9641 - loss: 0.0912 - val_accuracy: 0.8122 - val_loss: 0.9417\n",
            "Epoch 77/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9584 - loss: 0.1069 - val_accuracy: 0.8107 - val_loss: 0.9206\n",
            "Epoch 78/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9550 - loss: 0.1064 - val_accuracy: 0.8162 - val_loss: 0.9648\n",
            "Epoch 79/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9699 - loss: 0.0902 - val_accuracy: 0.7981 - val_loss: 0.9486\n",
            "Epoch 80/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9669 - loss: 0.0846 - val_accuracy: 0.7956 - val_loss: 0.9501\n",
            "Epoch 81/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9635 - loss: 0.0915 - val_accuracy: 0.8137 - val_loss: 0.9506\n",
            "Epoch 82/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9669 - loss: 0.0863 - val_accuracy: 0.8087 - val_loss: 0.9398\n",
            "Epoch 83/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9659 - loss: 0.0880 - val_accuracy: 0.8117 - val_loss: 0.9969\n",
            "Epoch 84/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9584 - loss: 0.0928 - val_accuracy: 0.8016 - val_loss: 0.9456\n",
            "Epoch 85/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9716 - loss: 0.0766 - val_accuracy: 0.7905 - val_loss: 0.9779\n",
            "Epoch 86/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9645 - loss: 0.0951 - val_accuracy: 0.8192 - val_loss: 0.9743\n",
            "Epoch 87/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9663 - loss: 0.0856 - val_accuracy: 0.7951 - val_loss: 0.9762\n",
            "Epoch 88/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9745 - loss: 0.0736 - val_accuracy: 0.8112 - val_loss: 0.9662\n",
            "Epoch 89/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9637 - loss: 0.0905 - val_accuracy: 0.8142 - val_loss: 0.9740\n",
            "Epoch 90/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9713 - loss: 0.0758 - val_accuracy: 0.8066 - val_loss: 0.9798\n",
            "Epoch 91/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9677 - loss: 0.0810 - val_accuracy: 0.8001 - val_loss: 0.9942\n",
            "Epoch 92/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9682 - loss: 0.0805 - val_accuracy: 0.7996 - val_loss: 1.0154\n",
            "Epoch 93/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9712 - loss: 0.0751 - val_accuracy: 0.8077 - val_loss: 1.0117\n",
            "Epoch 94/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9661 - loss: 0.0865 - val_accuracy: 0.8127 - val_loss: 1.0225\n",
            "Epoch 95/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9702 - loss: 0.0807 - val_accuracy: 0.8102 - val_loss: 1.0194\n",
            "Epoch 96/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9742 - loss: 0.0734 - val_accuracy: 0.7936 - val_loss: 1.0230\n",
            "Epoch 97/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9720 - loss: 0.0797 - val_accuracy: 0.8061 - val_loss: 1.0183\n",
            "Epoch 98/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9722 - loss: 0.0767 - val_accuracy: 0.7895 - val_loss: 1.0226\n",
            "Epoch 99/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9678 - loss: 0.0850 - val_accuracy: 0.7946 - val_loss: 1.0296\n",
            "Epoch 100/100\n",
            "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9678 - loss: 0.0803 - val_accuracy: 0.8072 - val_loss: 1.0452\n"
          ]
        },
        {
          "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": "1ThZiy3irI5k"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}