CancerDetectionModel.ipynb

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        "<a href=\"https://colab.research.google.com/github/kiruthikpurpose/CancerDetectionModel/blob/main/CancerDetectionModel.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "text": [
            "Epoch 1/100\n",
            "12/12 [==============================] - 1s 23ms/step - loss: 0.6242 - accuracy: 0.6346 - val_loss: 0.5428 - val_accuracy: 0.7692\n",
            "Epoch 2/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.4798 - accuracy: 0.8269 - val_loss: 0.4352 - val_accuracy: 0.8791\n",
            "Epoch 3/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.3761 - accuracy: 0.8709 - val_loss: 0.3791 - val_accuracy: 0.8571\n",
            "Epoch 4/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.3549 - accuracy: 0.8709 - val_loss: 0.3336 - val_accuracy: 0.8901\n",
            "Epoch 5/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.3110 - accuracy: 0.8956 - val_loss: 0.3345 - val_accuracy: 0.8791\n",
            "Epoch 6/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.2572 - accuracy: 0.9121 - val_loss: 0.2848 - val_accuracy: 0.8681\n",
            "Epoch 7/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.2398 - accuracy: 0.9121 - val_loss: 0.2755 - val_accuracy: 0.8791\n",
            "Epoch 8/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.2383 - accuracy: 0.9148 - val_loss: 0.2649 - val_accuracy: 0.8681\n",
            "Epoch 9/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.2412 - accuracy: 0.9148 - val_loss: 0.2511 - val_accuracy: 0.8791\n",
            "Epoch 10/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.2306 - accuracy: 0.9148 - val_loss: 0.2478 - val_accuracy: 0.8791\n",
            "Epoch 11/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.2143 - accuracy: 0.9231 - val_loss: 0.2600 - val_accuracy: 0.8901\n",
            "Epoch 12/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.2167 - accuracy: 0.9203 - val_loss: 0.3011 - val_accuracy: 0.8791\n",
            "Epoch 13/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.2429 - accuracy: 0.9148 - val_loss: 0.2456 - val_accuracy: 0.9011\n",
            "Epoch 14/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.2469 - accuracy: 0.9093 - val_loss: 0.2344 - val_accuracy: 0.9011\n",
            "Epoch 15/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.2153 - accuracy: 0.9203 - val_loss: 0.2289 - val_accuracy: 0.9121\n",
            "Epoch 16/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.2144 - accuracy: 0.9148 - val_loss: 0.2617 - val_accuracy: 0.8901\n",
            "Epoch 17/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.2194 - accuracy: 0.9203 - val_loss: 0.2694 - val_accuracy: 0.9121\n",
            "Epoch 18/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.2928 - accuracy: 0.8956 - val_loss: 0.2711 - val_accuracy: 0.8791\n",
            "Epoch 19/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.2581 - accuracy: 0.8956 - val_loss: 0.3826 - val_accuracy: 0.8791\n",
            "Epoch 20/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.2600 - accuracy: 0.9066 - val_loss: 0.2863 - val_accuracy: 0.8901\n",
            "Epoch 21/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.2402 - accuracy: 0.9093 - val_loss: 0.2717 - val_accuracy: 0.8901\n",
            "Epoch 22/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.2205 - accuracy: 0.9066 - val_loss: 0.2284 - val_accuracy: 0.9011\n",
            "Epoch 23/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.2117 - accuracy: 0.9231 - val_loss: 0.2428 - val_accuracy: 0.9121\n",
            "Epoch 24/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.2146 - accuracy: 0.9121 - val_loss: 0.2727 - val_accuracy: 0.8901\n",
            "Epoch 25/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.2106 - accuracy: 0.9148 - val_loss: 0.2360 - val_accuracy: 0.9011\n",
            "Epoch 26/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.2017 - accuracy: 0.9231 - val_loss: 0.2576 - val_accuracy: 0.8901\n",
            "Epoch 27/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1972 - accuracy: 0.9258 - val_loss: 0.2203 - val_accuracy: 0.9121\n",
            "Epoch 28/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1942 - accuracy: 0.9203 - val_loss: 0.3053 - val_accuracy: 0.8462\n",
            "Epoch 29/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1992 - accuracy: 0.9258 - val_loss: 0.2123 - val_accuracy: 0.9011\n",
            "Epoch 30/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1925 - accuracy: 0.9258 - val_loss: 0.2511 - val_accuracy: 0.9121\n",
            "Epoch 31/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1929 - accuracy: 0.9313 - val_loss: 0.2184 - val_accuracy: 0.9121\n",
            "Epoch 32/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1811 - accuracy: 0.9286 - val_loss: 0.2239 - val_accuracy: 0.9121\n",
            "Epoch 33/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1835 - accuracy: 0.9148 - val_loss: 0.2188 - val_accuracy: 0.9121\n",
            "Epoch 34/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.1970 - accuracy: 0.9258 - val_loss: 0.2087 - val_accuracy: 0.9121\n",
            "Epoch 35/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1910 - accuracy: 0.9203 - val_loss: 0.2202 - val_accuracy: 0.9121\n",
            "Epoch 36/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.2173 - accuracy: 0.9258 - val_loss: 0.2278 - val_accuracy: 0.9231\n",
            "Epoch 37/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.2056 - accuracy: 0.9148 - val_loss: 0.2113 - val_accuracy: 0.9121\n",
            "Epoch 38/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1742 - accuracy: 0.9423 - val_loss: 0.2330 - val_accuracy: 0.9121\n",
            "Epoch 39/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1794 - accuracy: 0.9341 - val_loss: 0.2071 - val_accuracy: 0.9121\n",
            "Epoch 40/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1807 - accuracy: 0.9258 - val_loss: 0.2332 - val_accuracy: 0.9121\n",
            "Epoch 41/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1959 - accuracy: 0.9286 - val_loss: 0.2228 - val_accuracy: 0.9121\n",
            "Epoch 42/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1865 - accuracy: 0.9258 - val_loss: 0.2197 - val_accuracy: 0.9121\n",
            "Epoch 43/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1881 - accuracy: 0.9286 - val_loss: 0.2059 - val_accuracy: 0.9121\n",
            "Epoch 44/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1865 - accuracy: 0.9231 - val_loss: 0.2043 - val_accuracy: 0.9121\n",
            "Epoch 45/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1890 - accuracy: 0.9341 - val_loss: 0.2081 - val_accuracy: 0.9121\n",
            "Epoch 46/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.1758 - accuracy: 0.9368 - val_loss: 0.2048 - val_accuracy: 0.9121\n",
            "Epoch 47/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1711 - accuracy: 0.9396 - val_loss: 0.2038 - val_accuracy: 0.9121\n",
            "Epoch 48/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1817 - accuracy: 0.9286 - val_loss: 0.3279 - val_accuracy: 0.8791\n",
            "Epoch 49/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.2314 - accuracy: 0.9093 - val_loss: 0.2472 - val_accuracy: 0.9121\n",
            "Epoch 50/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.2237 - accuracy: 0.9093 - val_loss: 0.2065 - val_accuracy: 0.9231\n",
            "Epoch 51/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1828 - accuracy: 0.9286 - val_loss: 0.1985 - val_accuracy: 0.9011\n",
            "Epoch 52/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1849 - accuracy: 0.9423 - val_loss: 0.1885 - val_accuracy: 0.9121\n",
            "Epoch 53/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1698 - accuracy: 0.9478 - val_loss: 0.2113 - val_accuracy: 0.9121\n",
            "Epoch 54/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.1889 - accuracy: 0.9341 - val_loss: 0.2923 - val_accuracy: 0.9121\n",
            "Epoch 55/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1968 - accuracy: 0.9368 - val_loss: 0.2323 - val_accuracy: 0.9121\n",
            "Epoch 56/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1900 - accuracy: 0.9286 - val_loss: 0.2372 - val_accuracy: 0.9121\n",
            "Epoch 57/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.2265 - accuracy: 0.9121 - val_loss: 0.2883 - val_accuracy: 0.8571\n",
            "Epoch 58/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.3186 - accuracy: 0.8764 - val_loss: 0.2983 - val_accuracy: 0.8571\n",
            "Epoch 59/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.1922 - accuracy: 0.9203 - val_loss: 0.2025 - val_accuracy: 0.9121\n",
            "Epoch 60/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.2228 - accuracy: 0.9231 - val_loss: 0.2433 - val_accuracy: 0.9121\n",
            "Epoch 61/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.1825 - accuracy: 0.9258 - val_loss: 0.2259 - val_accuracy: 0.9121\n",
            "Epoch 62/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.1955 - accuracy: 0.9258 - val_loss: 0.2107 - val_accuracy: 0.9121\n",
            "Epoch 63/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.1901 - accuracy: 0.9203 - val_loss: 0.2340 - val_accuracy: 0.9121\n",
            "Epoch 64/100\n",
            "12/12 [==============================] - 0s 15ms/step - loss: 0.1994 - accuracy: 0.9313 - val_loss: 0.2172 - val_accuracy: 0.9121\n",
            "Epoch 65/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.1833 - accuracy: 0.9258 - val_loss: 0.2254 - val_accuracy: 0.9011\n",
            "Epoch 66/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.1733 - accuracy: 0.9313 - val_loss: 0.2087 - val_accuracy: 0.9011\n",
            "Epoch 67/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.1731 - accuracy: 0.9396 - val_loss: 0.2144 - val_accuracy: 0.9121\n",
            "Epoch 68/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.1712 - accuracy: 0.9313 - val_loss: 0.2075 - val_accuracy: 0.9121\n",
            "Epoch 69/100\n",
            "12/12 [==============================] - 0s 13ms/step - loss: 0.1731 - accuracy: 0.9313 - val_loss: 0.2365 - val_accuracy: 0.9121\n",
            "Epoch 70/100\n",
            "12/12 [==============================] - 0s 15ms/step - loss: 0.1731 - accuracy: 0.9396 - val_loss: 0.2910 - val_accuracy: 0.9121\n",
            "Epoch 71/100\n",
            "12/12 [==============================] - 0s 14ms/step - loss: 0.2011 - accuracy: 0.9203 - val_loss: 0.2097 - val_accuracy: 0.9121\n",
            "Epoch 72/100\n",
            "12/12 [==============================] - 0s 13ms/step - loss: 0.2134 - accuracy: 0.9258 - val_loss: 0.2433 - val_accuracy: 0.9011\n",
            "Epoch 73/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.2029 - accuracy: 0.9341 - val_loss: 0.2070 - val_accuracy: 0.8901\n",
            "Epoch 74/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.2204 - accuracy: 0.9176 - val_loss: 0.2117 - val_accuracy: 0.9121\n",
            "Epoch 75/100\n",
            "12/12 [==============================] - 0s 13ms/step - loss: 0.2238 - accuracy: 0.9148 - val_loss: 0.2640 - val_accuracy: 0.9231\n",
            "Epoch 76/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.2248 - accuracy: 0.9148 - val_loss: 0.2566 - val_accuracy: 0.9011\n",
            "Epoch 77/100\n",
            "12/12 [==============================] - 0s 14ms/step - loss: 0.2086 - accuracy: 0.9231 - val_loss: 0.2753 - val_accuracy: 0.8901\n",
            "Epoch 78/100\n",
            "12/12 [==============================] - 0s 14ms/step - loss: 0.1910 - accuracy: 0.9203 - val_loss: 0.2463 - val_accuracy: 0.9011\n",
            "Epoch 79/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.1926 - accuracy: 0.9286 - val_loss: 0.2398 - val_accuracy: 0.9121\n",
            "Epoch 80/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.1881 - accuracy: 0.9286 - val_loss: 0.2124 - val_accuracy: 0.9011\n",
            "Epoch 81/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.1830 - accuracy: 0.9341 - val_loss: 0.2149 - val_accuracy: 0.9011\n",
            "Epoch 82/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1804 - accuracy: 0.9368 - val_loss: 0.2862 - val_accuracy: 0.8901\n",
            "Epoch 83/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1901 - accuracy: 0.9176 - val_loss: 0.1995 - val_accuracy: 0.9121\n",
            "Epoch 84/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.1787 - accuracy: 0.9286 - val_loss: 0.2436 - val_accuracy: 0.9121\n",
            "Epoch 85/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1845 - accuracy: 0.9341 - val_loss: 0.2143 - val_accuracy: 0.9121\n",
            "Epoch 86/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1608 - accuracy: 0.9368 - val_loss: 0.2079 - val_accuracy: 0.9121\n",
            "Epoch 87/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1700 - accuracy: 0.9313 - val_loss: 0.2243 - val_accuracy: 0.9121\n",
            "Epoch 88/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1653 - accuracy: 0.9368 - val_loss: 0.2142 - val_accuracy: 0.9121\n",
            "Epoch 89/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1767 - accuracy: 0.9258 - val_loss: 0.1846 - val_accuracy: 0.9231\n",
            "Epoch 90/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1798 - accuracy: 0.9368 - val_loss: 0.2417 - val_accuracy: 0.8901\n",
            "Epoch 91/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.2056 - accuracy: 0.9203 - val_loss: 0.2555 - val_accuracy: 0.9121\n",
            "Epoch 92/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1792 - accuracy: 0.9203 - val_loss: 0.2045 - val_accuracy: 0.9121\n",
            "Epoch 93/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.2261 - accuracy: 0.9286 - val_loss: 0.3503 - val_accuracy: 0.8681\n",
            "Epoch 94/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1870 - accuracy: 0.9258 - val_loss: 0.2434 - val_accuracy: 0.9121\n",
            "Epoch 95/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.2248 - accuracy: 0.9231 - val_loss: 0.1923 - val_accuracy: 0.9011\n",
            "Epoch 96/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.1871 - accuracy: 0.9313 - val_loss: 0.1944 - val_accuracy: 0.9121\n",
            "Epoch 97/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1741 - accuracy: 0.9341 - val_loss: 0.1807 - val_accuracy: 0.9121\n",
            "Epoch 98/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1632 - accuracy: 0.9423 - val_loss: 0.1785 - val_accuracy: 0.9121\n",
            "Epoch 99/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1550 - accuracy: 0.9533 - val_loss: 0.1602 - val_accuracy: 0.9121\n",
            "Epoch 100/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1603 - accuracy: 0.9451 - val_loss: 0.2348 - val_accuracy: 0.9121\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.1594 - accuracy: 0.9386\n",
            "Test Loss: 0.1593940258026123\n",
            "Test Accuracy: 0.9385964870452881\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf\n",
        "\n",
        "# Load and prepare the dataset\n",
        "dataset = pd.read_csv('CancerStats.csv')\n",
        "\n",
        "# Separate features and labels\n",
        "x = dataset.drop(columns=[\"diagnosis(1=m, 0=b)\"])\n",
        "y = dataset[\"diagnosis(1=m, 0=b)\"]\n",
        "\n",
        "# Split the dataset into training and testing sets\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Define the model\n",
        "model = tf.keras.models.Sequential()\n",
        "\n",
        "# Correct the input shape in the first layer\n",
        "model.add(tf.keras.layers.Dense(256, input_shape=(x_train.shape[1],), activation='sigmoid'))\n",
        "model.add(tf.keras.layers.Dense(256, activation='sigmoid'))\n",
        "model.add(tf.keras.layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "history = model.fit(x_train, y_train, epochs=100, validation_split=0.2)\n",
        "\n",
        "# Evaluate the model\n",
        "loss, accuracy = model.evaluate(x_test, y_test)\n",
        "print(f'Test Loss: {loss}')\n",
        "print(f'Test Accuracy: {accuracy}')\n"
      ]
    }
  ]
}