DeepLearningProj_MammographicMasses.ipynb

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      "source": [
        "## Predict whether a mammogram mass is benign or malignant"
      ],
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        "id": "vrthBK9xvNJ5"
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      "source": [
        "Data set used for this project: https://archive.ics.uci.edu/ml/datasets/Mammographic+Mass\n",
        "\n",
        "This data contains 961 instances of masses detected in mammograms, and contains the following attributes:\n",
        "\n",
        "\n",
        "   1. BI-RADS assessment: 1 to 5 (ordinal)  \n",
        "   2. Age: patient's age in years (integer)\n",
        "   3. Shape: mass shape: round=1 oval=2 lobular=3 irregular=4 (nominal)\n",
        "   4. Margin: mass margin: circumscribed=1 microlobulated=2 obscured=3 ill-defined=4 spiculated=5 (nominal)\n",
        "   5. Density: mass density high=1 iso=2 low=3 fat-containing=4 (ordinal)\n",
        "   6. Severity: benign=0 or malignant=1 (binominal)\n",
        "\n",
        "   Missing Attribute Values: Yes\n",
        "    - BI-RADS assessment:    2\n",
        "    - Age:                   5\n",
        "    - Shape:                31\n",
        "    - Margin:               48\n",
        "    - Density:              76\n",
        "    - Severity:              0\n",
        "\n",
        "  Class Distribution: benign: 516; malignant: 445"
      ],
      "metadata": {
        "id": "2-FWmchHvRNm"
      }
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    {
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      "source": [
        "## 1. Data Preprocessing"
      ],
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      "cell_type": "markdown",
      "source": [
        "Import the necessary libraries/packages"
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        "id": "Wl1fpVJy3Wpd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn import preprocessing\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "RQH0YPYsvfQh"
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      "execution_count": 35,
      "outputs": []
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      "source": [
        "Import the data set using pandas"
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      "source": [
        "_dataset = pd.read_csv(\"mammographic_masses.data\")\n",
        "_dataset.size\n",
        "_dataset.head()"
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              "   BI-Rads   Age  Shape  Margin  Density  Severity\n",
              "0      5.0  67.0    3.0     5.0      3.0         1\n",
              "1      4.0  43.0    1.0     1.0      NaN         1\n",
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        "Deal with the missing values. For the age column, replace NaN with the mean value. For the other columns, since they are categorical data, use the mode(most frequent value)"
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        "_imputer_mean = SimpleImputer(missing_values=np.nan, strategy=\"mean\", add_indicator=True )\n",
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        "_imputer_mean.fit(_age_col)\n",
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        "\n",
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              "   BI-Rads   Age  Shape  Margin  Density  Severity\n",
              "0      5.0  67.0    3.0     5.0      3.0         1\n",
              "1      4.0  43.0    1.0     1.0      NaN         1\n",
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        "_imputer_mode = SimpleImputer(missing_values = np.nan , strategy=\"most_frequent\", add_indicator=True)\n",
        "\n",
        "_shape_col = np.array(_dataset['Shape']).reshape(-1,1)\n",
        "_imputer_mode.fit(_shape_col)\n",
        "_shape_preproc = _imputer_mode.transform(_shape_col)\n",
        "_dataset['Shape'] = _shape_preproc\n",
        "\n",
        "_margin_col = np.array(_dataset['Margin']).reshape(-1,1)\n",
        "_imputer_mode.fit(_margin_col)\n",
        "_margin_preproc = _imputer_mode.transform(_margin_col)\n",
        "_dataset['Margin'] = _margin_preproc\n",
        "\n",
        "_density_col = np.array(_dataset['Density']).reshape(-1,1)\n",
        "_imputer_mode.fit(_density_col)\n",
        "_density_preproc = _imputer_mode.transform(_density_col)\n",
        "_dataset['Density'] = _density_preproc\n",
        "\n",
        "_dataset.head()"
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              "   BI-Rads   Age  Shape  Margin  Density  Severity\n",
              "0      5.0  67.0    3.0     5.0      3.0         1\n",
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          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Split the dataset into train and test"
      ],
      "metadata": {
        "id": "KudvbMbM3vNe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = _dataset.iloc[: , 1:-1]\n",
        "Y = _dataset.iloc[:,-1]\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)\n",
        "\n",
        "_scaler = preprocessing.StandardScaler()\n",
        "_scaler.fit_transform(X_train[['Age', 'Shape', 'Density', 'Margin']])\n",
        "_scaler.fit_transform(X_test[['Age', 'Shape', 'Density', 'Margin']])"
      ],
      "metadata": {
        "id": "CFKKJ23931yu",
        "outputId": "27ff1f88-4c02-4889-816a-8625f83e40c7",
        "colab": {
          "base_uri": "https://localhost:8080/"
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      },
      "execution_count": 39,
      "outputs": [
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              "       [-0.68481894,  0.93404422,  0.3230796 ,  0.80937073],\n",
              "       [-1.96217574, -0.68273187,  0.3230796 , -1.1191299 ],\n",
              "       [-1.53639014, -1.49111991,  0.3230796 , -1.1191299 ],\n",
              "       [-0.54289041,  0.12565617,  0.3230796 , -1.1191299 ],\n",
              "       [-0.11710481,  0.93404422,  0.3230796 ,  1.45220428],\n",
              "       [-1.46542587, -0.68273187,  0.3230796 , -1.1191299 ],\n",
              "       [ 0.37964506,  0.12565617,  0.3230796 , -1.1191299 ],\n",
              "       [-0.11710481, -0.68273187,  0.3230796 , -1.1191299 ],\n",
              "       [ 0.30868079, -0.68273187,  0.3230796 , -1.1191299 ],\n",
              "       [ 0.73446639,  0.12565617,  0.3230796 ,  0.16653719],\n",
              "       [-0.75578321, -1.49111991,  0.3230796 ,  0.80937073],\n",
              "       [-0.75578321, -0.68273187,  0.3230796 , -1.1191299 ],\n",
              "       [ 0.73446639,  0.93404422,  0.3230796 ,  0.80937073],\n",
              "       [-1.46542587,  0.93404422, -2.38797963,  1.45220428],\n",
              "       [ 0.45060932,  0.93404422,  0.3230796 ,  0.80937073],\n",
              "       [ 0.02482372,  0.93404422,  0.3230796 ,  1.45220428],\n",
              "       [ 1.16025199,  0.93404422,  0.3230796 ,  0.80937073],\n",
              "       [ 0.80543066, -1.49111991,  0.3230796 , -1.1191299 ],\n",
              "       [ 0.02482372,  0.93404422,  0.3230796 , -1.1191299 ],\n",
              "       [-0.54289041, -0.68273187,  0.3230796 , -1.1191299 ],\n",
              "       [-1.53639014,  0.93404422,  0.3230796 ,  0.80937073],\n",
              "       [ 1.79893039,  0.93404422,  0.3230796 ,  0.80937073],\n",
              "       [-1.32349734, -0.68273187,  0.3230796 ,  0.16653719],\n",
              "       [ 0.37964506,  0.93404422, -2.38797963,  0.16653719],\n",
              "       [ 0.52157359,  0.12565617,  0.3230796 ,  0.16653719],\n",
              "       [-1.60735441, -1.49111991,  0.3230796 , -1.1191299 ],\n",
              "       [ 1.30218052,  0.93404422,  0.3230796 ,  1.45220428],\n",
              "       [-1.39446161, -0.68273187,  0.3230796 , -1.1191299 ],\n",
              "       [ 2.72146585, -1.49111991,  0.3230796 ,  1.45220428],\n",
              "       [ 0.45060932,  0.93404422,  0.3230796 ,  0.16653719],\n",
              "       [-0.82674747,  0.93404422,  0.3230796 ,  0.16653719],\n",
              "       [-2.10410427, -0.68273187,  0.3230796 , -1.1191299 ],\n",
              "       [ 0.02482372, -0.68273187,  0.3230796 , -1.1191299 ],\n",
              "       [ 0.02482372, -1.49111991,  0.3230796 , -1.1191299 ],\n",
              "       [-0.96867601, -0.68273187,  0.3230796 , -1.1191299 ],\n",
              "       [ 0.23771652, -1.49111991,  0.3230796 ,  1.45220428],\n",
              "       [-0.96867601, -0.68273187,  0.3230796 , -1.1191299 ],\n",
              "       [ 1.16025199,  0.93404422,  0.3230796 ,  0.16653719],\n",
              "       [-0.25903334, -0.68273187,  0.3230796 ,  0.80937073],\n",
              "       [-0.61385467, -1.49111991, -5.09903886, -1.1191299 ],\n",
              "       [ 1.16025199,  0.93404422,  0.3230796 ,  0.80937073],\n",
              "       [ 1.16025199, -1.49111991,  0.3230796 , -1.1191299 ],\n",
              "       [ 0.87639492,  0.93404422,  0.3230796 ,  1.45220428],\n",
              "       [ 0.30868079, -0.68273187,  0.3230796 , -1.1191299 ],\n",
              "       [-0.82674747, -0.68273187,  0.3230796 , -1.1191299 ],\n",
              "       [-2.2460328 , -1.49111991,  0.3230796 , -1.1191299 ],\n",
              "       [-0.47192614, -1.49111991,  0.3230796 , -1.1191299 ],\n",
              "       [-1.11060454, -1.49111991,  0.3230796 , -1.1191299 ],\n",
              "       [ 1.30218052,  0.93404422,  0.3230796 ,  1.45220428],\n",
              "       [-1.67831867, -0.68273187,  0.3230796 , -1.1191299 ],\n",
              "       [-0.18806908,  0.93404422,  0.3230796 ,  0.80937073],\n",
              "       [ 0.05941509,  0.93404422,  0.3230796 ,  0.16653719],\n",
              "       [-1.96217574, -0.68273187,  0.3230796 , -1.1191299 ],\n",
              "       [-1.11060454, -1.49111991, -2.38797963, -1.1191299 ],\n",
              "       [-0.04614054,  0.93404422,  0.3230796 ,  0.80937073],\n",
              "       [ 0.73446639, -0.68273187, -2.38797963,  0.16653719],\n",
              "       [ 0.23771652,  0.93404422,  0.3230796 ,  0.80937073]])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Logistic Regression Model"
      ],
      "metadata": {
        "id": "CSTfgPJh6Re7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train the model"
      ],
      "metadata": {
        "id": "iUt-xHSIksHG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "_logistic_regressor = LogisticRegression().fit(X_train, Y_train)\n",
        "_logistic_regressor.score(X_train, Y_train)"
      ],
      "metadata": {
        "id": "5QGO0wkE6V-R",
        "outputId": "c85ff1fd-3afd-4034-b624-fc10173f5582",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7916666666666666"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate the Logistic Regressor"
      ],
      "metadata": {
        "id": "BpZj55yAkwJN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred_lr = _logistic_regressor.predict(X_test)\n",
        "_logistic_regressor.score(X_test, Y_test)"
      ],
      "metadata": {
        "id": "F89babcU80h3",
        "outputId": "e85e6722-725d-4bbc-871a-99ba102e3a3d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8134715025906736"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "ConfusionMatrixDisplay.from_predictions(Y_test, Y_pred_lr)"
      ],
      "metadata": {
        "id": "puKiOnan87YA",
        "outputId": "8f8fb865-0be0-440d-d236-ac6be7fc8db0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fc3db3d74d0>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Random Forest Model"
      ],
      "metadata": {
        "id": "YdNkhCs_k34P"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train the model"
      ],
      "metadata": {
        "id": "wvj30nljnL7U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "_random_forest = RandomForestClassifier(criterion = \"entropy\", random_state=0)\n",
        "_random_forest.fit(X_train, Y_train)\n"
      ],
      "metadata": {
        "id": "6e4e3-Lak7it",
        "outputId": "47a652dc-8cb0-49f1-ae21-2686e991f116",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(criterion='entropy', random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Test the Random Forest"
      ],
      "metadata": {
        "id": "9GpIfbm-nO_0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred_rf = _random_forest.predict(X_test)\n",
        "_random_forest.score(X_test, Y_test)"
      ],
      "metadata": {
        "id": "i4wLIRdHlpa2",
        "outputId": "6e79531b-d063-4285-9f0d-0ec2c18adef9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7772020725388601"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "ConfusionMatrixDisplay.from_predictions(Y_test, Y_pred_rf)"
      ],
      "metadata": {
        "id": "3aubL5IQmBDN",
        "outputId": "67ea296d-cca0-4aa3-eff6-24ce9dd22fed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fc3db373f50>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Multi-Layer Perceptron"
      ],
      "metadata": {
        "id": "QwsmLnxx5qNy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow import keras \n",
        "import tensorflow as tf\n",
        "from keras.layers import Dense, Dropout\n",
        "from keras.models import Sequential\n",
        "\n",
        "_model = Sequential()\n",
        "_model.add(Dense(4, input_dim=4, activation='relu', kernel_initializer='normal' ))\n",
        "_model.add(Dense(100, activation='relu', kernel_initializer='normal' ))\n",
        "_model.add(Dense(50, activation='relu', kernel_initializer='normal' ))\n",
        "_model.add(Dense(1,kernel_initializer='normal', activation='sigmoid'))\n",
        "\n",
        "_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=[\"accuracy\"])\n",
        "\n",
        "_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tctmpw0K5uJm",
        "outputId": "1b419d20-e4e5-4e26-cf0b-ad0b47690f28"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_18\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_52 (Dense)            (None, 4)                 20        \n",
            "                                                                 \n",
            " dense_53 (Dense)            (None, 100)               500       \n",
            "                                                                 \n",
            " dense_54 (Dense)            (None, 50)                5050      \n",
            "                                                                 \n",
            " dense_55 (Dense)            (None, 1)                 51        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5,621\n",
            "Trainable params: 5,621\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train the model"
      ],
      "metadata": {
        "id": "5yGNoNv9BOz_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "_history = _model.fit(X_train, Y_train, epochs = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ExU9cpl__3yK",
        "outputId": "3add25d2-f9ac-4061-b7a7-bde572c7b39e"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "24/24 [==============================] - 1s 2ms/step - loss: 0.6926 - accuracy: 0.5404\n",
            "Epoch 2/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.6907 - accuracy: 0.6198\n",
            "Epoch 3/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.6844 - accuracy: 0.6654\n",
            "Epoch 4/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.6649 - accuracy: 0.7266\n",
            "Epoch 5/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.6192 - accuracy: 0.7708\n",
            "Epoch 6/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.5533 - accuracy: 0.7591\n",
            "Epoch 7/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.5091 - accuracy: 0.7786\n",
            "Epoch 8/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.5226 - accuracy: 0.7604\n",
            "Epoch 9/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.4917 - accuracy: 0.7799\n",
            "Epoch 10/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4852 - accuracy: 0.7852\n",
            "Epoch 11/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4908 - accuracy: 0.7826\n",
            "Epoch 12/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.4947 - accuracy: 0.7839\n",
            "Epoch 13/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4812 - accuracy: 0.7760\n",
            "Epoch 14/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4795 - accuracy: 0.7747\n",
            "Epoch 15/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.4809 - accuracy: 0.7826\n",
            "Epoch 16/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4824 - accuracy: 0.7734\n",
            "Epoch 17/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.4844 - accuracy: 0.7839\n",
            "Epoch 18/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4970 - accuracy: 0.7721\n",
            "Epoch 19/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4794 - accuracy: 0.7878\n",
            "Epoch 20/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4793 - accuracy: 0.7786\n",
            "Epoch 21/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4780 - accuracy: 0.7760\n",
            "Epoch 22/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4823 - accuracy: 0.7773\n",
            "Epoch 23/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4774 - accuracy: 0.7878\n",
            "Epoch 24/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4756 - accuracy: 0.7786\n",
            "Epoch 25/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.4744 - accuracy: 0.7760\n",
            "Epoch 26/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4860 - accuracy: 0.7852\n",
            "Epoch 27/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4744 - accuracy: 0.7682\n",
            "Epoch 28/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4776 - accuracy: 0.7760\n",
            "Epoch 29/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4744 - accuracy: 0.7747\n",
            "Epoch 30/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4707 - accuracy: 0.7760\n",
            "Epoch 31/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4845 - accuracy: 0.7682\n",
            "Epoch 32/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4883 - accuracy: 0.7734\n",
            "Epoch 33/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4827 - accuracy: 0.7760\n",
            "Epoch 34/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4752 - accuracy: 0.7734\n",
            "Epoch 35/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4722 - accuracy: 0.7773\n",
            "Epoch 36/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4775 - accuracy: 0.7839\n",
            "Epoch 37/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4768 - accuracy: 0.7695\n",
            "Epoch 38/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4797 - accuracy: 0.7695\n",
            "Epoch 39/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4767 - accuracy: 0.7799\n",
            "Epoch 40/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4771 - accuracy: 0.7760\n",
            "Epoch 41/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4718 - accuracy: 0.7786\n",
            "Epoch 42/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4809 - accuracy: 0.7799\n",
            "Epoch 43/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4757 - accuracy: 0.7721\n",
            "Epoch 44/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4797 - accuracy: 0.7852\n",
            "Epoch 45/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4752 - accuracy: 0.7839\n",
            "Epoch 46/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4840 - accuracy: 0.7826\n",
            "Epoch 47/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4764 - accuracy: 0.7812\n",
            "Epoch 48/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4731 - accuracy: 0.7747\n",
            "Epoch 49/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4859 - accuracy: 0.7734\n",
            "Epoch 50/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4826 - accuracy: 0.7812\n",
            "Epoch 51/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4799 - accuracy: 0.7812\n",
            "Epoch 52/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4731 - accuracy: 0.7734\n",
            "Epoch 53/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4736 - accuracy: 0.7760\n",
            "Epoch 54/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4724 - accuracy: 0.7747\n",
            "Epoch 55/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4738 - accuracy: 0.7839\n",
            "Epoch 56/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4748 - accuracy: 0.7865\n",
            "Epoch 57/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4759 - accuracy: 0.7734\n",
            "Epoch 58/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4848 - accuracy: 0.7747\n",
            "Epoch 59/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4760 - accuracy: 0.7773\n",
            "Epoch 60/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4783 - accuracy: 0.7773\n",
            "Epoch 61/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4774 - accuracy: 0.7799\n",
            "Epoch 62/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4753 - accuracy: 0.7878\n",
            "Epoch 63/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4739 - accuracy: 0.7747\n",
            "Epoch 64/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4762 - accuracy: 0.7812\n",
            "Epoch 65/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4717 - accuracy: 0.7734\n",
            "Epoch 66/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4732 - accuracy: 0.7760\n",
            "Epoch 67/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4722 - accuracy: 0.7708\n",
            "Epoch 68/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4772 - accuracy: 0.7747\n",
            "Epoch 69/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4712 - accuracy: 0.7734\n",
            "Epoch 70/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4752 - accuracy: 0.7721\n",
            "Epoch 71/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.4704 - accuracy: 0.7799\n",
            "Epoch 72/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4730 - accuracy: 0.7708\n",
            "Epoch 73/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4739 - accuracy: 0.7773\n",
            "Epoch 74/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4695 - accuracy: 0.7773\n",
            "Epoch 75/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4731 - accuracy: 0.7839\n",
            "Epoch 76/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4739 - accuracy: 0.7786\n",
            "Epoch 77/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4720 - accuracy: 0.7852\n",
            "Epoch 78/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4697 - accuracy: 0.7786\n",
            "Epoch 79/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4692 - accuracy: 0.7786\n",
            "Epoch 80/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4727 - accuracy: 0.7708\n",
            "Epoch 81/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4735 - accuracy: 0.7878\n",
            "Epoch 82/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4766 - accuracy: 0.7604\n",
            "Epoch 83/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4740 - accuracy: 0.7734\n",
            "Epoch 84/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4804 - accuracy: 0.7734\n",
            "Epoch 85/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4765 - accuracy: 0.7760\n",
            "Epoch 86/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4738 - accuracy: 0.7786\n",
            "Epoch 87/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4711 - accuracy: 0.7773\n",
            "Epoch 88/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4684 - accuracy: 0.7760\n",
            "Epoch 89/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.4837 - accuracy: 0.7839\n",
            "Epoch 90/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.4666 - accuracy: 0.7865\n",
            "Epoch 91/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4851 - accuracy: 0.7747\n",
            "Epoch 92/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4767 - accuracy: 0.7786\n",
            "Epoch 93/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4724 - accuracy: 0.7747\n",
            "Epoch 94/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4692 - accuracy: 0.7839\n",
            "Epoch 95/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4783 - accuracy: 0.7812\n",
            "Epoch 96/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4870 - accuracy: 0.7760\n",
            "Epoch 97/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4723 - accuracy: 0.7760\n",
            "Epoch 98/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4687 - accuracy: 0.7760\n",
            "Epoch 99/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4740 - accuracy: 0.7812\n",
            "Epoch 100/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4704 - accuracy: 0.7747\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(_history.history['accuracy'])\n",
        "plt.title('Training Accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "2Re3cEHXBpGW",
        "outputId": "8aeba194-4cac-4fe2-d4cf-093d7e14ab3c"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(_history.history['loss'])\n",
        "plt.title('Training Loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "RM8vnMvXCsRY",
        "outputId": "cf97c0d6-e663-4477-bcf2-f56c5a8e4942"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate the ANN"
      ],
      "metadata": {
        "id": "xuzMtNE5BSP1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "_test_loss, _test_acc = _model.evaluate(X_test, Y_test)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tVxYp4bHAMqF",
        "outputId": "a28ef1c2-605c-4edc-aa38-c02f8661102f"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 3ms/step - loss: 0.4332 - accuracy: 0.7979\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred_ann = _model.predict(X_test)\n",
        "Y_pred_ann"
      ],
      "metadata": {
        "id": "V75FyeN390vn",
        "outputId": "8f125300-2b2d-4a2b-98af-af0165075d04",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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}