neural_network_predictions.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/mmkeyes140/ai_learning/blob/main/neural_network_predictions.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ecj6mkYT1dGC"
      },
      "source": [
        "# Make Predictions with a Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5n1OiHAY1dGF"
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      "source": [
        "# Initial imports\n",
        "import pandas as pd\n",
        "from sklearn.datasets import make_blobs\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Keras modules\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pWvoSOl41dGJ"
      },
      "source": [
        "## Data Generation and Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LS3gJykO1dGK"
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      "outputs": [],
      "source": [
        "# Generate 1000 demo data samples with 2 features and two centers\n",
        "X, y = make_blobs(n_samples=1000, centers=2, random_state=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4ZTOJjaK1dGK"
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      "outputs": [],
      "source": [
        "# Transforming y to a vertical vector\n",
        "y = y.reshape(-1, 1)"
      ]
    },
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              "   Feature 1  Feature 2  Target\n",
              "0  -3.058373   4.488258       0\n",
              "1  -8.609739  -3.727149       1\n",
              "2   1.371297   5.231074       0\n",
              "3  -9.339176  -2.954447       1\n",
              "4 -11.571786  -3.852755       1"
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          "metadata": {},
          "execution_count": 5
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      ],
      "source": [
        "# Creating a DataFrame with the dummy data\n",
        "df = pd.DataFrame(X, columns=[\"Feature 1\", \"Feature 2\"])\n",
        "df['Target'] = y\n",
        "\n",
        "df.head()"
      ]
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          "data": {
            "text/plain": [
              "<Axes: xlabel='Feature 1', ylabel='Feature 2'>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plotting the dummy data\n",
        "df.plot.scatter(x=\"Feature 1\", y=\"Feature 2\", c=\"Target\", colormap=\"winter\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7W4nYAP-1dGL"
      },
      "outputs": [],
      "source": [
        "# Create training and testing datasets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "g1n6TDBs1dGL",
        "outputId": "27da46f2-4334-4097-ce6e-65874a927f99"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          0         1\n",
              "0  1.347957  1.003797\n",
              "1  0.889623  1.245628\n",
              "2  0.981772  1.102287\n",
              "3 -0.888027 -1.032606\n",
              "4 -1.119879 -1.028622"
            ],
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              "      <td>1.347957</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Create scaler instance\n",
        "scaler = StandardScaler()\n",
        "\n",
        "# Fit the scaler\n",
        "X_scaler = scaler.fit(X_train)\n",
        "\n",
        "# Scale the data\n",
        "X_train = pd.DataFrame(X_scaler.transform(X_train))\n",
        "X_test = pd.DataFrame(X_scaler.transform(X_test))\n",
        "\n",
        "X_test.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3xa9Hxbe1dGL"
      },
      "source": [
        "## Creating a Neural Network Model"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "sKAR293o7HRn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "plxT0x2p1dGL"
      },
      "outputs": [],
      "source": [
        "# Create a sequential neural network model\n",
        "neuron = Sequential()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xBhoYA7W1dGL"
      },
      "outputs": [],
      "source": [
        "# Add the input and the hidden layer to the model\n",
        "number_inputs = 2\n",
        "number_hidden_nodes = 1\n",
        "neuron.add(Dense(units=number_hidden_nodes, activation=\"relu\", input_dim=number_inputs))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dsWjkJTR1dGM"
      },
      "outputs": [],
      "source": [
        "# Add the output layer\n",
        "number_classes = 1\n",
        "neuron.add(Dense(units=number_classes, activation=\"sigmoid\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f2enusvc1dGM",
        "outputId": "d06c7fcb-ddf4-46a2-90bd-3e28f487468c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 1)                 3         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 2         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5 (20.00 Byte)\n",
            "Trainable params: 5 (20.00 Byte)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Display model summary\n",
        "neuron.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a0fC7SFo1dGM"
      },
      "source": [
        "## Compiling a Neural Network Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GGxZlhqY1dGM"
      },
      "outputs": [],
      "source": [
        "# Compile the model\n",
        "neuron.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PUAQKdKH1dGM"
      },
      "source": [
        "## Fitting and Testing a Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a84MQKRf1dGN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "220deaf3-f0b6-4c4f-9b6b-a0196039f7e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "24/24 [==============================] - 1s 2ms/step - loss: 0.5538 - accuracy: 0.9880\n",
            "Epoch 2/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.5398 - accuracy: 0.9920\n",
            "Epoch 3/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.5257 - accuracy: 0.9933\n",
            "Epoch 4/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.5115 - accuracy: 0.9973\n",
            "Epoch 5/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4972 - accuracy: 0.9973\n",
            "Epoch 6/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.4830 - accuracy: 0.9987\n",
            "Epoch 7/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4690 - accuracy: 0.9987\n",
            "Epoch 8/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4551 - accuracy: 0.9987\n",
            "Epoch 9/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.4415 - accuracy: 0.9987\n",
            "Epoch 10/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4282 - accuracy: 1.0000\n",
            "Epoch 11/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.4152 - accuracy: 1.0000\n",
            "Epoch 12/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.4026 - accuracy: 1.0000\n",
            "Epoch 13/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.3905 - accuracy: 1.0000\n",
            "Epoch 14/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.3787 - accuracy: 1.0000\n",
            "Epoch 15/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.3673 - accuracy: 1.0000\n",
            "Epoch 16/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.3564 - accuracy: 1.0000\n",
            "Epoch 17/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.3459 - accuracy: 1.0000\n",
            "Epoch 18/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.3359 - accuracy: 1.0000\n",
            "Epoch 19/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.3262 - accuracy: 1.0000\n",
            "Epoch 20/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.3169 - accuracy: 1.0000\n",
            "Epoch 21/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.3081 - accuracy: 1.0000\n",
            "Epoch 22/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.2996 - accuracy: 1.0000\n",
            "Epoch 23/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.2914 - accuracy: 1.0000\n",
            "Epoch 24/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.2836 - accuracy: 1.0000\n",
            "Epoch 25/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.2761 - accuracy: 1.0000\n",
            "Epoch 26/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.2689 - accuracy: 1.0000\n",
            "Epoch 27/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.2620 - accuracy: 1.0000\n",
            "Epoch 28/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.2553 - accuracy: 1.0000\n",
            "Epoch 29/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.2490 - accuracy: 1.0000\n",
            "Epoch 30/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.2429 - accuracy: 1.0000\n",
            "Epoch 31/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.2370 - accuracy: 1.0000\n",
            "Epoch 32/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.2314 - accuracy: 1.0000\n",
            "Epoch 33/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.2259 - accuracy: 1.0000\n",
            "Epoch 34/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.2206 - accuracy: 1.0000\n",
            "Epoch 35/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.2155 - accuracy: 1.0000\n",
            "Epoch 36/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.2107 - accuracy: 1.0000\n",
            "Epoch 37/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.2059 - accuracy: 1.0000\n",
            "Epoch 38/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.2014 - accuracy: 1.0000\n",
            "Epoch 39/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1970 - accuracy: 1.0000\n",
            "Epoch 40/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1927 - accuracy: 1.0000\n",
            "Epoch 41/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1887 - accuracy: 1.0000\n",
            "Epoch 42/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1847 - accuracy: 1.0000\n",
            "Epoch 43/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1808 - accuracy: 1.0000\n",
            "Epoch 44/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1771 - accuracy: 1.0000\n",
            "Epoch 45/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1735 - accuracy: 1.0000\n",
            "Epoch 46/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1700 - accuracy: 1.0000\n",
            "Epoch 47/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.1666 - accuracy: 1.0000\n",
            "Epoch 48/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1633 - accuracy: 1.0000\n",
            "Epoch 49/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1601 - accuracy: 1.0000\n",
            "Epoch 50/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1570 - accuracy: 1.0000\n",
            "Epoch 51/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1540 - accuracy: 1.0000\n",
            "Epoch 52/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1510 - accuracy: 1.0000\n",
            "Epoch 53/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1481 - accuracy: 1.0000\n",
            "Epoch 54/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1454 - accuracy: 1.0000\n",
            "Epoch 55/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1427 - accuracy: 1.0000\n",
            "Epoch 56/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1400 - accuracy: 1.0000\n",
            "Epoch 57/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1375 - accuracy: 1.0000\n",
            "Epoch 58/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1350 - accuracy: 1.0000\n",
            "Epoch 59/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1325 - accuracy: 1.0000\n",
            "Epoch 60/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1302 - accuracy: 1.0000\n",
            "Epoch 61/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1278 - accuracy: 1.0000\n",
            "Epoch 62/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1256 - accuracy: 1.0000\n",
            "Epoch 63/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1234 - accuracy: 1.0000\n",
            "Epoch 64/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1212 - accuracy: 1.0000\n",
            "Epoch 65/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.1192 - accuracy: 1.0000\n",
            "Epoch 66/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1171 - accuracy: 1.0000\n",
            "Epoch 67/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1151 - accuracy: 1.0000\n",
            "Epoch 68/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1132 - accuracy: 1.0000\n",
            "Epoch 69/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1113 - accuracy: 1.0000\n",
            "Epoch 70/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1094 - accuracy: 1.0000\n",
            "Epoch 71/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1076 - accuracy: 1.0000\n",
            "Epoch 72/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1058 - accuracy: 1.0000\n",
            "Epoch 73/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1041 - accuracy: 1.0000\n",
            "Epoch 74/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1024 - accuracy: 1.0000\n",
            "Epoch 75/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1007 - accuracy: 1.0000\n",
            "Epoch 76/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.0991 - accuracy: 1.0000\n",
            "Epoch 77/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.0975 - accuracy: 1.0000\n",
            "Epoch 78/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.0960 - accuracy: 1.0000\n",
            "Epoch 79/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.0944 - accuracy: 1.0000\n",
            "Epoch 80/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.0929 - accuracy: 1.0000\n",
            "Epoch 81/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0915 - accuracy: 1.0000\n",
            "Epoch 82/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0901 - accuracy: 1.0000\n",
            "Epoch 83/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.0887 - accuracy: 1.0000\n",
            "Epoch 84/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.0873 - accuracy: 1.0000\n",
            "Epoch 85/100\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.0860 - accuracy: 1.0000\n",
            "Epoch 86/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0846 - accuracy: 1.0000\n",
            "Epoch 87/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0834 - accuracy: 1.0000\n",
            "Epoch 88/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0821 - accuracy: 1.0000\n",
            "Epoch 89/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0809 - accuracy: 1.0000\n",
            "Epoch 90/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0797 - accuracy: 1.0000\n",
            "Epoch 91/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0785 - accuracy: 1.0000\n",
            "Epoch 92/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0773 - accuracy: 1.0000\n",
            "Epoch 93/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0762 - accuracy: 1.0000\n",
            "Epoch 94/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.0751 - accuracy: 1.0000\n",
            "Epoch 95/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 0.0740 - accuracy: 1.0000\n",
            "Epoch 96/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0729 - accuracy: 1.0000\n",
            "Epoch 97/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0718 - accuracy: 1.0000\n",
            "Epoch 98/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0708 - accuracy: 1.0000\n",
            "Epoch 99/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0698 - accuracy: 1.0000\n",
            "Epoch 100/100\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0688 - accuracy: 1.0000\n"
          ]
        }
      ],
      "source": [
        "# Fitting the model\n",
        "model = neuron.fit(X_train, y_train, epochs=100)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2z8ONewA1dGN"
      },
      "source": [
        "## Plotting Loss and Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f6yEks0n1dGN"
      },
      "outputs": [],
      "source": [
        "# Create a DataFrame with the history dictionary\n",
        "df = pd.DataFrame(model.history, index=range(1, len(model.history[\"loss\"]) + 1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kX9aArn81dGN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "a62c4a81-3eb7-4115-9355-5f536e057fe5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot the loss\n",
        "df.plot(y=\"loss\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B3O5GRU_1dGN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "22cb8920-0ec6-4e34-9729-2790238f3bff"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot the accuracy\n",
        "df.plot(y=\"accuracy\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "57p2c0vx1dGO"
      },
      "source": [
        "### Evaluating the Model Performance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BCTtLqBx1dGO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c181ae48-4b72-4bf8-c3af-e2b8c9660e47"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 - 0s - loss: 0.0641 - accuracy: 1.0000 - 176ms/epoch - 22ms/step\n",
            "Loss: 0.0640796422958374, Accuracy: 1.0\n"
          ]
        }
      ],
      "source": [
        "# Evaluate the model using testing data\n",
        "model_loss, model_accuracy = neuron.evaluate(X_test, y_test, verbose=2)\n",
        "\n",
        "# Display evaluation results\n",
        "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "tags": [],
        "id": "LptGbFGB1dGO"
      },
      "source": [
        "## Making Predictions with a Neural Network Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w95HFOh81dGO"
      },
      "outputs": [],
      "source": [
        "# Create 10 new samples of dummy data\n",
        "new_X, new_y = make_blobs(n_samples = 10, centers = 2, n_features = 2, random_state = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xQND1eip1dGP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "157fec68-3f09-470c-e304-d4e1892a3c70"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 93ms/step\n",
            "[[1]\n",
            " [1]\n",
            " [1]\n",
            " [1]\n",
            " [0]\n",
            " [0]\n",
            " [0]\n",
            " [1]\n",
            " [0]\n",
            " [0]]\n"
          ]
        }
      ],
      "source": [
        "# Making predictions\n",
        "predictions = (neuron.predict(new_X) > 0.5).astype(\"int32\")\n",
        "print(predictions)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Fkr53hr31dGP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "e932e5c4-19ca-4663-9d14-51ee1dbb37cb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   predictions  actual\n",
              "0            1       1\n",
              "1            1       1\n",
              "2            1       1\n",
              "3            1       1\n",
              "4            0       0\n",
              "5            0       0\n",
              "6            0       0\n",
              "7            1       1\n",
              "8            0       0\n",
              "9            0       0"
            ],
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              "    async function quickchart(key) {\n",
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            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "# Create a DataFrame to compare the predictions with the actual values\n",
        "df_neuron_predict = pd.DataFrame({\"predictions\": predictions.ravel(), \"actual\": new_y})\n",
        "\n",
        "# Display sample data\n",
        "df_neuron_predict.head(10)"
      ]
    }
  ],
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    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
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      "pygments_lexer": "ipython3",
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}