Machine Learning/Health Costs Predictor with Regression.ipynb

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        "<a href=\"https://colab.research.google.com/github/noamsiegel/Projects/blob/main/Health%20Costs%20Predictor%20with%20Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1rRo8oNqZ-Rj"
      },
      "outputs": [],
      "source": [
        "# Import libraries\n",
        "!pip install -q git+https://github.com/tensorflow/docs\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# try:\n",
        "#   # %tensorflow_version only exists in Colab.\n",
        "#   %tensorflow_version 2.x\n",
        "# except Exception:\n",
        "#   pass\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow import keras\n",
        "# from tensorflow.keras import layers\n",
        "\n",
        "import tensorflow_docs as tfdocs\n",
        "import tensorflow_docs.plots\n",
        "import tensorflow_docs.modeling\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CiX2FI4gZtTt"
      },
      "outputs": [],
      "source": [
        "# Import data\n",
        "!wget https://cdn.freecodecamp.org/project-data/health-costs/insurance.csv\n",
        "dataset = pd.read_csv('insurance.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LcopvQh3X-kX"
      },
      "outputs": [],
      "source": [
        "# Convert categorical data to numbers\n",
        "dataset = pd.get_dummies(dataset, columns=['sex', 'smoker', 'region'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Uses 80% of the data as the train_dataset\n",
        "# Uses 20% of the data as the test_dataset\n",
        "train_dataset, test_dataset = train_test_split(dataset, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "GoQWONTZfEUR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Separates the labels\n",
        "train_labels = train_dataset.pop('expenses')\n",
        "test_labels = test_dataset.pop('expenses')"
      ],
      "metadata": {
        "id": "I0X9q8DagFZO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Creates the model\n",
        "model = keras.Sequential([\n",
        "  layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),\n",
        "  layers.Dense(64, activation='relu'),\n",
        "  layers.Dense(1)\n",
        "])\n",
        "\n",
        "optimizer = tf.keras.optimizers.RMSprop(0.01)\n",
        "\n",
        "model.compile(loss='mae',\n",
        "              optimizer=optimizer,\n",
        "              metrics=['mae', 'mse'])\n",
        "\n",
        "history = model.fit(\n",
        "  train_dataset, train_labels,\n",
        "  validation_split=0.2,\n",
        "  epochs=100,\n",
        "  batch_size=32,\n",
        "  verbose=0,\n",
        "  callbacks=[tfdocs.modeling.EpochDots()],\n",
        ")"
      ],
      "metadata": {
        "id": "74DVWqDhgNgx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xe7RXH3N3CWU"
      },
      "outputs": [],
      "source": [
        "# Tests model by checking how well the model generalizes using the test set.\n",
        "loss, mae, mse = model.evaluate(test_dataset, test_labels, verbose=2)\n",
        "\n",
        "print(\"Testing set Mean Abs Error: {:5.2f} expenses\".format(mae))\n",
        "\n",
        "if mae < 3500:\n",
        "  print(\"You passed the challenge. Great job!\")\n",
        "else:\n",
        "  print(\"The Mean Abs Error must be less than 3500. Keep trying.\")\n",
        "\n",
        "# Plot predictions.\n",
        "test_predictions = model.predict(test_dataset).flatten()\n",
        "\n",
        "a = plt.axes(aspect='equal')\n",
        "plt.scatter(test_labels, test_predictions)\n",
        "plt.xlabel('True values (expenses)')\n",
        "plt.ylabel('Predictions (expenses)')\n",
        "lims = [0, 50000]\n",
        "plt.xlim(lims)\n",
        "plt.ylim(lims)\n",
        "_ = plt.plot(lims,lims)"
      ]
    }
  ],
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