fcc_predict_health_costs_with_regression.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1rRo8oNqZ-Rj"
      },
      "outputs": [],
      "source": [
        "# Import libraries. You may or may not use all of these.\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"
      ]
    },
    {
      "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')\n",
        "dataset.tail()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset.shape)"
      ],
      "metadata": {
        "id": "pmN3P0vXJNn1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LcopvQh3X-kX"
      },
      "outputs": [],
      "source": [
        "len(dataset)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset=pd.get_dummies(dataset,columns=dataset.columns[[1]])\n",
        "#dataset.drop(dataset.columns[[6]],axis=1,inplace=True)\n",
        "dataset.head()"
      ],
      "metadata": {
        "id": "YjtNAKlBK18n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.drop(dataset.columns[[6]],axis=1,inplace=True)\n",
        "dataset.head()"
      ],
      "metadata": {
        "id": "thd3kenIT5A6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset=pd.get_dummies(dataset,columns=dataset.columns[[3]])\n",
        "#dataset.drop(dataset.columns[[6]],axis=1,inplace=True)\n",
        "dataset.head()"
      ],
      "metadata": {
        "id": "H2wDj6EUUNfF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.drop(dataset.columns[[6]],axis=1,inplace=True)\n",
        "dataset.head()"
      ],
      "metadata": {
        "id": "nGd3z1usUvFj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.drop(dataset.columns[[3]],axis=1,inplace=True)\n",
        "dataset.head()"
      ],
      "metadata": {
        "id": "yUpFvP7YU2KJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(dataset), len(target))\n"
      ],
      "metadata": {
        "id": "tvPeg2uRfBWO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Assuming 'dataset' is your DataFrame\n",
        "target_column_index = 3  # index of the column you want as the target\n",
        "\n",
        "labels = dataset.iloc[:, target_column_index]  # Extract the target column\n",
        "features = dataset.drop(dataset.columns[target_column_index], axis=1)  # Drop the target column from features\n",
        "\n",
        "# Split the data\n",
        "train_dataset, test_dataset, train_labels, test_labels = train_test_split(features, target, test_size=0.2)\n",
        "\n",
        "# Now 'train_target' and 'test_target' are your target variables, and 'train_dataset' and 'test_dataset' are your features\n"
      ],
      "metadata": {
        "id": "cIhnWok3WQcK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(train_labels))"
      ],
      "metadata": {
        "id": "N1dQext9kh2M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(train_dataset))"
      ],
      "metadata": {
        "id": "WOZQmt_cgW_U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "normalizer = layers.experimental.preprocessing.Normalization(axis=-1)\n",
        "normalizer.adapt(np.array(train_dataset))"
      ],
      "metadata": {
        "id": "9hKZJnvzlCPn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "normalizer"
      ],
      "metadata": {
        "id": "1ZfuPMFAlCaA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential([\n",
        "                          normalizer,\n",
        "                          layers.Dense(2),\n",
        "                          layers.Dense(1)\n",
        "])\n",
        "\n",
        "model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.1),\n",
        "              loss='mae',\n",
        "              metrics=['mae', 'mse'])\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "gIAEuKuLlJSG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_dataset, train_labels, epochs=150, validation_split=0.2)"
      ],
      "metadata": {
        "id": "hMKp-U4NlJZJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xe7RXH3N3CWU"
      },
      "outputs": [],
      "source": [
        "# RUN THIS CELL TO TEST YOUR MODEL. DO NOT MODIFY CONTENTS.\n",
        "# Test 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)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tVThHhqBT4FD"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "fcc_predict_health_costs_with_regression.ipynb",
      "private_outputs": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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