fcc_predict_health_costs_with_regression_attempt2.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",
        "from sklearn.model_selection import train_test_split\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",
        "from tensorflow.keras.layers import Dense, Activation\n",
        "from tensorflow.keras.models import Sequential\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",
      "execution_count": null,
      "metadata": {
        "id": "LcopvQh3X-kX"
      },
      "outputs": [],
      "source": [
        "dataset = pd.concat([dataset, pd.get_dummies(data = dataset['sex'], prefix = 'sex')], axis=1)\n",
        "dataset.drop(axis = 1, columns = ['sex'], inplace = True)\n",
        "\n",
        "dataset = pd.concat([dataset, pd.get_dummies(data = dataset['region'], prefix = 'region')], axis=1)\n",
        "dataset.drop(axis = 1, columns = ['region'], inplace = True)\n",
        "\n",
        "dataset = pd.concat([dataset, pd.get_dummies(data = dataset['smoker'], prefix = 'smoker')], axis=1)\n",
        "dataset.drop(axis = 1, columns = ['smoker'], inplace = True)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "id": "lNmfwpYzruio"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Seperate the features and the target values\n",
        "x_columns = dataset.columns.drop('expenses')\n",
        "x = dataset[x_columns].values\n",
        "y = dataset['expenses'].values"
      ],
      "metadata": {
        "id": "86SFX2RuoMOn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(64, activation='relu',input_dim=x.shape[1]))\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dense(1))\n",
        "model.compile(loss = 'mean_absolute_error',optimizer ='adam', metrics=['mae', 'mse'])"
      ],
      "metadata": {
        "id": "kQiPnRJooO3q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "callback = keras.callbacks.EarlyStopping(monitor='val_loss', min_delta = 0.1, patience=10, restore_best_weights = True, verbose = 1, mode = \"min\")\n",
        "model.fit(x = x, y = y, epochs=100, validation_split = 0.8)"
      ],
      "metadata": {
        "id": "R40gtwvloRT2"
      },
      "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"
      ]
    }
  ],
  "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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  },
  "nbformat": 4,
  "nbformat_minor": 0
}