{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9IIZwgmoUrmb"
   },
   "source": [
    "# Import and Split Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bqo23ud-Urme"
   },
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "colab_type": "code",
    "id": "os1H7RpwUrms",
    "outputId": "0794006a-0b2a-48b5-9191-7ac0f9b086cf"
   },
   "outputs": [],
   "source": [
    "# data doesn't have headers, so let's create headers\n",
    "_headers = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'car']\n",
    "# read in cars dataset\n",
    "df = pd.read_csv('../Dataset/car.data', names=_headers, index_col=None)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1tu3JxwoUrmz"
   },
   "outputs": [],
   "source": [
    "#split the data into 80% for training and 20% for evaluation\n",
    "training_df, eval_df = train_test_split(df, train_size=0.8, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "colab_type": "code",
    "id": "oEkV14eBUrm5",
    "outputId": "b04aae52-a6d7-4c13-8f00-e6e208aa9220"
   },
   "outputs": [],
   "source": [
    "training_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "colab_type": "code",
    "id": "cH8g00PnUrm_",
    "outputId": "a3f5230e-2f55-4444-f558-28ac1d458a91"
   },
   "outputs": [],
   "source": [
    "eval_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rOkZg2K8UrnE"
   },
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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