mlessentials/Lab06/Exercise6.10/Exercise6_10.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Compute the Accuracy Score of the  Classification Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "colab": {},
    "colab_type": "code",
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   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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   "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.head()\n",
    "\n",
    "# target column is 'car'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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    "colab_type": "code",
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    "outputId": "6fb93d35-62ab-4260-dc2b-56d1fc249d97"
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   "outputs": [],
   "source": [
    "# encode categorical variables\n",
    "_df = pd.get_dummies(df, columns=['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety'])\n",
    "_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
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    "id": "dyqJOifIFbId"
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   "source": [
    "# target column is 'car'\n",
    "\n",
    "features = _df.drop(['car'], axis=1).values\n",
    "labels = _df[['car']].values\n",
    "\n",
    "# split 80% for training and 20% into an evaluation set\n",
    "X_train, X_eval, y_train, y_eval = train_test_split(features, labels, test_size=0.3, random_state=0)\n",
    "\n",
    "# further split the evaluation set into validation and test sets of 10% each\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_eval, y_eval, test_size=0.5, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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    },
    "colab_type": "code",
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    "outputId": "f3cedafd-7544-4dc7-ae9e-bf8ac2ff2ef4"
   },
   "outputs": [],
   "source": [
    "# train a Logistic Regression model\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   "outputs": [],
   "source": [
    "# make predictions for the validation dataset\n",
    "y_pred = model.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EHQwM9V1FbIj"
   },
   "outputs": [],
   "source": [
    "# import necessary library\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "colab_type": "code",
    "id": "4qyAmnw7FbIl",
    "outputId": "752f9f1c-1879-44fe-a9e5-1c6b8df2788f"
   },
   "outputs": [],
   "source": [
    "_accuracy = accuracy_score(y_val, y_pred)\n",
    "print(_accuracy)"
   ]
  }
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