mlessentials/Lab07/Exercise7.06/Exercise7_06.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
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   "source": [
    "# LogisticRegression with Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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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.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "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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   "source": [
    "# separate features and labels DataFrames\n",
    "features = _df.drop(['car'], axis=1).values\n",
    "labels = _df[['car']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_uICHT5Np0Mr"
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   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegressionCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   "outputs": [],
   "source": [
    "model = LogisticRegressionCV(max_iter=2000, multi_class='auto', cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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    "outputId": "fcc92880-4dbf-4424-f8ba-ebc0c102e24a"
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   "outputs": [],
   "source": [
    "model.fit(features, labels.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "id": "IIy9tvVEp0M2",
    "outputId": "c66b57c9-9bbc-48dc-96e7-464944a2199e"
   },
   "outputs": [],
   "source": [
    "print(model.score(features, labels.ravel()))"
   ]
  }
 ],
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