51_Train_Test_Evaluation_with_Naive_Data_Preparation.ipynb

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  "metadata": {
    "colab": {
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      "authorship_tag": "ABX9TyNEMDmcu1q5BDuGdFS4YRW1",
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    "kernelspec": {
      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bahiensed/ml/blob/main/51_Train_Test_Evaluation_with_Naive_Data_Preparation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# naive approach to normalizing the data before splitting the data and evaluating the model\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "fSuE-N2Wmtys"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s5hxdX7Ql48V"
      },
      "outputs": [],
      "source": [
        "# define dataset\n",
        "X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5,\n",
        "random_state=7)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# explore data\n"
      ],
      "metadata": {
        "id": "y-FYhr4fpEur"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# define the scaler\n",
        "scaler = MinMaxScaler()"
      ],
      "metadata": {
        "id": "nkZAbTNQmX__"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# naively fit on the complete dataset\n",
        "X = scaler.fit_transform(X)"
      ],
      "metadata": {
        "id": "0Z2qIdM6pqhO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# split into train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)"
      ],
      "metadata": {
        "id": "TttxllylmZBX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# fit the model\n",
        "model = LogisticRegression()\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HcFAvi_zmdwL",
        "outputId": "24401158-fb56-4c17-fcfc-cdcdffed29c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate the model\n",
        "yhat = model.predict(X_test)\n",
        "\n",
        "# evaluate predictions\n",
        "accuracy = accuracy_score(y_test, yhat)\n",
        "\n",
        "print('Accuracy: %.3f' % (accuracy*100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8yk_6ztLnJ71",
        "outputId": "ed5117a8-7137-4b07-ff85-eb8a7b5a6e26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 84.848\n"
          ]
        }
      ]
    }
  ]
}