Breast_Cancer_logistic_regression.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Breast Cancer logistic regression.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "qixj155aezzo"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "dataset = pd.read_csv('Breast Cancer Detection.csv')\n",
        "X = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)\n",
        "\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.fit_transform(X_test)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FXF6C-11fJTq",
        "outputId": "16919f95-f3b1-4f38-afe6-da91b0260c6b"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "classifier = LogisticRegression(random_state = 21)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=21)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1LGYqlLWfJi7"
      },
      "source": [
        "y_pred= classifier.predict(X_test)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D7d6YMjzfOwW",
        "outputId": "6b5d595a-01c8-4e80-a0e2-c17d32932ff3"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm= confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[108   0]\n",
            " [  3  60]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9824561403508771"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    }
  ]
}