Logistic_regression_day_4-binary_classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Logistic regression day 4",
      "provenance": [],
      "toc_visible": true,
      "mount_file_id": "1iwyCeP6oVz115lXvRFFtLqHGQlwx_rfR",
      "authorship_tag": "ABX9TyN/YYRnccZ+kR5LHK6yaYTw",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/zohaib304/MachineLearning/blob/main/Logistic_regression_day_4-binary_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "44nucbyt0Rss"
      },
      "source": [
        "# Logistic Regression Binary Classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9qDgu-0L0RF4"
      },
      "source": [
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EGvuPUbU0dc9"
      },
      "source": [
        "# load the data\r\n",
        "data = pd.read_csv('/content/drive/MyDrive/Dataset/logistic_data.csv')\r\n"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "jSFxL0lF1Aig",
        "outputId": "ef38c889-a71b-42ad-c567-bc488866fcfb"
      },
      "source": [
        "# let's have look at first 5 rows\r\n",
        "data.head()\r\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>bought_insurance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  bought_insurance\n",
              "0   22                 0\n",
              "1   25                 0\n",
              "2   47                 1\n",
              "3   52                 0\n",
              "4   46                 1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CBYt9os-1KLY",
        "outputId": "e1687b7f-dee2-4d37-8e94-f4100c9d4576"
      },
      "source": [
        "# Dimension of the dataset\r\n",
        "data.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(27, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uTqohUL61PNv"
      },
      "source": [
        "# Splitting the data set\r\n",
        "X = data.age\r\n",
        "\r\n",
        "y= data.bought_insurance"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "OGrOf1Hs1g9i",
        "outputId": "13735ab3-b69c-41b5-8703-0c7be3be16c4"
      },
      "source": [
        "plt.scatter(X, y, marker='*')\r\n",
        "plt.xlabel('Age')\r\n",
        "plt.ylabel('Bought Insurance')\r\n",
        "plt.title('Age and Insurance')\r\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dquVfAEa2FKS"
      },
      "source": [
        "#Splitting the dataset into test set and training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Os-sONlN10ln"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\r\n",
        "X_train, X_test, y_train, y_test = train_test_split(data[['age']], y, test_size = 0.2, random_state = 42)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PZIfTx602qqA"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\r\n",
        "\r\n",
        "logr = LogisticRegression()"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pjE3tBOW3Kod",
        "outputId": "84cb639a-d6b0-4986-d478-6a114014a27f"
      },
      "source": [
        "logr.fit(X_train, y_train)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ERaCf06A3RPx"
      },
      "source": [
        "y_predict = logr.predict(X_test)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ndYKLrvO3r9A",
        "outputId": "3cc9e882-76e8-4453-eb24-886357ed4f3b"
      },
      "source": [
        "logr.predict_proba(X_test)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.05428681, 0.94571319],\n",
              "       [0.70555426, 0.29444574],\n",
              "       [0.06039343, 0.93960657],\n",
              "       [0.77085061, 0.22914939],\n",
              "       [0.84096546, 0.15903454],\n",
              "       [0.72848825, 0.27151175]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SwLmeP77330n",
        "outputId": "34844026-3a27-4e1d-9eb2-ef5b6612fa6e"
      },
      "source": [
        "logr.score(X_test, y_test)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xvBDpdfx391b",
        "outputId": "c7e145a4-ffa6-4217-ad5e-26d31b1610b6"
      },
      "source": [
        "logr.predict([[5]])"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3r7fWbxl4aGz"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}