Logistic_Regression_.ipynb

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    "colab": {
      "name": "Logistic Regression (1).ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/harshchan/AI-ML-python-codes/blob/main/Logistic_Regression_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KAG33Kk1iucs"
      },
      "source": [
        "## Import Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eqgM0fwgiucx"
      },
      "source": [
        "#Load the data from scikit\n",
        "#no need to import data we are using a sample dataset from scikit \n",
        "from sklearn.datasets import load_iris\n",
        "data = load_iris()"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1O9rFox_iucy",
        "outputId": "c7989a55-2f7b-4a61-8109-fb1b9b0f9441"
      },
      "source": [
        "#Look at the information of data\n",
        "#description printing\n",
        "#class is output\n",
        "#multi class classififcation\n",
        "#features/predictors are 4 sepal length,sepal width,petal length , petal width\n",
        "print(data.DESCR)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ".. _iris_dataset:\n",
            "\n",
            "Iris plants dataset\n",
            "--------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 150 (50 in each of three classes)\n",
            "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
            "    :Attribute Information:\n",
            "        - sepal length in cm\n",
            "        - sepal width in cm\n",
            "        - petal length in cm\n",
            "        - petal width in cm\n",
            "        - class:\n",
            "                - Iris-Setosa\n",
            "                - Iris-Versicolour\n",
            "                - Iris-Virginica\n",
            "                \n",
            "    :Summary Statistics:\n",
            "\n",
            "    ============== ==== ==== ======= ===== ====================\n",
            "                    Min  Max   Mean    SD   Class Correlation\n",
            "    ============== ==== ==== ======= ===== ====================\n",
            "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
            "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
            "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
            "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
            "    ============== ==== ==== ======= ===== ====================\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "    :Class Distribution: 33.3% for each of 3 classes.\n",
            "    :Creator: R.A. Fisher\n",
            "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
            "    :Date: July, 1988\n",
            "\n",
            "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
            "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
            "Machine Learning Repository, which has two wrong data points.\n",
            "\n",
            "This is perhaps the best known database to be found in the\n",
            "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
            "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
            "data set contains 3 classes of 50 instances each, where each class refers to a\n",
            "type of iris plant.  One class is linearly separable from the other 2; the\n",
            "latter are NOT linearly separable from each other.\n",
            "\n",
            ".. topic:: References\n",
            "\n",
            "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
            "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
            "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
            "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
            "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
            "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
            "     Structure and Classification Rule for Recognition in Partially Exposed\n",
            "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
            "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
            "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
            "     on Information Theory, May 1972, 431-433.\n",
            "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
            "     conceptual clustering system finds 3 classes in the data.\n",
            "   - Many, many more ...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yg-ylFJgiucz",
        "outputId": "7708ca67-b7d4-4ec0-8c99-fb266ff602f3"
      },
      "source": [
        "#If want to convert this data from scikit in pandas dataframe\n",
        "# import numpy as np\n",
        "# import pandas as pd\n",
        "# data1 = pd.DataFrame(data= np.c_[data['data'], data['target']],\n",
        "#                      columns= data['feature_names'] + ['target'])\n",
        "# data1.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
              "0                5.1               3.5                1.4               0.2   \n",
              "1                4.9               3.0                1.4               0.2   \n",
              "2                4.7               3.2                1.3               0.2   \n",
              "3                4.6               3.1                1.5               0.2   \n",
              "4                5.0               3.6                1.4               0.2   \n",
              "\n",
              "   target  \n",
              "0     0.0  \n",
              "1     0.0  \n",
              "2     0.0  \n",
              "3     0.0  \n",
              "4     0.0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0173dlLdiuc0"
      },
      "source": [
        "#Dividing the data into independant and dependant sets\n",
        "X = data.data\n",
        "Y = data.target"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78ekfhVIiuc1"
      },
      "source": [
        "## Train-Test Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vhmflAwLiuc2"
      },
      "source": [
        "#Dividing data in training and testing datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "# print(train_test_split.__doc__)\n",
        "Xtrn, Xtst, Ytrn, Ytst = train_test_split(X,Y, train_size = 0.8, random_state = 100)\n",
        "#80:20 proportion of data "
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UWBxZVodiuc2",
        "outputId": "5c5b0b9e-3795-49e0-bb01-7ea2f715c918"
      },
      "source": [
        "print(Xtrn.shape)\n",
        "print(Ytrn.shape)\n",
        "print(Xtst.shape)\n",
        "print(Ytst.shape)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(120, 4)\n",
            "(120,)\n",
            "(30, 4)\n",
            "(30,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "De4nuxGviuc3"
      },
      "source": [
        "## Training Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wRTFlyt1iuc4"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wVSuTKiliuc4"
      },
      "source": [
        "model = LogisticRegression()\r\n",
        "#model will be body of our classifier"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WYQnk4I8iuc4",
        "outputId": "67e970e0-0449-449e-f029-63fb11d187eb"
      },
      "source": [
        "model.fit(Xtrn,Ytrn)"
      ],
      "execution_count": 9,
      "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": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9L3tNCi2iuc5"
      },
      "source": [
        "## Evaluation of model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9-Imqhhyiuc5"
      },
      "source": [
        "Ypred = model.predict(Xtst)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M4GE962_iuc6",
        "outputId": "88ac1579-cb31-47bd-942a-3ab3cbba8eb1"
      },
      "source": [
        "print(Ytst)\n",
        "print(Ypred)\n",
        "#original and predicted values "
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[2 0 2 0 2 2 0 0 2 0 0 2 0 0 2 1 1 1 2 2 2 0 2 0 1 2 1 0 1 2]\n",
            "[2 0 2 0 2 2 0 0 2 0 0 2 0 0 2 1 1 2 2 2 2 0 2 0 1 2 1 0 1 2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B4oIHX5Niuc6"
      },
      "source": [
        "### 1.Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PlMdHB13iuc6",
        "outputId": "189ee7c1-bc2a-41fb-8975-db43175115ec"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "acc = accuracy_score(Ytst,Ypred)\n",
        "print(acc)\n",
        "print(\"Accuraccy of our algorithm is \",acc*100,\" %\")"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9666666666666667\n",
            "Accuraccy of our algorithm is  96.66666666666667  %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A1n3ifqaiuc7"
      },
      "source": [
        "### 2.Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fXWFtU5xiuc7",
        "outputId": "052082e3-eff3-41b5-dcd9-5585550c237a"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "#print(confusion_matrix.__doc__)\n",
        "# columns represent Predicted class, Rows represent Actual Class\n",
        "#as we are dealing with multiple class we have to  give the labels\n",
        "cm = confusion_matrix(Ytst,Ypred,labels=[0,1,2])\n",
        "cm\n",
        "#diagonals are accurate predictions"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[11,  0,  0],\n",
              "       [ 0,  5,  1],\n",
              "       [ 0,  0, 13]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kn331C95iuc8"
      },
      "source": [
        "### 3.Recall Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qEjxNxkTiuc8",
        "outputId": "fb18e128-3d85-4817-9304-19641deccd95"
      },
      "source": [
        "# #Predicted/ #Actual class\n",
        "rm =(((cm.T)/(cm.sum(axis=1))).T) #Recall matrix\n",
        "rm"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.        , 0.        , 0.        ],\n",
              "       [0.        , 0.83333333, 0.16666667],\n",
              "       [0.        , 0.        , 1.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b4arGmNyiuc9",
        "outputId": "7574399b-a01d-47c4-82cf-635e16d5fde3"
      },
      "source": [
        "from sklearn.metrics import recall_score\n",
        "rec = recall_score(Ytst,Ypred, average = 'micro')\n",
        "rec\n",
        "#it is recall score\n",
        "#by default average=\"binary \" because we are dealing with more than 2 classes"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9666666666666667"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rCATIupliuc-"
      },
      "source": [
        "### 4.Precision Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LT8C8DlNiuc-",
        "outputId": "6b15b1dc-8600-4904-88e7-738fe16b280e"
      },
      "source": [
        "# #Actual Class/ #Prediction\n",
        "pm =(cm/cm.sum(axis=0)) #Precision \n",
        "pm"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.        , 0.        , 0.        ],\n",
              "       [0.        , 1.        , 0.07142857],\n",
              "       [0.        , 0.        , 0.92857143]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ErIs0auaiuc-",
        "outputId": "39eee28f-737a-4ac8-b2f6-16d1bb7d9cf0"
      },
      "source": [
        "from sklearn.metrics import precision_score\n",
        "prec = precision_score(Ytst,Ypred, average = 'micro')\n",
        "prec\n",
        "print(\"Precision is \",prec*100,\" %\")"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Precision is  96.66666666666667  %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jaikpzGpnBpP"
      },
      "source": [
        "To clean data\r\n",
        "\r\n",
        "1.search missing values\r\n",
        "\r\n",
        "2.what are target variables\r\n",
        "\r\n",
        "3.balancing of data\r\n",
        "\r\n",
        "4.or drop one or the labels \r\n",
        "\r\n",
        "5.or include samples  of data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rn-S2FV3iuc_"
      },
      "source": [
        "### 5.F1 Score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Kh8mRn3iuc_",
        "outputId": "027f2a85-d68d-47be-a26e-6e42c17f664c"
      },
      "source": [
        "F1 = 2*pm*rm/(pm+rm)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in true_divide\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Na4CV3gYiudA",
        "outputId": "c35ed2f7-eee1-4fe9-a0bc-6e22a4726436"
      },
      "source": [
        "from sklearn.metrics import f1_score\n",
        "f1 = f1_score(Ytst,Ypred, average = 'micro')\n",
        "f1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9666666666666667"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    }
  ]
}