7. binary_classification.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0JyRUMBcF4LV",
        "outputId": "fddd07bd-0889-4d63-c400-4ce861c3a090"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/datasets/_openml.py:968: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n",
            "  warn(\n"
          ]
        }
      ],
      "source": [
        "from sklearn.datasets import fetch_openml\n",
        "import numpy as np\n",
        "\n",
        "# fetch_mldata downloads data in the file structure scikit_learn_data/mldata/mnist-original.mat\n",
        "# in your home directory.\n",
        "# you can also copy from our dataset using rsync -avz /cxldata/scikit_learn_data .\n",
        "# mnist = fetch_mldata(\"MNIST original\")\n",
        "mnist = fetch_openml('mnist_784', version=1, cache=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pQmGjihO58yw"
      },
      "source": [
        "### Looking into our Datasets\n",
        "\n",
        "Out datasets consists of 70,000 images and each image has 784 features. A image consists of 28x28 pixels, and each pixel is a value from 0 to 255 describing the pixel intensity. 0 for white and 255 for black.\n",
        "\n",
        "\n",
        "![MNIST dataset image](https://www.cntk.ai/jup/cntk103a_MNIST_input.png)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HuOfnhyQ58yx",
        "outputId": "78af98a8-114b-405a-a1a0-273207b70abb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(70000, 784)"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X, y = mnist[\"data\"].to_numpy(), mnist[\"target\"].to_numpy()\n",
        "X.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vHzQlqWr58yx"
      },
      "source": [
        "Since there are 70,000 images with 28x28 pixels\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ICOFCAxz58yy",
        "outputId": "2a823191-0db7-44ba-851f-cf6f56bdfbb2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(70000,)"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Av_16WMq58yz"
      },
      "source": [
        "This is label for each of the image. Since there are 70,000 images, hence there are 70,000 labels."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E5te5pgR58yz"
      },
      "source": [
        "### Looking at one of the datasamples\n",
        "\n",
        "To view the image of a single digit,all we need to do is grab an instance’s feature vector, reshape it to a 28×28 array, and display it using Matplotlib’s imshow() function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5FyazRBu58y0"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "C-lBg4rF58y0",
        "outputId": "ad200898-587a-42fb-9735-27c520552046"
      },
      "outputs": [
        {
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            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "some_digit = X[3600]   # Selecting the 3600th image.\n",
        "some_digit_image = some_digit.reshape(28, 28) # Reshaping it to get the 28x28 pixels\n",
        "plt.imshow(some_digit_image, cmap = matplotlib.cm.binary)\n",
        "plt.axis(\"off\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "bkTjOAUb6iIy",
        "outputId": "d8cf6da2-036e-4f21-960a-44c40f19439c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(28, 28)"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "some_digit_image.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bgzJx0li6kFC",
        "outputId": "67e16722-0e01-4d15-d9df-698092d8d623"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(784,)"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X[3600].shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "plMvNIEL6kQa",
        "outputId": "2a099fae-61e7-4e54-9eb7-c115b4d61b82"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'8'"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y[3600]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qfIoNQ4s58y3"
      },
      "source": [
        "## Test train split\n",
        "\n",
        "> Train set is the data on which our Machine learning model is trained.\n",
        "\n",
        "> Test set is the data on which our model is finally evaluated\n",
        "\n",
        "![Test Train Split](https://upload.wikimedia.org/wikipedia/commons/thumb/8/88/Machine_learning_nutshell_--_Split_into_train-test_set.svg/640px-Machine_learning_nutshell_--_Split_into_train-test_set.svg.png)\n",
        "\n",
        "We need to split the data into test and train data. The MNIST dataset is actually already split into a training set (the first 60,000 images) and a test set (the last 10,000 images)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R13fEQMk58y4",
        "outputId": "d6e737c2-9971-4fe3-de79-dd89b0d06095",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(60000, 784)\n",
            "(60000,)\n",
            "(10000, 784)\n",
            "(10000,)\n"
          ]
        }
      ],
      "source": [
        "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]\n",
        "\n",
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V9KzjtblKizA"
      },
      "source": [
        "# **Go back to slides**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6-rzr1SuKiFF"
      },
      "source": [
        "# **Binary Classification**\n",
        "## Classifying our Digits\n",
        "\n",
        "### Training a Binary Classifier\n",
        "\n",
        "A perfect binary classifier looks something like this.\n",
        "![Binary Classifier](https://classeval.files.wordpress.com/2015/06/perfect-classifier.png?w=480&h=192)\n",
        "\n",
        "Let us first simplify our problem and make a model that only predicts if the digit is 5 or not. This will be a example of a \"Binary Classifier\".\n",
        "\n",
        "Lets create target vector for our classification."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OpCFoKVI58y4"
      },
      "source": [
        "Also we need to **shuffle** our training data so that it ensures that we don't miss out any digit in a cross validation fold."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1uWydXrlG6bn"
      },
      "outputs": [],
      "source": [
        "y_train_8= y_train == '8'\n",
        "y_test_8= y_test == '8'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t6nEerVfHt1o",
        "outputId": "10cd79b6-b7d8-420a-ad6c-d4f31b1c83a7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "5851\n",
            "974\n",
            "True\n"
          ]
        }
      ],
      "source": [
        "print(np.count_nonzero(y_train_8))\n",
        "print(np.count_nonzero(y_test_8))\n",
        "print(y_train_8[3600])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b9Kdn-Q6KiFG"
      },
      "source": [
        "Now let’s pick a classifier and train it. A good place to start is with a **Stochastic Gradient Descent (SGD)** classifier, using **Scikit-Learn’s SGDClassifier class**. This classifier has the advantage of being\n",
        "capable of handling very large datasets efficiently. This is in part because SGD deals with training instances independently, one at a time (which also makes SGD well suited for online learning). Let’s create an **SGDClassifier** and train it on an example set:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tAB4yuf_KiFG"
      },
      "source": [
        "Now, for binary classification '8' and 'Not 8', we train the SGD Classifier on the training dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "id": "dwKspRYdKiFG",
        "outputId": "39c1b4bf-2c54-4c33-ccc1-117be75c0d14",
        "scrolled": false
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:702: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SGDClassifier(max_iter=10, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SGDClassifier</label><div class=\"sk-toggleable__content\"><pre>SGDClassifier(max_iter=10, random_state=42)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "SGDClassifier(max_iter=10, random_state=42)"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.linear_model import SGDClassifier\n",
        "\n",
        "sgd_clf = SGDClassifier(random_state=42, max_iter=10) # if you want reproducible results set the random_state value.\n",
        "sgd_clf.fit(X_train, y_train_8)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hWSmzfW8KiFG"
      },
      "source": [
        "### Testing using sample data\n",
        "We remember that a digit 8 was stored in some_digit and we test the classifier on this sample data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G7h9DklBKiFG"
      },
      "source": [
        "Since it gives a output of **True**, hence our binary classifier correctly identified the digit 8 from our dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CV6igVySKiFG",
        "outputId": "73a54001-e8a3-423b-de3b-dbe585c11bbb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([ True])"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sgd_clf.predict([X[3600]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "yBapqr_XKiFG",
        "outputId": "cc6c09be-034d-42bc-829f-a3a239dd7ca1"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'8'"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y[3600]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OOWUJYFSKiFI",
        "outputId": "68100c62-8a06-4120-b616-a7d67966e083"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:702: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:702: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:702: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "array([False, False, False, ..., False, False,  True])"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.model_selection import cross_val_predict\n",
        "y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_8, cv=3)\n",
        "y_train_pred"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0SRsH_llKiFH"
      },
      "source": [
        "## Measuring the performace of our Classifier\n",
        "\n",
        "Evaluating a classifier is often significantly trickier than evaluating a regressor.\n",
        "\n",
        "________\n",
        "+ **Measuring Accuracy Using Cross-Validation**\n",
        "______________\n",
        "We will use the cross_val_score() function to evaluate your **SGDClassifier model** using **K-fold cross-\n",
        "validation**, with three folds. **K-fold cross-validation** means splitting the training set into K-folds (in this case, three), then making predictions and evaluating them on each fold using a model trained on the remaining folds.\n",
        "\n",
        "Here is a example how a 10 fold cross validation works.\n",
        "\n",
        "![A 10 fold cross validation](https://sebastianraschka.com/images/faq/evaluate-a-model/k-fold.png)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bEzFT1ZNKiFH",
        "outputId": "ab9f85ae-76c8-492b-c963-20440d8d18bf"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:702: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:702: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:702: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:702: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:702: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:702: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:702: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:702: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:702: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:702: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "array([0.93316667, 0.92983333, 0.9175    , 0.92333333, 0.91416667,\n",
              "       0.92733333, 0.93733333, 0.938     , 0.93766667, 0.94666667])"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "cross_val_score(sgd_clf, X_train, y_train_8, cv=10, scoring=\"accuracy\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "At5TlDFVKiFI"
      },
      "source": [
        "____________\n",
        "+ **Measuring performace using a Confusion Matrix**\n",
        "____________\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tf1RbqWdKiFJ",
        "outputId": "838b00e2-759f-489e-9af5-f054662e11be"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[52054,  2095],\n",
              "       [ 2919,  2932]])"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "confusion_matrix(y_train_8, y_train_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "iBsGhr6RbNtu",
        "outputId": "8a479836-4503-4d3d-bd00-bbccd29e04d3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "# Calculate the confusion matrix\n",
        "cm = confusion_matrix(y_train_8, y_train_pred)\n",
        "\n",
        "# Create a heatmap for the confusion matrix\n",
        "sns.heatmap(cm, annot=True, cmap='Blues', fmt='d')\n",
        "\n",
        "# Set labels, title, and axis ticks\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix')\n",
        "\n",
        "# Customize the axis ticks\n",
        "class_names = ['Negative', 'Positive']\n",
        "tick_marks = [0.5, 1.5]\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wg45H8WIKiFI"
      },
      "source": [
        "Just like the cross_val_score() function, cross_val_predict() performs K-fold cross-validation,\n",
        "but instead of returning the evaluation scores, it returns the predictions made on each test fold"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-r61yrq4KiFJ"
      },
      "source": [
        "Each row in a confusion matrix represents an actual class, while each column represents a predicted class."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Om-ELeoPKiFJ"
      },
      "source": [
        "______________\n",
        "+ **Measuring accuracy using Precision, Recall and F1 score**\n",
        "________"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "59zgAFNnKiFK",
        "outputId": "d9c26fd5-a1a1-4390-9eef-4838233786d4",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Precision score is :  0.5832504475830516\n",
            "Recall score is :  0.5011109212100495\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import precision_score, recall_score\n",
        "\n",
        "print (\"Precision score is : \" , precision_score(y_train_8, y_train_pred))\n",
        "print (\"Recall score is : \" , recall_score(y_train_8, y_train_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GL6oywjZKiFL"
      },
      "source": [
        "It is often convenient to combine precision and recall into a single metric called the F1 score, in particular if you need a simple way to compare two classifiers. The F1 score is the harmonic mean of\n",
        "precision and recall.\n",
        "\n",
        "\n",
        "![F1 score](https://hassetukda.files.wordpress.com/2012/08/f12.jpg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R8995qVSKiFL"
      },
      "source": [
        "2/F1 = 1/recall + 1/precision\n",
        "\n",
        "1. f1/2 < smaller of recall and precision\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iIgeKS3PKiFL",
        "outputId": "f3372f8a-444b-49dd-fdc0-d8141e190026"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.5390696819268248"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import f1_score\n",
        "\n",
        "f1_score(y_train_8, y_train_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jaLtpoGpKiFM",
        "outputId": "933301f0-8814-46a0-d0dc-7e13d2ad92b4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([1235.10303855])"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_scores = sgd_clf.decision_function([some_digit])\n",
        "y_scores"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vLT0PZ8xKiFN"
      },
      "source": [
        "**Precision - Recall curve**\n",
        "\n",
        "Let's try to find the score from the decision_function. Using these scores we will plot a relation between Precision and Recall, by setting different values of the Threshold."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "drkhVIU3KiFN",
        "outputId": "c3c71e62-5c0a-49ff-dd14-1d6b6f7ed2f4",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:702: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:702: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:702: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_scores = cross_val_predict(sgd_clf, X_train, y_train_8, cv=3,\n",
        "method=\"decision_function\")\n",
        "len(y_scores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qNt6h9EjKiFN",
        "scrolled": true
      },
      "outputs": [],
      "source": [
        "# hack to work around issue #9589 in Scikit-Learn 0.19.0\n",
        "if y_scores.ndim == 2: #comes as 2 for logistic regrssion and linear regression, zeroth dimension is for negative class, first dimension is for positive class, we will go for 1 and look for positive class\n",
        "    y_scores = y_scores[:, 1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BlXhpR2zKiFO",
        "outputId": "551c7426-38da-4d41-c3a7-8472138e392b",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([0.09751667, 0.09751829, 0.09751992, ..., 1.        , 1.        ,\n",
              "        1.        ]),\n",
              " array([1.00000000e+00, 1.00000000e+00, 1.00000000e+00, ...,\n",
              "        3.41821911e-04, 1.70910955e-04, 0.00000000e+00]),\n",
              " array([-1180444.59862308, -1163426.32675707, -1082137.11831286, ...,\n",
              "          400356.63870881,   412488.9382906 ,   431961.48061614]))"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import precision_recall_curve\n",
        "precision_recall_curve(y_train_8, y_scores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6Kha5xHlKiFO"
      },
      "outputs": [],
      "source": [
        "# Now we will use these values of y_scores to find different values for precison and recall for varying thresholds.\n",
        "\n",
        "from sklearn.metrics import precision_recall_curve\n",
        "precisions, recalls, thresholds = precision_recall_curve(y_train_8, y_scores)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "yfAXWaNAKiFO",
        "outputId": "3cf21755-5914-446f-dddb-a55ec97bb1d9",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<Figure size 1600x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plotting our results\n",
        "\n",
        "def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n",
        "    plt.figure(figsize=(16,5))\n",
        "    plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\")\n",
        "    plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\")\n",
        "    plt.xlabel(\"Threshold\")\n",
        "    plt.legend(loc=\"upper left\")\n",
        "    plt.ylim([0, 1])\n",
        "\n",
        "plot_precision_recall_vs_threshold(precisions, recalls, thresholds)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DwF_6Y3DKiFR"
      },
      "source": [
        "**ROC curve**\n",
        "\n",
        "\n",
        "> The receiver operating characteristic (ROC) curve is another common tool used with binary classifiers.\n",
        "\n",
        "> It is very similar to the precision/recall curve, but instead of plotting precision versus recall, the ROC curve plots the **true positive rate (another name for recall)** against the **false positive rate**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "sO_VoXp_KiFR",
        "outputId": "5470a6a3-fdd0-4794-81a8-ba31782976b2",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Making the ROC curve\n",
        "\n",
        "\n",
        "from sklearn.metrics import roc_curve\n",
        "\n",
        "fpr, tpr, thresholds = roc_curve(y_train_8, y_scores)\n",
        "\n",
        "def plot_roc_curve(fpr, tpr, label=None):\n",
        "    plt.figure(figsize=(18,5))\n",
        "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
        "    plt.plot([0, 1], [0, 1], 'k--')\n",
        "    plt.axis([0, 1, 0, 1])\n",
        "    plt.xlabel('False Positive Rate')\n",
        "    plt.ylabel('True Positive Rate')\n",
        "\n",
        "plot_roc_curve(fpr, tpr)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tH9ddej0KiFS",
        "outputId": "b25a14f7-050a-4711-d53c-5d724bc4fd22"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8790860683665472"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Scikit-Learn provides a function to compute the ROC AUC:\n",
        "\n",
        "from sklearn.metrics import roc_auc_score\n",
        "roc_auc_score(y_train_8, y_scores)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R2M0-aSpKiFS"
      },
      "source": [
        "## Comparision of SGDClassifier and RandomForestClassifier on the basis of ROC-AUC\n",
        "\n",
        "Let’s train a RandomForestClassifier and compare its ROC curve and ROC AUC score to the\n",
        "SGDClassifier.\n",
        "\n",
        "First, you need to get scores for each instance in the training set. But due to the way it\n",
        "works, the **RandomForestClassifier class** does not have a **decision_function()**\n",
        "method. Instead it has a **predict_proba()** method.\n",
        "\n",
        "Scikit-Learn classifiers generally have one or the other.\n",
        "\n",
        "> The **predict_proba()** method returns an array containing a row per instance and a column per\n",
        "class, each containing the probability that the given instance belongs to the given class (e.g., 70% chance that the image represents a 5):"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9K9h1NbzHGiB"
      },
      "source": [
        "### **Modelling of Ranom Forest**\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DkdpC2HfKiFS",
        "outputId": "4b666221-386a-4d48-f566-c58dcce88518"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0.98, 0.02],\n",
              "       [1.  , 0.  ],\n",
              "       [0.96, 0.04],\n",
              "       ...,\n",
              "       [0.98, 0.02],\n",
              "       [1.  , 0.  ],\n",
              "       [0.29, 0.71]])"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "forest_clf = RandomForestClassifier(random_state=42)\n",
        "y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_8, cv=3, method=\"predict_proba\")\n",
        "y_probas_forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "GzYgqa4JKiFS",
        "outputId": "f4c839bd-4141-4fc6-a7a3-beb1f0167de3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# But to plot a ROC curve, you need scores, not probabilities.\n",
        "# A simple solution is to use the positive class’s probability as the score:\n",
        "\n",
        "y_scores_forest = y_probas_forest[:, 1] # score = proba of positive class\n",
        "fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_8,y_scores_forest)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(fpr, tpr, \"b:\", label=\"SGD\")\n",
        "ax.plot(fpr_forest, tpr_forest, linewidth=2, label=\"Random Forest\")\n",
        "ax.plot([0, 1], [0, 1], 'k--')\n",
        "ax.legend(loc=4);\n",
        "ax.set_title('ROC curves');\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Em4pS2mGKiFT",
        "outputId": "1d4e30f2-ba44-4634-a5d9-d3bfb01431d2",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The ROC AUC value for SGDClassifier :  0.8790860683665472\n",
            "The ROC AUC value for Random Forest Classifier is :  0.9965481456893603\n"
          ]
        }
      ],
      "source": [
        "# ROC auc score of SGDClassifier and RF\n",
        "\n",
        "from sklearn.metrics import roc_auc_score\n",
        "print (\"The ROC AUC value for SGDClassifier : \", roc_auc_score(y_train_8, y_scores))\n",
        "print (\"The ROC AUC value for Random Forest Classifier is : \" , roc_auc_score(y_train_8, y_scores_forest))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AAGEfY95VYpH",
        "outputId": "63886859-ad6e-4d20-86b2-9501f75d8b98"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.98283333, 0.97816667, 0.981     , 0.98266667, 0.98266667,\n",
              "       0.98316667, 0.982     , 0.97983333, 0.98316667, 0.9845    ])"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Measuring accuracy for Random forest Classifier\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "cross_val_score(forest_clf, X_train, y_train_8, cv=10, scoring=\"accuracy\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2_M2ARGTEBHR",
        "outputId": "06f883c5-4a87-48a7-8933-daa843245745"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([False, False, False, ..., False, False,  True])"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred_forest = cross_val_predict(forest_clf, X_train, y_train_8, cv=10)\n",
        "y_pred_forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2xKZrmuW_HA2",
        "outputId": "c6dc0420-bf27-4abd-966f-95a58f7b6bc3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Precision score is :  0.990339157245632\n",
            "Recall score is :  0.8234489830798154\n"
          ]
        }
      ],
      "source": [
        "#Precision and Recall values for RandomForest\n",
        "print (\"Precision score is : \" , precision_score(y_train_8, y_pred_forest))\n",
        "print (\"Recall score is : \" , recall_score(y_train_8, y_pred_forest))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "csqyvw3yhNym",
        "outputId": "ed48652c-4d27-4384-9c1a-b75656e8eacf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8992161254199328"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "f1_score(y_train_8, y_pred_forest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "s_1jrNT8E1Xx",
        "outputId": "c8ea4595-4e99-4a8f-e62d-1709046648c5"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "# Calculate the confusion matrix\n",
        "cm = confusion_matrix(y_train_8, y_pred_forest)\n",
        "\n",
        "# Create a heatmap for the confusion matrix\n",
        "sns.heatmap(cm, annot=True, cmap='Blues', fmt='d')\n",
        "\n",
        "# Set labels, title, and axis ticks\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix')\n",
        "\n",
        "# Customize the axis ticks\n",
        "class_names = ['Negative', 'Positive']\n",
        "tick_marks = [0.5, 1.5]\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tVcV2zZIOskQ"
      },
      "source": [
        "## **Support Vector Machine**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x51lb0icOpbQ",
        "outputId": "56788973-55a2-4411-c8bf-4552df2c49c2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([False, False, False, ..., False, False,  True])"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.svm import SVC\n",
        "svm_clf = SVC(random_state=42)\n",
        "y_pred_svm = cross_val_predict(svm_clf, X_train, y_train_8, cv=3)\n",
        "y_pred_svm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qwA3M04XPFWf",
        "outputId": "7d10d2b8-aec8-4de7-a89a-e3b83a277c43"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The ROC AUC value for SVM Classifier is :  0.9656751153652104\n",
            "Precision score is :  0.980606931226432\n",
            "Recall score is :  0.9333447273970261\n"
          ]
        }
      ],
      "source": [
        "print (\"The ROC AUC value for SVM Classifier is : \" , roc_auc_score(y_train_8, y_pred_svm))\n",
        "#Precision and Recall values for RandomForest\n",
        "print (\"Precision score is : \" , precision_score(y_train_8, y_pred_svm))\n",
        "print (\"Recall score is : \" , recall_score(y_train_8, y_pred_svm))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "j7m5oA5DQI7v",
        "outputId": "78c3853c-47d3-4345-a620-7a7679302a91"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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sW6dOHdSoUQMREREAgIiICDg7OyuTFQDw8vJCamoqrl27puzz+hj5ffLHyM7ORmRkpEofqVQKT09PZZ+iYsJCRESkbRqaEgoKCoKZmZnKKygoqNBLuri4IDQ0FAcPHsSqVasQExOD1q1b48WLF0hISIBMJoO5ubnKOZUrV0ZCQgIAICEhQSVZyT+ef+xdfVJTU5GZmYnHjx8jLy+v0D75YxQVdwkRERFpm4Z2CU2dOhX+/v4qbXK5vNC+HTt2VP66QYMGcHFxga2tLbZv3w5DQ0ONxFOSWGEhIiLSNg1VWORyOUxNTVVeb0tY3mRubo7atWvjzp07sLa2RnZ2Np4/f67SJzExEdbW1gAAa2vrAruG8n9+Xx9TU1MYGhqiYsWK0NPTK7RP/hhFxYSFiIioDEhLS8Pdu3dRpUoVNGnSBPr6+jh69KjyeHR0NGJjY+Hq6goAcHV1xZUrV1R284SHh8PU1BROTk7KPq+Pkd8nfwyZTIYmTZqo9FEoFDh69KiyT1FxSoiIiEjbdPDguIkTJ6JLly6wtbVFXFwcZsyYAT09PfTp0wdmZmYYMmQI/P39YWlpCVNTU4wZMwaurq5o0aIFAKB9+/ZwcnJC//79ERwcjISEBEybNg2+vr7Kqs7IkSOxfPlyTJ48GYMHD8axY8ewfft2hIWFKePw9/eHj48PmjZtiubNm2Px4sVIT0/HoEGDinU/TFiIiIi07QOeoaKuhw8fok+fPnjy5AkqVaqEVq1a4cyZM6hUqRIAYNGiRZBKpejRoweysrLg5eWFlStXKs/X09PDvn37MGrUKLi6usLY2Bg+Pj4IDAxU9rG3t0dYWBj8/PywZMkSVKtWDWvWrIGXl5eyT69evZCcnIzp06cjISEBjRo1wsGDBwssxH0fPoeFqAzhc1iICiqR57B0XKSRcTIP+GlknI8RKyxERETaxu8SUhsTFiIiIm3TwZRQacOUj4iIiESPFRYiIiJt45SQ2piwEBERaRsTFrXxHSQiIiLRY4WFiIhI27joVm1MWIiIiLSNU0JqY8JCRESkbaywqI0pHxEREYkeKyxERETaxikhtTFhISIi0jZOCamNKR8RERGJHissREREWiZhhUVtTFiIiIi0jAmL+jglRERERKLHCgsREZG2scCiNiYsREREWsYpIfVxSoiIiIhEjxUWIiIiLWOFRX1MWIiIiLSMCYv6mLAQERFpGRMW9XENCxEREYkeKyxERETaxgKL2piwEBERaRmnhNTHKSEiIiISPVZYiIiItIwVFvUxYSEiItIyJizq45QQERERiR4rLERERFrGCov6RFNh+euvv/D111/D1dUVjx49AgBs3LgRp06d0nFkREREapJo6FWGiSJh2bVrF7y8vGBoaIiLFy8iKysLAJCSkoK5c+fqODoiIiLSNVEkLD/88ANCQkKwevVq6OvrK9vd3Nxw4cIFHUZGRESkPolEopFXWSaKNSzR0dFo06ZNgXYzMzM8f/685AMiIiLSoLKebGiCKCos1tbWuHPnToH2U6dOoWbNmjqIiIiISHNYYVGfKBKWYcOGYdy4cTh79iwkEgni4uKwadMmTJw4EaNGjdJ1eERERKRjopgSmjJlChQKBdq1a4eMjAy0adMGcrkcEydOxJgxY3QdHhERkXrKdnFEI0SRsEgkEnz33XeYNGkS7ty5g7S0NDg5OcHExETXoREREamtrE/naIIopoR+/fVXZGRkQCaTwcnJCc2bN2eyQkREREqiSFj8/PxgZWWFvn37Yv/+/cjLy9N1SERERBrDRbfqE0XCEh8fj61bt0IikaBnz56oUqUKfH19cfr0aV2HRkREpDYmLOoTRcJSrlw5dO7cGZs2bUJSUhIWLVqE+/fvw8PDA7Vq1dJ1eERERKRjolh0+zojIyN4eXnh2bNnePDgAW7cuKHrkIiIiNRS1qsjmiCKCgsAZGRkYNOmTejUqROqVq2KxYsX48svv8S1a9d0HRoREZF6+OWHahNFhaV3797Yt28fjIyM0LNnT3z//fdwdXXVdVhEREQkEqJIWPT09LB9+3Z4eXlBT09P1+EQERFpFKeE1CeKhGXTpk26DoGIiEhrmLCoT2cJy9KlSzF8+HAYGBhg6dKl7+w7duzYEoqKiIhI85iwqE8iCIKgiwvb29vjn3/+QYUKFWBvb//WfhKJBPfu3SvW2IafjlY3PKJS6dn55boOgUh0DErgn+7VfX/XyDj/ruiqkXE+RjqrsMTExBT6ayIiolKHBRa1iWJbc2BgIDIyMgq0Z2ZmIjAwUAcRERERaQ6fdKs+USQss2bNQlpaWoH2jIwMzJo1SwcRERERkZiIImERBKHQzPHSpUuwtLTUQURl13cjOiHz4nKVV9Rv0wrtu2f5KGReXI4ubRuotFe3tsBvS0fiyemFeHA0CHPHd4OeXuF/1Fwb1sSL80twZusUlXa3xrWwc/EI3Ds8p9BrEIlN5D/nMeabkfBs2woN6zni2NEjKscFQcCKZUvQzr0VmjdugOFDBuLBg/sqfe7fj8G40aPg7uaCls0bw+frPjh39kwJ3gVpCyss6tPptmYLCwvlb0Lt2rVVfjPy8vKQlpaGkSNH6jDCsunanTh4j1ym/Dk3T1Ggz5h+HihsubZUKsFvS0ch8UkqPAYugHUlM6yZ3R85uXmYsXyvSl8zE0Osmd0ff567BasK5VWOGRvKceXWI2z4PQLbFg7XzI0RaVFmZgYcHR3RrXsP+I8ruPB/3drV2LJpI2bPnYeqVathxbIlGDV8CHb/sR9yuRwAMOabkbC1tcXq/66H3MAAmzasxxjfkQg7EI6KlSqV9C2RBpX1ZEMTdJqwLF68GIIgYPDgwZg1axbMzMyUx2QyGezs7PjEWx3IzVMg8cmLtx5vULsqxvX/DG79gnH/SJDKMU/Xuqhb0xreI5ch6ekLXL71CIErw/DD2K74IWQ/cnLzlH2XTeuNbQf/QV6egC4eqhWUw39fx+G/r2v2xoi0qFVrd7Rq7V7oMUEQsGnjBgwbMQoen3kCAH4ICsZnbVri2NEj6NjJG8+ePUXsg/uYNXsOajvWAQCM85+AbVs3486d20xYqMzTacLi4+MD4NUW55YtW0JfX1+X4dD/c6hRCfcOz8HLrBycvRyD6cv+wL8JzwAAhgb6CA0aiPHzthea1Lg0sMfVO3FIevq/Y+Gnb2DZd73hVKsKLkU/BAD0/6IF7KtWwKDv1mPK0A4lc2NEOvLo4UM8fpwMlxYtlW3ly5eHc4OGuHzpIjp28oa5uQXs7O2x9/c9qFPXCTKZDDu3b4NlhQpwcqqnw+hJE1hhUZ8onnTr7v6/f5W8fPkS2dnZKsdNTU1LOqQy6/zV+xg+/VfcepAI64pm+G5ERxz5rx+afDUHaRlZCJ7QA2cuxWDf8SuFnl+5gimS3khkkp6mvjpW0RSIBmrVqITZY7+A5+DFyCtkuomotHn8OBkAUKFiBZX2ChUq4PHjxwBe/YX2y5pQjB/7DVo2bwypVApLS0us/HkNTF+rPtNHivmK2kSx6DYjIwOjR4+GlZUVjI2NYWFhofJ6l6ysLKSmpqq8BEXeO8+htzv893X8duQirt6Ow5GIG+g2ehXMTAzRo31jeLs7o23z2pj0484PHl8qlWD93IH4IWQ/7sQmaTByoo+bIAiY+8MsWFpWwLoNm7Bp6w54fOaJsb4jkZzMzwqpb968eZBIJBg/fryy7eXLl/D19UWFChVgYmKCHj16IDExUeW82NhYeHt7w8jICFZWVpg0aRJyc3NV+hw/fhyNGzeGXC6Hg4MDQkNDC1x/xYoVsLOzg4GBAVxcXHDu3LlixS+KhGXSpEk4duwYVq1aBblcjjVr1mDWrFmwsbHBhg0b3nluUFAQzMzMVF65iZElFHnpl5KWiTuxSahVvRLaNquNmtUqIuHkj3hxfglenF8CANjy01AcWj0OAJD4JLXAAlory1cVssTHqShvZIAm9WyxKOA/yjG+Hd4BDR2r4cX5JXBvVrtkb5CoBFSs+Gr9yZPHT1Tanzx5gooVKwIAzp09g5MnjmP+T4vwaeMmqOtUD99NnwkDuQH+2LOnpEMmDdP1LqHz58/j559/RoMGqusF/fz8sHfvXuzYsQMnTpxAXFwcunfvrjyel5cHb29vZGdn4/Tp01i/fj1CQ0Mxffp0ZZ+YmBh4e3vDw8MDUVFRGD9+PIYOHYpDhw4p+2zbtg3+/v6YMWMGLly4gIYNG8LLywtJSUVPxkUxJbR3715s2LABbdu2xaBBg9C6dWs4ODjA1tYWmzZtQr9+/d567tSpU+Hv76/SZtU6QNshlxnGhjLYV6uIhLBz2HX4AtbtPq1yPHLnd5i8YBfCTlwFAJy9HIOAIV6oZGGC5Gevnq3TrkUdpLzIxI17CcjJzUOTr+aojDG8Z2u0bVYbfSetxf1Hqv9DJyoNqlarhooVK+Hs2QjUqVsXAJCWloYrly/hP736AHj1oEwAkL7xl5JEKoEgcOr0Y6fLNSxpaWno168fVq9ejR9++EHZnpKSgrVr12Lz5s347LPPAADr1q1D3bp1cebMGbRo0QKHDx/G9evXceTIEVSuXBmNGjXC7NmzERAQgJkzZ0ImkyEkJAT29vZYsGABAKBu3bo4deoUFi1aBC8vLwDAwoULMWzYMAwaNAgAEBISgrCwMPz3v//FlClTUBSiqLA8ffoUNWvWBPBqvcrTp08BAK1atcLJkyffea5cLoepqanKSyLV03rMpVWQ35do1cQBNapYokVDe2xbOBx5CgW2H4xE4pMXuH43XuUFAP/GP8ODuFeJxpGIG7hxLwFrf/CBc+2q8HStixm+nfHz9pPIzsmFIAgFxkh+moaX2bm4fjceGS9frV8yNpShQe2qaFC7KgDArmoFNKhdFdWt3z1FSKQrGenpuHnjBm7euAHg1ULbmzduID4uDhKJBP36D8Dqn1fh+LGjuH0rGtOmTkYlKyt81u7VrqGGjRrB1NQU076dguibN3H/fgwW/jQfjx4+Qus2bXV4Z6QJEolmXoUtg8jKynrntX19feHt7Q1PT0+V9sjISOTk5Ki016lTBzVq1EBERAQAICIiAs7OzqhcubKyj5eXF1JTU3Ht2jVlnzfH9vLyUo6RnZ2NyMhIlT5SqRSenp7KPkUhigpLzZo1ERMTgxo1aqBOnTrYvn07mjdvjr1798Lc3FzX4ZUpVSubY0PQIFiaGeHxszScjroH9wEL8PhZwScRF0ahENBj3Cos+bY3jodOQPrLLGzaew6Bq8KKFUdjJ1scXjNO+XPwxB4AgI1/nMHwGb8WayyiknDt2lUMHTRA+fNPwa+2/H/R9UvMnjsPg4YMe/V1IzOn48WLVHzauAlW/rxG+QwWC4tXC2yXLVmMYYN9kJubg1oOn2DJ8hVwrFNHJ/dE4hMUFFTgCfAzZszAzJkzC+2/detWXLhwAefPny9wLCEhATKZrMDfs5UrV0ZCQoKyz+vJSv7x/GPv6pOamorMzEw8e/YMeXl5hfa5efPmu2/4NaJIWAYNGoRLly7B3d0dU6ZMQZcuXbB8+XLk5ORg4cKFug6vTBkwZV2x+hf2zdix8c/w5ZhVRR5jzs/7Mefn/Sptf0Xe5rdu00elWXMXXLoW/dbjEokEvmPGwXfMuLf2qVffGSGr12ojPNIxTU0JFbYMIj/pfdO///6LcePGITw8HAYGBhq5vi6JImHx8/NT/trT0xM3b95EZGQkHBwcCiwQIiIi+thoagmLXC5/a4LypsjISCQlJaFx48bKtry8PJw8eRLLly/HoUOHkJ2djefPn6tUWRITE2FtbQ0AsLa2LrCbJ38X0et93txZlJiYCFNTUxgaGkJPTw96enqF9skfoyhEsYblTba2tujevTuTFSIiog/Url07XLlyBVFRUcpX06ZN0a9fP+Wv9fX1cfToUeU50dHRiI2NVT5l3tXVFVeuXFHZzRMeHg5TU1M4OTkp+7w+Rn6f/DFkMhmaNGmi0kehUODo0aPFepq9KCosS5cuLbRdIpHAwMAADg4OaNOmDfT0uJiWiIg+PrrYJVS+fHnUr19fpc3Y2BgVKlRQtg8ZMgT+/v6wtLSEqakpxowZA1dXV7Ro0QIA0L59ezg5OaF///4IDg5GQkICpk2bBl9fX2WlZ+TIkVi+fDkmT56MwYMH49ixY9i+fTvCwv63dtHf3x8+Pj5o2rQpmjdvjsWLFyM9PV25a6goRJGwLFq0CMnJycjIyFA+KO7Zs2cwMjKCiYkJkpKSULNmTfz555+oXr26jqMlIiIqHrE+mX/RokWQSqXo0aMHsrKy4OXlhZUrVyqP6+npYd++fRg1ahRcXV1hbGwMHx8fBAYGKvvY29sjLCwMfn5+WLJkCapVq4Y1a9YotzQDQK9evZCcnIzp06cjISEBjRo1wsGDBwssxH0XiSAU9p27JWvLli345ZdfsGbNGtSqVQsAcOfOHYwYMQLDhw+Hm5sbevfuDWtra+zc+f6nrHKxJlHhnp1frusQiETHoAT+6V5nyqH3dyqCm/O83t+plBJFhWXatGnYtWuXMlkBAAcHB/z000/o0aMH7t27h+DgYPTo0UOHURIREX0YqVSkJZaPiCgSlvj4+ALfSwAAubm5yn3eNjY2ePGi4LcDExERiZ1Yp4Q+JqLYJeTh4YERI0bg4sWLyraLFy9i1KhRyscFX7lyBfb29roKkYiIiHRIFAnL2rVrYWlpiSZNmij3mDdt2hSWlpZYu/bVQ5RMTEyU31NARET0MdH1lx+WBqKYErK2tkZ4eDhu3ryJW7duAQAcHR3h6Oio7OPh4aGr8IiIiNRSxnMNjRBFwpKvZs2akEgkqFWrFsqVE1VoREREH6ysV0c0QRRTQhkZGRgyZAiMjIxQr149xMbGAgDGjBmDefPm6Tg6IiIi0jVRJCxTp07FpUuXcPz4cZUvaPL09MS2bdt0GBkREZH6uIZFfaKYd9mzZw+2bduGFi1aqPyG1KtXD3fv3tVhZEREROor47mGRoiiwpKcnAwrK6sC7enp6WU+oyQiIiKRJCxNmzZV+ZKk/CRlzZo1xfomRyIiIjHilJD6RDElNHfuXHTs2BHXr19Hbm4ulixZguvXr+P06dM4ceKErsMjIiJSSxnPNTRCFBWWVq1aISoqCrm5uXB2dsbhw4dhZWWFiIgINGnSRNfhERERkY6JosICALVq1cLq1at1HQYREZHGlfXpHE3QacIilUrf+5sokUgK/WJEIiKijwXzFfXpNGHZvXv3W49FRERg6dKlUCgUJRgRERERiZFOE5auXbsWaIuOjsaUKVOwd+9e9OvXD4GBgTqIjIiISHM4JaQ+USy6BYC4uDgMGzYMzs7OyM3NRVRUFNavXw9bW1tdh0ZERKQWiUQzr7JM5wlLSkoKAgIC4ODggGvXruHo0aPYu3cv6tevr+vQiIiINILPYVGfTqeEgoODMX/+fFhbW2PLli2FThERERER6TRhmTJlCgwNDeHg4ID169dj/fr1hfb77bffSjgyIiIizSnjxRGN0GnCMmDAgDJf4iIiotKPf9epT6cJS2hoqC4vT0RERB8J0TzploiIqLRigUV9TFiIiIi0jFNC6tP5tmYiIiKi92GFhYiISMtYYFEfExYiIiIt45SQ+jglRERERKLHCgsREZGWscKiPiYsREREWsZ8RX1MWIiIiLSMFRb1cQ0LERERiR4rLERERFrGAov6mLAQERFpGaeE1McpISIiIhI9VliIiIi0jAUW9TFhISIi0jIpMxa1cUqIiIiIRI8VFiIiIi1jgUV9TFiIiIi0jLuE1MeEhYiISMukzFfUxjUsREREJHqssBAREWkZp4TUx4SFiIhIy5ivqI9TQkRERCR6rLAQERFpmQQssaiLCQsREZGWcZeQ+jglRERERKLHCgsREZGWcZeQ+piwEBERaRnzFfVxSoiIiIhEjxUWIiIiLZOyxKI2JixERERaxnxFfUxYiIiItIyLbtXHNSxEREQkeqywEBERaRkLLOpjwkJERKRlXHSrPk4JERERlUKrVq1CgwYNYGpqClNTU7i6uuLAgQPK4y9fvoSvry8qVKgAExMT9OjRA4mJiSpjxMbGwtvbG0ZGRrCyssKkSZOQm5ur0uf48eNo3Lgx5HI5HBwcEBoaWiCWFStWwM7ODgYGBnBxccG5c+eKfT9MWIiIiLRMoqFXcVSrVg3z5s1DZGQk/vnnH3z22Wfo2rUrrl27BgDw8/PD3r17sWPHDpw4cQJxcXHo3r278vy8vDx4e3sjOzsbp0+fxvr16xEaGorp06cr+8TExMDb2xseHh6IiorC+PHjMXToUBw6dEjZZ9u2bfD398eMGTNw4cIFNGzYEF5eXkhKSirW/UgEQRCK+R6InuGno3UdApEoPTu/XNchEImOQQksjuizIUoj42wZ0Eit8y0tLfHjjz/iq6++QqVKlbB582Z89dVXAICbN2+ibt26iIiIQIsWLXDgwAF07twZcXFxqFy5MgAgJCQEAQEBSE5OhkwmQ0BAAMLCwnD16lXlNXr37o3nz5/j4MGDAAAXFxc0a9YMy5e/+v+PQqFA9erVMWbMGEyZMqXIsbPCQkRE9JHIyspCamqqyisrK+u95+Xl5WHr1q1IT0+Hq6srIiMjkZOTA09PT2WfOnXqoEaNGoiIiAAAREREwNnZWZmsAICXlxdSU1OVVZqIiAiVMfL75I+RnZ2NyMhIlT5SqRSenp7KPkXFhIWIiEjLpBLNvIKCgmBmZqbyCgoKeut1r1y5AhMTE8jlcowcORK7d++Gk5MTEhISIJPJYG5urtK/cuXKSEhIAAAkJCSoJCv5x/OPvatPamoqMjMz8fjxY+Tl5RXaJ3+MoipSIeyPP/4o8oBffPFFsQIgIiIq7TT14LipU6fC399fpU0ul7+1v6OjI6KiopCSkoKdO3fCx8cHJ06c0EgsJa1ICUu3bt2KNJhEIkFeXp468RAREdFbyOXydyYob5LJZHBwcAAANGnSBOfPn8eSJUvQq1cvZGdn4/nz5ypVlsTERFhbWwMArK2tC+zmyd9F9HqfN3cWJSYmwtTUFIaGhtDT04Oenl6hffLHKKoiTQkpFIoivZisEBERFSSRaOalLoVCgaysLDRp0gT6+vo4evSo8lh0dDRiY2Ph6uoKAHB1dcWVK1dUdvOEh4fD1NQUTk5Oyj6vj5HfJ38MmUyGJk2aqPRRKBQ4evSosk9R8cFxREREWqaL7xKaOnUqOnbsiBo1auDFixfYvHkzjh8/jkOHDsHMzAxDhgyBv78/LC0tYWpqijFjxsDV1RUtWrQAALRv3x5OTk7o378/goODkZCQgGnTpsHX11dZ5Rk5ciSWL1+OyZMnY/DgwTh27Bi2b9+OsLAwZRz+/v7w8fFB06ZN0bx5cyxevBjp6ekYNGhQse7ngxKW9PR0nDhxArGxscjOzlY5Nnbs2A8ZkoiIqNSS6uBBt0lJSRgwYADi4+NhZmaGBg0a4NChQ/j8888BAIsWLYJUKkWPHj2QlZUFLy8vrFy5Unm+np4e9u3bh1GjRsHV1RXGxsbw8fFBYGCgso+9vT3CwsLg5+eHJUuWoFq1alizZg28vLyUfXr16oXk5GRMnz4dCQkJaNSoEQ4ePFhgIe77FPs5LBcvXkSnTp2QkZGB9PR0WFpa4vHjx8qn4N27d69YAWgDn8NCVDg+h4WooJJ4DsvALZc1Mk5onwYaGedjVOxtzX5+fujSpQuePXsGQ0NDnDlzBg8ePECTJk3w008/aSNGIiKij5pEItHIqywrdsISFRWFCRMmQCqVQk9PD1lZWahevTqCg4Px7bffaiNGIiKij5ouHs1f2hQ7YdHX14dU+uo0KysrxMbGAgDMzMzw77//ajY6IiIiInzAottPP/0U58+fxyeffAJ3d3dMnz4djx8/xsaNG1G/fn1txEhERPRRk5bx6RxNKHaFZe7cuahSpQoAYM6cObCwsMCoUaOQnJyMX375ReMBEhERfezE8hyWj1mxKyxNmzZV/trKykr5bYxERERE2sIHxxEREWlZWd/hownFTljs7e3f+caL4TksREREYsJ8RX3FTljGjx+v8nNOTg4uXryIgwcPYtKkSZqKi4iIiEip2AnLuHHjCm1fsWIF/vnnH7UDIiIiKm24S0h9xd4l9DYdO3bErl27NDUcERFRqcFdQurT2KLbnTt3wtLSUlPDERERlRpcdKu+D3pw3OtvvCAISEhIQHJyssq3PBIRERFpSrETlq5du6okLFKpFJUqVULbtm1Rp04djQb3oZ6e4zfSEhXm8YtsXYdAJDrVLGRav4bG1l+UYcVOWGbOnKmFMIiIiEovTgmpr9hJn56eHpKSkgq0P3nyBHp6ehoJioiIiOh1xa6wCIJQaHtWVhZkMu2X1YiIiD42UhZY1FbkhGXp0qUAXpW11qxZAxMTE+WxvLw8nDx5UjRrWIiIiMSECYv6ipywLFq0CMCrCktISIjK9I9MJoOdnR1CQkI0HyERERGVeUVOWGJiYgAAHh4e+O2332BhYaG1oIiIiEoTLrpVX7HXsPz555/aiIOIiKjU4pSQ+oq9S6hHjx6YP39+gfbg4GD85z//0UhQRERERK8rdsJy8uRJdOrUqUB7x44dcfLkSY0ERUREVJrwu4TUV+wpobS0tEK3L+vr6yM1NVUjQREREZUm/LZm9RW7wuLs7Ixt27YVaN+6dSucnJw0EhQREVFpItXQqywrdoXl+++/R/fu3XH37l189tlnAICjR49i8+bN2Llzp8YDJCIiIip2wtKlSxfs2bMHc+fOxc6dO2FoaIiGDRvi2LFjsLS01EaMREREHzXOCKmv2AkLAHh7e8Pb2xsAkJqaii1btmDixImIjIxEXl6eRgMkIiL62HENi/o+eErs5MmT8PHxgY2NDRYsWIDPPvsMZ86c0WRsRERERACKWWFJSEhAaGgo1q5di9TUVPTs2RNZWVnYs2cPF9wSERG9BQss6ityhaVLly5wdHTE5cuXsXjxYsTFxWHZsmXajI2IiKhUkEo08yrLilxhOXDgAMaOHYtRo0bhk08+0WZMRERERCqKXGE5deoUXrx4gSZNmsDFxQXLly/H48ePtRkbERFRqSCVSDTyKsuKnLC0aNECq1evRnx8PEaMGIGtW7fCxsYGCoUC4eHhePHihTbjJCIi+mjx0fzqK/YuIWNjYwwePBinTp3ClStXMGHCBMybNw9WVlb44osvtBEjERERlXFqPenX0dERwcHBePjwIbZs2aKpmIiIiEoVLrpV3wc9OO5Nenp66NatG7p166aJ4YiIiEoVCcp4tqEBGklYiIiI6O3KenVEE8r6lz8SERHRR4AVFiIiIi1jhUV9TFiIiIi0TFLW9yRrAKeEiIiISPRYYSEiItIyTgmpjwkLERGRlnFGSH2cEiIiIiLRY4WFiIhIy8r6FxdqAhMWIiIiLeMaFvVxSoiIiIhEjxUWIiIiLeOMkPqYsBAREWmZlF9+qDYmLERERFrGCov6uIaFiIiIRI8VFiIiIi3jLiH1MWEhIiLSMj6HRX2cEiIiIiLRY4WFiIhIy1hgUR8TFiIiIi3jlJD6OCVERERUCgUFBaFZs2YoX748rKys0K1bN0RHR6v0efnyJXx9fVGhQgWYmJigR48eSExMVOkTGxsLb29vGBkZwcrKCpMmTUJubq5Kn+PHj6Nx48aQy+VwcHBAaGhogXhWrFgBOzs7GBgYwMXFBefOnSvW/TBhISIi0jKJRDOv4jhx4gR8fX1x5swZhIeHIycnB+3bt0d6erqyj5+fH/bu3YsdO3bgxIkTiIuLQ/fu3ZXH8/Ly4O3tjezsbJw+fRrr169HaGgopk+fruwTExMDb29veHh4ICoqCuPHj8fQoUNx6NAhZZ9t27bB398fM2bMwIULF9CwYUN4eXkhKSmp6O+hIAhC8d4C8cvM0XUEROL0JC1b1yEQiU41C5nWrxF6PlYj4wxsVuODz01OToaVlRVOnDiBNm3aICUlBZUqVcLmzZvx1VdfAQBu3ryJunXrIiIiAi1atMCBAwfQuXNnxMXFoXLlygCAkJAQBAQEIDk5GTKZDAEBAQgLC8PVq1eV1+rduzeeP3+OgwcPAgBcXFzQrFkzLF++HACgUChQvXp1jBkzBlOmTClS/KywEBERfSSysrKQmpqq8srKyirSuSkpKQAAS0tLAEBkZCRycnLg6emp7FOnTh3UqFEDERERAICIiAg4OzsrkxUA8PLyQmpqKq5du6bs8/oY+X3yx8jOzkZkZKRKH6lUCk9PT2WfomDCQkREpGUSiUQjr6CgIJiZmam8goKC3nt9hUKB8ePHw83NDfXr1wcAJCQkQCaTwdzcXKVv5cqVkZCQoOzzerKSfzz/2Lv6pKamIjMzE48fP0ZeXl6hffLHKAruEiIiItIyTe0Rmjp1Kvz9/VXa5HL5e8/z9fXF1atXcerUKQ1FUvKYsBAREWmZprY1y+XyIiUorxs9ejT27duHkydPolq1asp2a2trZGdn4/nz5ypVlsTERFhbWyv7vLmbJ38X0et93txZlJiYCFNTUxgaGkJPTw96enqF9skfoyg4JURERFQKCYKA0aNHY/fu3Th27Bjs7e1Vjjdp0gT6+vo4evSosi06OhqxsbFwdXUFALi6uuLKlSsqu3nCw8NhamoKJycnZZ/Xx8jvkz+GTCZDkyZNVPooFAocPXpU2acoWGEhIiLSMl08Ns7X1xebN2/G77//jvLlyyvXi5iZmcHQ0BBmZmYYMmQI/P39YWlpCVNTU4wZMwaurq5o0aIFAKB9+/ZwcnJC//79ERwcjISEBEybNg2+vr7KSs/IkSOxfPlyTJ48GYMHD8axY8ewfft2hIWFKWPx9/eHj48PmjZtiubNm2Px4sVIT0/HoEGDinw/3NZMVIZwWzNRQSWxrXnzhYcaGadv42rv7/T/JG+Zhlq3bh0GDhwI4NWD4yZMmIAtW7YgKysLXl5eWLlypcpUzYMHDzBq1CgcP34cxsbG8PHxwbx581Cu3P9qHsePH4efnx+uX7+OatWq4fvvv1deI9/y5cvx448/IiEhAY0aNcLSpUvh4uJS9PthwkJUdjBhISqotCYspQ2nhIiIiLTsbdUOKjomLERERFrGHS7q43tIREREoscKCxERkZZxSkh9TFiIiIi0jOmK+jglRERERKLHCgsREZGWcUpIfUxYiIiItIzTGepjwkJERKRlrLCoj0kfERERiR4rLERERFrG+or6mLAQERFpGWeE1McpISIiIhI9VliIiIi0TMpJIbWJpsLy119/4euvv4arqysePXoEANi4cSNOnTql48iIiIjUI5Fo5lWWiSJh2bVrF7y8vGBoaIiLFy8iKysLAJCSkoK5c+fqODoiIiLSNVEkLD/88ANCQkKwevVq6OvrK9vd3Nxw4cIFHUZGRESkPomG/ivLRLGGJTo6Gm3atCnQbmZmhufPn5d8QERERBpU1qdzNEEUFRZra2vcuXOnQPupU6dQs2ZNHUREREREYiKKhGXYsGEYN24czp49C4lEgri4OGzatAkTJ07EqFGjdB0eERGRWqSQaORVloliSmjKlClQKBRo164dMjIy0KZNG8jlckycOBFjxozRdXhERERq4ZSQ+iSCIAi6DiJfdnY27ty5g7S0NDg5OcHExOSDxsnM0XBgRKXEk7RsXYdAJDrVLGRav8bhG8kaGad93UoaGedjJIopoV9//RUZGRmQyWRwcnJC8+bNPzhZISIiotJHFAmLn58frKys0LdvX+zfvx95eXm6DomIiEhjuK1ZfaJIWOLj47F161ZIJBL07NkTVapUga+vL06fPq3r0IiIiNQmlWjmVZaJag0LAGRkZGD37t3YvHkzjhw5gmrVquHu3bvFGoNrWIgKxzUsRAWVxBqWozcfa2ScdnUqamScj5Eodgm9zsjICF5eXnj27BkePHiAGzdu6DokIiIitZT16RxNEMWUEPCqsrJp0yZ06tQJVatWxeLFi/Hll1/i2rVrug6NiIhILfzyQ/WJosLSu3dv7Nu3D0ZGRujZsye+//57uLq66josIiIiEglRJCx6enrYvn07vLy8oKenp+twiIiINIpTQuoTRcKyadMmXYdARESkNWV9h48m6CxhWbp0KYYPHw4DAwMsXbr0nX3Hjh1bQlERERGRGOlsW7O9vT3++ecfVKhQAfb29m/tJ5FIcO/evWKNzW3N2rN962bs2LYFcXGPAAC1HD7B8JHfoFVrdwDAv7GxWPjTfERdjER2djZatmqNKVO/R4WK/9uKl5LyHPPmzsbJ439CIpXC07M9Jk/9DkZGxjq5p7KE25o1Y/3qldiwdpVKW3VbO4Ru26vSJggCpvqNwvkzf2PW/MVo5d5O5fjBfXuwc8sGPPz3AYyNTdDms88xbtI0AEB2VhYWzQ/E7ejreHA/Bi3c2mB28Lv/cUcfpiS2Nf9165lGxmld20Ij43yMdFZhiYmJKfTXJG6Vra0x1m8iatjaAoKAP37fg/FjfLF1525UtamKUcMHo7ZjHfyydj0AYMXyJRg7eiQ2bt4OqfTVprRvAyYiOTkZIavXITc3B9OnfYvAmdMxL3iBLm+NqFjsajrgx2WrlT8Xtv5u19aNkLxla8eOzeuxY8sGjBjtj7r1GiAzMwOJ8XHK43mKPMjlBvjyP/3w1/Ejmr8BKlFlfYePJohiW3NgYCAyMjIKtGdmZiIwMFAHEdHbuLf9DK3buMPW1g62dvYYM84PRkZGuHIpChcvXkBc3CMEzpmHT2o74pPajpg9Zz6uX7uKc2fPAADu3b2Lv0/9hRmzfoBzg4b4tHFTTPl2Gg4dCENSUqKO746o6PT09GBZoaLyZWau+i/fO7duYsfm9Zg0bXaBc1+kpmDdz8sxZfoctPPyhk216qj1iSNatvFQ9jE0NML4gO/h3e0rWFhW0Pr9kHZJNPQqy0SRsMyaNQtpaWkF2jMyMjBr1iwdRERFkZeXh4P7w5CZmYEGjT5FTk42JBIJZLL/lVflcjmkUikuXogEAFy+dBHlTU1Rr76zso9Li5aQSqW4evlyid8D0Yd69G8senb+DF9374C50wOQmBCvPPbyZSbmTA/A2EnfwbJCwSeTRp6LgEJQ4HFyEgb1+gK9urRD4HcTkJSYUJK3QPRREcUuIUEQCi2bXrp0CZaWlu88NysrC1lZWSptCqkccrlcozHS/9y+FY0B/XojOzsLhkZGWLhkBWrVcoCFhSUMDQ2xeOGPGDPOHxAELFm8AHl5eXj8+NVXqz9+/LjA72m5cuVgamam7EMkdnXqOWPy97NRrYYdnj55jA1rV2H8SB+s3bQbRsbGWLk4GPWcG8GtzWeFnh8f9xCCQoHN61fD128KjE1MsC5kGSaPHYbVv/4GfX39Er4j0jYp54TUptMKi4WFBSwtLSGRSFC7dm1YWloqX2ZmZvj888/Rs2fPd44RFBQEMzMzldeP84NK6A7KJjt7e2zbtQcbN29Hz559MP27ANy9eweWlpYIXrAEJ4//iZbNP0Ur16Z4kZqKuk71+GGlUsWlZWu4t/NCrU8c0ayFG4IWrkT6ixc4fvQQTp/8E1H/nIOvX8Bbz1coBOTm5mK0/1Q0a+EGp/oN8d3sYDz6NxZRkedK8E6opHBKSH06rbAsXrwYgiBg8ODBmDVrFszMzJTHZDIZ7Ozs3vvE26lTp8Lf31+lTSFldUWb9PVlqFHDFgDgVK8+rl27gs2/bsD3MwLR0q0V9h08gmfPnkJPrxxMTU3Rzt0NVTt0AgBUrFgRT58+VRkvNzcXqSkpqFixUonfC5EmmJQ3RbUatoh7GIuYu7cR9+hffPF5S5U+s6b6w7lhYyxctU65a87WvqbyuLmFJUzNzJH02tQSEf2PThMWHx8fAK+2OLds2fKDyqByecHpH25rLlkKhQLZ2arbZS0sXk37nDsbgadPn6Ctx6vSeIOGn+JFaiquX7sKp3r1/7/PGSgUCtRv0KBkAyfSkMyMDMQ9+heeHbqgracXOn3RXeX40H7dMWrcZLj+//b/eg0+BQD8++A+KllZAwBSU1KQmvIclavYlGzwVDLKenlEA3SWsKSmpsLU1BQA8OmnnyIzMxOZmZmF9s3vR7q3dNECuLVuA+sqVZCRno4DYfvwz/lzWPnzWgDAnt27ULNmLVhYWOLypYsInjcXXw8YCLv//5dkzVq14NaqNQJnfo/vps9Cbk4O5s2dDa+O3rCyqqzLWyMqspClP8G1lTsqW9vgyeNkhK5eAalUD5+17whzC8tCF9paWVujik01AED1GnZo2cYDKxbNh/+UGTAyNsaalUtQ3dYejZo0U55zP+YucnNy8CI1FRkZ6bhz6yYAwKF2nZK5UdIYPppffTpLWCwsLBAfHw8rKyuYm5sXuug2fzFuXl6eDiKkwjx9+gTTvg3A4+QkmJQvj9q1HbHy57VwbekGAHhwPwbLFi9ESkoKbKpWxdDhI/H1gIEqY8yd/xOC5szGiCE+kEqlaOfZHgHfTtPB3RB9mOSkRMyZHoDUlOcwM7dA/YaNsXzNJphbvHuTwOumzJiLlYuD8e2EbyCRSNHw06aYtzgE5cr9r9L8rd83SEz437NZRgz4DwDg6JkrmrsZoo+Ezp50e+LECbi5uaFcuXI4ceLEO/u6u7sXa2xOCREVjk+6JSqoJJ50e+5eikbGaV7T7P2dSimdJSzaxISFqHBMWIgKKomE5byGEpZmZThhEcWD4w4ePIhTp04pf16xYgUaNWqEvn374tkzzXz/AhEREX28RJGwTJo0CampqQCAK1euwN/fH506dUJMTEyBLctEREQfHT6IRW2ieNJtTEwMnJycAAC7du1Cly5dMHfuXFy4cAGdOnXScXRERETq4S4h9YmiwiKTyZRffnjkyBG0b98eAGBpaamsvBAREX2sJBLNvMoyUVRYWrVqBX9/f7i5ueHcuXPYtm0bAODWrVuoVq2ajqMjIiIiXRNFhWX58uUoV64cdu7ciVWrVqFq1aoAgAMHDqBDhw46jo6IiEg9XMKiPm5rJipDuK2ZqKCS2NZ84YFmljc0ti27T34XxZQQAOTl5WHPnj24ceMGAKBevXr44osvoKenp+PIiIiISNdEkbDcuXMHnTp1wqNHj+Do6AgACAoKQvXq1REWFoZatWrpOEIiIqIPx11C6hPFGpaxY8eiVq1a+Pfff3HhwgVcuHABsbGxsLe3x9ixY3UdHhERkVq4S0h9oljDYmxsjDNnzsDZ2Vml/dKlS3Bzc0NaWlqxxuMaFqLCcQ0LUUElsYYlKvaFRsZpVKO8Rsb5GIliSkgul+PFi4K/mWlpaZDJtP8HiYiISJvKeHFEI0QxJdS5c2cMHz4cZ8+ehSAIEAQBZ86cwciRI/HFF1/oOjwiIiL1cF+z2kSRsCxduhQODg5o2bIlDAwMYGBgADc3Nzg4OGDJkiW6Do+IiIh0TKcJi0KhwPz58+Ht7Y1Hjx6hW7du2LFjB3bu3Ino6Gjs3r0bZmZl96u0iYiodJBo6L/iOnnyJLp06QIbGxtIJBLs2bNH5bggCJg+fTqqVKkCQ0NDeHp64vbt2yp9nj59in79+sHU1BTm5uYYMmRIgbWlly9fRuvWrWFgYIDq1asjODi4QCw7duxAnTp1YGBgAGdnZ+zfv79Y96LThGXOnDn49ttvYWJigqpVq2L//v3Ys2cPunTpAgcHB12GRkREpDG62iWUnp6Ohg0bYsWKFYUeDw4OxtKlSxESEoKzZ8/C2NgYXl5eePnypbJPv379cO3aNYSHh2Pfvn04efIkhg8frjyempqK9u3bw9bWFpGRkfjxxx8xc+ZM/PLLL8o+p0+fRp8+fTBkyBBcvHgR3bp1Q7du3XD16tWiv4e63CX0ySefYOLEiRgxYgSAV1986O3tjczMTEilH55LcZcQUeG4S4iooJLYJXT1YfF2u75N/WomH3yuRCLB7t270a1bNwCvqis2NjaYMGECJk6cCABISUlB5cqVERoait69e+PGjRtwcnLC+fPn0bRpUwDAwYMH0alTJzx8+BA2NjZYtWoVvvvuOyQkJCg3ykyZMgV79uzBzZs3AQC9evVCeno69u3bp4ynRYsWaNSoEUJCQooUv04rLLGxsejUqZPyZ09PT0gkEsTFxekwKiIiInHKyspCamqqyisrK+uDxoqJiUFCQgI8PT2VbWZmZnBxcUFERAQAICIiAubm5spkBXj1d7VUKsXZs2eVfdq0aaOyq9fLywvR0dF49uyZss/r18nvk3+dotBpwpKbmwsDAwOVNn19feTksERCRESliIZ2CQUFBcHMzEzlFRQU9EEhJSQkAAAqV66s0l65cmXlsYSEBFhZWakcL1euHCwtLVX6FDbG69d4W5/840Wh0+ewCIKAgQMHQi6XK9tevnyJkSNHwtjYWNn222+/6SI8IiIijdDUo/mnTp0Kf39/lbbX/w4tzXSasPj4+BRo+/rrr3UQCRERkfjJ5XKNJSjW1tYAgMTERFSpUkXZnpiYiEaNGin7JCUlqZyXm5uLp0+fKs+3trZGYmKiSp/8n9/XJ/94Ueg0YVm3bp0uL09ERFQixPg9QPb29rC2tsbRo0eVCUpqairOnj2LUaNGAQBcXV3x/PlzREZGokmTJgCAY8eOQaFQwMXFRdnnu+++Q05ODvT19QEA4eHhcHR0hIWFhbLP0aNHMX78eOX1w8PD4erqWuR4RfHgOCIiotJMVw+6TUtLQ1RUFKKiogC8WmgbFRWF2NhYSCQSjB8/Hj/88AP++OMPXLlyBQMGDICNjY1yJ1HdunXRoUMHDBs2DOfOncPff/+N0aNHo3fv3rCxsQEA9O3bFzKZDEOGDMG1a9ewbds2LFmyRGXqaty4cTh48CAWLFiAmzdvYubMmfjnn38wevToor+HYvjyQ03jtmaiwnFbM1FBJbGt+UZcukbGqWtj/P5Orzl+/Dg8PDwKtPv4+CA0NBSCIGDGjBn45Zdf8Pz5c7Rq1QorV65E7dq1lX2fPn2K0aNHY+/evZBKpejRoweWLl0KE5P/bbG+fPkyfH19cf78eVSsWBFjxoxBQECAyjV37NiBadOm4f79+/jkk08QHBysslP4fZiwEJUhTFiICiqRhCVeQwlLleIlLKWJKL6tmYiIqDTT1C6hsoxrWIiIiEj0WGEhIiLSMjHuEvrYMGEhIiLSMuYr6mPCQkREpG3MWNTGNSxEREQkeqywEBERaRl3CamPCQsREZGWcdGt+jglRERERKLHCgsREZGWscCiPiYsRERE2saMRW2cEiIiIiLRY4WFiIhIy7hLSH1MWIiIiLSMu4TUxykhIiIiEj1WWIiIiLSMBRb1MWEhIiLSNmYsamPCQkREpGVcdKs+rmEhIiIi0WOFhYiISMu4S0h9TFiIiIi0jPmK+jglRERERKLHCgsREZGWcUpIfUxYiIiItI4Zi7o4JURERESixwoLERGRlnFKSH1MWIiIiLSM+Yr6OCVEREREoscKCxERkZZxSkh9TFiIiIi0jN8lpD4mLERERNrGfEVtXMNCREREoscKCxERkZaxwKI+JixERERaxkW36uOUEBEREYkeKyxERERaxl1C6mPCQkREpG3MV9TGKSEiIiISPVZYiIiItIwFFvUxYSEiItIy7hJSH6eEiIiISPRYYSEiItIy7hJSHxMWIiIiLeOUkPo4JURERESix4SFiIiIRI9TQkRERFrGKSH1MWEhIiLSMi66VR+nhIiIiEj0WGEhIiLSMk4JqY8JCxERkZYxX1Efp4SIiIhI9FhhISIi0jaWWNTGhIWIiEjLuEtIfZwSIiIiItFjhYWIiEjLuEtIfUxYiIiItIz5ivqYsBAREWkbMxa1cQ0LERERiR4rLERERFrGXULqY8JCRESkZVx0qz5OCREREZHoSQRBEHQdBJVOWVlZCAoKwtSpUyGXy3UdDpFo8LNBVHxMWEhrUlNTYWZmhpSUFJiamuo6HCLR4GeDqPg4JURERESix4SFiIiIRI8JCxEREYkeExbSGrlcjhkzZnBRIdEb+NkgKj4uuiUiIiLRY4WFiIiIRI8JCxEREYkeExYiIiISPSYsJCp2dnZYvHixrsMg0orjx49DIpHg+fPn7+zHzwFRQUxYypCBAwdCIpFg3rx5Ku179uyBpIS/mSs0NBTm5uYF2s+fP4/hw4eXaCxEb8r/rEgkEshkMjg4OCAwMBC5ublqjduyZUvEx8fDzMwMAD8HRMXBhKWMMTAwwPz58/Hs2TNdh1KoSpUqwcjISNdhEKFDhw6Ij4/H7du3MWHCBMycORM//vijWmPKZDJYW1u/9x8I/BwQFcSEpYzx9PSEtbU1goKC3trn1KlTaN26NQwNDVG9enWMHTsW6enpyuPx8fHw9vaGoaEh7O3tsXnz5gIl7IULF8LZ2RnGxsaoXr06vvnmG6SlpQF4VRYfNGgQUlJSlP+KnTlzJgDVUnjfvn3Rq1cvldhycnJQsWJFbNiwAQCgUCgQFBQEe3t7GBoaomHDhti5c6cG3ikq6+RyOaytrWFra4tRo0bB09MTf/zxB549e4YBAwbAwsICRkZG6NixI27fvq0878GDB+jSpQssLCxgbGyMevXqYf/+/QBUp4T4OSAqHiYsZYyenh7mzp2LZcuW4eHDhwWO3717Fx06dECPHj1w+fJlbNu2DadOncLo0aOVfQYMGIC4uDgcP34cu3btwi+//IKkpCSVcaRSKZYuXYpr165h/fr1OHbsGCZPngzgVVl88eLFMDU1RXx8POLj4zFx4sQCsfTr1w979+5VJjoAcOjQIWRkZODLL78EAAQFBWHDhg0ICQnBtWvX4Ofnh6+//honTpzQyPtFlM/Q0BDZ2dkYOHAg/vnnH/zxxx+IiIiAIAjo1KkTcnJyAAC+vr7IysrCyZMnceXKFcyfPx8mJiYFxuPngKiYBCozfHx8hK5duwqCIAgtWrQQBg8eLAiCIOzevVvI/6MwZMgQYfjw4Srn/fXXX4JUKhUyMzOFGzduCACE8+fPK4/fvn1bACAsWrTordfesWOHUKFCBeXP69atE8zMzAr0s7W1VY6Tk5MjVKxYUdiwYYPyeJ8+fYRevXoJgiAIL1++FIyMjITTp0+rjDFkyBChT58+734ziN7h9c+KQqEQwsPDBblcLnTr1k0AIPz999/Kvo8fPxYMDQ2F7du3C4IgCM7OzsLMmTMLHffPP/8UAAjPnj0TBIGfA6LiKKfTbIl0Zv78+fjss88K/Ivu0qVLuHz5MjZt2qRsEwQBCoUCMTExuHXrFsqVK4fGjRsrjzs4OMDCwkJlnCNHjiAoKAg3b95EamoqcnNz8fLlS2RkZBR5br5cuXLo2bMnNm3ahP79+yM9PR2///47tm7dCgC4c+cOMjIy8Pnnn6ucl52djU8//bRY7wfRm/bt2wcTExPk5ORAoVCgb9++6N69O/bt2wcXFxdlvwoVKsDR0RE3btwAAIwdOxajRo3C4cOH4enpiR49eqBBgwYfHAc/B0SvMGEpo9q0aQMvLy9MnToVAwcOVLanpaVhxIgRGDt2bIFzatSogVu3br137Pv376Nz584YNWoU5syZA0tLS5w6dQpDhgxBdnZ2sRYT9uvXD+7u7khKSkJ4eDgMDQ3RoUMHZawAEBYWhqpVq6qcx+9oIXV5eHhg1apVkMlksLGxQbly5fDHH3+897yhQ4fCy8sLYWFhOHz4MIKCgrBgwQKMGTPmg2Ph54CICUuZNm/ePDRq1AiOjo7KtsaNG+P69etwcHAo9BxHR0fk5ubi4sWLaNKkCYBX/8J7fddRZGQkFAoFFixYAKn01TKp7du3q4wjk8mQl5f33hhbtmyJ6tWrY9u2bThw4AD+85//QF9fHwDg5OQEuVyO2NhYuLu7F+/mid7D2Ni4wOegbt26yM3NxdmzZ9GyZUsAwJMnTxAdHQ0nJydlv+rVq2PkyJEYOXIkpk6ditWrVxeasPBzQFR0TFjKMGdnZ/Tr1w9Lly5VtgUEBKBFixYYPXo0hg4dCmNjY1y/fh3h4eFYvnw56tSpA09PTwwfPhyrVq2Cvr4+JkyYAENDQ+VWTQcHB+Tk5GDZsmXo0qUL/v77b4SEhKhc287ODmlpaTh69CgaNmwIIyOjt1Ze+vbti5CQENy6dQt//vmnsr18+fKYOHEi/Pz8oFAo0KpVK6SkpODvv/+GqakpfHx8tPCuUVn2ySefoGvXrhg2bBh+/vlnlC9fHlOmTEHVqlXRtWtXAMD48ePRsWNH1K5dG8+ePcOff/6JunXrFjoePwdExaDrRTRUcl5fSJgvJiZGkMlkwut/FM6dOyd8/vnngomJiWBsbCw0aNBAmDNnjvJ4XFyc0LFjR0Eulwu2trbC5s2bBSsrKyEkJETZZ+HChUKVKlUEQ0NDwcvLS9iwYYPKYkNBEISRI0cKFSpUEAAIM2bMEARBdbFhvuvXrwsABFtbW0GhUKgcUygUwuLFiwVHR0dBX19fqFSpkuDl5SWcOHFCvTeLyrTCPiv5nj59KvTv318wMzNT/vm+deuW8vjo0aOFWrVqCXK5XKhUqZLQv39/4fHjx4IgFFx0Kwj8HBAVlUQQBEGH+RKVAg8fPkT16tVx5MgRtGvXTtfhEBFRKcSEhYrt2LFjSEtLg7OzM+Lj4zF58mQ8evQIt27dUs6rExERaRLXsFCx5eTk4Ntvv8W9e/dQvnx5tGzZEps2bWKyQkREWsMKCxEREYkeH81PREREoseEhYiIiESPCQsRERGJHhMWIiIiEj0mLESl0MCBA9GtWzflz23btsX48eNLPI7jx49DIpHg+fPnJX5tIipdmLAQlaCBAwdCIpFAIpFAJpPBwcEBgYGByM3N1ep1f/vtN8yePbtIfZlkEJEY8TksRCWsQ4cOWLduHbKysrB//374+vpCX18fU6dOVemXnZ0NmUymkWtaWlpqZBwiIl1hhYWohMnlclhbW8PW1hajRo2Cp6cn/vjjD+U0zpw5c2BjY6P8Fu1///0XPXv2hLm5OSwtLdG1a1fcv39fOV5eXh78/f1hbm6OChUqYPLkyXjz8UpvTgllZWUhICAA1atXh1wuh4ODA9auXYv79+/Dw8MDAGBhYQGJRIKBAwcCABQKBYKCgmBvbw9DQ0M0bNgQO3fuVLnO/v37Ubt2bRgaGsLDw0MlTiIidTBhIdIxQ0NDZGdnAwCOHj2K6OhohIeHY9++fcjJyYGXlxfKly+Pv/76C3///TdMTEzQoUMH5TkLFixAaGgo/vvf/+LUqVN4+vQpdu/e/c5rDhgwAFu2bMHSpUtx48YN/PzzzzAxMUH16tWxa9cuAEB0dDTi4+OxZMkSAEBQUBA2bNiAkJAQXLt2DX5+fvj6669x4sQJAK8Sq+7du6NLly6IiorC0KFDMWXKFG29bURU1ujwixeJypzXvwVYoVAI4eHhglwuFyZOnCj4+PgIlStXFrKyspT9N27cKDg6Oqp8O29WVpZgaGgoHDp0SBAEQahSpYoQHBysPJ6TkyNUq1ZN5duG3d3dhXHjxgmCIAjR0dECACE8PLzQGAv7RuGXL18KRkZGwunTp1X6DhkyROjTp48gCIIwdepUwcnJSeV4QEBAgbGIiD4E17AQlbB9+/bBxMQEOTk5UCgU6Nu3L2bOnAlfX184OzurrFu5dOkS7ty5g/Lly6uM8fLlS9y9excpKSmIj4+Hi4uL8li5cuXQtGnTAtNC+aKioqCnpwd3d/cix3znzh1kZGTg888/V2nPzs7Gp59+CgC4ceOGShwA4OrqWuRrEBG9CxMWohLm4eGBVatWQSaTwcbGBuXK/e9jaGxsrNI3LS0NTZo0waZNmwqMU6lSpQ+6vqGhYbHPSUtLAwCEhYWhatWqKsfkcvkHxUFEVBxMWIhKmLGxMRwcHIrUt3Hjxti2bRusrKxgampaaJ8qVarg7NmzaNOmDQAgNzcXkZGRaNy4caH9nZ2doVAocOLECXh6ehY4nl/hycvLU7Y5OTlBLpcjNjb2rZWZunXr4o8//lBpO3PmzPtvkoioCLjolkjE+vXrh4oVK6Jr167466+/EBMTg+PHj2Ps2LF4+PAhAGDcuHGYN28e9uzZg5s3b+Kbb7555zNU7Ozs4OPjg8GDB2PPnj3KMbdv3w4AsLW1hUQiwb59+5CcnIy0tDSUL18eEydOhJ+fH9avX4+7d+/iwoULWLZsGdavXw8AGDlyJG7fvo1JkyYhOjoamzdvRmhoqLbfIiIqI5iwEImYkZERTp48iRo1aqB79+6oW7cuhgwZgpcvXyorLhMmTED//v3h4+MDV1dXlC9fHl9++eU7x121ahW++uorfPPNN6hTpw6GDRuG9PR0AEDVqlUxa9YsTJkyBZUrV8bo0aMBALNnz8b333+PoKAg1K1bFx06dEBYWBjs7e0BADVq1MCuXbuwZ88eNGzYECEhIZg7d64W3x0iKkskwttW5hERERGJBCssREREJHpMWIiIiEj0mLAQERGR6DFhISIiItFjwkJERESix4SFiIiIRI8JCxEREYkeExYiIiISPSYsREREJHpMWIiIiEj0mLAQERGR6DFhISIiItH7PzEbc36NG7sEAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "# Calculate the confusion matrix\n",
        "cm = confusion_matrix(y_train_8, y_pred_svm)\n",
        "\n",
        "# Create a heatmap for the confusion matrix\n",
        "sns.heatmap(cm, annot=True, cmap='Blues', fmt='d')\n",
        "\n",
        "# Set labels, title, and axis ticks\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix')\n",
        "\n",
        "# Customize the axis ticks\n",
        "class_names = ['Negative', 'Positive']\n",
        "tick_marks = [0.5, 1.5]\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pAhbh8beQQHx"
      },
      "source": [
        "## **K-Nearest Neighbors**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YOlZC3KiQU64",
        "outputId": "c2c7fd29-195d-42e1-c8ea-030d9ac8ad15"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([False, False, False, ..., False, False,  True])"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn_clf = KNeighborsClassifier(n_neighbors=2)\n",
        "y_pred_knn = cross_val_predict(svm_clf, X_train, y_train_8, cv=3)\n",
        "y_pred_knn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9jlizQ3AerwV",
        "outputId": "ec90eeb7-f0c0-450c-da9c-a03b8eef4401"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The ROC AUC value for SVM Classifier is :  0.9656751153652104\n",
            "Precision score is :  0.980606931226432\n",
            "Recall score is :  0.9333447273970261\n"
          ]
        }
      ],
      "source": [
        "print (\"The ROC AUC value for SVM Classifier is : \" , roc_auc_score(y_train_8, y_pred_knn))\n",
        "#Precision and Recall values for RandomForest\n",
        "print (\"Precision score is : \" , precision_score(y_train_8, y_pred_knn))\n",
        "print (\"Recall score is : \" , recall_score(y_train_8, y_pred_knn))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "zMlbOqIserwf",
        "outputId": "6dd25e58-6dc0-406d-ac70-35237b7e611b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "# Calculate the confusion matrix\n",
        "cm = confusion_matrix(y_train_8, y_pred_knn)\n",
        "\n",
        "# Create a heatmap for the confusion matrix\n",
        "sns.heatmap(cm, annot=True, cmap='Blues', fmt='d')\n",
        "\n",
        "# Set labels, title, and axis ticks\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix')\n",
        "\n",
        "# Customize the axis ticks\n",
        "class_names = ['Negative', 'Positive']\n",
        "tick_marks = [0.5, 1.5]\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8GTvCWxXXYHf",
        "outputId": "400c5057-e9e8-4408-82d9-f88e30f0377c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.9909, 0.9928, 0.9914])"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cross_val_score(svm_clf, X_train, y_train_8, cv=3, scoring=\"accuracy\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I-CERaE7hkXd",
        "outputId": "c1a455f6-d7ce-4243-db61-4d86abffad54"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.99      , 0.986     , 0.987     , 0.98983333, 0.98733333,\n",
              "       0.98733333, 0.98983333, 0.98683333, 0.98883333, 0.99166667])"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cross_val_score(knn_clf, X_train, y_train_8, cv=10, scoring=\"accuracy\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o0hZt0tKlLb-"
      },
      "outputs": [],
      "source": [
        "y_scores_svm = cross_val_predict(svm_clf, X_train, y_train_8, cv=3,\n",
        "method=\"decision_function\")\n",
        "if y_scores_svm.ndim == 2:\n",
        "  y_scores_svm=y_scores_svm[:,1]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pDCmHtxZzfuy"
      },
      "outputs": [],
      "source": [
        "y_scores_knn = cross_val_predict(knn_clf, X_train, y_train_8, cv=3,\n",
        "method=\"predict_proba\")\n",
        "if y_scores_knn.ndim == 2:\n",
        "  y_scores_knn=y_scores_knn[:,1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "PiPZ3tOjiKYd",
        "outputId": "a589ff10-c858-4e53-c121-2516ef75adaa"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# But to plot a ROC curve, you need scores, not probabilities.\n",
        "# A simple solution is to use the positive class’s probability as the score:\n",
        "\n",
        "y_scores_forest = y_probas_forest[:, 1] # score = proba of positive class\n",
        "fpr_svm, tpr_svm, thresholds_svm = roc_curve(y_train_8,y_scores_svm)\n",
        "fpr_knn, tpr_knn, thresholds_knn = roc_curve(y_train_8,y_scores_knn)\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(fpr, tpr, \"b:\", label=\"SGD\")\n",
        "ax.plot(fpr_forest, tpr_forest, linewidth=2, label=\"Random Forest\")\n",
        "ax.plot(fpr_svm, tpr_svm, linewidth=2, label=\"svm\")\n",
        "ax.plot(fpr_knn, tpr_knn, linewidth=2, label=\"knn\")\n",
        "ax.plot([0, 1], [0, 1], 'k--')\n",
        "ax.legend(loc=4);\n",
        "ax.set_title('ROC curves');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yAzv_VYBJFoF"
      },
      "source": [
        "### **Multiclass Classifiaction**"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}