principal_component_analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sunnyy6/first-repo/blob/main/principal_component_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VQ3syspj_rKn"
      },
      "source": [
        "# Principal Component Analysis (PCA)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xJGl9TcT_skx"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BNEgrGwd_29D"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hyp1gza1_6qX"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lPLTDBVI__ZQ"
      },
      "source": [
        "dataset = pd.read_csv('Wine.csv')\n",
        "X = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3bUhSHktAcOe"
      },
      "source": [
        "## Splitting the dataset into the Training set and Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L7hGLt1qAced"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1wrHODfJAEiI"
      },
      "source": [
        "## Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-UCD7ezAJG2"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S3i3lRiwASAX"
      },
      "source": [
        "## Applying PCA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TAi_sSw9AVzI"
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pca = PCA(n_components = 2)\n",
        "X_train = pca.fit_transform(X_train)\n",
        "X_test = pca.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UBx16JVLAuel"
      },
      "source": [
        "## Training the Logistic Regression model on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XDQahsqTAy44",
        "outputId": "8ff7589a-6bfb-4737-8856-f1433322675d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103
        }
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "classifier = LogisticRegression(random_state = 0)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "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=0, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MTck416XBPnD"
      },
      "source": [
        "## Making the Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2LO7H5LsBS1b",
        "outputId": "75ce1943-1cc9-4fe2-a18c-46048d6b2d0b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "y_pred = classifier.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[14  0  0]\n",
            " [ 1 15  0]\n",
            " [ 0  0  6]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9722222222222222"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h6pZMBrUBXwb"
      },
      "source": [
        "## Visualising the Training set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FK_LpLOeBdQ4",
        "outputId": "5a89fb09-2cab-4fe2-c527-d1d03c240801",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        }
      },
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = X_train, y_train\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
        "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = ListedColormap(('red', 'green', 'blue')))\n",
        "plt.xlim(X1.min(), X1.max())\n",
        "plt.ylim(X2.min(), X2.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
        "                c = ListedColormap(('red', 'green', 'blue'))(i), label = j)\n",
        "plt.title('Logistic Regression (Training set)')\n",
        "plt.xlabel('PC1')\n",
        "plt.ylabel('PC2')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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j6Rnc9cO7GjqOAoBIEaWXSqMWHD1e0/a4nnryKZ7Z/QwA+/bu40eDP2LgpMaSHjQHIG2t1vF8pZdKo65cvWPKHADA9BmTXLl6R0PH3TGyg4++/6NMTE7gk84Fb76Ac88/t6FjKgBI24paLmLoySGGnhwqGwyUXiqNKkz0rl0zl61P9LDg6HGuXL2j4QngRS9bxDfv+GYSRTxAAUDaVtR4fkG5tYMKvysLSBqx4uI9DVf4raAAILlWaYin2rh9YXK3tHJvVnqpSNYoAEhuVVsRtNx4frE8T+7qeoXWmWQSd2/4wqtmc3cmie71RlEAkNyqlLLZ39cfOZ5fKsnJ3VZWyFoOu7W27t3KnD1z6J3Zm9kg4O6M7Rlj696tsfdRAJDcqpayWTqeHyWpyd1WV8jVgp8k66bNN3EJl7BgxgK6Mpo9P8kkW/du5abNN8XeRwFAcitOymZhPH/Tk5vYMrrlkOdu2rnpwPMa0YwKuZ75jTwPaWXZ6MQo1z9yfdrFSFw2Q5lIDANHDNBlU0/hqJTNkdGRyMofYMInElnqIekKudqSFOWGrnS9gtRCPQDJrbgpm9WWcZj0SR5+6uGaW+rFLfRy6q2Q65nf0PUKUisFAMm1OCmbcVrh45PjjIyOxA4CpWP+UeqtkEdGRyr2KDb8ZgNjE2N0Wzc9XT2MT44rC0jqogAgbS9OOihQ03h9pYvMCq9ZT4VcCCyVFP6WCZ+giy4WH7lYFb/URQFA2t7AEQMMPTlU9Xm1jNdXeu7y45cf+L3W1NBqgaWUMn+kEZoElrbX39dPT1f1tk4t4/VxJmHrubdAPZPGyvyReqkHIB1hfLLyUrzVxutLW/JzZsxh27PbKk7C1pMaWi21VSuVSpJS6wGY2bFmdoeZ/dLMHjCzD6RVFml/lSrJavcTjmrJb3t2G/MPn1/xnsT1pIZGpbYCzJkxJ3baq0hcafYAxoEPuft9ZjYTuNfMbnf3X6ZYJmlDI6MjkT2ALuuqeiP5kdGRyPmDSZ9k596dnHXsWWX3refeAv19/ezet/uQ6xa2PbuNWdNnsWjOorqXm9DaQVIqtQDg7luBreHve8xsCDgaUACQxFRK15z0SYaeHGL3vt0sPHJh2X3LqTb2Xm+u/s69OyPLOvz0MGcde1ZdlbbWDpIomZgDMLPjgVcAP063JNIMabY842TVFFrbpUEgTqpnJfXeW6AZyzxo7SCJknoAMLM+4OvAB939mYjHVwGrAGx2Nlfhk/LSbnnGrTS3jG45JABU2jfu2Hs99xZoxm0ptXaQREk1AJjZNILK/0Z3/0bUc9x9HbAueP6rfPA9twGw/O/Pb1UxpQGNtjwb7T3EvQis1n2rzR00ohnLPOhexxIlzSwgA74ADLn7Z+Ps09dnLD9nGrNfOI3B99x24Eeyq5GWZz159KXKZdXUu2+XNf/K2/6+fhbNWVQxw6hWyiCSKGn2AF4DXAbcb2Ybw23Xuvut1XZcsgRgGgCD/7Z/ShBQzyAd5VrqjbQ8kxi3Lh2HNwzHD3neUX1HVd23lfMXSd+WUvc6lihpZgGtBxoe1F9+zrQDvxcHg57j72bpJz7R6OElhkrj/I0MZyQ1bl1amZbeG+CovqMis4Ci9s2zdvpbJBmpTwInaWowOPNAMLAXPMuyz701rWK1vUot9UKefD0tz2aNWy88cmHZCl+kk7RVACg2NRgcjiaPk1M63BPn1oz1tDy15r1Ic7VtAChWCAbr16P5ggZFDfeU02hLXePW2aIridtPRwSAgqVLIWryWENE8cVdrrhcS73WSkTj1tmQ9vUc0hwdFQCKlRsi0uRxZdVa/JUqdlUi8WWtta0riXNm8M5YT+vYAFCs3OTx7D+Zx5L5S9IqViZVmpittDAaqBKJK4uBUlcSZ9jPNsLu3VM23fHosin/NwYjd1UAKFE8X7Dr2u0H3jbNFwRqmZitdbJYApt2bqoaKJPqIcQ9jq4kzoiIyh4OrfDjUgAoo3i+QJPHB8WdmG3lZHE7GRkdYcInIh8rvIf19BCiKnog9nGUkZWSiKGcO741q3A1bMMUAGIoN3kM+QsGSbQc40zMNjpZnGXNHJ8ffnq47GO93b0V709QbiitXMAwLPaQnDKyWiSqwi9t3Sc4Kq0AUKM8X3ncyrHlRiaLs6zZ72Gl923OjDl13Z+g3NxL3OOUBrxmr4XUMRIezqmHAkADyk0eZzUYtHISttyYcbd1V50szrJmv4fl3reerh527t1Z1/0Jap1jibqxfZYmpHPrrvUwMXV4L8nhnHooACSkEAw2boRdj56ZySuPWzkJO3DEQNmhipHRkVxUHlFDPc1+D8uNtZ/0opMi38/i55QbSqsUVCZ9MvEb20uoZDjnjhu6YWnzhnPqoQCQsOKVSu/8t/FMzRe0MpOjv6+fh596+JB78Tre1MojyeyYqJZvt3VHTtIm9R5WGmsvbItSabnoSkGl3GsVKHMrpjiTtUtbWJ6YFACaaNk5B9/eLGQStTqTI+pG7NC8yiPJ4YpyLd+erh666Grqe1hukr3c51ftXgHVJnAr7av0zwhRQzk3dBeyRQ7KwSVECgAtkoVMolZncrS68khyuKJckBqfHGfxkYtTu0cA1Pf5aUG+BuS0dR+HAkAKymUSteLK41aurTNnxpwp6+4Xb2+GJIcrKgWvNNcnavVrd2T6Z4tTMdOkAJCyKcGg6MrjrGYS1WLn3p01bW9Ukj2OpFq+WVvTpx5tvSBfBlIx06QAkCF5yCSqRasnEJMcrkii5asUygzKYCpmmhQAMijLmUS1aPUcQNLDFY22fFudQtlIb6MdeiqHiKjsoX2Hc+qhAJBxxZlEebvyOI0JxCwNV7SyB9RIb6NteipRrfsOGcqplwJAjpQbIsrqstUdOYFYpJU9oEZ6G7m82KtsKqYq/FooAORQ8RDRxo3ZXrY6Sy3yVmvlRHKtvY3zfjTC5V8fZt7OMXr+J2Dx901FG6dipkkBIOfaZb6gWLuMR7dyIrmW3sZ5Pxrhw196iOnPB8c8bjc8NvvQ1071Yq8OSsVMkwJAGymeLygOBnm653HbjEeHWjWRXEtv4/KvDx+o/AHW/ABWrYDnXkDVfZsi6o5WUVfWSuIUANrU1Mnjw3MTDHI5Ht1EcYd2aultzNs5dd+V9wf/XnsebJ5N83tdcVr3qvtbQgGgA0y98vjwTE8eJ505k/fhpFqGduL2NrbP6WV+RBA4b0svb//fCS/VHeN+tZIeBYAOs/ycaYyMwIMPwq6PPhVOHk8w+8/mZiIYJJk50w7DSc1Ipb3+ooEpcwAA+17QxfUXNTjk0+FX1eaRAkCHGRmBhx4CdziY+tHDro/uYLDLgXQnj5Os8NphOKkZqbQ/ODvYt5AFtH1OLxtOncPlXx/m2nVDbJ/Ty/UXDRx4Xlkau889BYAMGBmB4WEYG4PeXhgYgP4m1U/DwzAZcVOp3t4ezjor/XseJ1nhtcta9s1Ipf3B2f0HKvjSrKD5O8f48JceOvC8A5SK2XYUAFJWaJEXKuWxseD/0JwgMFam7itsL54vSCuTKKkKT2vZx1OaFQQw/flJLr9xiB88/+CU7UrFbC8KACmLapFPTgbbmxEAenujg0BvRJ2Y10yiAq1lH09pVlDB/Gc1ft/uFABSVq1FnrSBgak9DoCurmB7JVOWoXjm8FysVNrpS1FUVDScs/3woLI/RFSrQNpK1QBgZi8E5rr7r0u2n+ruv2hayTpELS3yJBR6FfXOOZQuQ1E8X5BWWmmlVM9OXorigGpj98eN1NcqiNLKCS1pmHmQDhL9oNnbgOuA7QTf+ne5+0/Dx+5z91e2pJShmTNP89NOu6eVL9l0pXMAEHz3Fi3K1/dm/XoYn9wPtHal0tJUT4h3n9y2FudCq1LFFXdPT5AmNjFRWyXeLidzG7LBwXvd/bTS7dV6ANcCr3L3rWZ2OvAVM7vG3b9J5PJRUqtGW+RZMfWex2e2bL6gHVI9GxL3BuXV9PcHP41kJbR6QksaVi0AdLv7VgB3/4mZnQt818yOBcp3HaQmhe9eu2jllcftkuoZW7OXUWikEm/1hJY0rFoA2GNmJxTG/8OewHLgW8DLml04adzIvBsZHljNWO9meseOY2B4Df3bV7bs9QvBYP365ixb3dapnmlcWdtIJd7qCS1pWLUA8D5KhnrcfY+ZXQC8rWmlkkSMzLuRhxatYrL7OQDGpj/GQ4tWAbQ0CMDUIaL168PJ40kDugGnd852Bi66nv6zf1DTcdsq1TOp4ZxGJmIbqcTrTTGT1FQLAM8C/cCvSrafDtzd6IuHgeQvCWqB6939zxo9phw0PLD6QOVfMNn9HMMDq1seAIotXQojI9OK6gpjbOd8htatBqgpCOQ61bMZwzmNXlnYSCVey4SWsoUyoVoAuA64JmL7M+FjK+p9YTPrBv4W+E/A48BPzew77v7Leo/ZbHk7Z8d6N9e0vZWil6QwhtatZuj6j9SUSZSLVM+oyr4Z6+Y0OhHbaFZCnAmtVl/+LmVVCwD97n5/6UZ3v9/Mjm/wtU8HfuXuwwBmdhPwZiCTASAL52ytAah37DjGpj8WuT1t5YeUjeXnTJuSSdTKtNJExF0krRnr5iQxEdvkrIQbp29i9RWTbJ4V3I1szQ9g5f3KFkpDtQAQcaO4A2Y0+NpHA78p+v/jwBmlTzKzVcAqgN7e9CqutDPc6glAA8NrpswBAHRNHMbA8Joml7a6akPNU648fvTMbF95HDV2n9YiafWM4bewa3vjvBFWnT1x4O5jj80O7kYGsPJ+ZQu1WrUAcI+Z/aG7f754o5ldDtzbvGId5O7rgHUQXAjWiteMknaGW7kANDQU/ER9bwvj/GlmAZUTd6i5+MrjA5PHodSCQZbvV1vrGH6Lu7arB4an3HoSgltRrj4PVm5StlCrVQsAHwS+aWYrOVjhnwa8AGj06p4ngGOL/n9MuC2T0s5wqxZoyn1v+7evnFLhj4zAXZsONlh7euCkk6bu04oGYT1DzcWZRMUrlUITg0HebnJS6xvb4q7t5t7oE3nzLJQtlIKKAcDdR4CzwwvATg43/7O7/zCB1/4pcJKZvYSg4r8EeEcCx22KVme4lVbCPT0wPl55n8lJePjh8t/bkZGgt1BsfDy4Oxg0fiForRoZap66Uun+5K48TioVs1ZJRt1a3ti4XduEynfcWC+PTT/0NbscbjwFVm6v+ZDSgIoBwMymA+8FTgTuB77g7lWqoXjcfdzM3g/8K0Ea6N+7+wNJHLsZWrlkw6ZNsGXLwf8XvotmhTt5lTc+HnxXo8o1PBy9j/vBBl/acx31KHflcdXJ47it+2aP39cTdZMKGHG6trWWr0LZ1gwPsGrRQzzXPfUkm+iCVYuCY67cntETrQ1VGwL6MrAfuAt4PbCYYFgoEe5+K3BrUsdrtlYs2TAyMrXyL9bVFfQEqg0HlausK+1XeCztuY5GTQ0GZ05dhmIr2bxBea1RN8luWpyubS3lq1K2QuX+zt8aYqJr6q7PdU+yemC4sQCQt1ztlFULAC9191MAzOwLwE+aX6TOVq6VDsHIRE8PLF4c/L90OKegXGVdrrFXeKzSc/J4Nf+UZSiu2c4d3YfB0pQq/EoVU61RN8luWpyubS3li1G2ldv7uWxx9Mlbbo4glizkaudMtQCwv/BLOGTT5OJInMneBx8MGmnllKusBwaig4bZwQZfO17Nv3RpmLiT1s3Kq1VMtUbdpLtp1bq2tZQvZtnKzQUcN9ZASyOP45cpq1CNAPByM3sm/NkDnFr43cyeaUUBO02clnZhqfYoZsE8wOAgbNgQ1D0F/f1B76E0ji9YcPD70d8fLN9e3CNol+XcjzhjMJ0XrlQxQRBdSyN6pahb7iRpVjetlvLFLNua4QEOKxkDOmyiizXDDbQ08j5+mYJqWUDdrSqIBKJa4HEVMoUKwaFcD7h0MnnbNpg1a2oQaIcKv5iZsevTz8OyFK4dqFYx1Zph0OpuWi3li1m2wjj/6oFhNveOcdxYL2uGBxob/2+n8csW0T2BY2rV3FLpd60W3d2HpopOTgZZRcXH7cRe8rJzehi8c3/1JzZDnIqplqibxl2E4pavhrKt3N6fbMZPO45fNpkCQAytnlsq/q5F3WUvSrxg0d8AAA5kSURBVKUJ3omJg6mh6iWnoBkVU5a7aWmVrV1ur9dCCgAxpNlqLj2noy4IK9QllXoNhbJ2ei/53OPvbH3qpyqm1slyYMwgBYAYqrWamz08VHpOV3q9aqmhndxLNjM29js8msKLp1Ux1XNyKpe+YygAxFCp1ZxG6nG5uqS/P1gKImrJiEILv9Mbo6lNBKch6uQcGgouhlu4MP4+yqVvWwoAMVRqNWdtUvWkk6q38Du1l5zqRHAaou+6E1xqXpz2VW2ftE5o9USaTgEghkqt5kpDLhs2tP7c7fQWvhSpNLNf63ohlY5VXFEXNHriqSfSEgoAMZVrNVfKvileX6eV526ntvDjSmUiOA1xTs64+5TLEiiXptbognZxeyLqJTSk2pXAUkXURZJRii/8lBR10momlWb2K60XUstVyeWGmaDySV8IHKWtpJGR4CdO4Kp0DIlFPYAGRQ25KNc+u5afM43BL4/lcyK41tZuf38w4Vu6vGylCr3WMcQ4i1dFKdfC37Sp8prnxYErS/MVOaUAkIDSIZfC2H+prObaqxedA/WOiS9cGEz41ho44p4AlVo8hcejVLpqsZzSwKWWVsMUAJogT7n2mmvLiUZau82cFKq0eFW1Be1qrahLVyXs9KsaE6A5gCbI04qa1RaqbFfnHn/ojd0zLaut3dKTvaDaSV9urqGnTJu0t/fQY9U6XyGHUA+gSfKSiZPVeqWpjPzNA2SttdvouGG5uQaI331WznPDFAByQuP0yVl+zrT8XRCWpXHFRscNS0/mxYsP3S/uyZ6XllZGKQA0QbnKut5KXOP0kqnWbiPzEXFOZlXqLaMAkLBy5/fu3cGNV+qpxJuZ7Za1kYWWWr8+vdtE1iMrFWMj44ZK3cwUBYCElTu/S1OxC9vjnPfNHKfP0shCSxmce+kEdzyadkFyqJ5WQ9RyEcXqOZk1LtowZQElrNbzOM7zm3kL2DxlLCVt8MvtPNPdROWyb+bMCS6CKb0hdekVu1FqPZl1FXAi1ANIWK3pzXHO+2a30usZWch74yuXE8FZUPjgi0/G3t6g8i83xllpuQgIblJdOJnjnlgaSkqEAkDCylXW8+dP/X4UtsepxLM0/wealO5YUQu/xVkXvVqLqLD0Qy0nVr1DSXlvuSRMASBhlSrrWq/ILz1uVs7Ttmp85W0iOE31VPLVFsgqPnbheFHHLz2x6p2HqPUGOW1OAaAJKt2xK4kKMu1GTEdePCb1VfKFE7TcchHVjl3usWrjolFfknpukNPmNAmcM1mY+2rmpHQrLV82DZt4Lu1i5EelD77SsgzllosoPUYtJ1al7IVyX5JqN8jpQOoB5EwWhl86NnW001X64KtNVBW6v5XmEepZurqWCeJKOrT7qgCQM1kYfsnapLS0SNxKvp5jQJAlUWr+/NpPrHq+DHnrviZEASBnqs19tWp+IEuT0g3TRHB8SXzwUcfYsCG6lb5zZ+3Hr/QlmTOntl5Gm1MAyJlKvXClZ9bHJp7DydHKoK1WT6ui1n2S7NpWG6pqJB2vzSgAJKgVre9KvfCoRlRu0zNbZPkyXRBWUT2tinr2SXJRqiSGqjqEAkBCWtn6Lnf+ZmF+QNpMPVkH9eyTdGZBpUo+7TzqDFEASEizs3PinLMdvbKnNEc9rYp69om6gX3cCeBaKnSNk06h6wAS0szW98gIPPjg1LTmBx88NPdfd8hrwMaNaZcgm+q56KPSPiMj5ReMK80C2rat+gUutV4Y06n3QC1DASAhzbw46uGHDy6XUuAebC/WySt7Nsp2b0+7CNlUT6ui0mqh5SrreivmWvfTOOkUGgJKSDMvjhofj79d81u100RwBbVc9FE8FNPTE6zyOTFReSmGOGsJVVLrfhonnSKVAGBmnwFWAM8Dvwbe7e670ihLUnRxlLStOK2K0rH18fGgBVR8v9+hoeh9q60lVEmt++ky9inSGgK6HTjZ3U8FNgHXpFSORPX3w1lnwfLlwb9JVf7d3eUfKx1KlQZoHqB+cYZi6l1LqJJa99M46RSp9ADc/bai/94NXJxGOfJi4cLyjSeonMigjDdpiThDMY2sJVROPftpnPSALMwBvAf4arkHzWwVsAqgt/e4VpWpKeqtjEvP8ShRKafllj8vBBMFhIOWL5uG3bldVwTXK85QTLMu0FKFXremBQAz+z4wP+Kh1e7+7fA5q4Fx4MZyx3H3dcA6gJkzT/Nyz8u6RtOPi8/xwcHo55R+/6rdia/DU6AlSXHH1lVZZ0rTAoC7v67S42b2LuBC4Dz30iTH9pPkhWJx573iZLZpqQhJhLIgcimtLKALgKuBZe7eEXfkaNVaV8Xi3qC+Q1Ogo23cCEuWpF2KfFLrPnfSygL6G2AmcLuZbTSzz6VUjpZJ8kKxuIkMUQkSSZWhXemCMOkkaWUBnZjG66aplWtdFT8HgiuGy11M1sEp0IfQBWGSqByk4GUhC6gjpDVEWnwnvtIsoqTLkIPzXZpBH/yhcrLonAJAC6U1RNqqyj8H57skTR98tCzcvDsGLQbX5koXSyyotmhirbTIYofSBx8tJ4vOKQC0uUrXAiT5Pc3J+R6L3Xlb9SdJoJ0++CQ1c3ngBCkA5Fy55dUL6l1MsVY5Od+rWr5sWtpFyJd2+eCTlpObcygA5Fice2HEWUwxCTk53yVp+uCj5WTROU0C51iceaao9NOCJL+nuhC0Q+mDLy8HF8YpAKSo0ey5OMOv5RaSa8b3NAfnuzSDPvjcUgBISRLZc3HXBNL3s3Z25234Mq0MKu1NcwApSSJ7TsOvzaGJYOkU6gGkJInsOQ2/ikgjFABSktS9qTW8IyL10hBQSjR8k31HnDGYdhEkDdUurmkjCgApyUmacMdavmwauz79fNrFkFaLc3FNG9EQUIo0fCOSMTlZxC0p6gGIiBR02NpGCgAiIgUdtraRAoBIBVoZtMN0WHaGAoBIGbogrAN1WHaGJoFFRIp1UHaGegAiIh1KAUBEpEMpAIhUoYlgaVcKACIVaCJY2pkCgIhIh1IAEBHpUAoAIjFMG/5E2kUQSZyuA5BMafQ+yc2wfNk0Bu9cCu15Mah0MAUAyYwk7pMsIvFpCEgyI4n7JItIfAoAkhkdthKvSOoUACQzsr4Sb9eGb6RdBJFEKQBIZmR5JV5dECbtSJPAkhmFid6sZQEV+PN9aRdBJFEKAJIpHbQSr0jqNAQkItKhFABEaqCJYGknqQYAM/uQmbmZHZlmOUTimD1rmuYBpK2kFgDM7FjgfGBzWmUQqcWSJWmXQCRZafYA1gJXA55iGUREOlYqAcDM3gw84e4/j/HcVWZ2j5nds3//jhaUTqSyI84YTLsIIoloWhqomX0fmB/x0GrgWoLhn6rcfR2wDmDmzNPUW5BUzZ41jd3XPQVn/W7aRRFpWNMCgLu/Lmq7mZ0CvAT4uZkBHAPcZ2anu/u2ZpVHJCmaCJZ20fIhIHe/393nufvx7n488DjwSlX+kgeaCJZ2ousAREQ6VOoBIOwJPJl2OURqoVtESjtIPQCI5M3sWVoZVNqDAoBIjZYsgfHfLE27GCINUwAQEelQCgAiIh1KAUCkTpoIlrxTABCpQ0/3NM0DSO4pAIjUYanqfmkD5p6f5XXMbAfwWAtf8kig069R0HsQ0PsQ0PsQyNv78GJ3n1u6MVcBoNXM7B53Py3tcqRJ70FA70NA70OgXd4HDQGJiHQoBQARkQ6lAFDZurQLkAF6DwJ6HwJ6HwJt8T5oDkBEpEOpByAi0qEUAEREOpQCQAxm9iEzczM7Mu2ypMHMPmNmD5rZL8zsm2Y2O+0ytZKZXWBmD5nZr8zso2mXp9XM7Fgzu8PMfmlmD5jZB9IuU5rMrNvMfmZm3027LI1SAKjCzI4luIH95rTLkqLbgZPd/VRgE3BNyuVpGTPrBv4WeD3wUuDtZvbSdEvVcuPAh9z9pcCZwH/rwPeg2AeAobQLkQQFgOrWAlcDHTtb7u63uft4+N+7gWPSLE+LnQ78yt2H3f154CbgzSmXqaXcfau73xf+voeg8js63VKlw8yOAd4IXJ92WZKgAFCBmb0ZeMLdf552WTLkPcD30i5ECx0N/Kbo/4/ToZUfgJkdD7wC+HG6JUnNdQQNwsm0C5KEnrQLkDYz+z4wP+Kh1cC1BMM/ba/S++Du3w6fs5pgOODGVpZNssHM+oCvAx9092fSLk+rmdmFwHZ3v9fMlqddniR0fABw99dFbTezU4CXAD83MwiGPe4zs9PdfVsLi9gS5d6HAjN7F3AhcJ531sUjTwDHFv3/mHBbRzGzaQSV/43u/o20y5OS1wBvMrM3ANOBF5rZDe5+acrlqpsuBIvJzB4FTnP3PK0AmAgzuwD4LLDM3XekXZ5WMrMegonv8wgq/p8C73D3B1ItWAtZ0AL6MvCUu38w7fJkQdgD+LC7X5h2WRqhOQCJ42+AmcDtZrbRzD6XdoFaJZz8fj/wrwSTn1/rpMo/9BrgMuC14ee/MWwFS86pByAi0qHUAxAR6VAKACIiHUoBQESkQykAiIh0KAUAEZEOpQAgEoOZTYTpj/9hZv9kZoeF2+eb2U1m9mszu9fMbjWzheFj/2Jmu9ph1UhpTwoAIvHsdfcl7n4y8Dzw3vACqW8Cg+5+gru/imCl1P5wn88Q5M+LZJICgEjt7gJOBM4F9rv7gQvj3P3n7n5X+PsPgD3pFFGkOgUAkRqES0O8HrgfOBm4N90SidRPAUAknhlmthG4h+DmQF9IuTwiDev41UBFYtrr7kuKN5jZA8DFKZVHpGHqAYjU74dAr5mtKmwws1PN7LdTLJNIbAoAInUK74vwVuB1YRroA8CfAtsAzOwu4J+A88zscTP7nfRKK3IorQYqItKh1AMQEelQCgAiIh1KAUBEpEMpAIiIdCgFABGRDqUAICLSoRQAREQ61P8HEJ/ZgoaGWcoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Dbzx_KqBguX"
      },
      "source": [
        "## Visualising the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kk07XbUHBl0W",
        "outputId": "b127df80-990a-47ea-f908-5995eb1a3ebb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        }
      },
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = X_test, y_test\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
        "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = ListedColormap(('red', 'green', 'blue')))\n",
        "plt.xlim(X1.min(), X1.max())\n",
        "plt.ylim(X2.min(), X2.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
        "                c = ListedColormap(('red', 'green', 'blue'))(i), label = j)\n",
        "plt.title('Logistic Regression (Test set)')\n",
        "plt.xlabel('PC1')\n",
        "plt.ylabel('PC2')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}