Week12_principal_component_analysis.ipynb

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  "cells": [
    {
      "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": 1,
      "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": 2,
      "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": 3,
      "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": 4,
      "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": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UBx16JVLAuel"
      },
      "source": [
        "## Training the Logistic Regression model on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XDQahsqTAy44",
        "outputId": "7d1a8240-fbd0-44dd-e608-3d9829837359",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "classifier = LogisticRegression(random_state = 0)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=0)"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MTck416XBPnD"
      },
      "source": [
        "## Making the Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2LO7H5LsBS1b",
        "outputId": "bd02c5d4-7c1b-4986-c260-e7cb01ac5663",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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": 7,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "[[14  0  0]\n",
            " [ 1 15  0]\n",
            " [ 0  0  6]]\n"
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              "0.9722222222222222"
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          "metadata": {},
          "execution_count": 7
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h6pZMBrUBXwb"
      },
      "source": [
        "## Visualising the Training set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FK_LpLOeBdQ4",
        "outputId": "c159d7e2-b318-4802-a686-652c0c4df488",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "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": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-97c5a7ed0f37>:10: UserWarning: *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 the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
            "  plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Dbzx_KqBguX"
      },
      "source": [
        "## Visualising the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kk07XbUHBl0W",
        "outputId": "505d4b7d-17ec-4603-d584-80286f3d6ea3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "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": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-dcbaaf9cb35d>:10: UserWarning: *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 the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
            "  plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}