Day_47_PCAintution.ipynb

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        "<a href=\"https://colab.research.google.com/github/Manish-k723/100-days-of-ML/blob/main/Day_47_PCAintution.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "dibw47DpWEEI"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
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        "np.random.seed(23) \n",
        "\n",
        "mu_vec1 = np.array([0,0,0])\n",
        "cov_mat1 = np.array([[1,0,0],[0,1,0],[0,0,1]])\n",
        "class1_sample = np.random.multivariate_normal(mu_vec1, cov_mat1, 20)\n",
        "\n",
        "df = pd.DataFrame(class1_sample,columns=['feature1','feature2','feature3'])\n",
        "df['target'] = 1\n",
        "\n",
        "mu_vec2 = np.array([1,1,1])\n",
        "cov_mat2 = np.array([[1,0,0],[0,1,0],[0,0,1]])\n",
        "class2_sample = np.random.multivariate_normal(mu_vec2, cov_mat2, 20)\n",
        "\n",
        "df1 = pd.DataFrame(class2_sample,columns=['feature1','feature2','feature3'])\n",
        "\n",
        "df1['target'] = 0\n",
        "\n",
        "df = df.append(df1,ignore_index=True)\n",
        "\n",
        "df = df.sample(40)"
      ],
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      "execution_count": 19,
      "outputs": [
        {
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          "text": [
            "<ipython-input-19-a315fcc46096>:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append(df1,ignore_index=True)\n"
          ]
        }
      ]
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      "source": [
        "df.head()"
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              "    feature1  feature2  feature3  target\n",
              "2  -0.367548 -1.137460 -1.322148       1\n",
              "34  0.177061 -0.598109  1.226512       0\n",
              "14  0.420623  0.411620 -0.071324       1\n",
              "11  1.968435 -0.547788 -0.679418       1\n",
              "12 -2.506230  0.146960  0.606195       1"
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              "      <th>feature1</th>\n",
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              "      <th>2</th>\n",
              "      <td>-0.367548</td>\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So, Now we apply step-by step PCA without using any inbuilt library"
      ],
      "metadata": {
        "id": "_4yxi2fBZuN6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#1 Mean Centering, Applyin Standard Scaler\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "\n",
        "df.iloc[:,:3] = scaler.fit_transform(df.iloc[:,:3])"
      ],
      "metadata": {
        "id": "PL0EprzbZt27"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.round(df.describe(),2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "UiYQ1eAdaPfj",
        "outputId": "6564d660-2253-467c-ffda-9a5b78df61f1"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       feature1  feature2  feature3  target\n",
              "count     40.00     40.00     40.00   40.00\n",
              "mean       0.00      0.00     -0.00    0.50\n",
              "std        1.01      1.01      1.01    0.51\n",
              "min       -2.57     -2.00     -1.75    0.00\n",
              "25%       -0.68     -0.49     -0.68    0.00\n",
              "50%       -0.03     -0.04      0.03    0.50\n",
              "75%        0.72      0.57      0.47    1.00\n",
              "max        2.09      3.56      2.18    1.00"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-337aab6c-8a19-4863-8771-e78691cfce33\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>feature1</th>\n",
              "      <th>feature2</th>\n",
              "      <th>feature3</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>40.00</td>\n",
              "      <td>40.00</td>\n",
              "      <td>40.00</td>\n",
              "      <td>40.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>0.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.01</td>\n",
              "      <td>1.01</td>\n",
              "      <td>1.01</td>\n",
              "      <td>0.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-2.57</td>\n",
              "      <td>-2.00</td>\n",
              "      <td>-1.75</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.68</td>\n",
              "      <td>-0.49</td>\n",
              "      <td>-0.68</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-0.03</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.72</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.47</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.09</td>\n",
              "      <td>3.56</td>\n",
              "      <td>2.18</td>\n",
              "      <td>1.00</td>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-337aab6c-8a19-4863-8771-e78691cfce33');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **2. Finding Covariance Matrix**"
      ],
      "metadata": {
        "id": "6cfGBdgLaeW7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cov_matrix = np.cov([df.iloc[:,0],df.iloc[:,1],df.iloc[:,2]])"
      ],
      "metadata": {
        "id": "j295gGd7aeK8"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cov_matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CQENvtv2a44C",
        "outputId": "1c4774c0-e277-4286-afdb-10101e79e100"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.02564103, 0.20478114, 0.080118  ],\n",
              "       [0.20478114, 1.02564103, 0.19838882],\n",
              "       [0.080118  , 0.19838882, 1.02564103]])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Step-3 Finding Eigen Values and Vectors**"
      ],
      "metadata": {
        "id": "4_oWGasbbO0T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "eigen_values, eigen_vectors = np.linalg.eig(cov_matrix)"
      ],
      "metadata": {
        "id": "Sy-c7xrKbT6J"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eigen_values, eigen_vectors"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0GwKapnbbjc4",
        "outputId": "f51322c3-fba0-4270-a9d0-326015969b63"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1.3536065 , 0.94557084, 0.77774573]),\n",
              " array([[-0.53875915, -0.69363291,  0.47813384],\n",
              "        [-0.65608325, -0.01057596, -0.75461442],\n",
              "        [-0.52848211,  0.72025103,  0.44938304]]))"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%pylab inline\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from mpl_toolkits.mplot3d import proj3d\n",
        "from matplotlib.patches import FancyArrowPatch\n",
        "\n",
        "\n",
        "class Arrow3D(FancyArrowPatch):\n",
        "    def __init__(self, xs, ys, zs, *args, **kwargs):\n",
        "        FancyArrowPatch.__init__(self, (0,0), (0,0), *args, **kwargs)\n",
        "        self._verts3d = xs, ys, zs\n",
        "\n",
        "    def draw(self, renderer):\n",
        "        xs3d, ys3d, zs3d = self._verts3d\n",
        "        xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, renderer.M)\n",
        "        self.set_positions((xs[0],ys[0]),(xs[1],ys[1]))\n",
        "        FancyArrowPatch.draw(self, renderer)\n",
        "\n",
        "fig = plt.figure(figsize=(7,7))\n",
        "ax = fig.add_subplot(111, projection='3d')\n",
        "\n",
        "ax.plot(df['feature1'], df['feature2'], df['feature3'], 'o', markersize=8, color='blue', alpha=0.2)\n",
        "ax.plot([df['feature1'].mean()], [df['feature2'].mean()], [df['feature3'].mean()], 'o', markersize=10, color='red', alpha=0.5)\n",
        "for v in eigen_vectors.T:\n",
        "    a = Axes3D([df['feature1'].mean(), v[0]], [df['feature2'].mean(), v[1]], [df['feature3'].mean(), v[2]], mutation_scale=20, lw=3, arrowstyle=\"-|>\", color=\"r\")\n",
        "    ax.add_artist(a)\n",
        "ax.set_xlabel('x_values')\n",
        "ax.set_ylabel('y_values')\n",
        "ax.set_zlabel('z_values')\n",
        "\n",
        "plt.title('Eigenvectors')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 974
        },
        "id": "k4w1jLfebsst",
        "outputId": "b01a90a5-25f4-4742-a30c-10fbbe3e1ee0"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-41-ecc20acf69e5>\u001b[0m in \u001b[0;36m<cell line: 25>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'feature1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'feature2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'feature3'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'o'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarkersize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'red'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0meigen_vectors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m     \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAxes3D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'feature1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'feature2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'feature3'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmutation_scale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marrowstyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"-|>\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"r\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_artist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'x_values'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/mpl_toolkits/mplot3d/axes3d.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, rect, elev, azim, roll, sharez, proj_type, box_aspect, computed_zorder, focal_length, *args, **kwargs)\u001b[0m\n\u001b[1;32m    150\u001b[0m             )\n\u001b[1;32m    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 152\u001b[0;31m         super().__init__(\n\u001b[0m\u001b[1;32m    153\u001b[0m             \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrect\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframeon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbox_aspect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbox_aspect\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, facecolor, frameon, sharex, sharey, label, xscale, yscale, box_aspect, *args, **kwargs)\u001b[0m\n\u001b[1;32m    642\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    643\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_position\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_originalPosition\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmtransforms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 644\u001b[0;31m             \u001b[0msubplotspec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSubplotSpec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_from_subplot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    645\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_position\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_position\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheight\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    646\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Width and height specified must be non-negative'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/gridspec.py\u001b[0m in \u001b[0;36m_from_subplot_args\u001b[0;34m(figure, args)\u001b[0m\n\u001b[1;32m    583\u001b[0m             \u001b[0mrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    584\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 585\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0m_api\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnargs_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"subplot\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtakes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"1 or 3\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgiven\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    586\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    587\u001b[0m         \u001b[0mgs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSpec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_gridspec_exists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: subplot() takes 1 or 3 positional arguments but 2 were given"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pc = eigen_vectors[0:2]\n",
        "pc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5XJgZeO0bsfx",
        "outputId": "3d8159f4-508c-4f46-d907-66e16169c1a1"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.53875915, -0.69363291,  0.47813384],\n",
              "       [-0.65608325, -0.01057596, -0.75461442]])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "transformed_df = np.dot(df.iloc[:,0:3],pc.T)\n",
        "# 40,3 - 3,2\n",
        "new_df = pd.DataFrame(transformed_df,columns=['PC1','PC2'])\n",
        "new_df['target'] = df['target'].values\n",
        "new_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "CcmGtS_UcufG",
        "outputId": "e1fef815-1f55-4826-f26c-403636a4b119"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        PC1       PC2  target\n",
              "0  0.599433  1.795862       1\n",
              "1  1.056919 -0.212737       0\n",
              "2 -0.271876  0.498222       1\n",
              "3 -0.621586  0.023110       1\n",
              "4  1.567286  1.730967       1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-886e875a-43c2-43d8-a45c-94b41bcb97d0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PC1</th>\n",
              "      <th>PC2</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.599433</td>\n",
              "      <td>1.795862</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.056919</td>\n",
              "      <td>-0.212737</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.271876</td>\n",
              "      <td>0.498222</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.621586</td>\n",
              "      <td>0.023110</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.567286</td>\n",
              "      <td>1.730967</td>\n",
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        "new_df['target'] = new_df['target'].astype('str')\n",
        "fig = px.scatter(x=new_df['PC1'],\n",
        "                 y=new_df['PC2'],\n",
        "                 color=new_df['target'],\n",
        "                 color_discrete_sequence=px.colors.qualitative.G10\n",
        "                )\n",
        "\n",
        "fig.update_traces(marker=dict(size=12,\n",
        "                              line=dict(width=2,\n",
        "                                        color='DarkSlateGrey')),\n",
        "                  selector=dict(mode='markers'))\n",
        "fig.show()\n",
        "     "
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      "execution_count": 40,
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