Lecture_9_PCA_Dimensionality_Reduction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Lecture_9_PCA_Dimensionality Reduction.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyM9NNwn67v1yiyl8D4XXlzH",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ashishpatel26/Ganpat-University-Data-Science/blob/main/Lecture_9_PCA_Dimensionality_Reduction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4RnTFzJtupu9"
      },
      "source": [
        "![](https://devopedia.org/images/article/139/4543.1548137789.jpg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_nwMkHDVuxR_"
      },
      "source": [
        "### 1.Load the Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R8Mc0JLaujVj"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "%matplotlib inline\n",
        "np.random.seed(2021)"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wY9GSN4Mwejy"
      },
      "source": [
        "### 2.Read the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "pG27Egn4vMan",
        "outputId": "cabe9101-f839-423e-e4bd-1736f0e296dd"
      },
      "source": [
        "#Importing the data\n",
        "data= pd.read_csv(\"https://raw.githubusercontent.com/ashishpatel26/Ganpat-University-Data-Science/main/data/iris.csv\")\n",
        "data.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>sepal.length</th>\n",
              "      <th>sepal.width</th>\n",
              "      <th>petal.length</th>\n",
              "      <th>petal.width</th>\n",
              "      <th>variety</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal.length  sepal.width  petal.length  petal.width variety\n",
              "0           5.1          3.5           1.4          0.2  Setosa\n",
              "1           4.9          3.0           1.4          0.2  Setosa\n",
              "2           4.7          3.2           1.3          0.2  Setosa\n",
              "3           4.6          3.1           1.5          0.2  Setosa\n",
              "4           5.0          3.6           1.4          0.2  Setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Es9LSS_-wcaO"
      },
      "source": [
        "### 3.split data into x and y"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9jc6AdZmwTEe"
      },
      "source": [
        " X = data.iloc[:, 0:4]\n",
        "y = data.variety"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bt1-qveawqa8"
      },
      "source": [
        "### 4.Scale the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iY1LLXyQwhPh"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "X_scaled = StandardScaler().fit_transform(X)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vsk3Hl_IwuuU"
      },
      "source": [
        "### 5.Find the covariance matrix."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EgCTbF2ywsTG",
        "outputId": "b67d3783-0e07-4ef7-90e1-7e9df20c7d1f"
      },
      "source": [
        "covar_matrix = (1 / X_scaled.shape[0]) * np.matmul(X_scaled.T,X_scaled)\n",
        "print(covar_matrix.shape)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1k0xq4XRw2GV",
        "outputId": "4473cf88-d741-46ce-d8e9-a47107d4cdb9"
      },
      "source": [
        "covar_matrix"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.        , -0.11756978,  0.87175378,  0.81794113],\n",
              "       [-0.11756978,  1.        , -0.4284401 , -0.36612593],\n",
              "       [ 0.87175378, -0.4284401 ,  1.        ,  0.96286543],\n",
              "       [ 0.81794113, -0.36612593,  0.96286543,  1.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c1w9HDATw8nW"
      },
      "source": [
        "### 6.Find the eigenvalues and eigenvectors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7l0dFtv7w5J5"
      },
      "source": [
        "from scipy.linalg import eigh"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qaRN1YckxAzS",
        "outputId": "43da5bf0-6dc3-43d6-8272-cbf9c096f9bf"
      },
      "source": [
        "# eigh function returns the eigenvalues in ascending order\n",
        "# We specify the top 2 eigenvalues (out of 0, 1, 2, 3)\n",
        "values, vectors = eigh(covar_matrix, eigvals=(2, 3))\n",
        "print(f\"Shape of the Eigen value: {vectors.shape}\")\n",
        "print(f\"Vectors: \\n{vectors}\")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of the Eigen value: (4, 2)\n",
            "Vectors: \n",
            "[[-0.37741762  0.52106591]\n",
            " [-0.92329566 -0.26934744]\n",
            " [-0.02449161  0.5804131 ]\n",
            " [-0.06694199  0.56485654]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c0vByKjLxW_X"
      },
      "source": [
        "### 7.Project the original data on eigenvectors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "id": "68IN1WGHxHZH",
        "outputId": "a2c018e0-993b-4844-9537-7204c09ab88b"
      },
      "source": [
        "pca_components = np.matmul(X_scaled, vectors)\n",
        "print(f\"Principal Components: \")\n",
        "pca_data = pd.DataFrame(np.hstack((pca_components, y.to_numpy().reshape(-1, 1))), columns=[\"Component 1\", \"Component 2\", \"Species\"])\n",
        "pca_data.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Principal Components: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Component 1</th>\n",
              "      <th>Component 2</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.480027</td>\n",
              "      <td>-2.2647</td>\n",
              "      <td>Setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.674134</td>\n",
              "      <td>-2.08096</td>\n",
              "      <td>Setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.341908</td>\n",
              "      <td>-2.36423</td>\n",
              "      <td>Setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.597395</td>\n",
              "      <td>-2.29938</td>\n",
              "      <td>Setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.646835</td>\n",
              "      <td>-2.38984</td>\n",
              "      <td>Setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Component 1 Component 2 Species\n",
              "0   -0.480027     -2.2647  Setosa\n",
              "1    0.674134    -2.08096  Setosa\n",
              "2    0.341908    -2.36423  Setosa\n",
              "3    0.597395    -2.29938  Setosa\n",
              "4   -0.646835    -2.38984  Setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-C9bBQ4jx3Ni"
      },
      "source": [
        "### 8.Let's calculate the percentage of variation explained."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f5Gb4J1xxcI7",
        "outputId": "bc7731df-72fd-4196-8665-13df5e02f5e9"
      },
      "source": [
        "print(f\"Explained Variance : {values.sum() / eigh(covar_matrix)[0].sum() * 100} %\")"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Explained Variance : 95.8132072000017 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z6VvqBbWyFyL"
      },
      "source": [
        "### 9.Plot the Principal Component"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "dcrEV9r1x9Qm",
        "outputId": "26d96a0a-97fd-4d91-f880-f35ad78617e1"
      },
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "sns.scatterplot(x= \"Component 1\", y= \"Component 2\", hue= \"Species\", data= pca_data, s = 100)\n",
        "plt.title(\"PCA Component Result Analysis with Species\")\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZfIbhajPynmX"
      },
      "source": [
        "### 10.PCA With Scikit learn "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GZKldSaryOTU"
      },
      "source": [
        "pca = PCA(n_components= 2, random_state=2021)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3w7QNBJ3yvLd"
      },
      "source": [
        "pca_components = pca.fit_transform(X_scaled)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "1oiVxMoPyv5k",
        "outputId": "2806c561-56d8-41b3-f6b9-2bdb364011bc"
      },
      "source": [
        "pca_data = pd.DataFrame(np.hstack((pca_components, y.to_numpy().reshape(-1, 1))), columns=[\"Component 1\", \"Component 2\", \"Species\"])\n",
        "pca_data.head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Component 1</th>\n",
              "      <th>Component 2</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-2.2647</td>\n",
              "      <td>0.480027</td>\n",
              "      <td>Setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-2.08096</td>\n",
              "      <td>-0.674134</td>\n",
              "      <td>Setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-2.36423</td>\n",
              "      <td>-0.341908</td>\n",
              "      <td>Setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-2.29938</td>\n",
              "      <td>-0.597395</td>\n",
              "      <td>Setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-2.38984</td>\n",
              "      <td>0.646835</td>\n",
              "      <td>Setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Component 1 Component 2 Species\n",
              "0     -2.2647    0.480027  Setosa\n",
              "1    -2.08096   -0.674134  Setosa\n",
              "2    -2.36423   -0.341908  Setosa\n",
              "3    -2.29938   -0.597395  Setosa\n",
              "4    -2.38984    0.646835  Setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cGNDtvagywYQ",
        "outputId": "6a9463dc-ee83-401f-b5c9-e08e11dd455f"
      },
      "source": [
        "print(f\"Explained Variance : {pca.explained_variance_ratio_.sum() *100} %\")"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Explained Variance : 95.81320720000164 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "qYKT7BXvy_Gv",
        "outputId": "4b8f8671-3ce8-4464-fbdc-2ed4130257dd"
      },
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "sns.scatterplot(x= \"Component 1\", y= \"Component 2\", hue= \"Species\", data= pca_data, s = 100)\n",
        "plt.title(\"PCA Component Result Analysis with Species\")\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uao46-JH0O2E"
      },
      "source": [
        "### Apply classfication on data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ngYk_ZMUzFdG"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import *"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5sKQ6Fme0SB2"
      },
      "source": [
        "clf = RandomForestClassifier(n_estimators=10, random_state=2021)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "8OcjLMWN3JLi",
        "outputId": "6546aa92-ff5b-41f4-a985-0752c679ad24"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "pca_data[\"Species\"] = le.fit_transform(pca_data[\"Species\"])\n",
        "pca_data.head()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Component 1</th>\n",
              "      <th>Component 2</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-2.2647</td>\n",
              "      <td>0.480027</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-2.08096</td>\n",
              "      <td>-0.674134</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-2.36423</td>\n",
              "      <td>-0.341908</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-2.29938</td>\n",
              "      <td>-0.597395</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-2.38984</td>\n",
              "      <td>0.646835</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Component 1 Component 2  Species\n",
              "0     -2.2647    0.480027        0\n",
              "1    -2.08096   -0.674134        0\n",
              "2    -2.36423   -0.341908        0\n",
              "3    -2.29938   -0.597395        0\n",
              "4    -2.38984    0.646835        0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VhMgGQRk0VKi",
        "outputId": "fc2e718a-5441-448f-95ae-45733295fc3b"
      },
      "source": [
        "X = pca_data.iloc[:, [0,1]]\n",
        "y = pca_data.iloc[:, [2]]\n",
        "clf.fit(X, y)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
              "                       n_jobs=None, oob_score=False, random_state=2021,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EpdGSTYE0Ytq",
        "outputId": "3dd4749a-0fd4-42d4-c9d9-59e1cbd18274"
      },
      "source": [
        "y_pred = clf.predict(X)\n",
        "print(f\"Accuracy : {clf.score(X,y)}\")"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy : 1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KBVkXjGS0aUR",
        "outputId": "c38e3353-8046-48e4-8c7e-781184d05eeb"
      },
      "source": [
        "print(\"Classification Report : \\n\",classification_report(y, y_pred))\n",
        "print(\"Accuracy :\", accuracy_score(y, y_pred) * 100)\n",
        "print(\"Cohen Kappa :\", cohen_kappa_score(y, y_pred))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Classification Report : \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        50\n",
            "           1       1.00      1.00      1.00        50\n",
            "           2       1.00      1.00      1.00        50\n",
            "\n",
            "    accuracy                           1.00       150\n",
            "   macro avg       1.00      1.00      1.00       150\n",
            "weighted avg       1.00      1.00      1.00       150\n",
            "\n",
            "Accuracy : 100.0\n",
            "Cohen Kappa : 1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BCTzNKoJ2w7R",
        "outputId": "1f4907a9-9f02-4c23-8133-13a3f9a22651"
      },
      "source": [
        "np.unique(pca_data.iloc[:, [2]])"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "FGcPgEsm0uO1",
        "outputId": "ce33a1f3-d1c7-4b33-81d5-bf8ad200f19c"
      },
      "source": [
        "cm = confusion_matrix(y, y_pred)\n",
        "print(cm)\n",
        "sns.heatmap(cm, annot = True, cmap=\"YlGnBu\")\n",
        "plt.xlabel(\"Predicte\")\n",
        "plt.xlabel(\"True\")\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.show()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[50  0  0]\n",
            " [ 0 50  0]\n",
            " [ 0  0 50]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}