Iris_Prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Iris Prediction.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_GodLeWJRENj",
        "colab_type": "text"
      },
      "source": [
        "# Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r4O-TOs6COVu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cus4gXC_Tbce",
        "colab_type": "text"
      },
      "source": [
        "#Importing dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k2jD661eRWy4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset = pd.read_csv('Iris.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h3YaZEScT2mt",
        "colab_type": "text"
      },
      "source": [
        "#Understanding the dataset\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ps9hMLLDUTbo",
        "colab_type": "text"
      },
      "source": [
        "Checking for any missing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z1S-R8WiT0wl",
        "colab_type": "code",
        "outputId": "9d4ed21f-073c-49cc-bff7-82890893254a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "dataset.isnull().sum().sort_values(ascending = False)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Species          0\n",
              "PetalWidthCm     0\n",
              "PetalLengthCm    0\n",
              "SepalWidthCm     0\n",
              "SepalLengthCm    0\n",
              "Id               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z1D_VaHhUjJU",
        "colab_type": "text"
      },
      "source": [
        "As no data is mmissing, we shall move further to understand, what data is relevant and what is not"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I4ih-orGUZ31",
        "colab_type": "code",
        "outputId": "a10c8ab1-4e13-4f83-c961-eeb61285ed2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.countplot(data = dataset, x = 'SepalLengthCm', hue = 'Species')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa57444dd68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wv7obJbqU1Wl",
        "colab_type": "code",
        "outputId": "474d2912-2361-4bc1-aee3-862033edb2eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.countplot(data = dataset, x = 'PetalLengthCm', hue = 'Species')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa573dc9d68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oOl6rilJXtqg",
        "colab_type": "text"
      },
      "source": [
        "Performing Label Encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CZW6NeeCVSep",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(dataset.iloc[:, -1].values)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zB9cd2h4Vosx",
        "colab_type": "code",
        "outputId": "c01a9389-fd43-4313-e801-ce29917938b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        }
      },
      "source": [
        "print(y)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uNvlqgyEWD-f",
        "colab_type": "code",
        "outputId": "768ab2ea-9b16-4b65-c055-29feed4dadfb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "sns.scatterplot(x = dataset.iloc[:, 0].values, y = y)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa573327828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J6G_1dDqWOD8",
        "colab_type": "code",
        "outputId": "a800b7b1-9b33-4b86-c388-f2eea410899d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "sns.scatterplot(x = dataset.iloc[:, 1].values, y = y)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa5703393c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DsFpBsTZWphL",
        "colab_type": "code",
        "outputId": "a92f2ea8-6255-4a26-e697-bfddfc828bd9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "sns.scatterplot(x = dataset.iloc[:, 2].values, y = y)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa570373e48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PH0lXKliWqhS",
        "colab_type": "code",
        "outputId": "117da20d-5c5f-4332-9f27-19d8b9bb97aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "sns.scatterplot(x = dataset.iloc[:, 3].values, y = y)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa5702989b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tc7K9FlpWvmT",
        "colab_type": "code",
        "outputId": "b1338b88-6b24-4c8d-9a9e-1dcab388e44d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "sns.scatterplot(x = dataset.iloc[:, 4].values, y = y)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa5702556a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FDli7WmiW6EN",
        "colab_type": "text"
      },
      "source": [
        "From the above observations, we clearly see that Petal Width and Petal Length, are those independent variables that can be true factors for finding the dependent variables, but Sepal Length and Sepal Width **can** lead to poor results."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xW1TPvtdYJqb",
        "colab_type": "text"
      },
      "source": [
        "#Distribution of dataset into dependent and independent variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oQGSN3cLW2nh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = dataset.iloc[:, 1: -1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Baj6TESlYVjb",
        "colab_type": "code",
        "outputId": "e20ca8c4-cd7c-45d5-baf5-3fb7dd4eb566",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print(X)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[5.1 3.5 1.4 0.2]\n",
            " [4.9 3.  1.4 0.2]\n",
            " [4.7 3.2 1.3 0.2]\n",
            " [4.6 3.1 1.5 0.2]\n",
            " [5.  3.6 1.4 0.2]\n",
            " [5.4 3.9 1.7 0.4]\n",
            " [4.6 3.4 1.4 0.3]\n",
            " [5.  3.4 1.5 0.2]\n",
            " [4.4 2.9 1.4 0.2]\n",
            " [4.9 3.1 1.5 0.1]\n",
            " [5.4 3.7 1.5 0.2]\n",
            " [4.8 3.4 1.6 0.2]\n",
            " [4.8 3.  1.4 0.1]\n",
            " [4.3 3.  1.1 0.1]\n",
            " [5.8 4.  1.2 0.2]\n",
            " [5.7 4.4 1.5 0.4]\n",
            " [5.4 3.9 1.3 0.4]\n",
            " [5.1 3.5 1.4 0.3]\n",
            " [5.7 3.8 1.7 0.3]\n",
            " [5.1 3.8 1.5 0.3]\n",
            " [5.4 3.4 1.7 0.2]\n",
            " [5.1 3.7 1.5 0.4]\n",
            " [4.6 3.6 1.  0.2]\n",
            " [5.1 3.3 1.7 0.5]\n",
            " [4.8 3.4 1.9 0.2]\n",
            " [5.  3.  1.6 0.2]\n",
            " [5.  3.4 1.6 0.4]\n",
            " [5.2 3.5 1.5 0.2]\n",
            " [5.2 3.4 1.4 0.2]\n",
            " [4.7 3.2 1.6 0.2]\n",
            " [4.8 3.1 1.6 0.2]\n",
            " [5.4 3.4 1.5 0.4]\n",
            " [5.2 4.1 1.5 0.1]\n",
            " [5.5 4.2 1.4 0.2]\n",
            " [4.9 3.1 1.5 0.1]\n",
            " [5.  3.2 1.2 0.2]\n",
            " [5.5 3.5 1.3 0.2]\n",
            " [4.9 3.1 1.5 0.1]\n",
            " [4.4 3.  1.3 0.2]\n",
            " [5.1 3.4 1.5 0.2]\n",
            " [5.  3.5 1.3 0.3]\n",
            " [4.5 2.3 1.3 0.3]\n",
            " [4.4 3.2 1.3 0.2]\n",
            " [5.  3.5 1.6 0.6]\n",
            " [5.1 3.8 1.9 0.4]\n",
            " [4.8 3.  1.4 0.3]\n",
            " [5.1 3.8 1.6 0.2]\n",
            " [4.6 3.2 1.4 0.2]\n",
            " [5.3 3.7 1.5 0.2]\n",
            " [5.  3.3 1.4 0.2]\n",
            " [7.  3.2 4.7 1.4]\n",
            " [6.4 3.2 4.5 1.5]\n",
            " [6.9 3.1 4.9 1.5]\n",
            " [5.5 2.3 4.  1.3]\n",
            " [6.5 2.8 4.6 1.5]\n",
            " [5.7 2.8 4.5 1.3]\n",
            " [6.3 3.3 4.7 1.6]\n",
            " [4.9 2.4 3.3 1. ]\n",
            " [6.6 2.9 4.6 1.3]\n",
            " [5.2 2.7 3.9 1.4]\n",
            " [5.  2.  3.5 1. ]\n",
            " [5.9 3.  4.2 1.5]\n",
            " [6.  2.2 4.  1. ]\n",
            " [6.1 2.9 4.7 1.4]\n",
            " [5.6 2.9 3.6 1.3]\n",
            " [6.7 3.1 4.4 1.4]\n",
            " [5.6 3.  4.5 1.5]\n",
            " [5.8 2.7 4.1 1. ]\n",
            " [6.2 2.2 4.5 1.5]\n",
            " [5.6 2.5 3.9 1.1]\n",
            " [5.9 3.2 4.8 1.8]\n",
            " [6.1 2.8 4.  1.3]\n",
            " [6.3 2.5 4.9 1.5]\n",
            " [6.1 2.8 4.7 1.2]\n",
            " [6.4 2.9 4.3 1.3]\n",
            " [6.6 3.  4.4 1.4]\n",
            " [6.8 2.8 4.8 1.4]\n",
            " [6.7 3.  5.  1.7]\n",
            " [6.  2.9 4.5 1.5]\n",
            " [5.7 2.6 3.5 1. ]\n",
            " [5.5 2.4 3.8 1.1]\n",
            " [5.5 2.4 3.7 1. ]\n",
            " [5.8 2.7 3.9 1.2]\n",
            " [6.  2.7 5.1 1.6]\n",
            " [5.4 3.  4.5 1.5]\n",
            " [6.  3.4 4.5 1.6]\n",
            " [6.7 3.1 4.7 1.5]\n",
            " [6.3 2.3 4.4 1.3]\n",
            " [5.6 3.  4.1 1.3]\n",
            " [5.5 2.5 4.  1.3]\n",
            " [5.5 2.6 4.4 1.2]\n",
            " [6.1 3.  4.6 1.4]\n",
            " [5.8 2.6 4.  1.2]\n",
            " [5.  2.3 3.3 1. ]\n",
            " [5.6 2.7 4.2 1.3]\n",
            " [5.7 3.  4.2 1.2]\n",
            " [5.7 2.9 4.2 1.3]\n",
            " [6.2 2.9 4.3 1.3]\n",
            " [5.1 2.5 3.  1.1]\n",
            " [5.7 2.8 4.1 1.3]\n",
            " [6.3 3.3 6.  2.5]\n",
            " [5.8 2.7 5.1 1.9]\n",
            " [7.1 3.  5.9 2.1]\n",
            " [6.3 2.9 5.6 1.8]\n",
            " [6.5 3.  5.8 2.2]\n",
            " [7.6 3.  6.6 2.1]\n",
            " [4.9 2.5 4.5 1.7]\n",
            " [7.3 2.9 6.3 1.8]\n",
            " [6.7 2.5 5.8 1.8]\n",
            " [7.2 3.6 6.1 2.5]\n",
            " [6.5 3.2 5.1 2. ]\n",
            " [6.4 2.7 5.3 1.9]\n",
            " [6.8 3.  5.5 2.1]\n",
            " [5.7 2.5 5.  2. ]\n",
            " [5.8 2.8 5.1 2.4]\n",
            " [6.4 3.2 5.3 2.3]\n",
            " [6.5 3.  5.5 1.8]\n",
            " [7.7 3.8 6.7 2.2]\n",
            " [7.7 2.6 6.9 2.3]\n",
            " [6.  2.2 5.  1.5]\n",
            " [6.9 3.2 5.7 2.3]\n",
            " [5.6 2.8 4.9 2. ]\n",
            " [7.7 2.8 6.7 2. ]\n",
            " [6.3 2.7 4.9 1.8]\n",
            " [6.7 3.3 5.7 2.1]\n",
            " [7.2 3.2 6.  1.8]\n",
            " [6.2 2.8 4.8 1.8]\n",
            " [6.1 3.  4.9 1.8]\n",
            " [6.4 2.8 5.6 2.1]\n",
            " [7.2 3.  5.8 1.6]\n",
            " [7.4 2.8 6.1 1.9]\n",
            " [7.9 3.8 6.4 2. ]\n",
            " [6.4 2.8 5.6 2.2]\n",
            " [6.3 2.8 5.1 1.5]\n",
            " [6.1 2.6 5.6 1.4]\n",
            " [7.7 3.  6.1 2.3]\n",
            " [6.3 3.4 5.6 2.4]\n",
            " [6.4 3.1 5.5 1.8]\n",
            " [6.  3.  4.8 1.8]\n",
            " [6.9 3.1 5.4 2.1]\n",
            " [6.7 3.1 5.6 2.4]\n",
            " [6.9 3.1 5.1 2.3]\n",
            " [5.8 2.7 5.1 1.9]\n",
            " [6.8 3.2 5.9 2.3]\n",
            " [6.7 3.3 5.7 2.5]\n",
            " [6.7 3.  5.2 2.3]\n",
            " [6.3 2.5 5.  1.9]\n",
            " [6.5 3.  5.2 2. ]\n",
            " [6.2 3.4 5.4 2.3]\n",
            " [5.9 3.  5.1 1.8]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uL8C7Q3JYXbT",
        "colab_type": "code",
        "outputId": "f248aa85-1fcf-420b-e36e-8ae3bfe1931d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 602
        }
      },
      "source": [
        "print(y)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hgaDuC--Y60i",
        "colab_type": "code",
        "outputId": "977d3760-97ea-4930-9cba-c98de95d1754",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 602
        }
      },
      "source": [
        "print(y, len(y))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'] 150\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rph8ak8zY8Fo",
        "colab_type": "code",
        "colab": {}
      },
      "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 = 0.3, random_state = 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZRvOxJuHaV33",
        "colab_type": "text"
      },
      "source": [
        "#We will try different models for idetification\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QvR-fFuqae56",
        "colab_type": "text"
      },
      "source": [
        "1. Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZHf5AWGZZwF",
        "colab_type": "code",
        "outputId": "e2a5f676-d555-440e-9df2-f6ca05d74434",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        }
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "lr = LogisticRegression(random_state= 0)\n",
        "lr.fit(X_train, y_train)"
      ],
      "execution_count": 20,
      "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": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_7eya0Fubb1N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = lr.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "haRjWhjNbm0f",
        "colab_type": "code",
        "outputId": "b035e400-9a5a-4689-e7a5-efbb70013d3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "as1 = accuracy_score(y_test, y_pred)\n",
        "print(cm)\n",
        "as1"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[16  0  0]\n",
            " [ 0 17  1]\n",
            " [ 0  0 11]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9777777777777777"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j1bZtl4ScdxL",
        "colab_type": "text"
      },
      "source": [
        "2. KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YYE9jKSbqbY",
        "colab_type": "code",
        "outputId": "a1505e6c-4de2-419c-d7ed-a6afacc0887e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier()\n",
        "knn.fit(X_train, y_train)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wus1JzdXc4s3",
        "colab_type": "code",
        "outputId": "5fb20fd7-43b1-42d3-9b74-ba2aaabc0054",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "y_pred = knn.predict(X_test)\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "as1 = accuracy_score(y_test, y_pred)\n",
        "print(cm)\n",
        "print(as1)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[16  0  0]\n",
            " [ 0 17  1]\n",
            " [ 0  0 11]]\n",
            "0.9777777777777777\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IduMk_RldFwa",
        "colab_type": "text"
      },
      "source": [
        "3. SVM \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5EXnrnwNc9sW",
        "colab_type": "code",
        "outputId": "6026ba83-58c3-4a8c-e738-070d25ba6d4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "svc = SVC()\n",
        "svc.fit(X_train, y_train)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hxCE0pE7drqU",
        "colab_type": "code",
        "outputId": "c75b469a-d1ec-4770-84ed-2eeeadb3e4b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "y_pred = svc.predict(X_test)\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "as1 = accuracy_score(y_test, y_pred)\n",
        "print(cm)\n",
        "print(as1)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[16  0  0]\n",
            " [ 0 17  1]\n",
            " [ 0  0 11]]\n",
            "0.9777777777777777\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "24XE-RZ4eosy",
        "colab_type": "text"
      },
      "source": [
        "4. Kernal SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DqIb20Wuemjm",
        "colab_type": "code",
        "outputId": "5b66f56d-77c1-49e1-8dbf-b14a1d96d2f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "svc = SVC(kernel = 'rbf')\n",
        "svc.fit(X_train, y_train)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cll1aUTAeuEI",
        "colab_type": "code",
        "outputId": "53a37226-0fc2-45ef-ed75-7d597dd02acd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "y_pred = svc.predict(X_test)\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "as1 = accuracy_score(y_test, y_pred)\n",
        "print(cm)\n",
        "print(as1)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[16  0  0]\n",
            " [ 0 17  1]\n",
            " [ 0  0 11]]\n",
            "0.9777777777777777\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0V-dFBykeykn",
        "colab_type": "text"
      },
      "source": [
        "4. Naive Bayes "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oFxQO2hNewaF",
        "colab_type": "code",
        "outputId": "efbe211b-b641-45c2-feff-e2d7fe493a55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "gb = GaussianNB()\n",
        "gb.fit(X_train, y_train)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB(priors=None, var_smoothing=1e-09)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QoVSIN2QfCoF",
        "colab_type": "code",
        "outputId": "8b065601-8648-49c5-c805-7c5f2f0389d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "y_pred = gb.predict(X_test)\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "as1 = accuracy_score(y_test, y_pred)\n",
        "print(cm)\n",
        "print(as1)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[16  0  0]\n",
            " [ 0 18  0]\n",
            " [ 0  0 11]]\n",
            "1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dWVNRb_1fSDr",
        "colab_type": "text"
      },
      "source": [
        "5. Decision Tree "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DmwiOjskfGMF",
        "colab_type": "code",
        "outputId": "adba19a9-1cb3-4932-da2b-5de1763c4fd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        }
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "dtc = DecisionTreeClassifier()\n",
        "dtc.fit(X_train, y_train)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=None, max_features=None, max_leaf_nodes=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, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R-opn-cIf7n3",
        "colab_type": "code",
        "outputId": "9a69c706-1809-499b-e351-5d80a242d2ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "y_pred = dtc.predict(X_test)\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "as1 = accuracy_score(y_test, y_pred)\n",
        "print(cm)\n",
        "print(as1)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[16  0  0]\n",
            " [ 0 17  1]\n",
            " [ 0  0 11]]\n",
            "0.9777777777777777\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xx_HMysIgCqv",
        "colab_type": "text"
      },
      "source": [
        "6. Random Forest\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-5l-jvAygAY-",
        "colab_type": "code",
        "outputId": "c5c3cff9-7a5f-4835-fc77-12c1bd29459a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150
        }
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rfc = RandomForestClassifier()\n",
        "rfc.fit(X_train, y_train)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "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=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gYW9__Pugc_3",
        "colab_type": "code",
        "outputId": "9f83d4fc-5ee9-4895-9830-18ba95d6ce52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "y_pred = rfc.predict(X_test)\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "as1 = accuracy_score(y_test, y_pred)\n",
        "print(cm)\n",
        "print(as1)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[16  0  0]\n",
            " [ 0 17  1]\n",
            " [ 0  0 11]]\n",
            "0.9777777777777777\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S7peoTmEgygP",
        "colab_type": "text"
      },
      "source": [
        "After the evaluation of models, we come to conclusion that we get an acuraccy of 98% and 100%. Since 100% means overfitting of the model, so we discard the Naive Bayes model, and any other model from the rest 5 models can be used for an acuraccy of 98%\n"
      ]
    }
  ]
}