Iris_Classification_Dataset_ML_Project_7.ipynb

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  "metadata": {
    "colab": {
      "name": "Iris Classification Dataset - ML Project 7",
      "provenance": [],
      "authorship_tag": "ABX9TyMI+eWTYRvhQjFvFoSG7IEC",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Joykareko/Data-Science-Projects/blob/main/Iris_Classification_Dataset_ML_Project_7.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aJKLhn9EKh26"
      },
      "source": [
        "#importing dependencies\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import sklearn.datasets\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import svm"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HjA05X6IL1nS"
      },
      "source": [
        "#importing the dataset\n",
        "iris_dataset = sklearn.datasets.load_iris()"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "m6i8GfQhMuEk",
        "outputId": "e4fe4f9e-3592-41a4-ec7a-06d33e12971d"
      },
      "source": [
        "#importing the dataset as pandas dataframe\n",
        "iris_dataset_df = pd.DataFrame(iris_dataset.data, columns = iris_dataset.feature_names)\n",
        "iris_dataset_df.head(3)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</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",
              "    </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",
              "    </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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
              "0                5.1               3.5                1.4               0.2\n",
              "1                4.9               3.0                1.4               0.2\n",
              "2                4.7               3.2                1.3               0.2"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aXO4ew7yQSPx"
      },
      "source": [
        "#adding the target column to the dataframe\n",
        "iris_dataset_df['Class'] = iris_dataset.target"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "w_3RpKwARMb7",
        "outputId": "875af5c4-bfe7-4934-f195-2fb10acdb42e"
      },
      "source": [
        "iris_dataset_df.head(3)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>Class</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>0</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>0</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>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "   sepal length (cm)  sepal width (cm)  ...  petal width (cm)  Class\n",
              "0                5.1               3.5  ...               0.2      0\n",
              "1                4.9               3.0  ...               0.2      0\n",
              "2                4.7               3.2  ...               0.2      0\n",
              "\n",
              "[3 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_479KiGxRr1f"
      },
      "source": [
        "Class is--->\n",
        "2 ----> Iris Virginica\n",
        "1 ----> Iris Versicolour\n",
        "0 ----> Iris  Setosa"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TAo8BjKpReRn",
        "outputId": "856b3202-a435-4c65-c507-d972e2c8a544"
      },
      "source": [
        "#checking out the iris class\n",
        "iris_dataset_df['Class'].value_counts()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    50\n",
              "1    50\n",
              "0    50\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "tDEME6m6So1z",
        "outputId": "00d302fc-991b-4efc-8109-44fddcb38fd2"
      },
      "source": [
        "#shape of the data\n",
        "iris_dataset_df.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "rFCX2Z2PSu1U",
        "outputId": "f23f4ff9-99d3-471f-b198-1f3d958281d8"
      },
      "source": [
        "iris_dataset_df.info()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 5 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   sepal length (cm)  150 non-null    float64\n",
            " 1   sepal width (cm)   150 non-null    float64\n",
            " 2   petal length (cm)  150 non-null    float64\n",
            " 3   petal width (cm)   150 non-null    float64\n",
            " 4   Class              150 non-null    int64  \n",
            "dtypes: float64(4), int64(1)\n",
            "memory usage: 6.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OPXyQYEJUCpl",
        "outputId": "20816e2d-4e21-42ae-cc92-6c0fac437ea3"
      },
      "source": [
        "#checking for null values\n",
        "iris_dataset_df.isnull().sum()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sepal length (cm)    0\n",
              "sepal width (cm)     0\n",
              "petal length (cm)    0\n",
              "petal width (cm)     0\n",
              "Class                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "ZFdTwBaaUJlw",
        "outputId": "2b55b581-5b14-4496-ab7b-80e94e80f42b"
      },
      "source": [
        "#summary statistics\n",
        "iris_dataset_df.describe()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.057333</td>\n",
              "      <td>3.758000</td>\n",
              "      <td>1.199333</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.435866</td>\n",
              "      <td>1.765298</td>\n",
              "      <td>0.762238</td>\n",
              "      <td>0.819232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       sepal length (cm)  sepal width (cm)  ...  petal width (cm)       Class\n",
              "count         150.000000        150.000000  ...        150.000000  150.000000\n",
              "mean            5.843333          3.057333  ...          1.199333    1.000000\n",
              "std             0.828066          0.435866  ...          0.762238    0.819232\n",
              "min             4.300000          2.000000  ...          0.100000    0.000000\n",
              "25%             5.100000          2.800000  ...          0.300000    0.000000\n",
              "50%             5.800000          3.000000  ...          1.300000    1.000000\n",
              "75%             6.400000          3.300000  ...          1.800000    2.000000\n",
              "max             7.900000          4.400000  ...          2.500000    2.000000\n",
              "\n",
              "[8 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V1X5lNaNUXl7"
      },
      "source": [
        "#understanding if any correlation exists\n",
        "correlation = iris_dataset_df.corr()"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "id": "5Kf6l5OpUuHQ",
        "outputId": "a811dcfd-acf2-4326-dd3b-f696d98a5d43"
      },
      "source": [
        "#shows the variables are very much significant\n",
        "plt.figure(figsize = (10,10))\n",
        "sns.heatmap(correlation,cbar = True, square = True , fmt = '.1f',annot = True, annot_kws={'size':8}, cmap = 'Blues')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ffb9acce210>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CQDZYmylU-W4"
      },
      "source": [
        "#splitting the dataset into X and Y\n",
        "X = iris_dataset_df.drop(columns = 'Class', axis = 1)\n",
        "Y = iris_dataset_df['Class']"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OQT6A2s6VOK3",
        "outputId": "2d7aca8b-c66b-477c-f3c8-77cf274638af"
      },
      "source": [
        "print(X.shape, Y.shape)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(150, 4) (150,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yLZh_AfrVVcy",
        "outputId": "60d8fa3b-401f-4c72-b860-5be36afeeb93"
      },
      "source": [
        "#splitting the dataset to training and testing data\n",
        "X_train,X_test,Y_train,Y_test = train_test_split(X,Y, test_size = 0.2, stratify = Y, random_state = 2)\n",
        "print(X_train.shape,X_test.shape,Y_train.shape,Y_test.shape)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(120, 4) (30, 4) (120,) (30,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MdG7SHhTV7QK",
        "outputId": "a5c5acc5-fd00-4f8c-ac15-025845ed27d9"
      },
      "source": [
        "classifier = svm.SVC(kernel = 'rbf')\n",
        "classifier.fit(X_train,Y_train)"
      ],
      "execution_count": 21,
      "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": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fr6-PiM7Wotp",
        "outputId": "2a789f72-9695-4e74-f62c-d7f93dc353ba"
      },
      "source": [
        "#creating predictions and evaluating accuracy of the model\n",
        "training_pred = classifier.predict(X_train)\n",
        "score1 = accuracy_score(training_pred,Y_train)\n",
        "print('Accuracy Score:',score1)\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score: 0.9666666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cROr975KXEW7",
        "outputId": "714d7550-7a5e-45b3-f152-9a51945d3b37"
      },
      "source": [
        "testing_pred = classifier.predict(X_test)\n",
        "score2 = accuracy_score(testing_pred,Y_test)\n",
        "print('Accuracy Score:',score2)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6PPDaLGMXV-z"
      },
      "source": [
        "#making a prediction system\n",
        "input_data = (5.1,3.5,1.4,0.2)\n",
        "#changing the data as a numpy array\n",
        "input_data_as_numpy_array = np.asarray(input_data)\n",
        "#reshaping the array to perdict for one instance\n",
        "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LKIalz5TXV7V",
        "outputId": "e9becc3f-0d5f-4699-8554-3676bb575f2b"
      },
      "source": [
        "prediction = classifier.predict(input_data_reshaped)\n",
        "if (prediction[0]==0):\n",
        "  print(prediction,'class:Iris Sertosa')\n",
        "if (prediction[0]==1):\n",
        "  print(prediction,'class: Iris Versicolour')\n",
        "if (prediction[0]==2):\n",
        "  print('class: Iris Virginica')"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0] class:Iris Sertosa\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z7k9j-Z0ZBhr"
      },
      "source": [
        "#making a prediction system\n",
        "input_data = (4.9,3.1,5.0,0.2)\n",
        "#changing the data as a numpy array\n",
        "input_data_as_numpy_array = np.asarray(input_data)\n",
        "#reshaping the array to perdict for one instance\n",
        "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fgKx_H43ZSy3",
        "outputId": "e60f411e-207e-4698-a0e9-c7af9472ae54"
      },
      "source": [
        "prediction = classifier.predict(input_data_reshaped)\n",
        "if (prediction[0]==0):\n",
        "  print(prediction,'class:Iris Sertosa')\n",
        "if (prediction[0]==1):\n",
        "  print(prediction,'class: Iris Versicolour')\n",
        "if (prediction[0]==2):\n",
        "  print('class: Iris Virginica')"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1] class: Iris Versicolour\n"
          ]
        }
      ]
    }
  ]
}