Implement_K_NN_on_Iris_Dataset.ipynb

{
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        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Augusta02/Machine-Learning-Projects/blob/main/Implement_K_NN_on_Iris_Dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73,
          "resources": {
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              "headers": [
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                  "content-type",
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              "ok": true,
              "status": 200,
              "status_text": ""
            }
          }
        },
        "id": "pUqlUmH0qwnN",
        "outputId": "09a1f1e2-56f8-47f1-ee40-72a97340c38f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d20451ae-01d5-4748-9790-e0883a74e1ad\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-d20451ae-01d5-4748-9790-e0883a74e1ad\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving iris.xlsx to iris (2).xlsx\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "from google.colab import files\n",
        "uploaded= files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import io\n",
        "iris =pd.read_excel(io.BytesIO(uploaded[\"iris.xlsx\"]))\n",
        "iris.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "IU4TKFz_tHdB",
        "outputId": "38c35352-249d-41ef-fae0-756343f73590"
      },
      "execution_count": 7,
      "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": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# understand statistics of dataset\n",
        "iris.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "htbeq4fwuBNL",
        "outputId": "e40a3244-8a10-4cfb-ffa8-e58baf55697e"
      },
      "execution_count": 8,
      "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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       sepal.length  sepal.width  petal.length  petal.width\n",
              "count    150.000000   150.000000    150.000000   150.000000\n",
              "mean       5.843333     3.057333      3.758000     1.199333\n",
              "std        0.828066     0.435866      1.765298     0.762238\n",
              "min        4.300000     2.000000      1.000000     0.100000\n",
              "25%        5.100000     2.800000      1.600000     0.300000\n",
              "50%        5.800000     3.000000      4.350000     1.300000\n",
              "75%        6.400000     3.300000      5.100000     1.800000\n",
              "max        7.900000     4.400000      6.900000     2.500000"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# understand information of dataset\n",
        "iris.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dpIVlNajuIZi",
        "outputId": "c637eefe-2993-45b6-acae-2c92c8cd78fa"
      },
      "execution_count": 9,
      "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  150 non-null    float64\n",
            " 1   sepal.width   150 non-null    float64\n",
            " 2   petal.length  150 non-null    float64\n",
            " 3   petal.width   150 non-null    float64\n",
            " 4   variety       150 non-null    object \n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 6.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check for missing values\n",
        "iris.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QYCG9jOVuOuB",
        "outputId": "178ae456-69dc-46a8-d158-f5454f80285c"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sepal.length    0\n",
              "sepal.width     0\n",
              "petal.length    0\n",
              "petal.width     0\n",
              "variety         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# histogram visualization\n",
        "sns.histplot(data=iris, x=\"sepal.length\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "uh933M5juTHI",
        "outputId": "b87a7280-cfee-4878-ec6f-090be82c93dd"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f833823c790>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=iris, x=\"sepal.width\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "zCvMNe1RwmrC",
        "outputId": "c19ac3b7-8cd7-4d65-d82d-9e5b30b3c2de"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8336e74ad0>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=iris, x=\"petal.width\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "irLIddoUw0dV",
        "outputId": "afaab032-0dae-4e44-94a7-37515071a53c"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f83369c68d0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=iris, x=\"petal.length\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "-mKfNsTdw5EZ",
        "outputId": "4a1dd5c1-d123-47ce-8a0f-bcaac67c6d5a"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f833695a0d0>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check for correlation data\n",
        "iris.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "zXYljZBUz0K-",
        "outputId": "57196f91-fa14-417f-9f37-c84247010ba9"
      },
      "execution_count": 25,
      "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sepal.length</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.117570</td>\n",
              "      <td>0.871754</td>\n",
              "      <td>0.817941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sepal.width</th>\n",
              "      <td>-0.117570</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.428440</td>\n",
              "      <td>-0.366126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>petal.length</th>\n",
              "      <td>0.871754</td>\n",
              "      <td>-0.428440</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.962865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>petal.width</th>\n",
              "      <td>0.817941</td>\n",
              "      <td>-0.366126</td>\n",
              "      <td>0.962865</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              sepal.length  sepal.width  petal.length  petal.width\n",
              "sepal.length      1.000000    -0.117570      0.871754     0.817941\n",
              "sepal.width      -0.117570     1.000000     -0.428440    -0.366126\n",
              "petal.length      0.871754    -0.428440      1.000000     0.962865\n",
              "petal.width       0.817941    -0.366126      0.962865     1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# use heatmap to visualize corr information\n",
        "corr= iris.corr()\n",
        "fig,ax= plt.subplots(figsize=(5,5))\n",
        "sns.heatmap(corr, annot=True, ax=ax,cmap=\"coolwarm\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "rA-Q2Zg10Elv",
        "outputId": "322bda05-58f9-4c5f-8ac1-e405e98abe9b"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f832d849650>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# use labelencoder to change string values(\"variety\") to numerical data to better inform the model\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "le= LabelEncoder()\n",
        "\n",
        "iris[\"variety\"]= le.fit_transform(iris['variety'])\n",
        "iris.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "fXuNydBJ1K39",
        "outputId": "6f455078-74ad-41fa-ec53-b685aa8e9595"
      },
      "execution_count": 31,
      "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>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",
              "    <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>0</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>0</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        0\n",
              "1           4.9          3.0           1.4          0.2        0\n",
              "2           4.7          3.2           1.3          0.2        0\n",
              "3           4.6          3.1           1.5          0.2        0\n",
              "4           5.0          3.6           1.4          0.2        0"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare data for modelling\n",
        "from sklearn.model_selection import train_test_split\n",
        "x= iris.drop(columns=[\"variety\"])\n",
        "y= iris['variety']\n",
        "x_train,x_test,y_train,y_test= train_test_split(x,y,test_size=0.30)"
      ],
      "metadata": {
        "id": "0MGhAXP11v5i"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# model used is KNN\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "iris_mo= KNeighborsClassifier()"
      ],
      "metadata": {
        "id": "ZmmbF-Ac2jh3"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# fit model into KNN\n",
        "iris_mo.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gr9GYmpZ3DVO",
        "outputId": "76d7e76c-586e-49d3-8e1b-77b2e1b2e013"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# find accuracy of the model\n",
        "print(\"Accuracy: \", iris_mo.score(x_test,y_test)*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QGPbWAR43YN2",
        "outputId": "edd9dcd9-a727-4b76-d163-0c39d4fa6fad"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  93.33333333333333\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Implement K_NN on Iris Dataset.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM8PlQTzpddxs2VDv1VxkDU",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}