Iris_class.ipynb

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      "source": [
        "#IRIS FLOWER CLASSIFICATION\n",
        "\n"
      ],
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        "#Instructions:"
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        "* The Iris flower dataset consists of three species: setosa, versicolor, and virginica. These species can be distinguished based on their measurements. Now, imagine that you have the measurements\n",
        "of Iris flowers categorized by their respective species. Your\n",
        "objective is to train a machine learning model that can learn from\n",
        "these measurements and accurately classify the Iris flowers into\n",
        "their respective species.\n",
        "* Use the Iris dataset to develop a model that can classify iris\n",
        "flowers into different species based on their sepal and petal\n",
        "measurements. This dataset is widely used for introductory\n",
        "classification tasks."
      ],
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            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width      species\n",
              "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
              "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
              "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
              "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
              "4           5.0          3.6           1.4          0.2  Iris-setosa"
            ],
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              "      <td>5.1</td>\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4335943113621737,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522617,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7631607417008414,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Iris-setosa\",\n          \"Iris-versicolor\",\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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      ],
      "source": [
        "#Importing important libraries and reading dataset\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "df=pd.read_csv(\"IRIS.csv\")\n",
        "df.head()"
      ]
    },
    {
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      "source": [
        "#Getting the shape of dataset\n",
        "df.shape"
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          "data": {
            "text/plain": [
              "(150, 5)"
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          "execution_count": 2
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      "source": [
        "#Getting the information of dataset\n",
        "df.info()"
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          "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   species       150 non-null    object \n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 6.0+ KB\n"
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      "source": [
        "#Getting the Null values\n",
        "df.isnull().sum()"
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            "text/plain": [
              "sepal_length    0\n",
              "sepal_width     0\n",
              "petal_length    0\n",
              "petal_width     0\n",
              "species         0\n",
              "dtype: int64"
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              "      <th>sepal_length</th>\n",
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              "    <tr>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>petal_length</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>petal_width</th>\n",
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      "cell_type": "code",
      "source": [
        "#Getting the duplicates\n",
        "df.duplicated().sum()"
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              "3"
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          "metadata": {},
          "execution_count": 5
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      "source": [
        "#Getting the EDA\n",
        "df.describe()"
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      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       sepal_length  sepal_width  petal_length  petal_width\n",
              "count    150.000000   150.000000    150.000000   150.000000\n",
              "mean       5.843333     3.054000      3.758667     1.198667\n",
              "std        0.828066     0.433594      1.764420     0.763161\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"
            ],
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              "      <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>3.054000</td>\n",
              "      <td>3.758667</td>\n",
              "      <td>1.198667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.433594</td>\n",
              "      <td>1.764420</td>\n",
              "      <td>0.763161</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",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.24711349471842,\n        \"min\": 0.8280661279778629,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.843333333333334,\n          5.8,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.08647211421483,\n        \"min\": 0.4335943113621737,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.0540000000000003,\n          3.0,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.835227940958106,\n        \"min\": 1.0,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.758666666666666,\n          4.35,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.63663424340991,\n        \"min\": 0.1,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.1986666666666668,\n          1.3,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Making a copy of the original data to prevent any inconvenience"
      ],
      "metadata": {
        "id": "jmsdwsaoBMxM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df.copy()"
      ],
      "metadata": {
        "id": "QA3LEuuuBPuN"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Initiate Data Cleaning process...."
      ],
      "metadata": {
        "id": "yZfklMlKBdXG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Insights: No null values, no need to change data type, only 3 duplicate values"
      ],
      "metadata": {
        "id": "5EzgRBa6BscA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Handling the duplicates\n",
        "df1.drop_duplicates(inplace=True)\n",
        "df1.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "5xiET4EIBo_e",
        "outputId": "ae370707-dfea-417f-8b03-ce7a0193c98b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 147 entries, 0 to 149\n",
            "Data columns (total 5 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   sepal_length  147 non-null    float64\n",
            " 1   sepal_width   147 non-null    float64\n",
            " 2   petal_length  147 non-null    float64\n",
            " 3   petal_width   147 non-null    float64\n",
            " 4   species       147 non-null    object \n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 6.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking duplicates\n",
        "df1.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dVeWXniKCJLj",
        "outputId": "fc93cf06-f9c5-4f72-b3b1-cc529ac9c5a8"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#New Exploratory_Data_Analysis on cleaned data"
      ],
      "metadata": {
        "id": "kuvy7kEDCc6U"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Resetting the index"
      ],
      "metadata": {
        "id": "ZnKr785LChCZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1.reset_index(drop=True, inplace=True)\n",
        "df1.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "collapsed": true,
        "id": "DC6LT7HZCk7L",
        "outputId": "c1001f80-0dd9-4b03-9f71-08f26ce383d7"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width      species\n",
              "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
              "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
              "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
              "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
              "4           5.0          3.6           1.4          0.2  Iris-setosa"
            ],
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              "\n",
              "  <div id=\"df-6b51ec53-69d8-43f0-bb12-c655738be6d5\" class=\"colab-df-container\">\n",
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              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
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              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
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              "  <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>Iris-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>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>3.2</td>\n",
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              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6b51ec53-69d8-43f0-bb12-c655738be6d5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6b51ec53-69d8-43f0-bb12-c655738be6d5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7bea092b-37c2-418d-b8b7-c037cb4452e5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7bea092b-37c2-418d-b8b7-c037cb4452e5')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7bea092b-37c2-418d-b8b7-c037cb4452e5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 147,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8290998607345104,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4370087068034353,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7591108999509792,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7578742052400408,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Iris-setosa\",\n          \"Iris-versicolor\",\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Getting new EDA after removing duplicates\n",
        "df1.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "collapsed": true,
        "id": "9CBAb-Z-C3U_",
        "outputId": "e610d39a-f70e-4e95-d131-a9ec78f4ea35"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       sepal_length  sepal_width  petal_length  petal_width\n",
              "count    147.000000   147.000000    147.000000   147.000000\n",
              "mean       5.856463     3.055782      3.780272     1.208844\n",
              "std        0.829100     0.437009      1.759111     0.757874\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      4.400000     1.300000\n",
              "75%        6.400000     3.300000      5.100000     1.800000\n",
              "max        7.900000     4.400000      6.900000     2.500000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d8691763-b880-437a-9412-08199af62641\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>147.000000</td>\n",
              "      <td>147.000000</td>\n",
              "      <td>147.000000</td>\n",
              "      <td>147.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.856463</td>\n",
              "      <td>3.055782</td>\n",
              "      <td>3.780272</td>\n",
              "      <td>1.208844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.829100</td>\n",
              "      <td>0.437009</td>\n",
              "      <td>1.759111</td>\n",
              "      <td>0.757874</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>4.400000</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>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d8691763-b880-437a-9412-08199af62641')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d8691763-b880-437a-9412-08199af62641 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d8691763-b880-437a-9412-08199af62641');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b110220d-4350-41cd-ac55-2b6cf4e01fb0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b110220d-4350-41cd-ac55-2b6cf4e01fb0')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b110220d-4350-41cd-ac55-2b6cf4e01fb0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50.18661855588514,\n        \"min\": 0.8290998607345104,\n        \"max\": 147.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.856462585034014,\n          5.8,\n          147.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.025788925571604,\n        \"min\": 0.4370087068034353,\n        \"max\": 147.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.05578231292517,\n          3.0,\n          147.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50.772187180997165,\n        \"min\": 1.0,\n        \"max\": 147.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.780272108843538,\n          4.4,\n          147.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.57585291174468,\n        \"min\": 0.1,\n        \"max\": 147.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.2088435374149662,\n          1.3,\n          147.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Import stats and perform Z-score\n",
        "import scipy.stats as stats\n",
        "df1 = df1[(np.abs(stats.zscore(df1[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']], nan_policy='omit')) < 3).all(axis=1)]\n",
        "df1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MX1X8l9qEcKz",
        "outputId": "a7d7e094-bed2-430e-9d9f-0325835e5f5d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(146, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check distribution using histogram\n",
        "df1.hist(figsize=(10,10))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 853
        },
        "id": "BmaCYiZQiHqT",
        "outputId": "4bec6f42-2027-4da8-8c4d-6c58157277cc"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check outliers using boxplot\n",
        "df1.plot(kind='box',subplots=True,layout=(2,2),figsize=(15,10))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "id": "lFaMSfZ4ilah",
        "outputId": "3d62d1df-a86e-4402-8833-11065bf4edc6"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 4 Axes>"
            ],
            "image/png": 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2+2wAAHurvWHON27cuNTV1TXZy2v8+PHbtP13vXr1yv/9v/83K1asyJgxY/LFL34xX/nKV5r0Oeigg3LVVVflC1/4QgYMGJCLLrpot2oF9hwlDQ0NDZ1dBLBnGj9+fI455pjccMMNnV1Kp7vmmmty8803Z926dZ1dCgBAmzLnA4rMHl8A2/GNb3wjJ5xwQg488MD88pe/zPXXX++bPwAAgL2MVx2BPdqkSZOa/HT23x/XXnttu9336aefzhlnnJEjjzwyM2bMyGWXXZYrr7yy3e4HALAv66w5H1B8XnUE9mgvvvhiXn311e1+dsABB+SAAw7o4IoAAGhr5nxAexF8AQAAAFBIXnUEAAAAoJAEXwAAAAAUkuALAAAAgEISfAEAAABQSIIvAAAAAApJ8AUAAABAIQm+AAAAACik/x9Dq/TorwIt9wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#\"Unveiling Insights: A Dive into Univariate Analysis\""
      ],
      "metadata": {
        "id": "wcS9HkJNFM3O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Performing the violin plot\n",
        "import plotly.express as px\n",
        "fig = px.violin(data_frame=df1, x=\"sepal_length\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "collapsed": true,
        "id": "2gdCcnxGFRq9",
        "outputId": "79dd2043-94c5-447c-c2d5-44e8ff577e7d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Performing the violin plot\n",
        "import plotly.express as px\n",
        "fig = px.violin(data_frame=df1, x=\"sepal_width\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "collapsed": true,
        "id": "Q2E7qfUbGA9w",
        "outputId": "5e444818-0e55-479b-8e64-acb4d7a57042"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Insights: 'sepal_length' and 'sepal_width' Show a Symmetrical, Bell-Curve (Normal) Distribution\""
      ],
      "metadata": {
        "id": "YAQuNUA1HJu_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Performing the violin plot\n",
        "import plotly.express as px\n",
        "fig = px.violin(data_frame=df1, x=\"petal_length\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "collapsed": true,
        "id": "V2AYuAyGGYTX",
        "outputId": "87eeb676-a48f-4f3b-b872-84ca875b793c"
      },
      "execution_count": 13,
      "outputs": [
        {
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          "data": {
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Performing the violin plot\n",
        "import plotly.express as px\n",
        "fig = px.violin(data_frame=df1, x=\"petal_width\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "collapsed": true,
        "id": "7H63Le1oGv-8",
        "outputId": "97de334f-4666-407c-d9a8-5072cdd681d2"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "markdown",
      "source": [
        "#Preprocessing"
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      "metadata": {
        "id": "M3URb9PXjPBB"
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    {
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      "source": [
        "#Encoding the species\n",
        "y = df1['species']\n",
        "label_encoder = LabelEncoder()\n",
        "y_encoded = label_encoder.fit_transform(y)\n",
        "\n",
        "#Identify outliers using the IQR method\n",
        "Q1 = df1['sepal_width'].quantile(0.25)\n",
        "Q3 = df1['sepal_width'].quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "lower_bound = Q1 - 1.5 * IQR\n",
        "upper_bound = Q3 + 1.5 * IQR\n",
        "\n",
        "#Filter out the outliers\n",
        "df1 = df1[(df1['sepal_width'] >= lower_bound) & (df1['sepal_width'] <= upper_bound)]\n",
        "\n",
        "df1.head(), y_encoded[:5]"
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      "execution_count": 21,
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            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width         species\n",
              "0             5.1          3.5           1.4          0.2     Iris-setosa\n",
              "1             4.9          3.0           1.4          0.2     Iris-setosa\n",
              "2             4.7          3.2           1.3          0.2     Iris-setosa\n",
              "3             4.6          3.1           1.5          0.2     Iris-setosa\n",
              "4             5.0          3.6           1.4          0.2     Iris-setosa\n",
              "..            ...          ...           ...          ...             ...\n",
              "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
              "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
              "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
              "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
              "149           5.9          3.0           5.1          1.8  Iris-virginica\n",
              "\n",
              "[143 rows x 5 columns]"
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              "      <th>sepal_length</th>\n",
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              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
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              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>0.2</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <th>145</th>\n",
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              "      <td>Iris-virginica</td>\n",
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              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
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              "      <td>Iris-virginica</td>\n",
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              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>Iris-virginica</td>\n",
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              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
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              "      <td>Iris-virginica</td>\n",
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              "    <tr>\n",
              "      <th>149</th>\n",
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              "      <td>Iris-virginica</td>\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 143,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8350450917935364,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.39822194196149985,\n        \"min\": 2.2,\n        \"max\": 4.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          3.5,\n          3.9,\n          3.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7508239818395837,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7545384531993362,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Iris-setosa\",\n          \"Iris-versicolor\",\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Separating features and target variable\n",
        "X = df1.drop('species', axis=1)\n",
        "y = df1['species']\n",
        "\n",
        "#Standardizing the data\n",
        "scaler = StandardScaler()\n",
        "X_standardized = scaler.fit_transform(X)\n",
        "X_standardized_df = pd.DataFrame(X_standardized, columns=X.columns)\n",
        "\n",
        "#Normalizing the data\n",
        "normalizer = MinMaxScaler()\n",
        "X_normalized = normalizer.fit_transform(X)\n",
        "X_normalized_df = pd.DataFrame(X_normalized, columns=X.columns)\n",
        "\n",
        "X_standardized_df.head(), X_normalized_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1m-eKC3q_gz9",
        "outputId": "ae79980a-3946-4636-8c6e-4e969ff9ad67"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(   sepal_length  sepal_width  petal_length  petal_width\n",
              " 0     -0.926103     1.163072     -1.393237    -1.370894\n",
              " 1     -1.166453    -0.096923     -1.393237    -1.370894\n",
              " 2     -1.406803     0.407075     -1.450554    -1.370894\n",
              " 3     -1.526978     0.155076     -1.335921    -1.370894\n",
              " 4     -1.046278     1.415071     -1.393237    -1.370894,\n",
              "    sepal_length  sepal_width  petal_length  petal_width\n",
              " 0      0.222222     0.722222      0.067797     0.041667\n",
              " 1      0.166667     0.444444      0.067797     0.041667\n",
              " 2      0.111111     0.555556      0.050847     0.041667\n",
              " 3      0.083333     0.500000      0.084746     0.041667\n",
              " 4      0.194444     0.777778      0.067797     0.041667)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model Building"
      ],
      "metadata": {
        "id": "U3s3qXGR4Tqx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Split the standardized data\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_standardized_df, y, test_size=0.2, random_state=42)\n",
        "\n",
        "#Initialize and train the Logistic Regression model\n",
        "model = LogisticRegression(max_iter=200)\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "#Make predictions on the test set\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "#Calculate accuracy and classification report\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "classification_rep = classification_report(y_test, y_pred)\n",
        "\n",
        "print('Accuracy:', accuracy)\n",
        "print('\\nClassifi. Report:',classification_rep)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KsrbpoQnAvO0",
        "outputId": "b8ab1dc4-e5dd-41b3-c3b3-c11debb912c5"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.896551724137931\n",
            "\n",
            "Classifi. Report:                  precision    recall  f1-score   support\n",
            "\n",
            "    Iris-setosa       1.00      1.00      1.00         8\n",
            "Iris-versicolor       0.83      0.91      0.87        11\n",
            " Iris-virginica       0.89      0.80      0.84        10\n",
            "\n",
            "       accuracy                           0.90        29\n",
            "      macro avg       0.91      0.90      0.90        29\n",
            "   weighted avg       0.90      0.90      0.90        29\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Initialize KNN classifier\n",
        "knn = KNeighborsClassifier()\n",
        "\n",
        "#5-fold cross-validation\n",
        "knn_cv_scores = cross_val_score(knn, X_standardized_df, y_encoded, cv=5, scoring='accuracy')\n",
        "\n",
        "#mean accuracy across folds\n",
        "knn_cv_mean_accuracy = knn_cv_scores.mean()\n",
        "knn_cv_mean_accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7aCtgqgbB2Kf",
        "outputId": "8021ab9a-35ce-42c9-da6c-957710a39c65"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9440886699507388"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#range of 'n_neighbors'\n",
        "param_grid = {'n_neighbors': range(1, 21)}\n",
        "\n",
        "#Set up GridSearchCV with cross-validation\n",
        "grid_search = GridSearchCV(KNeighborsClassifier(), param_grid, cv=5, scoring='accuracy')\n",
        "grid_search.fit(X_standardized_df, y_encoded)\n",
        "\n",
        "#the best parameters & corresponding accuracy\n",
        "best_params = grid_search.best_params_\n",
        "best_score = grid_search.best_score_\n",
        "\n",
        "print(\"Best Parameters:\", best_params)\n",
        "print(\"Best Cross-Validated Accuracy:\", best_score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h3rupej4DaG3",
        "outputId": "c723cc0c-a208-4343-b7e7-84b7a5595c2b"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Parameters: {'n_neighbors': 8}\n",
            "Best Cross-Validated Accuracy: 0.9578817733990148\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_standardized_df, y, test_size=0.2, random_state=42)\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_train_std = scaler.fit_transform(X_train)\n",
        "X_test_std = scaler.transform(X_test)\n",
        "\n",
        "#KNN model (with n_neighbors=8)\n",
        "best_knn_model = KNeighborsClassifier(n_neighbors=8)\n",
        "best_knn_model.fit(X_train_std, y_train)\n",
        "\n",
        "#Predict on the test set\n",
        "y_pred_knn = best_knn_model.predict(X_test_std)\n",
        "\n",
        "#Evaluate accuracy and classification report\n",
        "accuracy_knn = accuracy_score(y_test, y_pred_knn)\n",
        "classification_rep_knn = classification_report(y_test, y_pred_knn)\n",
        "\n",
        "print(\"Test Accuracy:\", accuracy_knn)\n",
        "print(\"\\nClassification Report:\\n\", classification_rep_knn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aEyitcc6FWVe",
        "outputId": "642f8b0b-d5be-421a-af19-0d40ccb5232b"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy: 0.8620689655172413\n",
            "\n",
            "Classification Report:\n",
            "                  precision    recall  f1-score   support\n",
            "\n",
            "    Iris-setosa       1.00      1.00      1.00         8\n",
            "Iris-versicolor       0.77      0.91      0.83        11\n",
            " Iris-virginica       0.88      0.70      0.78        10\n",
            "\n",
            "       accuracy                           0.86        29\n",
            "      macro avg       0.88      0.87      0.87        29\n",
            "   weighted avg       0.87      0.86      0.86        29\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Insights"
      ],
      "metadata": {
        "id": "IJI20ADbHJaS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For Iris-setosa, the model performs very well; for Iris-versicolor, it performs mediocrely; and for Iris-virginica, it performs mediocrely. Although the weighted averages point to a strong overall performance, I may want to look into the reasons behind the lower-than-expected recall for Iris-virginica. This could point to possible problems with the data or the generalizability of the model for this class."
      ],
      "metadata": {
        "id": "nGT8P1HEHNwG"
      }
    }
  ]
}