Iris_using_LR,DT_and_RF.ipynb

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        "<a href=\"https://colab.research.google.com/github/AnmolArora15/ML-Projects/blob/main/Iris_using_LR%2CDT_and_RF.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# <center><b>IRIS DATA<b></center>"
      ],
      "metadata": {
        "id": "kGcJffFAnpaH"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "# **Table of Contents**\n",
        "---\n",
        "\n",
        "**1.** [**Introduction**](#Section1)<br>\n",
        "\n",
        "**2.** [**Primary Objectives**](#Section2)<br>\n",
        "**3.** [**Installing & Importing Libraries**](#Section3)<br>\n",
        "  - **3.1** [**Installing Libraries**](#Section31)\n",
        "  - **3.2** [**Upgrading Libraries**](#Section32)\n",
        "  - **3.3** [**Importing Libraries**](#Section33)\n",
        "\n",
        "**4.** [**Data Acquisition & Description**](#Section4)<br>\n",
        "  - **4.1** [**Data Informaton**](#Section41)\n",
        "  - **4.2** [**Data Description**](#Section42)\n",
        "\n",
        "**5.** [**Data Pre-processing**](#Section5)<br>\n",
        "  - **5.1** [**Pre-Profiling Report**](#Section51)<br>\n",
        "  - **5.2** [**Identification & Handling of Missing Data and Duplicates**](#Section52)<br>\n",
        "\n",
        "**6.** [**Exploratory Data Analysis**](#Section6)<br>\n",
        "**7.** [**Post Data Processing & Feature Selection**](#Section7)<br>\n",
        "  - **7.1** [**Feature Selection using Random Forest**](#Section71)<br>\n",
        "  - **7.2** [**Data Preparation**](#Section72)<br>\n",
        "\n",
        "**8.** [**Model Development & Evaluation**](#Section8)<br>\n",
        "  - **8.1** [**Logistic Regression**](#Section81)<br>\n",
        "  - **8.2** [**Logistic Regression Model Evaluation**](#Section82)<br>\n",
        "  - **8.3** [**Decision Tree**](#Section83)<br>\n",
        "  - **8.4** [**Decision Tree Model Evaluation**](#Section84)<br>\n",
        "  - **8.5** [**Random Forest**](#Section85)<br>\n",
        "  - **8.6** [**Random Forest Model Evaluation**](#Section86)<br>\n",
        "\n",
        "**9.** [**Conclusion**](#Section9)<br>"
      ],
      "metadata": {
        "id": "IiC3yXZ1n7pI"
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    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "<a name = Section1></a>\n",
        "# **1. Introduction**\n",
        "---\n",
        "The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by the British statistician and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis.\n",
        "- It is sometimes called Anderson's Iris data set because Edgar Anderson collected the data to quantify the morphologic variation of Iris flowers of three related species.\n",
        "- Two of the three species were collected in the Gaspé Peninsula \"all from the same pasture, and picked on the same day and measured at the same time by the same person with the same apparatus\".\n",
        "- The data set consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimetres. Based on the combination of these four features, Fisher developed a linear discriminant model to distinguish the species from each other.\n",
        "\n"
      ],
      "metadata": {
        "id": "r5CUy9tBoOXy"
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    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "<a name = Section2></a>\n",
        "# **2. Primary Objective**\n",
        "---\n",
        "-In this notebook, we will construct Logistic Regression,Decision Tree Algorithms to classify the data.\n",
        "\n",
        "We will also do a **Principal Component Analysis** later in this Notebook.\n",
        "\n",
        "The following steps will be implemented -\n",
        "\n",
        "1. Exploratory Data Analysis\n",
        "2. Logistic Regression\n",
        "3. Decision Tree\n",
        "4. PCA with Logistic Regression\n",
        "5. PCA with Decision Tree\n"
      ],
      "metadata": {
        "id": "l8Ahfa85pkJu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "<a name = Section3></a>\n",
        "# **3. Installing & Importing Libraries**\n",
        "---"
      ],
      "metadata": {
        "id": "qP0TEx5cqaEH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name = Section31></a>\n",
        "### **3.1 Installing Libraries**"
      ],
      "metadata": {
        "id": "fhO0YziMqgsd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q datascience                   # Package that is required by pandas profiling\n",
        "!pip install -q pandas-profiling              # Library to generate basic statistics about data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9QSDNse4nt2D",
        "outputId": "ece3138a-f263-4ee2-ef02-fc72a6f0f20d"
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      "source": [
        "<a name = Section32></a>\n",
        "### **3.2 Upgrading Libraries**\n",
        "\n",
        "- **After upgrading** the libraries, you need to **restart the runtime** to make the libraries in sync.\n",
        "\n",
        "- Make sure not to execute the cell above (3.1) and below (3.2) again after restarting the runtime."
      ],
      "metadata": {
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      "source": [
        "!pip install -q --upgrade pandas-profiling"
      ],
      "metadata": {
        "id": "M_HTPvLIqlSW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name = Section33></a>\n",
        "### **3.3 Importing Libraries**"
      ],
      "metadata": {
        "id": "GtncVbISq7ZO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#-------------------------------------------------------------------------------------------------------------------------------\n",
        "import pandas as pd                                                 # Importing for panel data analysis\n",
        "#from pandas_profiling import ProfileReport                          # Import Pandas Profiling (To generate Univariate Analysis)\n",
        "pd.set_option('display.max_columns', None)                          # Unfolding hidden features if the cardinality is high\n",
        "pd.set_option('display.max_colwidth', None)                         # Unfolding the max feature width for better clearity\n",
        "pd.set_option('display.max_rows', None)                             # Unfolding hidden data points if the cardinality is high\n",
        "pd.set_option('mode.chained_assignment', None)                      # Removing restriction over chained assignments operations\n",
        "pd.set_option('display.float_format', lambda x: '%.5f' % x)         # To suppress scientific notation over exponential values\n",
        "#-------------------------------------------------------------------------------------------------------------------------------\n",
        "import numpy as np                                                  # Importing package numpys (For Numerical Python)\n",
        "#-------------------------------------------------------------------------------------------------------------------------------\n",
        "import matplotlib.pyplot as plt                                     # Importing pyplot interface using matplotlib\n",
        "from matplotlib.pylab import rcParams                               # Backend used for rendering and GUI integration\n",
        "import seaborn as sns                                               # Importin seaborm library for interactive visualization\n",
        "%matplotlib inline\n",
        "#-------------------------------------------------------------------------------------------------------------------------------\n",
        "from sklearn.metrics import accuracy_score                          # For calculating the accuracy for the model\n",
        "from sklearn.metrics import precision_score                         # For calculating the Precision of the model\n",
        "from sklearn.metrics import recall_score                            # For calculating the recall of the model\n",
        "from sklearn.metrics import precision_recall_curve                  # For precision and recall metric estimation\n",
        "from sklearn.metrics import confusion_matrix                        # For verifying model performance using confusion matrix\n",
        "from sklearn.metrics import f1_score                                # For Checking the F1-Score of our model\n",
        "from sklearn.metrics import roc_curve                               # For Roc-Auc metric estimation\n",
        "#-------------------------------------------------------------------------------------------------------------------------------\n",
        "from sklearn.model_selection import train_test_split                # To split the data in training and testing part\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.decomposition import PCA\n",
        "#-------------------------------------------------------------------------------------------------------------------------------\n",
        "import warnings                                                     # Importing warning to disable runtime warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "metadata": {
        "id": "KvB0zt8Pq0Jh"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "<a name = Section4></a>\n",
        "# **4. Data Acquisition & Description**\n",
        "---"
      ],
      "metadata": {
        "id": "h48bAfdusHf2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset contains a set of 150 records under 5 attributes/features - Petal Length, Petal Width, Sepal Length, Sepal width and Class(Species)."
      ],
      "metadata": {
        "id": "-ADbSU9CufPK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip '/content/archive (5).zip'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zXAMpamfr6wD",
        "outputId": "2ed7e61d-f2ee-4636-eb4b-71ad6a78f6ed"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/archive (5).zip\n",
            "  inflating: IRIS.csv                \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/IRIS.csv')\n",
        "print(data.shape)\n",
        "data.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "VuYYdwyWuyrI",
        "outputId": "ceab818b-e3af-436b-8738-69e887cfdbe1"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(150, 5)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width      species\n",
              "0       5.10000      3.50000       1.40000      0.20000  Iris-setosa\n",
              "1       4.90000      3.00000       1.40000      0.20000  Iris-setosa\n",
              "2       4.70000      3.20000       1.30000      0.20000  Iris-setosa\n",
              "3       4.60000      3.10000       1.50000      0.20000  Iris-setosa\n",
              "4       5.00000      3.60000       1.40000      0.20000  Iris-setosa"
            ],
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              "<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>species</th>\n",
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              "      <th>0</th>\n",
              "      <td>5.10000</td>\n",
              "      <td>3.50000</td>\n",
              "      <td>1.40000</td>\n",
              "      <td>0.20000</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.90000</td>\n",
              "      <td>3.00000</td>\n",
              "      <td>1.40000</td>\n",
              "      <td>0.20000</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.70000</td>\n",
              "      <td>3.20000</td>\n",
              "      <td>1.30000</td>\n",
              "      <td>0.20000</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.60000</td>\n",
              "      <td>3.10000</td>\n",
              "      <td>1.50000</td>\n",
              "      <td>0.20000</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.00000</td>\n",
              "      <td>3.60000</td>\n",
              "      <td>1.40000</td>\n",
              "      <td>0.20000</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-7fa584df-795e-4cc0-a8a9-f3d74ef36040 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-7fa584df-795e-4cc0-a8a9-f3d74ef36040');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name = Section41></a>\n",
        "### **4.1 Data Information**\n",
        "\n",
        "- In this section we will see the **information about the types of features**."
      ],
      "metadata": {
        "id": "lA8tQn8JvKs2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('The Shape of our DataSet is',data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gN8P-FReu5XI",
        "outputId": "c2516a4a-dd83-498f-db02-e1775e1daa5e"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Shape of our DataSet is (150, 5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations** -\n",
        "- Our DataSet consists of 150 Rows and 5 Columns."
      ],
      "metadata": {
        "id": "ui-98IRWvZst"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZzD6pUGpvUFA",
        "outputId": "ab1fc6a1-cec9-4388-f393-7643326e7282"
      },
      "execution_count": 5,
      "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   species       150 non-null    object \n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 6.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations** -\n",
        "\n",
        "- We have 150 rows of observations across 5 features.(verified)\n",
        "- We don't have any Null values in our Data.\n",
        "- The datatype of all the features looks fine.\n",
        "- **Species** Column is our Target Feature."
      ],
      "metadata": {
        "id": "TKn27J4JxnUs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name = Section42></a>\n",
        "### **4.2 Data Description**\n",
        "\n",
        "- In this section we will get **information about the data** and see some observations."
      ],
      "metadata": {
        "id": "Ldek2A4zye3E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "ztU5C_V-xc2w",
        "outputId": "19b108c9-2f74-4e7d-e832-5598a32c1c70"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       sepal_length  sepal_width  petal_length  petal_width\n",
              "count     150.00000    150.00000     150.00000    150.00000\n",
              "mean        5.84333      3.05400       3.75867      1.19867\n",
              "std         0.82807      0.43359       1.76442      0.76316\n",
              "min         4.30000      2.00000       1.00000      0.10000\n",
              "25%         5.10000      2.80000       1.60000      0.30000\n",
              "50%         5.80000      3.00000       4.35000      1.30000\n",
              "75%         6.40000      3.30000       5.10000      1.80000\n",
              "max         7.90000      4.40000       6.90000      2.50000"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-c3e82ff2-970d-441c-997f-7f1fe95eef18\">\n",
              "    <div 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>150.00000</td>\n",
              "      <td>150.00000</td>\n",
              "      <td>150.00000</td>\n",
              "      <td>150.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.84333</td>\n",
              "      <td>3.05400</td>\n",
              "      <td>3.75867</td>\n",
              "      <td>1.19867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.82807</td>\n",
              "      <td>0.43359</td>\n",
              "      <td>1.76442</td>\n",
              "      <td>0.76316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.30000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.10000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.10000</td>\n",
              "      <td>2.80000</td>\n",
              "      <td>1.60000</td>\n",
              "      <td>0.30000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.80000</td>\n",
              "      <td>3.00000</td>\n",
              "      <td>4.35000</td>\n",
              "      <td>1.30000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.40000</td>\n",
              "      <td>3.30000</td>\n",
              "      <td>5.10000</td>\n",
              "      <td>1.80000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.90000</td>\n",
              "      <td>4.40000</td>\n",
              "      <td>6.90000</td>\n",
              "      <td>2.50000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c3e82ff2-970d-441c-997f-7f1fe95eef18')\"\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 0 24 24\"\n",
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              "      </button>\n",
              "\n",
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              "\n",
              "    <div id=\"df-1c86fea1-23cf-4e6b-8e01-1491a0ddc68f\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1c86fea1-23cf-4e6b-8e01-1491a0ddc68f')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-1c86fea1-23cf-4e6b-8e01-1491a0ddc68f button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-c3e82ff2-970d-441c-997f-7f1fe95eef18 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-c3e82ff2-970d-441c-997f-7f1fe95eef18');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**:\n",
        "- Our Datalooks Normally Distributed across all features.\n",
        "- Mean of Sepal Length is - 5.84 and Median is - 5.80\n",
        "- Mean of Sepal Width is - 3.05 and Median is - 3.00\n",
        "- Mean of Petal Length is - 3.75 and Median is - 4.35\n",
        "- Mean of Petal Width is - 1.19 and Median is - 1.30"
      ],
      "metadata": {
        "id": "02mAiMxsy24W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets see the skewness in our Data"
      ],
      "metadata": {
        "id": "eLILntA_z_M7"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.skew()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9MyO-NbTyzQT",
        "outputId": "d06392a3-3e4e-4899-92ca-be07eda5f1e9"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sepal_length    0.31491\n",
              "sepal_width     0.33405\n",
              "petal_length   -0.27446\n",
              "petal_width    -0.10500\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name = Section5></a>\n",
        "\n",
        "---\n",
        "# **5. Data Pre-Processing**\n",
        "---"
      ],
      "metadata": {
        "id": "uZoUX8Sp0GQt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name = Section51></a>\n",
        "### **5.1 Pre-Profiling Report**\n",
        "\n",
        "- For **quick analysis** pandas profiling is very handy.\n",
        "\n",
        "- Generates profile reports from a pandas DataFrame.\n",
        "\n",
        "- For each column **statistics** are presented in an interactive HTML report."
      ],
      "metadata": {
        "id": "xUTmegPF0Ndb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#profile = ProfileReport(df = data)\n",
        "#profile.to_file(output_file = 'Pre Profiling Report.html')\n",
        "#print('Accomplished!')"
      ],
      "metadata": {
        "id": "ULepj60Bz9Y3"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Lets see the Pandas Pre Profiling Report"
      ],
      "metadata": {
        "id": "tVzOeD_z0QW4"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#from google.colab import files                   # Use only if you are using Google Colab, otherwise remove it\n",
        "#files.download('Pre Profiling Report.html')"
      ],
      "metadata": {
        "id": "oZ5pOFXE0gQc"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**:\n",
        "- Reports shows that the **number of variables**\tin our data is **5**.\n",
        "- We have 2 Duplicate Rows in our Dataset(We will check after this)\n",
        "- No Missing Values\n",
        "- sepal_length is highly overall correlated with petal_length,petal_width and species(We will see the Correlation matrix)\n",
        "- petal_length is highly overall correlated with sepal_length,petal_width and species\n",
        "- petal_width is highly overall correlated with sepal_length,petal_length and species\n",
        "-species is highly overall correlated with sepal_length,petal_length and petal_width\n",
        "-species is uniformly distributed"
      ],
      "metadata": {
        "id": "aZ3iDXsn0tJ0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name = Section52></a>\n",
        "### **5.2 Identification & Handling of Missing Data and Duplicates**"
      ],
      "metadata": {
        "id": "SDoE4YDQ61V6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#We dont have any missing values in our Dataset"
      ],
      "metadata": {
        "id": "H6Csxjg77DWB"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JOYd03YE7IRK",
        "outputId": "e4399f8d-de65-466d-a9ed-91fb69b49256"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sepal_length    0\n",
              "sepal_width     0\n",
              "petal_length    0\n",
              "petal_width     0\n",
              "species         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets check the Duplicate Rows"
      ],
      "metadata": {
        "id": "S4Ocybhc0kov"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[data.duplicated(keep=False)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "jsaBk8Jb4Bd7",
        "outputId": "30e1cefa-018d-48fc-f2e8-ec7b1eecb4a1"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width         species\n",
              "9         4.90000      3.10000       1.50000      0.10000     Iris-setosa\n",
              "34        4.90000      3.10000       1.50000      0.10000     Iris-setosa\n",
              "37        4.90000      3.10000       1.50000      0.10000     Iris-setosa\n",
              "101       5.80000      2.70000       5.10000      1.90000  Iris-virginica\n",
              "142       5.80000      2.70000       5.10000      1.90000  Iris-virginica"
            ],
            "text/html": [
              "\n",
              "\n",
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              "      <div>\n",
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              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
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              "      <th>9</th>\n",
              "      <td>4.90000</td>\n",
              "      <td>3.10000</td>\n",
              "      <td>1.50000</td>\n",
              "      <td>0.10000</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "      <th>34</th>\n",
              "      <td>4.90000</td>\n",
              "      <td>3.10000</td>\n",
              "      <td>1.50000</td>\n",
              "      <td>0.10000</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>4.90000</td>\n",
              "      <td>3.10000</td>\n",
              "      <td>1.50000</td>\n",
              "      <td>0.10000</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "      <th>101</th>\n",
              "      <td>5.80000</td>\n",
              "      <td>2.70000</td>\n",
              "      <td>5.10000</td>\n",
              "      <td>1.90000</td>\n",
              "      <td>Iris-virginica</td>\n",
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              "      <th>142</th>\n",
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              "      <td>2.70000</td>\n",
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              "      <td>1.90000</td>\n",
              "      <td>Iris-virginica</td>\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
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              "        displayQuickchartButton(document);\n",
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              "      <style>\n",
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              "      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",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d9e92822-3511-4f68-8cb9-a8e9a63d7941 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-d9e92822-3511-4f68-8cb9-a8e9a63d7941');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Gnz2X8D3wT1",
        "outputId": "64302930-a14e-419b-80df-19e691fe8044"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets Remove the duplicate Rows"
      ],
      "metadata": {
        "id": "HR7mwFsi5ivq"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "SR_AK7Am6Aqb"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ttiwLfs-6XvW",
        "outputId": "994005d0-c5ed-4f70-e510-2be4b967ea29"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(147, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#We have removed the duplicate Rows and kept the First Rows dorp the duplicates"
      ],
      "metadata": {
        "id": "td_XKt9v6Zmj"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations** -\n",
        "- We had duplicates in our Dataset which we have removed.\n",
        "- No missing vlaues."
      ],
      "metadata": {
        "id": "VRamhKl27TY6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name = Section6></a>\n",
        "\n",
        "---\n",
        "# **6. Exploratory Data Analysis**\n",
        "---"
      ],
      "metadata": {
        "id": "IilbSAtJ7emz"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X719rDsLcOqv"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c3xqmwDI7uFQ",
        "outputId": "ea3d7173-ff44-4ecc-c3e1-2d01af4d9352"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n",
              "       'species'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 1**: What is the distribution of sepal_length, sepal_width, petal_length, and petal_width?"
      ],
      "metadata": {
        "id": "q40oH68ebTG3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Lets plot a histogram to see the distribution"
      ],
      "metadata": {
        "id": "2q4R141pcIJ5"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "paper, axes = plt.subplots(2, 2, figsize=(10, 6))\n",
        "data['sepal_length'].plot(kind='hist', ax=axes[0, 0], title='Sepal Length Distribution')\n",
        "data['sepal_width'].plot(kind='hist', ax=axes[0, 1], title='Sepal Width Distribution')\n",
        "data['petal_length'].plot(kind='hist', ax=axes[1, 0], title='Petal Length Distribution')\n",
        "data['petal_width'].plot(kind='hist', ax=axes[1, 1], title='Petal Width Distribution')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "kd-HTz0Nb1uM",
        "outputId": "1b5e4c6a-2595-406a-d7e4-481353e86f0d"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets plot a box plot for better understanding"
      ],
      "metadata": {
        "id": "ly22eAdBcCM2"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(data=data[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']], palette='viridis')\n",
        "plt.title('Box Plot of Sepal Length, Sepal Width, Petal Length, and Petal Width')\n",
        "plt.xlabel('Features')\n",
        "plt.ylabel('Values in cm')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "G944RtUicqcE",
        "outputId": "6e31e3b4-b46a-4d15-a558-0d62a51e1ad6"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**:\n",
        "- We can see how each feature is distributed in our Dataset.\n",
        "- sepal_width has few potential outliers. We will not remove them as they can be a useful info.\n",
        "- From the above Boxplot we can clearly see median value of **sepal_length > petal_length > sepal_width > petal_width**"
      ],
      "metadata": {
        "id": "qZo-p6w1fjsJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question**: What are the different 'species' and how are they distributed"
      ],
      "metadata": {
        "id": "dvWtt_3IjTXQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['species'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c2L0Qy2EjTCn",
        "outputId": "eb2bdd7b-e5fd-44d5-8692-3f05bfd053ca"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Iris-versicolor    50\n",
              "Iris-virginica     49\n",
              "Iris-setosa        48\n",
              "Name: species, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Earlier we had all 50 but we had few duplicate rows in our data"
      ],
      "metadata": {
        "id": "jZyz7JyhjMwZ"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "paper = plt.figure(figsize=(10,6))\n",
        "data['species'].value_counts().plot.pie(cmap ='crest',startangle =45,legend=True)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "nRFKLj0rjrzV",
        "outputId": "a307dfec-fd43-495f-cec6-87400fd9b576"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**:\n",
        "- All three species namely Iris-versicolor,Iris-virginica and Iris-setosa are equally distributed."
      ],
      "metadata": {
        "id": "iCFaCCu0luNk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets do some bi-variate analysis"
      ],
      "metadata": {
        "id": "624rif-HjwRj"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question**: What is the co-relation among different features in our Dataset?"
      ],
      "metadata": {
        "id": "QPMtrOgxmXgl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#To see the co-relation we can plot a heatmap"
      ],
      "metadata": {
        "id": "XpKlt5JfmCIb"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "paper = plt.figure(figsize=(10,6))\n",
        "sns.heatmap(data.corr(),annot=True,cmap ='coolwarm')\n",
        "plt.title('Co-relation among among different features')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "fAA-6CcZmkwK",
        "outputId": "3f6c72d5-3e20-495d-bc37-851a71ced632"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**:\n",
        "\n",
        "- petal_length is having high co-relation with sepal_length and petal_width.\n",
        "- petal_length is also showing negative co-relation with sepal_width.\n",
        "- petal_width is highly co-related with sepal_length and petal_length and negative co-related with sepal_width"
      ],
      "metadata": {
        "id": "zHJ4xmpYnSTk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question**: How does the distribution of each feature (sepal length, sepal width, petal length, and petal width) vary across different species (Iris-setosa, Iris-versicolor, and Iris-virginica)?"
      ],
      "metadata": {
        "id": "iLXMYidvoYIp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Lets create box plots for each feature grouped by species"
      ],
      "metadata": {
        "id": "toipgv5YAgY9"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.figure(figsize=(8, 4))\n",
        "sns.boxplot(x='species', y='sepal_length', data=data, palette='Set3')\n",
        "plt.title('Distribution of Sepal Length across Species')\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize=(8, 4))\n",
        "sns.boxplot(x='species', y='sepal_width', data=data, palette='Set3')\n",
        "plt.title('Distribution of Sepal Width across Species')\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize=(8, 4))\n",
        "sns.boxplot(x='species', y='petal_length', data=data, palette='Set3')\n",
        "plt.title('Distribution of Petal Length across Species')\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize=(8, 4))\n",
        "sns.boxplot(x='species', y='petal_width', data=data, palette='Set3')\n",
        "plt.title('Distribution of Petal Width across Species')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "J8UISebUAQXV",
        "outputId": "435809e8-434e-47dd-8e56-7dbede056119"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**:\n",
        "- We can see the distribution of different species based on their features.\n",
        "- We can make multiple conclusion based on the above graphs depending upon the requirements.\n",
        "\n",
        "Eg.\n",
        "- Avg Sepal Length of Iris-verginica > Iris-versicolor > Iris-setosa.\n",
        "- Avg Petal width of Iris-verginica > Iris-versicolor > Iris-setosa.\n"
      ],
      "metadata": {
        "id": "zlsNjZ785jL_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question**: Is there a correlation between the numerical features (sepal length, sepal width, petal length, and petal width)? How does the correlation vary for each species?"
      ],
      "metadata": {
        "id": "6IuMG0hIC40F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Its a multi-variate quesion"
      ],
      "metadata": {
        "id": "UhJc1njPC4c1"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets plot a pairplot to have a better visualization"
      ],
      "metadata": {
        "id": "otmOmVrZoW_s"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "paper = plt.figure(figsize=(15,6))\n",
        "sns.pairplot(data=data,hue='species')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 996
        },
        "id": "prk36DeECOI6",
        "outputId": "9172536e-d773-4007-be06-fda927b29bc2"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1143x1000 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**:\n",
        "- The pair plot shows distinct clusters for each species.\n",
        "\n",
        "-Iris-setosa appears to be well-separated from the other two species, while\n",
        " Iris-versicolor and Iris-virginica show some overlap.\n",
        "- Few features can be seen showing **positive linear co-relation** with each\n",
        " other like petal_length and petal_width, as one increases other also tends to increase.\n",
        "- With features like petal_length and petal_width we can clearly identify Iris-setosa some overlap in the other 2 species.\n",
        "- Few outliers can be seen.\n"
      ],
      "metadata": {
        "id": "QcyNpHXjED6P"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name = Section7></a>\n",
        "\n",
        "---\n",
        "# **7. Post Data Processing & Feature Selection**"
      ],
      "metadata": {
        "id": "5ApZQtn0GOur"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name = Section71></a>\n",
        "### **7.1 Feature Selection**"
      ],
      "metadata": {
        "id": "fjWiimUuGgRo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corr = data.corr()\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.heatmap(corr,vmax=.8,linewidth=.01, square = True, annot = True,cmap='YlGnBu',linecolor ='black')\n",
        "plt.title('Correlation between features')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "6Z6Grq--G1A7",
        "outputId": "04160166-ce32-4b46-f8a7-e806b733c173"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Correlation between features')"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**-\n",
        "- We have seen a few features that are highly correlated with each other.\n",
        "- We will keep all the features as of now."
      ],
      "metadata": {
        "id": "0YHbByHgG-QW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name = Section72></a>\n",
        "### **7.2 Data Preparation**\n",
        "\n",
        "- Now wlets **split** our **data** in **training** and **testing** part for further development."
      ],
      "metadata": {
        "id": "DbMXrNs8HfVE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "vLjkIWWLDWmw",
        "outputId": "4f82783c-3e04-4169-9a6f-3f3d6477a6bb"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width      species\n",
              "0       5.10000      3.50000       1.40000      0.20000  Iris-setosa\n",
              "1       4.90000      3.00000       1.40000      0.20000  Iris-setosa"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.10000</td>\n",
              "      <td>3.50000</td>\n",
              "      <td>1.40000</td>\n",
              "      <td>0.20000</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.90000</td>\n",
              "      <td>3.00000</td>\n",
              "      <td>1.40000</td>\n",
              "      <td>0.20000</td>\n",
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              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-0af25fdb-9c56-4c4e-8ad8-49274d0935b3 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-95bf54f9-ae7a-4d1a-8809-863cc2fe58e5 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-95bf54f9-ae7a-4d1a-8809-863cc2fe58e5');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.drop('species',axis=1)\n",
        "y = data['species']"
      ],
      "metadata": {
        "id": "fC51ZoaGGs5-"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "SKJUwHRAH4N6",
        "outputId": "fe98b3e8-c94f-4663-f643-44e69252c164"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width\n",
              "0       5.10000      3.50000       1.40000      0.20000\n",
              "1       4.90000      3.00000       1.40000      0.20000"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-efa67964-c69a-4387-8fc3-7e42793662da\">\n",
              "    <div 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>0</th>\n",
              "      <td>5.10000</td>\n",
              "      <td>3.50000</td>\n",
              "      <td>1.40000</td>\n",
              "      <td>0.20000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.90000</td>\n",
              "      <td>3.00000</td>\n",
              "      <td>1.40000</td>\n",
              "      <td>0.20000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-efa67964-c69a-4387-8fc3-7e42793662da')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-b9c2240a-88a8-44d8-8789-9a40534b6cdb\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b9c2240a-88a8-44d8-8789-9a40534b6cdb')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-b9c2240a-88a8-44d8-8789-9a40534b6cdb button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-efa67964-c69a-4387-8fc3-7e42793662da 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-efa67964-c69a-4387-8fc3-7e42793662da');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3CxsnroAH5td",
        "outputId": "c8cabb76-5f7c-4011-d6e4-d5a98879c6c1"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    Iris-setosa\n",
              "1    Iris-setosa\n",
              "Name: species, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42, stratify = y)"
      ],
      "metadata": {
        "id": "GkkNG75sIBh7"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Training Data Shape:', X_train.shape, y_train.shape)\n",
        "print('Testing Data Shape:', X_test.shape, y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nj82S-zwIbIe",
        "outputId": "a9acd75e-121c-422f-9ca0-a01cb7484ce2"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Data Shape: (117, 4) (117,)\n",
            "Testing Data Shape: (30, 4) (30,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name = Section8></a>\n",
        "\n",
        "---\n",
        "# **8. Model Development & Evaluation**\n",
        "---"
      ],
      "metadata": {
        "id": "fzI8CpvvIq8p"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- In this section we will **develop Logistic Regression using input features** and **tune** our **model if required**.\n",
        "\n",
        "- Then we will **analyze the results** obtained and **make our observation**.\n",
        "\n",
        "- For **evaluation purpose** we will look on **Accuracy**, also we will check for **Precision**, **Recall** and **F1-Score**.\n"
      ],
      "metadata": {
        "id": "wXwDm8mMI0GV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name = Section82></a>\n",
        "### **8.1 Logistic Regression**\n"
      ],
      "metadata": {
        "id": "i6FqTnfiJ4hL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LogisticRegression()"
      ],
      "metadata": {
        "id": "BZvwusaTIe59"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "8T1F8S_NKWNH",
        "outputId": "ae8df53e-118f-45ed-eb35-bb582ddfcef9"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds_lr_train = lr.predict(X_train)\n",
        "preds_lr_test = lr.predict(X_test)"
      ],
      "metadata": {
        "id": "g4JlxOchKbG3"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name = Section82></a>\n",
        "### **8.2 Logistic Regression Model Evaluation**\n",
        "\n"
      ],
      "metadata": {
        "id": "HAARBHoHLXl7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets import from sklearn lib"
      ],
      "metadata": {
        "id": "Oiog30OBL22O"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
      ],
      "metadata": {
        "id": "KvTfGHXCL6O-"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def model_eval(actual, predicted):\n",
        "  acc_score = accuracy_score(actual, predicted)\n",
        "  conf_matrix = confusion_matrix(actual, predicted)\n",
        "  clas_rep = classification_report(actual, predicted)\n",
        "  print('Model Accuracy is: ', round(acc_score, 2))\n",
        "  print(conf_matrix)\n",
        "  print(clas_rep)"
      ],
      "metadata": {
        "id": "rCrpzifmKkvK"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets see how our model predicts on the train data"
      ],
      "metadata": {
        "id": "ub8svmrAMJ8C"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_eval(y_train,preds_lr_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lumKuJEELttX",
        "outputId": "7e65d252-ad75-4f20-edd4-9ee08815a696"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Accuracy is:  0.97\n",
            "[[38  0  0]\n",
            " [ 0 38  2]\n",
            " [ 0  1 38]]\n",
            "                 precision    recall  f1-score   support\n",
            "\n",
            "    Iris-setosa       1.00      1.00      1.00        38\n",
            "Iris-versicolor       0.97      0.95      0.96        40\n",
            " Iris-virginica       0.95      0.97      0.96        39\n",
            "\n",
            "       accuracy                           0.97       117\n",
            "      macro avg       0.97      0.97      0.97       117\n",
            "   weighted avg       0.97      0.97      0.97       117\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Now Lets see how our model predicts on the test data"
      ],
      "metadata": {
        "id": "b4Il_BD9MQuw"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_eval(y_test,preds_lr_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CBsO-65PMaOt",
        "outputId": "f3a7312d-71d9-4b7f-c8ff-0d3985dee9c6"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Accuracy is:  0.97\n",
            "[[10  0  0]\n",
            " [ 0  9  1]\n",
            " [ 0  0 10]]\n",
            "                 precision    recall  f1-score   support\n",
            "\n",
            "    Iris-setosa       1.00      1.00      1.00        10\n",
            "Iris-versicolor       1.00      0.90      0.95        10\n",
            " Iris-virginica       0.91      1.00      0.95        10\n",
            "\n",
            "       accuracy                           0.97        30\n",
            "      macro avg       0.97      0.97      0.97        30\n",
            "   weighted avg       0.97      0.97      0.97        30\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**:\n",
        "- Our model is performing good on the train as well as the test data set.\n",
        "- We have an accuracy of 97% (wow....!!)\n",
        "- Let's check tree-based models and see if they perform better than Logistic Regression."
      ],
      "metadata": {
        "id": "Ve3tGhE4NGzQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name = Section83></a>\n",
        "### **8.3 Decision Tree**\n"
      ],
      "metadata": {
        "id": "eczZVnd2N1SE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Lets apply Decision Tree to our Model"
      ],
      "metadata": {
        "id": "dYfBP3UvOr2x"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dtree = DecisionTreeClassifier()\n",
        "dtree.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "sjZiUz6hMask",
        "outputId": "3db9be6e-f99c-4283-e4f8-a86a4620f9d0"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds_dtree_train = dtree.predict(X_train)\n",
        "preds_dtree_test = dtree.predict(X_test)"
      ],
      "metadata": {
        "id": "85ROU115Of4f"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name = Section84></a>\n",
        "### **8.4 Decision Tree Model Evaluation**\n"
      ],
      "metadata": {
        "id": "DxMbUPBVO0WJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_eval(y_train,preds_dtree_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UYampQYnOnyi",
        "outputId": "489e9dff-878b-4ed8-b0ba-f0ee4e611e84"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Accuracy is:  1.0\n",
            "[[38  0  0]\n",
            " [ 0 40  0]\n",
            " [ 0  0 39]]\n",
            "                 precision    recall  f1-score   support\n",
            "\n",
            "    Iris-setosa       1.00      1.00      1.00        38\n",
            "Iris-versicolor       1.00      1.00      1.00        40\n",
            " Iris-virginica       1.00      1.00      1.00        39\n",
            "\n",
            "       accuracy                           1.00       117\n",
            "      macro avg       1.00      1.00      1.00       117\n",
            "   weighted avg       1.00      1.00      1.00       117\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets see on the test dataset"
      ],
      "metadata": {
        "id": "AfYNuj-CPAG1"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_eval(y_test,preds_dtree_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c1JdsvfTPFoe",
        "outputId": "74a6e937-dea5-4ea4-ee3b-ac599f7df54b"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Accuracy is:  0.97\n",
            "[[10  0  0]\n",
            " [ 0  9  1]\n",
            " [ 0  0 10]]\n",
            "                 precision    recall  f1-score   support\n",
            "\n",
            "    Iris-setosa       1.00      1.00      1.00        10\n",
            "Iris-versicolor       1.00      0.90      0.95        10\n",
            " Iris-virginica       0.91      1.00      0.95        10\n",
            "\n",
            "       accuracy                           0.97        30\n",
            "      macro avg       0.97      0.97      0.97        30\n",
            "   weighted avg       0.97      0.97      0.97        30\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#We can see the Feature Importances List"
      ],
      "metadata": {
        "id": "c33ZzzZSRKUo"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "importances = list(zip(dtree.feature_importances_, X_train.columns))\n",
        "importances.sort(reverse=True)\n",
        "print(importances)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JbtO8a2CRJLK",
        "outputId": "e9ab47bc-2995-4aa2-ac90-107c8f7265f8"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(0.9164256007745706, 'petal_width'), (0.0600649735349429, 'petal_length'), (0.023509425690486634, 'sepal_width'), (0.0, 'sepal_length')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iKyit3gDRI_H"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#We can see our Decision Tree Split"
      ],
      "metadata": {
        "id": "zVR0CjiKQnx0"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import export_graphviz\n",
        "import pydotplus                                                    # To generate pydot file\n",
        "from IPython.display import Image                                   # To generate image using pydot file\n"
      ],
      "metadata": {
        "id": "eMDdJuUEQIwB"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate a pydot file\n",
        "dot_tree = export_graphviz(decision_tree=dtree,\n",
        "                           filled=True,\n",
        "                           rounded=True,\n",
        "                           special_characters=True,\n",
        "                           feature_names=X.columns)\n",
        "\n",
        "# Generate graph using pydot file\n",
        "graph = pydotplus.graph_from_dot_data(dot_tree)\n",
        "\n",
        "# Display the graph\n",
        "Image(graph.create_png())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 792
        },
        "id": "Xm87ls6fPGJI",
        "outputId": "4875848f-f87c-45c1-e73b-81248397eeba"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**-\n",
        "- We can see our Model is predicting 100% on train dataset but on test the accuracy has come down to 93%.\n",
        "- We have a over fitting model here.\n",
        "- This might be due to the co-relation present among features."
      ],
      "metadata": {
        "id": "RGa7jKg-PPSL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name = Section85></a>\n",
        "### **8.5 Random Forest**"
      ],
      "metadata": {
        "id": "ZYo64dQeQw7a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "Epec81mGR2DF"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestClassifier(n_estimators=150 ,verbose=2,max_depth=4,class_weight='balanced')"
      ],
      "metadata": {
        "id": "dJ6oV2wTQVGm"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "LHbwQixpRy43",
        "outputId": "981d8b76-db27-4671-90a6-806643d8b0f9"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "building tree 1 of 150\n",
            "building tree 2 of 150\n",
            "building tree 3 of 150\n",
            "building tree 4 of 150\n",
            "building tree 5 of 150\n",
            "building tree 6 of 150\n",
            "building tree 7 of 150\n",
            "building tree 8 of 150\n",
            "building tree 9 of 150\n",
            "building tree 10 of 150\n",
            "building tree 11 of 150\n",
            "building tree 12 of 150\n",
            "building tree 13 of 150\n",
            "building tree 14 of 150\n",
            "building tree 15 of 150\n",
            "building tree 16 of 150\n",
            "building tree 17 of 150\n",
            "building tree 18 of 150\n",
            "building tree 19 of 150\n",
            "building tree 20 of 150\n",
            "building tree 21 of 150\n",
            "building tree 22 of 150\n",
            "building tree 23 of 150\n",
            "building tree 24 of 150\n",
            "building tree 25 of 150\n",
            "building tree 26 of 150\n",
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(class_weight='balanced', max_depth=4, n_estimators=150,\n",
              "                       verbose=2)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, max_depth=4, n_estimators=150,\n",
              "                       verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, max_depth=4, n_estimators=150,\n",
              "                       verbose=2)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds_rf_train = rf.predict(X_train)\n",
        "preds_rf_test = rf.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U0D9ZH2yR8vQ",
        "outputId": "bbe2e733-bebc-4591-a401-3c1aeea4e684"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  40 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  40 tasks      | elapsed:    0.0s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_eval(y_train,preds_rf_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sx--WrdrSBgE",
        "outputId": "3e6ac5a5-14c8-4ae0-839d-3ae3a95d2389"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Accuracy is:  1.0\n",
            "[[38  0  0]\n",
            " [ 0 40  0]\n",
            " [ 0  0 39]]\n",
            "                 precision    recall  f1-score   support\n",
            "\n",
            "    Iris-setosa       1.00      1.00      1.00        38\n",
            "Iris-versicolor       1.00      1.00      1.00        40\n",
            " Iris-virginica       1.00      1.00      1.00        39\n",
            "\n",
            "       accuracy                           1.00       117\n",
            "      macro avg       1.00      1.00      1.00       117\n",
            "   weighted avg       1.00      1.00      1.00       117\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_eval(y_test,preds_rf_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DmeZQYpDSGup",
        "outputId": "b4cb9e1d-a5af-43b7-884d-780032d637d3"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Accuracy is:  0.97\n",
            "[[10  0  0]\n",
            " [ 0  9  1]\n",
            " [ 0  0 10]]\n",
            "                 precision    recall  f1-score   support\n",
            "\n",
            "    Iris-setosa       1.00      1.00      1.00        10\n",
            "Iris-versicolor       1.00      0.90      0.95        10\n",
            " Iris-virginica       0.91      1.00      0.95        10\n",
            "\n",
            "       accuracy                           0.97        30\n",
            "      macro avg       0.97      0.97      0.97        30\n",
            "   weighted avg       0.97      0.97      0.97        30\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations** -\n",
        "\n",
        "- We can see by hypertuning our Random Forest model we have got 100% train accuracy and 97 % on test.\n",
        "- A bit of Overfitting is still there.\n",
        "- Random Forest is performing better than Decision Tree."
      ],
      "metadata": {
        "id": "6KDpFBR4TJts"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name = Section9></a>\n",
        "\n",
        "---\n",
        "# **9. Summarization**\n",
        "---"
      ],
      "metadata": {
        "id": "3CT1QpiSTwsx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- We have seen how different features are responding to our Target feature.\n",
        "- We have also done EDA on different features and saw the distribution of different species.\n",
        "- In this Workbook, we have applied Logistic Regression, Decision Tree, and Random Forest.\n",
        "- We have compared all the 3 model's performances.\n",
        "- We can try to apply more complex classification algorithms like K-Nearest Neighbour or Support Vector Classifier and see if our Model performance increases.\n"
      ],
      "metadata": {
        "id": "aA3MUTOsT7Hp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Thankyou**"
      ],
      "metadata": {
        "id": "jtLb_sGPVH1J"
      }
    }
  ]
}