{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9e96a1dd-303c-4920-8d24-d5d7e58dc02d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "       Species  \n",
       "0  Iris-setosa  \n",
       "1  Iris-setosa  \n",
       "2  Iris-setosa  \n",
       "3  Iris-setosa  \n",
       "4  Iris-setosa  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Loading the iris dataset\n",
    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
    "df = pd.read_csv(url, header=None, names=['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n",
    "                                          'petal width (cm)', 'Species'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1a8f6c2f-d63a-4fc0-9e06-3c360e451852",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To know number of rows and collumns\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dc5283cb-d268-4373-99d1-94cf90c33edd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   sepal length (cm)  150 non-null    float64\n",
      " 1   sepal width (cm)   150 non-null    float64\n",
      " 2   petal length (cm)  150 non-null    float64\n",
      " 3   petal width (cm)   150 non-null    float64\n",
      " 4   Species            150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check the dataframe information\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "68c02ddc-9a88-45b1-8f86-9b2c9ebfc713",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal length (cm)    0\n",
       "sepal width (cm)     0\n",
       "petal length (cm)    0\n",
       "petal width (cm)     0\n",
       "Species              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To find if any null value is present\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "15fa05e3-9e35-44dc-a053-4021f64ec00b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <td>150.0</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>4.3</td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.80</td>\n",
       "      <td>6.4</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <td>150.0</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal length (cm)</th>\n",
       "      <td>150.0</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>4.35</td>\n",
       "      <td>5.1</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal width (cm)</th>\n",
       "      <td>150.0</td>\n",
       "      <td>1.198667</td>\n",
       "      <td>0.763161</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count      mean       std  min  25%   50%  75%  max\n",
       "sepal length (cm)  150.0  5.843333  0.828066  4.3  5.1  5.80  6.4  7.9\n",
       "sepal width (cm)   150.0  3.054000  0.433594  2.0  2.8  3.00  3.3  4.4\n",
       "petal length (cm)  150.0  3.758667  1.764420  1.0  1.6  4.35  5.1  6.9\n",
       "petal width (cm)   150.0  1.198667  0.763161  0.1  0.3  1.30  1.8  2.5"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To see summary statistics\n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25225fd1-313b-4dd7-81e4-63e8a09490db",
   "metadata": {},
   "source": [
    "### other options for palettes \n",
    "\n",
    "- \"PRGn\" \n",
    "- \"flare\"\n",
    "- \"colorblind\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6baff895-78f5-4c65-a804-32b372f95814",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1900x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "#%matplotlib inline # inline render graphs below cell (but maybe not necessary anymore) \n",
    "\n",
    "columns = df.columns # my data has 4 columns.\n",
    "\n",
    "fig, ax = plt.subplots(ncols = 4, figsize=(19,10))\n",
    "plt.subplots_adjust(wspace = 0.5) # wspace = width space\n",
    "\n",
    "for i in range(0,4):\n",
    "    s = sns.boxplot(ax = ax[i], data = df[columns[i]], showfliers = True)\n",
    "    ax[i].set_xlabel(columns[i])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "32b155aa-a95b-438d-a0ee-0d1336bc462d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(146, 5)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    # To remove outliers from 'sepal width (cm)'\n",
    "q1 = df['sepal width (cm)'].quantile(0.25)\n",
    "q3 = df['sepal width (cm)'].quantile(0.75)\n",
    "iqr = q3 - q1\n",
    "df = df[(df['sepal width (cm)'] >= q1-1.5*iqr) & (df['sepal width (cm)'] <= q3+1.5*iqr)]\n",
    "df.shape # To find out the number of rows and column after outlier treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "665e444d-3d65-4dc6-94c1-60fb0677dc77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot for sepal width (cm) after outlier treatment\n",
    "sns.boxplot(y=df['sepal width (cm)'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4e7a2ded-1e62-479f-82c9-2fe73e2af3b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Splitting the data into train and test sets\n",
    "X = df.drop(\"Species\",axis=1)\n",
    "y = df[\"Species\"]\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3, random_state= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "139a1941-1c75-4ead-81ed-83031c9f3ad6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=3, min_samples_leaf=10, random_state=1)</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=3, min_samples_leaf=10, random_state=1)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=3, min_samples_leaf=10, random_state=1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Defining an object for DTC and fitting for whole dataset\n",
    "dt = DecisionTreeClassifier(max_depth=3, min_samples_leaf=10, random_state=1 )\n",
    "dt.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9c6be7f6-ae65-4685-be32-5f945f53e869",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "\n",
    "#print(dt.feature_names_in_)\n",
    "\n",
    "t = tree.plot_tree(decision_tree = dt, feature_names=dt.feature_names_in_) # returns a array with each leaf and their values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "80282a49-1d47-4146-afd8-a9aa359e9791",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining an object for DTC and fitting for train dataset\n",
    "dt = DecisionTreeClassifier(random_state=1)\n",
    "dt.fit(X_train, y_train)\n",
    "\n",
    "y_pred_train = dt.predict(X_train)\n",
    "y_pred = dt.predict(X_test)\n",
    "y_prob = dt.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "af602439-ec2e-4ef7-88eb-6d3684fd70ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Decision Tree-Train:  1.0\n",
      "Accuracy of Decision Tree-Test:  0.9545454545454546\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print('Accuracy of Decision Tree-Train: ', accuracy_score(y_pred_train, y_train))\n",
    "print('Accuracy of Decision Tree-Test: ', accuracy_score(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0ae9915b-5145-4ffe-a49f-18d44064dfa5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        15\n",
      "Iris-versicolor       1.00      0.87      0.93        15\n",
      " Iris-virginica       0.88      1.00      0.93        14\n",
      "\n",
      "       accuracy                           0.95        44\n",
      "      macro avg       0.96      0.96      0.95        44\n",
      "   weighted avg       0.96      0.95      0.95        44\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report \n",
    "#Classification for test before hyperparameter tuning\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6906db27-13f0-4e27-b08c-924a303e61ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 3, 'min_samples_leaf': 3, 'min_samples_split': 2}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "# Hyperparameter Tuning of DTC\n",
    "\n",
    "dt = DecisionTreeClassifier(random_state=1)\n",
    "\n",
    "params = {'max_depth' : [2,3,4,5],\n",
    "        'min_samples_split': [2,3,4,5],\n",
    "        'min_samples_leaf': [1,2,3,4,5]}\n",
    "\n",
    "gsearch = GridSearchCV(dt, param_grid=params, cv=3)\n",
    "\n",
    "gsearch.fit(X,y)\n",
    "\n",
    "gsearch.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "56a75b7b-2915-47e7-9f8b-1f12c72c2196",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Passing best parameter for the Hyperparameter Tuning\n",
    "dt = DecisionTreeClassifier(**gsearch.best_params_, random_state=1)\n",
    "\n",
    "dt.fit(X_train, y_train)\n",
    "\n",
    "y_pred_train = dt.predict(X_train)\n",
    "y_prob_train = dt.predict_proba(X_train)[:,1]\n",
    "\n",
    "y_pred = dt.predict(X_test)\n",
    "y_prob = dt.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "59d0f61b-c0c7-4746-9706-2b0816dca073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.4, 0.875, 'petal width (cm) <= 0.75\\ngini = 0.666\\nsamples = 102\\nvalue = [32, 34, 36]'),\n",
       " Text(0.2, 0.625, 'gini = 0.0\\nsamples = 32\\nvalue = [32, 0, 0]'),\n",
       " Text(0.6, 0.625, 'petal width (cm) <= 1.65\\ngini = 0.5\\nsamples = 70\\nvalue = [0, 34, 36]'),\n",
       " Text(0.4, 0.375, 'petal length (cm) <= 4.95\\ngini = 0.149\\nsamples = 37\\nvalue = [0, 34, 3]'),\n",
       " Text(0.2, 0.125, 'gini = 0.0\\nsamples = 33\\nvalue = [0, 33, 0]'),\n",
       " Text(0.6, 0.125, 'gini = 0.375\\nsamples = 4\\nvalue = [0, 1, 3]'),\n",
       " Text(0.8, 0.375, 'gini = 0.0\\nsamples = 33\\nvalue = [0, 0, 33]')]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(decision_tree = dt, feature_names=dt.feature_names_in_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "eb0b0037-7305-4085-a668-92882fec7302",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Passing best parameter for the Hyperparameter Tuning\n",
    "dt = DecisionTreeClassifier(**gsearch.best_params_, random_state=1)\n",
    "\n",
    "dt.fit(X_train, y_train)\n",
    "\n",
    "y_pred_train = dt.predict(X_train)\n",
    "y_prob_train = dt.predict_proba(X_train)[:,1]\n",
    "\n",
    "y_pred = dt.predict(X_test)\n",
    "y_prob = dt.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d191383c-ba55-43c8-97bb-88816b9763d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix - Train: \n",
      " [[32  0  0]\n",
      " [ 0 33  1]\n",
      " [ 0  0 36]]\n",
      "\n",
      " Confusion Matrix - Test: \n",
      " [[15  0  0]\n",
      " [ 0 13  2]\n",
      " [ 0  0 14]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix \n",
    "\n",
    "print('Confusion Matrix - Train:','\\n',confusion_matrix(y_train,y_pred_train))\n",
    "print('\\n','Confusion Matrix - Test:','\\n',confusion_matrix(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2e5cdce5-ada0-493e-aefa-e3415abb1d54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        15\n",
      "Iris-versicolor       1.00      0.87      0.93        15\n",
      " Iris-virginica       0.88      1.00      0.93        14\n",
      "\n",
      "       accuracy                           0.95        44\n",
      "      macro avg       0.96      0.96      0.95        44\n",
      "   weighted avg       0.96      0.95      0.95        44\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Classification for test after hyperparameter tuning\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "55c4bb77-7646-4c0e-b2e2-24ae9e23e1db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Decision Tree-Train:  0.9901960784313726\n",
      "Accuracy of Decision Tree-Test:  0.9545454545454546\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy of Decision Tree-Train: ', accuracy_score(y_pred_train, y_train))\n",
    "print('Accuracy of Decision Tree-Test: ', accuracy_score(y_pred, y_test))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
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}