tst2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:2: SyntaxWarning: invalid escape sequence '\\p'\n",
      "<>:2: SyntaxWarning: invalid escape sequence '\\p'\n",
      "C:\\Users\\dev\\AppData\\Local\\Temp\\ipykernel_23172\\2065249256.py:2: SyntaxWarning: invalid escape sequence '\\p'\n",
      "  iris_data = pd.read_csv(\"C:\\prerna\\CodeSoft\\iris\\IRIS.csv\")\n"
     ]
    }
   ],
   "source": [
    "# Load your dataset\n",
    "iris_data = pd.read_csv(\"C:\\prerna\\CodeSoft\\iris\\IRIS.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</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  sepal_width  petal_length  petal_width      species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Iris.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667\n",
       "std        0.828066     0.433594      1.764420     0.763161\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of      sepal_length  sepal_width  petal_length  petal_width         species\n",
       "0             5.1          3.5           1.4          0.2     Iris-setosa\n",
       "1             4.9          3.0           1.4          0.2     Iris-setosa\n",
       "2             4.7          3.2           1.3          0.2     Iris-setosa\n",
       "3             4.6          3.1           1.5          0.2     Iris-setosa\n",
       "4             5.0          3.6           1.4          0.2     Iris-setosa\n",
       "..            ...          ...           ...          ...             ...\n",
       "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
       "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
       "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
       "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
       "149           5.9          3.0           5.1          1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_df.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 10,
     "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": [
    "df['sepal_length'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot of features (sepal length vs. sepal width)\n",
    "sns.scatterplot(x='sepal_length', y='sepal_width', hue='species', data=iris_data)\n",
    "plt.title('Scatter plot of Sepal Length vs. Sepal Width')\n",
    "plt.xlabel('Sepal Length')\n",
    "plt.ylabel('Sepal Width')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot of species distribution\n",
    "sns.countplot(x='species', data=iris_data)\n",
    "plt.title('Species Distribution')\n",
    "plt.xlabel('Species')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Separate features (X) and target variable (y)\n",
    "X = iris_data.drop('species', axis=1)  # Features (sepal length, sepal width, petal length, petal width)\n",
    "y = iris_data['species']  # Target variable (species)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the K-nearest neighbors classifier\n",
    "knn = KNeighborsClassifier(n_neighbors=3)  # You can adjust the number of neighbors as needed\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>KNeighborsClassifier(n_neighbors=3)</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;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the classifier\n",
    "knn.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the testing set\n",
    "y_pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Evaluate the accuracy of the classifier\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm, annot=True, cmap='Blues', fmt='g', xticklabels=iris_data['species'].unique(), yticklabels=iris_data['species'].unique())\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}