Iris.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b0c9db77",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "73312b4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=pd.read_csv('iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cc0b113d",
   "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>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "775c15eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=dataset.drop('species',axis=1)\n",
    "y=dataset['species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c8de6b39",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a814b1d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.20,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "405d1044",
   "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>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width\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",
       "145           6.7          3.0           5.2          2.3\n",
       "146           6.3          2.5           5.0          1.9\n",
       "147           6.5          3.0           5.2          2.0\n",
       "148           6.2          3.4           5.4          2.3\n",
       "149           5.9          3.0           5.1          1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5da544e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         setosa\n",
       "1         setosa\n",
       "2         setosa\n",
       "3         setosa\n",
       "4         setosa\n",
       "         ...    \n",
       "145    virginica\n",
       "146    virginica\n",
       "147    virginica\n",
       "148    virginica\n",
       "149    virginica\n",
       "Name: species, Length: 150, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "44209b7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4c0517cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler= StandardScaler()\n",
    "scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "483f8e98",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = scaler.transform(X_train)\n",
    "X_test=scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "17912128",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cbe7b0a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = KNeighborsClassifier(n_neighbors=5)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ba1e940f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7ba0b95a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['virginica', 'versicolor', 'setosa', 'virginica', 'setosa',\n",
       "       'virginica', 'setosa', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'virginica', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'setosa', 'versicolor', 'versicolor', 'setosa',\n",
       "       'setosa', 'virginica', 'versicolor', 'setosa', 'setosa',\n",
       "       'virginica', 'setosa', 'setosa', 'versicolor', 'versicolor',\n",
       "       'setosa'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cea143aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "error=[]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9c8f71ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range (1,40):\n",
    "    knn=KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(X_train,y_train)\n",
    "    pred_i = knn.predict(X_test)\n",
    "    error.append(np.mean(pred_i!=y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6e6a60ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mean Error')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(range(1,40),error,color='red', linestyle='dashed',marker='o',markerfacecolor='blue',markersize=10)\n",
    "plt.title('Error Rate K Value')\n",
    "plt.xlabel('K Value')\n",
    "plt.ylabel('Mean Error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a2a14173",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "xmytest=np.array([0,3.5,1.4,0]).reshape(1,-1)\n",
    "xt1=scaler.transform(xmytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3f878091",
   "metadata": {},
   "outputs": [],
   "source": [
    "ym=classifier.predict(xt1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "18c7694e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ym"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c0a81d2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}