Iris Flower Classification.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "589780b2",
   "metadata": {},
   "source": [
    "## Iris flower classification is a very popular machine learning project. The iris dataset contains three classes of flowers Versicolor, Setosa, Virginica, and each class contains 4 features, ‘Sepal length’, ‘Sepal width’, ‘Petal length’, ‘Petal width’. The aim of the iris flower classification is to predict flowers based on their specific features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1989b5f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#vERSICOLOR---- to differentiate we can use image processing---> focal length, pixel length, aperature length should be same\n",
    "#take these constraints for this by the help of these constraints we can make our flowers data (SEPAL LENGTH -- SEPAL WIDTH) MAIN CONSTRAINTS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68cb871b",
   "metadata": {},
   "source": [
    "# K NEAREST NEIGHBOURS CLASSIFICATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71c77eb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#IT ALL DEPENDS ON K VALUE WE HAVE TO TAKE K VALUE BY UNDRSTANDING THE DATASET   ---->K IS RELATIVE VALUE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cdf5a944",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "iris= load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "87c1c144",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2557c14d",
   "metadata": {},
   "source": [
    "# ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "007210aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#these are the constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e39bfe9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6cc09d3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#these are the datsets main cluster(flowers basically in which we have to differentiate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e4ddbc65",
   "metadata": {},
   "outputs": [
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       "  <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",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
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       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>0.2</td>\n",
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       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "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"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(iris.data,columns=iris.feature_names)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c98bf288",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5a823df9",
   "metadata": {},
   "outputs": [
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       "  <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>target</th>\n",
       "    </tr>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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      "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",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target']=iris.target\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "795f8758",
   "metadata": {},
   "source": [
    "# here we added target column and given the iris target as target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c6891527",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>target</th>\n",
       "      <th>flower_name</th>\n",
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       "      <td>5.1</td>\n",
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       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>setosa</td>\n",
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       "      <td>setosa</td>\n",
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       "</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",
       "   target flower_name  \n",
       "0       0      setosa  \n",
       "1       0      setosa  \n",
       "2       0      setosa  \n",
       "3       0      setosa  \n",
       "4       0      setosa  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['flower_name'] = df.target.apply(lambda x: iris.target_names[x])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d441cd57",
   "metadata": {},
   "source": [
    "# we create a function name as lambda and use it in the code by calling it again and again lambda is used to create a function -->X<--"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6d08bbf7",
   "metadata": {},
   "outputs": [
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       "  <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",
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       "      <td>2.3</td>\n",
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       "      <td>virginica</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\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",
       "     target flower_name  \n",
       "145       2   virginica  \n",
       "146       2   virginica  \n",
       "147       2   virginica  \n",
       "148       2   virginica  \n",
       "149       2   virginica  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['flower_name'] = df.target.apply(lambda x: iris.target_names[x])\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "23be07da",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
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       "      <td>versicolor</td>\n",
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       "      <th>51</th>\n",
       "      <td>6.4</td>\n",
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       "      <td>versicolor</td>\n",
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       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
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       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>6.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "50                7.0               3.2                4.7               1.4   \n",
       "51                6.4               3.2                4.5               1.5   \n",
       "52                6.9               3.1                4.9               1.5   \n",
       "53                5.5               2.3                4.0               1.3   \n",
       "54                6.5               2.8                4.6               1.5   \n",
       "\n",
       "    target flower_name  \n",
       "50       1  versicolor  \n",
       "51       1  versicolor  \n",
       "52       1  versicolor  \n",
       "53       1  versicolor  \n",
       "54       1  versicolor  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.target==1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fd046e28",
   "metadata": {},
   "outputs": [],
   "source": [
    "df0 = df[0:50]\n",
    "df1 = df[50:100]\n",
    "df2 = df[100:150]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "023e2f20",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "733608bb",
   "metadata": {},
   "source": [
    "# Sepal length VS Sepal Width (Setosa Vs Versicolor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d750004c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1ed4a56d4d0>"
      ]
     },
     "execution_count": 23,
     "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": [
    "plt.xlabel('sepal Length')\n",
    "plt.ylabel('sepal Width')\n",
    "plt.scatter(df0['sepal length (cm)'], df0['sepal width (cm)'],color= \"teal\", marker='*')\n",
    "plt.scatter(df1['sepal length (cm)'], df1['sepal width (cm)'],color= \"magenta\", marker='^')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61083bf1",
   "metadata": {},
   "source": [
    "# Sepal length VS Sepal Width (Setosa Vs Verginica)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9f311102",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1ed5057b990>"
      ]
     },
     "execution_count": 29,
     "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": [
    "plt.xlabel('sepal Length')\n",
    "plt.ylabel('sepal Width')\n",
    "plt.scatter(df0['petal length (cm)'], df0['petal width (cm)'],color= \"red\", marker='+')\n",
    "plt.scatter(df2['petal length (cm)'], df2['petal width (cm)'],color= \"blue\", marker='^')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "756cd7b3",
   "metadata": {},
   "source": [
    "# Train test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e6502ada",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "187ff3a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X= df.drop(['target','flower_name'], axis='columns')\n",
    "y= df.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "12c7da3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2, random_state=36)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f26f8207",
   "metadata": {},
   "source": [
    "# RANDOM STATE ---> RANDOM SPLIT = 36  IT WILL HELP US BY DOING THE SAME DATA WHICH IS HELP FOR US"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f84d3414",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b415875",
   "metadata": {},
   "source": [
    "# 80 PERCENT DATA IS IN TEST DO 30 IS IN TEST AND 120 IS IN TRAIN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6bc9b06e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5511fadc",
   "metadata": {},
   "source": [
    "# THIS IS TEST DATA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4062ea89",
   "metadata": {},
   "source": [
    "# Creating KNN Model-->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "05571697",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#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>KNeighborsClassifier(n_neighbors=10)</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\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=10)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=10)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn=KNeighborsClassifier(n_neighbors=10)\n",
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2e6658e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1a6afd3",
   "metadata": {},
   "source": [
    "# X's value is compared to y's value--->test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e0e111bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 8,  0,  0],\n",
       "       [ 0, 10,  1],\n",
       "       [ 0,  0, 11]], dtype=int64)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred = knn.predict(X_test)\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c8c869e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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