K Nearest Neighbors.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "797012cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "127446ff",
   "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",
       "  </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",
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       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</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"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ffdf6f73",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target'] = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "428efc87",
   "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",
       "  </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>0</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>0</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>0</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>0</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>0</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",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d3e5a407",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['flower_name']=df['target'].apply(lambda x : iris.target_names[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "365093f6",
   "metadata": {},
   "outputs": [
    {
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       "    }\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>target</th>\n",
       "      <th>flower_name</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>0</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>0</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>0</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>0</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>0</td>\n",
       "      <td>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",
       "   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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "77f3d749",
   "metadata": {},
   "outputs": [],
   "source": [
    "df0 = df[:50]\n",
    "df1 = df[50:100]\n",
    "df2 = df[100:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "237b3698",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5393bd60",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c202d457",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.drop(['target', 'flower_name'], axis = 1)\n",
    "y = df.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "86ab9302",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89a78564",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1b04f210",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=10)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# KNN Classifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=10) # preffered value of k = 3\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a50b495d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "db07c140",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.975"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "51022b6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred = knn.predict(X_test)\n",
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d8b18b82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(42.0, 0.5, 'Truth')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "plt.figure(figsize=(7,5))\n",
    "sn.heatmap(cm, annot=True)\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel('Truth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89689b18",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1c6b9acd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        11\n",
      "           1       1.00      0.92      0.96        13\n",
      "           2       0.86      1.00      0.92         6\n",
      "\n",
      "    accuracy                           0.97        30\n",
      "   macro avg       0.95      0.97      0.96        30\n",
      "weighted avg       0.97      0.97      0.97        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c349075",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}