Nearest_Neighbour.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K Nearest Neighbour Theory\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>46</td>\n",
       "      <td>41000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>51</td>\n",
       "      <td>23000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>50</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>36</td>\n",
       "      <td>33000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>49</td>\n",
       "      <td>36000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  EstimatedSalary  Purchased\n",
       "0     19            19000          0\n",
       "1     35            20000          0\n",
       "2     26            43000          0\n",
       "3     27            57000          0\n",
       "4     19            76000          0\n",
       "..   ...              ...        ...\n",
       "395   46            41000          1\n",
       "396   51            23000          1\n",
       "397   50            20000          1\n",
       "398   36            33000          0\n",
       "399   49            36000          1\n",
       "\n",
       "[400 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"Social_Network_Ads.csv\")\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[: , :-1].values\n",
    "y = dataset.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import  train_test_split\n",
    "X_train , X_test , y_train , y_test = train_test_split(X , y , test_size=0.2 , random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(320, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       [ 1.63131784e+00, -8.96051849e-01],\n",
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       "       [ 1.04805336e+00,  5.23579655e-01],\n",
       "       [ 1.33968560e+00,  2.31984809e+00],\n",
       "       [-3.12897090e-01, -1.42777990e-01],\n",
       "       [ 3.67578135e-01, -4.61470776e-01],\n",
       "       [-4.10107836e-01, -7.80163563e-01],\n",
       "       [-1.18475597e-01, -5.19414919e-01],\n",
       "       [ 9.50842613e-01, -1.15680049e+00],\n",
       "       [-8.96161568e-01, -7.80163563e-01],\n",
       "       [-2.15686344e-01, -5.19414919e-01],\n",
       "       [-1.09058306e+00, -4.61470776e-01],\n",
       "       [-1.18779381e+00,  1.39274180e+00]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#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()</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()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "classifier = KNeighborsClassifier(n_neighbors = 5 , metric = 'minkowski' , p = 2)\n",
    "classifier.fit(X_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "print(classifier.predict(sc.transform([[30 , 85000]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1,\n",
       "       0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = y_pred.reshape(len(y_pred) , 1)\n",
    "y_test = y_pred.reshape(len(y_test) , 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]]\n"
     ]
    }
   ],
   "source": [
    "print(np.concatenate((y_pred , y_test) , axis = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[56  0]\n",
      " [ 0 24]] 1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix  , accuracy_score\n",
    "cm = confusion_matrix(y_pred , y_test)\n",
    "acc = accuracy_score(y_pred , y_test)\n",
    "print(cm , acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_pred , y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sana\\AppData\\Local\\Temp\\ipykernel_7960\\3972071247.py:11: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "  plt.scatter(X_set[y_set ==j , 0 ] , X_set[y_set ==j , 1] , c = ListedColormap(('red' , 'green')) (i), label = j)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set , y_set = sc.inverse_transform(X_train) , y_train \n",
    "x1 , x2 = np.meshgrid(np.arange(start = X_set[: , 0].min() - 10, stop = X_set[: , 0].max() + 10 , step = 1), \n",
    "                      np.arange(start = X_set[: , 0].min() - 1000, stop = X_set[: , 0].max() + 1000 , step = 1))\n",
    "\n",
    "plt.contourf(x1 , x2 , classifier.predict(sc.transform(np.array([x1.ravel() , x2.ravel()]).T)).reshape(x1.shape),\n",
    "             alpha = 0.75 , cmap = ListedColormap(('red' , 'green')))\n",
    "plt.xlim(x1.min() , x1.max())\n",
    "plt.ylim(x2.min() , x2.max())\n",
    "for i , j in enumerate(np.unique(y_set)):\n",
    "      plt.scatter(X_set[y_set ==j , 0 ] , X_set[y_set ==j , 1] , c = ListedColormap(('red' , 'green')) (i), label = j) \n",
    "      plt.title('K-NN(Training set)')\n",
    "      plt.xlabel('Age')\n",
    "      plt.ylabel('Estimated Salary')\n",
    "      plt.show()\n",
    "\n",
    "\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": "3.11.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}