KthNearestNeighbour.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7d0f1f8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "from sklearn import preprocessing\n",
    "%matplotlib inline\n",
    "#produces plots inline with notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e4a1fbeb",
   "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>region</th>\n",
       "      <th>tenure</th>\n",
       "      <th>age</th>\n",
       "      <th>marital</th>\n",
       "      <th>address</th>\n",
       "      <th>income</th>\n",
       "      <th>ed</th>\n",
       "      <th>employ</th>\n",
       "      <th>retire</th>\n",
       "      <th>gender</th>\n",
       "      <th>reside</th>\n",
       "      <th>custcat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>64.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>136.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>116.0</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   region  tenure  age  marital  address  income  ed  employ  retire  gender  \\\n",
       "0       2      13   44        1        9    64.0   4       5     0.0       0   \n",
       "1       3      11   33        1        7   136.0   5       5     0.0       0   \n",
       "2       3      68   52        1       24   116.0   1      29     0.0       1   \n",
       "3       2      33   33        0       12    33.0   2       0     0.0       1   \n",
       "4       2      23   30        1        9    30.0   1       2     0.0       0   \n",
       "\n",
       "   reside  custcat  \n",
       "0       2        1  \n",
       "1       6        4  \n",
       "2       2        3  \n",
       "3       1        1  \n",
       "4       4        3  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/teleCust1000t.csv\"\n",
    "df= pd.read_csv(path)\n",
    "df.head()#gives first 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0171cb0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'income'}>]], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='income', bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4b31c728",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2.,  13.,  44.,   1.,   9.,  64.,   4.,   5.,   0.,   0.,   2.],\n",
       "       [  3.,  11.,  33.,   1.,   7., 136.,   5.,   5.,   0.,   0.,   6.],\n",
       "       [  3.,  68.,  52.,   1.,  24., 116.,   1.,  29.,   0.,   1.,   2.],\n",
       "       [  2.,  33.,  33.,   0.,  12.,  33.,   2.,   0.,   0.,   1.,   1.],\n",
       "       [  2.,  23.,  30.,   1.,   9.,  30.,   1.,   2.,   0.,   0.,   4.]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To use scikit-learn library, we have to convert the Pandas data frame to a Numpy array\n",
    "X = df[['region', 'tenure','age', 'marital', 'address', 'income', 'ed', 'employ','retire', 'gender', 'reside']] .values  #.astype(float)\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e67bfd00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 3, 1, 3], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['custcat'].values\n",
    "y[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "17c6c0b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.02696767, -1.055125  ,  0.18450456,  1.0100505 , -0.25303431,\n",
       "        -0.12650641,  1.0877526 , -0.5941226 , -0.22207644, -1.03459817,\n",
       "        -0.23065004],\n",
       "       [ 1.19883553, -1.14880563, -0.69181243,  1.0100505 , -0.4514148 ,\n",
       "         0.54644972,  1.9062271 , -0.5941226 , -0.22207644, -1.03459817,\n",
       "         2.55666158],\n",
       "       [ 1.19883553,  1.52109247,  0.82182601,  1.0100505 ,  1.23481934,\n",
       "         0.35951747, -1.36767088,  1.78752803, -0.22207644,  0.96655883,\n",
       "        -0.23065004],\n",
       "       [-0.02696767, -0.11831864, -0.69181243, -0.9900495 ,  0.04453642,\n",
       "        -0.41625141, -0.54919639, -1.09029981, -0.22207644,  0.96655883,\n",
       "        -0.92747794],\n",
       "       [-0.02696767, -0.58672182, -0.93080797,  1.0100505 , -0.25303431,\n",
       "        -0.44429125, -1.36767088, -0.89182893, -0.22207644, -1.03459817,\n",
       "         1.16300577]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data Standardization gives the data zero mean and unit variance, it is good practice, \n",
    "#especially for algorithms such as KNN which is based on the distance of data point\n",
    "X = preprocessing.StandardScaler().fit(X).transform(X.astype(float))\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "070f39a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (800, 11) (800,)\n",
      "Test set: (200, 11) (200,)\n"
     ]
    }
   ],
   "source": [
    "#Training and Testing\n",
    "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)\n",
    "print ('Train set:', X_train.shape,  y_train.shape)\n",
    "print ('Test set:', X_test.shape,  y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "074a04e0",
   "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(n_neighbors=4)</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=4)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=4)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Model\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "k = 4#Assume k=4 for now\n",
    "#Train Model and Predict  \n",
    "neigh = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\n",
    "neigh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "54b1a9e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 2, 4, 1], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat = neigh.predict(X_test)\n",
    "yhat[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e19ac48c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set Accuracy:  0.58375\n",
      "Test set Accuracy:  0.285\n"
     ]
    }
   ],
   "source": [
    "#Accuracy\n",
    "from sklearn import metrics\n",
    "print(\"Train set Accuracy: \", metrics.accuracy_score(y_train, neigh.predict(X_train)))\n",
    "print(\"Test set Accuracy: \", metrics.accuracy_score(y_test, yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ab0887c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.25 , 0.25 , 0.27 , 0.285, 0.32 , 0.31 , 0.31 , 0.31 , 0.33 ])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Model for different values of K\n",
    "Ks = 10\n",
    "mean_acc = np.zeros((Ks-1))\n",
    "\n",
    "for n in range(1,Ks):\n",
    "    \n",
    "    #Train Model and Predict  \n",
    "    neigh = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train)\n",
    "    yhat=neigh.predict(X_test)\n",
    "    mean_acc[n-1] = metrics.accuracy_score(y_test, yhat)\n",
    "\n",
    "mean_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "63282595",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot these accuracies for different Ks\n",
    "plt.plot(range(1,Ks),mean_acc,'g')\n",
    "plt.ylabel('Accuracy ')\n",
    "plt.xlabel('Number of Neighbors (K)')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5dbbcc4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best accuracy was with 0.33 with k= 9\n"
     ]
    }
   ],
   "source": [
    "#Best value of K for given dataset\n",
    "#numpy.argmax() function returns indices of the max element of the array\n",
    "print( \"The best accuracy was with\", mean_acc.max(), \"with k=\", mean_acc.argmax()+1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdbf2c00",
   "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}