K Means Cluster.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libarary\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from scipy.spatial.distance import cdist \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read csv file\n",
    "EastWestAirlines = pd.read_csv('~/Downloads/Data Science/data set/EastWestAirlines.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Balance</th>\n",
       "      <th>Qual_miles</th>\n",
       "      <th>cc1_miles</th>\n",
       "      <th>cc2_miles</th>\n",
       "      <th>cc3_miles</th>\n",
       "      <th>Bonus_miles</th>\n",
       "      <th>Bonus_trans</th>\n",
       "      <th>Flight_miles_12mo</th>\n",
       "      <th>Flight_trans_12</th>\n",
       "      <th>Days_since_enroll</th>\n",
       "      <th>Award</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>25447</td>\n",
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       "</table>\n",
       "<p>3999 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  Balance  Qual_miles  cc1_miles  cc2_miles  cc3_miles  Bonus_miles  \\\n",
       "0        1    28143           0          1          1          1          174   \n",
       "1        2    19244           0          1          1          1          215   \n",
       "2        3    41354           0          1          1          1         4123   \n",
       "3        4    14776           0          1          1          1          500   \n",
       "4        5    97752           0          4          1          1        43300   \n",
       "...    ...      ...         ...        ...        ...        ...          ...   \n",
       "3994  4017    18476           0          1          1          1         8525   \n",
       "3995  4018    64385           0          1          1          1          981   \n",
       "3996  4019    73597           0          3          1          1        25447   \n",
       "3997  4020    54899           0          1          1          1          500   \n",
       "3998  4021     3016           0          1          1          1            0   \n",
       "\n",
       "      Bonus_trans  Flight_miles_12mo  Flight_trans_12  Days_since_enroll  \\\n",
       "0               1                  0                0               7000   \n",
       "1               2                  0                0               6968   \n",
       "2               4                  0                0               7034   \n",
       "3               1                  0                0               6952   \n",
       "4              26               2077                4               6935   \n",
       "...           ...                ...              ...                ...   \n",
       "3994            4                200                1               1403   \n",
       "3995            5                  0                0               1395   \n",
       "3996            8                  0                0               1402   \n",
       "3997            1                500                1               1401   \n",
       "3998            0                  0                0               1398   \n",
       "\n",
       "      Award  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         1  \n",
       "...     ...  \n",
       "3994      1  \n",
       "3995      1  \n",
       "3996      1  \n",
       "3997      0  \n",
       "3998      0  \n",
       "\n",
       "[3999 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EastWestAirlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#feture selection\n",
    "\n",
    "X = np.array(EastWestAirlines.drop(['Award'], 1).astype(int))\n",
    "y = np.array(EastWestAirlines['Award'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=2, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=2) # want 2 cluster\n",
    "kmeans.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6444111027756939\n"
     ]
    }
   ],
   "source": [
    "#accuracy\n",
    "correct = 0\n",
    "for i in range(len(X)):\n",
    "    predict_me = np.array(X[i].astype(float))\n",
    "    predict_me = predict_me.reshape(-1, len(predict_me))\n",
    "    prediction = kmeans.predict(predict_me)\n",
    "    if prediction[0] == y[i]:\n",
    "        correct += 1\n",
    "\n",
    "print(correct/len(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=600,\n",
       "       n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# want 3 cluster\n",
    "kmeans1 = kmeans = KMeans(n_clusters=3, max_iter=600, algorithm = 'auto')\n",
    "kmeans1.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6274068517129282\n"
     ]
    }
   ],
   "source": [
    "#K Means Result\n",
    "correct = 0\n",
    "for i in range(len(X)):\n",
    "    predict_me = np.array(X[i].astype(float))\n",
    "    predict_me = predict_me.reshape(-1, len(predict_me))\n",
    "    prediction = kmeans.predict(predict_me)\n",
    "    if prediction[0] == y[i]:\n",
    "        correct += 1\n",
    "\n",
    "print(correct/len(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show Kmenas Predict labels\n",
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       0\n",
       "       ..\n",
       "3994    0\n",
       "3995    0\n",
       "3996    0\n",
       "3997    0\n",
       "3998    0\n",
       "Length: 3999, dtype: int32"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#converting numpy array into pandas series object \n",
    "md=pd.Series(kmeans.labels_)\n",
    "md"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x124e522b0>,\n",
       "  <matplotlib.axis.XTick at 0x124e52278>,\n",
       "  <matplotlib.axis.XTick at 0x124e52128>,\n",
       "  <matplotlib.axis.XTick at 0x124f07b00>,\n",
       "  <matplotlib.axis.XTick at 0x124f07e80>,\n",
       "  <matplotlib.axis.XTick at 0x124f1e470>,\n",
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       "  <matplotlib.axis.XTick at 0x124f25b00>,\n",
       "  <matplotlib.axis.XTick at 0x124f25e80>],\n",
       " <a list of 13 Text major ticklabel objects>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find best Value For K\n",
    "\n",
    "k = list(range(2,15))\n",
    "k\n",
    "TWSS = [] # variable for storing total within sum of squares for each kmeans \n",
    "for i in k:\n",
    "    kmeans = KMeans(n_clusters = i)\n",
    "    kmeans.fit(EastWestAirlines)\n",
    "    WSS = [] # variable for storing within sum of squares for each cluster \n",
    "    for j in range(i):\n",
    "        WSS.append(sum(cdist(EastWestAirlines.iloc[kmeans.labels_==j,:],kmeans.cluster_centers_[j].reshape(1,EastWestAirlines.shape[1]),\"euclidean\")))\n",
    "    TWSS.append(sum(WSS))\n",
    "\n",
    "\n",
    "# Scree plot \n",
    "plt.plot(k,TWSS, 'ro-');plt.xlabel(\"No_of_Clusters\");plt.ylabel(\"total_within_SS\");plt.xticks(k)"
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}