KNN_CreditCard.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"creditcard.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Class\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#taking first 20000 samples\n",
    "data_20000 = data[:20000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000, 31)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_20000.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    19915\n",
       "1       85\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_20000[\"Class\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000, 30)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data20000 = data_20000.drop(['Class'], axis=1)\n",
    "data20000.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000,)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data20000_labels = data_20000[\"Class\"]\n",
    "data20000_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20000, 30)\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "data20000_Std = StandardScaler().fit_transform(data20000)\n",
    "print(data20000_Std.shape)\n",
    "print(type(data20000_Std))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = data20000_Std[0:16000]\n",
    "XTest = data20000_Std[16000:20000]\n",
    "Y1 = data20000_labels[0:16000]\n",
    "YTest = data20000_labels[16000:20000]\n",
    "#taking last 4k points as test data and first 16k points as train data\n",
    "\n",
    "myList = list(range(0,50))\n",
    "neighbors = list(filter(lambda x: x%2!=0, myList))  #This will give a list of odd numbers only ranging from 0 to 50\n",
    "\n",
    "CV_Scores = []\n",
    "\n",
    "for k in neighbors:\n",
    "    KNN = KNeighborsClassifier(n_neighbors = k, algorithm = 'kd_tree')\n",
    "    scores = cross_val_score(KNN, X1, Y1, cv = 5, scoring='recall')\n",
    "    CV_Scores.append(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.741904761904762,\n",
       " 0.5142857142857142,\n",
       " 0.5142857142857143,\n",
       " 0.5009523809523809,\n",
       " 0.48761904761904756,\n",
       " 0.5009523809523809,\n",
       " 0.48571428571428577,\n",
       " 0.4990476190476191,\n",
       " 0.5123809523809524,\n",
       " 0.46952380952380957,\n",
       " 0.46952380952380957,\n",
       " 0.42666666666666664,\n",
       " 0.6,\n",
       " 0.5571428571428572,\n",
       " 0.5142857142857142,\n",
       " 0.45714285714285713,\n",
       " 0.41428571428571426,\n",
       " 0.41428571428571426,\n",
       " 0.4,\n",
       " 0.38666666666666666,\n",
       " 0.38666666666666666,\n",
       " 0.42857142857142866,\n",
       " 0.5085714285714286,\n",
       " 0.5085714285714286,\n",
       " 0.5085714285714286]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CV_Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14, 12))\n",
    "plt.plot(neighbors, CV_Scores)\n",
    "plt.title(\"Neighbors Vs Recall Score\", fontsize=25)\n",
    "plt.xlabel(\"Number of Neighbors\", fontsize=25)\n",
    "plt.ylabel(\"Recall Score\", fontsize=25)\n",
    "plt.grid(linestyle='-', linewidth=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_k = neighbors[CV_Scores.index(max(CV_Scores))]\n",
    "best_k"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Best 'K' value is chosen as 1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall Score of the knn classifier for best k values of 1 is: 0.8333333333333334\n",
      "[[3978   10]\n",
      " [   2   10]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(3978, 10, 2, 10)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score\n",
    "\n",
    "KNN_best = KNeighborsClassifier(n_neighbors = best_k, algorithm = 'kd_tree')\n",
    "\n",
    "KNN_best.fit(X1, Y1)\n",
    "\n",
    "prediction = KNN_best.predict(XTest)\n",
    "\n",
    "recallTest = recall_score(YTest, prediction)\n",
    "\n",
    "print(\"Recall Score of the knn classifier for best k values of \"+str(best_k)+\" is: \"+str(recallTest))\n",
    "\n",
    "cm = confusion_matrix(YTest, prediction)\n",
    "\n",
    "print(cm)\n",
    "\n",
    "tn, fp, fn, tp = cm.ravel()\n",
    "\n",
    "(tn, fp, fn, tp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3988\n",
       "1      12\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YTest.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall Score of the knn classifier for best k values of 1 is: 0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "# Calculating R square value of our model\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "print(\"Recall Score of the knn classifier for best k values of \"+str(best_k)+\" is: \"+str(recallTest))"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}