KNN Classifier.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# Credit Card Fraud Detection\n",
    "\n",
    "#### Problem Statement:- \n",
    "- The problem statement chosen for this project is to predict fraudulent credit card transactions with the help of machine learning models.\n",
    "- In this project, you will analyse customer-level data which has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group. \n",
    "- The dataset is taken from the Kaggle website and it has a total of 2,84,807 transactions, out of which 492 are fraudulent\n",
    "\n",
    "#### Business Problem Overview:-\n",
    "- For many banks, retaining high profitable customers is the number one business goal. Banking fraud, however, poses a significant threat to this goal for different banks. In terms of substantial financial losses, trust and credibility, this is a concerning issue to both banks and customers alike.\n",
    "- It has been estimated by Nilson report that by 2020 the banking frauds would account to USD 30 billion worldwide. With the rise in digital payment channels, the number of fraudulent transactions is also increasing with new and different ways. \n",
    "- In the banking industry, credit card fraud detection using machine learning is not just a trend but a necessity for them to put proactive monitoring and fraud prevention mechanisms in place. Machine learning is helping these institutions to reduce time-consuming manual reviews, costly chargebacks and fees, and denials of legitimate transactions.\n",
    "\n",
    "#### Data Dictionary:-\n",
    "The dataset can be download using this <a href=\"https://www.kaggle.com/mlg-ulb/creditcardfraud\">Link</a>\n",
    "- The data set includes credit card transactions made by European cardholders over a period of two days in September 2013. Out of a total of 2,84,807 transactions, 492 were fraudulent. This data set is highly unbalanced, with the positive class (frauds) accounting for 0.172% of the total transactions. The data set has also been modified with Principal Component Analysis (PCA) to maintain confidentiality. Apart from ‘time’ and ‘amount’, all the other features (V1, V2, V3, up to V28) are the principal components obtained using PCA. The feature 'time' contains the seconds elapsed between the first transaction in the data set and the subsequent transactions. The feature 'amount' is the transaction amount. The feature 'class' represents class labelling, and it takes the value 1 in cases of fraud and 0 in other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\arock.000\\Anaconda3\\lib\\site-packages\\statsmodels\\tools\\_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "# Loading Libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import norm, skew\n",
    "\n",
    "#Data Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from matplotlib.pyplot import figure\n",
    "from plotnine import *\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn import preprocessing\n",
    "from sklearn import model_selection\n",
    "\n",
    "# hide warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <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>V10</th>\n",
       "      <th>V11</th>\n",
       "      <th>V12</th>\n",
       "      <th>V13</th>\n",
       "      <th>V14</th>\n",
       "      <th>V15</th>\n",
       "      <th>V16</th>\n",
       "      <th>V17</th>\n",
       "      <th>V18</th>\n",
       "      <th>V19</th>\n",
       "      <th>V20</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>0.090794</td>\n",
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       "      <td>-0.617801</td>\n",
       "      <td>-0.991390</td>\n",
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       "      <td>1.468177</td>\n",
       "      <td>-0.470401</td>\n",
       "      <td>0.207971</td>\n",
       "      <td>0.025791</td>\n",
       "      <td>0.403993</td>\n",
       "      <td>0.251412</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",
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       "    <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>-0.166974</td>\n",
       "      <td>1.612727</td>\n",
       "      <td>1.065235</td>\n",
       "      <td>0.489095</td>\n",
       "      <td>-0.143772</td>\n",
       "      <td>0.635558</td>\n",
       "      <td>0.463917</td>\n",
       "      <td>-0.114805</td>\n",
       "      <td>-0.183361</td>\n",
       "      <td>-0.145783</td>\n",
       "      <td>-0.069083</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",
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       "    <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>0.207643</td>\n",
       "      <td>0.624501</td>\n",
       "      <td>0.066084</td>\n",
       "      <td>0.717293</td>\n",
       "      <td>-0.165946</td>\n",
       "      <td>2.345865</td>\n",
       "      <td>-2.890083</td>\n",
       "      <td>1.109969</td>\n",
       "      <td>-0.121359</td>\n",
       "      <td>-2.261857</td>\n",
       "      <td>0.524980</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",
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       "    <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>-0.054952</td>\n",
       "      <td>-0.226487</td>\n",
       "      <td>0.178228</td>\n",
       "      <td>0.507757</td>\n",
       "      <td>-0.287924</td>\n",
       "      <td>-0.631418</td>\n",
       "      <td>-1.059647</td>\n",
       "      <td>-0.684093</td>\n",
       "      <td>1.965775</td>\n",
       "      <td>-1.232622</td>\n",
       "      <td>-0.208038</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",
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       "    <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>0.753074</td>\n",
       "      <td>-0.822843</td>\n",
       "      <td>0.538196</td>\n",
       "      <td>1.345852</td>\n",
       "      <td>-1.119670</td>\n",
       "      <td>0.175121</td>\n",
       "      <td>-0.451449</td>\n",
       "      <td>-0.237033</td>\n",
       "      <td>-0.038195</td>\n",
       "      <td>0.803487</td>\n",
       "      <td>0.408542</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",
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      "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       V10       V11       V12       V13       V14  \\\n",
       "0  0.098698  0.363787  0.090794 -0.551600 -0.617801 -0.991390 -0.311169   \n",
       "1  0.085102 -0.255425 -0.166974  1.612727  1.065235  0.489095 -0.143772   \n",
       "2  0.247676 -1.514654  0.207643  0.624501  0.066084  0.717293 -0.165946   \n",
       "3  0.377436 -1.387024 -0.054952 -0.226487  0.178228  0.507757 -0.287924   \n",
       "4 -0.270533  0.817739  0.753074 -0.822843  0.538196  1.345852 -1.119670   \n",
       "\n",
       "        V15       V16       V17       V18       V19       V20       V21  \\\n",
       "0  1.468177 -0.470401  0.207971  0.025791  0.403993  0.251412 -0.018307   \n",
       "1  0.635558  0.463917 -0.114805 -0.183361 -0.145783 -0.069083 -0.225775   \n",
       "2  2.345865 -2.890083  1.109969 -0.121359 -2.261857  0.524980  0.247998   \n",
       "3 -0.631418 -1.059647 -0.684093  1.965775 -1.232622 -0.208038 -0.108300   \n",
       "4  0.175121 -0.451449 -0.237033 -0.038195  0.803487  0.408542 -0.009431   \n",
       "\n",
       "        V22       V23       V24       V25       V26       V27       V28  \\\n",
       "0  0.277838 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053   \n",
       "1 -0.638672  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724   \n",
       "2  0.771679  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752   \n",
       "3  0.005274 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458   \n",
       "4  0.798278 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   \n",
       "\n",
       "   Amount  Class  \n",
       "0  149.62      0  \n",
       "1    2.69      0  \n",
       "2  378.66      0  \n",
       "3  123.50      0  \n",
       "4   69.99      0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit = pd.read_csv(\"C:/Users/arock.000/Downloads/creditcardfraud/creditcard.csv\")\n",
    "df = credit.copy()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper Function - Missing data check\n",
    "def missing_data(data):\n",
    "    total = data.isnull().sum().sort_values(ascending = False)\n",
    "    percent = (data.isnull().sum()/data.isnull().count()*100).sort_values(ascending = False).round(4)\n",
    "    return pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float64    30\n",
       "int64       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#observe the different feature type present in the data\n",
    "\n",
    "#Data Types\n",
    "df.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <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>V10</th>\n",
       "      <th>V11</th>\n",
       "      <th>V12</th>\n",
       "      <th>V13</th>\n",
       "      <th>V14</th>\n",
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       "      <th>V17</th>\n",
       "      <th>V18</th>\n",
       "      <th>V19</th>\n",
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       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
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       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
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       "      <th>V27</th>\n",
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       "      <th>Amount</th>\n",
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       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>3.919560e-15</td>\n",
       "      <td>5.688174e-16</td>\n",
       "      <td>-8.769071e-15</td>\n",
       "      <td>2.782312e-15</td>\n",
       "      <td>-1.552563e-15</td>\n",
       "      <td>2.010663e-15</td>\n",
       "      <td>-1.694249e-15</td>\n",
       "      <td>-1.927028e-16</td>\n",
       "      <td>-3.137024e-15</td>\n",
       "      <td>1.768627e-15</td>\n",
       "      <td>9.170318e-16</td>\n",
       "      <td>-1.810658e-15</td>\n",
       "      <td>1.693438e-15</td>\n",
       "      <td>1.479045e-15</td>\n",
       "      <td>3.482336e-15</td>\n",
       "      <td>1.392007e-15</td>\n",
       "      <td>-7.528491e-16</td>\n",
       "      <td>4.328772e-16</td>\n",
       "      <td>9.049732e-16</td>\n",
       "      <td>5.085503e-16</td>\n",
       "      <td>1.537294e-16</td>\n",
       "      <td>7.959909e-16</td>\n",
       "      <td>5.367590e-16</td>\n",
       "      <td>4.458112e-15</td>\n",
       "      <td>1.453003e-15</td>\n",
       "      <td>1.699104e-15</td>\n",
       "      <td>-3.660161e-16</td>\n",
       "      <td>-1.206049e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>1.088850e+00</td>\n",
       "      <td>1.020713e+00</td>\n",
       "      <td>9.992014e-01</td>\n",
       "      <td>9.952742e-01</td>\n",
       "      <td>9.585956e-01</td>\n",
       "      <td>9.153160e-01</td>\n",
       "      <td>8.762529e-01</td>\n",
       "      <td>8.493371e-01</td>\n",
       "      <td>8.381762e-01</td>\n",
       "      <td>8.140405e-01</td>\n",
       "      <td>7.709250e-01</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>-2.458826e+01</td>\n",
       "      <td>-4.797473e+00</td>\n",
       "      <td>-1.868371e+01</td>\n",
       "      <td>-5.791881e+00</td>\n",
       "      <td>-1.921433e+01</td>\n",
       "      <td>-4.498945e+00</td>\n",
       "      <td>-1.412985e+01</td>\n",
       "      <td>-2.516280e+01</td>\n",
       "      <td>-9.498746e+00</td>\n",
       "      <td>-7.213527e+00</td>\n",
       "      <td>-5.449772e+01</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10%</th>\n",
       "      <td>35027.000000</td>\n",
       "      <td>-1.893272e+00</td>\n",
       "      <td>-1.359862e+00</td>\n",
       "      <td>-1.802587e+00</td>\n",
       "      <td>-1.656329e+00</td>\n",
       "      <td>-1.302171e+00</td>\n",
       "      <td>-1.167450e+00</td>\n",
       "      <td>-1.078148e+00</td>\n",
       "      <td>-4.589454e-01</td>\n",
       "      <td>-1.294047e+00</td>\n",
       "      <td>-1.032158e+00</td>\n",
       "      <td>-1.289015e+00</td>\n",
       "      <td>-1.148031e+00</td>\n",
       "      <td>-1.280012e+00</td>\n",
       "      <td>-1.003676e+00</td>\n",
       "      <td>-1.173945e+00</td>\n",
       "      <td>-1.059361e+00</td>\n",
       "      <td>-8.042644e-01</td>\n",
       "      <td>-9.769348e-01</td>\n",
       "      <td>-9.856163e-01</td>\n",
       "      <td>-3.896021e-01</td>\n",
       "      <td>-3.674467e-01</td>\n",
       "      <td>-8.674884e-01</td>\n",
       "      <td>-3.286342e-01</td>\n",
       "      <td>-8.703584e-01</td>\n",
       "      <td>-6.061010e-01</td>\n",
       "      <td>-5.480343e-01</td>\n",
       "      <td>-2.114656e-01</td>\n",
       "      <td>-1.556155e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>-5.354257e-01</td>\n",
       "      <td>-7.624942e-01</td>\n",
       "      <td>-4.055715e-01</td>\n",
       "      <td>-6.485393e-01</td>\n",
       "      <td>-4.255740e-01</td>\n",
       "      <td>-5.828843e-01</td>\n",
       "      <td>-4.680368e-01</td>\n",
       "      <td>-4.837483e-01</td>\n",
       "      <td>-4.988498e-01</td>\n",
       "      <td>-4.562989e-01</td>\n",
       "      <td>-2.117214e-01</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>-9.291738e-02</td>\n",
       "      <td>-3.275735e-02</td>\n",
       "      <td>1.400326e-01</td>\n",
       "      <td>-1.356806e-02</td>\n",
       "      <td>5.060132e-02</td>\n",
       "      <td>4.807155e-02</td>\n",
       "      <td>6.641332e-02</td>\n",
       "      <td>-6.567575e-02</td>\n",
       "      <td>-3.636312e-03</td>\n",
       "      <td>3.734823e-03</td>\n",
       "      <td>-6.248109e-02</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>4.539234e-01</td>\n",
       "      <td>7.395934e-01</td>\n",
       "      <td>6.182380e-01</td>\n",
       "      <td>6.625050e-01</td>\n",
       "      <td>4.931498e-01</td>\n",
       "      <td>6.488208e-01</td>\n",
       "      <td>5.232963e-01</td>\n",
       "      <td>3.996750e-01</td>\n",
       "      <td>5.008067e-01</td>\n",
       "      <td>4.589494e-01</td>\n",
       "      <td>1.330408e-01</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80%</th>\n",
       "      <td>145247.800000</td>\n",
       "      <td>1.694936e+00</td>\n",
       "      <td>9.573136e-01</td>\n",
       "      <td>1.215700e+00</td>\n",
       "      <td>9.861875e-01</td>\n",
       "      <td>8.022349e-01</td>\n",
       "      <td>6.289952e-01</td>\n",
       "      <td>6.983318e-01</td>\n",
       "      <td>4.355289e-01</td>\n",
       "      <td>7.915832e-01</td>\n",
       "      <td>6.482390e-01</td>\n",
       "      <td>9.132205e-01</td>\n",
       "      <td>7.306695e-01</td>\n",
       "      <td>8.290889e-01</td>\n",
       "      <td>6.087367e-01</td>\n",
       "      <td>7.950543e-01</td>\n",
       "      <td>6.414430e-01</td>\n",
       "      <td>5.189160e-01</td>\n",
       "      <td>6.374684e-01</td>\n",
       "      <td>5.856940e-01</td>\n",
       "      <td>1.957871e-01</td>\n",
       "      <td>2.354311e-01</td>\n",
       "      <td>6.485158e-01</td>\n",
       "      <td>1.942589e-01</td>\n",
       "      <td>5.323465e-01</td>\n",
       "      <td>4.175428e-01</td>\n",
       "      <td>3.602736e-01</td>\n",
       "      <td>1.352785e-01</td>\n",
       "      <td>1.013742e-01</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90%</th>\n",
       "      <td>157640.400000</td>\n",
       "      <td>2.015409e+00</td>\n",
       "      <td>1.326635e+00</td>\n",
       "      <td>1.676173e+00</td>\n",
       "      <td>1.482807e+00</td>\n",
       "      <td>1.407893e+00</td>\n",
       "      <td>1.509365e+00</td>\n",
       "      <td>1.039387e+00</td>\n",
       "      <td>7.693811e-01</td>\n",
       "      <td>1.301671e+00</td>\n",
       "      <td>1.119759e+00</td>\n",
       "      <td>1.318588e+00</td>\n",
       "      <td>1.017274e+00</td>\n",
       "      <td>1.251841e+00</td>\n",
       "      <td>9.814085e-01</td>\n",
       "      <td>1.124211e+00</td>\n",
       "      <td>9.908792e-01</td>\n",
       "      <td>9.086165e-01</td>\n",
       "      <td>1.020455e+00</td>\n",
       "      <td>9.538328e-01</td>\n",
       "      <td>4.445929e-01</td>\n",
       "      <td>3.761555e-01</td>\n",
       "      <td>9.148826e-01</td>\n",
       "      <td>3.392860e-01</td>\n",
       "      <td>7.054036e-01</td>\n",
       "      <td>6.009027e-01</td>\n",
       "      <td>6.889469e-01</td>\n",
       "      <td>2.653679e-01</td>\n",
       "      <td>1.799362e-01</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>164143.400000</td>\n",
       "      <td>2.081223e+00</td>\n",
       "      <td>1.808585e+00</td>\n",
       "      <td>2.062635e+00</td>\n",
       "      <td>2.566501e+00</td>\n",
       "      <td>2.098960e+00</td>\n",
       "      <td>3.160382e+00</td>\n",
       "      <td>1.407632e+00</td>\n",
       "      <td>1.049984e+00</td>\n",
       "      <td>1.780783e+00</td>\n",
       "      <td>1.548557e+00</td>\n",
       "      <td>1.614033e+00</td>\n",
       "      <td>1.243053e+00</td>\n",
       "      <td>1.607877e+00</td>\n",
       "      <td>1.393653e+00</td>\n",
       "      <td>1.373090e+00</td>\n",
       "      <td>1.325253e+00</td>\n",
       "      <td>1.274609e+00</td>\n",
       "      <td>1.394392e+00</td>\n",
       "      <td>1.286164e+00</td>\n",
       "      <td>8.361441e-01</td>\n",
       "      <td>5.378677e-01</td>\n",
       "      <td>1.128987e+00</td>\n",
       "      <td>4.880160e-01</td>\n",
       "      <td>8.663576e-01</td>\n",
       "      <td>7.606986e-01</td>\n",
       "      <td>9.209154e-01</td>\n",
       "      <td>3.877462e-01</td>\n",
       "      <td>2.560897e-01</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99%</th>\n",
       "      <td>170560.940000</td>\n",
       "      <td>2.237130e+00</td>\n",
       "      <td>3.801811e+00</td>\n",
       "      <td>2.728434e+00</td>\n",
       "      <td>4.248032e+00</td>\n",
       "      <td>3.424903e+00</td>\n",
       "      <td>4.200085e+00</td>\n",
       "      <td>2.696205e+00</td>\n",
       "      <td>2.075973e+00</td>\n",
       "      <td>2.986773e+00</td>\n",
       "      <td>3.253618e+00</td>\n",
       "      <td>2.290583e+00</td>\n",
       "      <td>1.698576e+00</td>\n",
       "      <td>2.513962e+00</td>\n",
       "      <td>2.150300e+00</td>\n",
       "      <td>1.925527e+00</td>\n",
       "      <td>1.874820e+00</td>\n",
       "      <td>2.289928e+00</td>\n",
       "      <td>2.068689e+00</td>\n",
       "      <td>2.262924e+00</td>\n",
       "      <td>2.412190e+00</td>\n",
       "      <td>1.931852e+00</td>\n",
       "      <td>1.530152e+00</td>\n",
       "      <td>1.508703e+00</td>\n",
       "      <td>1.063748e+00</td>\n",
       "      <td>1.203955e+00</td>\n",
       "      <td>1.158698e+00</td>\n",
       "      <td>9.313604e-01</td>\n",
       "      <td>5.411264e-01</td>\n",
       "      <td>1017.970000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>2.374514e+01</td>\n",
       "      <td>1.201891e+01</td>\n",
       "      <td>7.848392e+00</td>\n",
       "      <td>7.126883e+00</td>\n",
       "      <td>1.052677e+01</td>\n",
       "      <td>8.877742e+00</td>\n",
       "      <td>1.731511e+01</td>\n",
       "      <td>9.253526e+00</td>\n",
       "      <td>5.041069e+00</td>\n",
       "      <td>5.591971e+00</td>\n",
       "      <td>3.942090e+01</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  3.919560e-15  5.688174e-16 -8.769071e-15  2.782312e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "10%     35027.000000 -1.893272e+00 -1.359862e+00 -1.802587e+00 -1.656329e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "80%    145247.800000  1.694936e+00  9.573136e-01  1.215700e+00  9.861875e-01   \n",
       "90%    157640.400000  2.015409e+00  1.326635e+00  1.676173e+00  1.482807e+00   \n",
       "95%    164143.400000  2.081223e+00  1.808585e+00  2.062635e+00  2.566501e+00   \n",
       "99%    170560.940000  2.237130e+00  3.801811e+00  2.728434e+00  4.248032e+00   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean  -1.552563e-15  2.010663e-15 -1.694249e-15 -1.927028e-16 -3.137024e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "10%   -1.302171e+00 -1.167450e+00 -1.078148e+00 -4.589454e-01 -1.294047e+00   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "80%    8.022349e-01  6.289952e-01  6.983318e-01  4.355289e-01  7.915832e-01   \n",
       "90%    1.407893e+00  1.509365e+00  1.039387e+00  7.693811e-01  1.301671e+00   \n",
       "95%    2.098960e+00  3.160382e+00  1.407632e+00  1.049984e+00  1.780783e+00   \n",
       "99%    3.424903e+00  4.200085e+00  2.696205e+00  2.075973e+00  2.986773e+00   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "                V10           V11           V12           V13           V14  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   1.768627e-15  9.170318e-16 -1.810658e-15  1.693438e-15  1.479045e-15   \n",
       "std    1.088850e+00  1.020713e+00  9.992014e-01  9.952742e-01  9.585956e-01   \n",
       "min   -2.458826e+01 -4.797473e+00 -1.868371e+01 -5.791881e+00 -1.921433e+01   \n",
       "10%   -1.032158e+00 -1.289015e+00 -1.148031e+00 -1.280012e+00 -1.003676e+00   \n",
       "25%   -5.354257e-01 -7.624942e-01 -4.055715e-01 -6.485393e-01 -4.255740e-01   \n",
       "50%   -9.291738e-02 -3.275735e-02  1.400326e-01 -1.356806e-02  5.060132e-02   \n",
       "75%    4.539234e-01  7.395934e-01  6.182380e-01  6.625050e-01  4.931498e-01   \n",
       "80%    6.482390e-01  9.132205e-01  7.306695e-01  8.290889e-01  6.087367e-01   \n",
       "90%    1.119759e+00  1.318588e+00  1.017274e+00  1.251841e+00  9.814085e-01   \n",
       "95%    1.548557e+00  1.614033e+00  1.243053e+00  1.607877e+00  1.393653e+00   \n",
       "99%    3.253618e+00  2.290583e+00  1.698576e+00  2.513962e+00  2.150300e+00   \n",
       "max    2.374514e+01  1.201891e+01  7.848392e+00  7.126883e+00  1.052677e+01   \n",
       "\n",
       "                V15           V16           V17           V18           V19  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   3.482336e-15  1.392007e-15 -7.528491e-16  4.328772e-16  9.049732e-16   \n",
       "std    9.153160e-01  8.762529e-01  8.493371e-01  8.381762e-01  8.140405e-01   \n",
       "min   -4.498945e+00 -1.412985e+01 -2.516280e+01 -9.498746e+00 -7.213527e+00   \n",
       "10%   -1.173945e+00 -1.059361e+00 -8.042644e-01 -9.769348e-01 -9.856163e-01   \n",
       "25%   -5.828843e-01 -4.680368e-01 -4.837483e-01 -4.988498e-01 -4.562989e-01   \n",
       "50%    4.807155e-02  6.641332e-02 -6.567575e-02 -3.636312e-03  3.734823e-03   \n",
       "75%    6.488208e-01  5.232963e-01  3.996750e-01  5.008067e-01  4.589494e-01   \n",
       "80%    7.950543e-01  6.414430e-01  5.189160e-01  6.374684e-01  5.856940e-01   \n",
       "90%    1.124211e+00  9.908792e-01  9.086165e-01  1.020455e+00  9.538328e-01   \n",
       "95%    1.373090e+00  1.325253e+00  1.274609e+00  1.394392e+00  1.286164e+00   \n",
       "99%    1.925527e+00  1.874820e+00  2.289928e+00  2.068689e+00  2.262924e+00   \n",
       "max    8.877742e+00  1.731511e+01  9.253526e+00  5.041069e+00  5.591971e+00   \n",
       "\n",
       "                V20           V21           V22           V23           V24  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   5.085503e-16  1.537294e-16  7.959909e-16  5.367590e-16  4.458112e-15   \n",
       "std    7.709250e-01  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min   -5.449772e+01 -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "10%   -3.896021e-01 -3.674467e-01 -8.674884e-01 -3.286342e-01 -8.703584e-01   \n",
       "25%   -2.117214e-01 -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%   -6.248109e-02 -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    1.330408e-01  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "80%    1.957871e-01  2.354311e-01  6.485158e-01  1.942589e-01  5.323465e-01   \n",
       "90%    4.445929e-01  3.761555e-01  9.148826e-01  3.392860e-01  7.054036e-01   \n",
       "95%    8.361441e-01  5.378677e-01  1.128987e+00  4.880160e-01  8.663576e-01   \n",
       "99%    2.412190e+00  1.931852e+00  1.530152e+00  1.508703e+00  1.063748e+00   \n",
       "max    3.942090e+01  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   1.453003e-15  1.699104e-15 -3.660161e-16 -1.206049e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "10%   -6.061010e-01 -5.480343e-01 -2.114656e-01 -1.556155e-01       1.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "80%    4.175428e-01  3.602736e-01  1.352785e-01  1.013742e-01     100.000000   \n",
       "90%    6.009027e-01  6.889469e-01  2.653679e-01  1.799362e-01     203.000000   \n",
       "95%    7.606986e-01  9.209154e-01  3.877462e-01  2.560897e-01     365.000000   \n",
       "99%    1.203955e+00  1.158698e+00  9.313604e-01  5.411264e-01    1017.970000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "10%         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "80%         0.000000  \n",
       "90%         0.000000  \n",
       "95%         0.000000  \n",
       "99%         0.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data Distribution\n",
    "df.describe(percentiles = [.1,.25,.5,.75,.8,.9,.95,.99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V14</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V5</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V6</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V7</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V8</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V9</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V10</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V11</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V12</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V13</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V15</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amount</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V16</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V17</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V18</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V19</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V20</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V21</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V22</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V23</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V24</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V25</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V26</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V27</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V28</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Total  Percent\n",
       "Class       0      0.0\n",
       "V14         0      0.0\n",
       "V1          0      0.0\n",
       "V2          0      0.0\n",
       "V3          0      0.0\n",
       "V4          0      0.0\n",
       "V5          0      0.0\n",
       "V6          0      0.0\n",
       "V7          0      0.0\n",
       "V8          0      0.0\n",
       "V9          0      0.0\n",
       "V10         0      0.0\n",
       "V11         0      0.0\n",
       "V12         0      0.0\n",
       "V13         0      0.0\n",
       "V15         0      0.0\n",
       "Amount      0      0.0\n",
       "V16         0      0.0\n",
       "V17         0      0.0\n",
       "V18         0      0.0\n",
       "V19         0      0.0\n",
       "V20         0      0.0\n",
       "V21         0      0.0\n",
       "V22         0      0.0\n",
       "V23         0      0.0\n",
       "V24         0      0.0\n",
       "V25         0      0.0\n",
       "V26         0      0.0\n",
       "V27         0      0.0\n",
       "V28         0      0.0\n",
       "Time        0      0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will observe the distribution of our classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class Distribution:-\n",
      "Normal Share =  99.83\n",
      "Fraud Share =  0.17\n"
     ]
    }
   ],
   "source": [
    "classes=df['Class'].value_counts()\n",
    "normal_share= (classes[0]/df['Class'].count()*100).round(2)\n",
    "fraud_share=(classes[1]/df['Class'].count()*100).round(2)\n",
    "print('Class Distribution:-\\nNormal Share = ', normal_share)\n",
    "print('Fraud Share = ', fraud_share)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223371910725580776)>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a bar plot for the number and percentage of fraudulent vs non-fraudulent transcations\n",
    "\n",
    "#Reference:-\n",
    "# https://plotnine.readthedocs.io/en/stable/tutorials/miscellaneous-show-counts-and-percentages-for-bar-plots.html\n",
    "\n",
    "\n",
    "def combine(counts, percentages):\n",
    "    fmt = '{} ({:.2f}%)'.format\n",
    "    return [fmt(c, p) for c, p in zip(counts, percentages)]\n",
    "\n",
    "(ggplot(df, aes('Class', fill='factor(Class)'))\n",
    " + geom_bar()\n",
    " + labs(title='Class Distribution \\n [0: Normal | 1: Fraud]')\n",
    " + geom_text(\n",
    "     aes(label='stat(combine(count, 100*prop))', group=1),\n",
    "     stat='count',\n",
    "     nudge_y=0.125,\n",
    "     va='bottom'\n",
    " )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "variables": {
     "fraud_share": "0.17",
     "normal_share": "99.83"
    }
   },
   "source": [
    "### Observation:\n",
    "- Dataset is highly imbalanced. 9.83% are normal/non fraud transactions while only 0.17% are fraud transactions.\n",
    "- Using this data for model building will result in a model which is biased and predicts only non fraud transactions.\n",
    "- But the objective is to predict all fraud transactions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Distribution plots of all features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.subplot(121)\n",
    "plt.title('Amount Distribution Based on Class')\n",
    "sns.boxplot(y = 'Amount', x = 'Class', palette='pastel',data = df)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.title('Amount Distribution Based on Class w/o outliers')\n",
    "sns.boxplot(y = 'Amount', x = 'Class', palette='pastel',data = df,showfliers=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d5df0c40c8>"
      ]
     },
     "execution_count": 10,
     "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": [
    "# Distribution of feature Time\n",
    "sns.distplot(df['Time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d5de327c08>"
      ]
     },
     "execution_count": 11,
     "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": [
    "# Distribution of feature Amount\n",
    "sns.distplot(df['Amount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraud:\n",
      " count     492.000000\n",
      "mean      122.211321\n",
      "std       256.683288\n",
      "min         0.000000\n",
      "25%         1.000000\n",
      "50%         9.250000\n",
      "75%       105.890000\n",
      "max      2125.870000\n",
      "Name: Amount, dtype: float64\n",
      "Non Fraud:\n",
      " count    284315.000000\n",
      "mean         88.291022\n",
      "std         250.105092\n",
      "min           0.000000\n",
      "25%           5.650000\n",
      "50%          22.000000\n",
      "75%          77.050000\n",
      "max       25691.160000\n",
      "Name: Amount, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Distribution of Amount based on Class\n",
    "\n",
    "plt.figure(figsize=(12,4))\n",
    "plt.subplot(121)\n",
    "plt.title(\"Fraud Class\")\n",
    "sns.distplot(df['Amount'].loc[df['Class']==1])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.title(\"Normal Class\")\n",
    "sns.distplot(df['Amount'].loc[df['Class']==0])\n",
    "plt.show()\n",
    "\n",
    "print('Fraud:\\n', (df[df['Class'] == 1]).Amount.describe())\n",
    "print('Non Fraud:\\n', (df[df['Class'] == 0]).Amount.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:-\n",
    "- Average transaction amount for fraud cases = 122.2\n",
    "- Average transaction amount for fraud cases = 88.3\n",
    "- Though both these data seem to be skewed, the transaction amount for fraud cases are quite high making it critical to detect fraud cases so as to avoid losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot to observe the distribution of PCA features\n",
    "plt.figure(figsize=(16,4))\n",
    "\n",
    "plt.subplot(141)\n",
    "sns.distplot(df['V1'])\n",
    "\n",
    "plt.subplot(142)\n",
    "sns.distplot(df['V2'])\n",
    "\n",
    "plt.subplot(143)\n",
    "sns.distplot(df['V3'])\n",
    "\n",
    "plt.subplot(144)\n",
    "sns.distplot(df['V4'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,4))\n",
    "plt.subplot(141)\n",
    "sns.distplot(df['V5'])\n",
    "\n",
    "plt.subplot(142)\n",
    "sns.distplot(df['V6'])\n",
    "\n",
    "plt.subplot(143)\n",
    "sns.distplot(df['V7'])\n",
    "\n",
    "plt.subplot(144)\n",
    "sns.distplot(df['V8'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,4))\n",
    "plt.subplot(141)\n",
    "sns.distplot(df['V9'])\n",
    "\n",
    "plt.subplot(142)\n",
    "sns.distplot(df['V10'])\n",
    "\n",
    "plt.subplot(143)\n",
    "sns.distplot(df['V11'])\n",
    "\n",
    "plt.subplot(144)\n",
    "sns.distplot(df['V12'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,4))\n",
    "plt.subplot(141)\n",
    "sns.distplot(df['V13'])\n",
    "\n",
    "plt.subplot(142)\n",
    "sns.distplot(df['V14'])\n",
    "\n",
    "plt.subplot(143)\n",
    "sns.distplot(df['V15'])\n",
    "\n",
    "plt.subplot(144)\n",
    "sns.distplot(df['V16'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,4))\n",
    "plt.subplot(141)\n",
    "sns.distplot(df['V17'])\n",
    "\n",
    "plt.subplot(142)\n",
    "sns.distplot(df['V18'])\n",
    "\n",
    "plt.subplot(143)\n",
    "sns.distplot(df['V19'])\n",
    "\n",
    "plt.subplot(144)\n",
    "sns.distplot(df['V20'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,4))\n",
    "plt.subplot(141)\n",
    "sns.distplot(df['V21'])\n",
    "\n",
    "plt.subplot(142)\n",
    "sns.distplot(df['V22'])\n",
    "\n",
    "plt.subplot(143)\n",
    "sns.distplot(df['V23'])\n",
    "\n",
    "plt.subplot(144)\n",
    "sns.distplot(df['V24'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,4))\n",
    "plt.subplot(141)\n",
    "sns.distplot(df['V25'])\n",
    "\n",
    "plt.subplot(142)\n",
    "sns.distplot(df['V26'])\n",
    "\n",
    "plt.subplot(143)\n",
    "sns.distplot(df['V27'])\n",
    "\n",
    "plt.subplot(144)\n",
    "sns.distplot(df['V28'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Skewnesses of PCA trasnformed featured V-1 to 28')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "V1to28 = ['V%i' % k for k in range(1,29)]\n",
    "\n",
    "plt.figure(figsize=(14,5))\n",
    "sns.barplot(x=V1to28, y=df[V1to28].skew(), color='green')\n",
    "plt.xlabel('Column')\n",
    "plt.ylabel('Skewness')\n",
    "plt.title('Skewnesses of PCA trasnformed featured V-1 to 28')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness in Amount Data 16.98\n",
      "Skewness in Time Data -0.036\n"
     ]
    }
   ],
   "source": [
    "print('Skewness in Amount Data', round(df['Amount'].skew(),2))\n",
    "print('Skewness in Time Data', round(df['Time'].skew(),3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation:-\n",
    "Distribution plots clearly shows the skewness in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amount</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <td>0.005632</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Amount     Class\n",
       "Amount  1.000000  0.005632\n",
       "Class   0.005632  1.000000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Amount', 'Class']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <td>-0.012323</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Time     Class\n",
       "Time   1.000000 -0.012323\n",
       "Class -0.012323  1.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Time', 'Class']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# normalise the amount column\n",
    "scaler = StandardScaler()\n",
    "df['normAmount'] = scaler.fit_transform(np.array(df['Amount']).reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop unnecessary columns\n",
    "\n",
    "# drop Time and Amount columns as they are not relevant for prediction purpose  \n",
    "df_new = df.drop(['Time', 'Amount'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df_new[['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11',\n",
    "       'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21',\n",
    "       'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'normAmount']]\n",
    "y = df_new['Class']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estimate mutual information for a discrete target variable\n",
    "#Reference:\n",
    " # https://en.wikipedia.org/wiki/Mutual_information\n",
    " # https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.mutual_info_classif.html\n",
    "\n",
    "from sklearn.feature_selection import mutual_info_classif\n",
    "\n",
    "mutual_infos = pd.Series(data=mutual_info_classif(X, y, discrete_features=False, random_state=1), index=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d5e0bbc808>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mutual_infos.sort_values(ascending=False).plot(kind='bar',figsize = (12,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation:-\n",
    "- Mutual information between two random variables is a non-negative value, which measures the dependency between the variables\n",
    "- It is equal to zero if and only if two random variables are independent and higher values mean higher dependency\n",
    "- Top five most correlated features are V17, V14, V12, V10 and V11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>V10</th>\n",
       "      <th>V11</th>\n",
       "      <th>V12</th>\n",
       "      <th>V13</th>\n",
       "      <th>V14</th>\n",
       "      <th>V15</th>\n",
       "      <th>V16</th>\n",
       "      <th>V17</th>\n",
       "      <th>V18</th>\n",
       "      <th>V19</th>\n",
       "      <th>V20</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>normAmount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>94838.202258</td>\n",
       "      <td>0.008258</td>\n",
       "      <td>-0.006271</td>\n",
       "      <td>0.012171</td>\n",
       "      <td>-0.007860</td>\n",
       "      <td>0.005453</td>\n",
       "      <td>0.002419</td>\n",
       "      <td>0.009637</td>\n",
       "      <td>-0.000987</td>\n",
       "      <td>0.004467</td>\n",
       "      <td>0.009824</td>\n",
       "      <td>-0.006576</td>\n",
       "      <td>0.010832</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>0.012064</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>0.007164</td>\n",
       "      <td>0.011535</td>\n",
       "      <td>0.003887</td>\n",
       "      <td>-0.001178</td>\n",
       "      <td>-0.000644</td>\n",
       "      <td>-0.001235</td>\n",
       "      <td>-0.000024</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>-0.000072</td>\n",
       "      <td>-0.000089</td>\n",
       "      <td>-0.000295</td>\n",
       "      <td>-0.000131</td>\n",
       "      <td>88.291022</td>\n",
       "      <td>-0.000234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80746.806911</td>\n",
       "      <td>-4.771948</td>\n",
       "      <td>3.623778</td>\n",
       "      <td>-7.033281</td>\n",
       "      <td>4.542029</td>\n",
       "      <td>-3.151225</td>\n",
       "      <td>-1.397737</td>\n",
       "      <td>-5.568731</td>\n",
       "      <td>0.570636</td>\n",
       "      <td>-2.581123</td>\n",
       "      <td>-5.676883</td>\n",
       "      <td>3.800173</td>\n",
       "      <td>-6.259393</td>\n",
       "      <td>-0.109334</td>\n",
       "      <td>-6.971723</td>\n",
       "      <td>-0.092929</td>\n",
       "      <td>-4.139946</td>\n",
       "      <td>-6.665836</td>\n",
       "      <td>-2.246308</td>\n",
       "      <td>0.680659</td>\n",
       "      <td>0.372319</td>\n",
       "      <td>0.713588</td>\n",
       "      <td>0.014049</td>\n",
       "      <td>-0.040308</td>\n",
       "      <td>-0.105130</td>\n",
       "      <td>0.041449</td>\n",
       "      <td>0.051648</td>\n",
       "      <td>0.170575</td>\n",
       "      <td>0.075667</td>\n",
       "      <td>122.211321</td>\n",
       "      <td>0.135382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Time        V1        V2        V3        V4        V5  \\\n",
       "Class                                                                   \n",
       "0      94838.202258  0.008258 -0.006271  0.012171 -0.007860  0.005453   \n",
       "1      80746.806911 -4.771948  3.623778 -7.033281  4.542029 -3.151225   \n",
       "\n",
       "             V6        V7        V8        V9       V10       V11       V12  \\\n",
       "Class                                                                         \n",
       "0      0.002419  0.009637 -0.000987  0.004467  0.009824 -0.006576  0.010832   \n",
       "1     -1.397737 -5.568731  0.570636 -2.581123 -5.676883  3.800173 -6.259393   \n",
       "\n",
       "            V13       V14       V15       V16       V17       V18       V19  \\\n",
       "Class                                                                         \n",
       "0      0.000189  0.012064  0.000161  0.007164  0.011535  0.003887 -0.001178   \n",
       "1     -0.109334 -6.971723 -0.092929 -4.139946 -6.665836 -2.246308  0.680659   \n",
       "\n",
       "            V20       V21       V22       V23       V24       V25       V26  \\\n",
       "Class                                                                         \n",
       "0     -0.000644 -0.001235 -0.000024  0.000070  0.000182 -0.000072 -0.000089   \n",
       "1      0.372319  0.713588  0.014049 -0.040308 -0.105130  0.041449  0.051648   \n",
       "\n",
       "            V27       V28      Amount  normAmount  \n",
       "Class                                              \n",
       "0     -0.000295 -0.000131   88.291022   -0.000234  \n",
       "1      0.170575  0.075667  122.211321    0.135382  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Class').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Threshold based fraud detection\n",
    "# Reference:\n",
    "# https://www.opusconsulting.com/rule-based-vs-machine-learning-effective-fraud-prevention/\n",
    "# https://www.ijert.org/research/1-a-model-for-rule-based-fraud-detection-in-telecommunications-IJERTV1IS5337.pdf        \n",
    "\n",
    "#Threshold based on top 2 mostly correlated features\n",
    "df_thres = df.copy()\n",
    "df_thres['Fraud Tag'] = np.where(np.logical_and(df_thres['V17'] < -1, df_thres['V14'] < -1),1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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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>Predicted Class</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual Class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>282912</td>\n",
       "      <td>1403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>123</td>\n",
       "      <td>369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted Class       0     1\n",
       "Actual Class                 \n",
       "0                282912  1403\n",
       "1                   123   369"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_thres['Class'], df_thres['Fraud Tag'], rownames=['Actual Class'], colnames=['Predicted Class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Predicted Class</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual Class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.995065</td>\n",
       "      <td>0.004935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted Class         0         1\n",
       "Actual Class                       \n",
       "0                0.995065  0.004935\n",
       "1                0.250000  0.750000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_thres['Class'], df_thres['Fraud Tag'], rownames=['Actual Class'], colnames=['Predicted Class'],normalize = \"index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_thres.to_excel('C:/Users/arock.000/Downloads/rule.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation:-\n",
    "- The top 2 most correlated features base don on mutual information concept is V17 and V 14\n",
    "- The mean of these two features are calculated for each class\n",
    "- This mean class values is used to draw a cut off to identify the fraud and non fraud cases\n",
    "- After 2-4 iterations of cut off values, the optimal cut off value is found to be \"-1\" which gives below accuracy values\n",
    "  - Class 0 (Non Fraud) Accuracy = 99.5%\n",
    "  - Class 1 (Fraud) Accuracy     = 75%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function for ROC curve and dataset\n",
    "\n",
    "def plot_roc_curve(fpr, tpr):\n",
    "    plt.plot(fpr, tpr, color='orange', label='ROC')\n",
    "    plt.plot([0, 1], [0, 1], color='darkblue', linestyle='--')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('ROC Curve - ' + str_1)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "def plot_data(X,Y):\n",
    "    plt.scatter(X[Y == 0,0], X[Y == 0, 1], label=\"Class 0-Non Fraud\", alpha=0.4, c='b')\n",
    "    plt.scatter(X[Y == 1,0], X[Y == 1, 1], label=\"Class 1-Fraud\", alpha=0.4, c='g')\n",
    "    plt.legend()\n",
    "    \n",
    "    return plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before OverSampling, counts of label '1': 345\n",
      "Before OverSampling, counts of label '0': 199019 \n",
      "\n",
      "After OverSampling, the shape of train_X: (398038, 29)\n",
      "After OverSampling, the shape of train_y: (398038,) \n",
      "\n",
      "After OverSampling, counts of label '1': 199019\n",
      "After OverSampling, counts of label '0': 199019\n"
     ]
    }
   ],
   "source": [
    "# Handling Class Imabalance Using SMOTE\n",
    "# Reference: https://www.kaggle.com/residentmario/oversampling-with-smote-and-adasyn\n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "str_1 = 'Logistic Regression w/ SMOTE'\n",
    "sm = SMOTE(random_state = 2) \n",
    "X_train_res, y_train_res = sm.fit_sample(X_train, y_train.ravel())\n",
    "\n",
    "print(\"Before OverSampling, counts of label '1': {}\".format(sum(y_train == 1))) \n",
    "print(\"Before OverSampling, counts of label '0': {} \\n\".format(sum(y_train == 0))) \n",
    "  \n",
    "print('After OverSampling, the shape of train_X: {}'.format(X_train_res.shape)) \n",
    "print('After OverSampling, the shape of train_y: {} \\n'.format(y_train_res.shape)) \n",
    "  \n",
    "print(\"After OverSampling, counts of label '1': {}\".format(sum(y_train_res == 1))) \n",
    "print(\"After OverSampling, counts of label '0': {}\".format(sum(y_train_res == 0))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1dc496815f8>"
      ]
     },
     "execution_count": 67,
     "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": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "k_range = range(1, 11)\n",
    "\n",
    "score1=[]\n",
    "score2=[]\n",
    "for k in k_range:\n",
    "    knn=KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(X_train_res,y_train_res)\n",
    "    score1.append(knn.score(X_train_res,y_train_res))\n",
    "    score2.append(knn.score(X_test,y_test))\n",
    "    \n",
    "%matplotlib inline\n",
    "plt.plot(k_range,score1,label= 'Accuracy on training set')\n",
    "plt.plot(k_range,score2,label= 'Accuracy on testing set')\n",
    "plt.xlabel('Value of K in KNN')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing KNN classification using synthetic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs #synthetic dataset\n",
    "from sklearn.neighbors import KNeighborsClassifier #kNN classifier\n",
    "from sklearn.model_selection import train_test_split #train and test sets\n",
    "\n",
    "#create synthetic dataset\n",
    "X, y = make_blobs(n_samples = 100, n_features = 2, centers = 4,\n",
    "                       cluster_std = 1.5, random_state = 4)\n",
    "\n",
    "#scatter plot of dataset\n",
    "plt.figure(figsize = (10,6))\n",
    "plt.scatter(X[:,0], X[:,1], c=y, marker= 'o', s=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn5 = KNeighborsClassifier() #k=5\n",
    "knn1 = KNeighborsClassifier(n_neighbors=1) #k=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn5.fit(X_train, y_train)\n",
    "knn1.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_5 = knn5.predict(X_test)\n",
    "y_pred_1 = knn1.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of kNN with k=5 0.92\n",
      "Accuracy of kNN with k=1 0.92\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(\"Accuracy of kNN with k=5\", accuracy_score(y_test, y_pred_5))\n",
    "print(\"Accuracy of kNN with k=1\", accuracy_score(y_test, y_pred_1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "plt.scatter(X_test[:,0], X_test[:,1], c=y_test, marker= 'o', s=50)\n",
    "plt.title(\"Original data\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "plt.scatter(X_test[:,0], X_test[:,1], c=y_pred_5, marker= 'o', s=50)\n",
    "plt.title(\"Predicted values with k=5\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "plt.scatter(X_test[:,0], X_test[:,1], c=y_pred_1, marker= 'o', s=50)\n",
    "plt.title(\"Predicted values with k=1\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to find the best k value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer = load_breast_cancer()\n",
    "(X_cancer, y_cancer) = load_breast_cancer(return_X_y = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_grid = GridSearchCV(estimator = KNeighborsClassifier(), \n",
    "                        param_grid={'n_neighbors': np.arange(1,20)}, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30,\n",
       "                                            metric='minkowski',\n",
       "                                            metric_params=None, n_jobs=None,\n",
       "                                            n_neighbors=5, p=2,\n",
       "                                            weights='uniform'),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'n_neighbors': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19])},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_grid.fit(X_cancer, y_cancer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 13}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}