anomaly-detection-credit-card-fraud-analysis.ipynb

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    "# Introduction: Anomaly Detection \n",
    "\n",
    "Anomaly detection is a technique used to identify unusual patterns that do not conform to expected behavior, called outliers. It has many applications in business, from intrusion detection (identifying strange patterns in network traffic that could signal a hack) to system health monitoring (spotting a malignant tumor in an MRI scan), and from fraud detection in credit card transactions to fault detection in operating environments.\n",
    "\n",
    "In this jupyter notebook we are going to take the credit card fraud detection as the case study for understanding this concept in detail.\n",
    "\n",
    "## What Are Anomalies?\n",
    "In data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.\n",
    "Anomalies can be broadly categorized as:\n",
    "\n",
    "***Point anomalies:*** A single instance of data is anomalous if it's too far off from the rest. Business use case: Detecting credit card fraud based on \"amount spent.\"\n",
    "\n",
    "***Contextual anomalies:*** The abnormality is context specific. This type of anomaly is common in time-series data. Business use case: Spending $100 on food every day during the holiday season is normal, but may be odd otherwise.\n",
    "\n",
    "***Collective anomalies:*** A set of data instances collectively helps in detecting anomalies. Business use case: Someone is trying to copy data form a remote machine to a local host unexpectedly, an anomaly that would be flagged as a potential cyber attack. \n",
    "\n",
    "- Anomaly detection is similar to — but not entirely the same as — noise removal and novelty detection. \n",
    "\n",
    "- ***Novelty detection*** is concerned with identifying an unobserved pattern in new observations not included in training data like a sudden interest in a new channel on YouTube during Christmas, for instance. \n",
    "\n",
    "- ***Noise removal (NR)*** is the process of removing noise from an otherwise meaningful signal. \n",
    "\n",
    "## 1. Anomaly Detection Techniques\n",
    "#### Simple Statistical Methods\n",
    "\n",
    "The simplest approach to identifying irregularities in data is to flag the data points that deviate from common statistical properties of a distribution, including mean, median, mode, and quantiles. Let's say the definition of an anomalous data point is one that deviates by a certain standard deviation from the mean. Traversing mean over time-series data isn't exactly trivial, as it's not static. You would need a rolling window to compute the average across the data points. Technically, this is called a ***rolling average or a moving average***, and it's intended to smooth short-term fluctuations and highlight long-term ones. Mathematically, an n-period simple moving average can also be defined as a ***\"low pass filter.\"***\n",
    "\n",
    "#### Challenges with Simple Statistical Methods\n",
    "\n",
    "The low pass filter allows you to identify anomalies in simple use cases, but there are certain situations where this technique won't work. Here are a few:  \n",
    "\n",
    "- The data contains noise which might be similar to abnormal behavior, because the boundary between normal and abnormal behavior is often not precise. \n",
    "\n",
    "- The definition of abnormal or normal may frequently change, as malicious adversaries constantly adapt themselves. Therefore, the threshold based on moving average may not always apply.\n",
    "\n",
    "- The pattern is based on seasonality. This involves more sophisticated methods, such as decomposing the data into multiple trends in order to identify the change in seasonality.\n",
    "\n",
    "## 2. Machine Learning-Based Approaches\n",
    "\n",
    "Below is a brief overview of popular machine learning-based techniques for anomaly detection. \n",
    "\n",
    "#### a.Density-Based Anomaly Detection \n",
    "Density-based anomaly detection is based on the k-nearest neighbors algorithm.\n",
    "\n",
    "Assumption: Normal data points occur around a dense neighborhood and abnormalities are far away. \n",
    "\n",
    "The nearest set of data points are evaluated using a score, which could be Eucledian distance or a similar measure dependent on the type of the data (categorical or numerical). They could be broadly classified into two algorithms:\n",
    "\n",
    "***K-nearest neighbor***: k-NN is a simple, non-parametric lazy learning technique used to classify data based on similarities in distance metrics such as Eucledian, Manhattan, Minkowski, or Hamming distance.\n",
    "\n",
    "***Relative density of data***: This is better known as local outlier factor (LOF). This concept is based on a distance metric called reachability distance.\n",
    "\n",
    "#### b.Clustering-Based Anomaly Detection \n",
    "Clustering is one of the most popular concepts in the domain of unsupervised learning.\n",
    "\n",
    "Assumption: Data points that are similar tend to belong to similar groups or clusters, as determined by their distance from local centroids.\n",
    "\n",
    "***K-means*** is a widely used clustering algorithm. It creates 'k' similar clusters of data points. Data instances that fall outside of these groups could potentially be marked as anomalies.\n",
    "\n",
    "#### c.Support Vector Machine-Based Anomaly Detection \n",
    "\n",
    "- A support vector machine is another effective technique for detecting anomalies. \n",
    "- A SVM is typically associated with supervised learning, but there are extensions (OneClassCVM, for instance) that can be used to identify anomalies as an unsupervised problems (in which training data are not labeled). \n",
    "- The algorithm learns a soft boundary in order to cluster the normal data instances using the training set, and then, using the testing instance, it tunes itself to identify the abnormalities that fall outside the learned region.\n",
    "- Depending on the use case, the output of an anomaly detector could be numeric scalar values for filtering on domain-specific thresholds or textual labels (such as binary/multi labels).\n",
    "\n",
    "\n",
    "In this jupyter notebook we are going to take the credit card fraud detection as the case study for understanding this concept in detail using the following Anomaly Detection Techniques namely\n",
    "\n",
    "#### Isolation Forest Anomaly Detection Algorithm\n",
    "\n",
    "#### Density-Based Anomaly Detection (Local Outlier Factor)Algorithm\n",
    "\n",
    "#### Support Vector Machine Anomaly Detection Algorithm \n"
   ]
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   "source": [
    "## Credit Card Fraud Detection\n",
    "\n",
    "## Problem Statement:\n",
    "\n",
    "The Credit Card Fraud Detection Problem includes modeling past credit card transactions with the knowledge of the ones that turned out to be fraud. This model is then used to identify whether a new transaction is fraudulent or not. Our aim here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications.\n",
    "\n",
    "#### DataSet : \n",
    "\n",
    "The dataset that is used for credit card fraud detection is derived from the following Kaggle URL :\n",
    "\n",
    "https://www.kaggle.com/mlg-ulb/creditcardfraud\n",
    "\n"
   ]
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   "source": [
    "#### Observations\n",
    "\n",
    "- The data set is highly skewed, consisting of 492 frauds in a total of 284,807 observations. This resulted in only 0.172% fraud cases. This skewed set is justified by the low number of fraudulent transactions.\n",
    "\n",
    "- The dataset consists of numerical values from the 28 ‘Principal Component Analysis (PCA)’ transformed features, namely V1 to V28. Furthermore, there is no metadata about the original features provided, so pre-analysis or feature study could not be done.\n",
    "\n",
    "- The ‘Time’ and ‘Amount’ features are not transformed data.\n",
    "\n",
    "- There is no missing value in the dataset."
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Preprocessing \n",
    "\n",
    "### Import Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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    {
     "ename": "ImportError",
     "evalue": "\nThe plotly.plotly module is deprecated,\nplease install the chart-studio package and use the\nchart_studio.plotly module instead. \n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-efc2bb327014>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mRANDOM_SEED\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m42\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mLABELS\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Normal\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Fraud\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotly\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_objs\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mgo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/plotly/plotly/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m_plotly_future_\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_chart_studio_error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0m_chart_studio_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"plotly\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/_plotly_future_/__init__.py\u001b[0m in \u001b[0;36m_chart_studio_error\u001b[0;34m(submodule)\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[0mchart_studio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0msubmodule\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0minstead\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \"\"\".format(\n\u001b[0;32m---> 49\u001b[0;31m             \u001b[0msubmodule\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubmodule\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m         )\n\u001b[1;32m     51\u001b[0m     )\n",
      "\u001b[0;31mImportError\u001b[0m: \nThe plotly.plotly module is deprecated,\nplease install the chart-studio package and use the\nchart_studio.plotly module instead. \n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import scipy\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import classification_report,accuracy_score\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "from sklearn.svm import OneClassSVM\n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 14, 8\n",
    "RANDOM_SEED = 42\n",
    "LABELS = [\"Normal\", \"Fraud\"]\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import plotly\n",
    "import plotly.figure_factory as ff\n",
    "from plotly.offline import init_notebook_mode, iplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "254bbe6f4da144624ef2a95ff18c24d1ba1e0b27",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
      "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
      "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
      "       'Class'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('../input/creditcard.csv',sep=',')\n",
    "\n",
    "print(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "9f333f15665e79c05932856aad1c4b672e36c1bb",
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28481, 31)"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1= data.sample(frac = 0.1,random_state=1)\n",
    "\n",
    "data1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "4929d7a0c4344a55434b13a7d98c5650bebee8e9",
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       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</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>...</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>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>...</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",
       "<p>8 rows × 31 columns</p>\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",
       "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",
       "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",
       "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",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "           ...                 V21           V22           V23           V24  \\\n",
       "count      ...        2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean       ...        1.537294e-16  7.959909e-16  5.367590e-16  4.458112e-15   \n",
       "std        ...        7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min        ...       -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%        ...       -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%        ...       -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%        ...        1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max        ...        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",
       "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",
       "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",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "29c955006970e99513df449028c3f06245846177"
   },
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "d0ec36ceed7521cd1e770154d76ddc7cb7e820af",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b70e25fe4bb5020169d2a3d30fb878443377b0f7"
   },
   "source": [
    "Let us now check the missing values in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "40526b81aae8ee85a87c37941ab9ac6375f46044",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "5542372a56941a0fd5ee1bedf9923a274132c563",
    "collapsed": true
   },
   "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  \\\n",
       "0  0.098698  0.363787  ...   -0.018307  0.277838 -0.110474  0.066928   \n",
       "1  0.085102 -0.255425  ...   -0.225775 -0.638672  0.101288 -0.339846   \n",
       "2  0.247676 -1.514654  ...    0.247998  0.771679  0.909412 -0.689281   \n",
       "3  0.377436 -1.387024  ...   -0.108300  0.005274 -0.190321 -1.175575   \n",
       "4 -0.270533  0.817739  ...   -0.009431  0.798278 -0.137458  0.141267   \n",
       "\n",
       "        V25       V26       V27       V28  Amount  Class  \n",
       "0  0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.167170  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3  0.647376 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4 -0.206010  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "e9ed7b7baa60ac9d9a32354e9eb21d7529891cfa",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_classes = pd.value_counts(data['Class'], sort = True)\n",
    "count_classes.plot(kind = 'bar', rot=0)\n",
    "plt.title(\"Transaction Class Distribution\")\n",
    "plt.xticks(range(2), LABELS)\n",
    "plt.xlabel(\"Class\")\n",
    "plt.ylabel(\"Frequency\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "edfae305590a61af9fff147b3515508187c2a885"
   },
   "source": [
    "Determine the number of fraud and valid transactions in the entire dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "a10109bd86ec662ed70bbe5c61f3b9535e2c5fe3",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Fraud = data[data['Class']==1]\n",
    "\n",
    "Normal = data[data['Class']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "bbbfbadb438b51b65f71cc6ee8751d8491892c47",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(492, 31)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fraud.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "fb1db209eb0267b0c3e75be5a52c5caf9c1f8e0c",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284315, 31)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Normal.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "775a8c1dfc1a1f6480e353f230664ccb5d07ab53"
   },
   "source": [
    "How different are the amount of money used in different transaction classes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "312f579c38c1ae997f14b34371c028eddea839b6",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fraud.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "d3883604d25835afc6c8b67af283b284e15cec70",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Normal.Amount.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "33435c38bd419f715e3b4d2d2c21aeb2ef606962"
   },
   "source": [
    "Let's have a more graphical representation of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "7d280ac5f6e397fd60d6f6608201e7d3d1af734f",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(2, 1, sharex=True)\n",
    "f.suptitle('Amount per transaction by class')\n",
    "bins = 50\n",
    "ax1.hist(Fraud.Amount, bins = bins)\n",
    "ax1.set_title('Fraud')\n",
    "ax2.hist(Normal.Amount, bins = bins)\n",
    "ax2.set_title('Normal')\n",
    "plt.xlabel('Amount ($)')\n",
    "plt.ylabel('Number of Transactions')\n",
    "plt.xlim((0, 20000))\n",
    "plt.yscale('log')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "d31962c7d5bdb0d4daf86e6e1f635666c9f96010"
   },
   "source": [
    "Do fraudulent transactions occur more often during certain time frame ? Let us find out with a visual representation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "f703978db6f4fbec51dd4751db85a7e15954cc20",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(2, 1, sharex=True)\n",
    "f.suptitle('Time of transaction vs Amount by class')\n",
    "ax1.scatter(Fraud.Time, Fraud.Amount)\n",
    "ax1.set_title('Fraud')\n",
    "ax2.scatter(Normal.Time, Normal.Amount)\n",
    "ax2.set_title('Normal')\n",
    "plt.xlabel('Time (in Seconds)')\n",
    "plt.ylabel('Amount')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "6038cec0653e6065198c39aff009b032c0abf00a",
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'init_notebook_mode' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-33f4382ea43d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0minit_notebook_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconnected\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moffline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit_notebook_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconnected\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'init_notebook_mode' is not defined"
     ]
    }
   ],
   "source": [
    "init_notebook_mode(connected=True)\n",
    "plotly.offline.init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "72ad54185ab9c743963256ac60b204d86e87dadb",
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'go' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-a6e929903159>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Create a trace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m trace = go.Scatter(\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFraud\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTime\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFraud\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAmount\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'go' is not defined"
     ]
    }
   ],
   "source": [
    "# Create a trace\n",
    "\n",
    "trace = go.Scatter(\n",
    "    x = Fraud.Time,\n",
    "    y = Fraud.Amount,\n",
    "    mode = 'markers'\n",
    ")\n",
    "data = [trace]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "bfbb2f23af82604ea07dc5dcd8eff1caaffe0d10",
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plotly' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-8e74bdcef5c4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m plotly.offline.iplot({\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"data\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m })\n",
      "\u001b[0;31mNameError\u001b[0m: name 'plotly' is not defined"
     ]
    }
   ],
   "source": [
    "plotly.offline.iplot({\n",
    "    \"data\": data\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "4b930cb3ef7569933d13b3b1b5d22163e7e75a11"
   },
   "source": [
    "Doesn't seem like the time of transaction really matters here as per above observation.\n",
    "Now let us take a sample of the dataset for out modelling and prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "4122d5b053e8960e6df1376def862bb00f4f26b4",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28481, 31)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "2330356b551b7a9fb2ca4e7ec5e661cf1be7a745"
   },
   "source": [
    "Plot histogram of each parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "d83ae9773b6a1b2267d553025d3fd9fabcdcc26e",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1.hist(figsize=(20,20))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "19881d6fe2801dcfc778f5f87b3a20b811194bc4"
   },
   "source": [
    "Determine the number of fraud and valid transactions in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "f1679d140f46b8c6e7dc30c8c16825251628cc5a",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Fraud = data1[data1['Class']==1]\n",
    "Valid = data1[data1['Class']==0]\n",
    "outlier_fraction = len(Fraud)/float(len(Valid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "efa42f87a8d0df7b2f31d5f94a8ea28c12ee9f53"
   },
   "source": [
    "Now let us print the outlier fraction and no of Fraud and Valid Transaction cases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "537fb0a4da10d3699578d3e38043c8a78624ab4b",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0017234102419808666\n",
      "Fraud Cases : 49\n",
      "Valid Cases : 28432\n"
     ]
    }
   ],
   "source": [
    "print(outlier_fraction)\n",
    "print(\"Fraud Cases : {}\".format(len(Fraud)))\n",
    "print(\"Valid Cases : {}\".format(len(Valid)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "2ac58d3652f3d4d03b62ac90a6db1c5a9307d575"
   },
   "source": [
    "Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "add0d489bdabc1000d443dfdfb2c77c53524353e",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix = data1.corr()\n",
    "fig = plt.figure(figsize=(12,9))\n",
    "sns.heatmap(correlation_matrix,vmax=0.8,square = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "d755ac509a1414c058a018b41a62fdab72814437"
   },
   "source": [
    "The above correlation matrix shows that none of the V1 to V28 PCA components have any correlation to each other however if we observe Class has some form positive and negative correlations with the V components but has no correlation with Time and Amount."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "1c40d2ffe6c9ce35ba1f09ed42e646b120129905"
   },
   "source": [
    "Get all the columns from the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "374066c418100f772f7c3933558d902d0ce0e90d",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28481, 30)\n",
      "(28481,)\n"
     ]
    }
   ],
   "source": [
    "columns = data1.columns.tolist()\n",
    "# Filter the columns to remove data we do not want \n",
    "columns = [c for c in columns if c not in [\"Class\"]]\n",
    "# Store the variable we are predicting \n",
    "target = \"Class\"\n",
    "# Define a random state \n",
    "state = np.random.RandomState(42)\n",
    "X = data1[columns]\n",
    "Y = data1[target]\n",
    "X_outliers = state.uniform(low=0, high=1, size=(X.shape[0], X.shape[1]))\n",
    "# Print the shapes of X & Y\n",
    "print(X.shape)\n",
    "print(Y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "0a0261fea2a695dc3deeb6289a0a2a09cf7db9f1"
   },
   "source": [
    "## Model Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "22960294f6f02ba495ce4c4cdca1510c25e02e22"
   },
   "source": [
    "Now it is time to start building the model .The types of algorithms we are going to use to try to do anomaly detection on this dataset are as follows\n",
    "\n",
    "#### 1. Isolation Forest Algorithm: \n",
    "One of the newest techniques to detect anomalies is called Isolation Forests. The algorithm is based on the fact that anomalies are data points that are few and different. As a result of these properties, anomalies are susceptible to a mechanism called isolation.\n",
    "\n",
    "This method is highly useful and is fundamentally different from all existing methods. It introduces the use of isolation as a more effective and efficient means to detect anomalies than the commonly used basic distance and density measures. Moreover, this method is an algorithm with a low linear time complexity and a small memory requirement. It builds a good performing model with a small number of trees using small sub-samples of fixed size, regardless of the size of a data set.\n",
    "\n",
    "Typical machine learning methods tend to work better when the patterns they try to learn are balanced, meaning the same amount of good and bad behaviors are present in the dataset.\n",
    "\n",
    "#### How Isolation Forests Work\n",
    "\n",
    "The Isolation Forest algorithm isolates observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature. The logic argument goes: isolating anomaly observations is easier because only a few conditions are needed to separate those cases from the normal observations. On the other hand, isolating normal observations require more conditions. Therefore, an anomaly score can be calculated as the number of conditions required to separate a given observation.\n",
    "\n",
    "The way that the algorithm constructs the separation is by first creating isolation trees, or random decision trees. Then, the score is calculated as the path length to isolate the observation.\n",
    "\n",
    "#### 2. Local Outlier Factor(LOF) Algorithm\n",
    "\n",
    "The LOF algorithm is an unsupervised outlier detection method which computes the local density deviation of a given data point with respect to its neighbors. It considers as outlier samples that have a substantially lower density than their neighbors.\n",
    "\n",
    "The number of neighbors considered, (parameter n_neighbors) is typically chosen 1) greater than the minimum number of objects a cluster has to contain, so that other objects can be local outliers relative to this cluster, and 2) smaller than the maximum number of close by objects that can potentially be local outliers. In practice, such informations are generally not available, and taking n_neighbors=20 appears to work well in general.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8f84487627edf206d46e75f94e9cbfa137c2912d"
   },
   "source": [
    "Define the outlier detection methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "155355c45f18abc1f67fbdebb7498c498f88b81c",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifiers = {\n",
    "    \"Isolation Forest\":IsolationForest(n_estimators=100, max_samples=len(X), \n",
    "                                       contamination=outlier_fraction,random_state=state, verbose=0),\n",
    "    \"Local Outlier Factor\":LocalOutlierFactor(n_neighbors=20, algorithm='auto', \n",
    "                                              leaf_size=30, metric='minkowski',\n",
    "                                              p=2, metric_params=None, contamination=outlier_fraction),\n",
    "    \"Support Vector Machine\":OneClassSVM(kernel='rbf', degree=3, gamma=0.1,nu=0.05, \n",
    "                                         max_iter=-1, random_state=state)\n",
    "   \n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_kg_hide-input": false,
    "_uuid": "3f6139b57ec5464b72f2e8e1a3875f4e95ea7c05"
   },
   "source": [
    "Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "8b635e8a65b72a8107385d42c9ae31322df84be7",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/ensemble/iforest.py:247: FutureWarning:\n",
      "\n",
      "behaviour=\"old\" is deprecated and will be removed in version 0.22. Please use behaviour=\"new\", which makes the decision_function change to match other anomaly detection algorithm API.\n",
      "\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/ensemble/iforest.py:415: DeprecationWarning:\n",
      "\n",
      "threshold_ attribute is deprecated in 0.20 and will be removed in 0.22.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isolation Forest: 73\n",
      "Accuracy Score :\n",
      "0.9974368877497279\n",
      "Classification Report :\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     28432\n",
      "           1       0.26      0.27      0.26        49\n",
      "\n",
      "    accuracy                           1.00     28481\n",
      "   macro avg       0.63      0.63      0.63     28481\n",
      "weighted avg       1.00      1.00      1.00     28481\n",
      "\n",
      "Local Outlier Factor: 97\n",
      "Accuracy Score :\n",
      "0.9965942207085425\n",
      "Classification Report :\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     28432\n",
      "           1       0.02      0.02      0.02        49\n",
      "\n",
      "    accuracy                           1.00     28481\n",
      "   macro avg       0.51      0.51      0.51     28481\n",
      "weighted avg       1.00      1.00      1.00     28481\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/classes.py:1194: DeprecationWarning:\n",
      "\n",
      "The random_state parameter is deprecated and will be removed in version 0.22.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine: 8516\n",
      "Accuracy Score :\n",
      "0.7009936448860644\n",
      "Classification Report :\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.70      0.82     28432\n",
      "           1       0.00      0.37      0.00        49\n",
      "\n",
      "    accuracy                           0.70     28481\n",
      "   macro avg       0.50      0.53      0.41     28481\n",
      "weighted avg       1.00      0.70      0.82     28481\n",
      "\n"
     ]
    }
   ],
   "source": [
    "n_outliers = len(Fraud)\n",
    "for i, (clf_name,clf) in enumerate(classifiers.items()):\n",
    "    #Fit the data and tag outliers\n",
    "    if clf_name == \"Local Outlier Factor\":\n",
    "        y_pred = clf.fit_predict(X)\n",
    "        scores_prediction = clf.negative_outlier_factor_\n",
    "    elif clf_name == \"Support Vector Machine\":\n",
    "        clf.fit(X)\n",
    "        y_pred = clf.predict(X)\n",
    "    else:    \n",
    "        clf.fit(X)\n",
    "        scores_prediction = clf.decision_function(X)\n",
    "        y_pred = clf.predict(X)\n",
    "    #Reshape the prediction values to 0 for Valid transactions , 1 for Fraud transactions\n",
    "    y_pred[y_pred == 1] = 0\n",
    "    y_pred[y_pred == -1] = 1\n",
    "    n_errors = (y_pred != Y).sum()\n",
    "    # Run Classification Metrics\n",
    "    print(\"{}: {}\".format(clf_name,n_errors))\n",
    "    print(\"Accuracy Score :\")\n",
    "    print(accuracy_score(Y,y_pred))\n",
    "    print(\"Classification Report :\")\n",
    "    print(classification_report(Y,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "0d8f1173b6ef7c26922f56b4e868cff21f6f750c"
   },
   "source": [
    "#### Observations :\n",
    "- Isolation Forest detected 73 errors versus Local Outlier Factor detecting 97 errors vs. SVM detecting 8516 errors\n",
    "- Isolation Forest has a 99.74% more accurate than LOF of 99.65% and SVM of 70.09\n",
    "- When comparing error precision & recall for 3 models , the Isolation Forest performed much better than the LOF as we can see that the detection of fraud cases is around 27 % versus LOF detection rate of just 2 % and SVM of 0%.\n",
    "- So overall Isolation Forest Method performed much better in determining the fraud cases which is around 30%.\n",
    "- We can also improve on this accuracy by increasing the sample size or use deep learning algorithms however at the cost of computational expense.We can also use complex anomaly detection models to get better accuracy in determining more fraudulent cases"
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    "Now let us look at one particular Deep Learning Algorithm called ***Autoencoders***\n"
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    "## Autoencoders\n",
    "\n",
    "An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner.\n",
    "\n",
    "The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for the purpose of dimensionality reduction.\n",
    "\n",
    "An autoencoder learns to compress data from the input layer into a short code, and then uncompress that code into something that closely matches the original data. This forces the autoencoder to engage in dimensionality reduction, for example by learning how to ignore noise. Some architectures use stacked sparse autoencoder layers for image recognition. The first autoencoder might learn to encode easy features like corners, the second to analyze the first layer's output and then encode less local features like the tip of a nose, the third might encode a whole nose, etc., until the final autoencoder encodes the whole image into a code that matches (for example) the concept of \"cat\".An alternative use is as a generative model: for example, if a system is manually fed the codes it has learned for \"cat\" and \"flying\", it may attempt to generate an image of a flying cat, even if it has never seen a flying cat before.\n",
    "\n",
    "The simplest form of an autoencoder is a feedforward, non-recurrent neural network very similar to the many single layer perceptrons which makes a multilayer perceptron (MLP) – having an input layer, an output layer and one or more hidden layers connecting them – but with the output layer having the same number of nodes as the input layer, and with the purpose of reconstructing its own inputs (instead of predicting the target value Y  given inputs X). Therefore, autoencoders are unsupervised learning models. \n",
    "\n",
    "\n",
    "### If you like this  kernel Greatly Appreciate if you can  UPVOTE .Thank you \n"
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