Detection of Fraudulent Transactions.ipynb

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   "cell_type": "markdown",
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    "_cell_guid": "83708667-4fdc-1563-7b3a-06b6575d2865"
   },
   "source": [
    "\n",
    "\n",
    "# Detection of Fraudulent Transactions\n",
    "The objective of this case study is to employ different models based on classification techniques for the purpose of identifying whether a given transaction is a regular payment or a fraudulent one.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem Definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, the response variable takes a value of 1 in case the given transaction is fraud and 0 otherwise.\n",
    "\n",
    "The dataset provided contains information about credit card transactions carried out by European cardholders in September 2013. The data pertains to transactions that took place over a period of two days, during which there were a total of 492 fraudulent transactions out of 284,807. It is important to note that this dataset is highly imbalanced, with only 0.172% of all transactions being identified as frauds. The main objective is to predict instances of fraud. The response variable, 'Class', takes on a value of 1 if the transaction is fraudulent and 0 otherwise. The features in the dataset are a result of PCA transformation and may not be immediately interpretable based on their names.\n",
    "\n",
    "Here, you can find the dataset details as well as download it: https://www.kaggle.com/mlg-ulb/creditcardfraud\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the Packages and the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "5d8fee34-f454-2642-8b06-ed719f0317e1",
    "execution": {
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     "shell.execute_reply": "2023-04-03T07:13:34.897818Z",
     "shell.execute_reply.started": "2023-04-03T07:13:18.151144Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot\n",
    "from pandas import read_csv, set_option\n",
    "from pandas.plotting import scatter_matrix\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score, GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, RandomForestClassifier, ExtraTreesClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from keras.optimizers import SGD\n",
    "from pickle import dump\n",
    "from pickle import load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "787e35f7-bf9e-0969-8d13-a54fa87f3519",
    "execution": {
     "iopub.execute_input": "2023-04-03T07:13:51.799697Z",
     "iopub.status.busy": "2023-04-03T07:13:51.798922Z",
     "iopub.status.idle": "2023-04-03T07:13:56.231616Z",
     "shell.execute_reply": "2023-04-03T07:13:56.230487Z",
     "shell.execute_reply.started": "2023-04-03T07:13:51.799656Z"
    }
   },
   "outputs": [],
   "source": [
    "# load dataset\n",
    "dataset = read_csv('/kaggle/input/creditcardfraud/creditcard.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-03T07:14:00.729628Z",
     "iopub.status.busy": "2023-04-03T07:14:00.728945Z",
     "iopub.status.idle": "2023-04-03T07:14:00.734558Z",
     "shell.execute_reply": "2023-04-03T07:14:00.733344Z",
     "shell.execute_reply.started": "2023-04-03T07:14:00.729590Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "df6a4523-b385-69ee-c933-592826d81431"
   },
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "52f85dc2-0f91-3c50-400e-ddc38bea966b",
    "execution": {
     "iopub.execute_input": "2023-04-03T07:14:04.528522Z",
     "iopub.status.busy": "2023-04-03T07:14:04.527577Z",
     "iopub.status.idle": "2023-04-03T07:14:04.537296Z",
     "shell.execute_reply": "2023-04-03T07:14:04.536094Z",
     "shell.execute_reply.started": "2023-04-03T07:14:04.528485Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2023-04-03T07:14:06.253743Z",
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       "      <th>V27</th>\n",
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       "      <th>Class</th>\n",
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       "      <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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       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.792993</td>\n",
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       "      <td>-1.387024</td>\n",
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       "   Time        V1        V2        V3        V4        V5        V6        V7        V8        V9  \\\n",
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       "\n",
       "   ...       V21       V22       V23       V24       V25       V26       V27       V28  Amount  \\\n",
       "0  ... -0.018307  0.277838 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053  149.62   \n",
       "1  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724    2.69   \n",
       "2  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  378.66   \n",
       "3  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458  123.50   \n",
       "4  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   69.99   \n",
       "\n",
       "   Class  \n",
       "0      0  \n",
       "1      0  \n",
       "2      0  \n",
       "3      0  \n",
       "4      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set_option('display.width', 100)\n",
    "dataset.head()"
   ]
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    {
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       "Time      float64\n",
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   "source": [
    "set_option('display.max_rows', 500)\n",
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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       "      <td>88.350</td>\n",
       "      <td>0.002</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.146</td>\n",
       "      <td>1.959</td>\n",
       "      <td>1.651</td>\n",
       "      <td>1.516</td>\n",
       "      <td>1.416</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.332</td>\n",
       "      <td>1.237</td>\n",
       "      <td>1.194</td>\n",
       "      <td>1.099</td>\n",
       "      <td>...</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.624</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.521</td>\n",
       "      <td>0.482</td>\n",
       "      <td>0.404</td>\n",
       "      <td>0.330</td>\n",
       "      <td>250.120</td>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000</td>\n",
       "      <td>-56.408</td>\n",
       "      <td>-72.716</td>\n",
       "      <td>-48.326</td>\n",
       "      <td>-5.683</td>\n",
       "      <td>-113.743</td>\n",
       "      <td>-26.161</td>\n",
       "      <td>-43.557</td>\n",
       "      <td>-73.217</td>\n",
       "      <td>-13.434</td>\n",
       "      <td>...</td>\n",
       "      <td>-34.830</td>\n",
       "      <td>-10.933</td>\n",
       "      <td>-44.808</td>\n",
       "      <td>-2.837</td>\n",
       "      <td>-10.295</td>\n",
       "      <td>-2.605</td>\n",
       "      <td>-22.566</td>\n",
       "      <td>-15.430</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500</td>\n",
       "      <td>-0.920</td>\n",
       "      <td>-0.599</td>\n",
       "      <td>-0.890</td>\n",
       "      <td>-0.849</td>\n",
       "      <td>-0.692</td>\n",
       "      <td>-0.768</td>\n",
       "      <td>-0.554</td>\n",
       "      <td>-0.209</td>\n",
       "      <td>-0.643</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.228</td>\n",
       "      <td>-0.542</td>\n",
       "      <td>-0.162</td>\n",
       "      <td>-0.355</td>\n",
       "      <td>-0.317</td>\n",
       "      <td>-0.327</td>\n",
       "      <td>-0.071</td>\n",
       "      <td>-0.053</td>\n",
       "      <td>5.600</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.180</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>-0.054</td>\n",
       "      <td>-0.274</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.022</td>\n",
       "      <td>-0.051</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.029</td>\n",
       "      <td>0.007</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.017</td>\n",
       "      <td>-0.052</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.011</td>\n",
       "      <td>22.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500</td>\n",
       "      <td>1.316</td>\n",
       "      <td>0.804</td>\n",
       "      <td>1.027</td>\n",
       "      <td>0.743</td>\n",
       "      <td>0.612</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0.570</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.597</td>\n",
       "      <td>...</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.529</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.351</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.078</td>\n",
       "      <td>77.165</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000</td>\n",
       "      <td>2.455</td>\n",
       "      <td>22.058</td>\n",
       "      <td>9.383</td>\n",
       "      <td>16.875</td>\n",
       "      <td>34.802</td>\n",
       "      <td>73.302</td>\n",
       "      <td>120.589</td>\n",
       "      <td>20.007</td>\n",
       "      <td>15.595</td>\n",
       "      <td>...</td>\n",
       "      <td>27.203</td>\n",
       "      <td>10.503</td>\n",
       "      <td>22.528</td>\n",
       "      <td>4.585</td>\n",
       "      <td>7.520</td>\n",
       "      <td>3.517</td>\n",
       "      <td>31.612</td>\n",
       "      <td>33.848</td>\n",
       "      <td>25691.160</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time         V1         V2         V3         V4         V5         V6         V7  \\\n",
       "count 284807.000 284807.000 284807.000 284807.000 284807.000 284807.000 284807.000 284807.000   \n",
       "mean   94813.860      0.000      0.000     -0.000      0.000      0.000      0.000     -0.000   \n",
       "std    47488.146      1.959      1.651      1.516      1.416      1.380      1.332      1.237   \n",
       "min        0.000    -56.408    -72.716    -48.326     -5.683   -113.743    -26.161    -43.557   \n",
       "25%    54201.500     -0.920     -0.599     -0.890     -0.849     -0.692     -0.768     -0.554   \n",
       "50%    84692.000      0.018      0.065      0.180     -0.020     -0.054     -0.274      0.040   \n",
       "75%   139320.500      1.316      0.804      1.027      0.743      0.612      0.399      0.570   \n",
       "max   172792.000      2.455     22.058      9.383     16.875     34.802     73.302    120.589   \n",
       "\n",
       "              V8         V9  ...        V21        V22        V23        V24        V25  \\\n",
       "count 284807.000 284807.000  ... 284807.000 284807.000 284807.000 284807.000 284807.000   \n",
       "mean       0.000     -0.000  ...      0.000     -0.000      0.000      0.000      0.000   \n",
       "std        1.194      1.099  ...      0.735      0.726      0.624      0.606      0.521   \n",
       "min      -73.217    -13.434  ...    -34.830    -10.933    -44.808     -2.837    -10.295   \n",
       "25%       -0.209     -0.643  ...     -0.228     -0.542     -0.162     -0.355     -0.317   \n",
       "50%        0.022     -0.051  ...     -0.029      0.007     -0.011      0.041      0.017   \n",
       "75%        0.327      0.597  ...      0.186      0.529      0.148      0.440      0.351   \n",
       "max       20.007     15.595  ...     27.203     10.503     22.528      4.585      7.520   \n",
       "\n",
       "             V26        V27        V28     Amount      Class  \n",
       "count 284807.000 284807.000 284807.000 284807.000 284807.000  \n",
       "mean       0.000     -0.000     -0.000     88.350      0.002  \n",
       "std        0.482      0.404      0.330    250.120      0.042  \n",
       "min       -2.605    -22.566    -15.430      0.000      0.000  \n",
       "25%       -0.327     -0.071     -0.053      5.600      0.000  \n",
       "50%       -0.052      0.001      0.011     22.000      0.000  \n",
       "75%        0.241      0.091      0.078     77.165      0.000  \n",
       "max        3.517     31.612     33.848  25691.160      1.000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.float_format', '{:.3f}'.format)\n",
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To begin with, we will examine the number of instances of fraud as compared to non-fraud cases in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-03T07:14:27.281234Z",
     "iopub.status.busy": "2023-04-03T07:14:27.280001Z",
     "iopub.status.idle": "2023-04-03T07:14:27.292894Z",
     "shell.execute_reply": "2023-04-03T07:14:27.291816Z",
     "shell.execute_reply.started": "2023-04-03T07:14:27.281177Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not Fraud    284315\n",
      "Fraud           492\n",
      "Name: Class, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "class_names = {0:'Not Fraud', 1:'Fraud'}\n",
    "print(dataset.Class.value_counts().rename(index = class_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of instances in the dataset is skewed, with a significant majority of the transactions being classified as non-fraudulent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-03T07:14:35.284678Z",
     "iopub.status.busy": "2023-04-03T07:14:35.283993Z",
     "iopub.status.idle": "2023-04-03T07:14:38.673026Z",
     "shell.execute_reply": "2023-04-03T07:14:38.672065Z",
     "shell.execute_reply.started": "2023-04-03T07:14:35.284640Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histograms\n",
    "dataset.hist(sharex=False, sharey=False, xlabelsize=1, ylabelsize=1, figsize=(12,12))\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-03T07:14:42.345755Z",
     "iopub.status.busy": "2023-04-03T07:14:42.345336Z",
     "iopub.status.idle": "2023-04-03T07:14:42.365069Z",
     "shell.execute_reply": "2023-04-03T07:14:42.363775Z",
     "shell.execute_reply.started": "2023-04-03T07:14:42.345717Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null Values = False\n"
     ]
    }
   ],
   "source": [
    "print('Null Values =',dataset.isnull().values.any())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no null in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-03T07:14:44.201197Z",
     "iopub.status.busy": "2023-04-03T07:14:44.200266Z",
     "iopub.status.idle": "2023-04-03T07:14:44.387471Z",
     "shell.execute_reply": "2023-04-03T07:14:44.386204Z",
     "shell.execute_reply.started": "2023-04-03T07:14:44.201137Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Specs     Score\n",
      "17   V17 33979.169\n",
      "14   V14 28695.548\n",
      "12   V12 20749.822\n",
      "10   V10 14057.980\n",
      "16   V16 11443.349\n",
      "3     V3 11014.508\n",
      "7     V7 10349.605\n",
      "11   V11  6999.355\n",
      "4     V4  5163.832\n",
      "18   V18  3584.381\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "\n",
    "bestfeatures = SelectKBest( k=10)\n",
    "bestfeatures\n",
    "Y= dataset[\"Class\"]\n",
    "X = dataset.loc[:, dataset.columns != 'Class']\n",
    "fit = bestfeatures.fit(X,Y)\n",
    "dfscores = pd.DataFrame(fit.scores_)\n",
    "dfcolumns = pd.DataFrame(X.columns)\n",
    "\n",
    "featureScores = pd.concat([dfcolumns,dfscores],axis=1)\n",
    "featureScores.columns = ['Specs','Score']  \n",
    "print(featureScores.nlargest(10,'Score'))  #print 10 best features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While certain features in the dataset may be important for detecting fraud, the process of selecting these features has not been emphasized or prioritized."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Algorithms and Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Train-Test-Split and Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-03T07:14:52.470582Z",
     "iopub.status.busy": "2023-04-03T07:14:52.470089Z",
     "iopub.status.idle": "2023-04-03T07:14:52.576059Z",
     "shell.execute_reply": "2023-04-03T07:14:52.574982Z",
     "shell.execute_reply.started": "2023-04-03T07:14:52.470536Z"
    }
   },
   "outputs": [],
   "source": [
    "Y= dataset[\"Class\"]\n",
    "X = dataset.loc[:, dataset.columns != 'Class']\n",
    "validation_size = 0.2\n",
    "seed = 7\n",
    "X_train, X_validation, Y_train, Y_validation = train_test_split(X, Y, test_size=validation_size, random_state=seed)\n",
    "scoring = 'accuracy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-03T07:14:57.162591Z",
     "iopub.status.busy": "2023-04-03T07:14:57.162229Z",
     "iopub.status.idle": "2023-04-03T07:14:57.170467Z",
     "shell.execute_reply": "2023-04-03T07:14:57.169412Z",
     "shell.execute_reply.started": "2023-04-03T07:14:57.162559Z"
    }
   },
   "outputs": [],
   "source": [
    "num_folds = 10\n",
    "seed = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-03T07:15:02.547759Z",
     "iopub.status.busy": "2023-04-03T07:15:02.547049Z",
     "iopub.status.idle": "2023-04-03T07:15:02.555234Z",
     "shell.execute_reply": "2023-04-03T07:15:02.553768Z",
     "shell.execute_reply.started": "2023-04-03T07:15:02.547720Z"
    }
   },
   "outputs": [],
   "source": [
    "models = []\n",
    "models.append(('LR', LogisticRegression()))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('NB', GaussianNB()))\n",
    "models.append(('SVM', SVC()))\n",
    "models.append(('NN', MLPClassifier()))\n",
    "models.append(('AB', AdaBoostClassifier()))\n",
    "models.append(('GBM', GradientBoostingClassifier()))\n",
    "models.append(('RF', RandomForestClassifier()))\n",
    "models.append(('ET', ExtraTreesClassifier()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-03T07:15:09.873086Z",
     "iopub.status.busy": "2023-04-03T07:15:09.872111Z",
     "iopub.status.idle": "2023-04-03T09:24:47.941171Z",
     "shell.execute_reply": "2023-04-03T09:24:47.939875Z",
     "shell.execute_reply.started": "2023-04-03T07:15:09.873040Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.998885 (0.000205)\n",
      "LDA: 0.999364 (0.000136)\n",
      "KNN: 0.998310 (0.000290)\n",
      "CART: 0.999109 (0.000245)\n",
      "NB: 0.992745 (0.000733)\n",
      "SVM: 0.998280 (0.000278)\n",
      "NN: 0.998429 (0.000361)\n",
      "AB: 0.999192 (0.000154)\n",
      "GBM: 0.999008 (0.000285)\n",
      "RF: 0.999570 (0.000107)\n",
      "ET: 0.999565 (0.000122)\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "names = []\n",
    "for name, model in models:\n",
    "    kfold = KFold(n_splits=num_folds)\n",
    "    cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-03T09:26:23.161115Z",
     "iopub.status.busy": "2023-04-03T09:26:23.160735Z",
     "iopub.status.idle": "2023-04-03T09:26:23.487252Z",
     "shell.execute_reply": "2023-04-03T09:26:23.486137Z",
     "shell.execute_reply.started": "2023-04-03T09:26:23.161081Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare algorithms\n",
    "fig = pyplot.figure()\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "pyplot.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "fig.set_size_inches(15,7)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-03T09:27:00.165501Z",
     "iopub.status.busy": "2023-04-03T09:27:00.164826Z",
     "iopub.status.idle": "2023-04-03T09:27:17.472290Z",
     "shell.execute_reply": "2023-04-03T09:27:17.471138Z",
     "shell.execute_reply.started": "2023-04-03T09:27:00.165463Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = DecisionTreeClassifier()\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-03T09:28:28.843225Z",
     "iopub.status.busy": "2023-04-03T09:28:28.842180Z",
     "iopub.status.idle": "2023-04-03T09:28:28.922788Z",
     "shell.execute_reply": "2023-04-03T09:28:28.921576Z",
     "shell.execute_reply.started": "2023-04-03T09:28:28.843129Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9991397773954567\n",
      "[[56833    29]\n",
      " [   20    80]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     56862\n",
      "           1       0.73      0.80      0.77       100\n",
      "\n",
      "    accuracy                           1.00     56962\n",
      "   macro avg       0.87      0.90      0.88     56962\n",
      "weighted avg       1.00      1.00      1.00     56962\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rescaledValidationX = X_validation\n",
    "predictions = model.predict(rescaledValidationX)\n",
    "print(accuracy_score(Y_validation, predictions))\n",
    "print(confusion_matrix(Y_validation, predictions))\n",
    "print(classification_report(Y_validation, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-03T09:28:34.685116Z",
     "iopub.status.busy": "2023-04-03T09:28:34.684749Z",
     "iopub.status.idle": "2023-04-03T09:28:34.945090Z",
     "shell.execute_reply": "2023-04-03T09:28:34.944077Z",
     "shell.execute_reply.started": "2023-04-03T09:28:34.685081Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cm = pd.DataFrame(confusion_matrix(Y_validation, predictions), columns=np.unique(Y_validation), index = np.unique(Y_validation))\n",
    "df_cm.index.name = 'Actual'\n",
    "df_cm.columns.name = 'Predicted'\n",
    "sns.heatmap(df_cm, cmap=\"Blues\", annot=True,annot_kws={\"size\": 16});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Despite achieving good results, it is worth noting that 20 out of 100 fraud cases are not detected. Therefore, it is crucial to prioritize the metric of recall, which minimizes false negatives."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-03T09:28:48.463614Z",
     "iopub.status.busy": "2023-04-03T09:28:48.462622Z",
     "iopub.status.idle": "2023-04-03T09:28:48.468981Z",
     "shell.execute_reply": "2023-04-03T09:28:48.467792Z",
     "shell.execute_reply.started": "2023-04-03T09:28:48.463576Z"
    }
   },
   "outputs": [],
   "source": [
    "scoring = 'recall'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-03T09:28:50.663038Z",
     "iopub.status.busy": "2023-04-03T09:28:50.661913Z",
     "iopub.status.idle": "2023-04-03T09:28:50.670880Z",
     "shell.execute_reply": "2023-04-03T09:28:50.669905Z",
     "shell.execute_reply.started": "2023-04-03T09:28:50.662991Z"
    }
   },
   "outputs": [],
   "source": [
    "models = []\n",
    "models.append(('LR', LogisticRegression()))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('NB', GaussianNB()))\n",
    "models.append(('SVM', SVC()))\n",
    "models.append(('NN', MLPClassifier()))\n",
    "models.append(('AB', AdaBoostClassifier()))\n",
    "models.append(('GBM', GradientBoostingClassifier()))\n",
    "models.append(('RF', RandomForestClassifier()))\n",
    "models.append(('ET', ExtraTreesClassifier()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-03T09:29:10.592020Z",
     "iopub.status.busy": "2023-04-03T09:29:10.591652Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.631287 (0.116650)\n",
      "LDA: 0.758283 (0.045450)\n",
      "KNN: 0.023882 (0.019671)\n",
      "CART: 0.749286 (0.070769)\n",
      "NB: 0.659465 (0.080686)\n",
      "SVM: 0.000000 (0.000000)\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "names = []\n",
    "for name, model in models:\n",
    "    kfold = KFold(n_splits=num_folds)\n",
    "    cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the LDA has the best recall out of all the models, it is used to ealuate the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearDiscriminantAnalysis()\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9995435553526912\n",
      "\n",
      "[[56854     8]\n",
      "\n",
      " [   18    82]]\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "\n",
      "\n",
      "           0       1.00      1.00      1.00     56862\n",
      "\n",
      "           1       0.91      0.82      0.86       100\n",
      "\n",
      "\n",
      "\n",
      "    accuracy                           1.00     56962\n",
      "\n",
      "   macro avg       0.96      0.91      0.93     56962\n",
      "\n",
      "weighted avg       1.00      1.00      1.00     56962\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rescaledValidationX = X_validation\n",
    "predictions = model.predict(rescaledValidationX)\n",
    "print(accuracy_score(Y_validation, predictions))\n",
    "print(confusion_matrix(Y_validation, predictions))\n",
    "print(classification_report(Y_validation, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20b99399978>"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cm = pd.DataFrame(confusion_matrix(Y_validation, predictions), columns=np.unique(Y_validation), index = np.unique(Y_validation))\n",
    "df_cm.index.name = 'Actual'\n",
    "df_cm.columns.name = 'Predicted'\n",
    "sns.heatmap(df_cm, cmap=\"Blues\", annot=True,annot_kws={\"size\": 16});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44828     0\n",
       "221877    0\n",
       "278826    0\n",
       "149792    0\n",
       "226041    0\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.360</td>\n",
       "      <td>-0.073</td>\n",
       "      <td>2.536</td>\n",
       "      <td>1.378</td>\n",
       "      <td>-0.338</td>\n",
       "      <td>0.462</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.364</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018</td>\n",
       "      <td>0.278</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.129</td>\n",
       "      <td>-0.189</td>\n",
       "      <td>0.134</td>\n",
       "      <td>-0.021</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>1.192</td>\n",
       "      <td>0.266</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.060</td>\n",
       "      <td>-0.082</td>\n",
       "      <td>-0.079</td>\n",
       "      <td>0.085</td>\n",
       "      <td>-0.255</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.226</td>\n",
       "      <td>-0.639</td>\n",
       "      <td>0.101</td>\n",
       "      <td>-0.340</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.126</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.015</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>-1.358</td>\n",
       "      <td>-1.340</td>\n",
       "      <td>1.773</td>\n",
       "      <td>0.380</td>\n",
       "      <td>-0.503</td>\n",
       "      <td>1.800</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.248</td>\n",
       "      <td>-1.515</td>\n",
       "      <td>...</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.772</td>\n",
       "      <td>0.909</td>\n",
       "      <td>-0.689</td>\n",
       "      <td>-0.328</td>\n",
       "      <td>-0.139</td>\n",
       "      <td>-0.055</td>\n",
       "      <td>-0.060</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>-0.966</td>\n",
       "      <td>-0.185</td>\n",
       "      <td>1.793</td>\n",
       "      <td>-0.863</td>\n",
       "      <td>-0.010</td>\n",
       "      <td>1.247</td>\n",
       "      <td>0.238</td>\n",
       "      <td>0.377</td>\n",
       "      <td>-1.387</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>0.005</td>\n",
       "      <td>-0.190</td>\n",
       "      <td>-1.176</td>\n",
       "      <td>0.647</td>\n",
       "      <td>-0.222</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.061</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>-1.158</td>\n",
       "      <td>0.878</td>\n",
       "      <td>1.549</td>\n",
       "      <td>0.403</td>\n",
       "      <td>-0.407</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.593</td>\n",
       "      <td>-0.271</td>\n",
       "      <td>0.818</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.798</td>\n",
       "      <td>-0.137</td>\n",
       "      <td>0.141</td>\n",
       "      <td>-0.206</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.215</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     V8     V9  ...    V21    V22    V23  \\\n",
       "0   0.0 -1.360 -0.073  2.536  1.378 -0.338  0.462  0.240  0.099  0.364  ... -0.018  0.278 -0.110   \n",
       "1   0.0  1.192  0.266  0.166  0.448  0.060 -0.082 -0.079  0.085 -0.255  ... -0.226 -0.639  0.101   \n",
       "2   1.0 -1.358 -1.340  1.773  0.380 -0.503  1.800  0.791  0.248 -1.515  ...  0.248  0.772  0.909   \n",
       "3   1.0 -0.966 -0.185  1.793 -0.863 -0.010  1.247  0.238  0.377 -1.387  ... -0.108  0.005 -0.190   \n",
       "4   2.0 -1.158  0.878  1.549  0.403 -0.407  0.096  0.593 -0.271  0.818  ... -0.009  0.798 -0.137   \n",
       "\n",
       "     V24    V25    V26    V27    V28  Amount  Class  \n",
       "0  0.067  0.129 -0.189  0.134 -0.021  149.62      0  \n",
       "1 -0.340  0.167  0.126 -0.009  0.015    2.69      0  \n",
       "2 -0.689 -0.328 -0.139 -0.055 -0.060  378.66      0  \n",
       "3 -1.176  0.647 -0.222  0.063  0.061  123.50      0  \n",
       "4  0.141 -0.206  0.502  0.219  0.215   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([X_train, Y_train], axis=1)\n",
    "\n",
    "fraud_df = df.loc[df['Class'] == 1]\n",
    "non_fraud_df = df.loc[df['Class'] == 0][:492]# fraud classes = 492.\n",
    "\n",
    "normal_distributed_df = pd.concat([fraud_df, non_fraud_df])\n",
    "\n",
    "df_new = normal_distributed_df.sample(frac=1, random_state=42)\n",
    "Y_train_new= df_new[\"Class\"]\n",
    "X_train_new = df_new.loc[:, dataset.columns != 'Class']\n",
    "\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of the Classes in the subsample dataset\n",
      "\n",
      "1    0.5\n",
      "\n",
      "0    0.5\n",
      "\n",
      "Name: Class, dtype: float64\n"
     ]
    },
    {
     "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": [
    "print('Distribution of the Classes in the subsample dataset')\n",
    "print(df_new['Class'].value_counts()/len(df_new))\n",
    "sns.countplot('Class', data=df_new)\n",
    "pyplot.title('Equally Distributed Classes', fontsize=14)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "scoring='accuracy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# spot check the algorithms\n",
    "models = []\n",
    "models.append(('LR', LogisticRegression()))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('NB', GaussianNB()))\n",
    "models.append(('SVM', SVC()))\n",
    "models.append(('NN', MLPClassifier()))\n",
    "models.append(('AB', AdaBoostClassifier()))\n",
    "models.append(('GBM', GradientBoostingClassifier()))\n",
    "models.append(('RF', RandomForestClassifier()))\n",
    "models.append(('ET', ExtraTreesClassifier()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "EnableDLModelsFlag = 1\n",
    "if EnableDLModelsFlag == 1 :   \n",
    "    def create_model(neurons=12, activation='relu', learn_rate = 0.01, momentum=0):\n",
    "        # create model\n",
    "        model = Sequential()\n",
    "        model.add(Dense(X_train.shape[1], input_dim=X_train.shape[1], activation=activation))\n",
    "        model.add(Dense(32, activation=activation))\n",
    "        model.add(Dense(1, activation='sigmoid'))\n",
    "        optimizer = SGD(lr=learn_rate, momentum=momentum)\n",
    "        model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "        return model    \n",
    "    models.append(('DNN', KerasClassifier(build_fn=create_model, epochs=50, batch_size=10, verbose=0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.931911 (0.024992)\n",
      "\n",
      "LDA: 0.905473 (0.027422)\n",
      "\n",
      "KNN: 0.648258 (0.044550)\n",
      "\n",
      "CART: 0.907565 (0.022669)\n",
      "\n",
      "NB: 0.860771 (0.027234)\n",
      "\n",
      "SVM: 0.522356 (0.048395)\n",
      "\n",
      "NN: 0.648712 (0.100137)\n",
      "\n",
      "AB: 0.924830 (0.024068)\n",
      "\n",
      "GBM: 0.934982 (0.015132)\n",
      "\n",
      "RF: 0.932931 (0.015859)\n",
      "\n",
      "ET: 0.931962 (0.031043)\n",
      "\n",
      "WARNING:tensorflow:From D:\\Anaconda\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "\n",
      "Instructions for updating:\n",
      "\n",
      "Colocations handled automatically by placer.\n",
      "\n",
      "WARNING:tensorflow:From D:\\Anaconda\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "\n",
      "Instructions for updating:\n",
      "\n",
      "Use tf.cast instead.\n",
      "\n",
      "DNN: 0.498011 (0.050742)\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "names = []\n",
    "for name, model in models:\n",
    "    kfold = KFold(n_splits=num_folds, random_state=seed)\n",
    "    cv_results = cross_val_score(model, X_train_new, Y_train_new, cv=kfold, scoring=scoring)\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = pyplot.figure()\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "pyplot.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "fig.set_size_inches(8,4)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since GBM has been determined as the top-performing model among all other models, a grid search is conducted to optimize the GBM model's performance by varying the number of estimators and maximum depth parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.936992 using {'max_depth': 5, 'n_estimators': 1000}\n",
      "\n",
      "#3 0.931911 (0.016958) with: {'max_depth': 2, 'n_estimators': 20}\n",
      "\n",
      "#6 0.929878 (0.017637) with: {'max_depth': 2, 'n_estimators': 180}\n",
      "\n",
      "#9 0.924797 (0.021358) with: {'max_depth': 2, 'n_estimators': 1000}\n",
      "\n",
      "#6 0.929878 (0.020476) with: {'max_depth': 3, 'n_estimators': 20}\n",
      "\n",
      "#3 0.931911 (0.011120) with: {'max_depth': 3, 'n_estimators': 180}\n",
      "\n",
      "#3 0.931911 (0.017026) with: {'max_depth': 3, 'n_estimators': 1000}\n",
      "\n",
      "#8 0.928862 (0.022586) with: {'max_depth': 5, 'n_estimators': 20}\n",
      "\n",
      "#2 0.934959 (0.015209) with: {'max_depth': 5, 'n_estimators': 180}\n",
      "\n",
      "#1 0.936992 (0.012639) with: {'max_depth': 5, 'n_estimators': 1000}\n"
     ]
    }
   ],
   "source": [
    "n_estimators = [20,180,1000]\n",
    "max_depth= [2, 3,5]\n",
    "param_grid = dict(n_estimators=n_estimators, max_depth=max_depth)\n",
    "model = GradientBoostingClassifier()\n",
    "kfold = KFold(n_splits=num_folds, random_state=seed)\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, scoring=scoring, cv=kfold)\n",
    "grid_result = grid.fit(X_train_new, Y_train_new)\n",
    "\n",
    "#Print Results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "ranks = grid_result.cv_results_['rank_test_score']\n",
    "for mean, stdev, param, rank in zip(means, stds, params, ranks):\n",
    "    print(\"#%d %f (%f) with: %r\" % (rank, mean, stdev, param))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.1, loss='deviance', max_depth=5,\n",
       "                           max_features=None, max_leaf_nodes=None,\n",
       "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                           min_samples_leaf=1, min_samples_split=2,\n",
       "                           min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
       "                           n_iter_no_change=None, presort='auto',\n",
       "                           random_state=None, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = GradientBoostingClassifier(max_depth= 5, n_estimators = 1000)\n",
    "model.fit(X_train_new, Y_train_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9668199852533268\n",
      "\n",
      "[[54972  1890]\n",
      "\n",
      " [    0   100]]\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "\n",
      "\n",
      "           0       1.00      0.97      0.98     56862\n",
      "\n",
      "           1       0.05      1.00      0.10       100\n",
      "\n",
      "\n",
      "\n",
      "    accuracy                           0.97     56962\n",
      "\n",
      "   macro avg       0.53      0.98      0.54     56962\n",
      "\n",
      "weighted avg       1.00      0.97      0.98     56962\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(X_validation)\n",
    "print(accuracy_score(Y_validation, predictions))\n",
    "print(confusion_matrix(Y_validation, predictions))\n",
    "print(classification_report(Y_validation, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26e0cc0bb70>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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evIDKleOoVLkyrdv4xtB3aNWMK67qyRNPDc3ab+SrwzmvSVMqVarMli2beHPSBKKiorj7vvuztmndth1du1/B4AcHcUuvPjRt1pyICCM1dStfL1zAwEEPUrde/QLXvVef/sz8/FMeuO8u+vZPZN/evfzrlZdo2qw5l3S+PGu7hwcNpPF5TWh0TmPOPLMCmzf9wjtvTSYyKpI/9+5b4POXRoXZsjezBOAKYCjwgPkO3hm4xdtkMjAEX9j39F4DvA+M9LbvCUx1zh0GNppZEr4GNECScy7ZO9dUb9uSCftQsWbDNv7UtTUDbvo9Z0SXZfvOPXzy5Xc8O/pzdu7e721lREVFEmHHL7D+vGk7V3duztWXXkBshfLs2X+IJd8lM+Cpt1nmfyduxm9cdddrPNSvC7dd15F68VXYf/AIG1P+x8yFazhy9PS+CK3+eStX3f0aQ++7ho9GDCB930He+exb/j5y+mkdV2DwQ9m7vV4Y+jQArdq0Zcx438X5Y8eOcey37Jep0nbuZPiLz5OWlkZcXByXdL6cxLsGZpumAODp517kvXff4tOPP2TiG2MoU7YstWrF06FjJ6pUqXpada9eowaj35jMyy8N49EH7qNMmTJcfGlnBj34SLZ7CJo1v4C5s7/g7TcncfToUWrUOIvWbdrRt39iSF2cBYgo3Au0rwCPABW991WA3c65zKvtKfh6PfB+bgFwzmWYWbq3fTzZG8z++2w5obx9fhWygvQ7F4fS3mcvJWP7koKPL5fQFRN9+kl97uBZAWfOTy90uwNvMIlnrHfNETO7EujhnLvLzC4BHgL6AUuccw29bWoDM5xz55vZGqCrcy7FW7cBXwv+aW+ft7zy8cAMfDfDdnXO/cUr7wW0c87dk1ed1bIXEeHUWvb+g0ly0Am42sx6ANH4+uxfASqZWZTXuk/Ad6Mp+FrmtYEUM4sCYoE0v/JM/vvkVp4rTZcgIoLvAm2gS16cc4855xKcc/XwXWD90jn3Z+ArIPO25T7AJ97r6d57vPVfOl+Xy3TgJjMr543kaYTvJtWlQCMzq29mZb1z5Nsvq5a9iAjFMs7+UWCqmT0LrATGe+XjgTe9C7Bp+MIb59waM5uG78JrBnB35mhGMxsIzMI39HKCc25NfidX2IuIUCRDL3HOzQfme6+TOT6axn+bQ8ANuew/FN+InhPLZ+Drvw+Ywl5EBD28REQkLIT4bAkKexER0Nw4IiJhIcSzXmEvIgJq2YuIhIUQz3qFvYgIFPrcOKWOwl5EBHXjiIiEhRDPeoW9iAioZS8iEhZCPOsV9iIioAu0IiJhQd04IiJhQGEvIhIGQjzrFfYiIqCWvYhIWAjxrFfYi4iARuOIiISFiBBv2ivsRURQN46ISFjQBVoRkTAQ4l32CnsREdAFWhGRsGAo7EVEQl6IN+wV9iIioAu0IiJhIcSzXmEvIgK6qUpEJCxoNI6ISBgI8YY9ESVdARGR0iDCLOAlL2YWbWbfmtl3ZrbGzJ7yyuub2f+Z2Xoze8/Mynrl5bz3Sd76en7Heswr/8nMuvqVd/PKksxscCC/X64tezP7FHC5rXfOXR3ICUREgkEhNuwPA52dc/vMrAywyMxmAg8ALzvnpprZaKA/MMr7ucs519DMbgJeAG40sybATUBToBYw18zO8c7xGvAHIAVYambTnXNr86pUXt04LxX4VxURCTKFNfTSOeeAfd7bMt7igM7ALV75ZGAIvrDv6b0GeB8Yab7K9ASmOucOAxvNLAlo522X5JxL9uo91du2YGHvnPtP4L+eiEhwK8zrs2YWCSwHGuJrhW8AdjvnMrxNUoB473U8sAXAOZdhZulAFa/8G7/D+u+z5YTy9vnVKd8LtGbWCHgeaAJEZ5Y75xrkt6+ISLA4ldE4ZpYIJPoVjXXOjc1845w7BrQws0rAR8B5ORwms5s8pxO7PMpzutaaa5d7pkBG40wEngReBi4F+uVSCRGRoHUq3ThesI8NYLvdZjYf6ABUMrMor3WfAKR6m6UAtYEUM4sCYoE0v/JM/vvkVp6rQEbjlHfOzQPMObfJOTcEX9+TiEjIiLDAl7yYWTWvRY+ZlQcuB9YBXwHXe5v1AT7xXk/33uOt/9Lr958O3OSN1qkPNAK+BZYCjbzRPWXxXcSdnt/vF0jL/pCZRQDrzWwgsBWoHsB+IiJBoxDnxqkJTPb67SOAac65z8xsLTDVzJ4FVgLjve3HA296F2DT8IU3zrk1ZjYN34XXDOBur3sIL4tnAZHABOfcmvwqFUjYDwLOAO4FnsHXqu+T5x4iIkGmsKLeObcaaJlDeTLHR9P4lx8CbsjlWEOBoTmUzwBmnEq98g1759xS7+U+fP31IiIhJzLcp0sws6/I4Uqvc0799iISMjTFMTzk9zoa+CO+/iMRkZAR4lkfUDfO8hOKFpuZbrgSkZAS9lMcm1mc39sIoDVwVpHVSESkBIR41gfUjbOc43dzZQAb8U3cU6R2LR1Z1KcQEcmiPns4zxsalMXMyhVRfURESkRkiId9IHfQfp1D2ZLCroiISEkqrDtoS6u85rM/C98Ma+XNrCXH7zmIwXeTlYhIyAjWEA9UXt04XYG++CbZ+SfHw34P8HjRVktEpHiFbZ+9c24yvvkd/uic+6AY6yQiUuxCvWUfSJ9968wZ3ADMrLI3kY+ISMgwC3wJRoGEfXfn3O7MN865XUCPoquSiEjxizILeAlGgQy9jDSzct5zEDPnZ9bQSxEJKUGa4QELJOzfAuaZ2UTvfT98D8sVEQkZYT9dgnPuRTNbje9pKwZ8AdQt6oqJiBSnEM/6gFr2AL8CvwF/wjddgkbniEhICfXROHndVHUOvsdj3QzsBN7D9xzaS4upbiIixSacH17yI7AQuMo5lwRgZvcXS61ERIpZiGd9nkMv/4iv++YrMxtnZpdReI9pFBEpVewU/glGuYa9c+4j59yNwLnAfOB+oIaZjTKzLsVUPxGRYhHqE6Hle1OVc26/c+5t59yV+ObJWQUMLvKaiYgUo7APe3/OuTTn3Bg9bFxEQo2ZBbwEo0CHXoqIhLTIU2r6Bh+FvYgIuoNWRCQsBGtffKAU9iIiaLoEEZGwEBGk4+cDpbAXEUEtexGRsBAV4p32CnsREUK/ZR/iI0tFRAITYRbwkhczq21mX5nZOjNbY2b3eeVxZjbHzNZ7Pyt75WZmI8wsycxWm1krv2P18bZfb2Z9/Mpbm9n33j4jLIA7vRT2IiIU6gPHM4AHnXPnAR2Au82sCb5pZuY55xoB8zg+7Ux3oJG3JAKjfPWxOOBJoD3QDngy8wPC2ybRb79u+VVKYS8igi8MA13y4pzb5pxb4b3eC6wD4oGeHH+k62TgGu91T2CK8/kGqGRmNYGuwBxvmppdwBygm7cuxjm3xDnngCl+x8qV+uxFRCiaO2jNrB7QEvg/oIZzbhv4PhDMrLq3WTywxW+3FK8sr/KUHMrzpJa9iAin1mdvZolmtsxvSTzxeGZWAd8jXAc55/bkceqcPmVcAcrzpJa9iAin9mQm59xYYGyuxzIrgy/o33bOfegVbzezml6rviawwytPAWr77Z4ApHrll5xQPt8rT8hh+zypZS8iQuFdoPVGxowH1jnnhvutmg5kjqjpA3ziV97bG5XTAUj3untmAV3MrLJ3YbYLMMtbt9fMOnjn6u13rFypZS8iAoU5T30noBfwvZmt8soeB4YB08ysP7AZuMFbNwPoASQBB4B+4Ht+iJk9Ayz1tnvaOZfmvR4ATALKAzO9JU/mu5hb+hzKyL8PSkQEIDrq9Ce2eW/l1oAz58aW8UF3C5Za9iIiaD57EZGwEKyPGwyUwl5EhNAfraKwFxFBLXsRkbAQ2lGvsBcRASBSLXsRkdAX4lmvsBcRAbAQ78hR2IuIoJa9iEhYiFDLXkQk9KllLyISBjRdgpSIX7dt4x8vPM83SxbjnKP9hR155NHHqVmrVklXTQKw/ddfmTB+HGvX/MDPP/3IoUOHmDF7HvHxCfnvXIi+nDeX0a+PZGPyBqpUqcp1199A/9vvIDIyEoBjx47x1pRJLFzwHzZsSOLQwYPUqVuPG2++hWuu/SMREaF+X+lxEaGd9SF/h3BQOnjwILff1oeNG5N55rkXGDrsRTZv2sRfbuvNgQMHSrp6EoDNmzcxe9ZMYmJiaNmqTYnUYfGihTw46B6aNTuf10aP45ZbezNuzChGvHJ8ivXDhw4xbswoGjY6h78PeZpX/vU6bdu15+knn+CV4f8okXqXFDuFf4KRWval0IfvTyMlZQuffPYFderWBaDROY25ukdX3p/2Hr379ivhGkp+Wrdpy1cLvgbgw/f/zZKvFxXq8fv37UWtWvE889ywXLd59eV/0rJVa/7+1DMAtGvfgQMH9jNuzGh69e5L1WrVKBcdzedfzCW2UqWs/dp3uJA9e9J59+23uGvgfURHRxdq3UurEO/FUcu+NJr/1Zc0b35BVtADJCTUpkXLVsz/al4J1kwCFWj3x65daTz79JNcfulFtGnRjJ5XduP9ae+d9vl/3baNn35cxxVXXp2t/Mqre5KRcZRFixYAEBkZmS3oMzVrdj5Hjhxh965dp12XYKGWvRS7DUlJXNL5spPKzz67IXNmf1ECNZKisG/fPvrcejOHDx1mwF33EJ+QwNeLFzL0mSEcOXqEW/7cq8DH3pC0HoCGjRplK09IqE10+fIkb0jKc/9ly5ZSMSaGqtWqFbgOwSbU++wV9qVQeno6MTExJ5XHxsayZ09eD6mXYPL2m5PZlprK+x9/St269QDocGFH9u7Zy5jXR/KnG28mKsr3v2hGRka2fZ1zOOeylZtZ1oXX9PR0gBz/jmJiYrLW52TxooXM/mImd99zX9b5w4FG4xQyM+vnnJtY3OcNNjlNt6rnNIaWrxct5PzmFxAfn5AttDt2+h0ffvBvkjckcU7jc9m6NYUeXU7+precpXw6/eOs97VqxTNzzpcAOO+vJce/ozweRbohKYnBDz9Im7bt6Nf/9gL/bsEotKO+ZFr2TwE5hr2ZJQKJACNfH0P/2xOLs16lRkxszi2vPbm0+CU4paWlsXnzJlpf0DTH9bt37wagerXqvPPe+9nWPfPUk1SrVp0777o7q6xs2bJZr2NjYwFy/Dvau3dv1np/KVu2cMft/YhPSOCVf70eVq16UMu+QMxsdW6rgBq57eecGwuMhfB+4PjZZzfM6nP1l5y8gQZnNyyBGklRiK1UiRZxcTzy2F9zXF+vfn0AypQtS9Nm52dbd+aZZ1KpUqWTyjOdfbavr35DUhIXtGiZVb51awqHDh486e9o+6+/cnv/PlSoUIHXx7xBhQoVCvx7BavQjvqia9nXALoCJ17KN+DrIjpnyLjk0s4Mf+lFUrZsIaF2bcD3P+mqlSu49/4HS7h2Ulg6/e4i3n37Lc6qWYsqVaoU6rFr1qpF48bn8vln07nu+huyyj//dDpRUWX43e8uzipLS0sj8S99ARgzbiJxcXGFWpegEeJpX1Rh/xlQwTm36sQVZja/iM4ZMq67/k9Mfedt7rvnLgbeex+G8dq/XqXGWWdxww03lnT1JEBzZvlGTq1d+wMAixcuoHLlOCrHxdGmbTtu7d2XWV/MoF/vW7i1d1/q1avPwYMH+WVjMiuWL+PVkaNO6/z3DHqAe+66g6eH/J3uPa7gx3XrGDdmFH/u1StrlM2hQ4cYkNif1K1beeqZ59i+/Ve2b/816xgNzm4YNq38UO/Gsbwu1pSkcO7GAdiWmpp9uoQOF/Lw4MeL/XZ7KbgLmjbOsbxN23aMn/Qm4LsOM2bUa3z55Vx2bN9BxZiK1KtXn8v/0IVbe/fN9diB3FQFMHfObMa8PpKNG5OpUqUq1/7xem6/Y0DWqJ3cLv5memPiFNq2a5/Pb1ryoqNOv12+NDk94Mxp2yA26D4ZFPYiEvQKJew3nkLY1w++sA+vy+0iIrkI1jtjA6WwFxEh9OfGUdiLiBDyg3EU9iIikPPdxqFEYS8igrpxRETCQohnveazFxEBfGkf6JLfocwmmNkOM/vBryzOzOaY2XrvZ2Wv3MxshJklmdlqM2vlt08fb/v1ZtbHr7y1mX3v7TPCAuiDUtiLiFDoDy+ZBHQ7oWwwMM/4TRUwAAAGPUlEQVQ51wiY570H6A408pZEYBT4PhyAJ4H2QDvgycwPCG+bRL/9TjzXSRT2IiL4+uwDXfLjnFsApJ1Q3BOY7L2eDFzjVz7F+XwDVDKzmvjmF5vjnEtzzu0C5gDdvHUxzrklzndX7BS/Y+VKYS8iwqmFvZklmtkyvyWQ+dhrOOe2AXg/q3vl8cAWv+1SvLK8ylNyKM+TLtCKiHBqd9D6T8deKKfO4RQFKM+TWvYiIhRuN04utntdMHg/d3jlKUBtv+0SgNR8yhNyKM+Twl5EhEIdjJOb6UDmiJo+wCd+5b29UTkdgHSvm2cW0MXMKnsXZrsAs7x1e82sgzcKp7ffsXKlbhwRESjUgfZm9i5wCVDVzFLwjaoZBkwzs/7AZiDzqTIzgB5AEnAA6AfgnEszs2eApd52TzvnMi/6DsA34qc8MNNb8q6TpjgWkWBXGFMc//TrgYAzp/FZZwTdPVhq2YuIEPp30CrsRUQg5NNeYS8igh5eIiISFjTrpYhIGAjxrFfYi4iAHl4iIhIWQjzrFfYiIqBuHBGR8BDiaa+wFxFBQy9FRMKC+uxFRMJAhMJeRCQchHbaK+xFRFA3johIWAjxrFfYi4iAWvYiImFB0yWIiISB0I56hb2ICKBuHBGRsKA7aEVEwkFoZ73CXkQEQj7rFfYiIgARId5pr7AXESH0L9BGlHQFRESk6KllLyJC6LfsFfYiImjopYhIWFDLXkQkDCjsRUTCQKh342g0jogIvpZ9oEv+x7JuZvaTmSWZ2eCir33+FPYiIvjuoA10yfM4ZpHAa0B3oAlws5k1KaJqB0xhLyIChZf20A5Ics4lO+eOAFOBnkVT6cCpz15EhEKdLiEe2OL3PgVoX1gHL6hSG/bRUSF+teQUmFmic25sSddDShf9XRSuU8kcM0sEEv2Kxvr9t8jpOO506lYY1I0THBLz30TCkP4uSohzbqxzro3f4v+hmwLU9nufAKQWbw1PprAXESlcS4FGZlbfzMoCNwHTS7hOpbcbR0QkGDnnMsxsIDALiAQmOOfWlHC1FPZBQv2ykhP9XZRSzrkZwIySroc/c67ErxuIiEgRU5+9iEgYUNiXcqXxtmspWWY2wcx2mNkPJV0XCR4K+1KstN52LSVuEtCtpCshwUVhX7qVytuupWQ55xYAaSVdDwkuCvvSLafbruNLqC4iEsQU9qVbqbztWkSCj8K+dCuVt12LSPBR2JdupfK2axEJPgr7Usw5lwFk3na9DphWGm67lpJlZu8CS4DGZpZiZv1Luk5S+ukOWhGRMKCWvYhIGFDYi4iEAYW9iEgYUNiLiIQBhb2ISBhQ2EuhM7NjZrbKzH4ws3+b2RmncaxLzOwz7/XVec38aWaVzOyuApxjiJk9VNA6igQDhb0UhYPOuRbOuWbAEeBO/5Xmc8p/e8656c65YXlsUgk45bAXCQcKeylqC4GGZlbPzNaZ2evACqC2mXUxsyVmtsL7BlABsubw/9HMFgHXZR7IzPqa2UjvdQ0z+8jMvvOWjsAw4GzvW8U/vO0eNrOlZrbazJ7yO9ZfvecEzAUaF9u/DZESorCXImNmUfjm4v/eK2oMTHHOtQT2A38DLnfOtQKWAQ+YWTQwDrgKuAg4K5fDjwD+45y7AGgFrAEGAxu8bxUPm1kXoBG+qaJbAK3N7GIza41v6omW+D5M2hbyry5S6uiB41IUypvZKu/1QmA8UAvY5Jz7xivvgO+BLIvNDKAsvikAzgU2OufWA5jZW0BiDufoDPQGcM4dA9LNrPIJ23TxlpXe+wr4wr8i8JFz7oB3Ds03JCFPYS9F4aBzroV/gRfo+/2LgDnOuZtP2K4FhTeNswHPO+fGnHCOQYV4DpGgoG4cKSnfAJ3MrCGAmZ1hZucAPwL1zexsb7ubc9l/HjDA2zfSzGKAvfha7ZlmAbf5XQuIN7PqwALgWjMrb2YV8XUZiYQ0hb2UCOfcf4G+wLtmthpf+J/rnDuEr9vmc+8C7aZcDnEfcKmZfQ8sB5o653bi6xb6wcz+4ZybDbwDLPG2ex+o6JxbAbwHrAI+wNfVJBLSNOuliEgYUMteRCQMKOxFRMKAwl5EJAwo7EVEwoDCXkQkDCjsRUTCgMJeRCQMKOxFRMLA/wNCnk3mvXeJRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cm = pd.DataFrame(confusion_matrix(Y_validation, predictions), columns=np.unique(Y_validation), index = np.unique(Y_validation))\n",
    "df_cm.index.name = 'Actual'\n",
    "df_cm.columns.name = 'Predicted'\n",
    "sns.heatmap(df_cm, cmap=\"Blues\", annot=True,annot_kws={\"size\": 16});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The performance of the model on the test set is highly satisfactory, as it demonstrates superior results with no instances of fraud being missed."
   ]
  }
 ],
 "metadata": {
  "_change_revision": 206,
  "_is_fork": false,
  "authors": [
   {
    "name": "Ayoub HAIDA"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "title": "Detection of Fraudulent Transactions"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}