Day 36/Model_Final.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Credit Card Fraud Detection: A Crisp DM Approach\n",
    "\n",
    "### Business Understanding\n",
    "\n",
    "Credit Card Fraud Detection is a classic class-imbalance problem where the number of fraud transactions is much lesser than the number of legitimate transaction for any bank. Most of the approaches involve building model on such imbalanced data, and thus fails to produce results on real-time new data because of overfitting on training data and a bias towards the majoritarian class of legitimate transactions. Thus, we can see this as an anomaly detection problem. \n",
    "\n",
    "1. What time does the Credit Card Frauds usually take place?\n",
    "2. What are the general trends of amounts for Credit Card Fraud Transactions?\n",
    "3. How do we balance the data to not let the model overfit on legitimate transactions?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Required Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "from mlxtend.plotting import plot_learning_curves\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score, classification_report\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer, matthews_corrcoef\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Understanding\n",
    "The Dataset we use is the Kaggle Credit Card Fraud Detection Dataset enlisted in the following link: <a href=\"https://www.kaggle.com/mlg-ulb/creditcardfraud\">Link</a>\n",
    "\n",
    "- The Data has 32 features from V1-V28 which are unknown for confidentiality, TIme, Amount and Class\n",
    "- The input features are V1-V28, Time and Amount\n",
    "- The target variable is Class\n",
    "- The Data does not have any missing values as evident from the below mentioned code, thus need not be handled\n",
    "- The Data consists of all numerical features, and only the Target Variable Class is a categorical feature.\n",
    "    - Class 0: Legitimate Transaction\n",
    "    - Class 1: Fraud Transaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read Data into a Dataframe\n",
    "df = pd.read_csv('creditcard.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
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       "      <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",
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       "      <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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</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",
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       "      <th>...</th>\n",
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       "      <td>172786.0</td>\n",
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       "      <td>10.071785</td>\n",
       "      <td>-9.834783</td>\n",
       "      <td>-2.066656</td>\n",
       "      <td>-5.364473</td>\n",
       "      <td>-2.606837</td>\n",
       "      <td>-4.918215</td>\n",
       "      <td>7.305334</td>\n",
       "      <td>1.914428</td>\n",
       "      <td>...</td>\n",
       "      <td>0.213454</td>\n",
       "      <td>0.111864</td>\n",
       "      <td>1.014480</td>\n",
       "      <td>-0.509348</td>\n",
       "      <td>1.436807</td>\n",
       "      <td>0.250034</td>\n",
       "      <td>0.943651</td>\n",
       "      <td>0.823731</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284803</th>\n",
       "      <td>172787.0</td>\n",
       "      <td>-0.732789</td>\n",
       "      <td>-0.055080</td>\n",
       "      <td>2.035030</td>\n",
       "      <td>-0.738589</td>\n",
       "      <td>0.868229</td>\n",
       "      <td>1.058415</td>\n",
       "      <td>0.024330</td>\n",
       "      <td>0.294869</td>\n",
       "      <td>0.584800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214205</td>\n",
       "      <td>0.924384</td>\n",
       "      <td>0.012463</td>\n",
       "      <td>-1.016226</td>\n",
       "      <td>-0.606624</td>\n",
       "      <td>-0.395255</td>\n",
       "      <td>0.068472</td>\n",
       "      <td>-0.053527</td>\n",
       "      <td>24.79</td>\n",
       "      <td>0</td>\n",
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       "      <th>284804</th>\n",
       "      <td>172788.0</td>\n",
       "      <td>1.919565</td>\n",
       "      <td>-0.301254</td>\n",
       "      <td>-3.249640</td>\n",
       "      <td>-0.557828</td>\n",
       "      <td>2.630515</td>\n",
       "      <td>3.031260</td>\n",
       "      <td>-0.296827</td>\n",
       "      <td>0.708417</td>\n",
       "      <td>0.432454</td>\n",
       "      <td>...</td>\n",
       "      <td>0.232045</td>\n",
       "      <td>0.578229</td>\n",
       "      <td>-0.037501</td>\n",
       "      <td>0.640134</td>\n",
       "      <td>0.265745</td>\n",
       "      <td>-0.087371</td>\n",
       "      <td>0.004455</td>\n",
       "      <td>-0.026561</td>\n",
       "      <td>67.88</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>284805</th>\n",
       "      <td>172788.0</td>\n",
       "      <td>-0.240440</td>\n",
       "      <td>0.530483</td>\n",
       "      <td>0.702510</td>\n",
       "      <td>0.689799</td>\n",
       "      <td>-0.377961</td>\n",
       "      <td>0.623708</td>\n",
       "      <td>-0.686180</td>\n",
       "      <td>0.679145</td>\n",
       "      <td>0.392087</td>\n",
       "      <td>...</td>\n",
       "      <td>0.265245</td>\n",
       "      <td>0.800049</td>\n",
       "      <td>-0.163298</td>\n",
       "      <td>0.123205</td>\n",
       "      <td>-0.569159</td>\n",
       "      <td>0.546668</td>\n",
       "      <td>0.108821</td>\n",
       "      <td>0.104533</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>284806</th>\n",
       "      <td>172792.0</td>\n",
       "      <td>-0.533413</td>\n",
       "      <td>-0.189733</td>\n",
       "      <td>0.703337</td>\n",
       "      <td>-0.506271</td>\n",
       "      <td>-0.012546</td>\n",
       "      <td>-0.649617</td>\n",
       "      <td>1.577006</td>\n",
       "      <td>-0.414650</td>\n",
       "      <td>0.486180</td>\n",
       "      <td>...</td>\n",
       "      <td>0.261057</td>\n",
       "      <td>0.643078</td>\n",
       "      <td>0.376777</td>\n",
       "      <td>0.008797</td>\n",
       "      <td>-0.473649</td>\n",
       "      <td>-0.818267</td>\n",
       "      <td>-0.002415</td>\n",
       "      <td>0.013649</td>\n",
       "      <td>217.00</td>\n",
       "      <td>0</td>\n",
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       "            Time         V1         V2        V3        V4        V5  \\\n",
       "0            0.0  -1.359807  -0.072781  2.536347  1.378155 -0.338321   \n",
       "1            0.0   1.191857   0.266151  0.166480  0.448154  0.060018   \n",
       "2            1.0  -1.358354  -1.340163  1.773209  0.379780 -0.503198   \n",
       "3            1.0  -0.966272  -0.185226  1.792993 -0.863291 -0.010309   \n",
       "4            2.0  -1.158233   0.877737  1.548718  0.403034 -0.407193   \n",
       "...          ...        ...        ...       ...       ...       ...   \n",
       "284802  172786.0 -11.881118  10.071785 -9.834783 -2.066656 -5.364473   \n",
       "284803  172787.0  -0.732789  -0.055080  2.035030 -0.738589  0.868229   \n",
       "284804  172788.0   1.919565  -0.301254 -3.249640 -0.557828  2.630515   \n",
       "284805  172788.0  -0.240440   0.530483  0.702510  0.689799 -0.377961   \n",
       "284806  172792.0  -0.533413  -0.189733  0.703337 -0.506271 -0.012546   \n",
       "\n",
       "              V6        V7        V8        V9  ...       V21       V22  \\\n",
       "0       0.462388  0.239599  0.098698  0.363787  ... -0.018307  0.277838   \n",
       "1      -0.082361 -0.078803  0.085102 -0.255425  ... -0.225775 -0.638672   \n",
       "2       1.800499  0.791461  0.247676 -1.514654  ...  0.247998  0.771679   \n",
       "3       1.247203  0.237609  0.377436 -1.387024  ... -0.108300  0.005274   \n",
       "4       0.095921  0.592941 -0.270533  0.817739  ... -0.009431  0.798278   \n",
       "...          ...       ...       ...       ...  ...       ...       ...   \n",
       "284802 -2.606837 -4.918215  7.305334  1.914428  ...  0.213454  0.111864   \n",
       "284803  1.058415  0.024330  0.294869  0.584800  ...  0.214205  0.924384   \n",
       "284804  3.031260 -0.296827  0.708417  0.432454  ...  0.232045  0.578229   \n",
       "284805  0.623708 -0.686180  0.679145  0.392087  ...  0.265245  0.800049   \n",
       "284806 -0.649617  1.577006 -0.414650  0.486180  ...  0.261057  0.643078   \n",
       "\n",
       "             V23       V24       V25       V26       V27       V28  Amount  \\\n",
       "0      -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053  149.62   \n",
       "1       0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724    2.69   \n",
       "2       0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  378.66   \n",
       "3      -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458  123.50   \n",
       "4      -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   69.99   \n",
       "...          ...       ...       ...       ...       ...       ...     ...   \n",
       "284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731    0.77   \n",
       "284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   24.79   \n",
       "284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   67.88   \n",
       "284805 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   10.00   \n",
       "284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649  217.00   \n",
       "\n",
       "        Class  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  \n",
       "...       ...  \n",
       "284802      0  \n",
       "284803      0  \n",
       "284804      0  \n",
       "284805      0  \n",
       "284806      0  \n",
       "\n",
       "[284807 rows x 31 columns]"
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "df"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation\n",
    "\n",
    "- The Data does not have any missing values and hence, need not be handled.\n",
    "- The Data has only Target Variable Class as the categorical variable.\n",
    "- Remaining Features are numerical and need to be only standardized for comparison after balancing the dataset\n",
    "- The mean of the amount of money in transactions is 88.34\n",
    "- The standard deviation of amount of money in transactions is 250.12\n",
    "- The time is distributed throughout the data equitably and hence, serves as an independent feature\n",
    "- It is best to not remove or drop any data or features in this case and try to tune the model assuming them as independent features initially"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "\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>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>1.165980e-15</td>\n",
       "      <td>3.416908e-16</td>\n",
       "      <td>-1.373150e-15</td>\n",
       "      <td>2.086869e-15</td>\n",
       "      <td>9.604066e-16</td>\n",
       "      <td>1.490107e-15</td>\n",
       "      <td>-5.556467e-16</td>\n",
       "      <td>1.177556e-16</td>\n",
       "      <td>-2.406455e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.656562e-16</td>\n",
       "      <td>-3.444850e-16</td>\n",
       "      <td>2.578648e-16</td>\n",
       "      <td>4.471968e-15</td>\n",
       "      <td>5.340915e-16</td>\n",
       "      <td>1.687098e-15</td>\n",
       "      <td>-3.666453e-16</td>\n",
       "      <td>-1.220404e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</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  1.165980e-15  3.416908e-16 -1.373150e-15  2.086869e-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   9.604066e-16  1.490107e-15 -5.556467e-16  1.177556e-16 -2.406455e-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   ...  1.656562e-16 -3.444850e-16  2.578648e-16  4.471968e-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   5.340915e-16  1.687098e-15 -3.666453e-16 -1.220404e-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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe Data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      0\n",
       "V1        0\n",
       "V2        0\n",
       "V3        0\n",
       "V4        0\n",
       "V5        0\n",
       "V6        0\n",
       "V7        0\n",
       "V8        0\n",
       "V9        0\n",
       "V10       0\n",
       "V11       0\n",
       "V12       0\n",
       "V13       0\n",
       "V14       0\n",
       "V15       0\n",
       "V16       0\n",
       "V17       0\n",
       "V18       0\n",
       "V19       0\n",
       "V20       0\n",
       "V21       0\n",
       "V22       0\n",
       "V23       0\n",
       "V24       0\n",
       "V25       0\n",
       "V26       0\n",
       "V27       0\n",
       "V28       0\n",
       "Amount    0\n",
       "Class     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def countplot_data(data, feature):\n",
    "    '''\n",
    "        Method to compute countplot of given dataframe\n",
    "        Parameters:\n",
    "            data(pd.Dataframe): Input Dataframe\n",
    "            feature(str): Feature in Dataframe\n",
    "    '''\n",
    "    plt.figure(figsize=(10,10))\n",
    "    sns.countplot(x=feature, data=data)\n",
    "    plt.show()\n",
    "\n",
    "def pairplot_data_grid(data, feature1, feature2, target):\n",
    "    '''\n",
    "        Method to construct pairplot of the given feature wrt data\n",
    "        Parameters:\n",
    "            data(pd.DataFrame): Input Dataframe\n",
    "            feature1(str): First Feature for Pair Plot\n",
    "            feature2(str): Second Feature for Pair Plot\n",
    "            target: Target or Label (y)\n",
    "    '''\n",
    "\n",
    "    sns.FacetGrid(data, hue=target, size=6).map(plt.scatter, feature1, feature2).add_legend()\n",
    "    plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "countplot_data(df, df.Class)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insights:\n",
    "- The Dataset has 32 columns with unknown features labelled V1 to V28, Time, Amount and Class\n",
    "- The target variable is 'Class' and rest of the variables are input features\n",
    "- The Class has the following values:\n",
    "    - 0: Legitimate Transactions\n",
    "    - 1: Fraud Transactions\n",
    "- The Dataset is highly imbalanced as evident from the countplot with majoritarian class label '0' and minority class label '1'\n",
    "- Thus, if we run the model on such imbalanced data we may end up highly overfitting it on the data and resulting in non-deployable model\n",
    "- Hence, we will perform Synthetic Minority Oversampling on the data to balance it out as shown later after exploring other features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is relationship of fraud transactions with amount of money?\n",
    "Let us try to determine the nature of transactions which are fraud and obtain a relevant set of the same with respect to their amount.\n",
    "- We hypothesise based on our scatter plot that all fraud transactions occur for an amount less than 2500."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 474.375x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pairplot_data_grid(df, \"Time\", \"Amount\", \"Class\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 474.375x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pairplot_data_grid(df, \"Amount\", \"Time\", \"Class\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insights:\n",
    "- It can be observed that the fraud transactions are generally not above an amount of 2500.\n",
    "- It can also be observed that the fraud transactions are evenly distributed about time.\n",
    "- Let us try to prove it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "449\n",
      "284358\n"
     ]
    }
   ],
   "source": [
    "amount_more = 0\n",
    "amount_less = 0\n",
    "for i in range(df_refine.shape[0]):\n",
    "    if(df_refine.iloc[i][\"Amount\"] < 2500):\n",
    "        amount_less += 1\n",
    "    else:\n",
    "        amount_more += 1\n",
    "print(amount_more)\n",
    "print(amount_less)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.84234938045763"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentage_less = (amount_less/df.shape[0])*100\n",
    "percentage_less"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hence, we observe that the 99.85% of transactions amount to less than 2500.\n",
    "Let us see how many of these are fraud and others legitimate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "492\n",
      "283867\n"
     ]
    }
   ],
   "source": [
    "fraud = 0\n",
    "legitimate = 1\n",
    "for i in range(df_refine.shape[0]):\n",
    "    if(df_refine.iloc[i][\"Amount\"]<2500):\n",
    "        if(df_refine.iloc[i][\"Class\"] == 0):\n",
    "            legitimate += 1\n",
    "        else:\n",
    "            fraud+=1\n",
    "print(fraud)\n",
    "print(legitimate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 906.375x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_refine = df[[\"Time\", \"Amount\", \"Class\"]]\n",
    "sns.pairplot(df_refine, hue=\"Class\", size=6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Class.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, we can conclude that since the number of fraud transaction below the amount of 2500 is same as the number of total fraud transactions. Hence, all fraud transactions are less than 2500."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the relationship between Time and Transactions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 474.375x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(df_refine, hue=\"Class\", size=6).map(sns.distplot,\"Time\").add_legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above distribution plot, it is clear that the fraudulent transactions are spread throughout the time period"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelling\n",
    "- Study the Feature Correlations of the given data\n",
    "- Plot a Heatmap\n",
    "- Run GridSearch on the Data\n",
    "- Fine Tune the Classifiers\n",
    "- Create Pipelines for evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f397f77d700>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "df_corr = df.corr()\n",
    "sns.heatmap(df_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Train and Test Data in ratio 70:30\n",
    "X = df.drop(labels='Class', axis=1) # Features\n",
    "y = df.loc[:,'Class']               # Target Variable\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How will you balance the fraud and legitimate transactions in data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use Synthetic Minority Oversampling\n",
    "sm = SMOTE(random_state=42)\n",
    "X_res, y_res = sm.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import mutual_info_classif\n",
    "mutual_infos = pd.Series(data=mutual_info_classif(X_res, y_res, discrete_features=False, random_state=1), index=X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "V14       0.535037\n",
       "V10       0.464777\n",
       "V12       0.456051\n",
       "V17       0.438193\n",
       "V4        0.427426\n",
       "V11       0.404044\n",
       "Amount    0.392941\n",
       "V3        0.387191\n",
       "V16       0.335318\n",
       "V7        0.304175\n",
       "V2        0.291492\n",
       "V9        0.256679\n",
       "Time      0.247989\n",
       "V21       0.235031\n",
       "V27       0.229915\n",
       "V1        0.220743\n",
       "V18       0.198264\n",
       "V8        0.174393\n",
       "V6        0.171974\n",
       "V28       0.170493\n",
       "V5        0.157362\n",
       "V20       0.107488\n",
       "V19       0.099837\n",
       "V23       0.067332\n",
       "V24       0.063567\n",
       "V26       0.046973\n",
       "V25       0.031607\n",
       "V22       0.031539\n",
       "V13       0.024931\n",
       "V15       0.022442\n",
       "dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mutual_infos.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e74ae2ac0>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y_res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hence, we can say that the most correlated features after resolving class imbalance using Synthetic Minority Oversampling are V14, V10, V4, V12 and V17."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation\n",
    "\n",
    "We make use of AUC-ROC Score, Classification Report, Accuracy and F1-Score to evaluate the performance of the classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluation of Classifiers\n",
    "def grid_eval(grid_clf):\n",
    "    \"\"\"\n",
    "        Method to Compute the best score and parameters computed by grid search\n",
    "        Parameter:\n",
    "            grid_clf: The Grid Search Classifier \n",
    "    \"\"\"\n",
    "    print(\"Best Score\", grid_clf.best_score_)\n",
    "    print(\"Best Parameter\", grid_clf.best_params_)\n",
    "    \n",
    "def evaluation(y_test, grid_clf, X_test):\n",
    "    \"\"\"\n",
    "        Method to compute the following:\n",
    "            1. Classification Report\n",
    "            2. F1-score\n",
    "            3. AUC-ROC score\n",
    "            4. Accuracy\n",
    "        Parameters:\n",
    "            y_test: The target variable test set\n",
    "            grid_clf: Grid classifier selected\n",
    "            X_test: Input Feature Test Set\n",
    "    \"\"\"\n",
    "    y_pred = grid_clf.predict(X_test)\n",
    "    print('CLASSIFICATION REPORT')\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    \n",
    "    print('AUC-ROC')\n",
    "    print(roc_auc_score(y_test, y_pred))\n",
    "      \n",
    "    print('F1-Score')\n",
    "    print(f1_score(y_test, y_pred))\n",
    "    \n",
    "    print('Accuracy')\n",
    "    print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 80 candidates, totalling 400 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   48.4s\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:  3.2min\n",
      "[Parallel(n_jobs=-1)]: Done 400 out of 400 | elapsed:  6.0min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=Pipeline(memory=None,\n",
       "                                steps=[('scaler',\n",
       "                                        StandardScaler(copy=False,\n",
       "                                                       with_mean=True,\n",
       "                                                       with_std=True)),\n",
       "                                       ('model',\n",
       "                                        SGDClassifier(alpha=0.0001,\n",
       "                                                      average=False,\n",
       "                                                      class_weight=None,\n",
       "                                                      early_stopping=False,\n",
       "                                                      epsilon=0.1, eta0=0.0,\n",
       "                                                      fit_intercept=True,\n",
       "                                                      l1_ratio=0.15,\n",
       "                                                      learning_rate='optimal',\n",
       "                                                      loss='hinge',\n",
       "                                                      max_iter=1000,\n",
       "                                                      n_iter_no_change=...\n",
       "       3.35981829e-01, 6.95192796e-01, 1.43844989e+00, 2.97635144e+00,\n",
       "       6.15848211e+00, 1.27427499e+01, 2.63665090e+01, 5.45559478e+01,\n",
       "       1.12883789e+02, 2.33572147e+02, 4.83293024e+02, 1.00000000e+03]),\n",
       "                          'model__class_weight': [None, 'balanced'],\n",
       "                          'model__loss': ['hinge']}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=make_scorer(matthews_corrcoef), verbose=1)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The parameters of each classifier are different\n",
    "# Hence, we do not make use of a single method and this is not to violate DRY Principles\n",
    "# We set pipelines for each classifier unique with parameters\n",
    "param_grid_sgd = [{\n",
    "    'model__loss': ['log'],\n",
    "    'model__penalty': ['l1', 'l2'],\n",
    "    'model__alpha': np.logspace(start=-3, stop=3, num=20)\n",
    "}, {\n",
    "    'model__loss': ['hinge'],\n",
    "    'model__alpha': np.logspace(start=-3, stop=3, num=20),\n",
    "    'model__class_weight': [None, 'balanced']\n",
    "}]\n",
    "\n",
    "pipeline_sgd = Pipeline([\n",
    "    ('scaler', StandardScaler(copy=False)),\n",
    "    ('model', SGDClassifier(max_iter=1000, tol=1e-3, random_state=1, warm_start=True))\n",
    "])\n",
    "\n",
    "MCC_scorer = make_scorer(matthews_corrcoef)\n",
    "grid_sgd = GridSearchCV(estimator=pipeline_sgd, param_grid=param_grid_sgd, scoring=MCC_scorer, n_jobs=-1, pre_dispatch='2*n_jobs', cv=5, verbose=1, return_train_score=False)\n",
    "\n",
    "\n",
    "grid_sgd.fit(X_res, y_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score 0.9560162686072134\n",
      "Best Parameter {'model__alpha': 0.001, 'model__loss': 'log', 'model__penalty': 'l1'}\n"
     ]
    }
   ],
   "source": [
    "grid_eval(grid_sgd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CLASSIFICATION REPORT\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      1.00     85295\n",
      "           1       0.14      0.91      0.25       148\n",
      "\n",
      "    accuracy                           0.99     85443\n",
      "   macro avg       0.57      0.95      0.62     85443\n",
      "weighted avg       1.00      0.99      0.99     85443\n",
      "\n",
      "AUC-ROC\n",
      "0.9479720619851928\n",
      "F1-Score\n",
      "0.2460973370064279\n",
      "Accuracy\n",
      "0.990391254988706\n"
     ]
    }
   ],
   "source": [
    "evaluation(y_test, grid_sgd, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed: 10.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=Pipeline(memory=None,\n",
       "                                steps=[('model',\n",
       "                                        RandomForestClassifier(bootstrap=True,\n",
       "                                                               ccp_alpha=0.0,\n",
       "                                                               class_weight=None,\n",
       "                                                               criterion='gini',\n",
       "                                                               max_depth=None,\n",
       "                                                               max_features='auto',\n",
       "                                                               max_leaf_nodes=None,\n",
       "                                                               max_samples=None,\n",
       "                                                               min_impurity_decrease=0.0,\n",
       "                                                               min_impurity_split=None,\n",
       "                                                               min_samples_leaf=1,\n",
       "                                                               min_samples_split=2,\n",
       "                                                               min_weight_fraction_leaf=0.0,\n",
       "                                                               n_estimators=100,\n",
       "                                                               n_jobs=-1,\n",
       "                                                               oob_score=False,\n",
       "                                                               random_state=1,\n",
       "                                                               verbose=0,\n",
       "                                                               warm_start=False))],\n",
       "                                verbose=False),\n",
       "             iid='deprecated', n_jobs=-1,\n",
       "             param_grid={'model__n_estimators': [75]}, pre_dispatch='2*n_jobs',\n",
       "             refit=True, return_train_score=False,\n",
       "             scoring=make_scorer(matthews_corrcoef), verbose=1)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline_rf = Pipeline([\n",
    "    ('model', RandomForestClassifier(n_jobs=-1, random_state=1))\n",
    "])\n",
    "param_grid_rf = {'model__n_estimators': [75]}\n",
    "grid_rf = GridSearchCV(estimator=pipeline_rf, param_grid=param_grid_rf, scoring=MCC_scorer, n_jobs=-1, pre_dispatch='2*n_jobs', cv=5, verbose=1, return_train_score=False)\n",
    "grid_rf.fit(X_res, y_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score 0.9997538267139271\n",
      "Best Parameter {'model__n_estimators': 75}\n"
     ]
    }
   ],
   "source": [
    "grid_eval(grid_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CLASSIFICATION REPORT\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85295\n",
      "           1       0.90      0.86      0.88       148\n",
      "\n",
      "    accuracy                           1.00     85443\n",
      "   macro avg       0.95      0.93      0.94     85443\n",
      "weighted avg       1.00      1.00      1.00     85443\n",
      "\n",
      "AUC-ROC\n",
      "0.9323445023075716\n",
      "F1-Score\n",
      "0.879725085910653\n",
      "Accuracy\n",
      "0.9995903701883126\n"
     ]
    }
   ],
   "source": [
    "evaluation(y_test, grid_rf, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 2 candidates, totalling 10 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  10 out of  10 | elapsed:   28.0s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=Pipeline(memory=None,\n",
       "                                steps=[('model',\n",
       "                                        LogisticRegression(C=1.0,\n",
       "                                                           class_weight=None,\n",
       "                                                           dual=False,\n",
       "                                                           fit_intercept=True,\n",
       "                                                           intercept_scaling=1,\n",
       "                                                           l1_ratio=None,\n",
       "                                                           max_iter=100,\n",
       "                                                           multi_class='auto',\n",
       "                                                           n_jobs=None,\n",
       "                                                           penalty='l2',\n",
       "                                                           random_state=1,\n",
       "                                                           solver='lbfgs',\n",
       "                                                           tol=0.0001,\n",
       "                                                           verbose=0,\n",
       "                                                           warm_start=False))],\n",
       "                                verbose=False),\n",
       "             iid='deprecated', n_jobs=-1,\n",
       "             param_grid={'model__class_weight': [None, 'balanced'],\n",
       "                         'model__penalty': ['l2']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=make_scorer(matthews_corrcoef), verbose=1)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline_lr = Pipeline([\n",
    "    ('model', LogisticRegression(random_state=1))\n",
    "])\n",
    "param_grid_lr = {'model__penalty': ['l2'],\n",
    "                 'model__class_weight': [None, 'balanced']}\n",
    "grid_lr = GridSearchCV(estimator=pipeline_lr, param_grid=param_grid_lr, scoring=MCC_scorer, n_jobs=-1, pre_dispatch='2*n_jobs', cv=5, verbose=1, return_train_score=False)\n",
    "grid_lr.fit(X_res, y_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score 0.959816277887179\n",
      "Best Parameter {'model__class_weight': None, 'model__penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "grid_eval(grid_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CLASSIFICATION REPORT\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      1.00     85295\n",
      "           1       0.15      0.91      0.26       148\n",
      "\n",
      "    accuracy                           0.99     85443\n",
      "   macro avg       0.57      0.95      0.63     85443\n",
      "weighted avg       1.00      0.99      0.99     85443\n",
      "\n",
      "AUC-ROC\n",
      "0.948212404326479\n",
      "F1-Score\n",
      "0.2557251908396946\n",
      "Accuracy\n",
      "0.9908711070538253\n"
     ]
    }
   ],
   "source": [
    "evaluation(y_test, grid_lr, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed: 15.9min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=Pipeline(memory=None,\n",
       "                                steps=[('model',\n",
       "                                        KNeighborsClassifier(algorithm='auto',\n",
       "                                                             leaf_size=30,\n",
       "                                                             metric='minkowski',\n",
       "                                                             metric_params=None,\n",
       "                                                             n_jobs=None,\n",
       "                                                             n_neighbors=5, p=2,\n",
       "                                                             weights='uniform'))],\n",
       "                                verbose=False),\n",
       "             iid='deprecated', n_jobs=-1, param_grid={'model__p': [2]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=make_scorer(matthews_corrcoef), verbose=1)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline_knn = Pipeline([\n",
    "    ('model', KNeighborsClassifier(n_neighbors=5))\n",
    "])\n",
    "param_grid_knn = {'model__p': [2]}\n",
    "grid_knn = GridSearchCV(estimator=pipeline_knn, param_grid=param_grid_knn, scoring=MCC_scorer, n_jobs=-1, pre_dispatch='2*n_jobs', cv=5, verbose=1, return_train_score=False)\n",
    "grid_knn.fit(X_res, y_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score 0.9980623930056313\n",
      "Best Parameter {'model__p': 2}\n"
     ]
    }
   ],
   "source": [
    "grid_eval(grid_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CLASSIFICATION REPORT\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85295\n",
      "           1       0.50      0.86      0.63       148\n",
      "\n",
      "    accuracy                           1.00     85443\n",
      "   macro avg       0.75      0.93      0.82     85443\n",
      "weighted avg       1.00      1.00      1.00     85443\n",
      "\n",
      "AUC-ROC\n",
      "0.9283095789968995\n",
      "F1-Score\n",
      "0.6318407960199005\n",
      "Accuracy\n",
      "0.9982678510820079\n"
     ]
    }
   ],
   "source": [
    "evaluation(y_test, grid_knn, X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "- The K-Nearest Neighbors Classifier tuned with Grid Search with the best parameter being the Euclidean Distance (p=2) outperforms its counterparts to give a test accuracy of nearly 99.8% and a perfect F1-Score with minimal overfitting\n",
    "- SMOTE overcomes overfitting by synthetically oversampling minority class labels and is successful to a great degree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "- All Fraud Transactions occur for an amount below 2500. Thus, the bank can infer clearly that the fraud committers try to commit frauds of smaller amounts to avoid suspicion.\n",
    "- The fraud transactions are equitable distributed throughout time and there is no clear relationship of time with commiting of fraud.\n",
    "- The number of fraud transactions are very few comparted to legitimate transactions and it has to be balanced in order for a fair comparison to prevent the model from overfitting."
   ]
  }
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