AI-ML-Project.ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":["# Importing the necessary libraries "]},{"cell_type":"code","execution_count":2,"metadata":{"_cell_guid":"95f30dd3-7b08-4087-b156-5bf3250ce742","_uuid":"dc31e50ac68836610ff16246bbda63d7ae6db49b","execution":{"iopub.execute_input":"2023-10-09T21:46:21.916502Z","iopub.status.busy":"2023-10-09T21:46:21.916117Z","iopub.status.idle":"2023-10-09T21:46:23.489698Z","shell.execute_reply":"2023-10-09T21:46:23.488483Z","shell.execute_reply.started":"2023-10-09T21:46:21.916475Z"},"trusted":true},"outputs":[],"source":["#Importing librairies\n","\n","import pandas as pd \n","import numpy as np\n","\n","# Scikit-learn library: For SVM\n","from sklearn import preprocessing\n","from sklearn.metrics import confusion_matrix\n","from sklearn import svm\n","\n","import itertools\n","\n","# Matplotlib library to plot the charts\n","import matplotlib.pyplot as plt\n","import matplotlib.mlab as mlab\n","\n","# Library for the statistic data vizualisation\n","import seaborn\n","\n","%matplotlib inline\n","\n"]},{"cell_type":"markdown","metadata":{"_cell_guid":"4c375090-4893-4b92-ad94-abb3fbc60e22","_uuid":"504f7342684d5bb9d64acbd7f48c89c2846e69a9"},"source":["# Data recuperation"]},{"cell_type":"code","execution_count":3,"metadata":{"_cell_guid":"b3cd20ab-f13e-4083-9c36-9291414694c2","_uuid":"232bc475856a4f5e92083e1757242020100ee284","execution":{"iopub.execute_input":"2023-10-09T21:46:33.068487Z","iopub.status.busy":"2023-10-09T21:46:33.067983Z","iopub.status.idle":"2023-10-09T21:46:38.452928Z","shell.execute_reply":"2023-10-09T21:46:38.451527Z","shell.execute_reply.started":"2023-10-09T21:46:33.068459Z"},"trusted":true},"outputs":[],"source":["data = pd.read_csv('../input/creditcard.csv') # Reading the file .csv\n","df = pd.DataFrame(data) # Converting data to Panda DataFrame"]},{"cell_type":"markdown","metadata":{"_cell_guid":"ae7d0f5e-f323-46f6-a454-a2a8065af693","_uuid":"dda60fcd5cb9953205c5ae716fcdf7cd5682f9eb"},"source":["# Data Visualization"]},{"cell_type":"code","execution_count":4,"metadata":{"_cell_guid":"baeaea24-4736-473a-a85c-cbc3ddde9ed3","_uuid":"9d450721809ddf6aa0754b705062bfdf837d9c54","execution":{"iopub.execute_input":"2023-10-09T21:46:38.456041Z","iopub.status.busy":"2023-10-09T21:46:38.455600Z","iopub.status.idle":"2023-10-09T21:46:38.562675Z","shell.execute_reply":"2023-10-09T21:46:38.561122Z","shell.execute_reply.started":"2023-10-09T21:46:38.456004Z"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Time</th>\n","      <th>V1</th>\n","      <th>V2</th>\n","      <th>V3</th>\n","      <th>V4</th>\n","      <th>V5</th>\n","      <th>V6</th>\n","      <th>V7</th>\n","      <th>V8</th>\n","      <th>V9</th>\n","      <th>...</th>\n","      <th>V21</th>\n","      <th>V22</th>\n","      <th>V23</th>\n","      <th>V24</th>\n","      <th>V25</th>\n","      <th>V26</th>\n","      <th>V27</th>\n","      <th>V28</th>\n","      <th>Amount</th>\n","      <th>Class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.0</td>\n","      <td>-1.359807</td>\n","      <td>-0.072781</td>\n","      <td>2.536347</td>\n","      <td>1.378155</td>\n","      <td>-0.338321</td>\n","      <td>0.462388</td>\n","      <td>0.239599</td>\n","      <td>0.098698</td>\n","      <td>0.363787</td>\n","      <td>...</td>\n","      <td>-0.018307</td>\n","      <td>0.277838</td>\n","      <td>-0.110474</td>\n","      <td>0.066928</td>\n","      <td>0.128539</td>\n","      <td>-0.189115</td>\n","      <td>0.133558</td>\n","      <td>-0.021053</td>\n","      <td>149.62</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.0</td>\n","      <td>1.191857</td>\n","      <td>0.266151</td>\n","      <td>0.166480</td>\n","      <td>0.448154</td>\n","      <td>0.060018</td>\n","      <td>-0.082361</td>\n","      <td>-0.078803</td>\n","      <td>0.085102</td>\n","      <td>-0.255425</td>\n","      <td>...</td>\n","      <td>-0.225775</td>\n","      <td>-0.638672</td>\n","      <td>0.101288</td>\n","      <td>-0.339846</td>\n","      <td>0.167170</td>\n","      <td>0.125895</td>\n","      <td>-0.008983</td>\n","      <td>0.014724</td>\n","      <td>2.69</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1.0</td>\n","      <td>-1.358354</td>\n","      <td>-1.340163</td>\n","      <td>1.773209</td>\n","      <td>0.379780</td>\n","      <td>-0.503198</td>\n","      <td>1.800499</td>\n","      <td>0.791461</td>\n","      <td>0.247676</td>\n","      <td>-1.514654</td>\n","      <td>...</td>\n","      <td>0.247998</td>\n","      <td>0.771679</td>\n","      <td>0.909412</td>\n","      <td>-0.689281</td>\n","      <td>-0.327642</td>\n","      <td>-0.139097</td>\n","      <td>-0.055353</td>\n","      <td>-0.059752</td>\n","      <td>378.66</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1.0</td>\n","      <td>-0.966272</td>\n","      <td>-0.185226</td>\n","      <td>1.792993</td>\n","      <td>-0.863291</td>\n","      <td>-0.010309</td>\n","      <td>1.247203</td>\n","      <td>0.237609</td>\n","      <td>0.377436</td>\n","      <td>-1.387024</td>\n","      <td>...</td>\n","      <td>-0.108300</td>\n","      <td>0.005274</td>\n","      <td>-0.190321</td>\n","      <td>-1.175575</td>\n","      <td>0.647376</td>\n","      <td>-0.221929</td>\n","      <td>0.062723</td>\n","      <td>0.061458</td>\n","      <td>123.50</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2.0</td>\n","      <td>-1.158233</td>\n","      <td>0.877737</td>\n","      <td>1.548718</td>\n","      <td>0.403034</td>\n","      <td>-0.407193</td>\n","      <td>0.095921</td>\n","      <td>0.592941</td>\n","      <td>-0.270533</td>\n","      <td>0.817739</td>\n","      <td>...</td>\n","      <td>-0.009431</td>\n","      <td>0.798278</td>\n","      <td>-0.137458</td>\n","      <td>0.141267</td>\n","      <td>-0.206010</td>\n","      <td>0.502292</td>\n","      <td>0.219422</td>\n","      <td>0.215153</td>\n","      <td>69.99</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>284802</th>\n","      <td>172786.0</td>\n","      <td>-11.881118</td>\n","      <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","    </tr>\n","    <tr>\n","      <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","    </tr>\n","    <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","    </tr>\n","    <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","    </tr>\n","  </tbody>\n","</table>\n","<p>284807 rows × 31 columns</p>\n","</div>"],"text/plain":["            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]"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["df = pd.DataFrame(data) # Converting data to Panda DataFrame\n","df"]},{"cell_type":"code","execution_count":5,"metadata":{"_cell_guid":"6aade7ce-2b87-4cd0-a998-3a54ac14d4df","_uuid":"4eafc2bb6228637b51ba4e1c97ded7456440cd0f","execution":{"iopub.execute_input":"2023-10-09T21:46:38.564768Z","iopub.status.busy":"2023-10-09T21:46:38.564323Z","iopub.status.idle":"2023-10-09T21:46:39.089886Z","shell.execute_reply":"2023-10-09T21:46:39.088404Z","shell.execute_reply.started":"2023-10-09T21:46:38.564728Z"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Time</th>\n","      <th>V1</th>\n","      <th>V2</th>\n","      <th>V3</th>\n","      <th>V4</th>\n","      <th>V5</th>\n","      <th>V6</th>\n","      <th>V7</th>\n","      <th>V8</th>\n","      <th>V9</th>\n","      <th>...</th>\n","      <th>V21</th>\n","      <th>V22</th>\n","      <th>V23</th>\n","      <th>V24</th>\n","      <th>V25</th>\n","      <th>V26</th>\n","      <th>V27</th>\n","      <th>V28</th>\n","      <th>Amount</th>\n","      <th>Class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>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>1.168375e-15</td>\n","      <td>3.416908e-16</td>\n","      <td>-1.379537e-15</td>\n","      <td>2.074095e-15</td>\n","      <td>9.604066e-16</td>\n","      <td>1.487313e-15</td>\n","      <td>-5.556467e-16</td>\n","      <td>1.213481e-16</td>\n","      <td>-2.406331e-15</td>\n","      <td>...</td>\n","      <td>1.654067e-16</td>\n","      <td>-3.568593e-16</td>\n","      <td>2.578648e-16</td>\n","      <td>4.473266e-15</td>\n","      <td>5.340915e-16</td>\n","      <td>1.683437e-15</td>\n","      <td>-3.660091e-16</td>\n","      <td>-1.227390e-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  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-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  1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-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   ...  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-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  1.683437e-15 -3.660091e-16 -1.227390e-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":5,"metadata":{},"output_type":"execute_result"}],"source":["df.describe() # Description of statistic features (Sum, Average, Variance, minimum, 1st quartile, 2nd quartile, 3rd Quartile and Maximum)"]},{"cell_type":"code","execution_count":46,"metadata":{"_cell_guid":"aaf455b3-f3a1-4316-99c6-cbd4c0a8b33d","_uuid":"346c8874f9118fcbcea8f028804179eed54eca69","execution":{"iopub.execute_input":"2023-10-09T22:13:04.593529Z","iopub.status.busy":"2023-10-09T22:13:04.591745Z","iopub.status.idle":"2023-10-09T22:13:04.975025Z","shell.execute_reply":"2023-10-09T22:13:04.974235Z","shell.execute_reply.started":"2023-10-09T22:13:04.593472Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["df_fraud = df[df['Class'] == 1] # Recovery of fraud data\n","plt.figure(figsize=(10,5))\n","plt.scatter(df_fraud['Time'], df_fraud['Amount']) # Display fraud amounts according to their time\n","plt.title('Scratter plot amount fraud')\n","plt.xlabel('Time')\n","plt.ylabel('Amount')\n","plt.xlim([0,175000])\n","plt.ylim([0,2500])\n","plt.show()"]},{"cell_type":"markdown","metadata":{"_cell_guid":"970cef6c-01b8-4d61-a3b5-38c3398f9c6f","_uuid":"61c0ec8f7ae47ca9514c2cf3d1e729ba7c528c4e"},"source":["We notive, first of all, the time doesn't impact the frequency of frauds. Moreover, the majority of frauds are small amounts."]},{"cell_type":"code","execution_count":7,"metadata":{"_cell_guid":"8b77e909-ab26-4783-8005-bf591d66cf5b","_uuid":"9a273b886643735328b0666edf70d12807804fe1","execution":{"iopub.execute_input":"2023-10-09T21:47:08.659336Z","iopub.status.busy":"2023-10-09T21:47:08.658659Z","iopub.status.idle":"2023-10-09T21:47:08.669821Z","shell.execute_reply":"2023-10-09T21:47:08.667741Z","shell.execute_reply.started":"2023-10-09T21:47:08.659295Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["There are only 9 frauds where the amount was bigger than 1000 over 492 frauds\n"]}],"source":["nb_big_fraud = df_fraud[df_fraud['Amount'] > 1000].shape[0] # Recovery of frauds over 1000\n","print('There are only '+ str(nb_big_fraud) + ' frauds where the amount was bigger than 1000 over ' + str(df_fraud.shape[0]) + ' frauds')"]},{"cell_type":"markdown","metadata":{"_cell_guid":"43c05bc7-c327-4120-9a4b-9b319457a0b5","_uuid":"c5b9104e688e7bcee494645969a7c8360aff515e"},"source":["**Unbalanced data**"]},{"cell_type":"code","execution_count":8,"metadata":{"_cell_guid":"02332e68-9fec-48c5-89cd-06ce360f2e9c","_uuid":"bd071ed3ed6dcf625adc45196f615d62cd462ab1","execution":{"iopub.execute_input":"2023-10-09T21:47:11.468733Z","iopub.status.busy":"2023-10-09T21:47:11.467831Z","iopub.status.idle":"2023-10-09T21:47:11.526012Z","shell.execute_reply":"2023-10-09T21:47:11.524930Z","shell.execute_reply.started":"2023-10-09T21:47:11.468699Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["There are only 492 frauds in the original dataset, even though there are 284315 no frauds in the dataset.\n"]}],"source":["number_fraud = len(data[data.Class == 1])\n","number_no_fraud = len(data[data.Class == 0])\n","print('There are only '+ str(number_fraud) + ' frauds in the original dataset, even though there are ' + str(number_no_fraud) +' no frauds in the dataset.')"]},{"cell_type":"markdown","metadata":{"_cell_guid":"a78cfc34-d5a4-45b7-abc1-c70dec7e1c9b","_uuid":"0fa59cb4bb66a0c62d3edb6eb2650d64e19fc113"},"source":["This dataset is unbalanced which means using the data as it is might result in unwanted behaviour from a supervised classifier.\n","To make it easy to understand if a classifier were to train with this data set trying to achieve the best accuracy possible it would most likely label every transaction as a non-fraud"]},{"cell_type":"code","execution_count":9,"metadata":{"_cell_guid":"32763345-e2ac-455b-9265-50bcc304da07","_uuid":"5139011572ad26820d148f408fb3d839899331b5","execution":{"iopub.execute_input":"2023-10-09T21:53:44.607501Z","iopub.status.busy":"2023-10-09T21:53:44.606804Z","iopub.status.idle":"2023-10-09T21:53:44.614795Z","shell.execute_reply":"2023-10-09T21:53:44.613355Z","shell.execute_reply.started":"2023-10-09T21:53:44.607458Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["The accuracy of the classifier then would be : 0.998269524998681 which is the number of good classification over the number of tuple to classify\n"]}],"source":["print(\"The accuracy of the classifier then would be : \"+ str((284315-492)/284315)+ \" which is the number of good classification over the number of tuple to classify\")"]},{"cell_type":"markdown","metadata":{"_cell_guid":"527f2804-0aa7-4220-aa9b-03f6b8c0a9fe","_uuid":"8a4df6becaba62481d52118f944612c8c579b8e7"},"source":["To answer this problem we could use the oversampling principle or the undersampling principle\n","The undersampling principle should be used only if we can be sure that the selected few tuples (in this case non-fraud) are representative of the whole non-fraud transactions of the dataset."]},{"cell_type":"markdown","metadata":{"_cell_guid":"934444aa-0a22-4b60-b363-c4af3ab8ffe9","_uuid":"ce9fa83195b843375a102593ee1ece0b9efdad97"},"source":["# Correlation of features"]},{"cell_type":"code","execution_count":10,"metadata":{"_cell_guid":"a575fa45-aa54-42d8-bc41-7901109b0a30","_uuid":"c79fd2aee5021d4f09523411513e43220bede053","execution":{"iopub.execute_input":"2023-10-09T21:53:46.818973Z","iopub.status.busy":"2023-10-09T21:53:46.818532Z","iopub.status.idle":"2023-10-09T21:53:47.656746Z","shell.execute_reply":"2023-10-09T21:53:47.655513Z","shell.execute_reply.started":"2023-10-09T21:53:46.818937Z"},"trusted":true},"outputs":[],"source":["df_corr = df.corr() # Calculation of the correlation coefficients in pairs, with the default method:\n","                    # Pearson, Standard Correlation Coefficient"]},{"cell_type":"code","execution_count":11,"metadata":{"_cell_guid":"f3fbb40b-c628-45bc-96df-599c83ea1a7f","_uuid":"238eb36a153c7049fef3fd5f4f07f363b4aa92b9","execution":{"iopub.execute_input":"2023-10-09T21:53:49.207047Z","iopub.status.busy":"2023-10-09T21:53:49.206710Z","iopub.status.idle":"2023-10-09T21:53:49.926809Z","shell.execute_reply":"2023-10-09T21:53:49.925201Z","shell.execute_reply.started":"2023-10-09T21:53:49.207021Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1500x1000 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(15,10))\n","seaborn.heatmap(df_corr, cmap=\"YlGnBu\") # Displaying the Heatmap\n","seaborn.set(font_scale=2,style='white')\n","\n","plt.title('Heatmap correlation')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"_cell_guid":"41acf9be-3920-47bd-88ec-14182bf970f8","_uuid":"1628a54bdc8639aa5eae20dd6d7c7197fe1cfee5"},"source":["As we can notice, most of the features are not correlated with each other. This corroborates the fact that a PCA was previously performed on the data."]},{"cell_type":"markdown","metadata":{"_cell_guid":"64510cda-f727-4c55-94e2-b5866b61ded6","_uuid":"183412c2da2fac035008f37fd94509c1dd59a343"},"source":["What can generally be done on a massive dataset is a dimension reduction.\n","By picking th emost important dimensions, there is a possiblity of explaining most of the problem, thus gaining\n","a considerable amount of time while preventing the accuracy to drop too much."]},{"cell_type":"markdown","metadata":{"_cell_guid":"2ba2b88a-7b27-4033-8e4a-73af650d1c06","_uuid":"d82ba55efd1b473742de899c8dd6b2c3215b70db"},"source":["However in this case given the fact that a PCA was previously performed, if the dimension reduction is effective then the PCA wasn't computed in the most effective way. Another way to put it is that no dimension reduction should be computed on a dataset on which a PCA was computed correctly."]},{"cell_type":"code","execution_count":13,"metadata":{"_cell_guid":"513071fd-e5e9-4ad5-a45a-bfcfa9376cdc","_uuid":"f5b17abf4e8a26f4f63f4379b1d0b2822b5a3c85","execution":{"iopub.execute_input":"2023-10-09T21:55:28.713718Z","iopub.status.busy":"2023-10-09T21:55:28.713310Z","iopub.status.idle":"2023-10-09T21:55:28.725612Z","shell.execute_reply":"2023-10-09T21:55:28.724079Z","shell.execute_reply.started":"2023-10-09T21:55:28.713691Z"},"trusted":true},"outputs":[],"source":["rank = df_corr['Class'] # Retrieving the correlation coefficients per feature in relation to the feature class\n","df_rank = pd.DataFrame(rank) \n","df_rank = np.abs(df_rank).sort_values(by='Class',ascending=False) # Ranking the absolute values of the coefficients\n","                                                                  # in descending order\n","df_rank.dropna(inplace=True) # Removing Missing Data (not a number)"]},{"cell_type":"markdown","metadata":{"_cell_guid":"00e91ae9-d53a-40af-a48a-095a4b67dff9","_uuid":"5a154f92f11ecfd72ad4eb2406ead5c24a9d00e3"},"source":["# Data Selection"]},{"cell_type":"markdown","metadata":{"_cell_guid":"a0432f2f-3926-46a0-ac53-a46cf05bb9c8","_uuid":"ecc0c8cd3849db0cbce7dc35e2ab532d13cc052a"},"source":["OVERSAMPLING"]},{"cell_type":"markdown","metadata":{"_cell_guid":"26829a30-0c31-4495-8b16-e96624583a3f","_uuid":"f2b9e864a17a8686b39024341699622a7988d4ff"},"source":["One way to do oversampling is to replicate the under-represented class tuples until we attain a correct proportion between the class"]},{"cell_type":"markdown","metadata":{"_cell_guid":"e87fe2fe-dad5-458a-940b-d94e25e150f8","_uuid":"7ba4b96fce4f0ecbcfc16781dce514baee56e58e"},"source":["However as we haven't infinite time nor the patience, we are going to run the classifier with the undersampled training data (for those using the undersampling principle if results are really bad just rerun the training dataset definition)"]},{"cell_type":"markdown","metadata":{"_cell_guid":"e08a9f1e-090b-402d-b5a3-05ece4881a60","_uuid":"e109c99ad8ae57199c62806c2d5cedb3aa481c41"},"source":["UNDERSAMPLING"]},{"cell_type":"code","execution_count":17,"metadata":{"_cell_guid":"679f5dd1-5665-4de2-942b-6ac95ff1a02f","_uuid":"d5b23951407345e6a89ca312967e582c66e11af3","execution":{"iopub.execute_input":"2023-10-09T21:57:34.523379Z","iopub.status.busy":"2023-10-09T21:57:34.522904Z","iopub.status.idle":"2023-10-09T21:57:34.551326Z","shell.execute_reply":"2023-10-09T21:57:34.549973Z","shell.execute_reply.started":"2023-10-09T21:57:34.523345Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["In this dataset, we have 293 frauds so we need to take a similar number of non-fraud\n"]}],"source":["# We seperate ours data in two groups : a train dataset and a test dataset\n","\n","# First we build our train dataset\n","df_train_all = df[0:150000] # We cut in two the original dataset\n","df_train_1 = df_train_all[df_train_all['Class'] == 1] # We seperate the data which are the frauds and the no frauds\n","df_train_0 = df_train_all[df_train_all['Class'] == 0]\n","print('In this dataset, we have ' + str(len(df_train_1)) +\" frauds so we need to take a similar number of non-fraud\")\n","\n","#df_sample=df_train_0.sample(300)\n","#df_train = df_train_1.append(df_sample) # We gather the frauds with the no frauds. \n","#df_train = df_train.sample(frac=1) # Then we mix our dataset\n","df_train = pd.concat([df_train_1, df_sample], ignore_index=True)\n","\n","df_train = df_train.sample(frac=1).reset_index(drop=True)"]},{"cell_type":"code","execution_count":18,"metadata":{"_cell_guid":"36f77663-2b2a-412f-a759-0fd3a0bf06ab","_uuid":"d9707da7dc04b2a415608f2833dd5d732dd98925","execution":{"iopub.execute_input":"2023-10-09T21:57:37.716736Z","iopub.status.busy":"2023-10-09T21:57:37.716160Z","iopub.status.idle":"2023-10-09T21:57:37.728856Z","shell.execute_reply":"2023-10-09T21:57:37.727672Z","shell.execute_reply.started":"2023-10-09T21:57:37.716698Z"},"trusted":true},"outputs":[],"source":["X_train = df_train.drop(['Time', 'Class'],axis=1) # We drop the features Time (useless), and the Class (label)\n","y_train = df_train['Class'] # We create our label\n","X_train = np.asarray(X_train)\n","y_train = np.asarray(y_train)"]},{"cell_type":"code","execution_count":19,"metadata":{"_cell_guid":"d7c350a4-6874-46b8-b699-4c27aa42dade","_uuid":"975a023dc24a60832b3a670aecf2dbe3b9390183","execution":{"iopub.execute_input":"2023-10-09T21:57:40.121890Z","iopub.status.busy":"2023-10-09T21:57:40.121233Z","iopub.status.idle":"2023-10-09T21:57:40.154339Z","shell.execute_reply":"2023-10-09T21:57:40.152574Z","shell.execute_reply.started":"2023-10-09T21:57:40.121843Z"},"trusted":true},"outputs":[],"source":["############################## with all the test dataset to see if the model learn correctly ##################\n","df_test_all = df[150000:]\n","\n","X_test_all = df_test_all.drop(['Time', 'Class'],axis=1)\n","y_test_all = df_test_all['Class']\n","X_test_all = np.asarray(X_test_all)\n","y_test_all = np.asarray(y_test_all)"]},{"cell_type":"markdown","metadata":{"_cell_guid":"0b4284ab-89fc-4992-919b-b2fce118c601","_uuid":"6506e5ce2aaa631f98c3290a6a35bd2cdc16a62d"},"source":["Then we define training and testing set after applying a dimension reduction to illustrate the fact that nothing will be gained because a PCA was previously computed"]},{"cell_type":"code","execution_count":20,"metadata":{"_cell_guid":"d7246290-1ada-4be9-87a3-833620d23e04","_uuid":"274f5e99f592cdc487e39eb60419a17a1cada5fe","execution":{"iopub.execute_input":"2023-10-09T21:57:44.231193Z","iopub.status.busy":"2023-10-09T21:57:44.230222Z","iopub.status.idle":"2023-10-09T21:57:44.238395Z","shell.execute_reply":"2023-10-09T21:57:44.237067Z","shell.execute_reply.started":"2023-10-09T21:57:44.231157Z"},"trusted":true},"outputs":[],"source":["X_train_rank = df_train[df_rank.index[1:11]] # We take the first ten ranked features\n","X_train_rank = np.asarray(X_train_rank)"]},{"cell_type":"code","execution_count":21,"metadata":{"_cell_guid":"1c9addbe-d94c-44e8-84a9-1afdd13291be","_uuid":"7a28465f8d7a4577ca6f4d1eb1f372a87da87c63","execution":{"iopub.execute_input":"2023-10-09T21:57:46.860050Z","iopub.status.busy":"2023-10-09T21:57:46.859619Z","iopub.status.idle":"2023-10-09T21:57:46.870434Z","shell.execute_reply":"2023-10-09T21:57:46.869203Z","shell.execute_reply.started":"2023-10-09T21:57:46.860021Z"},"trusted":true},"outputs":[],"source":["############################## with all the test dataset to see if the model learn correctly ##################\n","X_test_all_rank = df_test_all[df_rank.index[1:11]]\n","X_test_all_rank = np.asarray(X_test_all_rank)\n","y_test_all = np.asarray(y_test_all)"]},{"cell_type":"markdown","metadata":{"_cell_guid":"c54c8f9f-9301-4a96-9c8b-1845946187ff","_uuid":"a6896c291abc000d9d642fcc65bb93d45b118236"},"source":["# Confusion Matrix"]},{"cell_type":"code","execution_count":22,"metadata":{"_cell_guid":"e298b2fd-6569-4e38-97b1-227a0960e4fe","_uuid":"55d1987b1b3963fb9ca5fd1142faae095dfa338b","execution":{"iopub.execute_input":"2023-10-09T21:57:49.999036Z","iopub.status.busy":"2023-10-09T21:57:49.998643Z","iopub.status.idle":"2023-10-09T21:57:50.006593Z","shell.execute_reply":"2023-10-09T21:57:50.005269Z","shell.execute_reply.started":"2023-10-09T21:57:49.999007Z"},"trusted":true},"outputs":[],"source":["class_names=np.array(['0','1']) # Binary label, Class = 1 (fraud) and Class = 0 (no fraud)"]},{"cell_type":"code","execution_count":23,"metadata":{"_cell_guid":"c57ed880-9e78-4620-9158-bfe626bf75f6","_uuid":"a484397df965faf0e489e17a7e0b0ccf36c8bf84","execution":{"iopub.execute_input":"2023-10-09T21:57:56.353744Z","iopub.status.busy":"2023-10-09T21:57:56.352949Z","iopub.status.idle":"2023-10-09T21:57:56.366314Z","shell.execute_reply":"2023-10-09T21:57:56.364766Z","shell.execute_reply.started":"2023-10-09T21:57:56.353698Z"},"trusted":true},"outputs":[],"source":["# Function to plot the confusion Matrix\n","def plot_confusion_matrix(cm, classes,\n","                          title='Confusion matrix',\n","                          cmap=plt.cm.Blues):\n","    \n","    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n","    plt.title(title)\n","    plt.colorbar()\n","    tick_marks = np.arange(len(classes))\n","    plt.xticks(tick_marks, classes, rotation=45)\n","    plt.yticks(tick_marks, classes)\n","\n","    fmt = 'd' \n","    thresh = cm.max() / 2.\n","    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n","        plt.text(j, i, format(cm[i, j], fmt),\n","                 horizontalalignment=\"center\",\n","                 color=\"white\" if cm[i, j] > thresh else \"black\")\n","\n","    plt.tight_layout()\n","    plt.ylabel('True label')\n","    plt.xlabel('Predicted label')"]},{"cell_type":"markdown","metadata":{"_cell_guid":"f7de7e6f-e51d-4e83-9d4e-6b0abdd2c726","_uuid":"95ccdb3d1da9c590cb7b984f7f7a3151c316509c"},"source":["# Model Selection"]},{"cell_type":"markdown","metadata":{"_cell_guid":"08c5814b-f154-476b-a91b-09682911a7fc","_uuid":"940007266bccc4c732bfe0d990e7307eddb53293"},"source":["So now, we'll use a SVM model classifier, with the scikit-learn library."]},{"cell_type":"code","execution_count":24,"metadata":{"_cell_guid":"1402594d-a2bf-4bef-a4b1-ffbada37e8b6","_uuid":"54b464be3bacc3a8730ef22a44de83979a541952","execution":{"iopub.execute_input":"2023-10-09T21:58:37.037974Z","iopub.status.busy":"2023-10-09T21:58:37.037521Z","iopub.status.idle":"2023-10-09T21:58:37.044411Z","shell.execute_reply":"2023-10-09T21:58:37.043201Z","shell.execute_reply.started":"2023-10-09T21:58:37.037940Z"},"trusted":true},"outputs":[],"source":["classifier = svm.SVC(kernel='linear') # We set a SVM classifier, the default SVM Classifier (Kernel = Radial Basis Function)"]},{"cell_type":"code","execution_count":25,"metadata":{"_cell_guid":"671a5ed5-1103-4dd7-b7ef-f4959320b9b8","_uuid":"ae95b7f9abb4c55f97a7b0e844966d766acd14e1","execution":{"iopub.execute_input":"2023-10-09T21:58:40.385146Z","iopub.status.busy":"2023-10-09T21:58:40.384721Z","iopub.status.idle":"2023-10-09T21:58:42.703232Z","shell.execute_reply":"2023-10-09T21:58:42.701864Z","shell.execute_reply.started":"2023-10-09T21:58:40.385116Z"},"trusted":true},"outputs":[{"data":{"text/html":["<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. 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class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"],"text/plain":["SVC(kernel='linear')"]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["classifier.fit(X_train, y_train) # Then we train our model, with our balanced data train."]},{"cell_type":"markdown","metadata":{"_cell_guid":"33fd5429-184a-40ac-9b4f-cc66300eb75d","_uuid":"06c6624cc3199d8c61aa462a1ac5daea1af5f044"},"source":["# Testing the model"]},{"cell_type":"code","execution_count":26,"metadata":{"_cell_guid":"b7f42a0d-5dc2-482e-83dd-95be0b709fae","_uuid":"06c4a00a2aeb9989135ef954ade5bf1dd750f8ce","execution":{"iopub.execute_input":"2023-10-09T21:59:20.844319Z","iopub.status.busy":"2023-10-09T21:59:20.843839Z","iopub.status.idle":"2023-10-09T21:59:21.250754Z","shell.execute_reply":"2023-10-09T21:59:21.249435Z","shell.execute_reply.started":"2023-10-09T21:59:20.844285Z"},"trusted":true},"outputs":[],"source":["prediction_SVM_all = classifier.predict(X_test_all) #And finally, we predict our data test."]},{"cell_type":"code","execution_count":27,"metadata":{"_cell_guid":"5558cbc6-e22c-4748-8640-35cd43358d1c","_uuid":"cf58f6438e736b63903773caf38cd364a73178d4","execution":{"iopub.execute_input":"2023-10-09T21:59:24.016915Z","iopub.status.busy":"2023-10-09T21:59:24.016499Z","iopub.status.idle":"2023-10-09T21:59:24.404760Z","shell.execute_reply":"2023-10-09T21:59:24.403399Z","shell.execute_reply.started":"2023-10-09T21:59:24.016886Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["cm = confusion_matrix(y_test_all, prediction_SVM_all)\n","plot_confusion_matrix(cm,class_names)"]},{"cell_type":"markdown","metadata":{"_cell_guid":"fcc81707-fc91-4a66-945b-93fb7423dfcd","_uuid":"9866599a18a5ff8286d8fac8ce2dd7213ae2d310"},"source":["In this case we are gonna try to minimize the number of errors in our prediction results. Errors are on the anti-diagonal of the confusion matrix.  But we can infer that being wrong about an actual fraud is far worse than being wrong about a non-fraud transaction. "]},{"cell_type":"markdown","metadata":{"_cell_guid":"93871f02-4e10-4ae4-9724-0d673e56c255","_uuid":"b164a5ed0b0b36739ad8b96052a14d515bb277e9"},"source":["That is why  using the accuracy as only classification criterion could be considered unthoughtful. \n","During the remaining part of this study our criterion will consider precision on the real fraud 4 times more important than the general accuracy.\n","Even though the final tested result is accuracy."]},{"cell_type":"code","execution_count":28,"metadata":{"_cell_guid":"21cde17a-738a-4c75-a6b3-16b4c055188b","_uuid":"7000a8cec37987f50f8e398e48894c5ece0a7470","execution":{"iopub.execute_input":"2023-10-09T21:59:30.507714Z","iopub.status.busy":"2023-10-09T21:59:30.507347Z","iopub.status.idle":"2023-10-09T21:59:30.515444Z","shell.execute_reply":"2023-10-09T21:59:30.513954Z","shell.execute_reply.started":"2023-10-09T21:59:30.507687Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Our criterion give a result of 0.9217340122169073\n"]}],"source":["print('Our criterion give a result of ' \n","      + str( ( (cm[0][0]+cm[1][1]) / (sum(cm[0]) + sum(cm[1])) + 4 * cm[1][1]/(cm[1][0]+cm[1][1])) / 5))"]},{"cell_type":"code","execution_count":29,"metadata":{"_cell_guid":"08447323-4ed7-4e29-a3c1-c86682ff63f0","_uuid":"7c7bb0d2cfffdbdbe10df915a429c88e36a60957","execution":{"iopub.execute_input":"2023-10-09T21:59:33.321604Z","iopub.status.busy":"2023-10-09T21:59:33.321149Z","iopub.status.idle":"2023-10-09T21:59:33.330663Z","shell.execute_reply":"2023-10-09T21:59:33.329207Z","shell.execute_reply.started":"2023-10-09T21:59:33.321572Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["We have detected 183 frauds / 199 total frauds.\n","\n","So, the probability to detect a fraud is 0.9195979899497487\n","the accuracy is : 0.9302781012855416\n"]}],"source":["print('We have detected ' + str(cm[1][1]) + ' frauds / ' + str(cm[1][1]+cm[1][0]) + ' total frauds.')\n","print('\\nSo, the probability to detect a fraud is ' + str(cm[1][1]/(cm[1][1]+cm[1][0])))\n","print(\"the accuracy is : \"+str((cm[0][0]+cm[1][1]) / (sum(cm[0]) + sum(cm[1]))))"]},{"cell_type":"markdown","metadata":{"_cell_guid":"087b8a01-6a00-4bdd-ade9-08a3b5cd198b","_uuid":"13428d627950722f5c2dab060549bdd519a2981a"},"source":[" # Models Rank"]},{"cell_type":"markdown","metadata":{"_cell_guid":"37124bd8-5865-4ddc-b04f-f6d899340968","_uuid":"7bfed5e996206e6863601fcaa09761baf68fb361"},"source":["There is a need to compute the fit method again, as the dimension of the tuples to predict went from 29 to 10 because of the dimension reduction"]},{"cell_type":"code","execution_count":30,"metadata":{"_cell_guid":"5e73fef0-c4d4-450b-9961-e841767e0342","_uuid":"b4247cb1dd4d7af91421d01f7067898799fdbfd8","execution":{"iopub.execute_input":"2023-10-09T21:59:42.068439Z","iopub.status.busy":"2023-10-09T21:59:42.068057Z","iopub.status.idle":"2023-10-09T21:59:42.379306Z","shell.execute_reply":"2023-10-09T21:59:42.378184Z","shell.execute_reply.started":"2023-10-09T21:59:42.068412Z"},"trusted":true},"outputs":[],"source":["classifier.fit(X_train_rank, y_train) # Then we train our model, with our balanced data train.\n","prediction_SVM = classifier.predict(X_test_all_rank) #And finally, we predict our data test."]},{"cell_type":"code","execution_count":31,"metadata":{"_cell_guid":"e84710c9-59fe-4205-8c05-e989d5942712","_uuid":"bb4defbee6e8ebaff98eebaff7701503d2b65434","execution":{"iopub.execute_input":"2023-10-09T21:59:44.284213Z","iopub.status.busy":"2023-10-09T21:59:44.283817Z","iopub.status.idle":"2023-10-09T21:59:44.695355Z","shell.execute_reply":"2023-10-09T21:59:44.693673Z","shell.execute_reply.started":"2023-10-09T21:59:44.284184Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["cm = confusion_matrix(y_test_all, prediction_SVM)\n","plot_confusion_matrix(cm,class_names)"]},{"cell_type":"code","execution_count":32,"metadata":{"_cell_guid":"d310bf29-6673-4882-8387-e4b6fb53ee86","_uuid":"a0d4e792c37722f84d9b55432b286bfcafec1293","execution":{"iopub.execute_input":"2023-10-09T21:59:48.554986Z","iopub.status.busy":"2023-10-09T21:59:48.554262Z","iopub.status.idle":"2023-10-09T21:59:48.561232Z","shell.execute_reply":"2023-10-09T21:59:48.559834Z","shell.execute_reply.started":"2023-10-09T21:59:48.554954Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Our criterion give a result of 0.925250150102922\n"]}],"source":["print('Our criterion give a result of ' \n","      + str( ( (cm[0][0]+cm[1][1]) / (sum(cm[0]) + sum(cm[1])) + 4 * cm[1][1]/(cm[1][0]+cm[1][1])) / 5))"]},{"cell_type":"code","execution_count":33,"metadata":{"_cell_guid":"9fb3cb24-8f70-423c-89c7-aa91d15172ad","_uuid":"f39c12ef5d8c91185b1873483fd93e26c13c3cab","execution":{"iopub.execute_input":"2023-10-09T22:00:02.848844Z","iopub.status.busy":"2023-10-09T22:00:02.848421Z","iopub.status.idle":"2023-10-09T22:00:02.857736Z","shell.execute_reply":"2023-10-09T22:00:02.855859Z","shell.execute_reply.started":"2023-10-09T22:00:02.848816Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["We have detected 183 frauds / 199 total frauds.\n","\n","So, the probability to detect a fraud is 0.9195979899497487\n","the accuracy is : 0.9478587907156156\n"]}],"source":["print('We have detected ' + str(cm[1][1]) + ' frauds / ' + str(cm[1][1]+cm[1][0]) + ' total frauds.')\n","print('\\nSo, the probability to detect a fraud is ' + str(cm[1][1]/(cm[1][1]+cm[1][0])))\n","print(\"the accuracy is : \"+str((cm[0][0]+cm[1][1]) / (sum(cm[0]) + sum(cm[1]))))"]},{"cell_type":"markdown","metadata":{"_cell_guid":"74782013-18d1-4afb-8d3c-67dc8cae17d2","_uuid":"6b8d59c2aa9b0a07112a405d063120dc0e20e338"},"source":["We can see that the study using the reduced data is far from unrelevant, which means that the last step of the previously computed PCA could have been done in a more efficient way. Indeed one of the main question we have with the PCA once we calculated the principals components direction, is how many of this component are we gonna keep. This means that some of the 30 dimensions are do not discriminate classes that much."]},{"cell_type":"markdown","metadata":{"_cell_guid":"66ca64cd-078d-4a1c-8ecd-d85a247ef5f3","_uuid":"a38206000a28bcc2baf07d0d8e10f7eb1256f993"},"source":["# Re-balanced class weigh :"]},{"cell_type":"markdown","metadata":{"_cell_guid":"10579138-58f9-41db-b5a7-25f6c4741900","_uuid":"6cd3881e06f80ba87e4377c525572db1585e1c84"},"source":["In this previously used SVM model, the weigh of each class was the same, which means that missing a fraud is as bad as misjudging a non-fraud. The objective, for a bank, is to maximize the number of detected frauds! Even if it means considering more non-fraud tuple as fraudulent operation. So, we need to minimize the False positives : the number of no detected frauds.\n","\n","Indeed, by modifying the class_weight parameter, we can chose which class to give more importance during the training phase. In this case, the class_1 which describes the fraudulent operations will be considered more important than the class_0 (non-fraud operation). However, in this case we will give more importance to the class_0 due to the large number of misclassed non-fraud operation. Of course the goal is to lose as little effective fraud as possible in the process.\n"]},{"cell_type":"code","execution_count":34,"metadata":{"_cell_guid":"33f60bc0-38c6-4ee7-a348-e2254bca727c","_uuid":"5c36459b8b55ab20a57097d820f7f24a856c20f7","execution":{"iopub.execute_input":"2023-10-09T22:00:25.099477Z","iopub.status.busy":"2023-10-09T22:00:25.098606Z","iopub.status.idle":"2023-10-09T22:00:25.105774Z","shell.execute_reply":"2023-10-09T22:00:25.104338Z","shell.execute_reply.started":"2023-10-09T22:00:25.099425Z"},"trusted":true},"outputs":[],"source":["classifier_b = svm.SVC(kernel='linear',class_weight={0:0.60, 1:0.40})"]},{"cell_type":"code","execution_count":35,"metadata":{"_cell_guid":"07b6b2e3-4cd2-4202-b2a9-3d22edadf650","_uuid":"4b29ebf3f9fd2e591498e747d453297ccc9d95f0","execution":{"iopub.execute_input":"2023-10-09T22:00:27.277897Z","iopub.status.busy":"2023-10-09T22:00:27.277073Z","iopub.status.idle":"2023-10-09T22:00:28.127942Z","shell.execute_reply":"2023-10-09T22:00:28.126423Z","shell.execute_reply.started":"2023-10-09T22:00:27.277864Z"},"trusted":true},"outputs":[{"data":{"text/html":["<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(class_weight={0: 0.6, 1: 0.4}, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(class_weight={0: 0.6, 1: 0.4}, kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"],"text/plain":["SVC(class_weight={0: 0.6, 1: 0.4}, kernel='linear')"]},"execution_count":35,"metadata":{},"output_type":"execute_result"}],"source":["classifier_b.fit(X_train, y_train) # Then we train our model, with our balanced data train."]},{"cell_type":"markdown","metadata":{"_cell_guid":"711f7353-4f8d-494e-a2e3-e24c69da17a8","_uuid":"f441734ba20be6e81ead1f4fb1196c09095cc4f6"},"source":["# Testing the model"]},{"cell_type":"code","execution_count":36,"metadata":{"_cell_guid":"63406a9e-e7f7-4bbe-9945-55daf2c384a8","_uuid":"c2b29f36198561711dd23cdef243bd7d9b41bce7","execution":{"iopub.execute_input":"2023-10-09T22:01:00.722357Z","iopub.status.busy":"2023-10-09T22:01:00.721883Z","iopub.status.idle":"2023-10-09T22:01:01.101185Z","shell.execute_reply":"2023-10-09T22:01:01.100403Z","shell.execute_reply.started":"2023-10-09T22:01:00.722326Z"},"trusted":true},"outputs":[],"source":["prediction_SVM_b_all = classifier_b.predict(X_test_all) #We predict all the data set."]},{"cell_type":"code","execution_count":37,"metadata":{"_cell_guid":"42f417eb-5c5c-4d1d-af59-349d17d77f6f","_uuid":"e462d0526a115a87d27f8a1c24bbb6fd3b9e9eeb","execution":{"iopub.execute_input":"2023-10-09T22:01:07.492296Z","iopub.status.busy":"2023-10-09T22:01:07.491598Z","iopub.status.idle":"2023-10-09T22:01:07.866874Z","shell.execute_reply":"2023-10-09T22:01:07.865685Z","shell.execute_reply.started":"2023-10-09T22:01:07.492264Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["cm = confusion_matrix(y_test_all, prediction_SVM_b_all)\n","plot_confusion_matrix(cm,class_names)"]},{"cell_type":"code","execution_count":38,"metadata":{"_cell_guid":"9aa0594f-88da-4015-a712-c4e378d276c9","_uuid":"5f80761a24cf70ac29bbf3650a7db0ae6c273600","execution":{"iopub.execute_input":"2023-10-09T22:01:11.538676Z","iopub.status.busy":"2023-10-09T22:01:11.538330Z","iopub.status.idle":"2023-10-09T22:01:11.545138Z","shell.execute_reply":"2023-10-09T22:01:11.544368Z","shell.execute_reply.started":"2023-10-09T22:01:11.538652Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Our criterion give a result of 0.9333645312321248\n"]}],"source":["print('Our criterion give a result of ' \n","      + str( ( (cm[0][0]+cm[1][1]) / (sum(cm[0]) + sum(cm[1])) + 4 * cm[1][1]/(cm[1][0]+cm[1][1])) / 5))"]},{"cell_type":"code","execution_count":39,"metadata":{"_cell_guid":"d03a28ad-b9e1-4c2c-bed2-06308b1816cf","_uuid":"3d674db8f4eeb8ed4a8583e3d5cd40f8561ae33d","execution":{"iopub.execute_input":"2023-10-09T22:01:13.525990Z","iopub.status.busy":"2023-10-09T22:01:13.524948Z","iopub.status.idle":"2023-10-09T22:01:13.533908Z","shell.execute_reply":"2023-10-09T22:01:13.533060Z","shell.execute_reply.started":"2023-10-09T22:01:13.525945Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["We have detected 185 frauds / 199 total frauds.\n","\n","So, the probability to detect a fraud is 0.9296482412060302\n","the accuracy is : 0.9482296913365033\n"]}],"source":["print('We have detected ' + str(cm[1][1]) + ' frauds / ' + str(cm[1][1]+cm[1][0]) + ' total frauds.')\n","print('\\nSo, the probability to detect a fraud is ' + str(cm[1][1]/(cm[1][1]+cm[1][0])))\n","print(\"the accuracy is : \"+str((cm[0][0]+cm[1][1]) / (sum(cm[0]) + sum(cm[1]))))"]},{"cell_type":"markdown","metadata":{"_cell_guid":"7950e244-844f-418c-9898-2f06756dc125","_uuid":"f31fdcb8899711ce15b5a0760eced5741a9e1d97"},"source":[" # Models Rank"]},{"cell_type":"code","execution_count":40,"metadata":{"_cell_guid":"ef932b2b-72bf-47df-9157-ae3249665467","_uuid":"9d241abdaded4809eafe7e2c885150e63f4ae4d0","execution":{"iopub.execute_input":"2023-10-09T22:01:15.812382Z","iopub.status.busy":"2023-10-09T22:01:15.811643Z","iopub.status.idle":"2023-10-09T22:01:16.115353Z","shell.execute_reply":"2023-10-09T22:01:16.114018Z","shell.execute_reply.started":"2023-10-09T22:01:15.812347Z"},"trusted":true},"outputs":[],"source":["classifier_b.fit(X_train_rank, y_train) # Then we train our model, with our balanced data train.\n","prediction_SVM = classifier_b.predict(X_test_all_rank) #And finally, we predict our data test."]},{"cell_type":"code","execution_count":41,"metadata":{"_cell_guid":"a9ce5f16-08b3-4932-89ee-1ed934684482","_uuid":"3e6542b40144dbef93f27144198299e9b7a84bbe","execution":{"iopub.execute_input":"2023-10-09T22:01:17.643411Z","iopub.status.busy":"2023-10-09T22:01:17.642710Z","iopub.status.idle":"2023-10-09T22:01:18.133478Z","shell.execute_reply":"2023-10-09T22:01:18.132285Z","shell.execute_reply.started":"2023-10-09T22:01:17.643375Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["cm = confusion_matrix(y_test_all, prediction_SVM)\n","plot_confusion_matrix(cm,class_names)"]},{"cell_type":"code","execution_count":42,"metadata":{"_cell_guid":"ff879d9e-b9c3-40b7-a9a6-dae5ce11a88e","_uuid":"653485d7a5c7feb2052321b7a05b4415b63fdac8","execution":{"iopub.execute_input":"2023-10-09T22:01:18.316407Z","iopub.status.busy":"2023-10-09T22:01:18.315143Z","iopub.status.idle":"2023-10-09T22:01:18.324255Z","shell.execute_reply":"2023-10-09T22:01:18.323203Z","shell.execute_reply.started":"2023-10-09T22:01:18.316360Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Our criterion give a result of 0.9225905130778255\n"]}],"source":["print('Our criterion give a result of ' \n","      + str( ( (cm[0][0]+cm[1][1]) / (sum(cm[0]) + sum(cm[1])) + 4 * cm[1][1]/(cm[1][0]+cm[1][1])) / 5))"]},{"cell_type":"code","execution_count":43,"metadata":{"_cell_guid":"6aa458bb-3314-492b-b9c3-4d811c5a2db5","_uuid":"725c2ff781e0a1c86eb160eeab0da9723aee91cc","execution":{"iopub.execute_input":"2023-10-09T22:01:20.401981Z","iopub.status.busy":"2023-10-09T22:01:20.401584Z","iopub.status.idle":"2023-10-09T22:01:20.409766Z","shell.execute_reply":"2023-10-09T22:01:20.408304Z","shell.execute_reply.started":"2023-10-09T22:01:20.401953Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["We have detected 182 frauds / 199 total frauds.\n","\n","So, the probability to detect a fraud is 0.914572864321608\n","the accuracy is : 0.954661108102695\n"]}],"source":["print('We have detected ' + str(cm[1][1]) + ' frauds / ' + str(cm[1][1]+cm[1][0]) + ' total frauds.')\n","print('\\nSo, the probability to detect a fraud is ' + str(cm[1][1]/(cm[1][1]+cm[1][0])))\n","print(\"the accuracy is : \"+str((cm[0][0]+cm[1][1]) / (sum(cm[0]) + sum(cm[1]))))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.12"}},"nbformat":4,"nbformat_minor":4}