EDA+model dumping.ipynb

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  {
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    "### 1)identification of the probleme\n",
    ".probleme overexplained\n",
    "   today online transaction have dominent our world since the covid 19 and the technological advencent \n",
    "   as you can see from the diagrame we are becoming more and more dependentless from the old way of payment \n",
    "   but of course as bank,or any orgination that give this service there is a trade off:with the abilty of receving money off     your custormers directly wit ,you have the posibilty od receving a faulty trabsactiion ,ether from the user or the \n",
    "   organisation .and regarding how much tranaction processding , a human being can not indentify all the faulty transaction .\n",
    "      so here came the need of au an automated tools that help during this process of protecting the user from waking up in the       mornig and founding that his credit card got used to paying a party \n",
    "       \n",
    "          build and train a ML model to identify faulty tranaction so we can prevent them in the future"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec27e3f5",
   "metadata": {},
   "source": [
    "# 2)understanding the data \n",
    "        the data contains 14 column:\n",
    "        index - Unique Identifier for each row\n",
    "        transdatetrans_time - Transaction DateTime\n",
    "        cc_num - Credit Card Number of Customer\n",
    "        merchant - Merchant Name\n",
    "        category - Category of Merchant\n",
    "        amt - Amount of Transaction\n",
    "        first - First Name of Credit Card Holder\n",
    "        last - Last Name of Credit Card Holder\n",
    "        gender - Gender of Credit Card Holder\n",
    "        street - Street Address of Credit Card Holder\n",
    "        city - City of Credit Card Holder\n",
    "        state - State of Credit Card Holder\n",
    "        zip - Zip of Credit Card Holder\n",
    "        lat - Latitude Location of Credit Card Holder\n",
    "        long - Longitude Location of Credit Card Holder\n",
    "        city_pop - Credit Card Holder's City Population\n",
    "        job - Job of Credit Card Holder\n",
    "        dob - Date of Birth of Credit Card Holder\n",
    "        trans_num - Transaction Number\n",
    "        unix_time - UNIX Time of transaction\n",
    "        merch_lat - Latitude Location of Merchant\n",
    "        merch_long - Longitude Location of Merchant\n",
    "        is_fraud - Fraud Flag <--- Target Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d656e855",
   "metadata": {},
   "outputs": [],
   "source": [
    "### import all the tools \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split,cross_val_score,GridSearchCV\n",
    "from sklearn.metrics import *\n",
    "from sklearn.preprocessing import StandardScaler as sd\n",
    "from  imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.over_sampling import SMOTE,RandomOverSampler\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import LinearSVC\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c63a11e3",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trans_date_trans_time</th>\n",
       "      <th>cc_num</th>\n",
       "      <th>merchant</th>\n",
       "      <th>category</th>\n",
       "      <th>amt</th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "      <th>gender</th>\n",
       "      <th>street</th>\n",
       "      <th>city</th>\n",
       "      <th>...</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>city_pop</th>\n",
       "      <th>job</th>\n",
       "      <th>dob</th>\n",
       "      <th>trans_num</th>\n",
       "      <th>unix_time</th>\n",
       "      <th>merch_lat</th>\n",
       "      <th>merch_long</th>\n",
       "      <th>is_fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-01 00:00:18</td>\n",
       "      <td>2703186189652095</td>\n",
       "      <td>fraud_Rippin, Kub and Mann</td>\n",
       "      <td>misc_net</td>\n",
       "      <td>4.97</td>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Banks</td>\n",
       "      <td>F</td>\n",
       "      <td>561 Perry Cove</td>\n",
       "      <td>Moravian Falls</td>\n",
       "      <td>...</td>\n",
       "      <td>36.0788</td>\n",
       "      <td>-81.1781</td>\n",
       "      <td>3495</td>\n",
       "      <td>Psychologist, counselling</td>\n",
       "      <td>1988-03-09</td>\n",
       "      <td>0b242abb623afc578575680df30655b9</td>\n",
       "      <td>1325376018</td>\n",
       "      <td>36.011293</td>\n",
       "      <td>-82.048315</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-01 00:00:44</td>\n",
       "      <td>630423337322</td>\n",
       "      <td>fraud_Heller, Gutmann and Zieme</td>\n",
       "      <td>grocery_pos</td>\n",
       "      <td>107.23</td>\n",
       "      <td>Stephanie</td>\n",
       "      <td>Gill</td>\n",
       "      <td>F</td>\n",
       "      <td>43039 Riley Greens Suite 393</td>\n",
       "      <td>Orient</td>\n",
       "      <td>...</td>\n",
       "      <td>48.8878</td>\n",
       "      <td>-118.2105</td>\n",
       "      <td>149</td>\n",
       "      <td>Special educational needs teacher</td>\n",
       "      <td>1978-06-21</td>\n",
       "      <td>1f76529f8574734946361c461b024d99</td>\n",
       "      <td>1325376044</td>\n",
       "      <td>49.159047</td>\n",
       "      <td>-118.186462</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-01-01 00:00:51</td>\n",
       "      <td>38859492057661</td>\n",
       "      <td>fraud_Lind-Buckridge</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>220.11</td>\n",
       "      <td>Edward</td>\n",
       "      <td>Sanchez</td>\n",
       "      <td>M</td>\n",
       "      <td>594 White Dale Suite 530</td>\n",
       "      <td>Malad City</td>\n",
       "      <td>...</td>\n",
       "      <td>42.1808</td>\n",
       "      <td>-112.2620</td>\n",
       "      <td>4154</td>\n",
       "      <td>Nature conservation officer</td>\n",
       "      <td>1962-01-19</td>\n",
       "      <td>a1a22d70485983eac12b5b88dad1cf95</td>\n",
       "      <td>1325376051</td>\n",
       "      <td>43.150704</td>\n",
       "      <td>-112.154481</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-01 00:01:16</td>\n",
       "      <td>3534093764340240</td>\n",
       "      <td>fraud_Kutch, Hermiston and Farrell</td>\n",
       "      <td>gas_transport</td>\n",
       "      <td>45.00</td>\n",
       "      <td>Jeremy</td>\n",
       "      <td>White</td>\n",
       "      <td>M</td>\n",
       "      <td>9443 Cynthia Court Apt. 038</td>\n",
       "      <td>Boulder</td>\n",
       "      <td>...</td>\n",
       "      <td>46.2306</td>\n",
       "      <td>-112.1138</td>\n",
       "      <td>1939</td>\n",
       "      <td>Patent attorney</td>\n",
       "      <td>1967-01-12</td>\n",
       "      <td>6b849c168bdad6f867558c3793159a81</td>\n",
       "      <td>1325376076</td>\n",
       "      <td>47.034331</td>\n",
       "      <td>-112.561071</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-01-01 00:03:06</td>\n",
       "      <td>375534208663984</td>\n",
       "      <td>fraud_Keeling-Crist</td>\n",
       "      <td>misc_pos</td>\n",
       "      <td>41.96</td>\n",
       "      <td>Tyler</td>\n",
       "      <td>Garcia</td>\n",
       "      <td>M</td>\n",
       "      <td>408 Bradley Rest</td>\n",
       "      <td>Doe Hill</td>\n",
       "      <td>...</td>\n",
       "      <td>38.4207</td>\n",
       "      <td>-79.4629</td>\n",
       "      <td>99</td>\n",
       "      <td>Dance movement psychotherapist</td>\n",
       "      <td>1986-03-28</td>\n",
       "      <td>a41d7549acf90789359a9aa5346dcb46</td>\n",
       "      <td>1325376186</td>\n",
       "      <td>38.674999</td>\n",
       "      <td>-78.632459</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  trans_date_trans_time            cc_num                            merchant  \\\n",
       "0   2019-01-01 00:00:18  2703186189652095          fraud_Rippin, Kub and Mann   \n",
       "1   2019-01-01 00:00:44      630423337322     fraud_Heller, Gutmann and Zieme   \n",
       "2   2019-01-01 00:00:51    38859492057661                fraud_Lind-Buckridge   \n",
       "3   2019-01-01 00:01:16  3534093764340240  fraud_Kutch, Hermiston and Farrell   \n",
       "4   2019-01-01 00:03:06   375534208663984                 fraud_Keeling-Crist   \n",
       "\n",
       "        category     amt      first     last gender  \\\n",
       "0       misc_net    4.97   Jennifer    Banks      F   \n",
       "1    grocery_pos  107.23  Stephanie     Gill      F   \n",
       "2  entertainment  220.11     Edward  Sanchez      M   \n",
       "3  gas_transport   45.00     Jeremy    White      M   \n",
       "4       misc_pos   41.96      Tyler   Garcia      M   \n",
       "\n",
       "                         street            city  ...      lat      long  \\\n",
       "0                561 Perry Cove  Moravian Falls  ...  36.0788  -81.1781   \n",
       "1  43039 Riley Greens Suite 393          Orient  ...  48.8878 -118.2105   \n",
       "2      594 White Dale Suite 530      Malad City  ...  42.1808 -112.2620   \n",
       "3   9443 Cynthia Court Apt. 038         Boulder  ...  46.2306 -112.1138   \n",
       "4              408 Bradley Rest        Doe Hill  ...  38.4207  -79.4629   \n",
       "\n",
       "   city_pop                                job         dob  \\\n",
       "0      3495          Psychologist, counselling  1988-03-09   \n",
       "1       149  Special educational needs teacher  1978-06-21   \n",
       "2      4154        Nature conservation officer  1962-01-19   \n",
       "3      1939                    Patent attorney  1967-01-12   \n",
       "4        99     Dance movement psychotherapist  1986-03-28   \n",
       "\n",
       "                          trans_num   unix_time  merch_lat  merch_long  \\\n",
       "0  0b242abb623afc578575680df30655b9  1325376018  36.011293  -82.048315   \n",
       "1  1f76529f8574734946361c461b024d99  1325376044  49.159047 -118.186462   \n",
       "2  a1a22d70485983eac12b5b88dad1cf95  1325376051  43.150704 -112.154481   \n",
       "3  6b849c168bdad6f867558c3793159a81  1325376076  47.034331 -112.561071   \n",
       "4  a41d7549acf90789359a9aa5346dcb46  1325376186  38.674999  -78.632459   \n",
       "\n",
       "   is_fraud  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## PREPARE THE DATA\n",
    "data0=pd.read_csv('fraudTrain.csv')\n",
    "data1=pd.read_csv('fraudTest.csv')\n",
    "data=pd.concat([data0,data1]).reset_index()\n",
    "data.drop(['Unnamed: 0','index'],axis=1,inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d3e4d826",
   "metadata": {},
   "outputs": [
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       "      <th>...</th>\n",
       "      <th>trans_num</th>\n",
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       "      <th>merch_lat</th>\n",
       "      <th>merch_long</th>\n",
       "      <th>is_fraud</th>\n",
       "      <th>hour</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>age_holder</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2019-01-01 00:00:18</td>\n",
       "      <td>2703186189652095</td>\n",
       "      <td>fraud_Rippin, Kub and Mann</td>\n",
       "      <td>misc_net</td>\n",
       "      <td>4.97</td>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Banks</td>\n",
       "      <td>F</td>\n",
       "      <td>561 Perry Cove</td>\n",
       "      <td>Moravian Falls</td>\n",
       "      <td>...</td>\n",
       "      <td>0b242abb623afc578575680df30655b9</td>\n",
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       "      <td>-82.048315</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>January</td>\n",
       "      <td>2019</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-01 00:00:44</td>\n",
       "      <td>630423337322</td>\n",
       "      <td>fraud_Heller, Gutmann and Zieme</td>\n",
       "      <td>grocery_pos</td>\n",
       "      <td>107.23</td>\n",
       "      <td>Stephanie</td>\n",
       "      <td>Gill</td>\n",
       "      <td>F</td>\n",
       "      <td>43039 Riley Greens Suite 393</td>\n",
       "      <td>Orient</td>\n",
       "      <td>...</td>\n",
       "      <td>1f76529f8574734946361c461b024d99</td>\n",
       "      <td>1325376044</td>\n",
       "      <td>49.159047</td>\n",
       "      <td>-118.186462</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>January</td>\n",
       "      <td>2019</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-01-01 00:00:51</td>\n",
       "      <td>38859492057661</td>\n",
       "      <td>fraud_Lind-Buckridge</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>220.11</td>\n",
       "      <td>Edward</td>\n",
       "      <td>Sanchez</td>\n",
       "      <td>M</td>\n",
       "      <td>594 White Dale Suite 530</td>\n",
       "      <td>Malad City</td>\n",
       "      <td>...</td>\n",
       "      <td>a1a22d70485983eac12b5b88dad1cf95</td>\n",
       "      <td>1325376051</td>\n",
       "      <td>43.150704</td>\n",
       "      <td>-112.154481</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>January</td>\n",
       "      <td>2019</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-01 00:01:16</td>\n",
       "      <td>3534093764340240</td>\n",
       "      <td>fraud_Kutch, Hermiston and Farrell</td>\n",
       "      <td>gas_transport</td>\n",
       "      <td>45.00</td>\n",
       "      <td>Jeremy</td>\n",
       "      <td>White</td>\n",
       "      <td>M</td>\n",
       "      <td>9443 Cynthia Court Apt. 038</td>\n",
       "      <td>Boulder</td>\n",
       "      <td>...</td>\n",
       "      <td>6b849c168bdad6f867558c3793159a81</td>\n",
       "      <td>1325376076</td>\n",
       "      <td>47.034331</td>\n",
       "      <td>-112.561071</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>January</td>\n",
       "      <td>2019</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-01-01 00:03:06</td>\n",
       "      <td>375534208663984</td>\n",
       "      <td>fraud_Keeling-Crist</td>\n",
       "      <td>misc_pos</td>\n",
       "      <td>41.96</td>\n",
       "      <td>Tyler</td>\n",
       "      <td>Garcia</td>\n",
       "      <td>M</td>\n",
       "      <td>408 Bradley Rest</td>\n",
       "      <td>Doe Hill</td>\n",
       "      <td>...</td>\n",
       "      <td>a41d7549acf90789359a9aa5346dcb46</td>\n",
       "      <td>1325376186</td>\n",
       "      <td>38.674999</td>\n",
       "      <td>-78.632459</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>January</td>\n",
       "      <td>2019</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
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      ],
      "text/plain": [
       "  trans_date_trans_time            cc_num                            merchant  \\\n",
       "0   2019-01-01 00:00:18  2703186189652095          fraud_Rippin, Kub and Mann   \n",
       "1   2019-01-01 00:00:44      630423337322     fraud_Heller, Gutmann and Zieme   \n",
       "2   2019-01-01 00:00:51    38859492057661                fraud_Lind-Buckridge   \n",
       "3   2019-01-01 00:01:16  3534093764340240  fraud_Kutch, Hermiston and Farrell   \n",
       "4   2019-01-01 00:03:06   375534208663984                 fraud_Keeling-Crist   \n",
       "\n",
       "        category     amt      first     last gender  \\\n",
       "0       misc_net    4.97   Jennifer    Banks      F   \n",
       "1    grocery_pos  107.23  Stephanie     Gill      F   \n",
       "2  entertainment  220.11     Edward  Sanchez      M   \n",
       "3  gas_transport   45.00     Jeremy    White      M   \n",
       "4       misc_pos   41.96      Tyler   Garcia      M   \n",
       "\n",
       "                         street            city  ...  \\\n",
       "0                561 Perry Cove  Moravian Falls  ...   \n",
       "1  43039 Riley Greens Suite 393          Orient  ...   \n",
       "2      594 White Dale Suite 530      Malad City  ...   \n",
       "3   9443 Cynthia Court Apt. 038         Boulder  ...   \n",
       "4              408 Bradley Rest        Doe Hill  ...   \n",
       "\n",
       "                          trans_num   unix_time  merch_lat  merch_long  \\\n",
       "0  0b242abb623afc578575680df30655b9  1325376018  36.011293  -82.048315   \n",
       "1  1f76529f8574734946361c461b024d99  1325376044  49.159047 -118.186462   \n",
       "2  a1a22d70485983eac12b5b88dad1cf95  1325376051  43.150704 -112.154481   \n",
       "3  6b849c168bdad6f867558c3793159a81  1325376076  47.034331 -112.561071   \n",
       "4  a41d7549acf90789359a9aa5346dcb46  1325376186  38.674999  -78.632459   \n",
       "\n",
       "   is_fraud hour day_of_week    month  year  age_holder  \n",
       "0         0    0     Tuesday  January  2019          34  \n",
       "1         0    0     Tuesday  January  2019          44  \n",
       "2         0    0     Tuesday  January  2019          60  \n",
       "3         0    0     Tuesday  January  2019          55  \n",
       "4         0    0     Tuesday  January  2019          36  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##SPLIT THE trans_date_trans_time COLUMN TO DAY,hour,year\n",
    "# converting trans_date_trans_time into datetime\n",
    "data['trans_date_trans_time'] = pd.to_datetime(data['trans_date_trans_time'])\n",
    "#add hour colomn\n",
    "data['hour'] = data['trans_date_trans_time'].dt.hour\n",
    "#add day of the week\n",
    "data['day_of_week'] = data['trans_date_trans_time'].dt.day_name()\n",
    "#add month \n",
    "data['month'] = data['trans_date_trans_time'].dt.month_name()\n",
    "\n",
    "#add year\n",
    "data['year'] = data['trans_date_trans_time'].dt.year\n",
    "##add age column \n",
    "data['age_holder']=2022-pd.to_datetime(data['dob']).dt.year\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ead5f594",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cc_num</th>\n",
       "      <th>merchant</th>\n",
       "      <th>category</th>\n",
       "      <th>amt</th>\n",
       "      <th>gender</th>\n",
       "      <th>city</th>\n",
       "      <th>zip</th>\n",
       "      <th>is_fraud</th>\n",
       "      <th>hour</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>age_holder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2703186189652095</td>\n",
       "      <td>fraud_Rippin, Kub and Mann</td>\n",
       "      <td>misc_net</td>\n",
       "      <td>4.97</td>\n",
       "      <td>F</td>\n",
       "      <td>Moravian Falls</td>\n",
       "      <td>28654</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>January</td>\n",
       "      <td>2019</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>630423337322</td>\n",
       "      <td>fraud_Heller, Gutmann and Zieme</td>\n",
       "      <td>grocery_pos</td>\n",
       "      <td>107.23</td>\n",
       "      <td>F</td>\n",
       "      <td>Orient</td>\n",
       "      <td>99160</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>January</td>\n",
       "      <td>2019</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38859492057661</td>\n",
       "      <td>fraud_Lind-Buckridge</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>220.11</td>\n",
       "      <td>M</td>\n",
       "      <td>Malad City</td>\n",
       "      <td>83252</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>January</td>\n",
       "      <td>2019</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3534093764340240</td>\n",
       "      <td>fraud_Kutch, Hermiston and Farrell</td>\n",
       "      <td>gas_transport</td>\n",
       "      <td>45.00</td>\n",
       "      <td>M</td>\n",
       "      <td>Boulder</td>\n",
       "      <td>59632</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>January</td>\n",
       "      <td>2019</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>375534208663984</td>\n",
       "      <td>fraud_Keeling-Crist</td>\n",
       "      <td>misc_pos</td>\n",
       "      <td>41.96</td>\n",
       "      <td>M</td>\n",
       "      <td>Doe Hill</td>\n",
       "      <td>24433</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>January</td>\n",
       "      <td>2019</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             cc_num                            merchant       category  \\\n",
       "0  2703186189652095          fraud_Rippin, Kub and Mann       misc_net   \n",
       "1      630423337322     fraud_Heller, Gutmann and Zieme    grocery_pos   \n",
       "2    38859492057661                fraud_Lind-Buckridge  entertainment   \n",
       "3  3534093764340240  fraud_Kutch, Hermiston and Farrell  gas_transport   \n",
       "4   375534208663984                 fraud_Keeling-Crist       misc_pos   \n",
       "\n",
       "      amt gender            city    zip  is_fraud  hour day_of_week    month  \\\n",
       "0    4.97      F  Moravian Falls  28654         0     0     Tuesday  January   \n",
       "1  107.23      F          Orient  99160         0     0     Tuesday  January   \n",
       "2  220.11      M      Malad City  83252         0     0     Tuesday  January   \n",
       "3   45.00      M         Boulder  59632         0     0     Tuesday  January   \n",
       "4   41.96      M        Doe Hill  24433         0     0     Tuesday  January   \n",
       "\n",
       "   year  age_holder  \n",
       "0  2019          34  \n",
       "1  2019          44  \n",
       "2  2019          60  \n",
       "3  2019          55  \n",
       "4  2019          36  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#delete all the column that can be linked from another existing columns\n",
    "data.drop(['trans_date_trans_time',\n",
    "    'trans_num',#the uniqness of the row can be stored with indexes instead\n",
    "           'first',#droped because all user will be reffrenced only with their credigt card number\n",
    "           'last',#droped because all user will be reffrenced only with their credigt card number\n",
    "           'merch_lat',#out of context\n",
    "           'merch_long',#out of context\n",
    "           'dob',#replaced by the age \n",
    "           'job',#can be utile to classifie faulty transaction's holder\n",
    "           'state'#same\n",
    "           ,'street'#not helpful\n",
    "           ,'city_pop'#not helpful\n",
    "           ,'unix_time'#not helpful\n",
    "           ,'lat'#not helpful\n",
    "           ,'long'#not helpful\n",
    "          ],axis=1,inplace=True)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "60977fb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cc_num</th>\n",
       "      <th>merchant</th>\n",
       "      <th>category</th>\n",
       "      <th>amt</th>\n",
       "      <th>gender</th>\n",
       "      <th>city</th>\n",
       "      <th>zip</th>\n",
       "      <th>is_fraud</th>\n",
       "      <th>hour</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>age_holder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2449</th>\n",
       "      <td>4613314721966</td>\n",
       "      <td>fraud_Rutherford-Mertz</td>\n",
       "      <td>grocery_pos</td>\n",
       "      <td>281.06</td>\n",
       "      <td>M</td>\n",
       "      <td>Collettsville</td>\n",
       "      <td>28611</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>January</td>\n",
       "      <td>2019</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2472</th>\n",
       "      <td>340187018810220</td>\n",
       "      <td>fraud_Jenkins, Hauck and Friesen</td>\n",
       "      <td>gas_transport</td>\n",
       "      <td>11.52</td>\n",
       "      <td>F</td>\n",
       "      <td>San Antonio</td>\n",
       "      <td>78208</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>January</td>\n",
       "      <td>2019</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2523</th>\n",
       "      <td>340187018810220</td>\n",
       "      <td>fraud_Goodwin-Nitzsche</td>\n",
       "      <td>grocery_pos</td>\n",
       "      <td>276.31</td>\n",
       "      <td>F</td>\n",
       "      <td>San Antonio</td>\n",
       "      <td>78208</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>January</td>\n",
       "      <td>2019</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2546</th>\n",
       "      <td>4613314721966</td>\n",
       "      <td>fraud_Erdman-Kertzmann</td>\n",
       "      <td>gas_transport</td>\n",
       "      <td>7.03</td>\n",
       "      <td>M</td>\n",
       "      <td>Collettsville</td>\n",
       "      <td>28611</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>January</td>\n",
       "      <td>2019</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2553</th>\n",
       "      <td>340187018810220</td>\n",
       "      <td>fraud_Koepp-Parker</td>\n",
       "      <td>grocery_pos</td>\n",
       "      <td>275.73</td>\n",
       "      <td>F</td>\n",
       "      <td>San Antonio</td>\n",
       "      <td>78208</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>January</td>\n",
       "      <td>2019</td>\n",
       "      <td>62</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1813872</th>\n",
       "      <td>2242176657877538</td>\n",
       "      <td>fraud_Willms, Kris and Bergnaum</td>\n",
       "      <td>shopping_pos</td>\n",
       "      <td>1041.51</td>\n",
       "      <td>M</td>\n",
       "      <td>Senatobia</td>\n",
       "      <td>38668</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>December</td>\n",
       "      <td>2020</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1813949</th>\n",
       "      <td>2242176657877538</td>\n",
       "      <td>fraud_Kuhn LLC</td>\n",
       "      <td>shopping_pos</td>\n",
       "      <td>868.09</td>\n",
       "      <td>M</td>\n",
       "      <td>Senatobia</td>\n",
       "      <td>38668</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>December</td>\n",
       "      <td>2020</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1814016</th>\n",
       "      <td>2242176657877538</td>\n",
       "      <td>fraud_Mosciski, Ziemann and Farrell</td>\n",
       "      <td>shopping_net</td>\n",
       "      <td>1039.42</td>\n",
       "      <td>M</td>\n",
       "      <td>Senatobia</td>\n",
       "      <td>38668</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>December</td>\n",
       "      <td>2020</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1814204</th>\n",
       "      <td>2242176657877538</td>\n",
       "      <td>fraud_Bauch-Raynor</td>\n",
       "      <td>grocery_pos</td>\n",
       "      <td>289.27</td>\n",
       "      <td>M</td>\n",
       "      <td>Senatobia</td>\n",
       "      <td>38668</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>December</td>\n",
       "      <td>2020</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1814246</th>\n",
       "      <td>2242176657877538</td>\n",
       "      <td>fraud_Jaskolski-Vandervort</td>\n",
       "      <td>misc_net</td>\n",
       "      <td>766.38</td>\n",
       "      <td>M</td>\n",
       "      <td>Senatobia</td>\n",
       "      <td>38668</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>December</td>\n",
       "      <td>2020</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9651 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   cc_num                             merchant       category  \\\n",
       "2449        4613314721966               fraud_Rutherford-Mertz    grocery_pos   \n",
       "2472      340187018810220     fraud_Jenkins, Hauck and Friesen  gas_transport   \n",
       "2523      340187018810220               fraud_Goodwin-Nitzsche    grocery_pos   \n",
       "2546        4613314721966               fraud_Erdman-Kertzmann  gas_transport   \n",
       "2553      340187018810220                   fraud_Koepp-Parker    grocery_pos   \n",
       "...                   ...                                  ...            ...   \n",
       "1813872  2242176657877538      fraud_Willms, Kris and Bergnaum   shopping_pos   \n",
       "1813949  2242176657877538                       fraud_Kuhn LLC   shopping_pos   \n",
       "1814016  2242176657877538  fraud_Mosciski, Ziemann and Farrell   shopping_net   \n",
       "1814204  2242176657877538                   fraud_Bauch-Raynor    grocery_pos   \n",
       "1814246  2242176657877538           fraud_Jaskolski-Vandervort       misc_net   \n",
       "\n",
       "             amt gender           city    zip  is_fraud  hour day_of_week  \\\n",
       "2449      281.06      M  Collettsville  28611         1     1   Wednesday   \n",
       "2472       11.52      F    San Antonio  78208         1     1   Wednesday   \n",
       "2523      276.31      F    San Antonio  78208         1     3   Wednesday   \n",
       "2546        7.03      M  Collettsville  28611         1     3   Wednesday   \n",
       "2553      275.73      F    San Antonio  78208         1     3   Wednesday   \n",
       "...          ...    ...            ...    ...       ...   ...         ...   \n",
       "1813872  1041.51      M      Senatobia  38668         1    22     Tuesday   \n",
       "1813949   868.09      M      Senatobia  38668         1    22     Tuesday   \n",
       "1814016  1039.42      M      Senatobia  38668         1    22     Tuesday   \n",
       "1814204   289.27      M      Senatobia  38668         1    23     Tuesday   \n",
       "1814246   766.38      M      Senatobia  38668         1    23     Tuesday   \n",
       "\n",
       "            month  year  age_holder  \n",
       "2449      January  2019          34  \n",
       "2472      January  2019          62  \n",
       "2523      January  2019          62  \n",
       "2546      January  2019          34  \n",
       "2553      January  2019          62  \n",
       "...           ...   ...         ...  \n",
       "1813872  December  2020          63  \n",
       "1813949  December  2020          63  \n",
       "1814016  December  2020          63  \n",
       "1814204  December  2020          63  \n",
       "1814246  December  2020          63  \n",
       "\n",
       "[9651 rows x 13 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['is_fraud']==1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7bdbab5",
   "metadata": {},
   "source": [
    "# 4)EDA(exploratory data analysis):\n",
    "     first impression\n",
    "     DESCRIPTION FOR THE DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "86d21deb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cc_num           999\n",
       "merchant         693\n",
       "category          14\n",
       "amt            60616\n",
       "gender             2\n",
       "city             906\n",
       "zip              985\n",
       "is_fraud           2\n",
       "hour              24\n",
       "day_of_week        7\n",
       "month             12\n",
       "year               2\n",
       "age_holder        82\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## global statistic:\n",
    "\n",
    "data.nunique()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "01954936",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='is_fraud', ylabel='count'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAESCAYAAADnvkIDAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAS9UlEQVR4nO3dfZBdd13H8feHpA+DIBQSEJu2KRiVSmmhO0WxQstACQ40PoCmFilYjONQHHXsTDvOtEwYHRUdB6EIEUItI6mAFsNMoHQoGMdSyEYDfYBiDGiTURKbFiwgJeXrH/es3mx+m72b5uxddt+vmTt7z+/h3O/OpPvp75xzz0lVIUnSdI8ZdwGSpIXJgJAkNRkQkqQmA0KS1GRASJKaDAhJUtOiC4gkm5PsT3LXiON/Ick9Se5O8v6+65Ok7xVZbN+DSPIC4CHgxqp61ixj1wAfAF5UVQ8keUpV7Z+POiVpoVt0K4iq2g4cHG5L8owkH0uyM8k/JPnRrutXgeur6oFuruEgSZ1FFxAz2AS8sarOA34HeEfX/sPADyf5xyR3JFk7tgolaYFZPu4C+pbkccDzgQ8mmWo+qfu5HFgDXAisArYnObuqHpznMiVpwVn0AcFglfRgVZ3b6NsLfKaqvgN8OcmXGATGjnmsT5IWpEV/iKmqvs7gj/+rADJwTtf9YQarB5KsYHDIac8YypSkBWfRBUSSLcCngR9JsjfJFcBlwBVJPgfcDazrht8C3J/kHuCTwFVVdf846pakhWbRXeYqSTo+Ft0KQpJ0fCyqk9QrVqyo1atXj7sMSfqesXPnzv+qqpWtvkUVEKtXr2ZycnLcZUjS94wk/zZTn4eYJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTYvqm9SP1nlX3TjuErQA7XzLa8ZdgjQWriAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmnq7zDXJZuDlwP6qelaj/yrgsqE6ngmsrKqDSb4C/DfwCHCoqib6qlOS1NbnCuIGYO1MnVX1lqo6t6rOBa4B/r6qDg4NuajrNxwkaQx6C4iq2g4cnHXgwKXAlr5qkSTN3djPQSR5LIOVxt8MNRfw8SQ7k2yYZf6GJJNJJg8cONBnqZK0pIw9IIBXAP847fDSBVX1XOBlwBuSvGCmyVW1qaomqmpi5cqVfdcqSUvGQgiI9Uw7vFRV+7qf+4GbgfPHUJckLWljDYgkTwBeCPzdUNv3JXn81HvgYuCu8VQoSUtXn5e5bgEuBFYk2QtcB5wAUFXv7Ib9LPDxqvrG0NSnAjcnmarv/VX1sb7qlCS19RYQVXXpCGNuYHA57HDbHuCcfqqSJI1qIZyDkCQtQAaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqcmAkCQ19RYQSTYn2Z/krhn6L0zytSS7ute1Q31rk9ybZHeSq/uqUZI0sz5XEDcAa2cZ8w9VdW732giQZBlwPfAy4Czg0iRn9VinJKmht4Coqu3AwWOYej6wu6r2VNXDwE3AuuNanCRpVuM+B/ETST6X5KNJfqxrOxW4b2jM3q6tKcmGJJNJJg8cONBnrZK0pIwzIP4JOKOqzgHeBnz4WHZSVZuqaqKqJlauXHk865OkJW1sAVFVX6+qh7r324ATkqwA9gGnDQ1d1bVJkubR2AIiyQ8kSff+/K6W+4EdwJokZyY5EVgPbB1XnZK0VC3va8dJtgAXAiuS7AWuA04AqKp3Aq8Efj3JIeBbwPqqKuBQkiuBW4BlwOaquruvOiVJbb0FRFVdOkv/24G3z9C3DdjWR12SpNGM+yomSdICZUBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVJTbwGRZHOS/UnumqH/siSfT3JnktuTnDPU95WufVeSyb5qlCTNrM8VxA3A2qP0fxl4YVWdDbwZ2DSt/6KqOreqJnqqT5J0FMv72nFVbU+y+ij9tw9t3gGs6qsWSdLcLZRzEFcAHx3aLuDjSXYm2XC0iUk2JJlMMnngwIFei5SkpaS3FcSoklzEICAuGGq+oKr2JXkKcGuSL1bV9tb8qtpEd3hqYmKiei9YkpaIsa4gkjwbeDewrqrun2qvqn3dz/3AzcD546lQkpausQVEktOBvwV+uaq+NNT+fUkeP/UeuBhoXgklSepPb4eYkmwBLgRWJNkLXAecAFBV7wSuBZ4MvCMJwKHuiqWnAjd3bcuB91fVx/qqU5LU1udVTJfO0v964PWN9j3AOUfOkCTNp4VyFZMkaYExICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmkYKiCSfGKVNkrR4HPWBQUlOBh7L4KlwpwDpur4fOLXn2iRJYzTbE+V+DfhN4AeBnfx/QHwdeHt/ZUmSxu2oAVFVbwXemuSNVfW2eapJkrQAjHQOoqreluT5SX4pyWumXrPNS7I5yf4kd83QnyR/lmR3ks8nee5Q3+VJ/qV7XT76ryRJOh5mO8QEQJL3Ac8AdgGPdM0F3DjL1BsYHIqaadzLgDXd63nAnwPPS/Ik4DpgovucnUm2VtUDo9QrSXr0RgoIBn+oz6qqmsvOq2p7ktVHGbIOuLHb7x1JnpjkacCFwK1VdRAgya3AWmDLXD5fknTsRv0exF3AD/Tw+acC9w1t7+3aZmqXJM2TUVcQK4B7knwW+PZUY1Vd0ktVc5BkA7AB4PTTTx9zNZK0eIwaEG/q6fP3AacNba/q2vYxOMw03P6p1g6qahOwCWBiYmJOh8AkSTMbKSCq6u97+vytwJVJbmJwkvprVfUfSW4Bfr/7ch7AxcA1PdUgSWoY9Sqm/2ZwNRHAicAJwDeq6vtnmbeFwUpgRZK9DK5MOgGgqt4JbAN+GtgNfBN4Xdd3MMmbgR3drjZOnbCWJM2PUVcQj596nyQMrj768RHmXTpLfwFvmKFvM7B5lPokScffnO/mWgMfBl56/MuRJC0Uox5i+rmhzccw+F7E//RSkSRpQRj1KqZXDL0/BHyFwWEmSdIiNeo5iNf1XYgkaWEZ9YFBq5Lc3N14b3+Sv0myqu/iJEnjM+pJ6vcy+M7CD3avj3RtkqRFatSAWFlV762qQ93rBmBlj3VJksZs1IC4P8mrkyzrXq8G7u+zMEnSeI0aEL8C/ALwn8B/AK8EXttTTZKkBWDUy1w3ApdPPbCne6DPHzMIDknSIjTqCuLZw09z6+6L9Jx+SpIkLQSjBsRjhu6sOrWCGHX1IUn6HjTqH/k/AT6d5IPd9quA3+unJEnSQjDqN6lvTDIJvKhr+rmquqe/siRJ4zbyYaIuEAwFSVoi5ny7b0nS0mBASJKaDAhJUpMBIUlq6jUgkqxNcm+S3UmubvT/aZJd3etLSR4c6ntkqG9rn3VKko7U25fdkiwDrgdeAuwFdiTZOnx5bFX91tD4N3L4t7O/VVXn9lWfJOno+lxBnA/srqo9VfUwcBNHf0zppcCWHuuRJM1BnwFxKnDf0Pberu0ISc4AzgRuG2o+OclkkjuS/ExvVUqSmhbK/ZTWAx+qqkeG2s6oqn1Jng7cluTOqvrX6ROTbAA2AJx++unzU60kLQF9riD2AacNba/q2lrWM+3wUlXt637uAT7FDHePrapNVTVRVRMrV/qQO0k6XvoMiB3AmiRnJjmRQQgccTVSkh8FTgE+PdR2SpKTuvcrgJ/E23xI0rzq7RBTVR1KciVwC7AM2FxVdyfZCExW1VRYrAduqqoamv5M4F1JvssgxP7AmwNK0vzq9RxEVW0Dtk1ru3ba9psa824Hzu6zNknS0flNaklSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNvQZEkrVJ7k2yO8nVjf7XJjmQZFf3ev1Q3+VJ/qV7Xd5nnZKkIy3va8dJlgHXAy8B9gI7kmytqnumDf3rqrpy2twnAdcBE0ABO7u5D/RVryTpcH2uIM4HdlfVnqp6GLgJWDfi3JcCt1bVwS4UbgXW9lSnJKmhz4A4FbhvaHtv1zbdzyf5fJIPJTltjnMlST0Z90nqjwCrq+rZDFYJfznXHSTZkGQyyeSBAweOe4GStFT1GRD7gNOGtld1bf+nqu6vqm93m+8Gzht17tA+NlXVRFVNrFy58rgULknqNyB2AGuSnJnkRGA9sHV4QJKnDW1eAnyhe38LcHGSU5KcAlzctUmS5klvVzFV1aEkVzL4w74M2FxVdyfZCExW1VbgN5JcAhwCDgKv7eYeTPJmBiEDsLGqDvZVqyTpSL0FBEBVbQO2TWu7duj9NcA1M8zdDGzusz5J0szGfZJakrRAGRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmnoNiCRrk9ybZHeSqxv9v53kniSfT/KJJGcM9T2SZFf32tpnnZKkIy3va8dJlgHXAy8B9gI7kmytqnuGhv0zMFFV30zy68AfAb/Y9X2rqs7tqz5J0tH1uYI4H9hdVXuq6mHgJmDd8ICq+mRVfbPbvANY1WM9kqQ56DMgTgXuG9re27XN5Argo0PbJyeZTHJHkp+ZaVKSDd24yQMHDjyqgiVJ/6+3Q0xzkeTVwATwwqHmM6pqX5KnA7clubOq/nX63KraBGwCmJiYqHkpWJKWgD5XEPuA04a2V3Vth0nyYuB3gUuq6ttT7VW1r/u5B/gU8Jwea5UkTdNnQOwA1iQ5M8mJwHrgsKuRkjwHeBeDcNg/1H5KkpO69yuAnwSGT25LknrW2yGmqjqU5ErgFmAZsLmq7k6yEZisqq3AW4DHAR9MAvDvVXUJ8EzgXUm+yyDE/mDa1U+SpJ71eg6iqrYB26a1XTv0/sUzzLsdOLvP2iRJR+c3qSVJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqcmAkCQ19RoQSdYmuTfJ7iRXN/pPSvLXXf9nkqwe6ruma783yUv7rFOSdKTeAiLJMuB64GXAWcClSc6aNuwK4IGq+iHgT4E/7OaeBawHfgxYC7yj258kaZ4s73Hf5wO7q2oPQJKbgHXAPUNj1gFv6t5/CHh7knTtN1XVt4EvJ9nd7e/TPdYrLWj/vvHscZegBej0a+/sbd99BsSpwH1D23uB5800pqoOJfka8OSu/Y5pc09tfUiSDcCGbvOhJPc++tIFrAD+a9xFLAT548vHXYKO5L/PKdfl0e7hjJk6+gyIeVFVm4BN465jsUkyWVUT465DavHf5/zo8yT1PuC0oe1VXVtzTJLlwBOA+0ecK0nqUZ8BsQNYk+TMJCcyOOm8ddqYrcDU+v2VwG1VVV37+u4qpzOBNcBne6xVkjRNb4eYunMKVwK3AMuAzVV1d5KNwGRVbQXeA7yvOwl9kEGI0I37AIMT2oeAN1TVI33VqiYP22kh89/nPMjgf9glSTqc36SWJDUZEJKkJgNCR5jtFinSuCTZnGR/krvGXctSYEDoMCPeIkUalxsY3H5H88CA0HT/d4uUqnoYmLpFijR2VbWdwRWPmgcGhKZr3SKleZsTSYubASFJajIgNJ23OZEEGBA60ii3SJG0BBgQOkxVHQKmbpHyBeADVXX3eKuSBpJsYfBcmB9JsjfJFeOuaTHzVhuSpCZXEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQ0gyS3H8OcVyX5QpJP9lDPam9zrflkQEgzqKrnH8O0K4BfraqLhhuT9Pb8d6kvBoQ0gyQPdT+flmR7kl1J7kryUzOMvxa4AHhPkrckeW2SrUluAz6R5HFJPpHkn5LcmWRdN++wlUGS30nypu79eUk+l+RzwBt6/pWlwxgQ0ux+Cbilqs4FzgF2tQZV1UZgErisqq7qmp8LvLKqXgj8D/CzVfVc4CLgT5Jkls9+L/DGqjrnUf8W0hy57JVmtwPYnOQE4MNVtWsOc2+tqqkH3AT4/SQvAL7L4DkbT51pYpInAk/sHpID8D4GT/qT5oUrCGkW3R/oFzC47fkNSV4zh+nfGHp/GbASOK9bjXwVOBk4xOH/LZ78qAqWjhMDQppFkjOAr1bVXwDvZnDY6Fg8AdhfVd9JchFwRtf+VeApSZ6c5CTg5QBV9SDwYJILunGXHevvIB0LDzFJs7sQuCrJd4CHgLmsIIb9FfCRJHcyOFfxRYAuMDYCn2WwSvni0JzXMTi8VcDHj/FzpWPi7b4lSU0eYpIkNXmISToGST4DnDSt+Zer6s5x1CP1wUNMkqQmDzFJkpoMCElSkwEhSWoyICRJTf8LrIYsKFEOFy8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# general statistic on each numerical column \n",
    "data.describe()\n",
    "# number of fruad vs non frauds sample and % ===> conclusion need for data balncing\n",
    "data['is_fraud'].value_counts()/len(data)\n",
    "sns.countplot(x='is_fraud',data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1cc9b2f",
   "metadata": {},
   "source": [
    "## the data is imbalenced so next we will test it in three difftrent scinario:\n",
    "    overfit the positive class \n",
    "    underfit the negative class\n",
    "    SMOT technique"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f071629",
   "metadata": {},
   "source": [
    "# let's explore most relation between the feature"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cc504f6",
   "metadata": {},
   "source": [
    "## Effect of the amount on the faulty transaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "20c49bd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 4600x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 4600x1200 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig,axes=plt.subplots(nrows=3,ncols=2)\n",
    "#  showing outliers ===>\n",
    "plt.figure(figsize=(23,6),dpi=200)\n",
    "axes=[]\n",
    "axes.append(sns.boxplot(x='amt',data=data,ax=plt.subplot(234)))\n",
    "plt.xlim((0,1000))\n",
    "axes[0].set_title('distribution of the amount ')\n",
    "plt.show()\n",
    "\n",
    "#     dist on the fraud \n",
    "axes.append(sns.displot(x='amt',data=data[(data['amt']<2000)&data['is_fraud']==1],bins=12))\n",
    "axes.append(sns.displot(x='amt',data=data[(data['amt']<200)&(data['is_fraud']==0)],bins=5))\n",
    "# axes[1].axes[0][0].set_ylim((0,1000))\n",
    "axes[1].axes[0][0].set_title('fraudelent transaction')\n",
    "axes[1].set(xlim=(0, 2000), xticks=[250,500,1000,1500,2000])\n",
    "axes[2].axes[0][0].set_title(' none fraudelent transaction')\n",
    "\n",
    "axes[2].set(xlim=(0, 200), xticks=[50,100,150,200])\n",
    "plt.figure(figsize=(23,6),dpi=200)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0187831",
   "metadata": {},
   "source": [
    "# .### observation and precaution\n",
    "from the boxplot we can say that 50% of amount tranaction data are <200$ and looking more into the fraudelent tranaction i found that most of the them are >500$\n",
    "if we take that into considiration, any transaction that had more that 500$ should be controled and confirmed by a human contact"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5c05b63",
   "metadata": {},
   "source": [
    "### data VS FRAUD OF ALL TRANSACTION PER DAYS  WE WILL DISCOVER HOW MUCH FRAUD ATTEMP WHER COMMITED and how are the fraud distrubuted througth the time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a87bbfc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                is_fraud\n",
      "year month              \n",
      "2019 April         68078\n",
      "     August        87359\n",
      "     December     141060\n",
      "     February      49866\n",
      "     January       52525\n",
      "     July          86596\n",
      "     June          86064\n",
      "     March         70939\n",
      "     May           72532\n",
      "     November      70421\n",
      "     October       68758\n",
      "     September     70652\n",
      "2020 April         66892\n",
      "     August        88759\n",
      "     December     139538\n",
      "     February      47791\n",
      "     January       52202\n",
      "     July          85848\n",
      "     June          87805\n",
      "     March         72850\n",
      "     May           74343\n",
      "     November      72635\n",
      "     October       69348\n",
      "     September     69533\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year', ylabel='frauds%'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2400x2400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import calendar\n",
    "\n",
    "fraud_per_day=data[['day_of_week','is_fraud']].groupby('day_of_week').count()\n",
    "fig = plt.figure(figsize=(8,5))\n",
    "#    YEARS \n",
    "fraud_per_year=data[['year','month','is_fraud']].groupby(['year','month']).count()\n",
    "print(fraud_per_year)\n",
    "trans_per_year=sns.countplot(x='year',data=data,hue='month',palette='winter')\n",
    "trans_per_year.legend(loc=(-0.5,0))\n",
    "trans_per_year.set_title(\"total transaction on each month for 2019 ,2020\")\n",
    "trans_per_year.set_xlabel('total transaction')\n",
    "trans_per_year.set_ylabel('year')\n",
    "\n",
    "frauds=data[data['is_fraud']==1]\n",
    "fraud_per_year=data[['year','month','is_fraud']].groupby(['year','month']).count()\n",
    "fraud_per_year.columns=['total transaction']\n",
    "f=frauds[['year','month','is_fraud']].groupby(['year','month']).count()\n",
    "f.columns=['faulty transaction']\n",
    "results_date=pd.merge(on=['year','month'],left=fraud_per_year,right=f,how='outer')\n",
    "results_date['frauds%']=results_date['faulty transaction']*100/results_date['total transaction']\n",
    "#    WE WILL DISCOVER HOW MUCH FRAUD ATTEMP WHER COMMITE\n",
    "plt.figure(figsize=(12,12),dpi=200)\n",
    "results_date\n",
    "\n",
    "results_date=results_date.reset_index()\n",
    "\n",
    "## observation(date)\n",
    "\n",
    "plt.figure(figsize=(12,8),dpi=200)\n",
    "sns.barplot(y=results_date['frauds%'] ,x=results_date['year'],data=results_date,hue='month',hue_order=list(calendar.month_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7f1a5e1",
   "metadata": {},
   "source": [
    "### observation and precaution\n",
    "    january and december are the most month were most transaction, not only that ,the last diagram shows that they are also the first 2 to be suspected, so we should control the transaction more offten on this months (at least)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35be5e1a",
   "metadata": {},
   "source": [
    "### DOES GENDER AFFECT THE FRAUD VARIABLE\n",
    "     diagrames for distrubution of data on gender and for the frauds non fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "56ba2101",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>total transaction</th>\n",
       "      <th>faulty transaction</th>\n",
       "      <th>frauds%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>1014749</td>\n",
       "      <td>4899</td>\n",
       "      <td>0.482779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>837645</td>\n",
       "      <td>4752</td>\n",
       "      <td>0.567305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total transaction  faulty transaction   frauds%\n",
       "gender                                                 \n",
       "F                 1014749                4899  0.482779\n",
       "M                  837645                4752  0.567305"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dist of dataset over the gender with the two target category\n",
    "ax=sns.countplot(x='gender',data=data)\n",
    "for p in ax.patches:\n",
    "   ax.annotate('{:.1f}'.format(p.get_height()), (p.get_x()+0.25, p.get_height()+0.01))\n",
    "plt.show()\n",
    "frauds=data[data['is_fraud']==1]\n",
    "fraud_per_year=data[['gender','is_fraud']].groupby(['gender',]).count()\n",
    "fraud_per_year.columns=['total transaction']\n",
    "f=frauds[['gender','is_fraud']].groupby(['gender']).count()\n",
    "f.columns=['faulty transaction']\n",
    "results_gender=pd.merge(on='gender',left=fraud_per_year,right=f,how='outer')\n",
    "\n",
    "results_gender['frauds%']=results_gender['faulty transaction']*100/results_gender['total transaction']\n",
    "results_gender\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5da62c8",
   "metadata": {},
   "source": [
    "### observation and precaution\n",
    "   nothing can't be concluded from this analyst , although the gender doesn't have an effect,we can notice that femal tend to use their credit card more offten than man,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "df219d95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x2400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### kde of the distrubtion of diffrent sample on the age axes\n",
    "# and the age of most frauded account \n",
    "plt.figure(figsize=(8,12),dpi=200)\n",
    "sns.kdeplot(x='age_holder',data=data,ax=ax,label=\"Total Transaction\") \n",
    "\n",
    "sns.kdeplot(data=data[data['is_fraud']==1],x='age_holder',ax=ax,label=\"Fraudulent Transactions\")\n",
    "# sns.kdeplot(data=data[data['is_fraud']==0],x='age_holder',ax=ax,label=\" None Fraudulent Transactions\")\n",
    "ax.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "ax.set_title('Distibution of the total and fradulent transaction with age')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b4f5ed95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=data,x='age_holder',hue='is_fraud',common_norm=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45dbbf81",
   "metadata": {},
   "source": [
    "### observation and precaution\n",
    "we can clearly notice that most of credit card holder are aged between 30-40 and 45-55,so it goes to the fraudelent aged between 30-40 and 50-60\n",
    "this age should be more offten supervised"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1390b8b8",
   "metadata": {},
   "source": [
    "### top 5 CUSTOMER THAT HAVE THE HEIGHEST DIFRENCE BETWEEN FRAUD TRNASCTION AND NON FRAUD and their behaviour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d4af8980",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4593569795412,\n",
       " 4599285557366057,\n",
       " 3520550088202337,\n",
       " 6538441737335434,\n",
       " 4260128500325]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frauds=data[data['is_fraud']==1]\n",
    "fraud_per_year=frauds[['cc_num','is_fraud']].groupby('cc_num').count().sort_values('is_fraud',ascending=False)\n",
    "fraud_per_year.columns=['total frauds']\n",
    "sus_list=list(fraud_per_year[:5].index)\n",
    "sus_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b800899e",
   "metadata": {},
   "source": [
    "### TOP Category THAT HAVE THE HEIGHEST DIFFRENCE BETWEEN FRAUD TRNASCTION AND NON FRAUD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8fdf1a15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "      <th>faulty</th>\n",
       "      <th>%frauds</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>shopping_net</th>\n",
       "      <td>139322</td>\n",
       "      <td>2219</td>\n",
       "      <td>1.592713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>misc_net</th>\n",
       "      <td>90654</td>\n",
       "      <td>1182</td>\n",
       "      <td>1.303859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grocery_pos</th>\n",
       "      <td>176191</td>\n",
       "      <td>2228</td>\n",
       "      <td>1.264537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shopping_pos</th>\n",
       "      <td>166463</td>\n",
       "      <td>1056</td>\n",
       "      <td>0.634375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gas_transport</th>\n",
       "      <td>188029</td>\n",
       "      <td>772</td>\n",
       "      <td>0.410575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>misc_pos</th>\n",
       "      <td>114229</td>\n",
       "      <td>322</td>\n",
       "      <td>0.281890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grocery_net</th>\n",
       "      <td>64878</td>\n",
       "      <td>175</td>\n",
       "      <td>0.269737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>travel</th>\n",
       "      <td>57956</td>\n",
       "      <td>156</td>\n",
       "      <td>0.269170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>personal_care</th>\n",
       "      <td>130085</td>\n",
       "      <td>290</td>\n",
       "      <td>0.222931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entertainment</th>\n",
       "      <td>134118</td>\n",
       "      <td>292</td>\n",
       "      <td>0.217719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kids_pets</th>\n",
       "      <td>161727</td>\n",
       "      <td>304</td>\n",
       "      <td>0.187971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>food_dining</th>\n",
       "      <td>130729</td>\n",
       "      <td>205</td>\n",
       "      <td>0.156813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home</th>\n",
       "      <td>175460</td>\n",
       "      <td>265</td>\n",
       "      <td>0.151032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>health_fitness</th>\n",
       "      <td>122553</td>\n",
       "      <td>185</td>\n",
       "      <td>0.150955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 total  faulty   %frauds\n",
       "category                                \n",
       "shopping_net    139322    2219  1.592713\n",
       "misc_net         90654    1182  1.303859\n",
       "grocery_pos     176191    2228  1.264537\n",
       "shopping_pos    166463    1056  0.634375\n",
       "gas_transport   188029     772  0.410575\n",
       "misc_pos        114229     322  0.281890\n",
       "grocery_net      64878     175  0.269737\n",
       "travel           57956     156  0.269170\n",
       "personal_care   130085     290  0.222931\n",
       "entertainment   134118     292  0.217719\n",
       "kids_pets       161727     304  0.187971\n",
       "food_dining     130729     205  0.156813\n",
       "home            175460     265  0.151032\n",
       "health_fitness  122553     185  0.150955"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DIGRAME FOR SHOWING THE MOST %DIFF OF EACH CATEGORY\n",
    "def fraudy(category):\n",
    "    f=data[[category,'is_fraud']].groupby(category).count().sort_values('is_fraud')\n",
    "    frauds=data[data['is_fraud']==1]\n",
    "    g=frauds[[category,'is_fraud']].groupby(category).count().sort_values('is_fraud')\n",
    "    result=pd.merge(left=f,right=g,on=category)\n",
    "    result.columns=['total','faulty']\n",
    "    result['%frauds']=result['faulty']*100/result['total']\n",
    "    return result.sort_values('%frauds',ascending=False)\n",
    "# analysing\n",
    "def analyse(string):\n",
    "# filtreing\n",
    "    sample=data[data['merchant']==string]\n",
    "    print(f\"analising{string}\")\n",
    "    #analysing time\n",
    "    #mean of amt\n",
    "    return sample.describe()\n",
    "fraudy('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4296fd9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'% of fraudelent tranaction in each category '}, xlabel='% of the fraudenlent transaction compared to all tranaction', ylabel='CATEGORY'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "te=fraudy('category').reset_index()\n",
    "plt.figure(figsize=(12,8),dpi=200)\n",
    "def to_per(age:int):\n",
    "    if age <= 20 :\n",
    "        return '0<20'\n",
    "    elif 20<age<=40 :\n",
    "        return '20-40'\n",
    "    elif 40<age<=60 :\n",
    "        return '40-60'\n",
    "    elif 60<age<=80 :\n",
    "        return '60-80'\n",
    "    elif 80<age<=100 :\n",
    "        return '80-100'\n",
    "\n",
    "ax=sns.barplot(x='%frauds',data=te,y='category')\n",
    "ax.set_title(\"% of fraudelent tranaction in each category \")\n",
    "ax.set_ylabel('CATEGORY')\n",
    "ax.set_xlabel('% of the fraudenlent transaction compared to all tranaction')\n",
    "\n",
    "ax"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69c210c3",
   "metadata": {},
   "source": [
    "### observation and precaution\n",
    "we can see that categories like shopping_net misc_net,grocery_pos are the most suspected to be fraudelent so more supervising is needed with is logical since women tend to shop more online"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14091f47",
   "metadata": {},
   "source": [
    "### top 5 city that had the fraud\n",
    "*)distrubution of data on the city variable+frauds and the nonfruad\n",
    "*)analyse the transaction per city per gender\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2be4e4cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "te=fraudy('city')\n",
    "plt.figure(figsize=(12,8),dpi=200)\n",
    "def to_per(age:int):\n",
    "    if age <= 20 :\n",
    "        return '0<20'\n",
    "    elif 20<age<=40 :\n",
    "        return '20-40'\n",
    "    elif 40<age<=60 :\n",
    "        return '40-60'\n",
    "    elif 60<age<=80 :\n",
    "        return '60-80'\n",
    "    elif 80<age<=100 :\n",
    "        return '80-100'\n",
    "te['range%']=np.vectorize(to_per)(te['%frauds'])\n",
    "ax=sns.countplot(x='range%',data=te.sort_values('range%'))\n",
    "ax.set_title(\"% of fraudelent tranaction \")\n",
    "ax.set_ylabel('number of cities')\n",
    "ax.set_xlabel('% of the fraudenlent transaction compared to all tranaction')\n",
    "for p in ax.patches:\n",
    "   ax.annotate('{:.1f}'.format(p.get_height()), (p.get_x()+0.25, p.get_height()+0.01))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cdf0564",
   "metadata": {},
   "source": [
    "### observation and precaution\n",
    "70 city are having more than 80% of their tranasction  fraudelent :the only thing said here is :putting all the tranction of these cities under control "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0d93f21a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x274f6a9cbb0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2800x1600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8),dpi=200)\n",
    "\n",
    "# analse the zip per city\n",
    "\n",
    "l=data[['city','zip']].groupby(['city']).nunique().sort_values('zip',ascending=False)\n",
    "rrr=pd.merge(left=fraudy('city'),right=l,on='city')\n",
    "rrr[rrr['zip']>1]\n",
    "sns.catplot(x='zip',y='%frauds',data=rrr,legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da03d4e8",
   "metadata": {},
   "source": [
    "### observation and precaution\n",
    "     even though some cities have more than one zip code ,it doesn't indicate any relationj with          fraudelent transaction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b96fe66f",
   "metadata": {},
   "source": [
    "# TOP 10 MERCHANT THAT HAVE THE HEIGHEST % DIFRENCE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "941ac0e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '% of the fraudenlent transaction compared to their tranaction')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f=data[['merchant','is_fraud']].groupby('merchant').count().sort_values('is_fraud')\n",
    "frauds=data[data['is_fraud']==1]\n",
    "g=frauds[['merchant','is_fraud']].groupby('merchant').count().sort_values('is_fraud')\n",
    "results_merchant=pd.merge(left=f,right=g,on='merchant')\n",
    "\n",
    "results_merchant.columns=['total','faulty']\n",
    "results_merchant['%frauds']=results_merchant['faulty']*100/results_merchant['total']\n",
    "results_merchant.sort_values('%frauds',ascending=False)\n",
    "#analysing\n",
    "def analyse(string):\n",
    "#     filtreing\n",
    "    sample=data[data['merchant']==string]\n",
    "    print(f\"analising{string}\")\n",
    "    #analysing time\n",
    "    #mean of amt\n",
    "    return sample.describe()\n",
    "results_merchant=results_merchant.sort_values('%frauds',ascending=False).reset_index()\n",
    "plt.figure(figsize=(12,8),dpi=200)\n",
    "ax=sns.barplot(x='%frauds',y='merchant',data=results_merchant[:10])\n",
    "ax.set_title(\"% of fraudelent tranaction the most active merchant\")\n",
    "ax.set_ylabel('Mrchant Names')\n",
    "ax.set_xlabel('% of the fraudenlent transaction compared to their tranaction')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e73dc5a3",
   "metadata": {},
   "source": [
    "### observation and precaution\n",
    "      this merchant should be controled more offten"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddb5cda4",
   "metadata": {},
   "source": [
    "### encode the categoricale features and scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d94cd050",
   "metadata": {},
   "outputs": [],
   "source": [
    "### first lets try with the lR but before:\n",
    "# *)encode the categorical feature,since  the cc_num and the marcvhant are too much to encode and we cant risk droping some and overfitting the model\n",
    "##creating dummy variable:\n",
    "def to_dummy(column):\n",
    "     return pd.get_dummies(data[column],drop_first=True)\n",
    "# for )category:\n",
    "dummy_cat=to_dummy('category')\n",
    "# for)gender\n",
    "dummy_g=to_dummy('gender')\n",
    "# for age\n",
    "def zipping(age:int):\n",
    "    if age <= 20 :\n",
    "        return '0-20'\n",
    "    elif 20<age<=40 :\n",
    "        return '20-40'\n",
    "    elif 40<age<=60 :\n",
    "        return '40-60'\n",
    "    elif 60<age<=80 :\n",
    "        return '60-80'\n",
    "    elif 80<age<=100 :\n",
    "        return '80-100'\n",
    "    elif age > 100 :\n",
    "        return '>100'\n",
    "data['age_holder']=np.vectorize(zipping)(data['age_holder'])\n",
    "dummy_age=to_dummy('age_holder')\n",
    "dummy_day=to_dummy('day_of_week')\n",
    "dummy_month=to_dummy('month')\n",
    "test=data.drop(['category','age_holder','cc_num','merchant','month','day_of_week','gender','city','zip'],axis=1)\n",
    "test=pd.concat([test,dummy_g,dummy_age,dummy_day,dummy_cat,dummy_month],axis=1)\n",
    "\n",
    "\n",
    "#scaling\n",
    "scaler = sd()\n",
    "X= scaler.fit_transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8bca9956",
   "metadata": {},
   "outputs": [
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       "         August  December  February  January  July  June  March  May  \\\n",
       "0             0         0         0        1     0     0      0    0   \n",
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       "1852393       0         1         0        0     0     0      0    0   \n",
       "\n",
       "         November  October  September  \n",
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       "\n",
       "[1852394 rows x 11 columns]"
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     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
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  {
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   "id": "2ea5acf5",
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       "            amt  is_fraud  hour  year  M  20-40  40-60  60-80  80-100  Monday  \\\n",
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       "1852391   86.88         0    23  2020  0      0      1      0       0       0   \n",
       "1852392    7.99         0    23  2020  1      0      1      0       0       0   \n",
       "1852393   38.13         0    23  2020  1      1      0      0       0       0   \n",
       "\n",
       "         ...  December  February  January  July  June  March  May  November  \\\n",
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       "1852393  ...         1         0        0     0     0      0    0         0   \n",
       "\n",
       "         October  September  \n",
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       "[1852394 rows x 39 columns]"
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     "execution_count": 21,
     "metadata": {},
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    "test"
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  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4269db8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=test.drop('is_fraud',axis=1)\n",
    "y_train=test['is_fraud']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "46fa740f",
   "metadata": {},
   "outputs": [
    {
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>220.11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41.96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1852389</th>\n",
       "      <td>43.77</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1852390</th>\n",
       "      <td>111.84</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1852391</th>\n",
       "      <td>86.88</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1852392</th>\n",
       "      <td>7.99</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1852393</th>\n",
       "      <td>38.13</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1852394 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            amt  is_fraud  hour  year  M  20-40  40-60  60-80  80-100  Monday  \\\n",
       "0          4.97         0     0  2019  0      1      0      0       0       0   \n",
       "1        107.23         0     0  2019  0      0      1      0       0       0   \n",
       "2        220.11         0     0  2019  1      0      1      0       0       0   \n",
       "3         45.00         0     0  2019  1      0      1      0       0       0   \n",
       "4         41.96         0     0  2019  1      1      0      0       0       0   \n",
       "...         ...       ...   ...   ... ..    ...    ...    ...     ...     ...   \n",
       "1852389   43.77         0    23  2020  1      0      1      0       0       0   \n",
       "1852390  111.84         0    23  2020  1      1      0      0       0       0   \n",
       "1852391   86.88         0    23  2020  0      0      1      0       0       0   \n",
       "1852392    7.99         0    23  2020  1      0      1      0       0       0   \n",
       "1852393   38.13         0    23  2020  1      1      0      0       0       0   \n",
       "\n",
       "         ...  December  February  January  July  June  March  May  November  \\\n",
       "0        ...         0         0        1     0     0      0    0         0   \n",
       "1        ...         0         0        1     0     0      0    0         0   \n",
       "2        ...         0         0        1     0     0      0    0         0   \n",
       "3        ...         0         0        1     0     0      0    0         0   \n",
       "4        ...         0         0        1     0     0      0    0         0   \n",
       "...      ...       ...       ...      ...   ...   ...    ...  ...       ...   \n",
       "1852389  ...         1         0        0     0     0      0    0         0   \n",
       "1852390  ...         1         0        0     0     0      0    0         0   \n",
       "1852391  ...         1         0        0     0     0      0    0         0   \n",
       "1852392  ...         1         0        0     0     0      0    0         0   \n",
       "1852393  ...         1         0        0     0     0      0    0         0   \n",
       "\n",
       "         October  September  \n",
       "0              0          0  \n",
       "1              0          0  \n",
       "2              0          0  \n",
       "3              0          0  \n",
       "4              0          0  \n",
       "...          ...        ...  \n",
       "1852389        0          0  \n",
       "1852390        0          0  \n",
       "1852391        0          0  \n",
       "1852392        0          0  \n",
       "1852393        0          0  \n",
       "\n",
       "[1852394 rows x 39 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e5fc06b",
   "metadata": {},
   "source": [
    "# BALENCING THE DATA USING THE OVER SAMPLING UNDER SAPMLING AND SMOC AND TEST WITH EACH CASE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a252fc43",
   "metadata": {},
   "source": [
    "###### now that we encode our data we will  implement diffrente Classifier and compare their performance using the AUC to finaly choose which one we 'd pick up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6e6a9624",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "def split_scale(data,option=None):\n",
    "    x_train=data.drop('is_fraud',axis=1)\n",
    "    y_train=data['is_fraud']\n",
    "    scaler = sd()\n",
    "    x_train = scaler.fit_transform(x_train)\n",
    "    if option!=None:\n",
    "        try:\n",
    "            x_train,y_train=eval(option)(x_train,y_train)\n",
    "            \n",
    "        except:\n",
    "                raise ValueError(\"option must be valid string\")\n",
    "    \n",
    "\n",
    "    return train_test_split(x_train,y_train,test_size=0.4,random_state=42)\n",
    "def display_score(model,data,grid=None,option=None):\n",
    "    x_train,x_test,y_train,y_test=split_scale(data,option) \n",
    "    x_test = scaler.fit_transform(x_test)\n",
    "    name=model.__class__.__name__\n",
    "    if option:\n",
    "        name+=\" with '\"+option+\"' fitting \"\n",
    "    if grid:\n",
    "        model=GridSearchCV(estimator=model,param_grid=grid,cv=5)\n",
    "    \n",
    "    model.fit(x_train,y_train)\n",
    "    scores=cross_val_score(model,x_train,y_train,cv=5)\n",
    "    train_score=model.score(x_train,y_train)\n",
    "    test_score=model.score(x_test,y_test)\n",
    "    y_pred = model.predict(x_test)\n",
    "    ac=accuracy_score(y_pred,y_test)\n",
    "    ps=precision_score(y_pred,y_test)\n",
    "    recall=recall_score(y_pred,y_test)\n",
    "    rc=roc_auc_score(y_pred,y_test)\n",
    "    collection=[[name,train_score,test_score,ac,ps,rc,recall]]\n",
    "#     plot_roc_curve(model,x_test,y_test)\n",
    "    sample=pd.DataFrame(collection,columns=['model_name','train score','test_score','accuracy_score','precision','auc_score','recall'])\n",
    "#     print(sample)\n",
    "#extrtact the score of this params and them tp the sample\n",
    "    if grid:\n",
    "        name+=f\":withgridsearch{str(mode.best_params_)}\"\n",
    "    joblib.dump(model,f\"./models/{name}.pkl\")\n",
    "        \n",
    "    return sample    \n",
    "# .over sampling\n",
    "def over(x_train,y_train):\n",
    "    '''return the over samplade data'''\n",
    "    roc=RandomOverSampler()\n",
    "    new_x,new_y=roc.fit_resample(x_train,y_train)\n",
    "    return (new_x,new_y)\n",
    "    \n",
    "# .under samplung\n",
    "def under(x_train,y_train):\n",
    "    '''return the under sample'''\n",
    "    ruc=RandomUnderSampler()\n",
    "    new_x,new_y=ruc.fit_resample(x_train,y_train)\n",
    "    return (new_x,new_y)\n",
    "\n",
    "# .smote\n",
    "def smote(x_train,y_train):\n",
    "    '''return data using the SMOTe technique'''\n",
    "    soc=SMOTE()\n",
    "    new_x,new_y=soc.fit_resample(x_train,y_train)\n",
    "    return (new_x,new_y)\n",
    "# AUC for diffrent MODel in one graphe\n",
    "def AUC(data,clfs=None,option=None):\n",
    "    if clfs:\n",
    "        re=pl=pd.DataFrame(columns=['model_name','train score','test_score','accuracy_score','precision','auc_score'])\n",
    "        for i in clfs:\n",
    "            Auc=display_score(i,test)\n",
    "            re=pd.concat([re,Auc])\n",
    "            if op:\n",
    "                for j in op:\n",
    "                    Auc=display_score(i,test,option=j)\n",
    "                    re=pd.concat([re,Auc])\n",
    "            \n",
    "  \n",
    "        return re\n",
    "    \n",
    "#gridsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cc12acb7",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'op' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [25]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# op=['under','over','smote']\u001b[39;00m\n\u001b[0;32m      2\u001b[0m clf\u001b[38;5;241m=\u001b[39m[RandomForestClassifier(n_estimators\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m150\u001b[39m,n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m,random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m,max_depth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m)]\n\u001b[1;32m----> 3\u001b[0m RF\u001b[38;5;241m=\u001b[39mAUC(test,clf,\u001b[43mop\u001b[49m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'op' is not defined"
     ]
    }
   ],
   "source": [
    "# op=['under','over','smote']\n",
    "clf=[RandomForestClassifier(n_estimators=150,n_jobs=2,random_state=42,max_depth=4)]\n",
    "RF=AUC(test,clf,op)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9985c43a",
   "metadata": {},
   "outputs": [],
   "source": [
    "op=['under','over','smote']\n",
    "clf=[LogisticRegression(max_iter=350,random_state=0)]\n",
    "LR=AUC(test,clf,op)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e907ac94",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf=GridSearchCV(RandomForestClassifier(),)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3067446a",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf=RandomForestClassifier()\n",
    "{'n_estimator',[150,200,300,500,800],'max_depth':[5,8,15,25],'min_sample_split',[2,5,10,15],'min_sampe_leaf',[1,2,5]}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a219bab0",
   "metadata": {},
   "source": [
    "### model implementation\n",
    "build an APi to interact with the new data:\n",
    "visit the API Folder to get the api \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d2837d3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model=joblib.load(\"C:/Users/PC/fraude detect/observation/models/LinearSVC with 'under' fitting .pkl\")\n",
    "\n",
    "# fraud_sample=test[test['is_fraud']==1][7:25].drop('is_fraud',axis=1).reset_index(drop=True)\n",
    "# scaler = sd()\n",
    "# sample=scaler.fit_transform(fraud_sample)\n",
    "# prediction=model.predict_proba(sample)\n",
    "\n",
    "# d={i:prediction[i] for i in range(len(prediction))}\n",
    "# print(d)\n",
    "# test[test['is_fraud']==1][12:19].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6fd14fbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model=joblib.load(\"C:/Users/PC/fraude detect/observation/models/LinearSVC with 'under' fitting .pkl\")\n",
    "\n",
    "# fraud_sample=test[test['is_fraud']==0][7:1007].drop('is_fraud',axis=1).reset_index(drop=True)\n",
    "# scaler = sd()\n",
    "# sample=scaler.fit_transform(fraud_sample)\n",
    "# prediction=model.predict(sample)\n",
    "\n",
    "# d={i:prediction[i] for i in range(len(prediction))}\n",
    "# print(d)\n",
    "# test[test['is_fraud']==0][12:19].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abdd1444",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# fraud_sample=test[test['is_fraud']==0][12:19].drop('is_fraud',axis=1).reset_index(drop=True)\n",
    "# scaler = sd()\n",
    "# sample=scaler.fit_transform(fraud_sample)\n",
    "# prediction=model.predict_proba(sample)\n",
    "\n",
    "# d={i:prediction[i] for i in range(len(prediction))}\n",
    "# print(d)\n",
    "# test[test['is_fraud']==0][12:19].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c56066d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# g={'n_estimators':[150,200,300,500,800],'max_depth':[5,8,15,25],'min_samples_split':[2,5,10,15],'min_samples_leaf':[1,2,5]}\n",
    "# op='smote'\n",
    "# clf=RandomForestClassifier(random_state=0)\n",
    "# result=display_score(clf,test,grid=g,option=op)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "509f3159",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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