Online_Payments_Fraud_Detection.ipynb

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      "   step      type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
      "0     1   PAYMENT   9839.64  C1231006815       170136.0       160296.36   \n",
      "1     1   PAYMENT   1864.28  C1666544295        21249.0        19384.72   \n",
      "2     1  TRANSFER    181.00  C1305486145          181.0            0.00   \n",
      "3     1  CASH_OUT    181.00   C840083671          181.0            0.00   \n",
      "4     1   PAYMENT  11668.14  C2048537720        41554.0        29885.86   \n",
      "\n",
      "      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
      "0  M1979787155             0.0             0.0        0               0  \n",
      "1  M2044282225             0.0             0.0        0               0  \n",
      "2   C553264065             0.0             0.0        1               0  \n",
      "3    C38997010         21182.0             0.0        1               0  \n",
      "4  M1230701703             0.0             0.0        0               0  \n"
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   "source": [
    "# Online payment fraud detection \n",
    "\n",
    "# Importing required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.read_csv(\"cc_data.csv\")\n",
    "print(data.head())"
   ]
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      "step              0\n",
      "type              0\n",
      "amount            0\n",
      "nameOrig          0\n",
      "oldbalanceOrg     0\n",
      "newbalanceOrig    0\n",
      "nameDest          0\n",
      "oldbalanceDest    0\n",
      "newbalanceDest    0\n",
      "isFraud           0\n",
      "isFlaggedFraud    0\n",
      "dtype: int64\n"
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   ],
   "source": [
    "#checking if data is clean or we have null values\n",
    "print(data.isnull().sum())"
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   "execution_count": 3,
   "id": "3b8dd278",
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      "type\n",
      "CASH_OUT    2237500\n",
      "PAYMENT     2151495\n",
      "CASH_IN     1399284\n",
      "TRANSFER     532909\n",
      "DEBIT         41432\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Exploring transaction types\n",
    "print(data.type.value_counts())"
   ]
  },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing various transaction types\n",
    "\n",
    "type = data[\"type\"].value_counts()\n",
    "transactions = type.index\n",
    "quantity = type.values\n",
    "\n",
    "import plotly.offline as pyo  # added this as jupyter was not displaying plotly output\n",
    "pyo.init_notebook_mode(connected=True)\n",
    "\n",
    "import plotly.express as px\n",
    "figure = px.pie(data, \n",
    "             values=quantity, \n",
    "             names=transactions,hole = 0.5, \n",
    "             title=\"Distribution of Transaction Type\")\n",
    "figure.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2adf138a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "isFraud           1.000000\n",
      "amount            0.076688\n",
      "isFlaggedFraud    0.044109\n",
      "step              0.031578\n",
      "oldbalanceOrg     0.010154\n",
      "newbalanceDest    0.000535\n",
      "oldbalanceDest   -0.005885\n",
      "newbalanceOrig   -0.008148\n",
      "Name: isFraud, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Checking correlation between the features of the data with the isFraud column\n",
    "\n",
    "correlation = data.corr(numeric_only = True)\n",
    "print(correlation[\"isFraud\"].sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a70f01c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   step  type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
      "0     1     2   9839.64  C1231006815       170136.0       160296.36   \n",
      "1     1     2   1864.28  C1666544295        21249.0        19384.72   \n",
      "2     1     4    181.00  C1305486145          181.0            0.00   \n",
      "3     1     1    181.00   C840083671          181.0            0.00   \n",
      "4     1     2  11668.14  C2048537720        41554.0        29885.86   \n",
      "\n",
      "      nameDest  oldbalanceDest  newbalanceDest   isFraud  isFlaggedFraud  \n",
      "0  M1979787155             0.0             0.0  No Fraud               0  \n",
      "1  M2044282225             0.0             0.0  No Fraud               0  \n",
      "2   C553264065             0.0             0.0     Fraud               0  \n",
      "3    C38997010         21182.0             0.0     Fraud               0  \n",
      "4  M1230701703             0.0             0.0  No Fraud               0  \n"
     ]
    }
   ],
   "source": [
    "#Transform the categorical features into numerical\n",
    "\n",
    "data[\"type\"] = data[\"type\"].map({\"CASH_OUT\": 1, \"PAYMENT\": 2, \n",
    "                                 \"CASH_IN\": 3, \"TRANSFER\": 4,\n",
    "                                 \"DEBIT\": 5})\n",
    "data[\"isFraud\"] = data[\"isFraud\"].map({0: \"No Fraud\", 1: \"Fraud\"})\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "49a8b375",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "x = np.array(data[[\"type\", \"amount\", \"oldbalanceOrg\", \"newbalanceOrig\"]])\n",
    "y = np.array(data[[\"isFraud\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bdfdf434",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9997265277511466\n"
     ]
    }
   ],
   "source": [
    "# Training a machine learning model\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.10, random_state=42)\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(xtrain, ytrain)\n",
    "print(model.score(xtest, ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0d714b61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Fraud']\n"
     ]
    }
   ],
   "source": [
    "# Prediction\n",
    "# features = [type, amount, oldbalanceOrg, newbalanceOrig]\n",
    "\n",
    "features = np.array([[4, 9000.60, 9000.60, 0.0]])\n",
    "print(model.predict(features))"
   ]
  }
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