Fraud_Detection_Naive_Classification_bayes.ipynb

{
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   "cell_type": "code",
   "execution_count": 1,
   "id": "4221ba2b-c01f-4846-bfc8-cec03c6394a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "208ca7d3-b34d-4dff-ac0b-dbec06768b08",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"card_transdata.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4c38931c-d242-42dc-9f64-dc2925624689",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance_from_home</th>\n",
       "      <th>distance_from_last_transaction</th>\n",
       "      <th>ratio_to_median_purchase_price</th>\n",
       "      <th>repeat_retailer</th>\n",
       "      <th>used_chip</th>\n",
       "      <th>used_pin_number</th>\n",
       "      <th>online_order</th>\n",
       "      <th>fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>57.877857</td>\n",
       "      <td>0.311140</td>\n",
       "      <td>1.945940</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>10.829943</td>\n",
       "      <td>0.175592</td>\n",
       "      <td>1.294219</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.091079</td>\n",
       "      <td>0.805153</td>\n",
       "      <td>0.427715</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.247564</td>\n",
       "      <td>5.600044</td>\n",
       "      <td>0.362663</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44.190936</td>\n",
       "      <td>0.566486</td>\n",
       "      <td>2.222767</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance_from_home  distance_from_last_transaction  \\\n",
       "0           57.877857                        0.311140   \n",
       "1           10.829943                        0.175592   \n",
       "2            5.091079                        0.805153   \n",
       "3            2.247564                        5.600044   \n",
       "4           44.190936                        0.566486   \n",
       "\n",
       "   ratio_to_median_purchase_price  repeat_retailer  used_chip  \\\n",
       "0                        1.945940              1.0        1.0   \n",
       "1                        1.294219              1.0        0.0   \n",
       "2                        0.427715              1.0        0.0   \n",
       "3                        0.362663              1.0        1.0   \n",
       "4                        2.222767              1.0        1.0   \n",
       "\n",
       "   used_pin_number  online_order  fraud  \n",
       "0              0.0           0.0    0.0  \n",
       "1              0.0           0.0    0.0  \n",
       "2              0.0           1.0    0.0  \n",
       "3              0.0           1.0    0.0  \n",
       "4              0.0           1.0    0.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f129f46e-ca5f-4468-a852-106fe93dfa09",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance_from_home</th>\n",
       "      <th>distance_from_last_transaction</th>\n",
       "      <th>ratio_to_median_purchase_price</th>\n",
       "      <th>repeat_retailer</th>\n",
       "      <th>used_chip</th>\n",
       "      <th>used_pin_number</th>\n",
       "      <th>online_order</th>\n",
       "      <th>fraud</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>999995</th>\n",
       "      <td>2.207101</td>\n",
       "      <td>0.112651</td>\n",
       "      <td>1.626798</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>999996</th>\n",
       "      <td>19.872726</td>\n",
       "      <td>2.683904</td>\n",
       "      <td>2.778303</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>999997</th>\n",
       "      <td>2.914857</td>\n",
       "      <td>1.472687</td>\n",
       "      <td>0.218075</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>999998</th>\n",
       "      <td>4.258729</td>\n",
       "      <td>0.242023</td>\n",
       "      <td>0.475822</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>999999</th>\n",
       "      <td>58.108125</td>\n",
       "      <td>0.318110</td>\n",
       "      <td>0.386920</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "        distance_from_home  distance_from_last_transaction  \\\n",
       "999995            2.207101                        0.112651   \n",
       "999996           19.872726                        2.683904   \n",
       "999997            2.914857                        1.472687   \n",
       "999998            4.258729                        0.242023   \n",
       "999999           58.108125                        0.318110   \n",
       "\n",
       "        ratio_to_median_purchase_price  repeat_retailer  used_chip  \\\n",
       "999995                        1.626798              1.0        1.0   \n",
       "999996                        2.778303              1.0        1.0   \n",
       "999997                        0.218075              1.0        1.0   \n",
       "999998                        0.475822              1.0        0.0   \n",
       "999999                        0.386920              1.0        1.0   \n",
       "\n",
       "        used_pin_number  online_order  fraud  \n",
       "999995              0.0           0.0    0.0  \n",
       "999996              0.0           0.0    0.0  \n",
       "999997              0.0           1.0    0.0  \n",
       "999998              0.0           1.0    0.0  \n",
       "999999              0.0           1.0    0.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "943c0264-503d-444d-89c7-29f35d72b3dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "distance_from_home                0\n",
       "distance_from_last_transaction    0\n",
       "ratio_to_median_purchase_price    0\n",
       "repeat_retailer                   0\n",
       "used_chip                         0\n",
       "used_pin_number                   0\n",
       "online_order                      0\n",
       "fraud                             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ad2f51e8-92ca-482f-a9de-6f29d31b5220",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000000 entries, 0 to 999999\n",
      "Data columns (total 8 columns):\n",
      " #   Column                          Non-Null Count    Dtype  \n",
      "---  ------                          --------------    -----  \n",
      " 0   distance_from_home              1000000 non-null  float64\n",
      " 1   distance_from_last_transaction  1000000 non-null  float64\n",
      " 2   ratio_to_median_purchase_price  1000000 non-null  float64\n",
      " 3   repeat_retailer                 1000000 non-null  float64\n",
      " 4   used_chip                       1000000 non-null  float64\n",
      " 5   used_pin_number                 1000000 non-null  float64\n",
      " 6   online_order                    1000000 non-null  float64\n",
      " 7   fraud                           1000000 non-null  float64\n",
      "dtypes: float64(8)\n",
      "memory usage: 61.0 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1fb377c4-759d-45c6-8f0f-0aea62c957c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['distance_from_home', 'distance_from_last_transaction',\n",
       "       'ratio_to_median_purchase_price', 'repeat_retailer', 'used_chip',\n",
       "       'used_pin_number', 'online_order', 'fraud'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "976a6647-f5f4-4797-aee7-133ed4d8840a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance_from_home</th>\n",
       "      <th>distance_from_last_transaction</th>\n",
       "      <th>ratio_to_median_purchase_price</th>\n",
       "      <th>repeat_retailer</th>\n",
       "      <th>used_chip</th>\n",
       "      <th>used_pin_number</th>\n",
       "      <th>online_order</th>\n",
       "      <th>fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>26.628792</td>\n",
       "      <td>5.036519</td>\n",
       "      <td>1.824182</td>\n",
       "      <td>0.881536</td>\n",
       "      <td>0.350399</td>\n",
       "      <td>0.100608</td>\n",
       "      <td>0.650552</td>\n",
       "      <td>0.087403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>65.390784</td>\n",
       "      <td>25.843093</td>\n",
       "      <td>2.799589</td>\n",
       "      <td>0.323157</td>\n",
       "      <td>0.477095</td>\n",
       "      <td>0.300809</td>\n",
       "      <td>0.476796</td>\n",
       "      <td>0.282425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.004874</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.004399</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.878008</td>\n",
       "      <td>0.296671</td>\n",
       "      <td>0.475673</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.967760</td>\n",
       "      <td>0.998650</td>\n",
       "      <td>0.997717</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>25.743985</td>\n",
       "      <td>3.355748</td>\n",
       "      <td>2.096370</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10632.723672</td>\n",
       "      <td>11851.104565</td>\n",
       "      <td>267.802942</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       distance_from_home  distance_from_last_transaction  \\\n",
       "count      1000000.000000                  1000000.000000   \n",
       "mean            26.628792                        5.036519   \n",
       "std             65.390784                       25.843093   \n",
       "min              0.004874                        0.000118   \n",
       "25%              3.878008                        0.296671   \n",
       "50%              9.967760                        0.998650   \n",
       "75%             25.743985                        3.355748   \n",
       "max          10632.723672                    11851.104565   \n",
       "\n",
       "       ratio_to_median_purchase_price  repeat_retailer       used_chip  \\\n",
       "count                  1000000.000000   1000000.000000  1000000.000000   \n",
       "mean                         1.824182         0.881536        0.350399   \n",
       "std                          2.799589         0.323157        0.477095   \n",
       "min                          0.004399         0.000000        0.000000   \n",
       "25%                          0.475673         1.000000        0.000000   \n",
       "50%                          0.997717         1.000000        0.000000   \n",
       "75%                          2.096370         1.000000        1.000000   \n",
       "max                        267.802942         1.000000        1.000000   \n",
       "\n",
       "       used_pin_number    online_order           fraud  \n",
       "count   1000000.000000  1000000.000000  1000000.000000  \n",
       "mean          0.100608        0.650552        0.087403  \n",
       "std           0.300809        0.476796        0.282425  \n",
       "min           0.000000        0.000000        0.000000  \n",
       "25%           0.000000        0.000000        0.000000  \n",
       "50%           0.000000        1.000000        0.000000  \n",
       "75%           0.000000        1.000000        0.000000  \n",
       "max           1.000000        1.000000        1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6db28725-98a6-4337-b000-27724d0fb24d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'distance_from_home'}>,\n",
       "        <Axes: title={'center': 'distance_from_last_transaction'}>,\n",
       "        <Axes: title={'center': 'ratio_to_median_purchase_price'}>],\n",
       "       [<Axes: title={'center': 'repeat_retailer'}>,\n",
       "        <Axes: title={'center': 'used_chip'}>,\n",
       "        <Axes: title={'center': 'used_pin_number'}>],\n",
       "       [<Axes: title={'center': 'online_order'}>,\n",
       "        <Axes: title={'center': 'fraud'}>, <Axes: >]], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(15, 15),color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8fa20fd5-89e1-4367-b62d-c279cbab58e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(), annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "adaa5714-5a73-45fa-81f7-09bde332964d",
   "metadata": {},
   "outputs": [],
   "source": [
    "fraudValues=df['fraud'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cd4f0830-6d60-46da-97f5-022609c3d47c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar([\"Not Fraud\",\"Fraud\"],fraudValues.values,color=[\"lightgreen\",\"darkgreen\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d93fa693-61a4-4fcf-8e78-4fccdf6a682d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "35dbb3ce-57ed-4991-af80-86493b32f2c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop(\"fraud\",axis=1)\n",
    "y=df[\"fraud\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c04acdad-9204-479e-ae4f-7d6863cefe25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.95112\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)\n",
    "nb = GaussianNB()\n",
    "nb.fit(X_train, y_train)\n",
    "y_pred = nb.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "46687a4a-0346-4ada-a221-74110324b691",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy=accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7c472d1e-31c1-4c5c-8daa-1e473b270bc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.95112\n"
     ]
    }
   ],
   "source": [
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "409ae7ef-ce77-46fb-abc0-6fd833a19ec3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,y_pred,color='orange',edgecolors='blue')\n",
    "plt.xlabel('y_test')\n",
    "plt.ylabel('predicted')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}