Fraud_detection.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "### Description\n",
    "\n",
    "Implement machine learning models that can recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solution for this problem\n",
    "\n",
    "- Preprocess data (drop missing values, normalize data, ...)\n",
    "- Apply classification algorithms into original imbalanced dataset to evaluate their performance.\n",
    "- Apply undersampling, oversampling method to solve imbalanced issue.\n",
    "- Re-apply classifications algorithms into undersampled/oversampled dataset to see if the performance of models is improved.\n",
    "- Trying apply ensemble methods (Bagging, RandomForest, Boosting, ...) into undersampled/oversampled dataset to evaluate performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Load data"
   ]
  },
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    "id": "VxgaKX54cOoQ"
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   "source": [
    "# Go to dataset directory\n",
    "import os\n",
    "\n",
    "path = '/content/drive/MyDrive/Deep Learning/Data'\n",
    "os.chdir(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Cl8DZ-AMcuel"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"/content/drive/MyDrive/Deep Learning/Data/creditcard.csv\")"
   ]
  },
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       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
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       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
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       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
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LWlXVj4BVA27/AODUqrqrqn4BLAX2aK+lVXVd2/+pwAFJQu+Lys5o6y8CDuzb1qI2fQbwsjZekjQig4bLD5N8EHhskpcDXwX+z0Pc5zuTXN5Om23ZanOAG/rGLG+1tdWfAPymqu4dV19jW235bW28JGlEBg2XI4GVwBXA24FzgIfyDZTH0fuk/67AjcAnH8I2OpPk8CRLkixZuXLlVLYiSTPKoHeL3Q+c0F4PWVXdNDad5ATgm212BbB939DtWo211G8BtkiyYTs66R8/tq3l7fTd5m38RP0cDxwPMH/+fJ8+IEkdGfRusV8kuW7868HuLMk2fbOvpvfJf4CzgYPbnV5PAeYBFwOLgXntzrCN6V30P7s9hub7wOva+guAb/Rta0Gbfh1wgY+tkaTRejDPFhvzGOD1wFaTrZDkK8CLga2TLAeOAl6cZFd6NwMso3eKjaq6KsnpwM+Ae4Ejquq+tp13AufS+1zNwqq6qu3i/cCpST4K/AQ4sdVPBE5JspTeDQUHD/geJUkdyUP9R32SS6pq9477mTLz58+vJUuWTHUb6zT3yG9NdQszyrKPv2KqW5CmtZYF88fXB33k/m59s4+idyTzYL4LRpK0Hhk0IPrv6rqX3imtgzrvRpI0Iwx6t9hLht2IJGnmGPS02H+bbHlVfaqbdiRJM8GDuVvsefRu8wV4Jb1bha8dRlOSpOlt0HDZDtitqm6H3tONgW9V1ZuH1Zgkafoa9PEvs4G7++bvbjVJkh5g0COXk4GLk3y9zR/IH588LEnSGga9W+xjSb4NvLCV3lZVPxleW5Kk6WzQ02IAmwCrq+qf6T0U8ilD6kmSNM0N+uDKo+g9y+sDrbQR8MVhNSVJmt4GPXJ5NfAq4LcAVfVLYNNhNSVJmt4GDZe722PrCyDJ44bXkiRpuhs0XE5P8nl6X9B1GPA9HuYXh0mSZq513i2WJMBpwDOA1cDTgb+tqvOG3JskaZpaZ7hUVSU5p6qeBRgokqR1GvS02KVJnjfUTiRJM8agn9DfE3hzkmX07hgLvYOaZw+rMUnS9DVpuCTZoar+A9hnRP1IkmaAdR25nEXvacjXJzmzql47iqYkSdPbuq65pG/6qcNsRJI0c6wrXGot05IkrdW6Tos9J8lqekcwj23T8McL+psNtTtJ0rQ0abhU1QajakSSNHM8mEfuS5I0EMNFktQ5w0WS1DnDRZLUuaGFS5KFSW5OcmVfbask5yW5tv3cstWT5NgkS5NcnmS3vnUWtPHXJlnQV989yRVtnWPb05vXug9J0ugM88jlJGDfcbUjgfOrah5wfpsH2A+Y116HA8dBLyiAo+g922wP4Ki+sDgOOKxvvX3XsQ9J0ogMLVyq6kfAqnHlA4BFbXoRcGBf/eTquZDel5JtQ++ZZudV1aqqupXeI//3bcs2q6oL2zdknjxuWxPtQ5I0IqO+5jK7qm5s078CZrfpOcANfeOWt9pk9eUT1CfbhyRpRKbsgn474hjqI2XWtY8khydZkmTJypUrh9mKJK1XRh0uN7VTWrSfN7f6CmD7vnHbtdpk9e0mqE+2jweoquOran5VzZ81a9ZDflOSpDWNOlzOBsbu+FoAfKOvfki7a2wv4LZ2autcYO8kW7YL+XsD57Zlq5Ps1e4SO2TctibahyRpRAb9JsoHLclXgBcDWydZTu+ur48Dpyc5FLgeOKgNPwfYH1gK/A54G0BVrUryEWBxG/fhqhq7SeAd9O5Ieyzw7fZikn1IkkZkaOFSVW9cy6KXTTC2gCPWsp2FwMIJ6kuAXSao3zLRPiRJo+Mn9CVJnTNcJEmdM1wkSZ0zXCRJnTNcJEmdM1wkSZ0zXCRJnTNcJEmdM1wkSZ0zXCRJnTNcJEmdM1wkSZ0zXCRJnTNcJEmdM1wkSZ0zXCRJnTNcJEmdM1wkSZ0zXCRJnTNcJEmdM1wkSZ0zXCRJnTNcJEmdM1wkSZ0zXCRJnTNcJEmdM1wkSZ2bknBJsizJFUkuS7Kk1bZKcl6Sa9vPLVs9SY5NsjTJ5Ul269vOgjb+2iQL+uq7t+0vbetm9O9SktZfU3nk8pKq2rWq5rf5I4Hzq2oecH6bB9gPmNdehwPHQS+MgKOAPYE9gKPGAqmNOaxvvX2H/3YkSWMeSafFDgAWtelFwIF99ZOr50JgiyTbAPsA51XVqqq6FTgP2Lct26yqLqyqAk7u25YkaQSmKlwK+G6SS5Ic3mqzq+rGNv0rYHabngPc0Lfu8labrL58grokaUQ2nKL9/qeqWpHkicB5SX7ev7CqKkkNu4kWbIcD7LDDDsPenSStN6bkyKWqVrSfNwNfp3fN5KZ2Sov28+Y2fAWwfd/q27XaZPXtJqhP1MfxVTW/qubPmjXr4b4tSVIz8nBJ8rgkm45NA3sDVwJnA2N3fC0AvtGmzwYOaXeN7QXc1k6fnQvsnWTLdiF/b+Dctmx1kr3aXWKH9G1LkjQCU3FabDbw9XZ38IbAl6vqO0kWA6cnORS4HjiojT8H2B9YCvwOeBtAVa1K8hFgcRv34apa1abfAZwEPBb4dntJkkZk5OFSVdcBz5mgfgvwsgnqBRyxlm0tBBZOUF8C7PKwm5UkPSSPpFuRJUkzhOEiSeqc4SJJ6pzhIknqnOEiSeqc4SJJ6pzhIknqnOEiSeqc4SJJ6pzhIknqnOEiSeqc4SJJ6pzhIknqnOEiSeqc4SJJ6pzhIknqnOEiSeqc4SJJ6pzhIknqnOEiSeqc4SJJ6pzhIknqnOEiSeqc4SJJ6pzhIknqnOEiSeqc4SJJ6pzhIknq3IwNlyT7JrkmydIkR051P5K0PpmR4ZJkA+CzwH7AzsAbk+w8tV1J0vpjRoYLsAewtKquq6q7gVOBA6a4J0lab2w41Q0MyRzghr755cCe4wclORw4vM3ekeSaEfS2vtga+PVUN7Eu+cRUd6ApMC3+35xGnjxRcaaGy0Cq6njg+KnuYyZKsqSq5k91H9J4/r85GjP1tNgKYPu++e1aTZI0AjM1XBYD85I8JcnGwMHA2VPckyStN2bkabGqujfJO4FzgQ2AhVV11RS3tb7xdKMeqfx/cwRSVVPdgyRphpmpp8UkSVPIcJEkdc5wkSR1bkZe0NdoJXkGvScgzGmlFcDZVXX11HUlaSp55KKHJcn76T1eJ8DF7RXgKz4wVI9kSd421T3MZN4tpoclyb8Bz6yqe8bVNwauqqp5U9OZNLkk/1FVO0x1HzOVp8X0cN0PbAtcP66+TVsmTZkkl69tETB7lL2sbwwXPVzvBs5Pci1/fFjoDsCOwDunrCupZzawD3DruHqA/zf6dtYfhoselqr6TpKd6H3NQf8F/cVVdd/UdSYB8E3g8VV12fgFSX4w+nbWH15zkSR1zrvFJEmdM1wkSZ0zXKQpkORJSU5N8u9JLklyTpKdklw51b1JXfCCvjRiSQJ8HVhUVQe32nPw1ljNIB65SKP3EuCeqvpfY4Wq+il/vJWbJHOT/EuSS9vrT1t9myQ/SnJZkiuTvDDJBklOavNXJHnP6N+StCaPXKTR2wW4ZB1jbgZeXlV3JpkHfAWYD/w5cG5VfSzJBsAmwK7AnKraBSDJFsNrXRqM4SI9Mm0EfCbJrsB9wE6tvhhYmGQj4KyquizJdcBTk3wa+Bbw3SnpWOrjaTFp9K4Cdl/HmPcANwHPoXfEsjFAVf0IeBG9D6qelOSQqrq1jfsB8JfAF4bTtjQ4w0UavQuARyc5fKyQ5NnA9n1jNgdurKr7gbcAG7RxTwZuqqoT6IXIbkm2Bh5VVWcCHwJ2G83bkNbO02LSiFVVJXk18E/tKwvuBJbRe07bmM8BZyY5BPgO8NtWfzHwviT3AHcAh9B77M7/TjL2j8UPDP1NSOvg418kSZ3ztJgkqXOGiySpc4aLJKlzhoskqXOGiySpc4aLJKlzhoskqXOGiySpc/8fjkOmJJVUbIAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CHECKING THE TARGET CLASSES:\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "count_classes = pd.value_counts(df['Class'], sort = True).sort_index()\n",
    "print(count_classes)\n",
    "count_classes.plot(kind = 'bar')\n",
    "plt.title(\"Fraud class histogram\")\n",
    "plt.xlabel(\"Class\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kDubk3cQdTWN"
   },
   "outputs": [],
   "source": [
    "# Normalize the 'Amount' column\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "df['normAmount'] = StandardScaler().fit_transform(df['Amount'].values.reshape(-1, 1))\n",
    "data = df.drop(['Time','Amount'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "uY4NMUcldn0-",
    "outputId": "6ce40e21-bc5d-4ad8-cf8e-0dc78bcca94b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-1fee1418-eb1c-4167-84b4-8f68f300d926\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Class</th>\n",
       "      <th>normAmount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>0.090794</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>0</td>\n",
       "      <td>0.244964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.342475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>0.207643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>0</td>\n",
       "      <td>1.160686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>-0.054952</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>0</td>\n",
       "      <td>0.140534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.073403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
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      ],
      "text/plain": [
       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9       V10  ...       V21       V22       V23       V24  \\\n",
       "0  0.098698  0.363787  0.090794  ... -0.018307  0.277838 -0.110474  0.066928   \n",
       "1  0.085102 -0.255425 -0.166974  ... -0.225775 -0.638672  0.101288 -0.339846   \n",
       "2  0.247676 -1.514654  0.207643  ...  0.247998  0.771679  0.909412 -0.689281   \n",
       "3  0.377436 -1.387024 -0.054952  ... -0.108300  0.005274 -0.190321 -1.175575   \n",
       "4 -0.270533  0.817739  0.753074  ... -0.009431  0.798278 -0.137458  0.141267   \n",
       "\n",
       "        V25       V26       V27       V28  Class  normAmount  \n",
       "0  0.128539 -0.189115  0.133558 -0.021053      0    0.244964  \n",
       "1  0.167170  0.125895 -0.008983  0.014724      0   -0.342475  \n",
       "2 -0.327642 -0.139097 -0.055353 -0.059752      0    1.160686  \n",
       "3  0.647376 -0.221929  0.062723  0.061458      0    0.140534  \n",
       "4 -0.206010  0.502292  0.219422  0.215153      0   -0.073403  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Z83dglf6dsep"
   },
   "outputs": [],
   "source": [
    "# Assigning X and Y. No resampling\n",
    "X = data.iloc[:, data.columns != 'Class']\n",
    "y = data.iloc[:, data.columns == 'Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EFRMlpvzd5Qb",
    "outputId": "6819ce08-e45c-471c-ce01-0357b3a74ab1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 29)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E1lAcxQrd8bP",
    "outputId": "09607a08-c10c-4ac5-912d-8d6444d42b37"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Apply classification algorithms into original imbalanced dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rTzaEl08eAam"
   },
   "outputs": [],
   "source": [
    "# Splitting data into train and test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vgOBsnnFeEBY",
    "outputId": "a4a36df9-8f0a-4cc0-f57a-eecdb2e097d8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number transactions train dataset:  199364\n",
      "Number transactions test dataset:  85443\n",
      "Total number of transactions:  284807\n"
     ]
    }
   ],
   "source": [
    "print(\"Number transactions train dataset: \", len(X_train))\n",
    "print(\"Number transactions test dataset: \", len(X_test))\n",
    "print(\"Total number of transactions: \", len(X_train)+len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VhOaabnSeF1e",
    "outputId": "ca66824d-64af-4fa5-b9c5-858a099f31cf"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR : 99.9204147794436\n",
      "Confusion matrix:\n",
      " [[85284    12]\n",
      " [   56    91]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85296\n",
      "           1       0.88      0.62      0.73       147\n",
      "\n",
      "    accuracy                           1.00     85443\n",
      "   macro avg       0.94      0.81      0.86     85443\n",
      "weighted avg       1.00      1.00      1.00     85443\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LDA : 99.93914071369217\n",
      "Confusion matrix:\n",
      " [[85281    15]\n",
      " [   37   110]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85296\n",
      "           1       0.88      0.75      0.81       147\n",
      "\n",
      "    accuracy                           1.00     85443\n",
      "   macro avg       0.94      0.87      0.90     85443\n",
      "weighted avg       1.00      1.00      1.00     85443\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN : 99.9438221972543\n",
      "Confusion matrix:\n",
      " [[85289     7]\n",
      " [   41   106]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85296\n",
      "           1       0.94      0.72      0.82       147\n",
      "\n",
      "    accuracy                           1.00     85443\n",
      "   macro avg       0.97      0.86      0.91     85443\n",
      "weighted avg       1.00      1.00      1.00     85443\n",
      "\n",
      "CART : 99.92275552122467\n",
      "Confusion matrix:\n",
      " [[85267    29]\n",
      " [   37   110]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85296\n",
      "           1       0.79      0.75      0.77       147\n",
      "\n",
      "    accuracy                           1.00     85443\n",
      "   macro avg       0.90      0.87      0.88     85443\n",
      "weighted avg       1.00      1.00      1.00     85443\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NB : 97.84066570696254\n",
      "Confusion matrix:\n",
      " [[83475  1821]\n",
      " [   24   123]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99     85296\n",
      "           1       0.06      0.84      0.12       147\n",
      "\n",
      "    accuracy                           0.98     85443\n",
      "   macro avg       0.53      0.91      0.55     85443\n",
      "weighted avg       1.00      0.98      0.99     85443\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM : 99.93445923013002\n",
      "Confusion matrix:\n",
      " [[85291     5]\n",
      " [   51    96]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85296\n",
      "           1       0.95      0.65      0.77       147\n",
      "\n",
      "    accuracy                           1.00     85443\n",
      "   macro avg       0.97      0.83      0.89     85443\n",
      "weighted avg       1.00      1.00      1.00     85443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Import classification algorithms\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,precision_recall_curve,auc,roc_auc_score,roc_curve,recall_score,classification_report\n",
    "\n",
    "\n",
    "# Prepare models:\n",
    "models = []\n",
    "models.append(('LR', LogisticRegression()))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('NB', GaussianNB()))\n",
    "models.append(('SVM', SVC()))\n",
    "\n",
    "# Evaluate each model in turn:\n",
    "results=[]\n",
    "names=[]\n",
    "# scoring='accuracy'\n",
    "for name, model in models:\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    result = 100*accuracy_score(y_test, y_pred)\n",
    "    results.append(result)\n",
    "    names.append(name)\n",
    "    print(name,':', result)\n",
    "    print('Confusion matrix:\\n', confusion_matrix(y_test, y_pred))\n",
    "    print('Classification report:\\n', classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cmNyjKCxhYp8",
    "outputId": "a15f8d3f-b85c-46db-bfe6-adcf30bc1190"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =  99.9438221972543\n",
      "[[85289     7]\n",
      " [   41   106]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85296\n",
      "           1       0.94      0.72      0.82       147\n",
      "\n",
      "    accuracy                           1.00     85443\n",
      "   macro avg       0.97      0.86      0.91     85443\n",
      "weighted avg       1.00      1.00      1.00     85443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# model = KNeighborsClassifier()\n",
    "# model.fit(X_train, y_train)\n",
    "# y_pred = model.predict(X_test)\n",
    "# acc = 100*accuracy_score(y_test, y_pred)\n",
    "# print('Accuracy = ', acc)\n",
    "# print(confusion_matrix(y_test, y_pred))\n",
    "# print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Apply undersampling, oversampling method to solve imbalanced issue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sTXEp8Tbpu4p"
   },
   "outputs": [],
   "source": [
    "# Undersample data\n",
    "def under_sample(data):\n",
    "  # Number of data points in the minority class\n",
    "  number_records_fraud = len(data[data.Class == 1])\n",
    "  # Picking indices of the fraud classes\n",
    "  fraud_indices = np.array(data[data.Class == 1].index)\n",
    "  # Picking the indices of the normal classes\n",
    "  normal_indices = data[data.Class == 0].index\n",
    "  # Out of the indices we picked, randomly select \"x\" number (number_records_fraud)\n",
    "  random_normal_indices = np.random.choice(normal_indices, number_records_fraud, replace = False)\n",
    "  random_normal_indices = np.array(random_normal_indices)\n",
    "  # Appending the 2 indices\n",
    "  under_sample_indices = np.concatenate([fraud_indices,random_normal_indices])\n",
    "  # Under sample dataset\n",
    "  under_sampled_data = data.iloc[under_sample_indices,:]\n",
    "\n",
    "  return under_sampled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vbUOoiIMxtSo"
   },
   "outputs": [],
   "source": [
    "# Oversample data\n",
    "def over_sample(data):\n",
    "  # Number of data points in the minority class\n",
    "  number_records_normal = len(data[data.Class == 0])\n",
    "  # Picking indices of the normal classes\n",
    "  normal_indices = np.array(data[data.Class == 0].index)\n",
    "  # Picking the indices of the fraud classes\n",
    "  fraud_indices = data[data.Class == 1].index\n",
    "  # Out of the indices we picked, randomly select \"x\" number (number_records_fraud)\n",
    "  random_fraud_indices = np.random.choice(fraud_indices, number_records_normal, replace = True)\n",
    "  random_fraud_indices = np.array(random_fraud_indices)\n",
    "  # Appending the 2 indices\n",
    "  over_sample_indices = np.concatenate([normal_indices,random_fraud_indices])\n",
    "  # Under sample dataset\n",
    "  over_sampled_data = data.iloc[over_sample_indices,:]\n",
    "\n",
    "  return over_sampled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pSph0ZPTzdWb"
   },
   "outputs": [],
   "source": [
    "undersampled_data = under_sample(data)\n",
    "oversampled_data = over_sample(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iAQ-R-HgzoQL",
    "outputId": "77ca1562-92ca-4e1f-cdb8-d5f29c0ee561"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(984, 30)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "undersampled_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bJpZgu5gPk-E",
    "outputId": "1be4df56-29ff-4327-937f-ee062ee0f70c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(568630, 30)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oversampled_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "z3vKoZi_Pme1"
   },
   "outputs": [],
   "source": [
    "X_undersample = undersampled_data.iloc[:, undersampled_data.columns != 'Class']\n",
    "y_undersample = undersampled_data.iloc[:, undersampled_data.columns == 'Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "g9gGYk4sPmlG",
    "outputId": "dcdb36ba-a059-411e-ebe2-11f67cdcaeac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of normal transactions:  0.5\n",
      "Percentage of fraud transactions:  0.5\n",
      "Total number of transactions in resampled data:  984\n"
     ]
    }
   ],
   "source": [
    "# Showing ratio\n",
    "print(\"Percentage of normal transactions: \", len(undersampled_data[undersampled_data.Class == 0])/len(undersampled_data))\n",
    "print(\"Percentage of fraud transactions: \", len(undersampled_data[undersampled_data.Class == 1])/len(undersampled_data))\n",
    "print(\"Total number of transactions in resampled data: \", len(undersampled_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yhQ8Zf9QPmoZ"
   },
   "outputs": [],
   "source": [
    "# Split undersampled dataset\n",
    "X_train_undersample, X_test_undersample, y_train_undersample, y_test_undersample = train_test_split(X_undersample\n",
    "                                                                                                   ,y_undersample\n",
    "                                                                                                   ,test_size = 0.3\n",
    "                                                                                                   ,random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CgQorH-7Pmrh",
    "outputId": "84994fdc-3392-4113-b60c-b333a4db35ac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number transactions train dataset:  688\n",
      "Number transactions test dataset:  296\n",
      "Total number of transactions:  984\n"
     ]
    }
   ],
   "source": [
    "print(\"Number transactions train dataset: \", len(X_train_undersample))\n",
    "print(\"Number transactions test dataset: \", len(X_test_undersample))\n",
    "print(\"Total number of transactions: \", len(X_train_undersample)+len(X_test_undersample))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Re-apply classifications algorithms into undersampled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Tzz-UrAyPmue",
    "outputId": "3475c4fe-e3dd-4acd-e54a-920dde05e4a2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR : 93.91891891891892\n",
      "Confusion matrix:\n",
      " [[140   9]\n",
      " [  9 138]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.94      0.94       149\n",
      "           1       0.94      0.94      0.94       147\n",
      "\n",
      "    accuracy                           0.94       296\n",
      "   macro avg       0.94      0.94      0.94       296\n",
      "weighted avg       0.94      0.94      0.94       296\n",
      "\n",
      "LDA : 91.21621621621621\n",
      "Confusion matrix:\n",
      " [[145   4]\n",
      " [ 22 125]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.97      0.92       149\n",
      "           1       0.97      0.85      0.91       147\n",
      "\n",
      "    accuracy                           0.91       296\n",
      "   macro avg       0.92      0.91      0.91       296\n",
      "weighted avg       0.92      0.91      0.91       296\n",
      "\n",
      "KNN : 94.93243243243244\n",
      "Confusion matrix:\n",
      " [[148   1]\n",
      " [ 14 133]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.99      0.95       149\n",
      "           1       0.99      0.90      0.95       147\n",
      "\n",
      "    accuracy                           0.95       296\n",
      "   macro avg       0.95      0.95      0.95       296\n",
      "weighted avg       0.95      0.95      0.95       296\n",
      "\n",
      "CART : 91.21621621621621\n",
      "Confusion matrix:\n",
      " [[134  15]\n",
      " [ 11 136]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.90      0.91       149\n",
      "           1       0.90      0.93      0.91       147\n",
      "\n",
      "    accuracy                           0.91       296\n",
      "   macro avg       0.91      0.91      0.91       296\n",
      "weighted avg       0.91      0.91      0.91       296\n",
      "\n",
      "NB : 90.54054054054053\n",
      "Confusion matrix:\n",
      " [[142   7]\n",
      " [ 21 126]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.95      0.91       149\n",
      "           1       0.95      0.86      0.90       147\n",
      "\n",
      "    accuracy                           0.91       296\n",
      "   macro avg       0.91      0.91      0.91       296\n",
      "weighted avg       0.91      0.91      0.91       296\n",
      "\n",
      "SVM : 93.24324324324324\n",
      "Confusion matrix:\n",
      " [[147   2]\n",
      " [ 18 129]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.99      0.94       149\n",
      "           1       0.98      0.88      0.93       147\n",
      "\n",
      "    accuracy                           0.93       296\n",
      "   macro avg       0.94      0.93      0.93       296\n",
      "weighted avg       0.94      0.93      0.93       296\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,precision_recall_curve,auc,roc_auc_score,roc_curve,recall_score,classification_report\n",
    "\n",
    "# Prepare models:\n",
    "models = []\n",
    "models.append(('LR', LogisticRegression()))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('NB', GaussianNB()))\n",
    "models.append(('SVM', SVC()))\n",
    "\n",
    "# Evaluate each model in turn:\n",
    "results=[]\n",
    "names=[]\n",
    "# scoring='accuracy'\n",
    "for name, model in models:\n",
    "    model.fit(X_train_undersample, y_train_undersample)\n",
    "    y_pred = model.predict(X_test_undersample)\n",
    "    result = 100*accuracy_score(y_test_undersample, y_pred)\n",
    "    results.append(result)\n",
    "    names.append(name)\n",
    "    print(name,':', result)\n",
    "    print('Confusion matrix:\\n', confusion_matrix(y_test_undersample, y_pred))\n",
    "    print('Classification report:\\n', classification_report(y_test_undersample, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Tnlq70erPmyg",
    "outputId": "c0877091-440a-4ff2-8d98-af8c8dde4bc5"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_bagging.py:719: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:19: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bagging : 92.56756756756756\n",
      "Confusion matrix:\n",
      " [[143   6]\n",
      " [ 16 131]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.96      0.93       149\n",
      "           1       0.96      0.89      0.92       147\n",
      "\n",
      "    accuracy                           0.93       296\n",
      "   macro avg       0.93      0.93      0.93       296\n",
      "weighted avg       0.93      0.93      0.93       296\n",
      "\n",
      "RandomForest : 94.93243243243244\n",
      "Confusion matrix:\n",
      " [[145   4]\n",
      " [ 11 136]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.97      0.95       149\n",
      "           1       0.97      0.93      0.95       147\n",
      "\n",
      "    accuracy                           0.95       296\n",
      "   macro avg       0.95      0.95      0.95       296\n",
      "weighted avg       0.95      0.95      0.95       296\n",
      "\n",
      "ExtraTrees : 94.93243243243244\n",
      "Confusion matrix:\n",
      " [[147   2]\n",
      " [ 13 134]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.99      0.95       149\n",
      "           1       0.99      0.91      0.95       147\n",
      "\n",
      "    accuracy                           0.95       296\n",
      "   macro avg       0.95      0.95      0.95       296\n",
      "weighted avg       0.95      0.95      0.95       296\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:19: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_gb.py:494: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoosting : 93.91891891891892\n",
      "Confusion matrix:\n",
      " [[142   7]\n",
      " [ 11 136]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.95      0.94       149\n",
      "           1       0.95      0.93      0.94       147\n",
      "\n",
      "    accuracy                           0.94       296\n",
      "   macro avg       0.94      0.94      0.94       296\n",
      "weighted avg       0.94      0.94      0.94       296\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoost : 91.8918918918919\n",
      "Confusion matrix:\n",
      " [[140   9]\n",
      " [ 15 132]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.94      0.92       149\n",
      "           1       0.94      0.90      0.92       147\n",
      "\n",
      "    accuracy                           0.92       296\n",
      "   macro avg       0.92      0.92      0.92       296\n",
      "weighted avg       0.92      0.92      0.92       296\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Try using ensemble methods\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "models = []\n",
    "models.append(('Bagging', BaggingClassifier()))\n",
    "models.append(('RandomForest', RandomForestClassifier()))\n",
    "models.append(('ExtraTrees', ExtraTreesClassifier()))\n",
    "models.append(('GradientBoosting', GradientBoostingClassifier()))\n",
    "models.append(('AdaBoost', AdaBoostClassifier()))\n",
    "\n",
    "# Evaluate each model in turn:\n",
    "# scoring = 'accuracy'\n",
    "results=[]\n",
    "names=[]\n",
    "for name, model in models:\n",
    "    model.fit(X_train_undersample, y_train_undersample)\n",
    "    y_pred = model.predict(X_test_undersample)\n",
    "    result = 100*accuracy_score(y_test_undersample, y_pred)\n",
    "    results.append(result)\n",
    "    names.append(name)\n",
    "    print(name,':', result)\n",
    "    print('Confusion matrix:\\n', confusion_matrix(y_test_undersample, y_pred))\n",
    "    print('Classification report:\\n', classification_report(y_test_undersample, y_pred))\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Re-apply classifications algorithms into oversampled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "IBWE4PkDRX3I"
   },
   "outputs": [],
   "source": [
    "X_oversample = oversampled_data.iloc[:, oversampled_data.columns != 'Class']\n",
    "y_oversample = oversampled_data.iloc[:, oversampled_data.columns == 'Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "q5lD6sULRYGG",
    "outputId": "021ba9ae-8df9-4bf1-bb2d-5ca92b26e473"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of normal transactions:  0.5\n",
      "Percentage of fraud transactions:  0.5\n",
      "Total number of transactions in resampled data:  568630\n"
     ]
    }
   ],
   "source": [
    "# Showing ratio\n",
    "print(\"Percentage of normal transactions: \", len(oversampled_data[oversampled_data.Class == 0])/len(oversampled_data))\n",
    "print(\"Percentage of fraud transactions: \", len(oversampled_data[oversampled_data.Class == 1])/len(oversampled_data))\n",
    "print(\"Total number of transactions in resampled data: \", len(oversampled_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "V_ZanozBRYSG"
   },
   "outputs": [],
   "source": [
    "# Split oversampled dataset\n",
    "X_train_oversample, X_test_oversample, y_train_oversample, y_test_oversample = train_test_split(X_oversample\n",
    "                                                                                                   ,y_oversample\n",
    "                                                                                                   ,test_size = 0.3\n",
    "                                                                                                   ,random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "61tiwsivUejt",
    "outputId": "07427f23-eae2-4ef5-cc62-260c4a225df2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number transactions train dataset:  398041\n",
      "Number transactions test dataset:  170589\n",
      "Total number of transactions:  568630\n"
     ]
    }
   ],
   "source": [
    "print(\"Number transactions train dataset: \", len(X_train_oversample))\n",
    "print(\"Number transactions test dataset: \", len(X_test_oversample))\n",
    "print(\"Total number of transactions: \", len(X_train_oversample)+len(X_test_oversample))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Xixzj03kVAPM",
    "outputId": "f9d8a704-98cc-4a57-e057-919fc7092b02"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "398041\n",
      "170589\n"
     ]
    }
   ],
   "source": [
    "print(len(y_train_oversample))\n",
    "print(len(y_test_oversample))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Wjjkzz6kUqGN",
    "outputId": "8b4bb57a-3366-45de-eece-c1273d29573b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR : 94.86367819730464\n",
      "Confusion matrix:\n",
      " [[83188  1970]\n",
      " [ 6792 78639]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.98      0.95     85158\n",
      "           1       0.98      0.92      0.95     85431\n",
      "\n",
      "    accuracy                           0.95    170589\n",
      "   macro avg       0.95      0.95      0.95    170589\n",
      "weighted avg       0.95      0.95      0.95    170589\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LDA : 91.67414077109309\n",
      "Confusion matrix:\n",
      " [[84063  1095]\n",
      " [13108 72323]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.99      0.92     85158\n",
      "           1       0.99      0.85      0.91     85431\n",
      "\n",
      "    accuracy                           0.92    170589\n",
      "   macro avg       0.93      0.92      0.92    170589\n",
      "weighted avg       0.93      0.92      0.92    170589\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN : 99.95603467984454\n",
      "Confusion matrix:\n",
      " [[85083    75]\n",
      " [    0 85431]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85158\n",
      "           1       1.00      1.00      1.00     85431\n",
      "\n",
      "    accuracy                           1.00    170589\n",
      "   macro avg       1.00      1.00      1.00    170589\n",
      "weighted avg       1.00      1.00      1.00    170589\n",
      "\n",
      "CART : 99.96834496948807\n",
      "Confusion matrix:\n",
      " [[85104    54]\n",
      " [    0 85431]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85158\n",
      "           1       1.00      1.00      1.00     85431\n",
      "\n",
      "    accuracy                           1.00    170589\n",
      "   macro avg       1.00      1.00      1.00    170589\n",
      "weighted avg       1.00      1.00      1.00    170589\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NB : 91.62841683813141\n",
      "Confusion matrix:\n",
      " [[82921  2237]\n",
      " [12044 73387]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.97      0.92     85158\n",
      "           1       0.97      0.86      0.91     85431\n",
      "\n",
      "    accuracy                           0.92    170589\n",
      "   macro avg       0.92      0.92      0.92    170589\n",
      "weighted avg       0.92      0.92      0.92    170589\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM : 97.19794359542526\n",
      "Confusion matrix:\n",
      " [[84179   979]\n",
      " [ 3801 81630]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.99      0.97     85158\n",
      "           1       0.99      0.96      0.97     85431\n",
      "\n",
      "    accuracy                           0.97    170589\n",
      "   macro avg       0.97      0.97      0.97    170589\n",
      "weighted avg       0.97      0.97      0.97    170589\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Prepare models:\n",
    "models = []\n",
    "models.append(('LR', LogisticRegression()))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('NB', GaussianNB()))\n",
    "models.append(('SVM', SVC()))\n",
    "\n",
    "# Evaluate each model in turn:\n",
    "results=[]\n",
    "names=[]\n",
    "# scoring='accuracy'\n",
    "for name, model in models:\n",
    "    model.fit(X_train_oversample, y_train_oversample)\n",
    "    y_pred = model.predict(X_test_oversample)\n",
    "    result = 100*accuracy_score(y_test_oversample, y_pred)\n",
    "    results.append(result)\n",
    "    names.append(name)\n",
    "    print(name,':', result)\n",
    "    print('Confusion matrix:\\n', confusion_matrix(y_test_oversample, y_pred))\n",
    "    print('Classification report:\\n', classification_report(y_test_oversample, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZoCwPcn1UqN9"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, plot_confusion_matrix, f1_score, roc_auc_score\n",
    "\n",
    "def Confusion_Matrix(y_test,y_pred):\n",
    "    cfmat = confusion_matrix(y_test, y_pred)\n",
    "    print('Confusion Matrix: \\n',classification_report(y_test,y_pred,labels=[0,1]))\n",
    "    print(\"\\n\")\n",
    "    print('TN - True Negative {}'.format(cfmat[1,1]))\n",
    "    print('FP - False Positive {}'.format(cfmat[1,0]))\n",
    "    print('FN - False Negative {}'.format(cfmat[0,1]))\n",
    "    print('TP - True Positive {}'.format(cfmat[0,0]))\n",
    "    print('Accuracy Rate: {}'.format(np.divide(np.sum([cfmat[0,0],cfmat[1,1]]),np.sum(cfmat))))\n",
    "    print('Misclassification Rate: {}'.format(np.divide(np.sum([cfmat[0,1],cfmat[1,0]]),np.sum(cfmat))))\n",
    "    print('F1-Score: {}'.format(f1_score(y_test, y_pred,average='macro')))\n",
    "    print('ROC-AUC {}'.format(roc_auc_score(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nNmvb0BeUqYr"
   },
   "source": [
    "#### 6. SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WMZFh4vgUqh8"
   },
   "outputs": [],
   "source": [
    "# SMOTE method\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "sm = SMOTE()\n",
    "\n",
    "# data = pd.get_dummies(dataset,drop_first=True)\n",
    "\n",
    "# X = dataset_dummy.drop([\"Exited\"],axis=1)\n",
    "# y = dataset_dummy['Exited']\n",
    "\n",
    "X_sm, y_sm = sm.fit_resample(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5Kc6Xth0jpKo",
    "outputId": "744aac24-c291-4ac9-be88-cfbce9837f4e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((568630, 29), (568630, 1))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_sm.shape, y_sm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SOSuqiBMj7eE"
   },
   "outputs": [],
   "source": [
    "# Showing ratio\n",
    "print(\"Percentage of normal transactions: \", len(oversampled_data[oversampled_data.Class == 0])/len(oversampled_data))\n",
    "print(\"Percentage of fraud transactions: \", len(oversampled_data[oversampled_data.Class == 1])/len(oversampled_data))\n",
    "print(\"Total number of transactions in resampled data: \", len(oversampled_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B6v7YDpdkd1A",
    "outputId": "0e106345-620f-4ae3-ad9f-9d5df43ca96e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5\n",
      "0.5\n"
     ]
    }
   ],
   "source": [
    "print(len(y_sm[y_sm.Class == 0])/len(y_sm))\n",
    "print(len(y_sm[y_sm.Class == 1])/len(y_sm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "l1N0Up_bmI5t"
   },
   "outputs": [],
   "source": [
    "# Split smote data\n",
    "X_train_sm, X_test_sm, y_train_sm, y_test_sm = train_test_split(X_sm,y_sm,test_size = 0.3,random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vDsnB7qZmcSf",
    "outputId": "e97bec8b-099b-4f68-f176-f63d24fe1089"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(398041, 29)\n",
      "(170589, 29)\n"
     ]
    }
   ],
   "source": [
    "print(X_train_sm.shape)\n",
    "print(X_test_sm.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TDdcXCR3mtP2",
    "outputId": "0c09b2f5-ac13-48f4-cde4-cc9319fe12ea"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 97.05022011970291\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(X_train_sm, y_train_sm)\n",
    "y_sm_pred = model.predict(X_test_sm)\n",
    "\n",
    "acc = 100*accuracy_score(y_test_sm, y_pred)\n",
    "print('Accuracy Score:', acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IV1bK-n7nuDA",
    "outputId": "efe85325-e2d5-4ac4-8837-69fe90b8db48"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[83000  2172]\n",
      " [ 7038 78379]]\n",
      "Confusion Matrix: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.97      0.95     85172\n",
      "           1       0.97      0.92      0.94     85417\n",
      "\n",
      "    accuracy                           0.95    170589\n",
      "   macro avg       0.95      0.95      0.95    170589\n",
      "weighted avg       0.95      0.95      0.95    170589\n",
      "\n",
      "\n",
      "\n",
      "TN - True Negative 78379\n",
      "FP - False Positive 7038\n",
      "FN - False Negative 2172\n",
      "TP - True Positive 83000\n",
      "Accuracy Rate: 0.9460105868490934\n",
      "Misclassification Rate: 0.053989413150906565\n",
      "F1-Score: 0.9459709410392397\n",
      "ROC-AUC 0.9460514427577374\n"
     ]
    }
   ],
   "source": [
    "cfmat = confusion_matrix(y_test_sm, y_sm_pred)\n",
    "print('Confusion matrix:\\n', cfmat)\n",
    "\n",
    "Confusion_Matrix(y_test_sm,y_sm_pred)"
   ]
  }
 ],
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