Handling Imbalanced Data- Over Sampling.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "## Credit Card Kaggle- Fixing Imbalanced Dataset\n",
    "\n",
    "### Context\n",
    "It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.\n",
    "\n",
    "### Content\n",
    "The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.\n",
    "\n",
    "It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.\n",
    "\n",
    "### Inspiration\n",
    "Identify fraudulent credit card transactions.\n",
    "\n",
    "Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification.\n",
    "\n",
    "### Acknowledgements\n",
    "The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import scipy\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import classification_report,accuracy_score\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "from sklearn.svm import OneClassSVM\n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 14, 8\n",
    "RANDOM_SEED = 42\n",
    "LABELS = [\"Normal\", \"Fraud\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
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       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
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       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
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       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
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       "      <td>0.128539</td>\n",
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       "      <td>0.133558</td>\n",
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       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.101288</td>\n",
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       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
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       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
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       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
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       "<p>5 rows × 31 columns</p>\n",
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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  \\\n",
       "0  0.098698  0.363787  ...   -0.018307  0.277838 -0.110474  0.066928   \n",
       "1  0.085102 -0.255425  ...   -0.225775 -0.638672  0.101288 -0.339846   \n",
       "2  0.247676 -1.514654  ...    0.247998  0.771679  0.909412 -0.689281   \n",
       "3  0.377436 -1.387024  ...   -0.108300  0.005274 -0.190321 -1.175575   \n",
       "4 -0.270533  0.817739  ...   -0.009431  0.798278 -0.137458  0.141267   \n",
       "\n",
       "        V25       V26       V27       V28  Amount  Class  \n",
       "0  0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.167170  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3  0.647376 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4 -0.206010  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('creditcard.csv',sep=',')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      "Time      284807 non-null float64\n",
      "V1        284807 non-null float64\n",
      "V2        284807 non-null float64\n",
      "V3        284807 non-null float64\n",
      "V4        284807 non-null float64\n",
      "V5        284807 non-null float64\n",
      "V6        284807 non-null float64\n",
      "V7        284807 non-null float64\n",
      "V8        284807 non-null float64\n",
      "V9        284807 non-null float64\n",
      "V10       284807 non-null float64\n",
      "V11       284807 non-null float64\n",
      "V12       284807 non-null float64\n",
      "V13       284807 non-null float64\n",
      "V14       284807 non-null float64\n",
      "V15       284807 non-null float64\n",
      "V16       284807 non-null float64\n",
      "V17       284807 non-null float64\n",
      "V18       284807 non-null float64\n",
      "V19       284807 non-null float64\n",
      "V20       284807 non-null float64\n",
      "V21       284807 non-null float64\n",
      "V22       284807 non-null float64\n",
      "V23       284807 non-null float64\n",
      "V24       284807 non-null float64\n",
      "V25       284807 non-null float64\n",
      "V26       284807 non-null float64\n",
      "V27       284807 non-null float64\n",
      "V28       284807 non-null float64\n",
      "Amount    284807 non-null float64\n",
      "Class     284807 non-null int64\n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(284807, 30)\n",
      "(284807,)\n"
     ]
    }
   ],
   "source": [
    "#Create independent and Dependent Features\n",
    "columns = data.columns.tolist()\n",
    "# Filter the columns to remove data we do not want \n",
    "columns = [c for c in columns if c not in [\"Class\"]]\n",
    "# Store the variable we are predicting \n",
    "target = \"Class\"\n",
    "# Define a random state \n",
    "state = np.random.RandomState(42)\n",
    "X = data[columns]\n",
    "Y = data[target]\n",
    "# Print the shapes of X & Y\n",
    "print(X.shape)\n",
    "print(Y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_classes = pd.value_counts(data['Class'], sort = True)\n",
    "\n",
    "count_classes.plot(kind = 'bar', rot=0)\n",
    "\n",
    "plt.title(\"Transaction Class Distribution\")\n",
    "\n",
    "plt.xticks(range(2), LABELS)\n",
    "\n",
    "plt.xlabel(\"Class\")\n",
    "\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Get the Fraud and the normal dataset \n",
    "\n",
    "fraud = data[data['Class']==1]\n",
    "\n",
    "normal = data[data['Class']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(492, 31) (284315, 31)\n"
     ]
    }
   ],
   "source": [
    "print(fraud.shape,normal.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.combine import SMOTETomek\n",
    "from imblearn.under_sampling import NearMiss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Implementing Oversampling for Handling Imbalanced \n",
    "smk = SMOTETomek(random_state=42)\n",
    "X_res,y_res=smk.fit_sample(X,Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((567562, 30), (567562,))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_res.shape,y_res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape Counter({0: 284315, 1: 492})\n",
      "Resampled dataset shape Counter({0: 283781, 1: 283781})\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "from collections import Counter\n",
    "print('Original dataset shape {}'.format(Counter(Y)))\n",
    "print('Resampled dataset shape {}'.format(Counter(y_res)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "## RandomOverSampler to handle imbalanced data\n",
    "\n",
    "from imblearn.over_sampling import RandomOverSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "os =  RandomOverSampler(ratio=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_res, y_train_res = os.fit_sample(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((426472, 30), (426472,))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_res.shape,y_train_res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape Counter({0: 284315, 1: 492})\n",
      "Resampled dataset shape Counter({0: 284315, 1: 142157})\n"
     ]
    }
   ],
   "source": [
    "print('Original dataset shape {}'.format(Counter(Y)))\n",
    "print('Resampled dataset shape {}'.format(Counter(y_train_res)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In this example I use SMOTETomek which is a method of imblearn. SMOTETomek is a hybrid method\n",
    "# which uses an under sampling method (Tomek) in with an over sampling method (SMOTE).\n",
    "os_us = SMOTETomek(ratio=0.5)\n",
    "\n",
    "X_train_res1, y_train_res1 = os_us.fit_sample(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((424788, 30), (424788,))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_res1.shape,y_train_res1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape Counter({0: 284315, 1: 492})\n",
      "Resampled dataset shape Counter({0: 283473, 1: 141315})\n"
     ]
    }
   ],
   "source": [
    "print('Original dataset shape {}'.format(Counter(Y)))\n",
    "print('Resampled dataset shape {}'.format(Counter(y_train_res1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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