mini_project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# display plots in the notebook\n",
    "%matplotlib inline \n",
    "# NumPy for numerical computing\n",
    "import numpy as np\n",
    "\n",
    "# Pandas for DataFrames\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', 100)\n",
    "\n",
    "# Matplotlib for visualization\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "# Scikit-Learn for Modeling\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split \n",
    "\n",
    "# Function for creating model pipelines\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "# For standardization\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Helper for cross-validation\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import urllib.request\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will directly pass the best pipeline which is already trained\n",
    "def benchmark(clf, X_test, y_test, categories, name= 'classifier'):\n",
    "#     print(\"parameters:\", params)\n",
    "#     t0 = time()\n",
    "#     clf = clf_class(**params).fit(X_train, y_train)\n",
    "#     print(\"done in %fs\" % (time() - t0))\n",
    "\n",
    "    print(\"Predicting the outcomes of the testing set\")\n",
    "    t0 = time()\n",
    "    pred = clf.predict(X_test)\n",
    "    print(\"done in %fs\" % (time() - t0))\n",
    "    \n",
    "    print (\" Printing Accuracy of the classifier\")\n",
    "    print (accuracy_score(y_test, pred))\n",
    "    print ()\n",
    "\n",
    "    print(\"Classification report on test set for classifier:\")\n",
    "    print(clf)\n",
    "    print()\n",
    "    print(classification_report(y_test, pred, target_names=categories))\n",
    "    \n",
    "    print ()\n",
    "    print (\"FI score metrics\")\n",
    "    print (precision_recall_fscore_support(y_test, pred, average='weighted'))\n",
    "\n",
    "    cm = confusion_matrix(y_test, pred)\n",
    "    print(\"Confusion matrix:\")\n",
    "    print(cm)\n",
    "\n",
    "    # Show confusion matrix\n",
    "    plt.matshow(cm)\n",
    "    plt.title('CM of the %s ' % name)\n",
    "    plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download the dataset In case the below 2 steps throws error, else it will be automatically downloaded\n",
    "\n",
    "\n",
    "\n",
    "https://www.kaggle.com/mlg-ulb/creditcardfraud/downloads/creditcardfraud.zip/3\n",
    "\n",
    "\n",
    "and save to your folder.\n",
    "\n",
    "unzip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: wget in /Users/s2917623/anaconda3/lib/python3.6/site-packages (3.2)\n",
      "\u001b[33mYou are using pip version 18.0, however version 19.2.3 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Requirement already satisfied: googledrivedownloader in /Users/s2917623/anaconda3/lib/python3.6/site-packages (0.4)\n",
      "\u001b[33mYou are using pip version 18.0, however version 19.2.3 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install wget\n",
    "!pip install googledrivedownloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./creditcardfraud.zip'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import wget\n",
    "import os\n",
    "url = \"https://www.dropbox.com/s/z59v94oq1uiue5g/creditcardfraud.zip?dl=1\"\n",
    "try:\n",
    "    os.remove('./creditcardfraud.zip')\n",
    "except: \n",
    "    print (\"Downloading data\")\n",
    "wget.download(url, './creditcardfraud.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  ./creditcardfraud.zip\n",
      "  inflating: creditcard.csv          \n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "unzip -o ./creditcardfraud.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./creditcard.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
       "      <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>V11</th>\n",
       "      <th>V12</th>\n",
       "      <th>V13</th>\n",
       "      <th>V14</th>\n",
       "      <th>V15</th>\n",
       "      <th>V16</th>\n",
       "      <th>V17</th>\n",
       "      <th>V18</th>\n",
       "      <th>V19</th>\n",
       "      <th>V20</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>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\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>-0.551600</td>\n",
       "      <td>-0.617801</td>\n",
       "      <td>-0.991390</td>\n",
       "      <td>-0.311169</td>\n",
       "      <td>1.468177</td>\n",
       "      <td>-0.470401</td>\n",
       "      <td>0.207971</td>\n",
       "      <td>0.025791</td>\n",
       "      <td>0.403993</td>\n",
       "      <td>0.251412</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>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\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>1.612727</td>\n",
       "      <td>1.065235</td>\n",
       "      <td>0.489095</td>\n",
       "      <td>-0.143772</td>\n",
       "      <td>0.635558</td>\n",
       "      <td>0.463917</td>\n",
       "      <td>-0.114805</td>\n",
       "      <td>-0.183361</td>\n",
       "      <td>-0.145783</td>\n",
       "      <td>-0.069083</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>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\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>0.624501</td>\n",
       "      <td>0.066084</td>\n",
       "      <td>0.717293</td>\n",
       "      <td>-0.165946</td>\n",
       "      <td>2.345865</td>\n",
       "      <td>-2.890083</td>\n",
       "      <td>1.109969</td>\n",
       "      <td>-0.121359</td>\n",
       "      <td>-2.261857</td>\n",
       "      <td>0.524980</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>-0.054952</td>\n",
       "      <td>-0.226487</td>\n",
       "      <td>0.178228</td>\n",
       "      <td>0.507757</td>\n",
       "      <td>-0.287924</td>\n",
       "      <td>-0.631418</td>\n",
       "      <td>-1.059647</td>\n",
       "      <td>-0.684093</td>\n",
       "      <td>1.965775</td>\n",
       "      <td>-1.232622</td>\n",
       "      <td>-0.208038</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>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>-0.822843</td>\n",
       "      <td>0.538196</td>\n",
       "      <td>1.345852</td>\n",
       "      <td>-1.119670</td>\n",
       "      <td>0.175121</td>\n",
       "      <td>-0.451449</td>\n",
       "      <td>-0.237033</td>\n",
       "      <td>-0.038195</td>\n",
       "      <td>0.803487</td>\n",
       "      <td>0.408542</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>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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       V10       V11       V12       V13       V14  \\\n",
       "0  0.098698  0.363787  0.090794 -0.551600 -0.617801 -0.991390 -0.311169   \n",
       "1  0.085102 -0.255425 -0.166974  1.612727  1.065235  0.489095 -0.143772   \n",
       "2  0.247676 -1.514654  0.207643  0.624501  0.066084  0.717293 -0.165946   \n",
       "3  0.377436 -1.387024 -0.054952 -0.226487  0.178228  0.507757 -0.287924   \n",
       "4 -0.270533  0.817739  0.753074 -0.822843  0.538196  1.345852 -1.119670   \n",
       "\n",
       "        V15       V16       V17       V18       V19       V20       V21  \\\n",
       "0  1.468177 -0.470401  0.207971  0.025791  0.403993  0.251412 -0.018307   \n",
       "1  0.635558  0.463917 -0.114805 -0.183361 -0.145783 -0.069083 -0.225775   \n",
       "2  2.345865 -2.890083  1.109969 -0.121359 -2.261857  0.524980  0.247998   \n",
       "3 -0.631418 -1.059647 -0.684093  1.965775 -1.232622 -0.208038 -0.108300   \n",
       "4  0.175121 -0.451449 -0.237033 -0.038195  0.803487  0.408542 -0.009431   \n",
       "\n",
       "        V22       V23       V24       V25       V26       V27       V28  \\\n",
       "0  0.277838 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053   \n",
       "1 -0.638672  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724   \n",
       "2  0.771679  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752   \n",
       "3  0.005274 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458   \n",
       "4  0.798278 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   \n",
       "\n",
       "   Amount  Class  \n",
       "0  149.62      0  \n",
       "1    2.69      0  \n",
       "2  378.66      0  \n",
       "3  123.50      0  \n",
       "4   69.99      0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Explore your data and classify, Class column 0 = not fraud and 1 = fraud. highly imbalanced dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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