Random Forest.ipynb

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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Detect fake profiles in online social networks using Random Forest"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "import csv\n",
      "import datetime\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "from datetime import datetime\n",
      "import sexmachine.detector as gender\n",
      "from sklearn.preprocessing import Imputer\n",
      "from sklearn import cross_validation\n",
      "from sklearn import metrics\n",
      "from sklearn import preprocessing\n",
      "from sklearn.metrics import roc_curve, auc\n",
      "from  sklearn.ensemble import RandomForestClassifier\n",
      "from sklearn.cross_validation import StratifiedKFold, train_test_split\n",
      "from sklearn.grid_search import GridSearchCV\n",
      "from sklearn.metrics import accuracy_score\n",
      "from sklearn.learning_curve import learning_curve\n",
      "from sklearn.metrics import classification_report\n",
      "from sklearn.metrics import confusion_matrix\n",
      "%matplotlib inline "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "function for reading dataset from csv files"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "def read_datasets():\n",
      "    \"\"\" Reads users profile from csv files \"\"\"\n",
      "    genuine_users = pd.read_csv(\"data/users.csv\")\n",
      "    fake_users = pd.read_csv(\"data/fusers.csv\")\n",
      "    # print genuine_users.columns\n",
      "    # print genuine_users.describe()\n",
      "    #print fake_users.describe()\n",
      "    x=pd.concat([genuine_users,fake_users])   \n",
      "    y=len(fake_users)*[0] + len(genuine_users)*[1]\n",
      "    return x,y\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "function for predicting sex using name of person"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def predict_sex(name):\n",
      "    sex_predictor = gender.Detector(unknown_value=u\"unknown\",case_sensitive=False)\n",
      "    first_name= name.str.split(' ').str.get(0)\n",
      "    sex= first_name.apply(sex_predictor.get_gender)\n",
      "    sex_dict={'female': -2, 'mostly_female': -1,'unknown':0,'mostly_male':1, 'male': 2}\n",
      "    sex_code = sex.map(sex_dict).astype(int)\n",
      "    return sex_code"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 56
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "function for feature engineering"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def extract_features(x):\n",
      "    lang_list = list(enumerate(np.unique(x['lang'])))   \n",
      "    lang_dict = { name : i for i, name in lang_list }             \n",
      "    x.loc[:,'lang_code'] = x['lang'].map( lambda x: lang_dict[x]).astype(int)    \n",
      "    x.loc[:,'sex_code']=predict_sex(x['name'])\n",
      "    feature_columns_to_use = ['statuses_count','followers_count','friends_count','favourites_count','listed_count','sex_code','lang_code']\n",
      "    x=x.loc[:,feature_columns_to_use]\n",
      "    return x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "function for ploting learning curve"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
      "                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
      "    \n",
      "    plt.figure()\n",
      "    plt.title(title)\n",
      "    if ylim is not None:\n",
      "        plt.ylim(*ylim)\n",
      "    plt.xlabel(\"Training examples\")\n",
      "    plt.ylabel(\"Score\")\n",
      "    train_sizes, train_scores, test_scores = learning_curve(\n",
      "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
      "    train_scores_mean = np.mean(train_scores, axis=1)\n",
      "    train_scores_std = np.std(train_scores, axis=1)\n",
      "    test_scores_mean = np.mean(test_scores, axis=1)\n",
      "    test_scores_std = np.std(test_scores, axis=1)\n",
      "    plt.grid()\n",
      "\n",
      "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
      "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
      "                     color=\"r\")\n",
      "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
      "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
      "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
      "             label=\"Training score\")\n",
      "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
      "             label=\"Cross-validation score\")\n",
      "\n",
      "    plt.legend(loc=\"best\")\n",
      "    return plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 60
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "function for plotting confusion matrix"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):\n",
      "    target_names=['Fake','Genuine']\n",
      "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
      "    plt.title(title)\n",
      "    plt.colorbar()\n",
      "    tick_marks = np.arange(len(target_names))\n",
      "    plt.xticks(tick_marks, target_names, rotation=45)\n",
      "    plt.yticks(tick_marks, target_names)\n",
      "    plt.tight_layout()\n",
      "    plt.ylabel('True label')\n",
      "    plt.xlabel('Predicted label')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 61
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "function for plotting ROC curve"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_roc_curve(y_test, y_pred):\n",
      "    false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_pred)\n",
      "\n",
      "    print \"False Positive rate: \",false_positive_rate\n",
      "    print \"True Positive rate: \",true_positive_rate\n",
      "\n",
      "\n",
      "    roc_auc = auc(false_positive_rate, true_positive_rate)\n",
      "\n",
      "    plt.title('Receiver Operating Characteristic')\n",
      "    plt.plot(false_positive_rate, true_positive_rate, 'b',\n",
      "    label='AUC = %0.2f'% roc_auc)\n",
      "    plt.legend(loc='lower right')\n",
      "    plt.plot([0,1],[0,1],'r--')\n",
      "    plt.xlim([-0.1,1.2])\n",
      "    plt.ylim([-0.1,1.2])\n",
      "    plt.ylabel('True Positive Rate')\n",
      "    plt.xlabel('False Positive Rate')\n",
      "    plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "Function for training data using Random Forest"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def train(X_train,y_train,X_test):\n",
      "    \"\"\" Trains and predicts dataset with a Random Forest classifier \"\"\"\n",
      "    \n",
      "    clf=RandomForestClassifier(n_estimators=40,oob_score=True)\n",
      "    clf.fit(X_train,y_train)\n",
      "    print(\"The best classifier is: \",clf)\n",
      "    # Estimate score\n",
      "    scores = cross_validation.cross_val_score(clf, X_train,y_train, cv=5)\n",
      "    print scores\n",
      "    print('Estimated score: %0.5f (+/- %0.5f)' % (scores.mean(), scores.std() / 2))\n",
      "    title = 'Learning Curves (Random Forest)'\n",
      "    plot_learning_curve(clf, title, X_train, y_train, cv=5)\n",
      "    plt.show()\n",
      "    # Predict \n",
      "    y_pred = clf.predict(X_test)\n",
      "    return y_test,y_pred"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"reading datasets.....\\n\"\n",
      "x,y=read_datasets()\n",
      "x.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "reading datasets.....\n",
        "\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>id</th>\n",
        "      <th>statuses_count</th>\n",
        "      <th>followers_count</th>\n",
        "      <th>friends_count</th>\n",
        "      <th>favourites_count</th>\n",
        "      <th>listed_count</th>\n",
        "      <th>default_profile</th>\n",
        "      <th>default_profile_image</th>\n",
        "      <th>geo_enabled</th>\n",
        "      <th>profile_use_background_image</th>\n",
        "      <th>profile_background_tile</th>\n",
        "      <th>utc_offset</th>\n",
        "      <th>protected</th>\n",
        "      <th>verified</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td>2.818000e+03</td>\n",
        "      <td>2818.000000</td>\n",
        "      <td>2818.000000</td>\n",
        "      <td>2818.000000</td>\n",
        "      <td>2818.000000</td>\n",
        "      <td>2818.000000</td>\n",
        "      <td>1728</td>\n",
        "      <td>8</td>\n",
        "      <td>721</td>\n",
        "      <td>2760</td>\n",
        "      <td>489</td>\n",
        "      <td>1069.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>5.374889e+08</td>\n",
        "      <td>1672.198368</td>\n",
        "      <td>371.105039</td>\n",
        "      <td>395.363023</td>\n",
        "      <td>234.541164</td>\n",
        "      <td>2.818666</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1478.391020</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>2.977005e+08</td>\n",
        "      <td>4884.669157</td>\n",
        "      <td>8022.631339</td>\n",
        "      <td>465.694322</td>\n",
        "      <td>1445.847248</td>\n",
        "      <td>23.480430</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>8108.211889</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>3.610511e+06</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>-39600.000000</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>3.620867e+08</td>\n",
        "      <td>35.000000</td>\n",
        "      <td>17.000000</td>\n",
        "      <td>168.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>3600.000000</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>6.162253e+08</td>\n",
        "      <td>77.000000</td>\n",
        "      <td>26.000000</td>\n",
        "      <td>306.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>3600.000000</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>6.177673e+08</td>\n",
        "      <td>1087.750000</td>\n",
        "      <td>111.000000</td>\n",
        "      <td>519.000000</td>\n",
        "      <td>37.000000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>3600.000000</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>1.391998e+09</td>\n",
        "      <td>79876.000000</td>\n",
        "      <td>408372.000000</td>\n",
        "      <td>12773.000000</td>\n",
        "      <td>44349.000000</td>\n",
        "      <td>744.000000</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>36000.000000</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "                 id  statuses_count  followers_count  friends_count  \\\n",
        "count  2.818000e+03     2818.000000      2818.000000    2818.000000   \n",
        "mean   5.374889e+08     1672.198368       371.105039     395.363023   \n",
        "std    2.977005e+08     4884.669157      8022.631339     465.694322   \n",
        "min    3.610511e+06        0.000000         0.000000       0.000000   \n",
        "25%    3.620867e+08       35.000000        17.000000     168.000000   \n",
        "50%    6.162253e+08       77.000000        26.000000     306.000000   \n",
        "75%    6.177673e+08     1087.750000       111.000000     519.000000   \n",
        "max    1.391998e+09    79876.000000    408372.000000   12773.000000   \n",
        "\n",
        "       favourites_count  listed_count  default_profile  default_profile_image  \\\n",
        "count       2818.000000   2818.000000             1728                      8   \n",
        "mean         234.541164      2.818666                1                      1   \n",
        "std         1445.847248     23.480430                0                      0   \n",
        "min            0.000000      0.000000                1                      1   \n",
        "25%            0.000000      0.000000                1                      1   \n",
        "50%            0.000000      0.000000                1                      1   \n",
        "75%           37.000000      1.000000                1                      1   \n",
        "max        44349.000000    744.000000                1                      1   \n",
        "\n",
        "       geo_enabled  profile_use_background_image  profile_background_tile  \\\n",
        "count          721                          2760                      489   \n",
        "mean             1                             1                        1   \n",
        "std              0                             0                        0   \n",
        "min              1                             1                        1   \n",
        "25%              1                             1                        1   \n",
        "50%              1                             1                        1   \n",
        "75%              1                             1                        1   \n",
        "max              1                             1                        1   \n",
        "\n",
        "         utc_offset  protected  verified  \n",
        "count   1069.000000          0         0  \n",
        "mean    1478.391020        NaN       NaN  \n",
        "std     8108.211889        NaN       NaN  \n",
        "min   -39600.000000        NaN       NaN  \n",
        "25%     3600.000000        NaN       NaN  \n",
        "50%     3600.000000        NaN       NaN  \n",
        "75%     3600.000000        NaN       NaN  \n",
        "max    36000.000000        NaN       NaN  "
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"extracting featues.....\\n\"\n",
      "x=extract_features(x)\n",
      "print x.columns\n",
      "print x.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "extracting featues.....\n",
        "\n",
        "Index([u'statuses_count', u'followers_count', u'friends_count',\n",
        "       u'favourites_count', u'listed_count', u'sex_code', u'lang_code'],\n",
        "      dtype='object')"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "       statuses_count  followers_count  friends_count  favourites_count  \\\n",
        "count     2818.000000      2818.000000    2818.000000       2818.000000   \n",
        "mean      1672.198368       371.105039     395.363023        234.541164   \n",
        "std       4884.669157      8022.631339     465.694322       1445.847248   \n",
        "min          0.000000         0.000000       0.000000          0.000000   \n",
        "25%         35.000000        17.000000     168.000000          0.000000   \n",
        "50%         77.000000        26.000000     306.000000          0.000000   \n",
        "75%       1087.750000       111.000000     519.000000         37.000000   \n",
        "max      79876.000000    408372.000000   12773.000000      44349.000000   \n",
        "\n",
        "       listed_count     sex_code    lang_code  \n",
        "count   2818.000000  2818.000000  2818.000000  \n",
        "mean       2.818666    -0.180270     2.851313  \n",
        "std       23.480430     1.679125     1.992950  \n",
        "min        0.000000    -2.000000     0.000000  \n",
        "25%        0.000000    -2.000000     1.000000  \n",
        "50%        0.000000     0.000000     1.000000  \n",
        "75%        1.000000     2.000000     5.000000  \n",
        "max      744.000000     2.000000     7.000000  \n"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"spliting datasets in train and test dataset...\\n\"\n",
      "X_train,X_test,y_train,y_test = train_test_split(x, y, test_size=0.20, random_state=44)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "spliting datasets in train and test dataset...\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"training datasets.......\\n\"\n",
      "y_test,y_pred = train(X_train,y_train,X_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "training datasets.......\n",
        "\n",
        "('The best classifier is: ', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
        "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
        "            min_samples_leaf=1, min_samples_split=2,\n",
        "            min_weight_fraction_leaf=0.0, n_estimators=40, n_jobs=1,\n",
        "            oob_score=True, random_state=None, verbose=0, warm_start=False))"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[ 0.93791574  0.93791574  0.94678492  0.9578714   0.93777778]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimated score: 0.94365 (+/- 0.00395)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jjAnLgogJWrp4abyL0KZYfUaP1WXisiBijDGm2Wxg3QTZlUSH5vWFr/Po849S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\nnOSOwnB4swAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fd822417c10>"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'Classification Accuracy on Test dataset: ' ,accuracy_score(y_test, y_pred)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Classification Accuracy on Test dataset:  0.941489361702\n"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cm=confusion_matrix(y_test, y_pred)\n",
      "print('Confusion matrix, without normalization')\n",
      "print(cm)\n",
      "plot_confusion_matrix(cm)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Confusion matrix, without normalization\n",
        "[[265   3]\n",
        " [ 30 266]]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AS4BbUzPtbamh1oykDYFfANNsXyBpO0nfA1an6D/tf3zmMeBs4NruRBrD9Cjw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\nRZJQIyIqkoQaEVGR/w+/MTxLr6AbGQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fd821d1fc10>"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
      "print('Normalized confusion matrix')\n",
      "print(cm_normalized)\n",
      "plot_confusion_matrix(cm_normalized, title='Normalized confusion matrix')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Normalized confusion matrix\n",
        "[[ 0.98880597  0.01119403]\n",
        " [ 0.10135135  0.89864865]]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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xwLUR8cO0zjIRMa1zEVtfJH0QeIIsgT4GrAt8E7gQuDoinkqJ9BqySzFPdipW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\nWuAkambWAidRM7MW/H8AgA10yXRoHQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fd821b94410>"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(classification_report(y_test, y_pred, target_names=['Fake','Genuine']))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "             precision    recall  f1-score   support\n",
        "\n",
        "       Fake       0.90      0.99      0.94       268\n",
        "    Genuine       0.99      0.90      0.94       296\n",
        "\n",
        "avg / total       0.95      0.94      0.94       564\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      " plot_roc_curve(y_test, y_pred)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "False Positive rate:  [ 0.          0.01119403  1.        ]\n",
        "True Positive rate:  [ 0.          0.89864865  1.        ]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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grS2MPNAogESUFFLSnSkGVq8OZ99dObAPHqzasvQimZFFZ58NX/5y2tHUBSWFHrJoESxY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\n/Ft7zszuB54kdNTe4O41lxQ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       "text": [
        "<matplotlib.figure.Figure at 0x7fd821d1ff50>"
       ]
      }
     ],
     "prompt_number": 73
    }
   ],
   "metadata": {}
  }
 ]
}