diff --git a/Default Prediction.ipynb b/Default Prediction.ipynb
index e3aef97..bbd5d42 100644
--- a/Default Prediction.ipynb	
+++ b/Default Prediction.ipynb	
@@ -402,49 +402,6 @@
     "1 - train_y.mean()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "SVM"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Finished training 1\n",
-      "Finished training 0.1\n",
-      "Finished training 0.01\n",
-      "Finished training 10\n"
-     ]
-    }
-   ],
-   "source": [
-    "from itertools import product\n",
-    "import pickle\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "\n",
-    "for C in [1, .1, .01, 10]:\n",
-    "    lr = LogisticRegression(C=C)\n",
-    "\n",
-    "    lr.fit(train_x, train_y)\n",
-    "    with open('lr_{}.pickle'.format(C), 'w') as handle:\n",
-    "        pickle.dump(lr, handle)\n",
-    "        \n",
-    "    del(lr)\n",
-    "    print(\"Finished training {}\".format(C))\n",
-    "    #print(\"C: {}; gamma: {}; score: {}\".format(\n",
-    "    #        C, gamma, svc.score(valid_x, valid_y)))"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -456,36 +413,36 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/lib/python2.7/dist-packages/sklearn/discriminant_analysis.py:387: UserWarning: Variables are collinear.\n",
-      "  warnings.warn(\"Variables are collinear.\")\n"
+      "/usr/lib/python2.7/dist-packages/sklearn/linear_model/sag.py:267: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Finished training 342\n",
-      "Finished training 250\n",
-      "Finished training 150\n",
-      "Finished training 75\n"
+      "Finished training 1\n",
+      "Finished training 0.1\n",
+      "Finished training 0.01\n",
+      "Finished training 0.001\n",
+      "Finished training 0.0001\n"
      ]
     }
    ],
    "source": [
-    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
+    "from itertools import product\n",
     "import pickle\n",
+    "from sklearn.linear_model import LogisticRegression\n",
     "\n",
-    "# Number of columns is 342\n",
-    "for n_components in [342, 250, 150, 75]:\n",
-    "    lda = LinearDiscriminantAnalysis(n_components=n_components)\n",
-    "    lda.fit(train_x, train_y)\n",
-    "    with open('lda_{}.pickle'.format(n_components), 'w') as handle:\n",
-    "        pickle.dump(lda, handle)\n",
-    "    \n",
-    "    del(lda)\n",
-    "    print(\"Finished training {}\".format(n_components))\n",
-    "    #print(\"N_components: {}; score: {}\".format(\n",
-    "    #        n_components, lda.score(valid_x, valid_y)))"
+    "for C in [1, .1, .01, .001, .0001]:\n",
+    "    lr = LogisticRegression(C=C, solver='sag', max_iter=100)\n",
+    "\n",
+    "    lr.fit(train_x, train_y)\n",
+    "    with open('lr_{}.pickle'.format(C), 'w') as handle:\n",
+    "        pickle.dump(lr, handle)\n",
+    "        \n",
+    "    del(lr)\n",
+    "    print(\"Finished training {}\".format(C))"
    ]
   },
   {
@@ -494,36 +451,76 @@
    "metadata": {
     "collapsed": false
    },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Finished training 342\n",
+      "Finished training 150\n",
+      "Finished training 20\n",
+      "Finished training 5\n",
+      "Finished training 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
+    "import pickle\n",
+    "\n",
+    "# Number of columns is 342\n",
+    "for n_components in [342, 150, 20, 5, 1]:\n",
+    "    lda = LinearDiscriminantAnalysis(n_components=n_components,\n",
+    "                                     solver='lsqr')\n",
+    "    lda.fit(train_x, train_y)\n",
+    "    with open('lda_{}.pickle'.format(n_components), 'w') as handle:\n",
+    "        pickle.dump(lda, handle)\n",
+    "    \n",
+    "    del(lda)\n",
+    "    print(\"Finished training {}\".format(n_components))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Finished training 10\n",
-      "Finished training 50\n",
-      "Finished training 75\n",
-      "Finished training 100\n"
+      "Finished training 5\n",
+      "Finished training 3\n",
+      "Finished training 1\n"
      ]
     }
    ],
    "source": [
     "from sklearn.ensemble import RandomForestClassifier\n",
     "\n",
-    "for n_estimators in [10, 50, 75, 100]:\n",
+    "for n_estimators in [10, 5, 3, 1]:\n",
     "    rf = RandomForestClassifier(n_estimators=n_estimators)\n",
     "    rf.fit(train_x, train_y)\n",
     "    with open('rf_{}.pickle'.format(n_estimators), 'w') as handle:\n",
     "        pickle.dump(rf, handle)\n",
     "        \n",
     "    del(rf)        \n",
-    "    print(\"Finished training {}\".format(n_estimators))\n",
-    "    #print(\"N_estimators: {}; score: {}\".format(\n",
-    "    #        n_estimators, score(valid_x, valid_y)))"
+    "    print(\"Finished training {}\".format(n_estimators))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evaluating the Results"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
@@ -538,7 +535,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
@@ -550,13 +547,14 @@
       "Logistic Regression, C=1; Score: 0.89904155569\n",
       "Logistic Regression, C=0.1; Score: 0.89904155569\n",
       "Logistic Regression, C=0.01; Score: 0.89904155569\n",
-      "Logistic Regression, C=10; Score: 0.89904155569\n"
+      "Logistic Regression, C=0.001; Score: 0.89904155569\n",
+      "Logistic Regression, C=0.0001; Score: 0.89904155569\n"
      ]
     }
    ],
    "source": [
     "import pickle\n",
-    "lr_params = [1, .1, .01, 10]\n",
+    "lr_params = [1, .1, .01, .001, .0001]\n",
     "\n",
     "for C in lr_params:\n",
     "    with open('lr_{}.pickle'.format(C)) as handle:\n",
@@ -571,7 +569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
@@ -580,15 +578,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Linear Discriminant Analysis, components=342; Score: 0.897066249493\n",
-      "Linear Discriminant Analysis, components=250; Score: 0.897066249493\n",
-      "Linear Discriminant Analysis, components=150; Score: 0.897066249493\n",
-      "Linear Discriminant Analysis, components=75; Score: 0.897066249493\n"
+      "Linear Discriminant Analysis, components=342; Score: 0.896526629504\n",
+      "Linear Discriminant Analysis, components=150; Score: 0.896526629504\n",
+      "Linear Discriminant Analysis, components=20; Score: 0.896526629504\n",
+      "Linear Discriminant Analysis, components=5; Score: 0.896526629504\n",
+      "Linear Discriminant Analysis, components=1; Score: 0.896526629504\n"
      ]
     }
    ],
    "source": [
-    "lda_components = [342, 250, 150, 75]\n",
+    "lda_components = [342, 150, 20, 5, 1]\n",
     "\n",
     "for n_components in lda_components:\n",
     "    with open('lda_{}.pickle'.format(n_components)) as handle:\n",
@@ -603,7 +602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -612,15 +611,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Random Forests, estimators: 10; Score: 0.934468350545\n",
-      "Random Forests, estimators: 50; Score: 0.936468409953\n",
-      "Random Forests, estimators: 75; Score: 0.936324841332\n",
-      "Random Forests, estimators: 100; Score: 0.936740695268\n"
+      "Random Forests, estimators: 10; Score: 0.934577264671\n",
+      "Random Forests, estimators: 5; Score: 0.93108211135\n",
+      "Random Forests, estimators: 3; Score: 0.926601780251\n",
+      "Random Forests, estimators: 1; Score: 0.915037080309\n"
      ]
     }
    ],
    "source": [
-    "rf_estimators = [10, 50, 75, 100]\n",
+    "rf_estimators = [10, 5, 3, 1]\n",
     "\n",
     "for estimators in rf_estimators:\n",
     "    with open('rf_{}.pickle'.format(estimators)) as handle:\n",
@@ -632,6 +631,236 @@
     "                estimators, score\n",
     "            ))"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Column Significance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "train = pd.read_csv('processed_train.csv', index_col=0).dropna(axis=1)\n",
+    "valid = pd.read_csv('processed_validate.csv', index_col=0).dropna(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "activity_columns = ('Activity', list(filter(\n",
+    "            lambda x: 'activity' in x,\n",
+    "            train.columns)))\n",
+    "dcurrency_columns = ('Disbursal Currency', list(filter(\n",
+    "            lambda x: 'disbursal_currency' in x,\n",
+    "            train.columns)))\n",
+    "country_columns = ('Country', list(filter(\n",
+    "                lambda x: 'country' in x,\n",
+    "                train.columns)))\n",
+    "sector_columns = ('Sector', list(filter(\n",
+    "                lambda x: 'sector' in x,\n",
+    "                train.columns)))\n",
+    "repayment_columns = ('Repayment', list(filter(\n",
+    "                lambda x: 'repayment' in x,\n",
+    "                train.columns)))\n",
+    "nborrowers_columns = ('Number of Borrowers', ['num_borrowers'])\n",
+    "gratio_columns = ('Gender Ratio', ['gender_ratio'])\n",
+    "nlenders_columns = ('Lender Count', ['lender_count'])\n",
+    "damount_columns = ('Disbursal Amount', ['disbursal_amount'])\n",
+    "\n",
+    "colgroups = [\n",
+    "    activity_columns, dcurrency_columns, country_columns,\n",
+    "    sector_columns, repayment_columns, nborrowers_columns,\n",
+    "    gratio_columns, nlenders_columns, damount_columns\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Removing columns: Activity\n",
+      "Score: 93.569\n",
+      "\n",
+      "Removing columns: Disbursal Currency\n",
+      "Score: 93.458\n",
+      "\n",
+      "Removing columns: Country\n",
+      "Score: 93.381\n",
+      "\n",
+      "Removing columns: Sector\n",
+      "Score: 93.628\n",
+      "\n",
+      "Removing columns: Repayment\n",
+      "Score: 93.180\n",
+      "\n",
+      "Removing columns: Number of Borrowers\n",
+      "Score: 93.450\n",
+      "\n",
+      "Removing columns: Gender Ratio\n",
+      "Score: 93.422\n",
+      "\n",
+      "Removing columns: Lender Count\n",
+      "Score: 90.606\n",
+      "\n",
+      "Removing columns: Disbursal Amount\n",
+      "Score: 92.342\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "def validate_column_group(train, valid, colgroup):\n",
+    "    sub_group = train.drop(colgroup, axis=1)\n",
+    "    train_x = sub_group.drop('bad_loan', axis=1)\n",
+    "    train_y = sub_group['bad_loan']\n",
+    "    \n",
+    "    rf = RandomForestClassifier(n_estimators=10)\n",
+    "    rf.fit(train_x, train_y)\n",
+    "    \n",
+    "    valid_subgroup = valid.drop(colgroup, axis=1)\n",
+    "    valid_x = valid_subgroup.drop('bad_loan', axis=1)\n",
+    "    valid_y = valid_subgroup['bad_loan']\n",
+    "    score = rf.score(valid_x, valid_y)\n",
+    "    \n",
+    "    return score\n",
+    "    \n",
+    "for name, colgroup in colgroups:\n",
+    "    score = validate_column_group(train, valid, colgroup)\n",
+    "    \n",
+    "    print 'Removing columns: {}'.format(name)\n",
+    "    print 'Score: {:.3f}'.format(score*100)\n",
+    "    print"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Mjk5LXQmkp3nJ5BheeWV92j6N51+0aGUjx9o7n4iISHNSAJDGOUdtbWfqr8bBt6Skp6Xv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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f12b780c810>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "train.groupby('lender_count')['bad_loan'].mean().plot(style='o');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YDnhFo+aatn3Oa20jyZb1vpr7KfVz9kOSJI1pYyUU3Ux1uGs21ezMi4Hrk2xL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\n/wP64vtuq3uIGQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f129146ea90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train['lender_count'].plot(kind='hist', bins=200);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, want to get an idea of how well this model will perform in production:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "import pickle\n",
+    "\n",
+    "train = pd.read_csv('processed_train.csv', index_col=0).dropna(axis=1)\n",
+    "valid = pd.read_csv('processed_validate.csv', index_col=0).dropna(axis=1)\n",
+    "\n",
+    "full = pd.concat((train, valid))\n",
+    "full_x = full.drop('bad_loan', axis=1)\n",
+    "full_y = full['bad_loan']\n",
+    "rf = RandomForestClassifier(n_estimators=10)\n",
+    "\n",
+    "rf.fit(full_x, full_y)\n",
+    "\n",
+    "with open('rf_validated.pickle', 'w') as handle:\n",
+    "        pickle.dump(rf, handle)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy: 93.651%\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import pickle\n",
+    "\n",
+    "test = pd.read_csv('processed_test.csv', index_col=0).dropna(axis=1)\n",
+    "test_x = test.drop('bad_loan', axis=1)\n",
+    "test_y = test['bad_loan']\n",
+    "with open('rf_validated.pickle') as handle:\n",
+    "    rf = pickle.load(handle)\n",
+    "    score = rf.score(test_x, test_y)\n",
+    "    \n",
+    "    print 'Test accuracy: {:.3f}%'.format(score*100)"
+   ]
   }
  ],
  "metadata": {