diff --git a/notebooks/01-Exploration.ipynb b/notebooks/01-Exploration.ipynb
index 51d6a70..6fc55a8 100644
--- a/notebooks/01-Exploration.ipynb
+++ b/notebooks/01-Exploration.ipynb
@@ -777,7 +777,7 @@
       "text/plain": [
        "count                10989649\n",
        "unique               10989649\n",
-       "top       0000-0003-2532-0046\n",
+       "top       0000-0002-7963-4502\n",
        "freq                        1\n",
        "Name: orcid, dtype: object"
       ]
@@ -806,10 +806,10 @@
     {
      "data": {
       "text/plain": [
-       "count                       124722\n",
-       "unique                      124718\n",
-       "top       patrick.davey@monash.edu\n",
-       "freq                             2\n",
+       "count                     124722\n",
+       "unique                    124718\n",
+       "top       opercin@erbakan.edu.tr\n",
+       "freq                           2\n",
        "Name: primary_email, dtype: object"
       ]
      },
@@ -2412,9 +2412,9 @@
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\"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"constrain\": \"domain\", \"domain\": [0.0, 1.0], \"scaleanchor\": \"y\"}, \"yaxis\": {\"anchor\": \"x\", \"autorange\": \"reversed\", \"constrain\": \"domain\", \"domain\": [0.0, 1.0]}},                        {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('79146b9f-0db2-44c2-bf78-850a4711610c');\n",
+       "var gd = document.getElementById('a5c41718-5210-4e93-b149-be57c2c2616f');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -16262,7 +16262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -16291,7 +16291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -16888,7 +16888,7 @@
        "[2664886 rows x 31 columns]"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -16899,28 +16899,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
     "# (df.n_works > 0) & (df.n_ids > 1)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Serialise "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    " "
-   ]
   }
  ],
  "metadata": {
diff --git a/notebooks/03-Machine Learning.ipynb b/notebooks/03-Machine Learning.ipynb
index 5fa2601..0eed4e7 100644
--- a/notebooks/03-Machine Learning.ipynb	
+++ b/notebooks/03-Machine Learning.ipynb	
@@ -1,18 +1,8 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Todo in data\n",
-    "- Column names -> no space\n",
-    "- If a list is empty, serialise [] in the csv\n",
-    "- If a string is empty, serialise '' in the csv"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,7 +13,8 @@
     "import pandas as pd\n",
     "from sklearn.preprocessing import MultiLabelBinarizer\n",
     "from sklearn.svm import OneClassSVM \n",
-    "from sklearn.model_selection import train_test_split"
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import f1_score\n"
    ]
   },
   {
@@ -324,16 +315,303 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = df.fillna(0)"
+    "df.fillna(0, inplace=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      <th>verified_email</th>\n",
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+       "      <th>n_ids</th>\n",
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+       "      <th>n_employment</th>\n",
+       "      <th>n_education</th>\n",
+       "      <th>label</th>\n",
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+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10989649 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          verified_email  verified_primary_email  n_works  n_doi  n_arxiv  \\\n",
+       "0                      0                       0        0      0        0   \n",
+       "1                      1                       1        0      0        0   \n",
+       "2                      1                       1        0      0        0   \n",
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+       "10989647               1                       1        0      0        0   \n",
+       "10989648               1                       1        0      0        0   \n",
+       "\n",
+       "          n_pmc  n_other_pids  n_emails  n_urls  n_ids  n_keywords  \\\n",
+       "0             0             0       0.0     0.0    0.0         0.0   \n",
+       "1             0             0       0.0     0.0    0.0         0.0   \n",
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+       "10989648      0             0       0.0     0.0    0.0         0.0   \n",
+       "\n",
+       "          n_employment  n_education  label  \n",
+       "0                  0.0          0.0      0  \n",
+       "1                  1.0          0.0      0  \n",
+       "2                  0.0          0.0      0  \n",
+       "3                  1.0          0.0      0  \n",
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+       "...                ...          ...    ...  \n",
+       "10989644           1.0          2.0      0  \n",
+       "10989645           2.0          2.0      1  \n",
+       "10989646           0.0          0.0      0  \n",
+       "10989647           1.0          2.0      0  \n",
+       "10989648           0.0          0.0      0  \n",
+       "\n",
+       "[10989649 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -356,7 +634,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -367,7 +645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -390,7 +668,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -401,17 +679,299 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
     "# split into train/test sets\n",
     "X = df.loc[:,'verified_email':'n_education']\n",
     "y = df['label']\n",
-    "trainX, testX, trainy, testy = train_test_split(X, y, test_size=0.5, random_state=2, stratify=y)\n",
-    "\n",
+    "trainX, testX, trainy, testy = train_test_split(X, y, train_size=0.5, random_state=2, stratify=y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<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",
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+       "\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>verified_email</th>\n",
+       "      <th>verified_primary_email</th>\n",
+       "      <th>n_works</th>\n",
+       "      <th>n_doi</th>\n",
+       "      <th>n_arxiv</th>\n",
+       "      <th>n_pmc</th>\n",
+       "      <th>n_other_pids</th>\n",
+       "      <th>n_emails</th>\n",
+       "      <th>n_urls</th>\n",
+       "      <th>n_ids</th>\n",
+       "      <th>n_keywords</th>\n",
+       "      <th>n_employment</th>\n",
+       "      <th>n_education</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
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+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5494824 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          verified_email  verified_primary_email  n_works  n_doi  n_arxiv  \\\n",
+       "6325067                1                       1        0      0        0   \n",
+       "6140551                1                       1        0      0        0   \n",
+       "3258315                1                       1        0      0        0   \n",
+       "10948983               1                       1        0      0        0   \n",
+       "10089158               1                       1        0      0        0   \n",
+       "...                  ...                     ...      ...    ...      ...   \n",
+       "2398808                0                       0        1      1        0   \n",
+       "3622839                1                       1        0      0        0   \n",
+       "1389679                1                       1        0      0        0   \n",
+       "6594722                0                       0        0      0        0   \n",
+       "4589084                1                       1       11      0        0   \n",
+       "\n",
+       "          n_pmc  n_other_pids  n_emails  n_urls  n_ids  n_keywords  \\\n",
+       "6325067       0             0       0.0     0.0    1.0         0.0   \n",
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+       "10089158      0             0       0.0     0.0    0.0         0.0   \n",
+       "...         ...           ...       ...     ...    ...         ...   \n",
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+       "4589084       0             0       0.0     0.0    0.0         0.0   \n",
+       "\n",
+       "          n_employment  n_education  \n",
+       "6325067            1.0          1.0  \n",
+       "6140551            0.0          0.0  \n",
+       "3258315            0.0          0.0  \n",
+       "10948983           1.0          0.0  \n",
+       "10089158           0.0          0.0  \n",
+       "...                ...          ...  \n",
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+       "1389679            0.0          0.0  \n",
+       "6594722            0.0          0.0  \n",
+       "4589084            1.0          0.0  \n",
+       "\n",
+       "[5494824 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trainX"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "# define outlier detection model\n",
-    "model = OneClassSVM(gamma='scale', nu=0.01)\n",
+    "model = OneClassSVM(gamma='scale', nu=0.5)\n",
     "\n",
     "# fit on majority class\n",
     "trainX = trainX[trainy==1]\n",