diff --git a/Jonas_Solutions/Task_02_JonasWeinz.ipynb b/Jonas_Solutions/Task_02_JonasWeinz.ipynb
index 4caca7b..2cfca3e 100644
--- a/Jonas_Solutions/Task_02_JonasWeinz.ipynb
+++ b/Jonas_Solutions/Task_02_JonasWeinz.ipynb
@@ -14,17 +14,21 @@
     "* Tutorial on Datacamp: https://www.datacamp.com/community/tutorials/scikit-learn-fake-news\n",
     "\n",
     "* liar dataset paper: https://www.cs.ucsb.edu/~william/papers/acl2017.pdf\n",
-    "    * dataset: https://www.cs.ucsb.edu/~william/data/liar_dataset.zip\n",
-    "\n",
+    "    * dataset: https://www.cs.ucsb.edu/~william/data/liar_dataset.zip"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "## Dependencies for this Notebook:\n",
     "* library [rdflib](https://github.com/RDFLib/rdflib)\n",
-    "    * install: `pip3 install rdflib`\n",
-    "* "
+    "    * install: `pip3 install rdflib`\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -41,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,9 +53,65 @@
     "import numpy as np\n",
     "import itertools\n",
     "import sklearn.utils as sku\n",
+    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, HashingVectorizer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import PassiveAggressiveClassifier\n",
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "from sklearn import metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pprint import pprint as pp\n",
     "import os"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tools used later"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_confusion_matrix(cm, classes,\n",
+    "                          title,\n",
+    "                          normalize=False,\n",
+    "                          cmap=plt.cm.Blues):\n",
+    "    fig_1, ax_1 = plt.subplots()\n",
+    "    \"\"\"\n",
+    "    See full source and example: \n",
+    "    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
+    "    \n",
+    "    This function prints and plots the confusion matrix.\n",
+    "    Normalization can be applied by setting `normalize=True`.\n",
+    "    \"\"\"\n",
+    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
+    "    plt.title('Confusion Matrix for: ' + title)\n",
+    "    plt.colorbar()\n",
+    "    tick_marks = np.arange(len(classes))\n",
+    "    plt.xticks(tick_marks, classes, rotation=45)\n",
+    "    plt.yticks(tick_marks, classes)\n",
+    "\n",
+    "    if normalize:\n",
+    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
+    "        print(\"Normalized confusion matrix\")\n",
+    "    else:\n",
+    "        print('Confusion matrix, without normalization')\n",
+    "\n",
+    "    thresh = cm.max() / 2.\n",
+    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
+    "        plt.text(j, i, cm[i, j],\n",
+    "                 horizontalalignment=\"center\",\n",
+    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.ylabel('True label')\n",
+    "    plt.xlabel('Predicted label')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -63,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -103,37 +163,16 @@
       "        UNZIPOPT:  [none]\n",
       "         ZIPINFO:  [none]\n",
       "      ZIPINFOOPT:  [none]\n",
-      "Archive:  liar_dataset.zip\n",
-      "  inflating: README                  \n",
-      "  inflating: test.tsv                \n",
-      "  inflating: train.tsv               \n",
-      "  inflating: valid.tsv               \n",
       "================================================================================\n",
       "successfully finished action: downloading and unpacking https://www.cs.ucsb.edu/~william/data/liar_dataset.zip if not already existing\n",
       "================================================================================\n",
       "================================================================================\n",
       "downloading and unpacking https://raw.githubusercontent.com/GeorgeMcIntire/fake_real_news_dataset/master/fake_or_real_news.csv.zip if not already existing\n",
       "================================================================================\n",
-      "Archive:  fake_or_real_news.csv.zip\n",
-      "  inflating: fake_or_real_news.csv   \n",
-      "   creating: __MACOSX/\n",
-      "  inflating: __MACOSX/._fake_or_real_news.csv  \n",
       "================================================================================\n",
       "successfully finished action: downloading and unpacking https://raw.githubusercontent.com/GeorgeMcIntire/fake_real_news_dataset/master/fake_or_real_news.csv.zip if not already existing\n",
       "================================================================================\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
-      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
-      "100  989k  100  989k    0     0   366k      0  0:00:02  0:00:02 --:--:--  366k\n",
-      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
-      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
-      "100 11.3M  100 11.3M    0     0  2590k      0  0:00:04  0:00:04 --:--:-- 2751k\n"
-     ]
     }
    ],
    "source": [
@@ -150,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -166,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -321,28 +360,274 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def create_test_dset(dset, cutoff=0.7):\n",
-    "    shuffled = sku.shuffle(dset)\n",
+    "def create_training_and_test_set(dset, cutoff=0.7):\n",
+    "    shuffled = sku.shuffle(dset) # shuffle dataset\n",
     "    y = shuffled.label\n",
-    "    df_1 = shuffled.drop('label', axis=1)\n",
+    "    shuffled = shuffled.drop('label', axis=1)['text']\n",
+    "    size = int(cutoff * shuffled.shape[0])\n",
+    "    return shuffled[:size], y[:size], shuffled[size:], y[size:]\n",
     "    "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X,y, Xt,yt = create_training_and_test_set(df_1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#X2,y2, Xt2,yt2 = train_test_split(df_1['text'],df_1.label,test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Unnamed: 0\n",
+       "5507     Anthony Weiner Sends Apology Sext To Entire Cl...\n",
+       "1440     John Kasich was killing it with these Iowa vot...\n",
+       "3172     Yeah, yeah, with the rise of Der Trumper and t...\n",
+       "6962     Vermont fights the opioid epidemic by limiting...\n",
+       "6970     — Adam Baldwin (@AdamBaldwin) October 28, 2016...\n",
+       "7209     Militias prepare for election unrest while Chr...\n",
+       "4781     Raleigh, North Carolina (CNN) As soon as the f...\n",
+       "6137     Yemen’s Hudaydah suffering from dire humanitar...\n",
+       "8799     Subscribe \\nIn politics, the Third Way is a po...\n",
+       "4374     Hanging in the balance is Obama’s vision of Am...\n",
+       "9465     Under the Surface - Naomi Klein and the Great ...\n",
+       "10409    October 28, 2016 at 9:00 PM \\nWhy would Putin ...\n",
+       "2822     HAMPTON, N.H. -- Hillary Rodham Clinton said F...\n",
+       "5190     (CNN) Donald Trump on Wednesday refused to say...\n",
+       "2286     Gay rights won't fade as a political issue. Th...\n",
+       "4308     Fox Business’s Tuesday night Republican debate...\n",
+       "7305     Yes, There Are Paid Government Trolls On Socia...\n",
+       "6716     Vladimir Putin: The United States continues to...\n",
+       "6871     By wmw_admin on October 31, 2016 Sanchez Manni...\n",
+       "8527     Banana Republic Election in the United States?...\n",
+       "3658     Why are Americans expected to die sooner than ...\n",
+       "9721     Comments \\nFamous techno musician Moby tore in...\n",
+       "10001    Behind the headlines - conspiracies, cover-ups...\n",
+       "8920     Massachusetts +11% New York +10% \\nWhen home p...\n",
+       "8488     Ivana Says Young Donald Trump Was A Cry-Baby –...\n",
+       "8474       Recipient Email => \\nEnemies of the United S...\n",
+       "5789     Theresa May refuses to withdraw support for Sa...\n",
+       "1324     Even as Hillary Clinton has stepped up her rhe...\n",
+       "1744     Sally Kohn is an activist, columnist and telev...\n",
+       "3887     George Washington was the first President of t...\n",
+       "                               ...                        \n",
+       "643      Is anyone qualified to judge The Donald?\\n\\nDo...\n",
+       "6795     [Graphic: Calais street scene by Harriet Paint...\n",
+       "10444    By wmw_admin on October 30, 2016 By Timothy Fi...\n",
+       "8696     Unprecedented Surge In Election Fraud Incident...\n",
+       "5103     The Democratic National Committee is offering ...\n",
+       "8018     Comments \\nFOX News star Megyn Kelly has final...\n",
+       "2896     President Obama sent a draft Authorization for...\n",
+       "9188     Heseltine strangled dog as part of Thatcher ca...\n",
+       "6        It's been a big week for abortion news.\\n\\nCar...\n",
+       "9778     20 Foods That Naturally Unclog Arteries and Pr...\n",
+       "10062    FBI Releases Files on Bill Clinton's Cash for ...\n",
+       "8556     NTEB Ads Privacy Policy Black Americans Going ...\n",
+       "9757     10-27-1 6 The first Bill and Hillary Clinton c...\n",
+       "2524     President Obama's executive action preventing ...\n",
+       "8995     Rights? In The New America You Don’t Get Any R...\n",
+       "8602     By Amanda Froelich For a long time, green juic...\n",
+       "2886     W hen trying to explain the current unrest in ...\n",
+       "3456     WASHINGTON, June 21 (Reuters) - Tensions are b...\n",
+       "6564     This is how it works in the Clinton Cabal...or...\n",
+       "8406     posted by Eddie Angelina Jolie’s father, Jon V...\n",
+       "1151     As the Democratic presidential contest reaches...\n",
+       "3233     With the Department of Homeland Security’s fun...\n",
+       "4601     Enough Is Enough\\n\\nIf we really want to do so...\n",
+       "305      Top Dems want White House to call off Part B d...\n",
+       "842      Texas Sen. Ted Cruz’s popularity among Republi...\n",
+       "7085     Email \\nNot all invasions are hot — not all in...\n",
+       "530      President Obama's 2016 budget seeks higher spe...\n",
+       "5766       \\nThe decision of FBI Director Comey to go p...\n",
+       "976      Republican presidential candidate Ted Cruz is ...\n",
+       "2241     The simmering dispute over media access to Hil...\n",
+       "Name: text, Length: 4434, dtype: object"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count_vectorizer = CountVectorizer(stop_words='english')\n",
+    "count_train = count_vectorizer.fit_transform(X)\n",
+    "count_test = count_vectorizer.transform(Xt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tfidf_vectorizer = TfidfVectorizer(stop_words='english', max_df=0.7)\n",
+    "tfidf_train = tfidf_vectorizer.fit_transform(X)\n",
+    "tfidf_test = tfidf_vectorizer.transform(Xt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['00',\n",
+      " '000',\n",
+      " '0000',\n",
+      " '00000031',\n",
+      " '000035',\n",
+      " '00006',\n",
+      " '0001',\n",
+      " '0001pt',\n",
+      " '0002',\n",
+      " '000billion']\n",
+      "['حلب', 'عربي', 'عن', 'لم', 'ما', 'محاولات', 'من', 'هذا', 'والمرضى', 'ยงade']\n"
+     ]
+    }
+   ],
+   "source": [
+    "pp(count_vectorizer.get_feature_names()[0:10])\n",
+    "pp(count_vectorizer.get_feature_names()[-10:])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['00',\n",
+      " '000',\n",
+      " '0000',\n",
+      " '00000031',\n",
+      " '000035',\n",
+      " '00006',\n",
+      " '0001',\n",
+      " '0001pt',\n",
+      " '0002',\n",
+      " '000billion']\n",
+      "['حلب', 'عربي', 'عن', 'لم', 'ما', 'محاولات', 'من', 'هذا', 'والمرضى', 'ยงade']\n"
+     ]
+    }
+   ],
+   "source": [
+    "pp(tfidf_vectorizer.get_feature_names()[:10])\n",
+    "pp(tfidf_vectorizer.get_feature_names()[-10:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#count_df = pd.DataFrame(count_train.A, columns=count_vectorizer.get_feature_names())\n",
+    "#tfidf_df = pd.DataFrame(count_train.A, columns=tfidf_vectorizer.get_feature_names())\n",
+    "#diff = set(count_df.columns) - set(tfidf_df.columns)\n",
+    "#pp(count_df.equals(tfidf_df))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'score: 0.8527091004734351'\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "074a58de5e904298a7c212800fdc812b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>FigureCanvasNbAgg</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "FigureCanvasNbAgg()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confusion matrix, without normalization\n"
+     ]
+    }
+   ],
+   "source": [
+    "clf = MultinomialNB()\n",
+    "clf.fit(tfidf_train, y)\n",
+    "pred = clf.predict(tfidf_test)\n",
+    "score = metrics.accuracy_score(yt, pred)\n",
+    "pp(\"score: \" + str(score))\n",
+    "cm = metrics.confusion_matrix(yt, pred, labels=[\"FAKE\", \"REAL\"])\n",
+    "plot_confusion_matrix(cm, classes=[\"FAKE\", \"REAL\"], title= \"tfidf\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "cut = 0.7\n",
-    "\n",
-    "y = df_1.label\n",
-    "df_1 = df_1.drop('label', axis=1)\n"
-   ]
+   "source": []
   }
  ],
  "metadata": {