diff --git a/notebooks/baseline.ipynb b/notebooks/baseline.ipynb
index 3d8e4b7..83538e9 100644
--- a/notebooks/baseline.ipynb
+++ b/notebooks/baseline.ipynb
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,7 +75,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import preprocessing \n",
+    "\n",
+    "# encode labels to integers\n",
+    "label_encoder = preprocessing.LabelEncoder() \n",
+    "df['encoded_label'] = label_encoder.fit_transform(df['label']) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,21 +100,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "\n",
-    "X = df.drop(columns=['label'])\n",
-    "y=df['label']\n",
+    "X = df.drop(columns=['encoded_label'])\n",
+    "y=df['encoded_label']\n",
     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
     "\n",
     "# select small portion to get code running\n",
-    "X_train = X_train.iloc[0:50]\n",
-    "y_train = y_train.iloc[0:50]\n",
-    "X_test = X_test.iloc[0:10]\n",
-    "y_test = y_test.iloc[0:10]\n"
+    "# X_train = X_train.iloc[0:50]\n",
+    "# y_train = y_train.iloc[0:50]\n",
+    "# X_test = X_test.iloc[0:10]\n",
+    "# y_test = y_test.iloc[0:10]\n"
    ]
   },
   {
@@ -113,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -122,8 +135,8 @@
     "\n",
     "\n",
     "def run_baseline(baseline_function, dataframe, text_col):\n",
-    "    X = dataframe.drop(columns=['label'])\n",
-    "    y=dataframe['label']\n",
+    "    X = dataframe.drop(columns=['encoded_label'])\n",
+    "    y=dataframe['encoded_label']\n",
     "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
     "\n",
     "    vectorizer = TfidfVectorizer()\n",
@@ -142,7 +155,7 @@
     "    print(\"Accuracy:\", accuracy)\n",
     "\n",
     "    predictions = X_test.copy()\n",
-    "    predictions['label'] = y_test\n",
+    "    predictions['encoded_label'] = y_test\n",
     "    predictions['prediction'] = y_pred\n",
     "    return predictions"
    ]
@@ -156,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -189,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -209,43 +222,76 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Baseline 3: Linear Regression + tf-idf"
+    "### Baseline 3: Logistic Regression + tf-idf"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
-     "ename": "ValueError",
-     "evalue": "could not convert string to float: 'Motie'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[14], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlinear_model\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LinearRegression\n\u001b[0;32m----> 2\u001b[0m linear_reg \u001b[38;5;241m=\u001b[39m \u001b[43mrun_baseline\u001b[49m\u001b[43m(\u001b[49m\u001b[43mLinearRegression\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mclean_text\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "Cell \u001b[0;32mIn[11], line 19\u001b[0m, in \u001b[0;36mrun_baseline\u001b[0;34m(baseline_function, dataframe, text_col)\u001b[0m\n\u001b[1;32m     16\u001b[0m model \u001b[38;5;241m=\u001b[39m baseline_function\n\u001b[1;32m     18\u001b[0m \u001b[38;5;66;03m# Train the classifier on the training data\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train_tfidf_bin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     21\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(X_test_tfidf_bin)\n\u001b[1;32m     23\u001b[0m \u001b[38;5;66;03m# Calculate the accuracy of the classifier\u001b[39;00m\n",
-      "File \u001b[0;32m/anaconda/envs/AmsterdamInContextLearning/lib/python3.9/site-packages/sklearn/base.py:1474\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1467\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m   1469\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m   1470\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m   1471\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m   1472\u001b[0m     )\n\u001b[1;32m   1473\u001b[0m ):\n\u001b[0;32m-> 1474\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/anaconda/envs/AmsterdamInContextLearning/lib/python3.9/site-packages/sklearn/linear_model/_base.py:578\u001b[0m, in \u001b[0;36mLinearRegression.fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    574\u001b[0m n_jobs_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_jobs\n\u001b[1;32m    576\u001b[0m accept_sparse \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpositive \u001b[38;5;28;01melse\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsr\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsc\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcoo\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m--> 578\u001b[0m X, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    579\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_sparse\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_numeric\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmulti_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m    580\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    582\u001b[0m has_sw \u001b[38;5;241m=\u001b[39m sample_weight \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    583\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_sw:\n",
-      "File \u001b[0;32m/anaconda/envs/AmsterdamInContextLearning/lib/python3.9/site-packages/sklearn/base.py:650\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[1;32m    648\u001b[0m         y \u001b[38;5;241m=\u001b[39m check_array(y, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_y_params)\n\u001b[1;32m    649\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 650\u001b[0m         X, y \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_X_y\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcheck_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    651\u001b[0m     out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[1;32m    653\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m check_params\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mensure_2d\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m):\n",
-      "File \u001b[0;32m/anaconda/envs/AmsterdamInContextLearning/lib/python3.9/site-packages/sklearn/utils/validation.py:1279\u001b[0m, in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m   1259\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1260\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m requires y to be passed, but the target y is None\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1261\u001b[0m     )\n\u001b[1;32m   1263\u001b[0m X \u001b[38;5;241m=\u001b[39m check_array(\n\u001b[1;32m   1264\u001b[0m     X,\n\u001b[1;32m   1265\u001b[0m     accept_sparse\u001b[38;5;241m=\u001b[39maccept_sparse,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1276\u001b[0m     input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1277\u001b[0m )\n\u001b[0;32m-> 1279\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43m_check_y\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmulti_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmulti_output\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_numeric\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my_numeric\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1281\u001b[0m check_consistent_length(X, y)\n\u001b[1;32m   1283\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m X, y\n",
-      "File \u001b[0;32m/anaconda/envs/AmsterdamInContextLearning/lib/python3.9/site-packages/sklearn/utils/validation.py:1304\u001b[0m, in \u001b[0;36m_check_y\u001b[0;34m(y, multi_output, y_numeric, estimator)\u001b[0m\n\u001b[1;32m   1302\u001b[0m     _ensure_no_complex_data(y)\n\u001b[1;32m   1303\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y_numeric \u001b[38;5;129;01mand\u001b[39;00m y\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mO\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 1304\u001b[0m     y \u001b[38;5;241m=\u001b[39m \u001b[43my\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat64\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1306\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m y\n",
-      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'Motie'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.9148737137511693\n"
      ]
     }
    ],
    "source": [
-    "from sklearn.linear_model import LinearRegression\n",
-    "linear_reg = run_baseline(LinearRegression(), df, 'clean_text')"
+    "from sklearn.linear_model import LogisticRegression\n",
+    "log_reg = run_baseline(LogisticRegression(), df, 'clean_text')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Baseline 4: k Nearest Neigbors + tf-idf"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.7090739008419084\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "knn = run_baseline(KNeighborsClassifier(), df, 'clean_text')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Baseline 5: RandomForest + tf-idf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.9195509822263798\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "random_forest = run_baseline(RandomForestClassifier(), df, 'clean_text')"
+   ]
   }
  ],
  "metadata": {