diff --git a/deeplearning.ipynb b/deeplearning.ipynb
index fc53f37..7950a30 100644
--- a/deeplearning.ipynb
+++ b/deeplearning.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -230,7 +230,7 @@
        "[5 rows x 41 columns]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -245,6 +245,90 @@
     "df.head()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0            CONFIRMED\n",
+       "1            CONFIRMED\n",
+       "2       False_Positive\n",
+       "3       False_Positive\n",
+       "4            CONFIRMED\n",
+       "5            CONFIRMED\n",
+       "6            CONFIRMED\n",
+       "7            CONFIRMED\n",
+       "8       False_Positive\n",
+       "9            CONFIRMED\n",
+       "10           CONFIRMED\n",
+       "11           CONFIRMED\n",
+       "12           CONFIRMED\n",
+       "13           CONFIRMED\n",
+       "14      False_Positive\n",
+       "15      False_Positive\n",
+       "16      False_Positive\n",
+       "17      False_Positive\n",
+       "18           CONFIRMED\n",
+       "19           CONFIRMED\n",
+       "20      False_Positive\n",
+       "21           CONFIRMED\n",
+       "22           CONFIRMED\n",
+       "23           CONFIRMED\n",
+       "24      False_Positive\n",
+       "25           CONFIRMED\n",
+       "26           CONFIRMED\n",
+       "27           CONFIRMED\n",
+       "28      False_Positive\n",
+       "29      False_Positive\n",
+       "             ...      \n",
+       "9532    False_Positive\n",
+       "9533         CANDIDATE\n",
+       "9535    False_Positive\n",
+       "9536    False_Positive\n",
+       "9537    False_Positive\n",
+       "9538    False_Positive\n",
+       "9539         CANDIDATE\n",
+       "9540         CONFIRMED\n",
+       "9541    False_Positive\n",
+       "9542         CANDIDATE\n",
+       "9543    False_Positive\n",
+       "9544         CANDIDATE\n",
+       "9545    False_Positive\n",
+       "9546         CANDIDATE\n",
+       "9548    False_Positive\n",
+       "9549    False_Positive\n",
+       "9550         CANDIDATE\n",
+       "9551         CANDIDATE\n",
+       "9552    False_Positive\n",
+       "9553    False_Positive\n",
+       "9554    False_Positive\n",
+       "9555    False_Positive\n",
+       "9556    False_Positive\n",
+       "9557         CANDIDATE\n",
+       "9558         CANDIDATE\n",
+       "9559    False_Positive\n",
+       "9560    False_Positive\n",
+       "9561         CANDIDATE\n",
+       "9562    False_Positive\n",
+       "9563    False_Positive\n",
+       "Name: koi_disposition, Length: 8744, dtype: object"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Remove the space for 'FALSE POSITIVE'\n",
+    "new = df[\"koi_disposition\"] == \"FALSE POSITIVE\"\n",
+    "df.loc[new, \"koi_disposition\"] = \"False_Positive\"\n",
+    "df[\"koi_disposition\"]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -254,20 +338,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(8744, 40) (8744,)\n"
+     ]
+    }
+   ],
    "source": [
+    "# Asssign the X and y values\n",
     "X = df.drop(\"koi_disposition\", axis=1)\n",
-    "y = df[\"koi_disposition\"]"
+    "y = df[\"koi_disposition\"]\n",
+    "print(X.shape, y.shape)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Split the data into training and testing data\n",
     "from sklearn.model_selection import train_test_split\n",
     "\n",
     "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, stratify=y)"
@@ -275,7 +370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -487,7 +582,7 @@
        "[5 rows x 40 columns]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -511,6 +606,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Fit data to the MinMax Scaler\n",
     "from sklearn.preprocessing import MinMaxScaler\n",
     "\n",
     "X_scaler = MinMaxScaler().fit(X_train)"
@@ -526,6 +622,406 @@
     "X_test_scaled = X_scaler.transform(X_test)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
+    "from keras.utils import to_categorical\n",
+    "\n",
+    "# Step 1: Label-encode data set\n",
+    "label_encoder = LabelEncoder()\n",
+    "label_encoder.fit(y_train)\n",
+    "encoded_y_train = label_encoder.transform(y_train)\n",
+    "encoded_y_test = label_encoder.transform(y_test)\n",
+    "\n",
+    "# Step 2: Convert encoded labels to one-hot-encoding\n",
+    "y_train_categorical = to_categorical(encoded_y_train)\n",
+    "y_test_categorical = to_categorical(encoded_y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create a Deep Learning Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /Users/angie/anaconda3/envs/PythonData/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Colocations handled automatically by placer.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "\n",
+    "model = Sequential()\n",
+    "model.add(Dense(units=120, activation='relu', input_dim=40))\n",
+    "model.add(Dense(units=120, activation='relu'))\n",
+    "model.add(Dense(units=3, activation='softmax'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compile the model\n",
+    "model.compile(optimizer='adam',\n",
+    "              loss='categorical_crossentropy',\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "dense_1 (Dense)              (None, 120)               4920      \n",
+      "_________________________________________________________________\n",
+      "dense_2 (Dense)              (None, 120)               14520     \n",
+      "_________________________________________________________________\n",
+      "dense_3 (Dense)              (None, 3)                 363       \n",
+      "=================================================================\n",
+      "Total params: 19,803\n",
+      "Trainable params: 19,803\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /Users/angie/anaconda3/envs/PythonData/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.cast instead.\n",
+      "Epoch 1/100\n",
+      " - 2s - loss: 0.5112 - acc: 0.7438\n",
+      "Epoch 2/100\n",
+      " - 1s - loss: 0.3684 - acc: 0.8134\n",
+      "Epoch 3/100\n",
+      " - 1s - loss: 0.3509 - acc: 0.8193\n",
+      "Epoch 4/100\n",
+      " - 1s - loss: 0.3451 - acc: 0.8256\n",
+      "Epoch 5/100\n",
+      " - 1s - loss: 0.3326 - acc: 0.8425\n",
+      "Epoch 6/100\n",
+      " - 1s - loss: 0.3319 - acc: 0.8452\n",
+      "Epoch 7/100\n",
+      " - 1s - loss: 0.3247 - acc: 0.8464\n",
+      "Epoch 8/100\n",
+      " - 1s - loss: 0.3161 - acc: 0.8533\n",
+      "Epoch 9/100\n",
+      " - 1s - loss: 0.3127 - acc: 0.8544\n",
+      "Epoch 10/100\n",
+      " - 1s - loss: 0.3053 - acc: 0.8608\n",
+      "Epoch 11/100\n",
+      " - 1s - loss: 0.3031 - acc: 0.8618\n",
+      "Epoch 12/100\n",
+      " - 1s - loss: 0.2949 - acc: 0.8693\n",
+      "Epoch 13/100\n",
+      " - 1s - loss: 0.2985 - acc: 0.8634\n",
+      "Epoch 14/100\n",
+      " - 1s - loss: 0.2952 - acc: 0.8644\n",
+      "Epoch 15/100\n",
+      " - 1s - loss: 0.2913 - acc: 0.8678\n",
+      "Epoch 16/100\n",
+      " - 1s - loss: 0.2860 - acc: 0.8759\n",
+      "Epoch 17/100\n",
+      " - 1s - loss: 0.2861 - acc: 0.8704\n",
+      "Epoch 18/100\n",
+      " - 1s - loss: 0.2865 - acc: 0.8753\n",
+      "Epoch 19/100\n",
+      " - 1s - loss: 0.2756 - acc: 0.8794\n",
+      "Epoch 20/100\n",
+      " - 1s - loss: 0.2882 - acc: 0.8708\n",
+      "Epoch 21/100\n",
+      " - 1s - loss: 0.2747 - acc: 0.8792\n",
+      "Epoch 22/100\n",
+      " - 1s - loss: 0.2714 - acc: 0.8850\n",
+      "Epoch 23/100\n",
+      " - 1s - loss: 0.2711 - acc: 0.8809\n",
+      "Epoch 24/100\n",
+      " - 1s - loss: 0.2657 - acc: 0.8832\n",
+      "Epoch 25/100\n",
+      " - 1s - loss: 0.2743 - acc: 0.8782\n",
+      "Epoch 26/100\n",
+      " - 1s - loss: 0.2672 - acc: 0.8855\n",
+      "Epoch 27/100\n",
+      " - 1s - loss: 0.2691 - acc: 0.8803\n",
+      "Epoch 28/100\n",
+      " - 1s - loss: 0.2656 - acc: 0.8840\n",
+      "Epoch 29/100\n",
+      " - 1s - loss: 0.2654 - acc: 0.8808\n",
+      "Epoch 30/100\n",
+      " - 1s - loss: 0.2575 - acc: 0.8890\n",
+      "Epoch 31/100\n",
+      " - 1s - loss: 0.2625 - acc: 0.8850\n",
+      "Epoch 32/100\n",
+      " - 1s - loss: 0.2629 - acc: 0.8847\n",
+      "Epoch 33/100\n",
+      " - 1s - loss: 0.2600 - acc: 0.8875\n",
+      "Epoch 34/100\n",
+      " - 1s - loss: 0.2552 - acc: 0.8873\n",
+      "Epoch 35/100\n",
+      " - 1s - loss: 0.2550 - acc: 0.8884\n",
+      "Epoch 36/100\n",
+      " - 1s - loss: 0.2530 - acc: 0.8901\n",
+      "Epoch 37/100\n",
+      " - 1s - loss: 0.2639 - acc: 0.8801\n",
+      "Epoch 38/100\n",
+      " - 1s - loss: 0.2543 - acc: 0.8902\n",
+      "Epoch 39/100\n",
+      " - 1s - loss: 0.2520 - acc: 0.8887\n",
+      "Epoch 40/100\n",
+      " - 1s - loss: 0.2526 - acc: 0.8933\n",
+      "Epoch 41/100\n",
+      " - 1s - loss: 0.2524 - acc: 0.8879\n",
+      "Epoch 42/100\n",
+      " - 1s - loss: 0.2471 - acc: 0.8902\n",
+      "Epoch 43/100\n",
+      " - 1s - loss: 0.2526 - acc: 0.8919\n",
+      "Epoch 44/100\n",
+      " - 1s - loss: 0.2497 - acc: 0.8899\n",
+      "Epoch 45/100\n",
+      " - 1s - loss: 0.2541 - acc: 0.8901\n",
+      "Epoch 46/100\n",
+      " - 1s - loss: 0.2484 - acc: 0.8867\n",
+      "Epoch 47/100\n",
+      " - 1s - loss: 0.2480 - acc: 0.8902\n",
+      "Epoch 48/100\n",
+      " - 1s - loss: 0.2461 - acc: 0.8925\n",
+      "Epoch 49/100\n",
+      " - 1s - loss: 0.2450 - acc: 0.8928\n",
+      "Epoch 50/100\n",
+      " - 1s - loss: 0.2395 - acc: 0.8965\n",
+      "Epoch 51/100\n",
+      " - 1s - loss: 0.2421 - acc: 0.8957\n",
+      "Epoch 52/100\n",
+      " - 1s - loss: 0.2394 - acc: 0.8948\n",
+      "Epoch 53/100\n",
+      " - 1s - loss: 0.2414 - acc: 0.8943\n",
+      "Epoch 54/100\n",
+      " - 1s - loss: 0.2428 - acc: 0.8927\n",
+      "Epoch 55/100\n",
+      " - 1s - loss: 0.2383 - acc: 0.8951\n",
+      "Epoch 56/100\n",
+      " - 1s - loss: 0.2443 - acc: 0.8899\n",
+      "Epoch 57/100\n",
+      " - 1s - loss: 0.2392 - acc: 0.8954\n",
+      "Epoch 58/100\n",
+      " - 1s - loss: 0.2347 - acc: 0.8963\n",
+      "Epoch 59/100\n",
+      " - 1s - loss: 0.2413 - acc: 0.8975\n",
+      "Epoch 60/100\n",
+      " - 1s - loss: 0.2403 - acc: 0.8913\n",
+      "Epoch 61/100\n",
+      " - 1s - loss: 0.2350 - acc: 0.8986\n",
+      "Epoch 62/100\n",
+      " - 1s - loss: 0.2342 - acc: 0.8983\n",
+      "Epoch 63/100\n",
+      " - 1s - loss: 0.2365 - acc: 0.8975\n",
+      "Epoch 64/100\n",
+      " - 1s - loss: 0.2334 - acc: 0.9012\n",
+      "Epoch 65/100\n",
+      " - 1s - loss: 0.2378 - acc: 0.8955\n",
+      "Epoch 66/100\n",
+      " - 1s - loss: 0.2365 - acc: 0.8989\n",
+      "Epoch 67/100\n",
+      " - 1s - loss: 0.2342 - acc: 0.8975\n",
+      "Epoch 68/100\n",
+      " - 1s - loss: 0.2275 - acc: 0.9012\n",
+      "Epoch 69/100\n",
+      " - 1s - loss: 0.2350 - acc: 0.8962\n",
+      "Epoch 70/100\n",
+      " - 1s - loss: 0.2325 - acc: 0.8984\n",
+      "Epoch 71/100\n",
+      " - 1s - loss: 0.2299 - acc: 0.8998\n",
+      "Epoch 72/100\n",
+      " - 1s - loss: 0.2290 - acc: 0.8981\n",
+      "Epoch 73/100\n",
+      " - 1s - loss: 0.2306 - acc: 0.9009\n",
+      "Epoch 74/100\n",
+      " - 1s - loss: 0.2249 - acc: 0.9039\n",
+      "Epoch 75/100\n",
+      " - 1s - loss: 0.2281 - acc: 0.9007\n",
+      "Epoch 76/100\n",
+      " - 1s - loss: 0.2259 - acc: 0.8997\n",
+      "Epoch 77/100\n",
+      " - 1s - loss: 0.2311 - acc: 0.8963\n",
+      "Epoch 78/100\n",
+      " - 1s - loss: 0.2330 - acc: 0.8966\n",
+      "Epoch 79/100\n",
+      " - 1s - loss: 0.2281 - acc: 0.8992\n",
+      "Epoch 80/100\n",
+      " - 1s - loss: 0.2230 - acc: 0.9045\n",
+      "Epoch 81/100\n",
+      " - 1s - loss: 0.2227 - acc: 0.9055\n",
+      "Epoch 82/100\n",
+      " - 1s - loss: 0.2285 - acc: 0.8984\n",
+      "Epoch 83/100\n",
+      " - 1s - loss: 0.2258 - acc: 0.9003\n",
+      "Epoch 84/100\n",
+      " - 1s - loss: 0.2277 - acc: 0.8984\n",
+      "Epoch 85/100\n",
+      " - 1s - loss: 0.2237 - acc: 0.9044\n",
+      "Epoch 86/100\n",
+      " - 1s - loss: 0.2255 - acc: 0.9023\n",
+      "Epoch 87/100\n",
+      " - 1s - loss: 0.2206 - acc: 0.9033\n",
+      "Epoch 88/100\n",
+      " - 1s - loss: 0.2283 - acc: 0.9021\n",
+      "Epoch 89/100\n",
+      " - 1s - loss: 0.2244 - acc: 0.9052\n",
+      "Epoch 90/100\n",
+      " - 1s - loss: 0.2246 - acc: 0.9010\n",
+      "Epoch 91/100\n",
+      " - 1s - loss: 0.2211 - acc: 0.9053\n",
+      "Epoch 92/100\n",
+      " - 1s - loss: 0.2235 - acc: 0.8975\n",
+      "Epoch 93/100\n",
+      " - 1s - loss: 0.2197 - acc: 0.9055\n",
+      "Epoch 94/100\n",
+      " - 1s - loss: 0.2235 - acc: 0.9035\n",
+      "Epoch 95/100\n",
+      " - 1s - loss: 0.2174 - acc: 0.9052\n",
+      "Epoch 96/100\n",
+      " - 1s - loss: 0.2236 - acc: 0.9006\n",
+      "Epoch 97/100\n",
+      " - 1s - loss: 0.2188 - acc: 0.9047\n",
+      "Epoch 98/100\n",
+      " - 1s - loss: 0.2217 - acc: 0.9000\n",
+      "Epoch 99/100\n",
+      " - 1s - loss: 0.2160 - acc: 0.9058\n",
+      "Epoch 100/100\n",
+      " - 1s - loss: 0.2160 - acc: 0.9058\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x1051938d0>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(\n",
+    "    X_train_scaled,\n",
+    "    y_train_categorical,\n",
+    "    epochs=100,\n",
+    "    shuffle=True,\n",
+    "    verbose=2\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Quantify Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Normal Neural Network - Loss: 0.24744392827848502, Accuracy: 0.890210429900083\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_loss, model_accuracy = model.evaluate(\n",
+    "    X_test_scaled, y_test_categorical, verbose=2)\n",
+    "print(\n",
+    "    f\"Normal Neural Network - Loss: {model_loss}, Accuracy: {model_accuracy}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Make Predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "encoded_predictions = model.predict_classes(X_test_scaled[:5])\n",
+    "prediction_labels = label_encoder.inverse_transform(encoded_predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted classes: ['FALSE POSITIVE' 'CONFIRMED' 'FALSE POSITIVE' 'FALSE POSITIVE'\n",
+      " 'CONFIRMED']\n",
+      "Actual Labels: ['FALSE POSITIVE', 'CONFIRMED', 'FALSE POSITIVE', 'FALSE POSITIVE', 'CONFIRMED']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Predicted classes: {prediction_labels}\")\n",
+    "print(f\"Actual Labels: {list(y_test[:5])}\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/logisticregression.ipynb b/logisticregression.ipynb
index c3418db..be7e478 100644
--- a/logisticregression.ipynb
+++ b/logisticregression.ipynb
@@ -535,7 +535,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Train the Support Vector Machine"
+    "# Create and Train the Logistic Regression Model"
    ]
   },
   {