diff --git a/baselines/.ipynb_checkpoints/MNIST_baseline-checkpoint.ipynb b/baselines/.ipynb_checkpoints/MNIST_baseline-checkpoint.ipynb
index 58d307ae162a86fa6bdae50dab42a7cb8dcb8866..f7045c6dac50b106f3387a8e4ba985d7a56eb66c 100644
--- a/baselines/.ipynb_checkpoints/MNIST_baseline-checkpoint.ipynb
+++ b/baselines/.ipynb_checkpoints/MNIST_baseline-checkpoint.ipynb
@@ -142,23 +142,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'train_test_split' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-a7c485c3cf8f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_val\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'train_test_split' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "X_train, X_val= train_test_split(train_data, test_size=0.2, random_state=42) "
    ]
@@ -263,7 +249,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "y_pred = classifier.predict(X_test)"
+    "y_pred = classifier.predict(X_val)"
    ]
   },
   {
@@ -277,7 +263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -289,20 +275,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy of the model is : 0.89356210142727\n",
-      "Recall of the model is : 0.89356210142727\n",
-      "Precision of the model is : 0.89356210142727\n",
-      "F1 score of the model is : 0.6058161925441079\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(\"Accuracy of the model is :\" ,accuracy)\n",
     "print(\"Recall of the model is :\" ,recall)\n",
@@ -325,10 +300,10 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "def correctly_predicted():\n",
-    "    correct_pred = np.where(y_pred == y_test)\n",
+    "    correct_pred = np.where(y_pred == y_val)\n",
     "    fig = plt.figure()\n",
     "    for i in range(1,10):\n",
-    "      img = X_test.iloc[i,:]\n",
+    "      img = X_val.iloc[i,:]\n",
     "      img = np.array(img).reshape(28,28)\n",
     "      fig.add_subplot(3,3,i)\n",
     "      plt.imshow(img,cmap='gray')\n",
@@ -352,12 +327,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "final_test_path = \"test.csv\"\n",
-    "final_test = pd.read_csv(final_test_path)"
+    "final_test = pd.read_csv(final_test_path,header=None)"
    ]
   },
   {
diff --git a/baselines/MNIST_baseline.ipynb b/baselines/MNIST_baseline.ipynb
index 58d307ae162a86fa6bdae50dab42a7cb8dcb8866..f7045c6dac50b106f3387a8e4ba985d7a56eb66c 100644
--- a/baselines/MNIST_baseline.ipynb
+++ b/baselines/MNIST_baseline.ipynb
@@ -142,23 +142,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'train_test_split' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-a7c485c3cf8f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_val\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'train_test_split' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "X_train, X_val= train_test_split(train_data, test_size=0.2, random_state=42) "
    ]
@@ -263,7 +249,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "y_pred = classifier.predict(X_test)"
+    "y_pred = classifier.predict(X_val)"
    ]
   },
   {
@@ -277,7 +263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -289,20 +275,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy of the model is : 0.89356210142727\n",
-      "Recall of the model is : 0.89356210142727\n",
-      "Precision of the model is : 0.89356210142727\n",
-      "F1 score of the model is : 0.6058161925441079\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(\"Accuracy of the model is :\" ,accuracy)\n",
     "print(\"Recall of the model is :\" ,recall)\n",
@@ -325,10 +300,10 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "def correctly_predicted():\n",
-    "    correct_pred = np.where(y_pred == y_test)\n",
+    "    correct_pred = np.where(y_pred == y_val)\n",
     "    fig = plt.figure()\n",
     "    for i in range(1,10):\n",
-    "      img = X_test.iloc[i,:]\n",
+    "      img = X_val.iloc[i,:]\n",
     "      img = np.array(img).reshape(28,28)\n",
     "      fig.add_subplot(3,3,i)\n",
     "      plt.imshow(img,cmap='gray')\n",
@@ -352,12 +327,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "final_test_path = \"test.csv\"\n",
-    "final_test = pd.read_csv(final_test_path)"
+    "final_test = pd.read_csv(final_test_path,header=None)"
    ]
   },
   {