diff --git a/Code/Day 1_Data_Preprocessing.ipynb b/Code/Day 1_Data_Preprocessing.ipynb
index d19db6a..9f26c9c 100644
--- a/Code/Day 1_Data_Preprocessing.ipynb	
+++ b/Code/Day 1_Data_Preprocessing.ipynb	
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,14 +37,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
-      "Step 2: Importing dataset\nX\n[['France' 44.0 72000.0]\n ['Spain' 27.0 48000.0]\n ['Germany' 30.0 54000.0]\n ['Spain' 38.0 61000.0]\n ['Germany' 40.0 nan]\n ['France' 35.0 58000.0]\n ['Spain' nan 52000.0]\n ['France' 48.0 79000.0]\n ['Germany' 50.0 83000.0]\n ['France' 37.0 67000.0]]\nY\n['No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'Yes' 'No' 'Yes']\n"
+      "Step 2: Importing dataset\n",
+      "X\n",
+      "[['France' 44.0 72000.0]\n",
+      " ['Spain' 27.0 48000.0]\n",
+      " ['Germany' 30.0 54000.0]\n",
+      " ['Spain' 38.0 61000.0]\n",
+      " ['Germany' 40.0 nan]\n",
+      " ['France' 35.0 58000.0]\n",
+      " ['Spain' nan 52000.0]\n",
+      " ['France' 48.0 79000.0]\n",
+      " ['Germany' 50.0 83000.0]\n",
+      " ['France' 37.0 67000.0]]\n",
+      "Y\n",
+      "['No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'Yes' 'No' 'Yes']\n"
      ]
     }
    ],
@@ -71,40 +84,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
-      "[[44.0 72000.0]\n [27.0 48000.0]\n [30.0 54000.0]\n [38.0 61000.0]]\n"
-     ]
-    },
-    {
-     "output_type": "error",
-     "ename": "ValueError",
-     "evalue": "'X' and 'missing_values' types are expected to be both numerical. Got X.dtype=float64 and  type(missing_values)=<class 'str'>.",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-17-ee12d1366c46>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;31m#imputer = Imputer(missing_values = \"NaN\", strategy = \"mean\", axis = 0)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m4\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mimputer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimputer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m4\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m \u001b[1;33m:\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimputer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m \u001b[1;33m:\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"---------------------\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\impute\\_base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    275\u001b[0m         \u001b[0mself\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mSimpleImputer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    276\u001b[0m         \"\"\"\n\u001b[1;32m--> 277\u001b[1;33m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_input\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0min_fit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    278\u001b[0m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fit_indicator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    279\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\impute\\_base.py\u001b[0m in \u001b[0;36m_validate_input\u001b[1;34m(self, X, in_fit)\u001b[0m\n\u001b[0;32m    251\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mve\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 253\u001b[1;33m         \u001b[0m_check_inputs_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmissing_values\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    254\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\"i\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"u\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"f\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"O\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m             raise ValueError(\"SimpleImputer does not support data with dtype \"\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\impute\\_base.py\u001b[0m in \u001b[0;36m_check_inputs_dtype\u001b[1;34m(X, missing_values)\u001b[0m\n\u001b[0;32m     23\u001b[0m     if (X.dtype.kind in (\"f\", \"i\", \"u\") and\n\u001b[0;32m     24\u001b[0m             not isinstance(missing_values, numbers.Real)):\n\u001b[1;32m---> 25\u001b[1;33m         raise ValueError(\"'X' and 'missing_values' types are expected to be\"\n\u001b[0m\u001b[0;32m     26\u001b[0m                          \u001b[1;34m\" both numerical. Got X.dtype={} and \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     27\u001b[0m                          \u001b[1;34m\" type(missing_values)={}.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: 'X' and 'missing_values' types are expected to be both numerical. Got X.dtype=float64 and  type(missing_values)=<class 'str'>."
+      "---------------------\n",
+      "Step 3: Handling the missing data\n",
+      "step2\n",
+      "X\n",
+      "[['France' 44.0 72000.0]\n",
+      " ['Spain' 27.0 48000.0]\n",
+      " ['Germany' 30.0 54000.0]\n",
+      " ['Spain' 38.0 61000.0]\n",
+      " ['Germany' 40.0 63777.77777777778]\n",
+      " ['France' 35.0 58000.0]\n",
+      " ['Spain' 38.77777777777778 52000.0]\n",
+      " ['France' 48.0 79000.0]\n",
+      " ['Germany' 50.0 83000.0]\n",
+      " ['France' 37.0 67000.0]]\n"
      ]
     }
    ],
    "source": [
-    "# If you use the newest version of sklearn, use the lines of code commented out\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "imputer = SimpleImputer(missing_values=np.nan, strategy=\"mean\")\n",
-    "#from sklearn.preprocessing import Imputer\n",
+    "# If you use the newest version of sklearn, use the lines of code commented out",
+    "# from sklearn.impute import SimpleImputer",
+    "# imputer = SimpleImputer(missing_values=\"NaN\", strategy=\"mean\")",
+    "from sklearn.preprocessing import Imputer\n",
     "# axis=0表示按列进行\n",
-    "#imputer = Imputer(missing_values = \"NaN\", strategy = \"mean\", axis = 0)\n",
-    "print(X[ 0:4 , 1:3])\n",
-    "imputer = imputer.fit(X[ 0:4 , 1:3])\n",
+    "imputer = Imputer(missing_values = \"NaN\", strategy = \"mean\", axis = 0)\n",
+    "imputer = imputer.fit(X[ : , 1:3])\n",
     "X[ : , 1:3] = imputer.transform(X[ : , 1:3])\n",
     "print(\"---------------------\")\n",
     "print(\"Step 3: Handling the missing data\")\n",
@@ -304,13 +315,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.8.3 64-bit (conda)",
-   "metadata": {
-    "interpreter": {
-     "hash": "1b78ff499ec469310b6a6795c4effbbfc85eb20a6ba0cf828a15721670711b2c"
-    }
-   }
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -322,9 +329,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3-final"
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
\ No newline at end of file
+}