diff --git a/Code/Day 2_Simple_Linear_Regression.ipynb b/Code/Day 2_Simple_Linear_Regression.ipynb
index fc8f5d9..375277c 100644
--- a/Code/Day 2_Simple_Linear_Regression.ipynb	
+++ b/Code/Day 2_Simple_Linear_Regression.ipynb	
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,45 +37,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "    Hours  Scores\n",
-      "0     2.5      21\n",
-      "1     5.1      47\n",
-      "2     3.2      27\n",
-      "3     8.5      75\n",
-      "4     3.5      30\n",
-      "5     1.5      20\n",
-      "6     9.2      88\n",
-      "7     5.5      60\n",
-      "8     8.3      81\n",
-      "9     2.7      25\n",
-      "10    7.7      85\n",
-      "11    5.9      62\n",
-      "12    4.5      41\n",
-      "13    3.3      42\n",
-      "14    1.1      17\n",
-      "15    8.9      95\n",
-      "16    2.5      30\n",
-      "17    1.9      24\n",
-      "18    6.1      67\n",
-      "19    7.4      69\n",
-      "20    2.7      30\n",
-      "21    4.8      54\n",
-      "22    3.8      35\n",
-      "23    6.9      76\n",
-      "24    7.8      86\n"
+      "    Hours  Scores\n0     2.5      21\n1     5.1      47\n2     3.2      27\n3     8.5      75\n4     3.5      30\n5     1.5      20\n6     9.2      88\n7     5.5      60\n8     8.3      81\n9     2.7      25\n10    7.7      85\n11    5.9      62\n12    4.5      41\n13    3.3      42\n14    1.1      17\n15    8.9      95\n16    2.5      30\n17    1.9      24\n18    6.1      67\n19    7.4      69\n20    2.7      30\n21    4.8      54\n22    3.8      35\n23    6.9      76\n24    7.8      86\n25    2.1      93\n26    2.2      93\n27    2.5      93\n    Hours  Scores\n15    8.9      95\n27    2.5      93\n26    2.2      93\n25    2.1      93\n6     9.2      88\n24    7.8      86\n10    7.7      85\n8     8.3      81\n23    6.9      76\n3     8.5      75\n19    7.4      69\n18    6.1      67\n11    5.9      62\n7     5.5      60\n21    4.8      54\n1     5.1      47\n13    3.3      42\n12    4.5      41\n22    3.8      35\n20    2.7      30\n4     3.5      30\n16    2.5      30\n2     3.2      27\n9     2.7      25\n17    1.9      24\n0     2.5      21\n5     1.5      20\n14    1.1      17\n"
      ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "    Hours  Scores\n",
+       "0     2.5      21\n",
+       "1     5.1      47\n",
+       "2     3.2      27\n",
+       "3     8.5      75\n",
+       "4     3.5      30\n",
+       "5     1.5      20\n",
+       "6     9.2      88\n",
+       "7     5.5      60\n",
+       "8     8.3      81\n",
+       "9     2.7      25\n",
+       "10    7.7      85\n",
+       "11    5.9      62\n",
+       "12    4.5      41\n",
+       "13    3.3      42\n",
+       "14    1.1      17\n",
+       "15    8.9      95\n",
+       "16    2.5      30\n",
+       "17    1.9      24\n",
+       "18    6.1      67\n",
+       "19    7.4      69\n",
+       "20    2.7      30\n",
+       "21    4.8      54\n",
+       "22    3.8      35\n",
+       "23    6.9      76\n",
+       "24    7.8      86\n",
+       "25    2.1      93\n",
+       "26    2.2      93\n",
+       "27    2.5      93"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Hours</th>\n      <th>Scores</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2.5</td>\n      <td>21</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5.1</td>\n      <td>47</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.2</td>\n      <td>27</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>8.5</td>\n      <td>75</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3.5</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1.5</td>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>9.2</td>\n      <td>88</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>5.5</td>\n      <td>60</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>8.3</td>\n      <td>81</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2.7</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>7.7</td>\n      <td>85</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>5.9</td>\n      <td>62</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>4.5</td>\n      <td>41</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>3.3</td>\n      <td>42</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>1.1</td>\n      <td>17</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>8.9</td>\n      <td>95</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>2.5</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>1.9</td>\n      <td>24</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>6.1</td>\n      <td>67</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>7.4</td>\n      <td>69</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>2.7</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>4.8</td>\n      <td>54</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>3.8</td>\n      <td>35</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>6.9</td>\n      <td>76</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>7.8</td>\n      <td>86</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>2.1</td>\n      <td>93</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>2.2</td>\n      <td>93</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>2.5</td>\n      <td>93</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 90
     }
    ],
    "source": [
     "dataset = pd.read_csv('../datasets/studentscores.csv')\n",
-    "print(dataset)"
+    "print(dataset)\n",
+    "df = dataset.sort_values(\"Scores\",ascending=False)\n",
+    "print(df)\n",
+    "dataset.head(30)"
    ]
   },
   {
@@ -87,46 +104,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "X: [[2.5]\n",
-      " [5.1]\n",
-      " [3.2]\n",
-      " [8.5]\n",
-      " [3.5]\n",
-      " [1.5]\n",
-      " [9.2]\n",
-      " [5.5]\n",
-      " [8.3]\n",
-      " [2.7]\n",
-      " [7.7]\n",
-      " [5.9]\n",
-      " [4.5]\n",
-      " [3.3]\n",
-      " [1.1]\n",
-      " [8.9]\n",
-      " [2.5]\n",
-      " [1.9]\n",
-      " [6.1]\n",
-      " [7.4]\n",
-      " [2.7]\n",
-      " [4.8]\n",
-      " [3.8]\n",
-      " [6.9]\n",
-      " [7.8]]\n",
-      "Y: [21 47 27 75 30 20 88 60 81 25 85 62 41 42 17 95 30 24 67 69 30 54 35 76\n",
-      " 86]\n"
+      "X: [[2.5]\n [5.1]\n [3.2]\n [8.5]\n [3.5]\n [1.5]\n [9.2]\n [5.5]\n [8.3]\n [2.7]\n [7.7]\n [5.9]\n [4.5]\n [3.3]\n [1.1]\n [8.9]\n [2.5]\n [1.9]\n [6.1]\n [7.4]\n [2.7]\n [4.8]\n [3.8]\n [6.9]\n [7.8]]\nY: [[21]\n [47]\n [27]\n [75]\n [30]\n [20]\n [88]\n [60]\n [81]\n [25]\n [85]\n [62]\n [41]\n [42]\n [17]\n [95]\n [30]\n [24]\n [67]\n [69]\n [30]\n [54]\n [35]\n [76]\n [86]]\n"
      ]
     }
    ],
    "source": [
-    "X = dataset.iloc[ : ,   : 1 ].values\n",
-    "Y = dataset.iloc[ : , 1 ].values\n",
+    "X = dataset.iloc[ 0: 25,   : 1 ].values\n",
+    "Y = dataset.iloc[ 0: 25, -1: ].values\n",
     "print(\"X:\",X)\n",
     "print(\"Y:\",Y)"
    ]
@@ -140,13 +131,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 74,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[[7.8]\n [6.9]\n [1.1]\n [5.1]\n [7.7]\n [3.3]\n [8.3]\n [9.2]\n [6.1]\n [3.5]\n [2.7]\n [5.5]\n [2.7]\n [8.5]\n [2.5]\n [4.8]\n [8.9]\n [4.5]] [[1.5]\n [3.2]\n [7.4]\n [2.5]\n [5.9]\n [3.8]\n [1.9]]\n[[86]\n [76]\n [17]\n [47]\n [85]\n [42]\n [81]\n [88]\n [67]\n [30]\n [25]\n [60]\n [30]\n [75]\n [21]\n [54]\n [95]\n [41]] [[20]\n [27]\n [69]\n [30]\n [62]\n [35]\n [24]]\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "#拆分数据，0.25作为测试集\n",
-    "X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 1/4, random_state = 0) "
+    "X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 1/4, random_state = 0) \n",
+    "print(X_train,X_test)\n",
+    "print(Y_train,Y_test)"
    ]
   },
   {
@@ -158,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -177,11 +178,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 76,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[[16.84472176]\n [33.74557494]\n [75.50062397]\n [26.7864001 ]\n [60.58810646]\n [39.71058194]\n [20.8213931 ]]\n[[20]\n [27]\n [69]\n [30]\n [62]\n [35]\n [24]]\n"
+     ]
+    }
+   ],
    "source": [
-    "Y_pred = regressor.predict(X_test)"
+    "Y_pred = regressor.predict(X_test)\n",
+    "print(Y_pred)\n",
+    "print(Y_test)"
    ]
   },
   {
@@ -200,18 +211,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {
+      "needs_background": "light"
+     }
     }
    ],
    "source": [
@@ -231,18 +243,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {
+      "needs_background": "light"
+     }
     }
    ],
    "source": [
@@ -253,6 +266,23 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[[3.2]\n [3.8]\n [1.1]\n [1.9]\n [1.5]\n [5.9]\n [7.8]] [[27]\n [35]\n [17]\n [24]\n [20]\n [62]\n [86]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X_test,Y_test)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -277,9 +307,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.8.3-final"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file