diff --git a/Code/DAY 2.ipynb b/Code/DAY 2_Simple_Linear_Regression.ipynb
similarity index 94%
rename from Code/DAY 2.ipynb
rename to Code/DAY 2_Simple_Linear_Regression.ipynb
index 2a87940..657b00e 100644
--- a/Code/DAY 2.ipynb	
+++ b/Code/DAY 2_Simple_Linear_Regression.ipynb	
@@ -4,8 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#机器学习100天——第二天：简单线性回归\n",
-    "##第一步：数据预处理"
+    "# 机器学习100天——第二天：简单线性回归\n",
+    "## 第一步：数据预处理"
    ]
   },
   {
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {
     "collapsed": true
    },
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -76,7 +76,7 @@
     }
    ],
    "source": [
-    "dataset = pd.read_csv('datasets/studentscores.csv')\n",
+    "dataset = pd.read_csv('../datasets/studentscores.csv')\n",
     "print(dataset)"
    ]
   },
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {
     "collapsed": true
    },
@@ -108,14 +108,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "D:\\anaconda\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
+      "/home/ymao/usr/miniconda/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
       "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
      ]
     }
@@ -129,7 +129,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##第二步：训练集使用简单线性回归模型来训练"
+    "## 第二步：训练集使用简单线性回归模型来训练"
    ]
   },
   {
@@ -141,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {
     "collapsed": true
    },
@@ -159,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {
     "collapsed": true
    },
@@ -173,7 +173,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##第三步：预测结果"
+    "## 第三步：预测结果"
    ]
   },
   {
@@ -185,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {
     "collapsed": true
    },
@@ -198,14 +198,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##可视化"
+    "## 可视化"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "###训练集结果可视化"
+    "### 训练集结果可视化"
    ]
   },
   {
@@ -217,16 +217,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x1dd7e8ba978>"
+       "<matplotlib.collections.PathCollection at 0x7fe708af19b0>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -244,16 +244,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1dd7e8cb160>]"
+       "[<matplotlib.lines.Line2D at 0x7fe708b04550>]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -271,14 +271,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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2NIlUSwu0toY9v83CY2trxVMXZmF6uyf3iGZEavCpQiRNSu3QfwlcCnT3ODba3TsyX68B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\nKNBFRFJCgS4ikhIKdBGRlPh/Rc+jTuQbrmQAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1dd7bcd2828>"
+       "<matplotlib.figure.Figure at 0x7fe70c5abac8>"
       ]
      },
      "metadata": {},
@@ -293,19 +293,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "###测试集结果可视化"
+    "### 测试集结果可视化"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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cEQ/zG28MXbnCXGR4cunQzwAuMrMLgNHAwWZ2D7DNzGrcvdXMaoC2gZ7s7vVAPUBdXZ1O\nAxHefrv/Usrevf0vESciQzPo/0Lufp27T3b3WuALwOPufimwCJgdPWw2sLBoVUpmfPrT8TCfP3/g\n632KyNDlcxz6LcACM7sMaAEuKUxJkkVbtsDkyfE5bXErUlhD6ovc/Ul3/0x0/w13P9vdp7n7Oe6+\nvTglStp98IPxMH/kEW1xK1IMOlNUimb9+v6n6GszLZHi0cqlFIVZPMxXrlSYixSbAl0K6le/ii+l\nHHhgCPK+296KSOFpyUUKpu+aeHMzHHtsMrWIVCJ16JK3Bx+Mh/mf/EnoyhXmIqWlDl2GbaDjx7dt\ngyOOSKYekUqnDl2G5c4742H+uc+FgFeYiyRHHboMyd69MKLPv5p33oHx45OpR0R6qEOXnM2bFw/z\nq64KXbnCXKQ8KNBlUJ2d4UPPm2/umdu1C269Nc9v3NgYLhJ6wAHhtrExz28oUtkU6LJfl14K48b1\njG+9NXTlI0fm+Y0bG8NVLFpawjdsaQljhbrIsJmX8PS9uro6b2pqKtnryfC98QYcfnh8rqCbadXW\nhhDva+pUePXVAr2ISDaY2Sp3rxvscerQpZ/TT4+H+X33FWEzrU2bhjYvIoPSUS7yvldfhQ98ID5X\ntF/gpkwZuEOfMqVILyiSferQBYDDDouH+eOPF3kzrZtugrFj43Njx4Z5ERkWBXqFW706LKVs77Wb\nvTt88pNFfuFZs6C+PqyZm4Xb+vowLyLDoiWXCtZ3TfyFF8I+LCUza5YCXKSA1KFXoGXL4mFeXR26\n8pKGuYgUnDr0CtO3K29p0eeQIlmhDr1C/Oxn8TA/88zQlSvMRbJDHXrGDbTF7RtvwMSJydQjIsWj\nDj3DfvCDeJj/1V+FgFeYi2STOvQM2r0bRo2Kz3V2wpgxydQjIqWhDj1jrroqHub/9E+hK1eYi2Sf\nOvSMeOcdOPjg+NyePVBVlUw9IlJ6g3boZjbazFaa2QtmttbMbojmJ5rZUjNrjm4nFL9cGcjMmfEw\n//d/D125wlyksuSy5LIT+JS7nwTMAM4zs48Bc4Fl7j4NWBaNZTAFvKjDtm3hUMSFC3vmurrg8svz\nrlJEUmjQQPdgRzQcGf1x4GKgIZpvAGYWpcIsKeBFHaZPhyOP7BkvXFiELW5FJFVy+lDUzKrMbDXQ\nBix19xVAtbu3Rg/ZClQXqcbsmDcvHG7SW2dnmM9Rc3MI7bVre+bc4aKLClSjiKRWToHu7nvdfQYw\nGTjVzKb3+boTuvZ+zGyOmTWZWVN7e3veBadanhd1GDUKjjuuZ/zrXxd5i1sRSZUhHbbo7m8BTwDn\nAdvMrAYgum3bx3Pq3b3O3esmTZqUb73ptq/z7Ac5/37lytCV797dM+ceriwkItItl6NcJpnZodH9\nMcC5wAZgETA7ethsYOHA30HeN4yLOpjBaaf1jNevV1cuIgPLpUOvAZ4wszXAbwhr6IuBW4BzzawZ\nOCcay/4M4aIODz8c/4Bz2rQQ5CecUMJ6RSRVzEvY7tXV1XlTU1PJXi+NBtpM6//+D2pqkqlHRJJn\nZqvcvW6wx+nU/zIyf348zM8/PwS8wlxEcqFT/8tAV1f/szrffrv/qfwiIvujDj1h//Iv8TD/2tdC\nV16yMC/gmasikix16Al5773+OyDu3Nl/29ui6j5ztftkp+4zV0EXbxZJIXXoCZg7Nx7mt9wSuvKS\nhjkU5MxVESkf6tBL6L33wgecb73VM7d3b/+jWkomzzNXRaS8qEMvkYaG0JV3h/mKFQMfolhSwzxz\nVUTKkwK9yDo6wglCf/3XYfylL4UgP/XURMsKhnHmqoiULwV6Ed12GxxySM/45ZfL7CCSIZy5KiLl\nT2voRdDWBtW9NhO+6iq49dbk6tmvWbMU4CIZoQ69wObNi4f5li1lHOYikikK9AJpaQmrFjffHMY3\n3RTWyo86Ktm6RKRyVGagF/jsyK9+NXybbtu3w7e/nde3FBEZssoL9AJe13PdutCV3313GP/Hf4Rv\nOWFCgWsWEclB5QV6Ac6O7L6G54c/HMajRsGOHT1nzYuIJKHyAj3PsyNXrgwrNQ89FMYLFoQ9WMaN\nK1B9IiLDVHmHLU6ZEpZZBprfj66ucA3PlSt7Ht7cnMD+KyIi+1B5Hfowzo5cujRscdsd5v/zP+Fn\ngsJcRMpJ5XXo3SfRzJsXllmmTAlhPsDJNbt3w7HH9qzGfPSjsHx5wvuviIjsQ+UFOuR0duT998Pn\nP98zXr4cTjutyHWJiOShMgN9Pzo7w2GHu3aF8Wc/CwsXhsMTRUTKmRYPeqmvD0erdIf52rWwaJHC\nXETSQR068OabMHFiz/iyy3pOFhIRSYuK79C/+914mL/6qsJcRNKpYjv01tb4xlnXXdezsZaISBoN\n2qGb2TFm9oSZrTOztWZ2ZTQ/0cyWmllzdFu8HUwKvJnWo4/Gw3zbNoW5iKRfLksue4Br3P1E4GPA\n183sRGAusMzdpwHLonHhFXAzre3bw6XgzjsPxo+HH/wgfMsjjih82SIipTZooLt7q7s/F91/B1gP\nHA1cDDRED2sAZhalwgJspgXwy1/CiSfCPfeEp7a3w9VXF7BOEZGEDWkN3cxqgZOBFUC1u7dGX9oK\nVO/jOXOAOQBThnM1+Tw309q6Fb7xDXjgATj5ZFiyBGbMGHoZIiLlLuejXMxsPPAA8C137+j9NXd3\nwAd6nrvXu3udu9dNmjRp6BXu64fAID8c3OEnPwld+eLF4WiWFSsU5iKSXTkFupmNJIR5o7v/Mpre\nZmY10ddrgLaiVDiMzbQ2bYILLoDZs0Ogr14Nc+fCyJFFqVBEpCzkcpSLAT8G1rt778sdLwJmR/dn\nAwsLXx5hz5X6epg6NZyyOXVqGA+wF0tXF9x5Z7jwxDPPwB13wNNPwwknFKUyEZGyYmG1ZD8PMDsT\neAZ4EeiKpr9NWEdfAEwBWoBL3H37/r5XXV2dNzU15VvzgJqb4W//NgT4ueeGzO99nU8RkbQys1Xu\nXjfY4wb9UNTdnwX2tZvJ2UMtrND27IHbboPrr4fRo2H+/HBoovZfEZFKk+ozRdesCfuuNDXBzJlh\nuaWmJumqRESSkcq9XHbuhH/+ZzjllPAB6IIF4ThzhbmIVLLUdegrVoSufO1auPRSuP12OOywpKsS\nEUleajr0zk645hr40z+Ft9+Ghx+Gn/5UYS4i0i0VHfpLL8GFF8LGjXDFFfC978HBByddlYhIeUlF\noE+ZAscdF/YpP+uspKsRESlPqQj0MWPgkUeSrkJEpLylZg1dRET2T4EuIpIRCnQRkYxQoIuIZIQC\nXUQkIxToIiIZoUAXEckIBbqISEYMeoGLgr6YWTvhYhjl7HDg9aSLKKAsvZ8svRfI1vvReymuqe4+\n6EWZSxroaWBmTblcGSQtsvR+svReIFvvR++lPGjJRUQkIxToIiIZoUDvrz7pAgosS+8nS+8FsvV+\n9F7KgNbQRUQyQh26iEhGKNAjZnaMmT1hZuvMbK2ZXZl0TcNlZqPNbKWZvRC9lxuSrilfZlZlZs+b\n2eKka8mXmb1qZi+a2Woza0q6nnyZ2aFmdr+ZbTCz9WZ2etI1DYeZHR/9nXT/6TCzbyVd11BoySVi\nZjVAjbs/Z2YHAauAme6+LuHShszMDBjn7jvMbCTwLHCluy9PuLRhM7OrgTrgYHf/TNL15MPMXgXq\n3L3cjnUeFjNrAJ5x97vNbBQw1t3fSrqufJhZFbAFOM3dy/3cmfepQ4+4e6u7PxfdfwdYDxydbFXD\n48GOaDgy+pPan9xmNhm4ELg76VokzswOAT4B/BjA3XelPcwjZwMb0xTmoEAfkJnVAicDK5KtZPii\nJYrVQBuw1N1T+16A24Frga6kCykQBx4zs1VmNifpYvL0AaAd+K9oSexuMxuXdFEF8AXgZ0kXMVQK\n9D7MbDzwAPAtd+9Iup7hcve97j4DmAycambTk65pOMzsM0Cbu69KupYCOjP6uzkf+LqZfSLpgvIw\nAvgIcJe7nwy8C8xNtqT8RMtGFwG/SLqWoVKg9xKtNz8ANLr7L5OupxCiX3+fAM5LupZhOgO4KFp3\nvg/4lJndk2xJ+XH3LdFtG/AgcGqyFeVlM7C512+A9xMCPs3OB55z921JFzJUCvRI9EHij4H17n5r\n0vXkw8wmmdmh0f0xwLnAhmSrGh53v87dJ7t7LeHX4Mfd/dKEyxo2MxsXfehOtDTx58Bvk61q+Nx9\nK/CamR0fTZ0NpO5Agj6+SAqXWyD8uiTBGcCXgRejtWeAb7v7IwnWNFw1QEP0Sf0BwAJ3T/3hfhlR\nDTwY+gdGAPe6+5JkS8rb3wON0VLFK8BXEq5n2KIfsucClyddy3DosEURkYzQkouISEYo0EVEMkKB\nLiKSEQp0EZGMUKCLiGSEAl1EJCMU6CIiGaFAFxHJiP8PldXB71FyIikAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1dd7e863c50>"
+       "<matplotlib.figure.Figure at 0x7fe708b04dd8>"
       ]
      },
      "metadata": {},
@@ -318,6 +318,15 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "<b>完整的项目请前往Github项目<a href=\"https://github.com/MachineLearning100/100-Days-Of-ML-Code\">100-Days-Of-ML-Code</a>查看。有任何的建议或者意见欢迎在issue中提出~</b>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -344,7 +353,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,
diff --git a/Code/Data.csv b/Code/Data.csv
deleted file mode 100644
index 564b65b..0000000
--- a/Code/Data.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-Country,Age,Salary,Purchased
-France,44,72000,No
-Spain,27,48000,Yes
-Germany,30,54000,No
-Spain,38,61000,No
-Germany,40,,Yes
-France,35,58000,Yes
-Spain,,52000,No
-France,48,79000,Yes
-Germany,50,83000,No
-France,37,67000,Yes
\ No newline at end of file
diff --git a/Code/Day 13_SVM.ipynb b/Code/Day 13_SVM.ipynb
index 8508e27..0477659 100644
--- a/Code/Day 13_SVM.ipynb	
+++ b/Code/Day 13_SVM.ipynb	
@@ -42,7 +42,7 @@
    },
    "outputs": [],
    "source": [
-    "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
+    "dataset = pd.read_csv('../datasets/Social_Network_Ads.csv')\n",
     "X = dataset.iloc[:, [2, 3]].values\n",
     "y = dataset.iloc[:, 4].values"
    ]
diff --git a/Code/Day 1_Data Preprocessing.ipynb b/Code/Day 1_Data Preprocessing.ipynb
index b4d80b8..921b622 100644
--- a/Code/Day 1_Data Preprocessing.ipynb	
+++ b/Code/Day 1_Data Preprocessing.ipynb	
@@ -62,7 +62,7 @@
     }
    ],
    "source": [
-    "dataset = pd.read_csv('Data.csv')\n",
+    "dataset = pd.read_csv('../datasets/Data.csv')\n",
     "X = dataset.iloc[ : , :-1].values\n",
     "Y = dataset.iloc[ : , 3].values\n",
     "print(\"Step 2: Importing dataset\")\n",
@@ -304,6 +304,15 @@
    "source": [
     "<b>完整的项目请前往Github项目<a href=\"https://github.com/MachineLearning100/100-Days-Of-ML-Code\">100-Days-Of-ML-Code</a>查看。有任何的建议或者意见欢迎在issue中提出~</b>"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/Code/Social_Network_Ads.csv b/Code/Social_Network_Ads.csv
deleted file mode 100644
index e139dea..0000000
--- a/Code/Social_Network_Ads.csv
+++ /dev/null
@@ -1,401 +0,0 @@
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\ No newline at end of file