up

1year/2trimester/Coding for Data Science - Python language/Python/Examples/.ipynb_checkpoints/Diamonds ML regression-checkpoint.ipynb

@@ -623,10 +623,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
+      "  FutureWarning)\n",
+      "E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
+      "  \"this warning.\", FutureWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SCORE:  0.009547645532072673 \n",
+      "--------------\n"
+     ]
+    }
+   ],
    "source": [
+    "from sklearn import linear_model\n",
     "from sklearn.linear_model import LogisticRegression\n",
     "logistic = linear_model.LogisticRegression(random_state=0)  # create object for the class\n",
     "logistic.fit(X_train, y_train) # perform logistic regression\n",
@@ -636,13 +656,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1662 3370\n",
+      "1882 3321\n",
+      "776 698\n",
+      "530 646\n",
+      "1882 4839\n",
+      "1076 1851\n",
+      "984 1624\n",
+      "802 665\n",
+      "872 596\n",
+      "394 1154\n"
+     ]
+    }
+   ],
    "source": [
-    "Y_pred = logistic.predict(X_test, y_test)  # make predictions\n",
     "for X,y in list(zip(X_test, y_test))[:10]:\n",
-    "    print(Y_pred[x], y)"
+    "    print(logistic.predict([X])[0], y)\n",
+    "    "
    ]
   },
   {
@@ -650,11 +687,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "for X,y in list(zip(X_test, y_test))[:10]:\n",
-    "    print(logistic.predict([X])[0], y)\n",
-    "    "
-   ]
+   "source": []
   }
  ],
  "metadata": {
@@ -677,5 +710,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

1year/2trimester/Coding for Data Science - Python language/Python/Examples/Diamonds ML regression.ipynb

@@ -656,25 +656,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
-     "ename": "TypeError",
-     "evalue": "predict() takes 2 positional arguments but 3 were given",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-8-7941666c0bd3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mY_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlogistic\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# make predictions\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m10\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      3\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY_pred\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mTypeError\u001b[0m: predict() takes 2 positional arguments but 3 were given"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1662 3370\n",
+      "1882 3321\n",
+      "776 698\n",
+      "530 646\n",
+      "1882 4839\n",
+      "1076 1851\n",
+      "984 1624\n",
+      "802 665\n",
+      "872 596\n",
+      "394 1154\n"
      ]
     }
    ],
    "source": [
-    "Y_pred = logistic.predict(X_test, y_test)  # make predictions\n",
     "for X,y in list(zip(X_test, y_test))[:10]:\n",
-    "    print(Y_pred[x], y)"
+    "    print(logistic.predict([X])[0], y)\n",
+    "    "
    ]
   },
   {
@@ -682,11 +687,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "for X,y in list(zip(X_test, y_test))[:10]:\n",
-    "    print(logistic.predict([X])[0], y)\n",
-    "    "
-   ]
+   "source": []
   }
  ],
  "metadata": {