Updated title for graphs.

stackoverflow-survey.ipynb

@@ -157,7 +157,7 @@
     "\n",
     "        sb.scatterplot(data=self.devs, x=key_x, y=key_y, hue=hue, palette=palette)\n",
     "        plt.legend(loc='lower center', bbox_to_anchor=(1.5,0)) \n",
-    "        title = \"Annual Salary of %s Developers Over Years of Experience\" % self.language\\\n",
+    "        title = \"Annual Compensation of %s Programmers Over Years of Experience\" % self.language\\\n",
     "                + \"\\nsample size=%i\" %  len (self.devs)\\\n",
     "                + \"\\ncountry=%s\" % self.country\n",
     "        plt.title(title)\n",
@@ -178,19 +178,20 @@
     "\n",
     "        model.fit(X_train, y_train)\n",
     "        y_pred = model.predict(X_test)\n",
-    "\n",
+    "    \n",
+    "        m = model.coef_[0][0]\n",
+    "        b = model.intercept_[0]\n",
     "        print(\"+----------------------+\")\n",
     "        print(\"%s regression line for %s\" % (line_color, self.language))\n",
-    "        print(\"coefficient =\", model.coef_)\n",
-    "        print('intercept=', model.intercept_)\n",
+    "        print(\"coefficient = %0.2f\" % m)\n",
+    "        print('intercept = %0.2f' % b)\n",
     "        rmse = root_mean_squared_error(y_test, y_pred)\n",
-    "        print(\"rmse = \", rmse)\n",
+    "        print(\"rmse = %0.2f\" % rmse)\n",
     "        r2   = r2_score(y_test, y_pred)\n",
-    "        print(\"r2 score = \", r2)\n",
+    "        print(\"r2 score = %0.2f\" % r2)\n",
     "        print(\"sample predictions:\")\n",
     "        print(y_pred[3:6])\n",
     "        print(\"+----------------------+\")\n",
-    "        b = model.intercept_[0]\n",
     "\n",
     "        plt.figure(self.canvas)\n",
     "        plt.plot(X_test, y_pred, color=line_color, label='Regression Line')\n",
@@ -247,7 +248,7 @@
     "\n",
     "* The income of a data professional can either increase by 2,000 per year (red) or 10,000 per year (cyan).\n",
     "\n",
-    "* For both models, the r2 score ranges from poor to moderate = 0.20 - 0.37 depending on the random number. The variability not explained by the model could be the result of the fields such as advertising, finance, or bio/medical technology.\n",
+    "* For both models, the r2 score ranges from poor to moderate = 0.20 - 0.37 depending on the random number. The variability not explained by the model could be the result of different fields that employ dats scientists/analysts/engineers such as finance, bio/med, or advertising.\n",
     "\n",
     "* For any given point in the career, the model is off by 39,000 or 42,000 dollars.\n",
     "\n",