Print only 2 significant figures of regression results.
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@@ -178,19 +178,20 @@
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"\n",
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" model.fit(X_train, y_train)\n",
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" y_pred = model.predict(X_test)\n",
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"\n",
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" \n",
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" m = model.coef_[0][0]\n",
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" b = model.intercept_[0]\n",
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" print(\"+----------------------+\")\n",
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" print(\"%s regression line for %s\" % (line_color, self.language))\n",
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" print(\"coefficient =\", model.coef_)\n",
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" print('intercept=', model.intercept_)\n",
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" print(\"coefficient = %0.2f\" % m)\n",
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" print('intercept = %0.2f' % b)\n",
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" rmse = root_mean_squared_error(y_test, y_pred)\n",
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" print(\"rmse = \", rmse)\n",
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" print(\"rmse = %0.2f\" % rmse)\n",
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" r2 = r2_score(y_test, y_pred)\n",
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" print(\"r2 score = \", r2)\n",
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" print(\"r2 score = %0.2f\" % r2)\n",
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" print(\"sample predictions:\")\n",
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" print(y_pred[3:6])\n",
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" print(\"+----------------------+\")\n",
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" b = model.intercept_[0]\n",
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"\n",
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" plt.figure(self.canvas)\n",
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" plt.plot(X_test, y_pred, color=line_color, label='Regression Line')\n",
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@@ -247,7 +248,7 @@
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"\n",
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"* The income of a data professional can either increase by 2,000 per year (red) or 10,000 per year (cyan).\n",
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"\n",
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"* 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",
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"* 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",
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"\n",
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"* For any given point in the career, the model is off by 39,000 or 42,000 dollars.\n",
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"\n",
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