Add (USD) after GDP per capita
parent
48dfa2ce67
commit
f8f2b9e4bb
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@ -353,6 +353,7 @@
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" plt.annotate(country, xy=(pos_data_x, pos_data_y), xytext=pos_text,\n",
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" arrowprops=dict(facecolor='black', width=0.5, shrink=0.1, headwidth=5))\n",
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" plt.plot(pos_data_x, pos_data_y, \"ro\")\n",
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"plt.xlabel(\"GDP per capita (USD)\")\n",
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"save_fig('money_happy_scatterplot')\n",
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"plt.show()"
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]
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@ -384,6 +385,7 @@
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"import numpy as np\n",
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"\n",
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"sample_data.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(5,3))\n",
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"plt.xlabel(\"GDP per capita (USD)\")\n",
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"plt.axis([0, 60000, 0, 10])\n",
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"X=np.linspace(0, 60000, 1000)\n",
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"plt.plot(X, 2*X/100000, \"r\")\n",
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@ -421,6 +423,7 @@
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"outputs": [],
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"source": [
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"sample_data.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(5,3))\n",
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"plt.xlabel(\"GDP per capita (USD)\")\n",
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"plt.axis([0, 60000, 0, 10])\n",
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"X=np.linspace(0, 60000, 1000)\n",
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"plt.plot(X, t0 + t1*X, \"b\")\n",
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@ -449,6 +452,7 @@
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"outputs": [],
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"source": [
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"sample_data.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(5,3), s=1)\n",
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"plt.xlabel(\"GDP per capita (USD)\")\n",
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"X=np.linspace(0, 60000, 1000)\n",
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"plt.plot(X, t0 + t1*X, \"b\")\n",
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"plt.axis([0, 60000, 0, 10])\n",
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@ -598,6 +602,7 @@
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"t0full, t1full = lin_reg_full.intercept_[0], lin_reg_full.coef_[0][0]\n",
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"X = np.linspace(0, 110000, 1000)\n",
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"plt.plot(X, t0full + t1full * X, \"k\")\n",
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"plt.xlabel(\"GDP per capita (USD)\")\n",
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"\n",
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"save_fig('representative_training_data_scatterplot')\n",
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"plt.show()"
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@ -623,6 +628,7 @@
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"pipeline_reg.fit(Xfull, yfull)\n",
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"curve = pipeline_reg.predict(X[:, np.newaxis])\n",
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"plt.plot(X, curve)\n",
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"plt.xlabel(\"GDP per capita (USD)\")\n",
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"save_fig('overfitting_model_plot')\n",
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"plt.show()"
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]
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@ -672,6 +678,7 @@
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"\n",
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"plt.legend(loc=\"lower right\")\n",
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"plt.axis([0, 110000, 0, 10])\n",
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"plt.xlabel(\"GDP per capita (USD)\")\n",
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"save_fig('ridge_model_plot')\n",
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"plt.show()"
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]
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@ -726,6 +733,13 @@
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"X_new = np.array([[22587.0]]) # Cyprus' GDP per capita\n",
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"print(model.predict(X_new)) # outputs [[ 5.76666667]]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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