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468
ml1/2_3_1_Advanced_Visualisation.ipynb
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202
ml1/2_5_1_kNN_Model.ipynb
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ml1/2_5_2_Decision_Tree_Model.ipynb
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@ -74,7 +74,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@ -124,25 +124,9 @@
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort=False, random_state=1,\n",
" splitter='best')"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.tree import DecisionTreeClassifier\n",
"import numpy as np\n",
@ -161,24 +145,9 @@
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Prediction [1 0 1 1 1 0 0 1 0 2 0 0 1 2 0 1 2 2 1 1 0 0 2 0 0 2 1 1 2 2 2 2 0 0 1 1 0\n",
" 1 2 1 2 0 2 0 1 0 2 1 0 2 2 0 0 2 0 0 0 2 2 0 1 0 1 0 1 1 1 1 1 0 1 0 1 2\n",
" 0 0 0 0 2 2 0 1 1 2 1 0 0 2 1 1 0 1 1 0 2 1 2 1 2 0 1 0 0 0 2 1 2 1 2 1 2\n",
" 0]\n",
"Expected [1 0 1 1 1 0 0 1 0 2 0 0 1 2 0 1 2 2 1 1 0 0 2 0 0 2 1 1 2 2 2 2 0 0 1 1 0\n",
" 1 2 1 2 0 2 0 1 0 2 1 0 2 2 0 0 2 0 0 0 2 2 0 1 0 1 0 1 1 1 1 1 0 1 0 1 2\n",
" 0 0 0 0 2 2 0 1 1 2 1 0 0 1 1 1 0 1 1 0 2 2 2 1 2 0 1 0 0 0 2 1 2 1 2 1 2\n",
" 0]\n"
]
}
],
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Prediction \", model.predict(x_train))\n",
"print(\"Expected \", y_train)"
@ -193,26 +162,9 @@
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted probabilities [[0. 0.97368421 0.02631579]\n",
" [1. 0. 0. ]\n",
" [0. 0.97368421 0.02631579]\n",
" [0. 0.97368421 0.02631579]\n",
" [0. 0.97368421 0.02631579]\n",
" [1. 0. 0. ]\n",
" [1. 0. 0. ]\n",
" [0. 0.97368421 0.02631579]\n",
" [1. 0. 0. ]\n",
" [0. 0. 1. ]]\n"
]
}
],
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Print the \n",
"print(\"Predicted probabilities\", model.predict_proba(x_train[:10]))"
@ -220,17 +172,9 @@
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy in training 0.9821428571428571\n"
]
}
],
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Evaluate Accuracy in training\n",
"\n",
@ -241,17 +185,9 @@
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy in testing 0.9210526315789473\n"
]
}
],
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Now we evaluate error in testing\n",
"y_test_pred = model.predict(x_test)\n",
@ -273,21 +209,9 @@
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'pydotplus'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-1bf5ec7fb043>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexternals\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msix\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpydotplus\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpydot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdot_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pydotplus'"
]
}
],
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import Image \n",
"from sklearn.externals.six import StringIO\n",
@ -544,7 +468,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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4
ml1/requirements.txt
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@ -1,4 +0,0 @@
scikit-learn
seaborn
pydotplus
graphviz

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ml2/3_0_0_Intro_ML_2.ipynb
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@ -18,7 +18,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias"
"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias"
]
},
{
@ -86,7 +86,7 @@
"## Licence\n",
"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
"\n",
"© Carlos A. Iglesias, Universidad Politécnica de Madrid."
"© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid."
]
}
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@ -106,26 +106,9 @@
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ml2/3_1_Read_Data.ipynb
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"© Carlos A. Iglesias, Universidad Politécnica de Madrid."
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ml2/3_2_Pandas.ipynb
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ml2/3_3_Data_Munging_with_Pandas.ipynb
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ml2/3_4_Visualisation_Pandas.ipynb
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@ -18,7 +18,7 @@
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"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias"
"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias"
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@ -478,24 +478,21 @@
"outputs": [],
"source": [
"# Now we make that the plot shows both values combined, and change the labels\n",
"df.query('Age < 20 and Survived == 1').groupby(['Sex','Pclass']).size().unstack(['Sex']).plot(kind='bar', stacked=True) "
"df.query('Age < 20 and Survived == 1').groupby(['Sex','Pclass']).size().unstack(['Sex']).plot(kind='bar', \\\n",
" \n",
" stacked=True) "
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"end_time": "2019-03-06T17:31:13.351504Z",
"start_time": "2019-03-06T17:31:13.092499Z"
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"source": [
"#Small touches\n",
"\n",
"pclass_labels = ['First', 'Second', 'Third']\n",
"sex_labels = ['Female', 'Male']\n",
"sex_labels = {'Female': 0, 'Male': 1}\n",
"\n",
"plt = df.query('Age < 20 and Survived == 1').groupby(['Sex','Pclass']).size().unstack(['Sex']).plot(kind='bar', \n",
" stacked=True, rot=0, subplots=False, figsize=(5,10))\n",
@ -508,17 +505,12 @@
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"source": [
"#The same horizontal\n",
"pclass_labels = ['First', 'Second', 'Third']\n",
"sex_labels = ['Female', 'Male']\n",
"sex_labels = {'Female': 0, 'Male': 1}\n",
"\n",
"plt = df.query('Age > 25 and Survived == 1').groupby(['Sex','Pclass']).size().unstack(['Sex']).plot(kind='barh', \n",
" stacked=True, rot=0, subplots=False)\n",
@ -1570,7 +1562,7 @@
"source": [
"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
"\n",
"© Carlos A. Iglesias, Universidad Politécnica de Madrid."
"© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid."
]
}
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ml2/3_5_Exercise_1.ipynb
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ml2/3_6_Machine_Learning.ipynb
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ml2/3_7_SVM.ipynb
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ml2/3_8_Exercise_2.ipynb
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python/1_5_ControlFlow.ipynb
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python/1_8_Classes.ipynb
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python/1_9_Errors_Exceptions.ipynb
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python/1__10_Modules_Packages.ipynb
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