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Upgrade dependency versions
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.dvc/.gitignore

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/updater.lock
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/state-journal
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/state-wal
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/cache
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/cache
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/tmp

README.md

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*WARNING: the exercises build on top of each other, so you will not be able to
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skip steps ahead without executing them.*
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*WARNING 2: the workshop requires infrastructure that we only provision when
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needed, therefore you won't be able to execute the exercises on your own that
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require that shared infrastructure. We are working on a setup that allows
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running the workshop locally, but that is work in progress.*
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## The Machine Learning Problem
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We built a simplified solution to a Kaggle problem posted by Corporación Favorita,

requirements.txt

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dvc[gs]==0.35.7
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numpy==1.16.3
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pandas==0.24.2
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pylint==2.1.1
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pytest==4.4.1
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scikit-learn==0.20.3
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flask==1.0.2
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mlflow==0.9.1
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lime==0.1.1.33
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fluent-logger==0.9.3
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dvc[gs]==0.70.0
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numpy==1.17.4
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pandas==0.25.3
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pylint==2.4.4
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pytest==5.3.0
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scikit-learn==0.21.3
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flask==1.1.1
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mlflow==1.4.0
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lime==0.1.1.36
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fluent-logger==0.9.4
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s3fs==0.1.2
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seaborn==0.9.0
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joblib==0.14.0

src/app.py

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from flask import Flask, render_template, jsonify, request
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from datetime import datetime
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from sklearn.externals import joblib
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import joblib
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import pandas as pd
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import decision_tree
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import os

src/decision_tree.py

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import pandas as pd
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import sys, os, json
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from sklearn.preprocessing import LabelEncoder
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from sklearn.externals import joblib
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import joblib
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sys.path.append(os.path.join('..', 'src'))
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sys.path.append(os.path.join('src'))
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from sklearn import tree, ensemble, metrics

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