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diff --git a/.gitignore b/.gitignore
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+.vscode
+.idea
+env
+science
diff --git a/README.md b/README.md
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+# example-datascience
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diff --git a/__init__.py b/__init__.py
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diff --git a/__pycache__/app.cpython-37.pyc b/__pycache__/app.cpython-37.pyc
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diff --git a/__pycache__/run.cpython-37.pyc b/__pycache__/run.cpython-37.pyc
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diff --git a/app.py b/app.py
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+from flask import Blueprint
+from flask_restful import Api
+from resources.Sales import Popularity
+
+api_bp = Blueprint('api', __name__)
+api = Api(api_bp)
+
+# Route
+api.add_resource(Popularity, '/sales/popularity')
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diff --git a/requirements.txt b/requirements.txt
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+++ b/requirements.txt
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+flask
+flask_restful
+flask_mysql
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diff --git a/resources/Sales.py b/resources/Sales.py
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+++ b/resources/Sales.py
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+from flask import jsonify, render_template, make_response
+from flask_restful import Resource
+import pandas as pd
+import numpy as np
+import time
+import turicreate as tc
+import sqlalchemy as sql
+from sklearn.model_selection import train_test_split
+import json
+
+def load_data():
+ try:
+ connect_string = 'mysql://root:123456@127.0.0.1:3306/example_science'
+ sql_engine = sql.create_engine(connect_string)
+ query = "select * from Transactions"
+ return pd.read_sql_query(query, sql_engine)
+ except Exception as e:
+ print(e)
+
+def model(train_data, name, user_id, item_id, target, n_rec):
+ if name == 'popularity':
+ model = tc.popularity_recommender.create(tc.SFrame(train_data),
+ user_id=user_id,
+ item_id=item_id,
+ target=target)
+ elif name == 'cosine':
+ model = tc.item_similarity_recommender.create(train_data,
+ user_id=user_id,
+ item_id=item_id,
+ target=target,
+ similarity_type='cosine')
+ elif name == 'pearson':
+ model = tc.item_similarity_recommender.create(train_data,
+ user_id=user_id,
+ item_id=item_id,
+ target=target,
+ similarity_type='pearson')
+
+ recom = model.recommend(k=n_rec)
+ return recom
+
+def split_data(data):
+ train, test = train_test_split(data, test_size = .2)
+ train_data = train
+ return train_data
+
+class Popularity(Resource):
+ def get(self):
+ try:
+ transactions = load_data()
+ data = pd.melt(transactions.set_index('customerId')['product_name'].apply(pd.Series).reset_index(),
+ id_vars=['customerId'],
+ value_name='product_name').groupby(['customerId', 'product_name']) \
+ .agg({'product_name' : 'count'}) \
+ .rename(columns={'product_name' : 'product_count'}) \
+ .reset_index() \
+ .sort_values(by='product_count', ascending=False)
+
+ name = 'popularity'
+ user_id = 'customerId'
+ item_id = 'product_name'
+ target = 'product_count'
+ n_rec = 10 # number of items to recommend
+ train_data = split_data(data)
+ result = model(train_data, name, user_id, item_id, target, n_rec)
+ df_result = pd.DataFrame(result)
+ headers = {'Content-Type': 'text/html'}
+ return make_response(render_template('view.html', tables=[df_result.to_html(classes='data', header="true")]),200,headers)
+ except Exception as e:
+ print(e)
+ return { "success" : False, "message" : "Internal server error" }
+
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diff --git a/resources/__pycache__/Sales.cpython-37.pyc b/resources/__pycache__/Sales.cpython-37.pyc
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diff --git a/run.py b/run.py
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+++ b/run.py
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+from flask import Flask
+
+def create_app(config_filename):
+ app = Flask(__name__)
+
+ from app import api_bp
+ app.register_blueprint(api_bp, url_prefix='/api')
+
+ return app
+
+
+if __name__ == "__main__":
+ app = create_app("config")
+ app.run(debug=True)
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diff --git a/templates/view.html b/templates/view.html
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+++ b/templates/view.html
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+
+
+
+
+ Popularity
+
+
+
+{% for table in tables %}
+ {{ table|safe }}
+{% endfor %}
+
+
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