{"cells":[{"metadata":{"_uuid":"9c761c52fae9d86379f7d84f188cc84d85d70852"},"cell_type":"markdown","source":"# Overview\n\nGoogle has a large amount of different serives, Google Merchandise Store is one of them. Google is also know for applying and incentivating the use of Data Science and related fields to improve their business. It's with this intention that the company brings a challenge to help predict revenue per customer. This kernel is a Exploratory Data Analysis (EDA) that tries to help other competitors and entusiasts in having a better understanding of the challenge data.\n\nIn this notebook we are going to use Tableau framework to help us analyse this data."},{"metadata":{"_uuid":"9b8f2245ec51e3392c01a472b0bf644e738af466"},"cell_type":"markdown","source":"## Analysis"},{"metadata":{"_uuid":"9d6bac0590b8fa5a72053d08f7f8d95fd416bdd3"},"cell_type":"markdown","source":"Here we have the transactions revenue across countries, as expected, most of them come from USA, with EU coming in second."},{"metadata":{"_kg_hide-input":true,"trusted":true,"_uuid":"83c4fad2f517ba8af975f800ab887a0cd7a945d0"},"cell_type":"code","source":"%%html\n
","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"2cac7ec1bcc206bd2368da18d9bd9393ecbdd491"},"cell_type":"markdown","source":"Same map here but with revenue across the years, the sale first started on USA and expanded to other countries."},{"metadata":{"_kg_hide-input":true,"trusted":true,"_uuid":"f13982708c4f818dc6d4dafa2eb489e318f1ed58"},"cell_type":"code","source":"%%html\n
","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"a5483de772cd2836ecf3efced7e280a161ffa57e"},"cell_type":"markdown","source":"A chart indicating the amount of customers by percentage."},{"metadata":{"_kg_hide-input":true,"trusted":true,"_uuid":"4fcfc9dd10c9b124da184937723a9ea045aefd05"},"cell_type":"code","source":"%%html\n
","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"43377df18830b6c309c074ace64dfe959b8401f6"},"cell_type":"markdown","source":"This is an analysis for the 80/20 pareto. The bars represents the revenue sum by customer in a descending order, at the right side we have a vertical axis representing the percentage of the revenue. As we move the scroll bar to the right we see that the blue line (which represents the percentage) increases, **when it gets to 80 the actual % of customers is not 20 but 25**."},{"metadata":{"_kg_hide-input":true,"trusted":true,"_uuid":"e26ebd92c33a5acf0927437b4952310bc3665ebe"},"cell_type":"code","source":"%%html\n
","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"8d9d2c07f1b7ce65364b50af518d4d1b17479dcd","_kg_hide-input":false},"cell_type":"markdown","source":"Distribution of the revenue with the top 80% and the other 20%."},{"metadata":{"trusted":true,"_uuid":"8566112ed6f976bbd469c2b031300868038429ec","_kg_hide-input":true},"cell_type":"code","source":"%%html\n
","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"11cce0d3f76f9e386e98eac9d00e6fe50539f4fd"},"cell_type":"markdown","source":"Timeline of the revenue. \nNote: I think that the decrease in August is caused by missing data."},{"metadata":{"trusted":true,"_uuid":"83e57580410ccfa39973a202c06561d3e8390ad3","_kg_hide-input":true},"cell_type":"code","source":"%%html\n
","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"aaee03228acdacf5d59c0896f6ea6263ef83b83c"},"cell_type":"markdown","source":"Timeline of revenue and weekdays. It is clear a correlationship between the both of them."},{"metadata":{"_kg_hide-input":true,"trusted":true,"_uuid":"e0a95e6e7457a6eb02557bbc2edda54f2208c53b"},"cell_type":"code","source":"%%html\n
","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"93dc354e345c8f068e3b54b6f767a7ae590c847e"},"cell_type":"markdown","source":"Customers that buy more tend to have more hits as well."},{"metadata":{"_kg_hide-input":true,"trusted":true,"_uuid":"ceff4255562a6365957bc4da043510254deaa765"},"cell_type":"code","source":"%%html\n
","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}