/Module-11-Timed-Series-Challenge

In this assignment, I've been tasked with analyzing the company's financial and user data in clever ways to help the company grow. So, I want to find out if the ability to predict search traffic can translate into the ability to successfully trade the stock.

Primary LanguageJupyter Notebook

Module-11-Timed-Series-Challenge

In this assignment, I've been tasked with analyzing the company's financial and user data in clever ways to help the company grow. So, I want to find out if the ability to predict search traffic can translate into the ability to successfully trade the stock. In this module, I’ll learn to analyze time series data, as well as continue my journey into machine learning by automating time series forecasting using Prophet, a tool created by Facebook. An analysis that previously took financial professionals hours to complete can now be automated and completed within minutes using the machine learning algorithms that power Prophet.

I'm a growth analyst at MercadoLibre (http://investor.mercadolibre.com/investor-relations). With over 200 million users, MercadoLibre is the most popular e-commerce site in Latin America. In a bid to drive revenue, I’ll produce a Jupyter notebook that contains my data preparation, my analysis, and my visualizations for all the timeseries data that the company needs to understand. I’ll use text and comments to document my findings. And, I’ll answer the question prompts in the instructions.

Specifically, this notebook should contain the following:

  • Visual depictions of seasonality (as measured by Google Search traffic) that are of interest to the company.
  • An evaluation of how the company stock price correlates to its Google Search traffic.
  • A Prophet forecast model that can predict hourly user search traffic.
  • Answers to the questions in the instructions that I will write in my Jupyter notebook.
  • (Optional) A plot of a forecast for the company’s future revenue.

Instructions

  • Step 1: Find Unusual Patterns in Hourly Google Search Traffic
  • Step 2: Mine the Search Traffi c Data for Seasonality
  • Step 3: Relate the Search Traffic to Stock Price Patterns
  • Step 4: Create a Time Series Model by Using Prophet
  • Step 5 (Optional): Forecast the Revenue by Using Time Series Models

Instructions on how to use

1. Launch forecasting_net_prophet.ipynb from JuypterLab to run JupyterLab version

  • Run through each line of code to view the output

2. Launch google_colab_version_forecasting_net_prophet.ipynb and upload into your Google Colab:

  • download the resource files in this repository to upload using the Google Colab prompts within the code

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