Business decisions are all about predictions: “to hold or to sell my stock” depends on how well you can predict the stock market performance; “to increase or decrease my inventory” depends on how well you can forecast your sales.
Can we use Predictive Machine Learning Models as a game-changer in this domain? Join us in this full-day workshop to discover!
10:00-12:00: Intro to Python and Data Visualization
1.1 Learning the basics of Python language
Python is a powerful, flexible programming language you can use in web development and to process and manipulate lots of data. Python and its various libraries enable programmers to scrape the web for data, and then manipulate and even visualize that data.
1.2 Learning how to visualize data with Python
When we watch reports or presentations, we naturally first look at the graphs and charts. Data Visualization is a key aspect of anyone working with Data Science or Analytics, as it helps you not only locate trends and insight, but also present your findings in a compelling way. And to stay relevant with a world bulging? With data, we need a better tool than Excel - Python.
Part 2: Building your first Predictive Machine Learning Model in Python
2.1 How predictions are done with Python and Machine Learning
Predictive models use a technique called Time Series - a basic concept in Machine Learning, which processes past data to forecast future outcomes. . But how do these things work? Predictive models are used by companies to forecast business outcomes, sales, product demands, workforce planning, competition analysis, and more, so it’s a very useful tool.
2.2 Predicting stock prices and forecasting sales using your Prediction Model
After discovering the tools, we are going to build our own model to make predictions on sales data and stock prices. We will then explore how we evaluate the performance of our model and visualize its results.
⏰ Saturday 2021/8/15 10:00 -17:00
📌XNode 9/F, Yan'an West Road 129, Jingan District, Shanghai
💻 Please bring your own laptop. No coding experience is needed.
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