Auto EDA (Exploratory Data Analysis) Library | Mitosheet
A food company collaborated with a fitness company to examine the relationship between a person's weight and calories consumed, aiming to design effective diet plans. Business goals include maximizing customer attraction while ensuring health risks are minimized. Key success metrics are a 20-30% profit increase, 95% machine learning accuracy, and a profit growth of 10 lakhs.
The speaker demonstrates manual Exploratory Data Analysis (EDA) using the company's data, showcasing techniques like data visualization (histograms, scatter plots) and checking for outliers and correlations. This process is emphasized as time-consuming, taking up to 60-80% of the analysis time.
Mitosheet, a Python package, is introduced as a solution to speed up the EDA process. Instead of numerous manual steps, users can achieve comprehensive EDA with just a couple of lines of code using Mitosheet.
With Mitosheet, a user-friendly dashboard is generated, allowing for the easy creation and editing of various chart types without writing extensive code. It simplifies tasks like viewing, editing, and importing data, as well as drawing graphs.
The speaker encourages the audience to explore Mitosheet to save time and improve efficiency during the EDA process, emphasizing its powerful capabilities and user-friendly interface.
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