Auto EDA (Exploratory Data Analysis) Library | Pandas Profiling | 360DigiTMG

Introduction:
- Exploratory Data Analysis (EDA) is a fundamental technique in data exploration, helping in understanding data, detecting patterns, and uncovering insights.
- With the surge of data in the digital age, manual EDA becomes time-consuming. Hence, the need for Auto EDA arises.

Business Case:
- Companies in the data science domain face challenges analyzing large datasets manually.
- The objective of Auto EDA is to reduce the time spent on manual analysis.
- Success criteria include business (reducing manual EDA time), economic (reducing costs by up to 50%), and ML (achieving accuracy up to 90%).

Manual EDA vs. Auto EDA:
- Manual EDA involves steps like calculating mean, median, handling duplicates, dealing with missing values, identifying outliers, and visualizations like box plots.
- Auto EDA, particularly using the Pandas Profiling library, simplifies this process, achieving the same results with fewer lines of code.

Pandas Profiling Library:
- It's a Python library that generates comprehensive reports with statistics and visualizations.
- Users can install it using 'pip install pandas-profiling', and with a few lines of code, they can have detailed insights and visualizations of their data.

Conclusion:
- Automating EDA using python libraries like Pandas Profiling enhances efficiency and productivity in data science projects. Sharing and engaging with fellow enthusiasts is encouraged.

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