Data Profiling and Exploration
We will use the Column Profiler in Power Query to evaluate our bicycle dataset to identify errors, necessary transformations, and other data quality needs. The Column Profiler provides detailed insights into the data, including value distribution, data quality, and summary statistics. By analyzing these metrics, we can pinpoint issues such as duplicate or missing values, understand the distribution of our data, and decide on the necessary transformations to clean and prepare our dataset for analysis.
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Can you explain how to fix the misspelled "casual" value in the writer type column?
What are the best ways to handle the missing values in the riders and humidity columns?
How can I deal with the zero and outlier values in the wind speed column?
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Data Profiling and Exploration
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We will use the Column Profiler in Power Query to evaluate our bicycle dataset to identify errors, necessary transformations, and other data quality needs. The Column Profiler provides detailed insights into the data, including value distribution, data quality, and summary statistics. By analyzing these metrics, we can pinpoint issues such as duplicate or missing values, understand the distribution of our data, and decide on the necessary transformations to clean and prepare our dataset for analysis.
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