Mining data to identify valuable patterns is a skill ostensibly more art than science. Pressure improves the interest of early obvious outcomes, yet it’s too simple to trick yourself.
How would you resist the siren tunes of the information and keep up an analysis discipline that will prompt huge outcomes?
Below are the most widely recognized errors made in data mining.
Here, we quickly define the top 10 errors made in data mining, as far as frequency and seriousness.
0. Lack of adequate data
1. Concentrate on training
2. Depend on one strategy
3. Ask the wrong inquiry
4. Listen (only) to the data
5. Accept leaks from the future
6. Discount pesky cases
8. Answer each query
9. Test casually
10. Trust the best mode
Follow this link to read more about these mistakes. The top 10 data mining mistakes were discussed by John Elder, PhD in part 20 of the Handbook of Statistical Analysis and Data Mining Applications.
Here is the video of his popular talk on the top 10 data mining mistakes.