For how many years have you been experimenting with data? | 5 |
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## Popular articles by zenpanik

### ACADEMIA DATATHON CASE: THE A.I. CRYPTO TRADER

### Datathon Ontotext Mentors’ Guidelines – Text Mining Classification

### The SAP Case using KNIME and Multiple Linear Regression Method

### THE A.I. CRYPTO TRADER: cryptomonkeys

### Tiny smart data modelled with a not-so-tiny smart model – the Case of SAP

### Antelope SAP

### Critical Outliers – VMware Case

### Datathon Kaufland Mentors’ Guidelines – On Predictive Maintenance

### Datathon Sofia Air Mentors’ Guidelines – On IOT Prediction

### Datathon Telenor Mentors’ Guidelines – On TelCo predictions

### Datathon NSI Mentors’ Guidelines – Economic Time Series Prediction

## Popular comments by zenpanik

### Cryptocurrency Prediction by Kautilya

6. Would you bet your own money on your predictions? If so how much?

### Cryptocurrency Prediction by Kautilya

1. You may want to include some evaluation metrics for your models both on train & test sets.

2. On the data prep part – it is not the best solution to just remove rows where you see missing values because it is time-series data and could seriously bias your next steps.

3. Assumption you have made about the “large number of missing values” is probably poor. Do you have any data/metric you used to prove it?

4. You may want to include more detailed explanation why the data is not continuous (here is a link on discrete and continuous data https://www.mathsisfun.com/data/data-discrete-continuous.html)

5. How you would rank your model? What are the metrics you used?

### Antelope SAP

Hi team,

You have underestimated the data understanding, EDA and feature engineering. It is an important part of data science. Having a visual representations would be nice. Also tables and some numbers are welcome in the paper.

– zenpanik

### By KrYpToNiAnS

6. Would you bet your own money on your predictions? If so how much?

### Prediction Model for Crypto Currency in R

1. You say there are missing timestamps but are there missing values?

2. How did you imputed the missing values?

3. Did you find a trend/seasonality in the data provided?

4. How good is your predictive model? What are the metrics you have used to evaluate it?

5. Would you bet your own money on your predictions? If so how much?