GPc | Gaussian process code in C++ including some implementations | Analytics library

So I have been trying to get things on track. Finally, I have been able to do it: The jQuery AJAX code that I was looking for to solve my problem is as below:

You have to jump through some loops with Python to get the file you're after. Mainly, you need to get the request header cookie part right, otherwise you'll keep getting 401 code.

First, you need to get the regular cookies from the authority www.nseindia.com. Then, you need to get the bm_sv cookie from the https://www.nseindia.com/json/quotes/equity-historical.json. Finally, add something that's called nseQuoteSymbols.

Glue all that together and make the request to get the file.

Here's how:

Keep it simple. My NGINX looks like:

Hello seeing to the network requests of website I was able to get the data via below code but you might need to get the new password if website blocks it jwt token which is generated below is valid for like 6 to 8 mins you can re use the jwt token till that time and then you need to get new jwt token by calling that back login url like mentioned in below code.

Code:

If you want to do nested lists, MenuItem is not the right component for this.

It's using the li tag as a base and this is why you're getting this warning.

I suggest you use an external package for this, material-ui-nested-menu-item, created exactly for this.

All you have to do is to replace MenuItem by its default NestedMenuItem component, wrap them with a Menu container and use the container ref:

By including [1].text after row.findAll('td') you are retrieving the text of the first item in the list that findAll returns. Removing it should make it work. Also, replace('\n','') can be replaced by strip():

First of all you have to clarify which type of machine learning problem you are trying to solve:

• Categorical: you are trying to predict one class (example: good, normal, bad)
• Continuous/Regressor: you are trying to predict one continuous value (example: house price)

So, what is your y=updated['Encounters'], categorical or continuous?

I will assume (watching the error) your desired output is continuous. So you are working with Regressor problem. In you random forest you used the model: RandomForestRegressor(), so, as the name say, it is a Random Forest used for Regression, so it will work.

But, when you are trying the decision tree, you applied the next model: DecisionTreeClassifier(). As you can see, now, you are implementing a Classifier model, which won't suit your problem. To solve it use the next code instead of DecisionTreeClassifier():

JupyterLab 3.0 requires jinja2 >= 2.10. You must have installed a different version after installing JupyterLab or have broken dependencies for another reason. First, check for broken dependencies running: