It's changed!

It's changed now! The domain as well as the scope of this site. All my technical learning will be shared in this blog. It's not specific to any one or two technoligies. Rather, any technology which I come across and learn which I feel like sharing with others, I'll publish in this site.

I hope to keep my writing as regular and consistent as possible. Let's see, how it goes!

Comments

Post a Comment

Popular posts from this blog

How to set JSON as a data source to a Combo Box in Power Apps

I was creating a form mockup in Power Apps. Since it was a mockup, I didn't want to connect to any data source. One of the fields in the form was Combo Box. When you add this control, by default the Items property is set to ComboBoxSample . When you run the app, the combo box shows values such as Item 1, Item 2,... Item 15. For the purpose of the mockup, I wanted to put some other values to this control. However, creating a table and connecting to that data source is over engineering for a mockup. Hence, I wanted to create a sample JSON object and assign that to Items property. This is where things got little tricky. I tried forming JSON text something like below (the same example given in Microsoft website). [ { "id": 1, "name": "Option 1"}, { "id": 2, "name": "Option 2"}, { "id": 3, "name": "Option 3"} ] When I put this in Items property, it showed errors like below for the string part ...
Read more

Ordinal Encoder, OneHotEncoder and LabelBinarizer in Python

Usually in the data set we work on, we will have both numerical data as well as categorical data (non-numeric). There are algorithms which do not work on categorical data. These algorithms are designed that way to enhance efficiency. So in such cases how do we train the model with categorical data? This is where we need to apply a technique called Encoding. Encoding transforms categorical data into numeric. There are various Encoders available in sci-kit learn library. In this post, I'll share my learning on three Encoders. Ordinal Encoder This performs ordinal (integer) encoding of categorical data. Let's look at an example. I have an array of categorical data. This is a 2 dimensional array. multiArray = np.array([[ 'Karnataka' ,  'KA' ], [ 'Maharastra' ,  'MH' ], [ 'Gujarat' ,  'GJ' ]]) Then I use OrdinalEncoder to transform this data. ordinalEncoder = OrdinalEncoder() ordinalEncoderArray = ordinalEncoder.fit_transform(mult...
Read more