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Friday, July 28, 2017
Tuesday, May 23, 2017
We concluded our previous attempt to predict the onset of diabetes with a few questions. Lets try to answer the questions in this post.
Recap
The model that we built previously had an accuracy of 77.73% - with a caveat that we used the same data for training the model and do the prediction. And we...
Tuesday, May 9, 2017
One of the applications of Machine Learning is to predict likelihood of occurrence of an event from the past data. With our learning from exploring data to testing hypothesis, lets see if we can put together the learning to predict likelihood of onset of diabetes from diagnostic measures.
Data Source
We...
Wednesday, April 26, 2017
Having explored the mtcars dataset and building a good understanding about the dataset, its time for us to do some "Hypothesis Testing" (without getting much into math and statistics behind it).
Hypothesis Testing
So, the objective of this testing is to state our hypothesis about cars and validate...
Tuesday, April 18, 2017
In the previous post we understood the "mtcars" data set and did some univariate analysis to understand the features from the data set.
The objective of this post is to understand how multiple features (aka variables) are related. For example, one might be interested in understanding how weight of...
Saturday, February 25, 2017
I thought it would be a good idea to visually explain the inferences from the previous post because a picture is worth a thousand words. Visualization is one of the most effective ways to communicate the findings from Data Analytics.
This post would be a shorter one as we are going to visually represent...
Tuesday, February 14, 2017
Having acquainted with The ABCs of Machine Learning, its time to do some data exploration to understand the nature of data to graduate to the next level.
After we acquire data, the immediate next step would be to make sense of the data, quickly. This step would be of paramount importance as doing...
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