If you want to break into competitive data science, then this course is for you! Participating in predictive modelling competitions can help you gain practical experience, improve and harness your data modelling skills in various domains such as credit, insurance, marketing, natural language processing, sales’ forecasting and computer vision to name a few. At the same time you get to do it in a competitive context against thousands of participants where each one tries to build the most predictive algorithm. Pushing each other to the limit can result in better performance and smaller prediction errors. Being able to achieve high ranks consistently can help you accelerate your career in data science. In this course, you will learn to analyse and solve competitively such predictive modelling tasks. When you finish this class, you will: - Understand how to solve predictive modelling competitions efficiently and learn which of the skills obtained can be applicable to real-world tasks. - Learn how to preprocess the data and generate new features from various sources such as text and images. - Be taught advanced feature engineering techniques like generating mean-encodings, using aggregated statistical measures or finding nearest neighbors as a means to improve your predictions. - Be able to form reliable cross validation methodologies that help you benchmark your solutions and avoid overfitting or underfitting when tested with unobserved (test) data. - Gain experience of analysing and interpreting the data. You will become aware of inconsistencies, high noise levels, errors and other data-related issues such as leakages and you will learn how to overcome them. - Acquire knowledge of different algorithms and learn how to efficiently tune their hyperparameters and achieve top performance. - Master the art of combining different machine learning models and learn how to ensemble. - Get exposed to past (winning) solutions and codes and learn how to read them. Disclaimer : This is not a machine learning course in the general sense. This course will teach you how to get high-rank solutions against thousands of competitors with focus on practical usage of machine learning methods rather than the theoretical underpinnings behind them. Prerequisites: - Python: work with DataFrames in pandas, plot figures in matplotlib, import and train models from scikit-learn, XGBoost, LightGBM. - Machine Learning: basic understanding of linear models, K-NN, random forest, gradient boosting and neural networks.
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Data Analysis, Feature Extraction, Feature Engineering, Xgboost
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MM
Nov 10, 2017
This course is fantastic. It's chock full of practical information that is presented clearly and concisely. I would like to thank the team for sharing their knowledge so generously.
SA
Feb 25, 2019
I really enjoyed this course but it was probably 2-3 times more work than I anticipated. Most of that extra time comes from working on the final project, testing things out, etc.
From the lesson
Exploratory Data Analysis
We will start this week with Exploratory Data Analysis (EDA). It is a very broad and exciting topic and an essential component of solving process. Besides regular videos you will find a walk through EDA process for Springleaf competition data and an example of prolific EDA for NumerAI competition with extraordinary findings.
Taught By
Dmitry Ulyanov
Visiting lecturer
Alexander Guschin
Visiting lecturer at HSE, Lecturer at MIPT
Mikhail Trofimov
Visiting lecturer
Dmitry Altukhov
Visiting lecturer
Marios Michailidis
Research Data Scientist
In the previous video, we've tried to decode anonymized features and guess their types.
In fact, we want to do more.
We want to generate new features and to find insights in a data.
And in this lesson,
we will talk about various visualizations that can help us with it.
We will first to see what plots we can draw to explore individual features,
and then we will get to exploration of feature relations.
We'll explore pairs first and then we'll try to find feature groups in a dataset.
First, there is no recipe how you find interesting things in the data.
You should just spend some time looking closely at the data table,
printing it, and examining.
If we found something interesting,
we then can take a closer look.
So, EDA is kind of an art,
but we have a bunch of tools for it which we'll discuss right now.
The first, we can build histograms.
Histograms split feature edge into bins and show how many points fall into each bin.
Note that histograms may be misleading in some cases,
so try to overwrite its number of bins when using it.
Also, know that it aggregates in the data,
so we cannot see,
for example, if all the values are unique or there are a lot of repeated values.
Let's see in other example.
The first thing that I want to illustrate here is that histograms can confuse.
Looking at this histogram,
we could probably think that there are a lot of zero values in this feature.
But in fact, if we take logarithm of the values and build histogram again,
we'll clearly see that distribution is
non-degenerate and there are many more distinct values than one.
So my point is never make a conclusion based on a single plot.
If you have a hypothesis,
try to make several different plots to prove it.
The second interesting thing here is that peak.
What is it?
It turns out that the peak is located exactly at the mean value of this feature.
Seems like organizers filled the missing values with the mean values for us.
So, now we understand that values were originally missing.
How can we use this information?
We can replace the missing values we found with not numbers, nulls again.
For example, [inaudible] has a special algorithm that can fill missing values on its own and so,
maybe [inaudible] will benefit from explicit missing values.
Or we can fill the missing values with something other than feature mean,
for example, with -999.
Or we can generate a new feature which will indicate that the value was missing.
This can be particularly useful for linear models.
We can also build the plot where on X axis,
we have a row index,
and on the Y axis, we have feature values.
It is convenient not to connect points with
line segments but only draw them with circles.
Now, if we observe horizontal lines on this kind of plot,
we understand there are a lot of repeated values in this feature.
Also, note the randomness over the indices.
That is, we see some horizontal patterns but no vertical ones.
It means that the data is properly shuffled.
We can also color code the points according to their labels.
Here, we see that the feature is quite
good as it presumably gives a nice class separation.
And also, we clearly see that the data is not shuffled here.
It is, in fact, sorted by class label.
It is useful to examine statistics with Pandas' describe function.
You can see examples of its output on the screenshot.
It gives you information about mean,
standard deviation, and several percentiles of the feature distribution.
Of course, you can manually compute those statistics.
In Pandas' nan type,
you can find functions named by statistics they compute.
Mean for mean value, var for variance,
and so on, but it's really convenient to have them all in once.
And finally, as we already discussed in the previous video,
there is value_counts function to examine
the number of occurrences of distinct feature values,
and a function is null,
which helps to find the missing values in the data.
For example, you can visualize nulls patterns in the data as on the picture you see.
So, here's the full list of functions we've discussed.
Make sure you remember each of them.
To this end, we've discussed visualizations for individual features.
And now, let's get to the next topic of our discussion,
exploration of feature relations.
It turns out that sometimes,
it's hard to make conclusions looking at one feature at a time.
So let's look at the pairs.
The best two here is a scatter plot.
With it, we can draw one sequence of values versus another one.
And usually, we plot one feature versus another feature.
So each point on the figure correspond to an object
with the feature values shown by points position.
If it's a classification task,
it's convenient to color code the points with their labels like on this picture.
The color indicates the class of the object.
For regression, the heat map light coloring can be used, too.
Or alternatively, the target value can be visualized by point size.
We can effectively use scatter plots to check if
the data distribution in the train and test sets are the same.
In this example, the red points correspond to class zero,
and the blue points to class one.
And on top of red and blue points,
we see gray points.
They correspond to test set.
We don't have labels for the test set,
that is why they are gray.
And we clearly see that the red points are mixed with part of the gray ones,
and that that is good actually.
But other gray points are located in the region
where we don't have any training data, and that is bad.
If you see some kind of discrepancy between colored and gray points distribution,
you should probably stop and think if you're doing it right.
It can be just a bug in the code,
or it can be completely overfitted feature,
or something else that is for sure not healthy.
Now, take a look at this scatter plot.
Say, we plot feature X1 versus feature X2.
What can we say about their relation?
The right answer is X2 is less or equal than one_minus_X1.
Just realize that the equation for the diagonal line is X1 + X2 = 1,
and for all the points below the line,
X2 is less or equal than one_minus_X1.
So, suppose we found this relation between two features,
how do we use this fact?
Of course, it depends,
but at least there are some obvious features to generate.
For tree-based models, we can create a new features like
the difference or ratio between X1 and X2.
Now, take a look at this scatter plot.
It's hard to say what is the true relation between the features, but after all,
our goal is not to decode the data here but to
generate new features and get a better score.
And this plot gives us an idea how to generate the features out of these two features.
We see several triangles on the picture,
so we could probably make a feature to each triangle a given point belongs,
and hope that this feature will help.
When you have a small number of features,
you can plot all the pairwise scatter plots at once
using scatter metrics function from Pandas. It's pretty handy.
It's also nice to have histogram and scatter plot before the eyes at
the same time as scatter plot gives you very vague information about densities,
while histograms do not show feature interactions.
We can also compute some kind of distance between the columns of
our feature table and store them into
a matrix of size number of features by a number of features.
For example, we can compute correlation between the counts.
It's the most common type of matrices people build, correlation metric.
But we can compute other things than correlation.
For example, how many times one feature is larger than the other?
I mean, how many rows are there such that the value of
the first feature is larger than the value of the second one?
Or another example, we can compute
how many distinct combinations the features have in the dataset.
With such custom functions,
we should build the metrics manually,
and we can use matshow function from
Matplotlib to visualize it like on the slide you see.
If the metrics looks like a total mess like in here,
we can run some kind of clustering like K-means clustering on
the rows and columns of this matrix and reorder the features.
This one looks better, isn't it?
We actually came to the last topic of our discussion, feature groups.
And it's what we see here.
There are groups of very similar features, and usually,
it's a good idea to generate new features based on the groups.
Again, it depends, but maybe some statistics
could collated over the group will work fine as features.
Another visualization that helps to find feature groups is the
following: We calculate the statistics of each feature,
for example, mean value,
and then plot it against column index.
This plot can look quite random if the columns are shuffled.
So, what if we sorted the columns based on this statistic?
Feature and mean, in this case.
It looks like it worked out.
We clearly see the groups here.
So, now we can take a closer look to each group and
use the imagination to generate new features.
And here is a list of all the functions we've just discussed.
Pause the video and check if you remember the examples we saw.
So, finally in this video,
we we're talking about the tools and functions that help us with data exploration.
For example, to explore features one by one,
we can use histograms,
plots, and we can also examine statistics.
To explore a relation between the features,
the best tool is a scatter plot.
Scatter metrics combines several scatter plots and histograms on one figure.
Correlation plot is useful to understand how similar the features are.
And if we reorder the columns and rows of the correlation metrics,
we'll probably find feature groups.
And feature groups was the last topic we discussed in this lesson.
We also saw a plot of sorted feature statistics and how it can reveal as feature groups.
Well, of course, we've discussed only a fraction of helpful plots there are.
With practice, you will develop and find your own tools further exploration.
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