Python Machine Learning

Python Machine Learning

Python Machine Learning : Python is one of the most popular programming languages for machine learning.

It is easy to use and read, and it has many powerful packages that could simplify our code and analysis.

If you are not familiar with coding or machine learning, Python would be a good choice to start with. In this post we will go over some basic machine learning tools in Python.

        Firstly, we need to get familiar with some basic scientific Python libraries. When dealing with data analysis, the initial step requires us to import our data from some excel or csv files, ‘pandas’ would be a helpful tool here, as we could easily select data, edit data and plot data using the features of ‘pandas’. 

We may also need to do some operations between data, such as multiplying some arrays, or matrices, to get inner product or other product, then ‘numpy’ would be a strong tool to simplify your code. It includes almost all matrix operations we could learn from the textbook including matrix multiplication, matrix inverse, pseudo inverse and SVD. It could also help us generate random numbers. 

Another tool is ‘matplotlib’, which is a library that could help us visualize the data you have, ‘seaborn’ is an advanced library based on ‘matplotlib’ which allows us to draw higher level attractive and informative statistical graphics.   

        After getting familiar with basic Python libraries, we could start to use other packages designed for machine learning algorithms. The most well-known and widely used package is ‘scikit-learn’ (sklearn). It is a standard machine learning package and it is simple and efficient to use if we want to do data analysis with machine learning. We know that preprocessing data is necessary since some data might have different scales. 

Standardization might be a common way for data preprocess to get a standard normally distributed data set. ‘sklearn’ has the preprocessing tool ‘StandardScaler’ that allows us to standardize data, and it could also rescale data in our desired range if we use ‘MinMaxScaler’. After we preprocess data. We might want to split out the train set and test set, which can be done by ‘train_test_split’. 

We can also define our test set size and random state in this step. If we want to do model selection, ‘cross_validate’ would be helpful. The machine learning algorithms we choose for analysis depend on our goals, but no matter if we want to do regression analysis or classification, ‘sklearn’ has the corresponding package that could support our analysis. For example, if we want to fit a linear model, the ‘linear_model’ module could meet our requirements. 

We could use the standard OLS to achieve goals, or we could apply lasso or ridge with our choice of regularization parameter. Another package I would recommend is ‘statsmodels’ (sm). It is a statistical package but could also be used as a machine learning package in Python. 

When we fit a linear model using the ‘sm’ regression tools, it will automatically generate a detailed report, including R square, F-statistic, log likelihood and AIC. It will also include the parameter results including the standard error, t statistics, p value and the confidence interval. ‘sm’ also has a time series analysis section ‘tsa’ which allows us to fit our data with different time series models. The time series report is also very detailed. 

We could also easily check for models by generating the auto correlation function and partial autocorrelation function. In addition to the above two packages. There are many other advanced tools in Python. Such as ‘keras’. Which could allow you to build a neural network easily. We cannot go over each package. But the more we learn from machine learning and Python. The more we could get familiar with the features of different packages. And how we can apply machine learning in the financial market.   

        In conclusion, machine learning with Python is very popular in today’s financial market. The basic machine learning tools in Python include ‘numpy’, ‘pandas’, ‘matplotlib’, ‘scikit-learn’ and ‘statsmodels’. We need to continue our learning journey and use the power of Python to achieve our goals using machine learning.

Python Machine Learning

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