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Whenever a new project is assigned to a data science team they must be worried about doing some sort of boring stuff again and again. At this moment I am talking about writing the same code blocks, it’s like a vicious cycle where the person has to do the same monotonous work again and again. Isn’t it interesting to convert these tasks into automation and rather investing the time in some useful tasks such as:
- Hyperparameter Tuning
- Feature Engineering
- Model Selection
- Business Insights
In this article we will be discussing python functions to rescue us from the same boring stuff. We are going to deep into the Top 10 Python Functions to Automate the Steps in Data Science – let’s go without further a ado. In order to automate the data science process, we will start to discuss the basics of python function and machine learning and move into the actual automation process.
2. Function in Python
A function is nothing but reusable code that helps in performing various tasks. It helps the data scientist or programmer to decompose the problem into small chunks of different modules. Thus, Python functions help in the reusability of code when required by calling it. Python provides you with an ‘n’ number of in-built functions to accomplish your concerned tasks.
Syntax: Python Function
def name_your_function( parameters ): "function_docstring" function_suite return [expression]
Parameters have a positional nature by default, and we have to inform them in the same order in which they were defined.
Making a Function Call
A function is defined by:
- Giving it a name,
- Specifying the arguments that must be included in the function, and structuring the code blocks.
Once a function’s fundamental structure is complete, you can call it from another function or directly from the Python prompt to run it.
The process of automating the tasks of applying machine learning to real-world issues is known as automated machine learning. From the raw dataset to the deployable machine learning model, Automated machine learning covers the entire pipeline and is offered as an AI-based answer to the ever-increasing issue of machine learning applications. Automated machine learning’s high level of automation enables non-experts to employ machine learning models and techniques without having to become machine learning professionals.
Automating the entire machine learning process has the added benefits of:
- Providing simpler solutions,
- Faster generation of those solutions, and models that frequently outperform hand-designed models.
In a prediction model, Automated machine learning was utilized to compare the relative relevance of each factor.
4. Let’s compare to the standard approach
Data Scientists in a typical machine learning application have a collection of input data points to train with. The raw data may not be in a format that can be used by all algorithms. Data Scientists need to use the following to make the data suitable for machine learning:
- Proper data pre-processing,
- Feature engineering – feature extraction and feature selection.
Following these, they must choose an algorithm and optimize hyperparameters to improve their model’s prediction performance. Each of these phases might be difficult, making machine learning difficult to implement.
Automated Machine Learning substantially simplifies these tasks!
5. How to automate boring stuff and save time?
There is a saying doing a smarter way is more impactful than doing the hard way. You are a data scientist in XYZ company, you have been assigned to develop a product for your organization having x objectives. So, whenever you are designing the architecture of the data science project in your workspace; you have to do the following tasks such as importing data, data preprocessing, building machine learning models, performance measurement of models, deployment, and much more.
In a typical end to end project, there are many ways, 2 common ways are,
- You will be having a compressed data file.
- The data will be stored in a relational database having multiple tables/ documents/ files.
In both instances, you have to gather the data, perform data preprocessing, and built the model in which you have to write the same sorts of multiple lines of code.
In this blog we will be discussing the following in context to function:
- Focusing on Data and Training
- Performing EDA (Exploratory Data Analysis)
- Building Machine Learning Models
- Model Deployment
5.1 Focusing on Data and Training
If you are assigned a task to build a model the first thought comes to in mind:
Let’s concentrate on what is both and how it is significant:
1. Batch Learning
- The system cannot learn gradually in batch learning; it must be trained to utilize all the available data because this takes a long time and a lot of computing power, it’s usually done offline.
- The system is first trained, and then it is put into production, where it operates without having to learn anything new; it simply applies what it has learned. This is referred to as “offline learning.”
- Unless you want a batch learning system to learn about new data (such as a new type of spam), they must first train a new version of the system from scratch on the entire dataset (not just the new data), then terminate the old one and replace it with the new one.
- Moreover, the entire process of training, testing, and releasing a Machine Learning system can be automated, allowing even batch learning systems to adapt to changing conditions. Maintain current data and retrain a new version of the system when needed.
- Furthermore, training on the entire collection of data necessitates a significant amount of computer resources (CPU, memory, disc space, disc I/O, network I/O, and so on). It will cost you a lot of money if you have a lot of data and you automate your system to train from scratch every day. It may even be impossible to employ a batch learning method if the amount of data is enormous.
2. Online Learning
- In online learning, the system is progressively trained back to back giving it data instances, either individually or in small groups known as mini-batches. Each learning phase is quick and inexpensive, allowing the system to learn about new data as it comes in. For systems that receive data in a continuous stream and must adapt to change quickly or autonomously, online learning is ideal.
- Online learning methods can also be used to train systems on massive datasets that are too large to fit in the main memory of a single machine (this is called out-of-core learning). The algorithm loads a portion of the data, performs a training step on that data, and then continues the process until all of the data has been processed.
While building batch learning as mentioned above it has to be trained with available data to do so here is the below code.
Note: All CODE BLOCKS are BEST viewed in DESKTOP MODE, kindly switch for BEST experience!
As we have to work on available data so no such automation is required and it is a one time process, however, if you want to train the model progressively for example in the stock price sector you need to update the data in each instance that is a type of online learning as mentioned above:
5.2 Performing EDA (Exploratory Data Analysis)
Exploratory data analysis, or EDA for short, is a term coined by John W. Tukey for describing the act of looking at data to see what it seems to say. It assists data scientists in determining how to best manipulate data sources to obtain the answers they require, making it easier for them to find patterns, test hypotheses, and verify assumptions.
EDA is primarily used to examine what data can disclose outside of formal modeling or hypothesis testing tasks, and to gain a better knowledge of data set variables and their interactions. It might also assist you in determining whether the statistical techniques, you’re contemplating for data analysis are suitable.
The primary goal of EDA is to assist in the analysis of data before making any assumptions. It can aid in the detection of evident errors, as well as a better understanding of data patterns, the detection of outliers or unusual events, and the discovery of interesting relationships between variables. Exploratory analysis can be used by data scientists to guarantee that the results they create are accurate and appropriate to any targeted business outcomes and goals. EDA also assists stakeholders by ensuring that they are asking the appropriate questions. Standard deviations, categorical variables, and confidence intervals can all be answered with EDA. Following the completion of EDA and the extraction of insights, its features can be applied to more advanced data analysis or modelling, including machine learning.
There are 4 primary types of EDA:
This is the simplest method of data analysis where the data being investigated only has one variable. Because it is a single variable, it does not deal with causes or relationships. The basic purpose of the univariate analysis is to describe and detect patterns in the data.
2. Univariate with graphs
Single variable Visualisation to seek more information and patterns. As a result, graphical methods are required. Common types of univariate graphics include:
- Stem-and-leaf plots
- Box plots
Multivariate data arises from more than one variable. Multivariate EDA techniques generally show the relationship between two or more variables of the data through cross-tabulation or statistics.
4. Multivariate with graphs
Graphics are used to demonstrate relationships Other common types of multivariate graphics include:
- Scatter plot
- Multivariate chart
- Run chart
- Bubble chart
- Heat map
To do so we can create a python function:
In python, pandas have an in-built function to look and understand the attributes, sometimes it will be too clumsy to write each line of code and run it, here is how you can write a function to make work easier.
However, pandas_profiling and sweetviz are two libraries that perform exploratory data analysis with few lines of code. Also, once the EDA is done, data preprocessing and cleaning is initiated that will comprise of but are not limited to:
- Conversion of Categorical Data into Numerical ones
- Replacing missing values or unwanted values
- Removing Outlier
- Scaling or Normalization of data
NOTE: Business Context should be taken into consideration while performing the above measures.
5.3 Building Machine Learning Models
Machine learning (ML) is a sort of artificial intelligence (AI) that allows applications to improve their prediction metric over time without being expressly designed to do so. In order to forecast new output values, machine learning algorithms use historical data as input.
Building a machine learning model is a strenuous exercise, each time you have to start from scratch, with the use function you can simply pass whichever model you want to work on.
Whenever you are building a model each time you have to write the same few lines of code, this will be going to fetch extra seconds, these seconds could be used to improve model performance in different ways.
However, in the above model function, I am just returning the accuracy of each model, we can return other performance metrics also. Such as in the case of the classification model:
In the above scenario, as it is a classification model precision, recall and F1-score is returned as seen in the below output:
When estimating the likelihood of a given result, such as whether or not a customer would churn in 30 days, “prediction” refers to the output of an algorithm after it has been trained on a previous dataset and applied to new data. For each record in the new data, the algorithm will generate probable values for an unknown variable, allowing the model builder to determine what that value will most likely be.
The term “prediction” can be deceptive. In some circumstances, such as when utilizing machine learning to pick the next best move in a marketing campaign, it means you’re forecasting a future outcome. Other times, the “prediction” concerns, for example, whether or not a previously completed transaction was fraudulent. In that situation, the transaction has already occurred, but you’re attempting to determine whether it was legitimate, allowing you to take necessary action.
Why are Predictions Important?
Machine learning model predictions allow organizations to generate very accurate guesses about the likely outcomes of a query based on historical data, which might be about anything from customer attrition to possible fraud. These supply the company with information that has a measurable business value.
For example, if modelling predicts that a client is likely to churn, the company can reach out to them with tailored messaging and outreach to prevent the customer from leaving.
To do so we can create a python function
6. Model Deployment
It’s quite difficult for laymen to understand or infer the machine learning code and output. In this section, we will be discussing the function that will enable the deployment of the machine learning model.
Deployment is the process of integrating a machine learning model into an existing production environment to make data-driven business decisions. For doing so, we have to save our code file as your_file.pickle using python library pickle and joblib and save the column as a JSON file.
Henceforth, we have two create two python files that will be util.py file (a set of simple Python functions and classes that shorten and simplify common tasks)and app.py (contains the application’s code, where you create the app and its views) and later on you have to create a frontend using HTML, CSS, and JS.
However, if you want to pass categorical data that you can do using a dictionary.
7. Top 10 Python Functions to Automate the Steps in Data Science
Key Takeaways are
- Basics of Reusable Python Functions
- Automation Significance
- Batch vs Online Learning Understanding
- Exploratory Data Analysis Understanding
8. Benefits of employing Python functions
- Reducing code duplication
- Taking big problems and breaking them down into smaller chunks
- Increasing the code’s clarity
- Reusability of code
- Readability of code
- Information is being kept hidden.
With the continuous inflow of data in the world, in fact, all the sectors whether it be finance, sports, marketing, supply chain or social media, the task of data scientists have been increasing drastically and the challenges increase more if you are a solo data scientist in your firm. The development of machine learning products has become more complex than ever due to the increment of volume and varieties of data. Working with data is tedious work and doing the same sorts of code, again and again, can affect the quality.
With automation, the following benefits can be achieved:
- Time Management: You can easily segregate your time where to invest and how much, with the above codes you have to only pass several models, the other tasks can be automated.
- Trade-off: There is always a trade-off about how much time you want to invest in Exploratory Data Analysis as it is a never-ending task when the data is on a larger scale, with automation you can easily pass the features or call the function to do so.
- Business Insights: Time is money, with getting more minutes in your bucket you can drill down more into the data to get deeper business narratives or insights.
With the usage of the above code snippets, you can develop a package for the XYZ Team and that could be used by all of the members of that team and help in automation.
If you liked this Blog, leave your thoughts and feedback in the comments section, See you again in the next interesting read!
Until Next Time, Take care!
– By Swetank
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