Data Science Project lifecycle, A step by step Approach
Data science projects are not only getting the data & creating a Machine learning model. For a perfect data science project, you need to perform several tasks & combinedly all the performing tasks are known as the data science project lifecycle as a whole. As a data scientist, you need to know these steps for a complete data science lifecycle project. So, what are they?
In this article, we’ll discuss all the steps you need to know for completing a data science project perfectly.
- Get a proper idea about the problem or what you want to do.
- Get data
- Exploratory analysis for insights
- Preprocessing or Feature Engineering
- Select the best Machine learning Model
- Evaluate the model
- Launch the model into production
Get a proper idea about the problem:
Without getting a proper idea about the data or the problem you can’t move ahead. If you do so data will not give you a solid solution. A dataset may have so many features that will say a lot about the final output that you want if you don’t have a solid idea you can’t get the perfect outcome.
For example, you got a dataset of real estate business, the dataset has lots of features like location, area of house, avg. area of rooms, No. of a bedroom, the price per sqrt feet, and so on. Without a proper idea about all these features, you can’t compete with a good model.
The second step is to get data. If you are working for a company you may get company data for the project else you may get data from a different online open-source platform or by conducting online or offline surveys. Apart from that, you can collect data by web scraping. Most of the time you will get raw data from these sources.
The third step is to make some analysis of the available data. This section is about to find the information about the data like the number of features the dataset has, the null values, types of data in the set & some statistical information about the data like min, max, mean, percentiles & so on, values of each data features. Sometimes the analysis is to perform by visualizing the data through some bar-plot, line-plot, scatter-plot, etc. This provides a solid idea about what the data wants to say.
Preprocessing or Feature Engineering:
This is one of the most important parts of creating a robust Machine Learning model. In a real-world dataset, there will be a lot of missing values in the dataset as many people won’t like to share some of their personal information, this missing value should be removed or be filled up with some other data based on others information. Aside from that, there will be some categorical variables like male, female, religions & so on. Machine learning algorithms are not capable with these types of categorical data so it needs to convert into numeric form. This missing value imputation, convert category into numeric data are done through data preprocessing or feature engineering. Not only these two, but they’re also are some other things to do like, variable transformation, outlier handling, feature scaling, etc.
Select an ML algorithm:
This is the most exciting part of a data science project. Here you need to select which type of machine learning algorithm you need depending on your problem either you will apply supervised learning, unsupervised learning, or reinforcement learning. Depending on your problem you need to select the type of learning & can perform fewer algorithms for the same problem for better understanding which is working better. For a supervised regression problem, you may perform, linear regression, lasso regression, logistic regression & so on.
As you’ve created the model now it’s time to evaluate how the model is performing. This step is done with some new data that the model hasn’t seen yet. Here you will find how the model will do in the production by finding the model accuracy or percentage of errors. In the earlier section, you’ve performed several ml algorithms & in this part, you need to evaluate all of them & find the optimal algorithm which is performing the best on the new data & this will be ready for production.
Launch the model to Production:
This is the last step you need to do. You’ve created the model & now it will go production & perform for the desire purposes. The launching can be done through amazon’s web service (AWS), Microsoft Azure, or using some other platform.
Remember a data science project can be very time-consuming. It can take a week, a month, or even a year. You may spend most of your time completing the project in preprocessing or feature engineering. Generally, 19/20% of the time goes with collecting the data, 9/10 % of the time goes for exploratory analysis, almost 60% time goes with feature engineering, creating training set & building model takes only 6/7% of the time & left takes other tasks to perform.