Data Science Model Building Life Cycle
Introduction
When we come across a business analytical problem, without acknowledging the stumbling blocks, we proceed towards the execution. Before realizing the misfortunes, we try to implement and predict the outcomes.
But do those outcomes reveal the strategies to decode the problems?
The answer is NO. We can’t come up with a solution having zero significance in business understanding. To improve the quality of the product, create market strategies, establish brand perceptions, and upgrade customer satisfaction, we have to demolish the complications.
At the end of this article, you will get to know about the problem-solving steps involved in the data science model-building life cycle. Further, you can foretell more meaningful solutions that help the organization in productivity growth.
Table of Content
- Problem Definition
- Hypothesis Generation
- Data Collection
- Data Exploration/Transformation
- Predictive Modelling
- Model Deployment
- Key Takeaways
Let’s understand every model building step in-depth,
Data Science is the process of extracting meaningful insights from the enormous amount of data. Data-driven science comprises of statistics, pre-defined scientific functions, analytical methodologies, and visualization techniques to deliver a message.
The data science model-building life cycle includes some important steps to follow. If you are anxious about developing the data science model, then just stick to the following steps.
1. Problem Definition
The first step in constructing a model is to understand the industrial problem in a more comprehensive way. In business, an issue doesn’t occur until a customer encounters any difficulty while utilizing the services.
To identify the purpose of the problem and the prediction target, we must define the project objectives appropriately. Therefore, to proceed with an analytical approach, we have to recognize the obstacles first. Remember, excellent results always depend on a better understanding of the problem.
2. Hypothesis Generation
Hypothesis generation is the guessing approach through which we derive some essential data parameters that have a significant correlation with the prediction target. Before data collection, we figure out the vital features that impact on the target variable.
Your hypothesis research must be in-depth, looking for every perceptive of all stakeholders into account. We search for every suitable factor that can influence the outcome. Hypothesis generation focuses on what you can create rather than what is available in the dataset.
Let’s take an example of loan approval prediction. We have to derive some critical data features that will approve the loan request of an applicant or not. Here we introduce some features:
- Income: If an applicant has a higher income, should get a loan easily.
- Education: Higher education results in higher income, so we can approve the loan request.
- Loan Amount: Lesser the amount, chances of loan approval are high.
- Job Type: Permanent or temporary
- Previous History: If the applicant has not repaid his last loan amount, then additional loan requests can’t be approved.
- Property Area: Looking for an applicant’s private property area (urban/rural).
- EMI: Lesser the EMI to pay higher the possibility of loan approval.
As you can see, we have structured some factors that might influence the loan approval request. Remember, the intelligence of the model would directly depend on the quality of your research.
3. Data Collection
If you have generated a hypothesis well, then you know the data you want to collect from respective sources. Data collection is gathering data from relevant sources regarding the analytical problem, then we extract meaningful insights from the data for prediction.
The data gathered must have:
- Proficiency in answer hypothesis questions.
- Capacity to elaborate on every data parameter.
- Effectiveness to justify your research.
- Competency to predict outcomes accurately.
To make effective decisions, we collect data from established resources. From the above image, we list down all the primary and secondary data collection methods from which you can gather the data. Data is collected on product requirements, services, ongoing trends, and customer feedback at innumerable instants.
4. Data Exploration/Transformation
The data you collected may be in unfamiliar shapes and sizes. It may contain unnecessary features, null values, unanticipated small values, or immense values. So, before applying any algorithmic model to data, we have to explore it first.
By inspecting the data, we get to understand the explicit and hidden trends in data. We find the relation between data features and the target variable. You may hear about some techniques: ‘Exploratory Data Analysis and Feature Engineering’, that come under data exploration.
Usually, a data scientist invests his 60–70% of project time dealing with data exploration only.
There are several substeps involved in data exploration:
Feature Identification:
- You need to analyze which data features are available and which ones are not.
- Identify independent and target variables.
- Identify data types and categories of these variables.
Univariate Analysis:
We inspect each variable one by one. This kind of analysis depends on the variable type whether it is categorical and continuous.
- Continuous variable: We mainly look for statistical trends like mean, median, standard deviation, skewness, and many more in the dataset.
- Categorical variable: We use a frequency table to understand the spread of data for each category. We can apply value_counts() or values_counts(normalize = True) function measure the counts and frequency of occurrence of values.
Multi-variate Analysis:
The bi-variate analysis helps to discover the relation between two or more variables. We can find the correlation heatmap in case of continuous variables and the case of categorical, we look for association and dissociation between them.
Filling Null Values:
Usually, the dataset contains null values which lead to lower the potential of the model. With a continuous variable, we fill these null values using the mean or mode of that specific column. For the null values present in the categorical column, we replace them with the most frequently occurred categorical value. Remember, don’t delete that rows because you may lose the information.
Feature Engineering:
We design more meaningful input data from the existing filtered data to strengthen the machine learning model. In this, we combine two data features, convert categorical parameters into continuous parameters, reduce the range of the continuous variable, and many more. There are some meaningful techniques of feature engineering, such as,
- Binning
- Log Transform
- One-Hot Encoding
- Scaling
- Grouping
- Outlier Treatment
- Feature Split
Read here for more information about feature engineering techniques.
5. Predictive Modeling
Predictive modeling is a mathematical approach to create a statistical model to forecast future behavior based on input test data.
Steps involved in predictive modeling:
Algorithm Selection:
When we have the structured dataset, and we want to estimate the continuous or categorical outcome then we use supervised machine learning methodologies like regression and classification techniques. When we have unstructured data and want to predict the clusters of items to which a particular input test sample belongs, we use unsupervised algorithms. An actual data scientist applies multiple algorithms to get a more accurate model.
Train Model:
After assigning the algorithm and getting the data handy, we train our model using the input data applying the preferred algorithm. It is an action to determine the correspondence between independent variables, and the prediction targets.
Model Prediction:
We make predictions by giving the input test data to the trained model. We measure the accuracy by using a cross-validation strategy or ROC curve which performs well to derive model output for test data.
6. Model Deployment
There is nothing better than deploying the model in a real-time environment. It helps us to gain analytical insights into the decision-making procedure. You constantly need to update the model with additional features for customer satisfaction.
To predict business decisions, plan market strategies, and create personalized customer interests, we integrate the machine learning model into the existing production domain.
When you go through the Amazon website and notice the product recommendations completely based on your curiosities. Same as Amazon, Netflix gives you the movie’s suggestion based on your watching history and several interests. You can experience the increase in the involvement of the customers utilizing these services. That’s how a deployed model changes the mindset of the customer and convince him to purchase the product.
7. Key Takeaways
By reading the article, we summarize the following points:
- Understand the purpose of the business analytical problem.
- Generate hypotheses before looking at data.
- Collect reliable data from well-known resources.
- Invest most of the time in data exploration to extract meaningful insights from the data.
- Choose the signature algorithm to train the model and use test data to evaluate.
- Deploy the model into the production environment so it will be available to users and strategize to make business decisions effectively.
Final Roadmap
Hey everyone!
I’ve been getting a lot of DMs for guidance, so decided to take action on it. I’m excited to help folks out and give back to the community via Topmate. Feel free to reach out if you have any questions or just want to say hi!
That’s all folks,
See you in my next article.
