9 Steps for Solving Data Science Problems

An outline of the actions you need to start today to address any problem with machine learning in your business. W Edwards Deming, an American statistician who was instrumental in developing the techniques for sampling that are employed in both the U.S. Department of the Census as well as the Bureau of Labor Statistics, once stated, "If you can't describe what you are doing as a process, you don't know what you're doing. While studying Machine Learning, I was constantly confronted with the issue that I needed to learn the precise order of the steps in every data science procedure.

Numerous resources describe the various steps in an extremely detailed way, but I couldn't find any source that could provide me with a close-up of the entire procedure. After having learned all the steps from various sources, I created the steps in this procedure, which comprise nine steps that I'd like to share with you. This is a brief introduction, and for further details on each step, it is recommended to study the details with the relevant literature. Before studying this article, you are expected to be acquainted with the machine learning methods and the associated literature. As examples, some applications that can be developed using machine learning include a Virtual Truck Customization App and websites as well as a Sw418 Login and Registration app.

Step 1: Problem Formulation

With experience in machine-learning algorithms, it's easy to forget that the goal of Machine Learning is solving problems by using data. It can be either predictive analytics, where the goal is to anticipate the outcome of events, or exploratory analytics, which attempts to answer the question about how something occurred. We employ Data Sciences not because we want to implement some complex neural networks that use TPUs. Still, rather we're trying to find answers to questions that will assist our business, our nation, and the planet to grow better. So, it's crucial to begin by defining the question you would like to be able to answer with your data.

 Step 2: Data Cleaning

When you have identified the issue assertion, it is crucial to cleanse the data. Some statistics show that more than 80 percent time, Data Science includes data cleansing. Data cleaning ensures that the data is correct or when spelling errors are present, as well as if an alternative method for entering data was used in different locations, like kgs or pounds in the weight column.

The most crucial subject in Data Cleaning is Missing Values Treatment. Based on the situation you have in mind, you can decide to eliminate any missing values or replace them with zero or the median or mean of other columns. Which method is best is open to debate. However, what can be helpful is looking at my business challenge to come to the right choice here.

 Step 3: Exploratory Data Analysis

 After data has been cleansed and sorted, it is crucial to analyze it through an eye-to-eye view. I typically look over the data types for the variables and then see how they compare to the data they are supposed to be. The second thing I perform is to examine the range of variables that interest me. I also observe how numerous categorical and numerical variables are present. The calculation of certain statistics, such as median, mean, and quartiles, will also help you gain some understanding of the information. Based on the situation, the data may be useful to find out whether some variables are linked.

 Multicollinearity and Autocorrelation tests from Statistics assist me in this. Most of the easy problems can be solved through a graph. When I was analyzing NASA Airfoil's data, I concluded from the graph that Linear Regression might not be the best machine-learning algorithm in this instance. My favorite library for the visualization of data is Seaborn, which is built upon Matplotlib. Therefore all functions that are part of the Matplotlib library are available.

Step 4: Data Preparation and Preprocessing

 Many Machine Learning algorithms, such as Support Vector Machines, require the preprocessing of data before they can be utilized. Coding categorical variables is also required before incorporating them into the models. sklearn has several built-in preprocessing classes, such as MinMaxScaler() or StandardScaler(). One Hot Encoding can be achieved using pandas' get dummies() function. This procedure may not be necessary based on how machine-learning algorithms work.

 Step 5: Feature Selection

 The data sets you receive will differ in size. One might only need some of the factors (or functions) to solve some questions in the business world. Consulting with an expert is the best option at this point. Based on their assessment, you can save time deciding what features to keep and which ones to eliminate within the model. Another option is to utilize functions built into your Machine Learning algorithms to see the importance of different features to explain the observed variability within the model.

The third option that is extremely scientific is to make use of Applied Machine Learning. This may seem counterintuitive since one may think, how do we apply ML when we intend to employ ML in our model? Principal Component Analysis can be used to discover important characteristics from the data.

Step 6: Model Development

 This method is the most popular in the world and comprises data science. The researcher will likely utilize a supervised method or an unsupervised algorithm based on the issue. Some algorithms are faster, and others are slower. Presently, Random Forests and Gradient Boosting Random Forests are well-known for solving the most challenging machine-learning problems. If you have the time and money, then using Neural Networks with TensorFlow is an excellent choice.

Step 7: Hyper parameter Tuning and Cross-Validation

 This is my absolute favorite aspect of the machine learning process. It is crucial to building the model in the most efficient way feasible. Both can be serious issues when it comes to developing models. Using incorrect techniques and parameters can complicate a generalization of the universe.

Utilizing Grid Search CV in sklearn lets, you simultaneously carry out cross-validation and hyper parameter tuning while creating ML models. The examples on the web of sklearn to demonstrate this method will help you understand the whole procedure. The use of tuned hyper parameters gives the ability to machine learning algorithms to operate optimally.

Step 8: Model Evaluation

 After tuning the hyper parameters and evaluating the accuracy scores, it's time to analyze the model thoroughly. Confusion Matrix Precision-Recall, Accuracy, and f-Score are the most important measurements to solve classification problems. This article and the introduction to KDNuggetsexplain the concepts in a clear way. Regarding the regression issue, R Square can effectively measure modeling evaluation. The majority of the time that I follow the seven steps above meticulously, my evaluation matrix gives me a positive answer to my model and the hyper parameters I choose to use.

Step 9: Communication

The final and most important step is to convey the outcomes from the algorithm for machine learning to the appropriate public in a language they can comprehend. Most of the time, the management will not be interested in studying AUC and the ROC Curve and AUC. They'd appreciate your advice or suggestions from a data scientist about the issues they raised.

 However, no matter how skilled a data scientist you are, If you cannot impact your target audience, your algorithm will remain on your computer and not be utilized within the actual world. Therefore, communicating the outcome is a vital step in data science. Data Science Process