7 Key Steps To Build Your Machine Learning Model From Scratch

Data science, as perceived by most of the online courses and recent public discourse, has been around to help develop accurate models for prediction. The key areas within Data Science are focused on the development of models, that is, Artificial Intelligence, Machine Learning, and Deep Learning.

For fresher’s who are starting their journey in Data Science, the first thing they need to learn is the process of developing a Machine Learning model and interpreting them. So let’s try to understand how to build a machine learning model from scratch.

1. Problem definition: Any data analysis starts with setting up an objective that we want to achieve out of the model development exercise. These objectives can be in terms of hypothesis or target results in business metrics after using the models.

2. Data collection: Now, the data which can help solve the problem statement is gathered through different channels and sources. Best efforts are made to have accurate and timely data for the analysis.

3. Data wrangling: Data wrangling has many parts to it, including cleaning the data from missing and erroneous values, removing outliers, transforming the data, feature engineering, and other steps to make data ready for empirical analysis.

4. Exploratory Data Analysis (EDA): EDA step is a pre-model-analysis of descriptive and diagnostic nature where we use visualizations, distributions, frequency tables, and other techniques to understand the data relationships and make the choice of the right algorithm for desired analysis.

5. Machine Learning Algorithms: Now, we train, test, and validate algorithms with appropriate dependent and independent variables, with appropriate techniques from the set of supervised, unsupervised, and reinforcement learning algorithms.

6. Prediction and insights: The algorithms will quantify all the relationships and allow us to make predictions and derive insights from the model outputs. The model results need to be transformed back to business language and presented on the same scale as of original data.

7. Visualization and communication: The results need to communicate back to business executives or end-users for making decisions. The results need to be communicated in simple terms while not undermining the assumptions of probability and modeling techniques.

The process is what has been observed in most of the cases, but it does not limit the data science professionals to explore new ways to bring value out of their data. Exploration and being curious is the key to develop good models that derive business.

If you are a Data Science enthusiast who wants to explore various aspects of Data Science then check out our book on “Data Science for Business Professionals”.

The book focuses on the fundamental concepts of Data Science, such as Statistics, Machine Learning, Business Intelligence, Data pipeline, and Cloud Computing. By the end, you will be able to build Data Science solutions with ease.