Steps for Machine Learning Project
2 min readJan 24, 2023
- Problem Statement: This step is about identifying the problem that needs to be solved using data science. It involves defining the goals and objectives of the project, as well as understanding the context in which the problem exists.
- Data Gathering and Data Validation: This step involves collecting the data that will be used for the project. It also includes validating the data to ensure it is accurate and reliable. The schema file is a blueprint that describes the structure of the data, and the good and bad folders are used to separate the data that is usable from that which is not.
- EDA: Exploratory Data Analysis (EDA) is the process of analyzing and understanding the data. It involves visualizing the data, finding patterns and insights, and identifying any potential issues or problems.
- Feature Engineering: This step is about preparing the data for analysis by cleaning, transforming, and normalizing it. Handling missing values, outliers, and encoding the data are some of the tasks that are performed in this step. MENTOS is a mnemonic that stands for Missing, Encoding, Transformation, Outlier, and Scaling.
- Feature Selection: This step is about selecting the best set of features to use for the project. Filter method is one of the techniques used to select the features.
- Model Training: This step is about building the model or method that will be used to make predictions or decisions based on the data. Linear Regression, KNN Regressor, Random Forest, Decision Tree, and AdaBoost are some of the common models used in data science projects.
- Model Evaluation: This step is about evaluating the performance of the model and determining how well it is able to solve the problem.
- Web Development Framework: This step is about developing the web-based interface of the project. Flask, Django, and FastAPI are some of the commonly used frameworks for building web-based applications.
- Deployment: This step is about putting the final model or method into production and making it available for use by stakeholders.
