8 Steps in Your First Machine Learning Project

Data Collection

Gather relevant data from various sources

Ensure data quality and completeness

Data Preprocessing

Clean and transform data for analysis

Handle missing values, outliers, and categorical data encoding

Model Selection

Choose an appropriate machine learning algorithm

Consider problem type, data characteristics, and objectives

Model Training

Train the model using a labeled training dataset

Optimize model parameters to improve performance

Model Evaluation

Assess model performance with evaluation metrics

Validate against a separate test dataset to check generalization

Deployment

Implement the trained model in a production environment

Create an interface for real-time predictions or decision-making

Monitoring and Maintenance

Continuously monitor model predictions in real-world use

Update the model with new data

Read the Ultimate Guide to Become a Machine Learning Expert