– Collect relevant data for your problem. – Use quality data and address missing values. – Explore and analyze the dataset using statistical and visual methods. – Gain insights into the distribution, relationships, and patterns within the data
Clean the data by handling outliers, missing values, and irrelevant features. – Convert categorical variables into a format suitable for machine learning. – Split the dataset into training and testing sets to evaluate the model's performance.
– Select a suitable machine learning algorithm based on the problem type (classification, regression, clustering). – Split the data into features and labels, and train the model using the training set.