This repository outlines a typical workflow for a professional machine learning project. The workflow includes loading the dataset, performing data preprocessing, training the model, and evaluating the results.
The first step involves loading the dataset into the environment. This can be done using various libraries such as Pandas for CSV files, SQLAlchemy for databases, or custom data loaders for other formats.
Data preprocessing involves several steps to prepare the data for model training. This step ensures the quality and suitability of the data.
Handle missing values by either removing them or imputing them with appropriate values.
Convert categorical variables into a numerical format using techniques like one-hot encoding or label encoding.
Scale the features to ensure they are on a similar scale, which helps certain algorithms perform better.
Split the dataset into training and testing sets to evaluate the model's performance on unseen data.
Choose a machine learning algorithm and train the model using the training data.
Evaluate the model's performance using appropriate metrics and the testing set.
Visualize the results using libraries like Matplotlib or Seaborn to interpret and present the findings effectively.