  Algorithms are used in eCommerce product recommendation systems. These
systems just recently began utilizing machine learning algorithms due to the
development and growth of the artificial intelligence research community. This
project aspires to transform how eCommerce platforms communicate with their
users. We have created a model that can customize product recommendations and
offers for each unique customer using cutting-edge machine learning techniques,
we used PCA to reduce features and four machine learning algorithms like
Gaussian Naive Bayes (GNB), Random Forest (RF), Logistic Regression (LR),
Decision Tree (DT), the Random Forest algorithms achieve the highest accuracy
of 99.6% with a 96.99 r square score, 1.92% MSE score, and 0.087 MAE score. The
outcome is advantageous for both the client and the business. In this research,
we will examine the model's development and training in detail and show how
well it performs using actual data. Learning from machines can change of
eCommerce world.
