A comparison of a performance of various
machine learning models to predict the sales components
is presented in this paper. The general aim of this thesis
was to find a suitable machine learning model that can fit
and well forecast the sales components before they
happen and thus create the purchase orders before the
product runs out of stock or sold. The dataset used in the
thesis is from a product supplier consisting of product
sales data for about a thousand assorted products on a
time of three years. Firstly, a Literature review used to
find suitable machine learning algorithms and then based
on the results obtained, an experiment was performed to
evaluate the performances of machine learning
algorithms. Results from the literature review shown that
regression algorithms namely Supports Vector Machine
Regression, Ridge Regression, Gradient Boosting
Regression, and Random Forest Regression are suitable
algorithms and results from the experiment showed that
gradient boosting has performed well than the other
machine learning algorithms for the chosen dataset, The
range of supervised and unsupervised algorithms is
provided. The different models were compared using the
performance metrics such as mean squared error (MSE),
Mean Absolute Error (MAE) and Root Squared (R^2)
called coefficient of determination as well based on the
dataset’s values against the predicted values. The results
shows that the Gradient boosting have shown the highly
fitting capability and as well with the highly accuracy
compared to other Machine learning models. To conclude
After the experimentation and the analysis, the Gradient
boosting algorithm has been performed well when
compared with the performances of the other algorithms
and therefore, gradient boosting is chosen as the optimal
algorithm for performing the sales forecasting of the sales
components at Maison Gil Lt.
Keywords :
Time Series Forecasting, Sales Forecasting, Mean Absolute Error