Abstract:
"Machine learning techniques are becoming increasingly popular around the world. However, when
considering Sri Lanka’s Tea industry; this area or its application is very limited. The aim of this
study is to use machine learning algorithms to predict the Revenue at Dessford Estate. The
objectives of this research are to Identify the main factors that affect revenue and identify how it
has impacted over time, building a model to Predict the revenue of the Business, identify the most
suitable model to predict the revenue at Dessford Estate and its relationship with the other variables
and lastly to identify the significant cost contributions and provide a roadmap or recommendations
to Dessford through the use of analytics.
The data was provided from year 2011 till 2021 from Dessford Estate with formal consent to be
utilized for this thesis. 7 Machine Learning Regression models were implemented for conduct the
analysis - Multiple Linear Regression, Ridge Regression, LASSO Regression, Elastic Net
Regression, Decision Tree Regression, XG Boost and K-Nearest Neighbor. Two Strategies were
followed to analyze the results of these respective Regression models. Out of the models
implemented the best 3 performing algorithms were Ridge Regression, LASSO Regression, Elastic
Net Regression.
Furthermore, Four Time Series Forecasting models were implemented to analyze and predict
Revenue based on past trends, seasonality of the data. Simple Exponential Smoothing (SES),
Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Moving Average
(SARIMA) and Holt Winters were analyzed. Out of which SARIMA was selected as the best
performing model due to having the least number of error values."
