Teff Scarcity Prediction Model for Ethiopian Context Using Multiple Linear Regression
| dc.contributor.advisor | Ayalew Belay | |
| dc.contributor.author | Taye Mohammed | |
| dc.date.accessioned | 2025-09-05T23:08:17Z | |
| dc.date.available | 2025-09-05T23:08:17Z | |
| dc.date.issued | 2023-07 | |
| dc.description.abstract | Scarcity prediction is vital in avoiding economic and political disturbances from food resource scarcity. In addition, it allows for well-managing resources for maintaining and improving livelihood, population's quality of life, use of budget, reducing cost, boosting productivity, seeing potential resource conflicts in the early stage for more responsive mitigation, reducing wastage of resources, and contributing sustainable and reasonable growth. Hence, the purpose of this paper is to use multiple linear regression models to predict the scarcity of Teff resources in the context of Ethiopia. However, predicting Teff's scarcity based on factors like resource consumption, population growth, productivity, and other factors is a significant problem to address. Thus, in this thesis work, we present a system that predicts Teff scarcity. We use a multilinear regression approach to design the system. The teff scarcity prediction model consists of three components: the preprocessing components, the train-validate-test component, and the prediction component. The preprocessing part consists of data cleansing and data transformation. The train-validate-test consists of the training, validating, and testing data partition. The prediction part removes insignificant attributes and trains, validates, and tests the designed models. The preprocess component receives the raw dataset and performs data cleansing and transformation. The train-validate-test component performs partitioning into a train, validate, and test dataset. The prediction component predicts teff scarcity with the partitioned data and evaluates the model with the result. The experiment result shows scarcity prediction statistically based on mean absolute error, root mean squared error, and R squared values. Hence, the experiment produced a mean absolute error of 7%, a root mean squared error of 6.43%, and an R squared of 97.07%. The designed model can predict Teff scarcity with an accuracy of 97.07%. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/7389 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | Crop Production | |
| dc.subject | Population Growth | |
| dc.subject | Consumption | |
| dc.subject | Scarcity Prediction | |
| dc.subject | Multiple Linear Regression | |
| dc.subject | Root Mean Square Root Error | |
| dc.subject | R Squared Value | |
| dc.title | Teff Scarcity Prediction Model for Ethiopian Context Using Multiple Linear Regression | |
| dc.type | Thesis |