ADDIS ABABA UNIVERSITY SCHOOL OF GRADUATE STUDIES COLLEGE OF NATURAL SCIENCES DEPARTMENT OF COMPUTER SCIENCE Predictive Model for ECX Coffee Contracts Frehiwot Mulugeta Deressa A THESIS SUBMITTED TO THE SCHOOL OF GRADUATE STUDIES OF ADDIS ABABA UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE October 2014 Addis Ababa ADDIS ABABA UNIVERSITY SCHOOL OF GRADUATE STUDIES COLLEGE OF NATURAL SCIENCES DEPARTMENT OF COMPUTER SCIENCE Predictive Model for ECX Coffee Contracts Frehiwot Mulugeta Deressa Advisor: Sebsibe Hailemariam(Ph.D) Approved by Board of Examiners: Name Signature 1. Sebisbe Hailemariam (Ph.D), Advisor __________________ 2. Yaregal Assabie(PhD), Examiner __________________ 3. Dida Midekso (PhD), Examiner ___________________ Acknowledgement First and foremost, I would like to express my deepest gratitude to my advisor Dr. Sebsibe Hailemariam for his continuous support and professional guidance throughout this thesis work. I would like to extend my thanks to staff of ECX especially to market data specialists for their cooperation. My deepest gratitude goes to my family who were always motivating me to work hard. I would also like to thank my friends for their support and companionship. i Table of Content Acknowledgement ........................................................................................................................... i List of Figures ................................................................................................................................... v List of Tables ................................................................................................................................... vi Acronyms ....................................................................................................................................... vii Abstract ......................................................................................................................................... viii CHAPTER ONE INTRODUCTION ............................................................................................................................... 1 1.1. Statement of the Problem ............................................................................................................ 9 1.2. Significance of the Study ............................................................................................................... 9 1.3. Objective of the Study ................................................................................................................ 10 1.3.1. General Objective ............................................................................................................... 10 1.3.2. Specific Objectives .............................................................................................................. 10 1.4. Scope and Limitation................................................................................................................... 10 1.5. Research Methodology ............................................................................................................... 11 1.5.1. Literature Review ................................................................................................................ 11 1.5.2. Data Mining Process Model ................................................................................................ 11 1.5.3. Tools .................................................................................................................................... 13 1.6. Organization of the Thesis .......................................................................................................... 13 CHAPTER TWO LITERATURE REVIEW AND RELATED WORK .................................................................................. 14 2.1. Overview of Data Mining ............................................................................................................ 14 2.1.1. The Data Mining Task .......................................................................................................... 14 2.1.2. Data Mining Process Models............................................................................................... 16 2.2. Machine Learning ........................................................................................................................ 18 2.3. Artificial Neural Networks ........................................................................................................... 18 2.3.1. Advantage of using Artificial Neural Network .................................................................... 19 2.3.2. Taxonomy of Neural Network Architecture ........................................................................ 20 2.3.3. Applications of Neural Networks ........................................................................................ 23 2.4. Function Approximation by Neural Networks ............................................................................ 23 2.5. Multilayer Perceptron Neural Network Model ........................................................................... 24 2.6. Radial Basis Function Neural Network ........................................................................................ 24 2.7. Related Work .............................................................................................................................. 26 2.8. Summary ..................................................................................................................................... 29 CHAPTER THREE DATA ANALYSIS ............................................................................................................................. 31 3.1. Business and Data Understanding .............................................................................................. 31 3.1.1. Data Collection .................................................................................................................... 32 3.1.2. Descriptive Data Visualisation ............................................................................................ 32 3.1.3. Data Preparation ................................................................................................................. 33 3.1.4. Attribute Selection .............................................................................................................. 34 3.1.5. Description of the Selected Attributes ............................................................................... 34 3.1.6. Data Cleaning ...................................................................................................................... 35 3.1.7. Selecting Best Contract ....................................................................................................... 35 3.1.8. Relationship Analysis .......................................................................................................... 38 3.1.9. Scatter plot and Scatter Matrix ........................................................................................... 38 3.1.10. Correlation Coefficient Analysis .......................................................................................... 41 3.2. Data Preparation for Analysis ..................................................................................................... 45 CHAPTER FOUR DESIGN OF THE SYSTEM ................................................................................................................ 47 4.1. Architecture of the System ......................................................................................................... 47 4.2. Model Construction .................................................................................................................... 48 4.2.1. Reference Contract Selection ............................................................................................. 48 4.2.2. Merging Data ...................................................................................................................... 48 4.2.3. Data Transformation and Data Division .............................................................................. 48 4.2.4. ANN Model Parameterization ............................................................................................. 53 4.2.5. Multilayer Preceptor Neural Network ................................................................................ 53 4.2.6. Radial basis Function ........................................................................................................... 58 4.2.7. Model Evaluation ................................................................................................................ 59 iii 4.3. Prediction .................................................................................................................................... 60 4.3.1. Data Normalization ............................................................................................................. 60 4.3.2. Coffee Contracts Price Prediction ....................................................................................... 60 CHAPTER FIVE EXPERIMENT ................................................................................................................................. 62 5.1. Data Set ....................................................................................................................................... 62 5.2. Experimental Setup ....................................................................................................................