A data analysis project exploring global coffee prices using statistical modeling and time series forecasting techniques. The study applies methods such as STL decomposition, exponential smoothing, weighted moving average, and regression models to analyze historical price movements and predict future trends for major coffee companies.
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- Exploratory Data Analysis (EDA)
- Time Series Decomposition (STL)
- Exponential Smoothing (Single & Trend-adjusted)
- Weighted Moving Average
- Linear Regression Forecasting
- Model Evaluation via MAPE
/code/– R source code for analysis and modeling/data/– CSV file containing raw coffee price data/assets/– Visualization figures used in the report/report/– Final project report and slide deckREADME.md– You are here
Selected charts from the report:
| Price Distribution | EDA of Rating Grouping |
|---|---|
 |
 |
Additional visuals available in /assets/.
I'm a graduate student in Analytics with a strong interest in time-series modeling, financial forecasting, and simulation-based evaluation of investment strategies.
Feel free to connect via LinkedIn or email me at: [email protected]