Analysis on the impact of trade in Ghana
This project aims to dive into the impact of trade(import and export) on the growth of the manufacturing industry in Ghana. We would be using a case study of its impact on 1D1F( 1 district and 1 factory) initiative in Ghana.
We gathered the following problems regarding the import and export of agricultural products in Ghana.
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The location of plantations. Some farm plantations are very far from factories that can process the raw materials into finished goods for export. So, a substantial quantity of the raw materials get rotten
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There are insufficient factories in areas that grow the raw materials that are in high demand
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There are bad road networks in areas with plantations
This is an effort to explore the impact of trade on the manufacturing sector and its development on Ghana.
Our main hypothesis is that trade impacts negatively on the growth of the manufacturing industry in Ghana.
This hypothesis was motivated by the assumption that there are a lot of goods being imported into Ghana rather than goods being exported. Hence, the manufacturing industry of Ghana is not growing and this negatively affects industrialization in Ghana.
-Questions
To be able to test the hypothesis, a few questions were asked. This will help in gathering information for data analysis.
Which raw materials are more available in which districts?
Which factories are fully operational in which districts?
Which goods are imported most into Ghana?
Which goods are exported most out of Ghana?
Which countries are more goods imported into Ghana?
What was the highest amount paid for goods exported from Ghana?
What is the highest number of package of goods exported from Ghana?
What is the highest number of packages of goods imported into Ghana?
What is the highest amount paid for goods imported into Ghana?
Was the number of exports increasing over the years?
Was the number of imports increasing over the years?
-ANALYSIS
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Jupyter notebook was used to perform analysis on the data
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Power BI was used for visualisations
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Azure Machine Learning studio was used to create a predictive model for 1D1F