This is a personal project with objectives of:
- Investigating the factors influencing adoption of innovation among farmers(hopefully generalizable). This will be achieved through Exploratory Data Analysis
- Predicting Farmers' likelihood to adopt innovation. A model will be trained with the available data to achieve this.
- Assign individual farmers to various adoption clusters(Innovators....Laggards). Clustering will come into play, here.
This project is intended to be revisited quarterly, as more data becomes available, or better still deployed in order to make use of dynamic data.
The data for this work was extracted from various data sets collected primarily by students and staff of the Agricultural Extension and Management department of Federal College of Forestry, Jos, Plateau state, Nigeria, in their academic researches in various fields. It is also important to note at this point that, some of these data(definitely, more than one source) was not geared towards innovation adoption, hence, additional but non-intrusive work had to be done on the data to make it usable and relevant.
In summary, the data was explored, visualized, and a model was trained with above average performance. The farmers who adopted were also clustered(attempted) into 4 groups. We shall attempt to deploy the model and see how it works on dynamic data.