Abstract:
Nowadays most business fields using many strategies to improve business profits. Most of
them used traditional methods. Therefore, those company‘s efficiency and profit goes to the
critical situation. So the improve efficiency of the company is a major requirement for
nowadays business platform. Using new technologies companies can improve their profit and
efficiency. Also companies can identify their sales life-cycle.
This sales prediction was carried for Alfred Edirisinghe (PVT) LTD which is a medium scale
tyre dealer in Colombo. Decision Tree, Association Rules and Naïve Bayes data mining
models were attempted for the prediction. The best algorithm was selected for each model.
Item Code, Item Type, Item Quantity, Item Value, Item Sold Date, etc. variables were used in
data mining process. Among those variables five variables were selected for the mining
process.
A sales data sample with 5000 records were provided by the client for the analysis. Out of the
5000 records 30% was used in the mining process. According to the predicting probabilities,
Decision Tree algorithm were performed 98.65%, Association Rules algorithm were
performed 100.00% and Naïve Bayes algorithm were performed 99.57%. Decision Tree
belongs to the lowest predict probability value. Therefore Decision Tree model was the worst
model. Association Rule model contains highest predicted value 100.00%. Therefore it was
the best model. Naïve Bayes model was also a good model. The Score results indicate that
Decision Trees and Naïve Bayes mining model has the best score 1.00 and followed by
Association Rule mining algorithm with score of 0.99. By considering score and target
population with predicting probabilities, Association Rule algorithm was the best one for
prediction process.
Data mining model was implemented using Association Rule algorithm. According to these
predicting results, the company can handle their imports optimizing the available resources;
storage, time, money. Therefore this research would benefit the Company to improve their
incomes.
