asuane-coda/FinalYearSalesPredictionProject

This project created prediction models to predict the sales of medical laboratory products in a medical laboratory in Nigeria. # FinalYearSalesPredictionProject This project created prediction models to predict the sales of medical laboratory products in a medical laboratory in Nigeria. Data source: Local Excel file Tools used: Jupyter notebook Libraries used: Pandas for exploratory data analysis, Numpy for mathematical equations, Matplotlib for graph plots, Models used: Random Forest Regressor, Support Vector Regressor, Extreme Gradient Boost, MultiLayer Perceptron, Ensemble model What I did: -Data cleaning involved using replacing missing values, dropping unnecessary columns, encoding values -Plotted graphs of the products using matplotlib -Split the dataset into test and train dataset -Built models based using the train dataset and used the test dataset to evaluate the models -Got the RMSE, MAE and MSE values -Used Gridsearch to optimize the parameters of the models -Built an ensemble using XGBoost as the meta model