FORECASTING THE TIME DELAY IN DELIVERY OF PHARMACEUTICAL PRODUCTS

Supply chain management system is a centralized system which controls and plans the activities involved from production to delivery of a product. Disruption in treatment and loss of life occurs due to delay in delivery of pharmaceutical products. The objective is to do a model using Machine learning algorithms to determine: Classification to predict which product will be delayed and Regression shows how much time it will be delayed exactly. This study will use publicly available supply chain data which helps to identify primary aspect of predicting whether HIV drugs are delivered in time or not. It will then use these factors to predict how long delays are likely to be, thus allowing HIV/Supply Chain program managers to know details of the products which are going to be delayed and quantify the exact delay. and how much it will be delayed. so that they can take mitigating action to save lives and avoid additional supply chain costs. We will use Machine learning prediction model to predict which product will be delayed and regression model shows how much time it will be delayed exactly

Fortune of smart-phones by A model recommendation

In recent market, there are several cell phones available, Smart phones differ based on their Operating system. Here we are going to do a comparison between two Operating systems i.e., “IOS and ANDROID”. The comparison starts including the basic features of smart phones. The features are varying from each other. Some of them are categorical and some are numerical. According to these data, classify the smart phones using machine learning classification model. After the classification we are going to analyse which operating system-based Smartphone will be taken for further classification. A “Recommendation system”, will be designed which recommend a better smart phone to the customer. In market a lot of smart phones are available, which are of different companies but with same cost. From classification model we will find which set is more affordable with good combination of features. Further Recommendation model will help us to find which model will be the best model according to the customer requirement and budget. On the basis of customer requirement that is what are the features and price of the phone our model is going to predict which model will be more suitable and gives the solution in a form of recommendation. It will give us the exact phone, which is having all the features and also pocket friendly