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A FMCG company has entered into the instant noodles business two years back

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A FMCG company has entered into the instant noodles business two years back. Their higher management has notices that there is a miss match in the demand and supply. Where the demand is high, supply is pretty low and where the demand is low, supply is pretty high. In both the ways it is an inventory cost loss to the company; hence, the higher management wants to optimize the supply quantity in each and every warehouse in entire country. Goal & Objective: The objective of this exercise is to build a model, using historical data that will determine an optimum weight of the product to be shipped each time to the warehouse. Also try to analysis the demand pattern in different pockets of the country so management can drive the advertisement campaign particular in those pockets. This is the first phase of the agreement; hence, company has shared very limited information. Once you are able to showcase a tangible impact with this much of information then company will open the 360 degree data lake for your consulting company to build a more robust model. File: Data.csv Target variable: product_wg_ton 1) Introduction of the business problem 4 a) Defining problem statement b) Need of the study/project c) Understanding business/social opportunity 2)Data Report 2 a) Understanding how data was collected in terms of time, frequency and methodology b) Visual inspection of data (rows, columns, descriptive details) c) Understanding of attributes (variable info, renaming if required) 3) Exploratory data analysis 10 a) Univariate analysis (distribution and spread for every continuous attribute, distribution of data in categories for categorical ones) b) Bivariate analysis (relationship between different variables , correlations) a) Removal of unwanted variables (if applicable) b) Missing Value treatment (if applicable) d) Outlier treatment (if required) e) Variable transformation (if applicable) f) Addition of new variables (if required) 4) Business insights from EDA 4 a) Is the data unbalanced? If so, what can be done? Please explain in the context of the business b) Any business insights using clustering (if applicable) c) Any other business insights 1. Model building and interpretation. a. Build various models (You can choose to build models for either or all of descriptive, predictive or prescriptive purposes) b. Test your predictive model against the test set using various appropriate performance metrics c.Interpretation of the model(s) 10 2. Model Tuning a.Ensemble modelling, wherever applicable b. Any other model tuning measures(if applicable) c. Interpretation of the most optimum model and its implication on the business