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Sales forecasting is the process or method of evaluating future sales. Perfect sales estimation forecasts enable almost every company to make informed business important decisions and predict short-term and long-term performance. Companies will be able to base their forecasts on past sales data or information, comparisons between different industries, and the economic trends. Prediction of sales gives an idea about how a company should be able to manage its workforce, cash flow, and resources. It will be easy for established firms to predict future sales based on past business data. forecasts have been a base for most Newly founded companies to focus on market research and competitive strategies to forecast their future business. Moreover, predictive sales data is playing a crucial role in businesses when focussing on acquiring investment capital. Sales forecasts perform a vital role to make a number of decisions, such as hiring and resource management to goal-setting and budgeting. Sales forecasts can also be considered as a powerful motivation tool. Sales forecasts will allow us to fix vital issues while there's still time to rectify them. According to research, it is noted that firms with perfect sales forecasts are 10% more likely to grow their revenue for the next crucial year.
Why: Problem statement
Recently information technology in this century is reaching a high level where large-scale data can be processed and easily studied to make sense or meaning of data where the normal approach is no more effective. Now, corporates need an entire view of their consumers; without that information, they will be missing the competitive edge of the market. retailers have to create the best promotions and offer to meet its sales and marketing goals, else they will fail the most important opportunities that the current market has to offer. Most of the time it is hard for the retail firms to grasp or to get a clear idea of the market condition since their stores are at different geographical locations. Big Data applications such as python enable these organizations to use past data to give a better forecast and prediction for the future year’s sales. It also helps retailers with analytical insights, specifically analyzing customers' desired products in a particular store at different locations. In this paper, we analyze the data sets of the world’s largest retailers, Walmart Store to determine the business drivers and predict sales. We have made use of Anaconda and Python to gain new information about consumer behaviors and help Walmart’s marketing efforts and their data-driven strategies through visual analytical representation data.
How: Solution description
The 2018 sales data for WalMart has been collected for 1564 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store using Numpy and Pandas.
Linear Regression Model Algorithm:
Linear regressionis a basic and commonlyusedtype of predictiveanalysis. Theseregressionestimates areused toexplain the relationship between one dependent variable and one or more independent variables.
How is it different from competition
The major problem in the business market is that companies or organizations do not have the perfect future plans to develop their business. The unplanned way of making the business leads to the loss of business or other unwanted problems. I am going to choose and train the data using machine learning algorithms to predict sales. It is the perfect project for learning Data Analytics and apply machine learning algorithms to predict outlet production sales.We could put this model into production and turn it into an interactive that business users within the organization could use to forecast sales for any store or department for however many weeks they wanted.
In this paper, we study the usage of Anaconda and Python version 3.6 for sales prediction. The main goal of this paper is to consider important approaches using the above-said tools for sales forecasting. This method can be used to make sales predictions when there is a small quantity of data for specific sales time series when a new product is launched. A stacking approach for building regression has been discussed. The results explain that by using stacking ideas, we will be able to improve the performance of predictive models for sales or time series forecasting. Discovering problems through forecasting has a huge impact on business plans
Who are your customers
Corporate companies, Businessmen and all others who need to predict their future sales and business growth.