Sales Prediction With Machine Learning

Project Code :TCPGPY332

Objective

The main goal of this analysis study is predicting the sales price and compare the algorithm which algorithm provide high accuracy. Finally select the best algorithm to predict the sales price at early stage.

Abstract

Analytical System requires integration of decision analysis and predictions. Most of the business organizations heavily depend on a knowledge base and demand prediction of sales trends. The accuracy in sales forecast provides a big impact in business. Data mining techniques are very effective tools in extracting hidden knowledge from an enormous dataset to enhance accuracy and efficiency of forecasting. The detailed study and analysis of comprehensible predictive models to improve future sales predictions are carried out in this research. Traditional forecast systems are difficult to deal with the big data and accuracy of sales forecasting. These issues could be overcome by using various data mining techniques. The various techniques and measures for sales predictions are described in the later part of the research work. On the basis of a performance evaluation, a best suited predictive model is suggested for the sales trend forecast. The results are summarized in terms of reliability and accuracy of efficient techniques taken for prediction and forecasting. The studies found that the best fit model is Support Vector Model Algorithm, which shows better result in sales prediction. 

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

HARDWARE SPECIFICATIONS:
  • Processor: I3/Intel
  • Processor RAM: 4GB (min)
  • Hard Disk: 128 GB
  • Key Board: Standard Windows Keyboard
  • Mouse: Two or Three Button Mouse
  • Monitor: Any
SOFTWARE SPECIFICATIONS:
  • Operating System: Windows 7+
  • Server-side Script: Python 3.6+
  • IDE: Jupyter Notebook
  • Libraries Used: Pandas, Numpy.

Learning Outcomes

  • About Python.
  • About Pandas.
  • About Numpy.
  • About Machine Learning.
  • About Artificial Intelligent.
  • About how to use the libraries.
  • Virtualization.
  • About model choosing.
  • About future outcomes for the stock prices.
  • About how to generate the predictions with python code.
    • Project Development Skills:
    • Problem analyzing skills.
    • Problem solving skills.
    • Creativity and imaginary skills.
    • Programming skills.
    • Deployment.
    • Testing skills.
    • Debugging skills.
    • Project presentation skills.
    • Thesis writing skills.

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