Big Mart Sales Prediction Machine Learning Project

The Big Mart Sales Prediction is a famous starter-to-intermediate level regression project in the machine learning (ML) area. Get one to one support for all research work under one stop. Our researcher’s team will help scholars to assist in all research circumstances if you are struck up with. The choice of the relevant topic that interests you will be shared under                      Big Mart Sales Prediction Machine Learning Project. Research proposal will be clearly stated in this a complete research methodology will be stated. Thesis writing team will frame out the best thesis for you we assure that it will improve your academic career.

 The aim of our work is to detect the sales of different Big Mart outlets. The following is a literate guide we use to define this project:

  1. Objective Definition

      We design a ML framework to detect sales for every item at various Big Mart stores.

  1. Data Collection

      Majorly, this type of work offers us a dataset that has information about the products, their properties and previous sales representations.

  1. Data Exploration & Cleaning
  • Interpreting the Data: View the first few rows, data types and general statistics for better understanding of our project.
  • Missing Values: We find and remove the lost values which are depending on the state of our data.
  • Outliers: To forecast the errors and outliers we visualize scattered data using box plot and histograms.
  1. Exploratory Data Analysis (EDA)
  • Univariate Analysis: Understanding the dispersion of our individual variables, specifically in aim variable like sales.
  • Bivariate Analysis: By inspecting the relationship between the predictors and the key variable we find sales across various stores in type and size.
  • Correlation Analysis: We examine how variables are related to each other.
  1. Feature Engineering
  • Encoding Categorical Variables: For transforming the categorical variables into numerical format we utilize approaches such as one-hot encoding and label encoding.
  • Feature Creation: Generating latest features based on traditional ones, we derive a “product_age” property when we get details about the product was firstly innovated.
  • Scaling: We normalize and standardize numerical features to handle them in relevant measurement.
  1. Model Selection

Here are the following methods we consider for regression task,

  • Linear Regression
  • Decision Trees and Random Forest
  • Gradient Boosting Machines like XGBoost and LightGBM.
  • Neural Networks for difficult datasets.
  1. Training & Validation
  • Data Splitting: We divide the data for instruction and evaluation sets and when a potential test set is offered.
  • Framework Training: In this step we instruct the chose model into our training data.
  • Model Validation: For evaluating framework’s efficiency we use the validation set.
  1. Model Evaluation
  • Regression Metrics: The metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R^2 and Mean Absolute Percentage Error (MAPE) are assist us in the model validation.
  • Residual Analysis: We check the residuals by making sure our assumptions of regression are met.
  1. Optimization & Hyperparameter Tuning
  • Grid Search & Random Search: To identify the optimal configuration for our frameworks we test with several hyperparameters.
  • Feature Importance: After modelling we evaluate which features are to make detections. By this we offer understanding and simplifying our framework by discarding the unessential features.
  1. Deployment

When the objective involving in developing a necessary product:

  • We create a web and mobile application for Big Mart managers to input product information and receive sales detections.
  • Basically to make sure the predictions results accurate we retrain the model on the latest data.
  1. Feedback & Continuous Learning
  • For scale the accuracy over time we monitor the model’s predictions.
  • To find the possible field of enhancement we collect review from customers.
  1. Conclusion & Future Work
  • We file our findings, techniques, faced challenges and learnt lessons in this work.
  • Explain potential future improvements like incorporating time-sequences detections when we get series of sales data and combining external factors like vacations and promotions.


  • Ensembling: We integrate detections from various frameworks to significantly boost accuracy in our project.
  • Temporal Dynamics: When the data life extended for many years we consider the temporal dynamics in sales like patterns and seasonality.
  • Domain Skills: To gain some insights we collaborate with domain masters who can guide us in feature engineering and model understanding.

            While finishing our project we gain deep understanding of regression methods, feature engineering, and the limitations which associated with detecting sales depends on product and store attributes.

Big Mart Sales Prediction Machine Learning Project Thesis Ideas

Almost all the research aspects will be covered by us which you may find difficult to handle, while proper explanation will be given. Thesis editing and formatting services is also available so if you are struck up anywhere contact us.

Big mart sales prediction machine learning project Ideas

Have a look at our projects we have worked with.


  1. BMSP-ML: big mart sales prediction using different machine learning techniques


Linear regression, Machine learning, Random Forest, Ridge regression, Sales prediction

In our paper, various ML approaches are utilized to interpret the data and forecast the sales. Our approach obtains the data by utilizing feature engineering and assigning missing values to improve the quality of data. We compared several methods such as random forest, ridge regression, linear regression, and decision tree. As a result, Random Forest method achieved greater outcomes than others.

  1. Predictive Analysis for Big Mart Sales Using Machine Learning Algorithms


Polynomial Regression, Xgboost Regression

            Our paper demonstrated that, by employing ML methodologies, vendors can predict the future sales through the use of finding data. Here we have constructed a prediction framework for the forecasting purpose in BigMart sales through the utilization of various methods including Xgboost, Linear regression, Polynomial regression, and Ridge regression. We conclude that, our suggested framework provides highest end results than other previous frameworks.

  1. Fusing Clustering and Machine Learning Techniques for Big-Mart Sales Predication


Data mining techniques, reliability

            Through the integration of clustering and ML approaches, clustering related prediction model is proposed in our project to forecast the sales. Firstly, we split the dataset into groups by utilizing clustering method. After that, prediction model for every group is trained by employing ML methods. Various ML techniques like Generalized LR, DT, and Gradient Boosted Tree and some clustering methods like Self Organizing Map (SOM) and K-mean clustering are utilized.

  1. Comparative Analysis of Regression Algorithms used to predict the Sales of Big Marts



            Different ML regression methods like SMO regression, simple LR, LR, additive regression, multi-layer perceptron, RF, and M5P are compared in our research. From that we have to find out the optimal method for forecasting the BigMart sales and have to discover the method which has the largest correlation coefficient value and the least values of mean absolute error, relative absolute error, root mean squared error, and root relative squared error.

  1. Machine Learning for Sales Prediction in Big Mart


Gradient Boosting, Predictive Analytics

            Our article stated that, for efficient inventory administration, supply chain management and revenue maximization, accurate sales forecasting is very important. We carried out a comparative analysis for several ML regression techniques to forecast the BigMart sales. To detect the precise sales forecasting model, we compared the efficiency of methods like Linear Regression, Decision Trees, Random Forests, Gradient Boosting, and Neural Networks.


  1. Big Mart Sales Predictive Analysis using machine Learning


Revenue optimization, Data Visualization

            Our study focused on the enhancement of BigMart sales prediction capabilities. By utilizing ML approaches including XG Boost, linear regression, and time series techniques, we accomplished this by constructing a forecasting analytics framework. This framework assists the BigMart to optimize the supply range and can minimize the transport costs. Here we discussed about the improvement of market sector’s profitability through the use of ML and data analytics.

  1. Machine Learning Approach for Big-Mart Sales Prediction Framework


Prediction, Sales Forecasting

            To detect an efficient ML based sales forecasting model to increase the profit is the main objective of our article. Sales prediction can assist the administrator by providing ideas related to the handling of workers, properties and working capitals.  We constructed the model by employing ML techniques including GLL (Generalized Linear Model), GBT (Gradient Boosted Trees), and Decision Trees. In that,  GBT provides efficient results in prediction process.

  1. Predictive Analysis for Big Mart Sales Using Machine Learning Algorithm


Mean Absolute Error, Root Mean Square Error, Mean Square Error

            An advanced machine learning approaches are proposed in our study which helps in forecasting or reading process that are carried out with different kinds of associations. We examined the company’s transactions by constructing an efficient forecasting framework through the use of linear retrogression and Ridge retrogression techniques. Other measurable factors generate large number of transactions that evaluate morality.

  1. Sales Prediction of Big Mart based on Linear Regression, Random Forest, and Gradient Boosting


            In our research, we demonstrated the possibility of sales forecasting in a compact market. We examined the efficiency of various methods such as linear regression, random forest, and gradient boosting. We have done an experimental analysis by considering various performance metrics. Results show that, gradient boosting method provides better outcomes. We conclude that, with a limited data, we can perform sales prediction through the use of ML methods.

  1. Prediction System for BigMart Sales using Machine Learning


            A major goal of our paper is to develop a forecasting framework by utilizing ML methods in order to evaluate each product’s sales. This will assist the retailers to enhance their profits and can improve their products by forecasting the future sales. For forecasting the sales, we employed various ML based supervised learning methods including Linear Regression Algorithm and Random Forest algorithm and it also provides awareness about Big Mart sales.


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