Big Mart Sales Prediction Machine Learning Project

Have a look at our projects we have worked with.

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

Keywords

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.

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

Keywords

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.

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

Keywords

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.

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

Keyword

Regression

            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.

5. Machine Learning for Sales Prediction in Big Mart

Keywords

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.

6. Big Mart Sales Predictive Analysis using machine Learning

Keywords

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.

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

Keywords

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.

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

Keywords

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.

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

Keywords

            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.

10. Prediction System for BigMart Sales using Machine Learning

Keywords

            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.