Sales Prediction Project

The data scientists at BigMart have collected sales data for 1559 products across 10 stores in different cities for the year 2013. Each product has certain attributes that set it apart from other products, and each store has unique characteristics as well.

The aim of this project is to build a predictive model using R to determine the sales of each product at a particular store. This model will help decision-makers at BigMart identify the key properties of products and stores that contribute to increasing overall sales.

Project Structure

• Data Collection: Gathering sales data for products across various stores.
• Data Preprocessing: Cleaning and preparing the data for analysis.
• Exploratory Data Analysis (EDA): Understanding the data through visualization and statistical analysis.
• Model Building: Creating predictive models using R.
• Model Evaluation: Assessing the performance of the models.
• Deployment: Implementing the model for practical use.

Getting Started

To get started with this project, you will need to have R installed on your system. You can download it from CRAN.

Prerequisites

• R (version 3.6 or higher)
• RStudio (optional, but recommended)
• Required R packages: dplyr, ggplot2, caret, randomForest

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/sales-prediction.git

2. Navigate to the project directory:

   cd sales-prediction

3. Install the required packages:

   install.packages(c("dplyr", "ggplot2", "caret", "randomForest"))

Usage

1. Load the dataset:

   data <- read.csv("path/to/dataset.csv")

2. Preprocess the data:

   # Example preprocessing steps
   data <- na.omit(data)
   data$Store <- as.factor(data$Store)

3. Perform EDA:

   library(ggplot2)
   ggplot(data, aes(x=Store, y=Sales)) + geom_boxplot()

4. Build the model:

   library(caret)
   model <- train(Sales ~ ., data=data, method="rf")

5. Evaluate the model:

   predictions <- predict(model, newdata=test_data)
   confusionMatrix(predictions, test_data$Sales)

Contributing

Contributions are welcome! Please read the contributing guidelines for more details.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• BigMart for providing the dataset.
• The R community for their invaluable packages and support. he data scientists at BigMart have collected sales data for 1559 products across 10 stores in different cities for the year 2013. Now each product has certain attributes that sets it apart from other products. Same is the case with each store.

--date="2023-05-29T07:43:58-08:00"