imed-ha / bigmart-product-outlet-sales-analysis-and-prediction

The aim of this data science project is to build a predictive model and find out the sales of each product at a particular store.

BigMart Product Outlet Sales Analysis and Prediction

Welcome to the BigMart Sales Prediction project! In this data science project, my goal is to analyze and predict the sales of various products across multiple stores operated by BigMart in different cities. By leveraging the power of data science and machine learning, I will uncover valuable insights and develop accurate models to forecast future sales.

Project Overview

BigMart has provided me with a comprehensive dataset containing sales data for 1559 different products across 10 stores, covering a full year of transactions in 2013. Each product in the dataset possesses distinct attributes that make it unique and different from others. These attributes could include factors such as weight, size, brand, location, and promotional offers among others.

Project Goals

The primary objective of this project is to build a predictive model that can accurately estimate the sales of products based on their attributes and other relevant factors. By achieving this goal, I will equip BigMart with a powerful tool to optimize their inventory management, streamline their supply chain operations, and maximize their profitability.

Methodology

To accomplish my project goals, I will employ a variety of data science techniques and machine learning algorithms. The initial phase will involve exploratory data analysis (EDA), where I will thoroughly examine the dataset, visualize the relationships between variables, and identify any patterns or trends that can inform my predictive models.

Following the EDA, I will preprocess and clean the data, handling missing values, outliers, and transforming variables as necessary. I will also engineer new features that could potentially enhance the predictive power of my models. Once the data is properly prepared, I will proceed to develop and train various machine learning models using regression algorithms.

To assess the performance of my models, I will employ suitable evaluation metrics and perform cross-validation to ensure their robustness. I will fine-tune the models by tuning hyperparameters and selecting the best-performing model for final deployment. The resulting model will provide accurate sales predictions for new data points, enabling BigMart to make informed decisions about inventory stocking, pricing strategies, and store-specific optimizations.

Project Deliverables

Throughout this project, I will provide the following deliverables:

1. Loading dataset
2. Exploratory data analysis report
3. Preprocessed and cleaned dataset
4. Feature engineering documentation
5. Trained machine learning models
6. Model evaluation and selection report Sales prediction tool for new data points Final project report summarizing my findings, insights, and recommendations

I am excited to embark on this data science journey and contribute to the success of BigMart. Let's dive into the data, extract meaningful patterns, and build accurate sales prediction models that will drive informed decision-making and elevate the company's overall performance.