Big Mart Sales Prediction
The Big Mart - Sales Prediction project aims to predict future sales for Big Mart stores by analyzing historical sales data. By leveraging machine learning techniques, the project identifies key trends and patterns that influence sales performance.
The objective is to enhance inventory management and optimize marketing strategies for the fast-growing grocery and express home delivery brand.
Tech Stack:
Key Features:
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Comprehensive Data Analysis:
Utilizing historical sales data, the project uncovers valuable insights through exploratory data analysis (EDA).
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Data Preprocessing:
Efficiently handles missing values, reduces noise, and encodes data to prepare the dataset for machine learning algorithms.
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Multiple Machine Learning Models:
Implements various regression models, including K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Gradient Boosting, and XGBoost for accurate sales predictions.
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Model Evaluation and Selection:
Evaluates models using metrics such as R² Score, Mean Squared Error (MSE), and Mean Absolute Error (MAE) to determine the best predictor.
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Hyperparameter Tuning:
Employs Randomized Search Cross-Validation to optimize model parameters for enhanced predictive accuracy.
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Deployment Ready:
The trained model is serialized for deployment, enabling seamless integration into Big Mart’s sales forecasting operations.