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@article{169882,
        author = {Sameer Tadvi and Mrunal Shinde and Akash Sonawane and Prasad Shevale},
        title = {Sales predictor system using Artificial Intelligence},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {3319-3323},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169882},
        abstract = {Predicting sales accurately is crucial for businesses to optimize inventory management, plan marketing strategies, and maximize revenue. We propose a neural network-based approach for real-time sales prediction of products. The proposed model leverages historical sales data, pricing information, promotional activities, seasonality indicators, and other relevant factors to forecast future sales volumes. The neural network architecture consists of multiple hidden layers with appropriate activation functions and a linear output layer for regression tasks. Training is performed using optimization algorithms such as Adam, with regularization techniques like dropout to prevent overfitting. The model's performance is evaluated using metrics such as mean squared error (MSE) and mean absolute error (MAE) on separate validation and test datasets. Through experimentation and fine-tuning of hyperparameters, the proposed neural network model demonstrates promising accuracy and generalization ability in predicting sales based on real-time data. This research contributes to the advancement of predictive analytics in retail and e-commerce industries, enabling businesses to make data-driven decisions and improve operational efficiency.},
        keywords = {Artificial Intelligence, Sales Prediction, Machine learning},
        month = {November},
        }