Restaurant Sales Prediction Using Machine Learning

Restaurant Sales Prediction Using Machine Learning

DOI: 10.4018/978-1-6684-7105-0.ch011
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In general, the revenue forecast, offer information, and the weather gauge setting will record an accurate estimate of any restaurant's future revenue. The turnover is significantly focused on the need of the customers. Either way, the performance has transformed over the past couple of years with the presentation of huge amounts of information and calculations during the time taken to gain the upper hand. It is fundamental to learn and understand the importance of the information that will be used in any business process. Again, climate forecasting can be done alongside business expectations with the organization.
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Different researchers have contributed toward development process based on machine learning techniques in our daily life routine activities. Sales predication based on weather forecasting with the help of machine learning concepts has being an effective approach to detail with. Machine learning has provided unique mechanism that can be utilized as an effective solution to deal with different real time problem.

Key Terms in this Chapter

Mean Squared Error (MSE): It is a measure of the difference between predicted values and actual values in a dataset, calculated as the average of the squared differences.

Root Mean Square Error (RMSE): It is a measure of the difference between predicted values and actual values in a dataset.

Support Vector Regression: Refers to a machine learning algorithm used for regression analysis, which uses a subset of training points in the decision function.

Normalization: It is the process of scaling data to a range of 0 to 1, making it easier to compare and analyze.

Data Cleaning: The process of identifying and removing or correcting errors, inconsistencies, and inaccuracies in a dataset in order to improve its quality and usefulness for analysis.

Economic Conditions: Refers to the state of the economy, such as inflation, unemployment, and economic growth, which can impact consumer spending and restaurant sales.

Customer Feedback: Refers to the opinions and comments shared by customers about their experience with a restaurant. Customer reviews and sentiment analysis can provide valuable information on factors that drive customer satisfaction and dissatisfaction, which can help restaurants improve their offerings and increase revenue.

Dataset: A collection of data that can be analyzed and used for different purposes.

Weather Forecasting: It is the process of using scientific and mathematical techniques to predict atmospheric conditions for a particular location and time.

Extreme Gradient Boosting (XG Boost): It is a machine learning algorithm that uses decision trees to improve the accuracy of predictions.

Sales: The exchange of goods or services for money or other valuable consideration.

Deep Learning Models: Refers to a subset of machine learning algorithms that use artificial neural networks to model and solve complex problems.

Pre-Order: A feature provided by online food delivery services that allows customers to book food in advance for a specific time or date.

Order Wise Sales Summary (OSS) Dataset: A dataset that contains information on the sales generated through online services during a certain time period. It includes different features related to the orders, such as order date, delivery time, payment mode, etc.

Long Short-Term Memory (LSTM) Neural Network: It is a type of recurrent neural network that can learn long-term dependencies in sequential data.

Information Technology: The use of technology to store, process, transmit and retrieve information.

Machine Learning Algorithms: Refers to a set of statistical models and techniques that enable computers to learn from data, identify patterns, and make predictions without being explicitly programmed.

Data Pre-Processing: Refers to the technique of data mining utilized for turning raw data into a more appropriate format. This includes removing missing entries, null values, normalization, enrichment, and cleaning of data.

Machine Learning: A subfield of Artificial Intelligence (AI) that enables systems to learn and improve from experience without being explicitly programmed.

Absolute Error: It is the absolute value of the difference between predicted values and actual values in a dataset.

Mode of Payment: The method used by customers to make payment for the food they order online, such as online banking, credit/debit card, cash on delivery, etc.

Computer Science: The study of computing, programming, and computation in correspondence with computer systems.

Sales Prediction: The process of forecasting future sales of a business based on historical sales data, market trends, and other relevant information.

Public Holidays and Weekend Impact on Sales: The influence of public holidays and weekends on the sales and orders of a restaurant, as customers may have more free time and prefer to order food online during these times.

LightGBM: Refers to a gradient boosting framework that uses tree-based learning algorithms.

Customer Demographics: Refers to the characteristics of a customer base, such as age, gender, income, and education, which can impact their preferences and purchasing behavior.

Decision Tree: Refers to a tree-shaped model used to make decisions, with each internal node representing a “test” on an attribute, and each leaf node representing a class label.

Standardization: It is the process of transforming data to have a mean of zero and standard deviation of one, making it easier to compare and analyze.

Customer Data of Restaurant (CDR) Dataset: A dataset that contains information on the customers who have used the online services of a restaurant, such as their phone number, name, total orders, revenue generated, etc.

Accuracy: It is the percentage of correct predictions in a dataset.

Seasonal Trends: Refers to the recurring patterns of behavior, preferences, and activities that change with the seasons, which can impact restaurant sales.

Weather Impact on Sales: The effect of weather conditions, such as rain, fog, or sunshine, on the number of orders and daily sales of a restaurant.

Features Selection: Refers to the selection of valuable features related to the research perspective and objective. The removal of unnecessary features is also performed through this method.

Tensorflow: It is an open-source software library for dataflow and differentiable programming across a range of tasks. It is used for machine learning applications such as neural networks.

Correlation: It is a statistical measure that indicates the degree of association between two variables.

K-Fold Cross Validation: Refers to a method of splitting data into K folds to provide an effective implementation of data and provide a unique pattern that is easily understandable.

Location: Refers to the physical place of a restaurant and its influence on customer needs and preferences, which can affect sales.

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