Restaurant Revenue Prediction

This project aims to predict the revenue of a restaurant using three different regression models.The goal is to analyze the performance of these models and determine which one provides the most accurate revenue predictions.

Dataset

The dataset used for this project consists of various features related to a restaurant, such as the opening date, location, city, and other factors that may influence its revenue. The dataset is divided into two parts: the train set and the test set.

Dataset used here is from https://www.kaggle.com/competitions/restaurant-revenue-prediction/data.

Process

1. Importing required libraries.

2. Data Visualisation: Mainly using graphs.

3. Preprocess the dataset: This involves cleaning the data, handling missing value, etc

4. Train the models: Fitting data into each of the three regression models (Linear Regression, Random Forest Regression, and Support Vector Regression).

5. Evaluate the models and Compare the results: Analyze the performance of each model and identify the one that provides the most accurate predictions for restaurant revenue.

Results

After evaluating the models on the test data, the score for each model is compared to determine the best model for restaurant revenue prediction.

Conclusion

This project demonstrates the use of Regression models for predicting restaurant revenue. By comparing the performance of these models, we can identify the most suitable model for this particular task.