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| 1 | +# Restaurant Revenue Prediction |
| 2 | + |
| 3 | +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. |
| 4 | + |
| 5 | +## Dataset |
| 6 | + |
| 7 | +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. |
| 8 | + |
| 9 | +Dataset used here is from https://www.kaggle.com/competitions/restaurant-revenue-prediction/data. |
| 10 | + |
| 11 | +## Process |
| 12 | + |
| 13 | +1. Importing required libraries. |
| 14 | + |
| 15 | +2. Data Visualisation: Mainly using graphs. |
| 16 | + |
| 17 | +3. Preprocess the dataset: This involves cleaning the data, handling missing value, etc |
| 18 | + |
| 19 | +4. Train the models: Fitting data into each of the three regression models (Linear Regression, Random Forest Regression, and Support Vector Regression). |
| 20 | + |
| 21 | +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. |
| 22 | + |
| 23 | +## Results |
| 24 | + |
| 25 | +After evaluating the models on the test data, the score for each model is compared to determine the best model for restaurant revenue prediction. |
| 26 | + |
| 27 | +## Conclusion |
| 28 | + |
| 29 | +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. |
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