Added Restaurant Revenue Prediction

Author: AnkitaBarbora

Restaurant_Revenue_Prediction/README.md

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+# 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.