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Restaurant Visitor Forecasting

Running a thriving local restaurant isn’t always as charming as first impressions appear. There are often all sorts of unexpected troubles popping up that could hurt business.

One common predicament is that restaurants need to know how many customers to expect each day to effectively purchase ingredients and schedule staff members. This forecast isn’t easy to make because many unpredictable factors affect restaurant attendance, like weather and local competition. It’s even harder for newer restaurants with little historical data.

Restaurant had a unique access to key datasets that could make automated future customer prediction possible. Specifically, Restaurant owns Hot Pepper Gourmet (a restaurant review service), AirREGI (a restaurant point of sales service), and Restaurant Board (reservation log management software).

In this Case Study we had to use reservation and visitation data to predict the total number of visitors to a restaurant for future dates. This information helped restaurants to be much more efficient and allowed them to focus on creating an enjoyable dining experience for their customers.

In this competition, we were provided a time-series forecasting problem centered around restaurant visitors. The data comes from two separate sites:

• Hot Pepper Gourmet (hpg): similar to Yelp, here users can search restaurants and also make a reservation online
• AirREGI / Restaurant Board (air): similar to Square, a reservation control and cash register system

Introduction to Time Series:
Time Series is a series of time stamped values. Time stamped data is basically a sequence of data that has time values attached to the sequence of values.

Steps for modeling a time series:

1. Visualize the time series.

2. Recognize the trend and seasonality component.

3. Apply regression to model the trend and seasonality.

4. Remove the seasonal component from the series. What remains is the stationary part : a combination of autoregressive and white noise.

5. Model this stationary time series.

6. Combine the forecast of this model with the trend and seasonal component.

7. Find the residual series by subtracting the forecasted value from the actual observed value.

8. Check if the residual series is pure white noise.

Evaluation:

Submissions were evaluated on the root mean squared logarithmic error.

The RMSLE is calculated as

Result:

We used the reservations, visits, and other information from these sites to forecast future restaurant visitor totals on given date by generating a CSV file also which helped in focusing on creating an enjoyable dining experience for their customers. This forecasting will help the restaurant to manage the visitors according to the predicted values and it will also help the customers to get confirmed table reservation at last moment also.