Using SQL to manipulate and clean the data, analyzing to discover trends and patterns, dig insights in an understandable format.

The fields present in Items dataset are,

 1. id 2. Order_id 3. Name 4. Is_veg/not 

The fields present in Orders dataset are,

 1. id 2. order_id 3. order_Total 4. restaurent name 5. order_time 6. rain_mode 7. on_time 

1. Count of unique number of orders that are placed ?

   

   95 unique orders are placed according to this dataset.

2. At what modes of rain were orders placed?

   

   There are 3 modes namely -Heavy,moderate,drizzle with 0,2 and 5 as it's respective identifiers from the dataset.

3. What are the uniqiue restaurent names from the overall orders that have been placed?

There are 49 unique restaurents name present in the order history in the given dataset.

4. Which Restaurent holds most of the orders ?

   

   

   

   

   The Bowl company is the restaurent which holds the highest number of orders according to the swiggy customers present in the dataset. from the above results we can also categorize the restaurents as most favourite , favourite and least favourite by which required recommendations can be activated by the company to attract its customer to place an order.