Task: equi-join-detect (source dataset: Auto-BI)

Your are given a database with related tables used for business intelligence analysis. Your task is to identify all key/foreign-key join relationships between the tables for this database. Recall that a join is a relationship between two tables, that allows rows from one table to be combined with rows from another, based on a set of common join columns.
 
 
No explanation needed. Please return all join relationships in the following JSON format:
{
  "joins": [
    {
      "from_table": "table-1",
      "from_column": "column-1",
      "to_table": "table-2",
      "to_column": "column-3"
    }, 
	...
  ]
}

Tables:

Table: Buyers
| Category    | Buyer            |
|:------------|:-----------------|
| Womens      | Amy Buchanan     |
| Mens        | Bruce Curran     |
| Kids        | Elizabeth Gentry |
| Juniors     | Chester George   |
| Shoes       | Howard Wright    |
| Intimate    | Sara Ferrell     |
| Hosiery     | Ella Hickman     |
| Accessories | Maggie Mayer     |
| Home        | Richard Carr     |
| Groceries   | Chris Monroe     |


Table: Dates
| Date                    | Financial Year   | FY Qtr   |   FY Month |
|:------------------------|:-----------------|:---------|-----------:|
| 2016-01-01 00:00:00.000 | 2015/16          | 2016 Q3  |          7 |
| 2016-02-01 00:00:00.000 | 2015/16          | 2016 Q3  |          8 |
| 2016-03-01 00:00:00.000 | 2015/16          | 2016 Q3  |          9 |
| 2016-04-01 00:00:00.000 | 2015/16          | 2016 Q4  |         10 |
| 2016-05-01 00:00:00.000 | 2015/16          | 2016 Q4  |         11 |
| 2016-06-01 00:00:00.000 | 2015/16          | 2016 Q4  |         12 |
| 2016-07-01 00:00:00.000 | 2016/17          | 2017 Q1  |          1 |
| 2016-08-01 00:00:00.000 | 2016/17          | 2017 Q1  |          2 |
| 2016-09-01 00:00:00.000 | 2016/17          | 2017 Q1  |          3 |
| 2016-10-01 00:00:00.000 | 2016/17          | 2017 Q2  |          4 |


Table: Managers
| Suburb        |   Postcode | Manager        |
|:--------------|-----------:|:---------------|
| Darwin        |        800 | Michael Carney |
| Alice Springs |        870 | Michael Carney |
| Alexandria    |       2015 | Jeremy Garcia  |
| Mascot        |       2020 | Jeremy Garcia  |
| Randwick      |       2031 | Jeremy Garcia  |
| Artarmon      |       2064 | Jeremy Garcia  |
| Chatswood     |       2067 | Jeremy Garcia  |
| Narrabeen     |       2101 | Lillian Pruitt |
| Rydalmere     |       2116 | Lillian Pruitt |
| Thornleigh    |       2120 | Lillian Pruitt |


Table: Regions
| State   | Suburb       |   Postcode | State,Country   |
|:--------|:-------------|-----------:|:----------------|
| ACT     | Belconnen    |       2617 | ACT,Australia   |
| ACT     | Fyshwick     |       2609 | ACT,Australia   |
| NSW     | Alexandria   |       2015 | NSW,Australia   |
| NSW     | Armidale     |       2350 | NSW,Australia   |
| NSW     | Artarmon     |       2064 | NSW,Australia   |
| NSW     | Ashfield     |       2131 | NSW,Australia   |
| NSW     | Ballina      |       2478 | NSW,Australia   |
| NSW     | Bathurst     |       2795 | NSW,Australia   |
| NSW     | Campbelltown |       2560 | NSW,Australia   |
| NSW     | Castle Hill  |       2154 | NSW,Australia   |


Table: Sales
| Date                    | Chain      |   Postcode | Category    |   Total Units |   Sale Price |   Cost Price |   Sales |   Cost |   Profit |
|:------------------------|:-----------|-----------:|:------------|--------------:|-------------:|-------------:|--------:|-------:|---------:|
| 2016-01-01 00:00:00.000 | Ready Wear |       2560 | Intimate    |             1 |            1 |            1 |       1 |      1 |        0 |
| 2016-01-01 00:00:00.000 | Ready Wear |       2830 | Accessories |             1 |            1 |            1 |       1 |      1 |        0 |
| 2016-01-01 00:00:00.000 | Ready Wear |       3066 | Accessories |             1 |            1 |            1 |       1 |      1 |        0 |
| 2016-01-01 00:00:00.000 | Ready Wear |       4053 | Shoes       |             1 |            1 |            1 |       1 |      1 |        0 |
| 2016-01-01 00:00:00.000 | Bellings   |       6112 | Hosiery     |             1 |            1 |            1 |       1 |      1 |        0 |
| 2016-02-01 00:00:00.000 | Ready Wear |        800 | Accessories |             1 |            1 |            1 |       1 |      1 |        0 |
| 2016-02-01 00:00:00.000 | Ready Wear |       2015 | Accessories |             1 |            1 |            1 |       1 |      1 |        0 |
| 2016-02-01 00:00:00.000 | Ready Wear |       2350 | Shoes       |             1 |            1 |            1 |       1 |      1 |        0 |
| 2016-02-01 00:00:00.000 | Ready Wear |       2350 | Shoes       |             1 |            1 |            1 |       1 |      1 |        0 |
| 2016-02-01 00:00:00.000 | Bellings   |       2478 | Groceries   |             1 |            1 |            1 |       1 |      1 |        0 |

 


tables: name: Buyers
data: Buyers.csv
name: Dates
data: Dates.csv
name: Managers
data: Managers.csv
name: Regions
data: Regions.csv
name: Sales
data: Sales.csv
label: Sales
Category
Buyers
Category
Sales
Postcode
Managers
Postcode
Regions
Postcode
Managers
Postcode
Sales
Date
Dates
Date
output: {
    "joins": [
        {
            "from_table": "Sales",
            "from_column": "Category",
            "to_table": "Buyers",
            "to_column": "Category"
        },
        {
            "from_table": "Sales",
            "from_column": "Postcode",
            "to_table": "Managers",
            "to_column": "Postcode"
        },
        {
            "from_table": "Regions",
            "from_column": "Postcode",
            "to_table": "Managers",
            "to_column": "Postcode"
        },
        {
            "from_table": "Sales",
            "from_column": "Date",
            "to_table": "Dates",
            "to_column": "Date"
        }
    ]
}