### Dataset attributes & their meaning

The dataset has 22 attributes, each explained below, including the numerical mapping made to the categorical variables

- **age** (Age of suitor)
- **gender**('female':0,'male':1 )
- **height**( Height in inches)
- **kgs** (Weight in kgs)
- **bmi** (BMI calculated based on height and weight)
- **eating** ('Doesn't Matter':0, 'Jain': 1, 'Vegetarian': 2, 'Vegetarian With Eggs': 3, 'Non Vegetarian':4)
- **family_type** ('Doesn't Matter': 0, 'Others':3, 'Nuclear': 1, 'Joint family both parents': 2, 'Joint family only mother':2, 'Joint family father mother and brothers sisters':2, 'Joint family single parent brothers and or sisters':2, 'Joint family only father': 2)
- **status** ('Doesn't Matter': 0, 'Middle Class': 1, 'Upper Middle Class': 2, 'High Class': 3, 'Rich / Affluent':4)
- **marital status** ('Doesn't Matter': 0, 'Never Married': 1, 'Divorced': 2, 'Annulled': 3, 'Widowed': 4, 'Married': 5)
- **manglik** ('No': 0, 'Yes': 1, 'Do Not Know': 2)
- **Horoscope** Believe in horoscope matching ('No': 0, 'Yes': 1)
- **drinking** ('Doesn't Matter':0, 'No': 1, 'Occasionally': 2, 'Yes': 3)
- **smoking** ('Doesn't Matter': 0, 'No': 1, 'Moderate': 2, 'Yes': 3)
- **values** ('Orthodox': 1, 'Traditional': 2, 'Moderate': 3, 'Liberal': 4, 'Modern': 5, 'International':6, 'Others': 7)
- **caste_imp** ('No': 0, 'Yes': 1)
- **complexion** ('Very Fair ': 1, 'Very Fair': 1, 'Fair ': 2, 'Fair': 2, 'Whetish Medium ': 4, 'Whetish Medium': 4, 'Wheatish': 3, 'Wheatish ':3, 'Dark ':5, 'Dark':5)
- **body** ('Slim': 1, 'Average': 2, 'Heavy': 3, 'Athletic': 4)
- **education** ('High School':0, 'Some college':1,'Undergrad':2, 'Grad':3, 'Doctorate':4) 
- **employed** ('Not Working':0, 'Public Sector': 1, 'State Government': 2, 'Central Government': 3, 'Private Sector':4, 'MNC':5, 'Others':6, "Doesn't Matter": 7)
- **living** Living with parents ('No': 0, 'Yes': 1)
-** city**('International': 1, 'Mumbai': 2, 'Delhi':3, 'Kolkata':4,'Bengaluru':5, 'Chennai':6, 'Hyderabad':7, 'Pune':8, 'Ahmedabad':9,'Surat':10, 'Vishakapatnam':11, 'Others':12)
- **income** (Annual income in dollars)

The response variable is '**income**', the other 21 attributes can be used as both categorical or continuous for model fitting.