i have a data frame like this

and then i change it like this

my “Area” type was str so i change all data fram type to int64 with this code:
Numeric_HDS = coded_address_HDS.astype("int64")

so i want to fitting a curve to my data

1. non-linear

2. multiple linear

for “multiple linear” i think this is the formula:
Y = aX1 + bX2 +cX3 + dX4 + gX5 + hX6
and for “non-linear” i chose:
Y = 1 / 1 + e^beta1(X - beta2)
were X is aX1 + bX2 +cX3 + dX4 + gX5 + hX6

and my code is like this:

def Multiple_Sigmoid_f(beta1, beta2, theta1, theta2, theta3, theta4, theta5, theta6, Numeric_HDS):
    Area,Room,Parking,Warehouse,Elevator,Address 
    = Numeric_HDS[0],Numeric_HDS[1],Numeric_HDS[2],Numeric_HDS[3],Numeric_HDS[4],Numeric_HDS[5]
    X = (theta1*Area) + (theta2*Room) + (theta3*Parking) + (theta4*Warehouse) + (theta5*Elevator) + (theta6*Address)
    Y = 1 / (1 + np.exp(beta1*(X-beta2)))
    return Y

def Multiple_f(theta1, theta2, theta3, theta4, theta5, theta6, Numeric_HDS):
    Area,Room,Parking,Warehouse,Elevator,Address 
    = Numeric_HDS[0],Numeric_HDS[1],Numeric_HDS[2],Numeric_HDS[3],Numeric_HDS[4],Numeric_HDS[5]
    Y = (theta1*Area) + (theta2*Room) + (theta3*Parking) + (theta4*Warehouse) + (theta5*Elevator) + (theta6*Address)
    return Y

msk = np.random.rand(len(Numeric_HDS)) < 0.8
train = Numeric_HDS[msk]
test = Numeric_HDS[~msk]

X_train = np.asanyarray(train[["Area","Room","Parking","Warehouse","Elevator","Address"]]).transpose()
Y_real_train = np.asanyarray(train["Price"])

I want to fitting a curve but i get “Index error” when using curve_fit

using python