LRMLModel.py

#!/Users/prafulla/miniconda2/bin/python
# path for python
# give exc permission

# -*- coding: utf-8 -*-

#to read cvs execell etc
import pandas
#get file from : https://drive.google.com/open?id=0B_P-kZ2NQv5aZWNSYUR2b2VrbFE
dataset = pandas.read_csv('50_Startups.csv')

#Features : Are the variables in the row over which result is dependent
X = dataset.iloc[:,:-1].values
#Labels : this is what we going to predict for future data
y = dataset.iloc[:,3].values

#Encoding categorical data (As linear regression deals with number)
#anything string need to be change to numerical value if needed
#from sklearn.preprocessing import LabelEncoder,OneHotEncoder
#encode = LabelEncoder()  # assign 0,1 value per label in row
#X[:,3] = encode.fit_transform(X[:,3])
#hotencode = OneHotEncoder(categorical_features=[3])
#X = hotencode.fit_transform(X).toarray()

# To avoid Dummy Variable trap
# This is automatically managed in Regression algorithm !!! (sci-kit)
#X = X[:,1:]// it handled in library automatically

# Training and Testing Set will enable me for two things:
# 1. Training set will help me to create a model. (70% dataset)
# 2. Testing set will help me evaluate my model for accuracy of predn (30% dataset)

from sklearn.cross_validation import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.3,
                                                 random_state=0)

# Fitting Multiple LR to training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()

regressor.fit(X_train,y_train)   #The output of this operation is a model


#Test the model for predictions based on test split

#y_pred = regressor.predict(X_test)  # use this to check prediction i.e
                                     # compare with achual result



# coefficients
regressor.intercept_
regressor.coef_


# now we will deploy above model
import coremltools
# convert to Core ML Model
coreml_model = coremltools.converters.sklearn.convert(regressor, input_features=["R&D Spend","Administration","Marketing Spend"], output_feature_names="Profit")

# deploy your model
coreml_model.save("ProfitPredictor.mlmodel")
#now this saved model we are going to use in our iOS App
#you can create various models with deferent algoritums and
#deploy it for you App