Common Steps for using Machine Learning Model

Common Steps to Use a Machine Learning Model

1) Load the data & Split data into X & y 

import pandas   
pandas.read_csv("./data/1.csv")
X = df.drop("target", axis=1) # using all columns besides target
y = df["target"] #  predicting y using X

2) Model selection  & Split the data into training and test sets

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.2)

3) Instantiate the Model  

from sklearn.ensemble import RandomForestClassifier
m= RandomForestClassifier(n_estimators=50)

4) Fit the model using function 

m.fit(X_train,y_train); 

5) Make prediction

ypreds=m.predict(X_test)

7) To Evaluate Model use  score() function on test and train data

m.score(X_test, y_test)
m.score(X_train,y_train)

8) To improve the model by changing its hyperParameters

from sklearn.model_selection import cross_val_score

import numpy as np

# Use different numbers of  n_estimators  as hyperparameter 

np.random.seed(40)
for i in range(10, 100, 5):
print(f"Trying model with {i} estimators...")
m= RandomForestClassifier(n_estimators=i).fit(X_train, y_train)
print(f"Model accruacy on test data set: {m.score(X_test, y_test)}")

9) Save trained model to file using pickle

import pickle
pickle.dump(m, open("My_Random_forest_model.pkl", "wb"))

10) Load a saved model and make a prediction on a single example
saved_model = pickle.load(open("My_Random_forest_model.pkl", "rb"))
saved_model.score(X_train,y_train)