Save & Load Machine Learning Model using Pickle & Joblib

In this ML Algorithms course tutorial, we are going to learn “How to save machine learning Model in detail. we covered it by practically and theoretical intuition.

1. How do you save a ML model?
2. How can we save the machine learning model?
3. How do you pickle a machine learning model?
4. Where are machine learning models stored?
5. What is Joblib file?
6. How do I load a python model?
7. What is model in python?
8. What is serialization in python?
9. How do you deploy ML models in production?

How to Save & Load Machine Learning Model

# -*- coding: utf-8 -*-
"""Save Model.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1BNLslMmXZ00oP-fCL9eD_3owzsY5zp7G

##Random Forest Classification

### Import Libraries
"""

# import libraries
import numpy as np
import pandas as pd

"""### Load Dataset"""

#load dataset
from sklearn.datasets import load_breast_cancer
data = load_breast_cancer()

data.data

data.feature_names

data.target

data.target_names

# create dtaframe
df = pd.DataFrame(np.c_[data.data, data.target], columns=[list(data.feature_names)+['target']])
df.head()

df.tail()

df.shape

"""### Split Data"""

X = df.iloc[:, 0:-1]
y = df.iloc[:, -1]

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, random_state=2020)

print('Shape of X_train = ', X_train.shape)
print('Shape of y_train = ', y_train.shape)
print('Shape of X_test = ', X_test.shape)
print('Shape of y_test = ', y_test.shape)

"""## Train Random Forest Classification Model"""

from sklearn.ensemble import RandomForestClassifier

classifier = RandomForestClassifier(n_estimators=100, criterion='gini')
classifier.fit(X_train, y_train)

classifier.score(X_test, y_test)

"""## Predict Cancer"""

patient1 = [17.99,
 10.38,
 122.8,
 1001.0,
 0.1184,
 0.2776,
 0.3001,
 0.1471,
 0.2419,
 0.07871,
 1.095,
 0.9053,
 8.589,
 153.4,
 0.006399,
 0.04904,
 0.05373,
 0.01587,
 0.03003,
 0.006193,
 25.38,
 17.33,
 184.6,
 2019.0,
 0.1622,
 0.6656,
 0.7119,
 0.2654,
 0.4601,
 0.1189]

patient1 = np.array([patient1])
patient1

classifier.predict(patient1)

data.target_names

pred = classifier.predict(patient1)

if pred[0] == 0:
  print('Patient has Cancer (malignant tumor)')
else:
  print('Patient has no Cancer (malignant benign)')

"""# Save Model

## Save Model using Pickle
"""

import pickle

pickle.dump(classifier, open('model_save', 'wb'))

model = pickle.load(open('model_save', 'rb'))

model.predict(patient1)[0]

"""## Save Model using Joblib"""

import joblib

joblib.dump(classifier, 'model_save2')

model2 = joblib.load('model_save2')

model2.predict(patient1)