1.
Python Machine Learning
With
Jupyter Notebook
Speakers
Thong Nguyen
Truoc Pham
1

2.
Target & Audiences
2

3.
Agenda
What is Machine Learning?
How to get started?
Our suggestion
Simple Machine Learning project
Q&A
3
1
2
3
4
5

4.
What is Machine Learning?
4
1

5.
5
1

6.
YouTube
6
1

7.
7
1

8.
Machine Learning is
using data to answer questions
8
1

9.
How to get started?
9
2

10.
10
Languages
2

11.
11
2

12.
Our suggestion
12
3

13.
Python
13
# Python 3: Fibonacci series up to n
>>> def fib(n):
>>> a, b = 0, 1
>>> while a < n:
>>> print(a, end=' ')
>>> a, b = b, a+b
>>> print()
>>> fib(1000)
0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987
3

14.
18K users
14
83% using Python
https://www.kaggle.com/kaggle/kaggle-survey-2018/discussion/74297
3

15.
Libs for ML
15
3

16.
Jupyter Notebook
16
3

17.
17https://www.kaggle.com/kaggle/kaggle-survey-2018/discussion/74297
3

18.
18
3

19.
19
3

20.
20
3

21.
Simple Machine Learning project
21
4

22.
22
4

23.
Demo
https://devdayml2019.herokuapp.com/
23
4

24.
24
Data → Processing data → Training → Model → Serve Predictions
4

25.
25
Preparing Data → Processing Data → Training → Model → Serve Predictions
4

26.
26
Preparing Data → Processing Data → Training → Model → Serve Predictions
Data for demo
https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data
4

27.
Preparing Data → Processing Data → Training → Model → Serve Predictions
27
- 1460 rows, 81 columns
- Data types include:
int64
float64
object
4

28.
Preparing Data → Processing Data → Training → Model → Serve Predictions
28
- Fill NaN
- Outlier
- Data Transformation
4

29.
Preparing Data → Processing Data → Training → Model → Serve Predictions
29
4

30.
30
train = train.replace([-np.inf, np.inf], 0.0)
train = train.fillna(0.0)
test = test.replace([-np.inf, np.inf], 0.0)
test = test.fillna(0.0)
Preparing Data → Processing Data → Training → Model → Serve Predictions
Sample code for filling NaN by Zero
4

31.
Preparing Data → Processing Data → Training → Model → Serve Predictions
31
4

32.
32
train.drop(
train[(train['GrLivArea'] > 4000)].index,
inplace=True
)
Preparing Data → Processing Data → Training → Model → Serve Predictions
Sample code for removing outliers
4

33.
Preparing Data → Processing Data → Training → Model → Serve Predictions
33
4

34.
34
def label_encoding(df):
cat_features = df.select_dtypes(include=['object']).columns
lbl = LabelEncoder()
for col in cat_features:
df[col] = lbl.fit_transform(list(df[col].values.astype('str')))
return df
train = label_encoding(train)
Preparing Data → Processing Data → Training → Model → Serve Predictions
Sample code for label encoding
4

35.
Preparing Data → Processing Data → Training → Model → Serve Predictions
35
Linear regression
4

36.
36
ignore_cols = ['Id', 'SalePrice']
train_features = [col for col in train.select_dtypes(include=['float64',
'int64']).columns
if col not in ignore_cols]
X = train[train_features].copy()
y = np.log1p(train['SalePrice'])
clf = LinearRegression()
clf.fit(X, y)
Using simple Linear Regression model
4
Preparing Data → Processing Data → Training → Model → Serve Predictions

37.
Preparing Data → Processing Data → Training → Model → Serve Predictions
37
Evaluation Model
4

38.
38
def rmse_score(y_obs, y_hat):
rmse = np.sqrt(mean_squared_error(y_obs, y_hat))
return rmse
X_trn, X_val, y_trn, y_val = train_test_split(
X, y, test_size=0.2, random_state=42)
clf.fit(X_trn, y_trn)
y_hat = clf.predict(X_val)
rmse = rmse_score(y_val, y_hat)
print('RMSE score: {:.6f}'.format(rmse))
Preparing Data → Processing Data → Training → Model → Serve Predictions
Sample code for Evaluation Model
4

39.
Prepare Data → Processing Data → Training → Model → Serve Predictions
39
What is my house price?
New Info ML Web Service Predicted Price
4

40.
Let’s getting started your wonderful Machine Learning project with
Python & Jupyter Notebook
40
4

41.
References
41
http://bit.ly/agilityio-ml-2019
4

42.
Thank You
42
Speakers
Thong Nguyen - nguyenthonght@gmail.com
Truoc Pham - khactruoc09dce@gmail.com

43.
43
5