Machine Learning - Supervised Learning

Author

sulthan a. karimov

Published

July 17, 2024

import graphviz

# This output is generated from the model
# as you can see, there are several decisions 
# to make, and the hyrarchy can be compiled like a tree
graphviz.Source.from_file('cache/iristree.dot')

first we need to import pandas and load the data with pd.read_csv(file)

import pandas as pd

iris = pd.read_csv('datasets/iris/Iris.csv')

this is the overview of the data

iris
Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species
0 1 5.1 3.5 1.4 0.2 Iris-setosa
1 2 4.9 3.0 1.4 0.2 Iris-setosa
2 3 4.7 3.2 1.3 0.2 Iris-setosa
3 4 4.6 3.1 1.5 0.2 Iris-setosa
4 5 5.0 3.6 1.4 0.2 Iris-setosa
... ... ... ... ... ... ...
145 146 6.7 3.0 5.2 2.3 Iris-virginica
146 147 6.3 2.5 5.0 1.9 Iris-virginica
147 148 6.5 3.0 5.2 2.0 Iris-virginica
148 149 6.2 3.4 5.4 2.3 Iris-virginica
149 150 5.9 3.0 5.1 1.8 Iris-virginica

150 rows × 6 columns

if we wan to check detail about the data, use data.info()

iris.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 6 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Id             150 non-null    int64  
 1   SepalLengthCm  150 non-null    float64
 2   SepalWidthCm   150 non-null    float64
 3   PetalLengthCm  150 non-null    float64
 4   PetalWidthCm   150 non-null    float64
 5   Species        150 non-null    object 
dtypes: float64(4), int64(1), object(1)
memory usage: 7.2+ KB

the Id is not needed in this case, therefore we can just drop it

# stropping unneeded data
iris.drop('Id', axis=1, inplace=True)
Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species
0 1 5.1 3.5 1.4 0.2 Iris-setosa
1 2 4.9 3.0 1.4 0.2 Iris-setosa
2 3 4.7 3.2 1.3 0.2 Iris-setosa
3 4 4.6 3.1 1.5 0.2 Iris-setosa
4 5 5.0 3.6 1.4 0.2 Iris-setosa

now, we separate the data label from its features, and save it to X and y, we need also to separate the data to train and split

X = iris[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']]
y = iris['Species']

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=123)

we prepare the model by calling it from sklearn.tree

from sklearn import tree

clf = tree.DecisionTreeClassifier()
# With defined train test split

clf = clf.fit(X_train, y_train)

conclusion on cross_val_score

cross_val_score is for validating the quality of data set, it’s consider good if it’s more than 0.85

# with cross validation
from sklearn.model_selection import cross_val_score

scores = cross_val_score(clf, X, y, cv=5)
scores
array([0.96666667, 0.96666667, 0.9       , 0.96666667, 1.        ])
# model evaluation
from sklearn.metrics import accuracy_score

y_pred = clf.predict(X_test)

print(y_pred)
print(y_test)

acc_score = round(accuracy_score(y_pred, y_test), 3)

print('accuracy', acc_score)
['Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-versicolor'
 'Iris-setosa' 'Iris-virginica' 'Iris-versicolor' 'Iris-setosa'
 'Iris-setosa' 'Iris-versicolor' 'Iris-virginica' 'Iris-setosa'
 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica']
72     Iris-versicolor
112     Iris-virginica
132     Iris-virginica
88     Iris-versicolor
37         Iris-setosa
138     Iris-virginica
87     Iris-versicolor
42         Iris-setosa
8          Iris-setosa
90     Iris-versicolor
141     Iris-virginica
33         Iris-setosa
59     Iris-versicolor
116     Iris-virginica
135     Iris-virginica
Name: Species, dtype: object
accuracy 0.933
print(clf.predict([[6.2, 3.4, 5.4, 2.3]])[0])
from sklearn.tree import export_graphviz
export_graphviz(
    clf,
    out_file = 'cache/iristree.dot',
    feature_names = ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'],
    class_names = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica' ],
    rounded= True,
    filled =True)

Regression

Linear Regression

import numpy as np

# make dummy data of rooms
bedrooms = np.array([1,1,2,2,3,4,4,5,5,5])
 
# make dummy price data in dolar
house_price = np.array([15000, 18000, 27000, 34000, 50000, 68000, 65000, 81000,85000, 90000])
# visualize in scatterplot
import matplotlib.pyplot as plt
%matplotlib inline
 
plt.scatter(bedrooms, house_price)

from sklearn.linear_model import LinearRegression
 
# train the model with LinearRegression.fit()
bedrooms = bedrooms.reshape(-1, 1)
linreg = LinearRegression()
linreg.fit(bedrooms, house_price)
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. 
# plotting the corelation between number of rooms and house_prices
plt.scatter(bedrooms, house_price)
plt.plot(bedrooms, linreg.predict(bedrooms))

Logistic Regression

import pandas as pd

df = pd.read_csv('datasets/socmedAds/Social_Network_Ads.csv')
df
User ID Gender Age EstimatedSalary Purchased
0 15624510 Male 19 19000 0
1 15810944 Male 35 20000 0
2 15668575 Female 26 43000 0
3 15603246 Female 27 57000 0
4 15804002 Male 19 76000 0
... ... ... ... ... ...
395 15691863 Female 46 41000 1
396 15706071 Male 51 23000 1
397 15654296 Female 50 20000 1
398 15755018 Male 36 33000 0
399 15594041 Female 49 36000 1

400 rows × 5 columns

df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 400 entries, 0 to 399
Data columns (total 5 columns):
 #   Column           Non-Null Count  Dtype 
---  ------           --------------  ----- 
 0   User ID          400 non-null    int64 
 1   Gender           400 non-null    object
 2   Age              400 non-null    int64 
 3   EstimatedSalary  400 non-null    int64 
 4   Purchased        400 non-null    int64 
dtypes: int64(4), object(1)
memory usage: 15.8+ KB
data = df.drop(columns=['User ID'])

data = pd.get_dummies(data)
data
Age EstimatedSalary Purchased Gender_Female Gender_Male
0 19 19000 0 False True
1 35 20000 0 False True
2 26 43000 0 True False
3 27 57000 0 True False
4 19 76000 0 False True
... ... ... ... ... ...
395 46 41000 1 True False
396 51 23000 1 False True
397 50 20000 1 True False
398 36 33000 0 False True
399 49 36000 1 True False

400 rows × 5 columns

X = data[['Age', 'EstimatedSalary', 'Gender_Female', 'Gender_Male']]
y = data['Purchased']
# data normalization
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
# calculating the mean and standard deviation of every attribute column
# to be used on every transform function
scaler.fit(X)
scaled_data = scaler.transform(X)
scaled_data = pd.DataFrame(scaled_data, columns=X.columns)
scaled_data
Age EstimatedSalary Gender_Female Gender_Male
0 -1.781797 -1.490046 -1.020204 1.020204
1 -0.253587 -1.460681 -1.020204 1.020204
2 -1.113206 -0.785290 0.980196 -0.980196
3 -1.017692 -0.374182 0.980196 -0.980196
4 -1.781797 0.183751 -1.020204 1.020204
... ... ... ... ...
395 0.797057 -0.844019 0.980196 -0.980196
396 1.274623 -1.372587 -1.020204 1.020204
397 1.179110 -1.460681 0.980196 -0.980196
398 -0.158074 -1.078938 -1.020204 1.020204
399 1.083596 -0.990844 0.980196 -0.980196

400 rows × 4 columns

# validation with cross validation
from sklearn.model_selection import cross_val_score
from sklearn import linear_model

model = linear_model.LogisticRegression()
scores = cross_val_score(model, scaled_data, y, cv=5)
scores
array([0.7   , 0.95  , 0.9375, 0.8125, 0.7   ])
from sklearn.model_selection import train_test_split
 
X_train, X_test, y_train, y_test = train_test_split(scaled_data, y, test_size=0.2, random_state=1)
model.fit(X_train, y_train)
LogisticRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. 
# examine model accuracy
model.score(X_test, y_test)
0.825
Back to top