Logistic Regression Implementation

Logistic Regression එකේ, දෙවෙනි ශක්තිමත් කරන pass එකක් — core idea එකම, අලුතින් apply කරනවා, "linear score → sigmoid → probability" pattern එක automatic වෙනකම්.

A second, reinforcing pass at Logistic Regression — same core idea, applied fresh, so the pattern of "linear score → sigmoid → probability" becomes automatic.

13 May 20265 min readSelf Guide · Stage 15#Building Models from Scratch#Algorithms from Scratch#Machine learning

Lesson 37 of 64 · Part 3 · Building Models from Scratch · Algorithms from Scratch · bilingual notes (English + සිංහල)

Why this matters. A second, reinforcing pass at Logistic Regression — same core idea, applied fresh, so the pattern of "linear score → sigmoid → probability" becomes automatic.

සිංහලෙන්: Logistic Regression එකේ, දෙවෙනි ශක්තිමත් කරන pass එකක් — core idea එකම, අලුතින් apply කරනවා, "linear score → sigmoid → probability" pattern එක automatic වෙනකම්.

What you'll use this for. The more comfortable this pattern feels, the faster you'll recognise it hiding inside far more complex models, including neural networks, later on.

සිංහලෙන්: මේ pattern එකේ ලේසි වෙන තරමට, පස්සේ එන neural networks ඇතුළු ගොඩක් සංකීර්ණ models ඇතුළෙ මේක හංගිලා ඉන්නවා ඉක්මනට හඳුනාගන්නවා.

Key principles

The decision boundary is where probability crosses 0.5. Everything above that threshold gets classified as one class, everything below as the other — a single number quietly controls the entire decision.
Decision boundary එක තියෙන්නෙ probability එක 0.5 cross වෙන තැන

Walkthrough

Importing the Dependencies

ExamplePython

import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import Log_Reg

Data Collection and Analysis

PIMA Diabetes Dataset

ExamplePython

# loading the diabetes dataset to a pandas DataFrame
diabetes_dataset = pd.read_csv('/content/diabetes.csv')

ExamplePython

# printing the first 5 rows of the dataset
diabetes_dataset.head()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	…
0	6	148	72	35	0	33.6	0.627	…
1	1	85	66	29	0	26.6	0.351	…
2	8	183	64	0	0	23.3	0.672	…
3	1	89	66	23	94	28.1	0.167	…
4	0	137	40	35	168	43.1	2.288	…

ExamplePython

# number of rows and Columns in this dataset
diabetes_dataset.shape

Output

Output

(768, 9)

ExamplePython

# getting the statistical measures of the data
diabetes_dataset.describe()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	…
count	768.000000	768.000000	768.000000	768.000000	768.000000	768.000000	768.000000	…
mean	3.845052	120.894531	69.105469	20.536458	79.799479	31.992578	0.471876	…
std	3.369578	31.972618	19.355807	15.952218	115.244002	7.884160	0.331329	…
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.078000	…
25%	1.000000	99.000000	62.000000	0.000000	0.000000	27.300000	0.243750	…
50%	3.000000	117.000000	72.000000	23.000000	30.500000	32.000000	0.372500	…

… 2 more rows

ExamplePython

diabetes_dataset['Outcome'].value_counts()

Output

Output

0    500
1    268
Name: Outcome, dtype: int64

0 --> Non-Diabetic

1 --> Diabetic

ExamplePython

diabetes_dataset.groupby('Outcome').mean()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age
Outcome
0	3.298000	109.980000	68.184000	19.664000	68.792000	30.304200	0.429734	31.190000
1	4.865672	141.257463	70.824627	22.164179	100.335821	35.142537	0.550500	37.067164

ExamplePython

# separating the data and labels
features = diabetes_dataset.drop(columns = 'Outcome', axis=1)
target = diabetes_dataset['Outcome']

ExamplePython

print(features)

Output

Output

     Pregnancies  Glucose  BloodPressure  ...   BMI  DiabetesPedigreeFunction  Age
0              6      148             72  ...  33.6                     0.627   50
1              1       85             66  ...  26.6                     0.351   31
2              8      183             64  ...  23.3                     0.672   32
3              1       89             66  ...  28.1                     0.167   21
4              0      137             40  ...  43.1                     2.288   33
..           ...      ...            ...  ...   ...                       ...  ...
763           10      101             76  ...  32.9                     0.171   63
764            2      122             70  ...  36.8                     0.340   27
765            5      121             72  ...  26.2                     0.245   30
766            1      126             60  ...  30.1                     0.349   47
767            1       93             70  ...  30.4                     0.315   23

[768 rows x 8 columns]

ExamplePython

print(target)

Output

Output

0      1
1      0
2      1
3      0
4      1
      ..
763    0
764    0
765    0
766    1
767    0
Name: Outcome, Length: 768, dtype: int64

Data Standardization

ExamplePython

scaler = StandardScaler()

ExamplePython

scaler.fit(features)

Output

Output

StandardScaler(copy=True, with_mean=True, with_std=True)

ExamplePython

standardized_data = scaler.transform(features)

ExamplePython

print(standardized_data)

Output

Output

[[ 0.63994726  0.84832379  0.14964075 ...  0.20401277  0.46849198
   1.4259954 ]
 [-0.84488505 -1.12339636 -0.16054575 ... -0.68442195 -0.36506078
  -0.19067191]
 [ 1.23388019  1.94372388 -0.26394125 ... -1.10325546  0.60439732
  -0.10558415]
 ...
 [ 0.3429808   0.00330087  0.14964075 ... -0.73518964 -0.68519336
  -0.27575966]
 [-0.84488505  0.1597866  -0.47073225 ... -0.24020459 -0.37110101
   1.17073215]
 [-0.84488505 -0.8730192   0.04624525 ... -0.20212881 -0.47378505
  -0.87137393]]

ExamplePython

features = standardized_data
target = diabetes_dataset['Outcome']

ExamplePython

print(features)
print(target)

Output

Output

[[ 0.63994726  0.84832379  0.14964075 ...  0.20401277  0.46849198
   1.4259954 ]
 [-0.84488505 -1.12339636 -0.16054575 ... -0.68442195 -0.36506078
  -0.19067191]
 [ 1.23388019  1.94372388 -0.26394125 ... -1.10325546  0.60439732
  -0.10558415]
 ...
 [ 0.3429808   0.00330087  0.14964075 ... -0.73518964 -0.68519336
  -0.27575966]
 [-0.84488505  0.1597866  -0.47073225 ... -0.24020459 -0.37110101
   1.17073215]
 [-0.84488505 -0.8730192   0.04624525 ... -0.20212881 -0.47378505
  -0.87137393]]
0      1
1      0
2      1
3      0
4      1
… (7 more lines)

Train Test Split

ExamplePython

X_train, X_test, Y_train, Y_test = train_test_split(features,target, test_size = 0.2, random_state=2)

ExamplePython

print(features.shape, X_train.shape, X_test.shape)

Output

Output

(768, 8) (614, 8) (154, 8)

Training the Model

ExamplePython

classifier = Log_Reg.Logistic_Regression(learning_rate=0.01, no_of_iterations=1000)

ExamplePython

#training the support vector Machine Classifier
classifier.fit(X_train, Y_train)

Model Evaluation

Accuracy Score

ExamplePython

# accuracy score on the training data
X_train_prediction = classifier.predict(X_train)
training_data_accuracy = accuracy_score( Y_train, X_train_prediction)

ExamplePython

print('Accuracy score of the training data : ', training_data_accuracy)

Output

Output

Accuracy score of the training data :  0.7768729641693811

ExamplePython

# accuracy score on the test data
X_test_prediction = classifier.predict(X_test)
test_data_accuracy = accuracy_score( Y_test, X_test_prediction)

ExamplePython

print('Accuracy score of the test data : ', test_data_accuracy)

Output

Output

Accuracy score of the test data :  0.7662337662337663

Making a Predictive System

ExamplePython

input_data = (5,166,72,19,175,25.8,0.587,51)

# changing the input_data to numpy array
input_data_as_numpy_array = np.asarray(input_data)

# reshape the array as we are predicting for one instance
input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)

# standardize the input data
std_data = scaler.transform(input_data_reshaped)
print(std_data)

prediction = classifier.predict(std_data)
print(prediction)

if (prediction[0] == 0):
  print('The person is not diabetic')
else:
  print('The person is diabetic')

Output

Output

[[ 0.3429808   1.41167241  0.14964075 -0.09637905  0.82661621 -0.78595734
   0.34768723  1.51108316]]
[1]
The person is diabetic

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Logistic Regression Implementation

Key principles

Walkthrough

Importing the Dependencies

Data Standardization

Train Test Split

Training the Model

Model Evaluation

Accuracy Score

Making a Predictive System

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