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Supervised Machine Learning Algorithms

REGRESSSION ALGORITHMS
CLASSIFICATION ALGORITHMS
Linear regression Logistic Regression
Polynomial regression Naive Bayes
Decision Trees Decision Trees
Random Forests Random Forests
Support Vector Machine(SVM) Support Vector Machine(SVM)
K-Nearest Neighbors K-Nearest Neighbors
Gradient Boosting Gradient Boosting

Naive Bayes

In machine learning, Naive Bayes is a probabilistic classifier. It is said to be "naive" since it makes the assumption that every characteristic is independent, which is sometimes untrue in practice, but it nevertheless functions surprisingly well.
Applications where it is used.
Spam Email Filtering: Uses features to determine whether an email is spam or not.

Text classification: Applied to topic classification, document classification, and sentiment analysis.

Medical diagnosis: Based on symptoms, it helps determine the likelihood of a disease.

Credit Scoring: Evaluates creditworthiness of individuals for loan approval.

Weather Prediction: Classifies weather conditions based on various factors.



Let's see the following example to understand better






Example 2




USE CASE 1: Use Naive Bayes with scikit-learn. Dependent variable: Default (0 = No Default, 1 = Default) Independent variables (3): Income (monthly income, e.g., 1000–10000) CreditScore (300–850) LoanAmount (1000–50000). 





import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

# -----------------------------------
# 1. Load data from Excel
# -----------------------------------
data = pd.read_excel("product_data.xlsx")

print("Dataset Preview:")
print(data.head())

#  Split dataset into features (X) and target (y)
X = df[['Income', 'CreditScore', 'LoanAmount']]
y = df['Default']

# Split into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25, random_state=42
)

#  Initialize Gaussian Naive Bayes
gnb = GaussianNB()

#  Train the model
gnb.fit(X_train, y_train)

#  Make predictions
y_pred = gnb.predict(X_test)

#  Evaluate the model
print("Accuracy:", accuracy_score(y_test, y_pred))
print("\nConfusion Matrix:\n", confusion_matrix(y_test, y_pred))
print("\nClassification Report:\n", classification_report(y_test, y_pred))


# Predict default probability for a new customer
new_customer = np.array([[5500, 710, 8000]])  # Income, CreditScore, LoanAmount
pred_class = gnb.predict(new_customer)
pred_prob = gnb.predict_proba(new_customer)

print("Predicted Class:", pred_class[0])
print("Probability of Default [No, Yes]:", pred_prob[0])

USE CASE 2: Use Naive Bayes Customer Churn example using Naive Bayes with scikit-learn in Python. We’ll assume 4 independent variables, for example: Tenure (months with company) - 1–60 months MonthlyCharges (amount billed per month) - 30–120 ContractType (0=Month-to-month, 1=One-year, 2=Two-year) SupportCalls (number of calls to support) 0–10 The dependent variable is Churn (0=Stay, 1=Churn).. 







import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

# -----------------------------------
# 1. Load data from Excel
# -----------------------------------
#sample data can be exported to 
#excel from the URL
# https://pythonPlaza.com/linear_school_grade_data.html

data = pd.read_excel("student_data.xlsx")

print("Dataset Preview:")
print(data.head())

# -----------------------------------
# 2. Define features and target
# -----------------------------------
X = data[['Hours_Studied', 'Attendance_%', 'Previous_Score']]
y = data['Final_Grade']

# -----------------------------------
# 3. Split into training and testing
# -----------------------------------
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25, random_state=42
)

# -----------------------------------
# 4. Train the Linear Regression model
# -----------------------------------
model = LinearRegression()
model.fit(X_train, y_train)


# -----------------------------
# Predictions
y_pred = model.predict(X_test)

# -----------------------------
# Evaluation
print("Predicted grades:", y_pred)
print("Actual grades:   ", y_test)

print("\nMean Squared Error:", mean_squared_error(y_test, y_pred))
print("R² Score:", r2_score(y_test, y_pred))


Example: Predict a new student’s grade
# New student: [hours_studied, attendance %, previous_score]
new_student = np.array([[6, 85, 78]])

predicted_grade = model.predict(new_student)
print("Predicted final grade:", predicted_grade[0])

USE CASE 3: Use Naive Bayes. What learning style does a student prefer
Visual, Auditory, Reading/Writing, Kinesthetic (Dependent Variable)

Independent variables (How a student prefers to learn)

prefers_diagrams – How much a student likes diagrams (1-5)
prefers_lectures – How much a student likes lectures (1-5)
prefers_notes – How much a student likes reading/writing notes (1-5)
prefers_hands_on – How much a student likes hands-on activities (1-5) 







import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

# -----------------------------------
# 1. Load data from Excel
# -----------------------------------
#sample data can be exported to 
#excel from the URL

  
  Get the Profit Optimization data in Excel
  

data = pd.read_excel("profit_optimization.xlsx")

print("Dataset Preview:")
print(data.head())

# -----------------------------------
# 2. Define features and target  Price (P)	
# -----------------------------------
X = data[['Price', 'Advertising', 'Units_Sold']]
y = data['Profit']

# -----------------------------------
# 3. Split into training and testing
# -----------------------------------
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25, random_state=42
)

# -----------------------------------
# 4. Train the Linear Regression model
# -----------------------------------
model = LinearRegression()
model.fit(X_train, y_train)


#Predict profit
y_pred = model.predict(X_test)

print("Predicted profit:", y_pred)
print("Actual profit:   ", y_test)

#Evaluate the model
print("\nMean Squared Error:", mean_squared_error(y_test, y_pred))
print("R² Score:", r2_score(y_test, y_pred))

#Profit equation (key for optimization)
print("Intercept:", model.intercept_)
print("Coefficients [Price, Advertising, Units Sold]:", model.coef_)


#Predict profit for a new business strategy
# Example: Price = 15, Advertising = 165, Units Sold = 460
new_strategy = np.array([[15, 165, 460]])

predicted_profit = model.predict(new_strategy)
print("Predicted profit:", predicted_profit[0])

USE CASE 4: Use Naive Bayes to predict if a person has a disease. Age, BloodPressure,Cholesterol,FamilyHistory, are independent variables and Disease is dependent variables. 







import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
# -----------------------------------
# 1. Load data from Excel
# -----------------------------------
#sample data can be exported to 
#excel from the URL

  
  Get the Patient Response Data in Excel
  

data = pd.read_excel("patient_dosage_response.xlsx")

print("Dataset Preview:")
print(data.head())

# -----------------------------------
# 2. Define features and target  Price (P)	
# -----------------------------------
X = data[['Dosage', 'Age', 'Weight']]
y = data['Patient_Response']

# -----------------------------------
# 3. Split into training and testing
# -----------------------------------
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25, random_state=42
)

# -----------------------------------
# 4. Train the Linear Regression model
# -----------------------------------
model = LinearRegression()
model.fit(X_train, y_train)


#Predict profit
y_pred = model.predict(X_test)

print("Predicted responses:", y_pred)
print("Actual responses:   ", y_test)

#Evaluate the model
print("\nMean Squared Error:", mean_squared_error(y_test, y_pred))
print("R² Score:", r2_score(y_test, y_pred))

#Profit equation (key for optimization)
print("Intercept:", model.intercept_)
print("Coefficients [Dosage, Age, Weight]:", model.coef_)


Predict response for a new patient
# New patient: Dosage=72mg, Age=36yrs, Weight=172lbs
new_patient = np.array([[72, 36, 172]])

predicted_response = model.predict(new_patient)
print("Predicted patient response:", predicted_response[0])