Random Forest (Regression Algorithm)

Random Forest is a machine learning algorithm that creates many decision trees with various subsets data. Each tree can have a different root node. The method uses these trees to make predictions by randomly looking at different parts of the data. For classification tasks, the final answer is chosen based on what most trees agree on. For regression tasks, the result is the average of all the trees' predictions. This method is called ensemble learning, which helps make predictions more accurate and reduces mistakes. The way the trees are built, including their root and decision points, makes Random Forest powerful and less likely to learn from the training data too closely.

USE CASE 1: Using Random Forest with scikit-learn, predict the product price. The Production cost, Advertising spend, and Demand level are the independent variables.

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error, r2_score
# -----------------------------------
# 1. Load data from Excel
# -----------------------------------
data = pd.read_excel("product_data.xlsx")

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

# -----------------------------------
# 2. Define features and target
# -----------------------------------
X = data[['Production_Cost', 'Advertising_Spend', 'Demand_Level']]
y = data['Product_Price']

# -----------------------------------
# 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 = RandomForestRegressor(
    n_estimators=100,
    max_depth=5,
    random_state=42
)
model.fit(X_train, y_train)

# -----------------------------------
# 5. Model parameters
# -----------------------------------
print("\nIntercept:", model.intercept_)
print("Coefficients:")
for feature, coef in zip(X.columns, model.coef_):
    print(f"  {feature}: {coef}")

# -----------------------------------
# 6. Evaluate the model
# -----------------------------------
y_pred = model.predict(X_test)

r2 = r2_score(y_test, y_pred)
mae = mean_absolute_error(y_test, y_pred)
mse = mean_squared_error(y_test, y_pred)

print("\nModel Evaluation:")
print("R² Score:", r2)
print("Mean Absolute Error:", mae)
print("Mean Squared Error:", mse)

# -----------------------------------
# 7. Predict price for a new product
# -----------------------------------
# New product: Cost=32, Advertising=160, Demand=62
new_product = np.array([[32, 160, 62]])

predicted_price = model.predict(new_product)
print("Predicted product price:", predicted_price[0])

Why Random Forest works well here

1. It captures non-linear relationships very well.
2. It handles feature interactions automatically.
3. It is more robust than a single decision tree.
4. It reduces overfitting by averaging.

import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# -----------------------------------
# 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])

import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# -----------------------------------
# 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])

import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# -----------------------------------
# 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])