Wednesday, April 9, 2025

Real Estate Investment Analysis - Loan Amortization Schedule

 

Notes:

  • What problem does it solve?
    This code generates a loan amortization schedule for a mortgage, showing the principal and interest breakdown over time.

  • How can businesses or users benefit from customizing the code?
    Users can understand how their mortgage payments affect principal reduction and interest over time.

  • How can businesses or users adopt the solution further, if needed?
    This can be integrated into property management tools or used to plan refinancing options.

Actual Python Code:


class LoanAmortization:
    def __init__(self, loan_amount, interest_rate, loan_term):
        self.loan_amount = loan_amount
        self.interest_rate = interest_rate / 100 / 12  # Monthly interest rate
        self.loan_term = loan_term * 12  # Convert years to months
    
    def monthly_payment(self):
        # Formula for monthly mortgage payment
        return (self.loan_amount * self.interest_rate) / (1 - (1 + self.interest_rate) ** -self.loan_term)
    
    def amortization_schedule(self):
        balance = self.loan_amount
        monthly_payment = self.monthly_payment()
        schedule = []
        for month in range(1, self.loan_term + 1):
            interest_payment = balance * self.interest_rate
            principal_payment = monthly_payment - interest_payment
            balance -= principal_payment
            schedule.append((month, principal_payment, interest_payment, balance))
        return schedule

# Example usage
amortization = LoanAmortization(loan_amount=300000, interest_rate=4, loan_term=30)

schedule = amortization.amortization_schedule()
for month, principal, interest, balance in schedule[:5]:  # Displaying the first 5 months
    print(f"Month {month}: Principal: ${principal:.2f}, Interest: ${interest:.2f}, Balance: ${balance:.2f}")

Real Estate Investment Analysis - Depreciation Estimator

 

Notes:

  • What problem does it solve?
    This code calculates the depreciation of a property for tax purposes based on a given depreciation schedule.

  • How can businesses or users benefit from customizing the code?
    This can help property owners calculate tax savings over time due to property depreciation.

  • How can businesses or users adopt the solution further, if needed?
    The model can be adjusted for different property types or tax regulations.

Actual Python Code:


class DepreciationEstimator:
    def __init__(self, purchase_price, useful_life, depreciation_method='linear'):
        self.purchase_price = purchase_price
        self.useful_life = useful_life
        self.depreciation_method = depreciation_method
    
    def annual_depreciation(self):
        if self.depreciation_method == 'linear':
            return self.purchase_price / self.useful_life
        # Other depreciation methods can be added here
        return 0

# Example usage
depreciation = DepreciationEstimator(purchase_price=500000, useful_life=27.5)

print(f"Annual Depreciation: ${depreciation.annual_depreciation():.2f}")

Real Estate Investment Analysis - Equity Growth Estimator

 

Notes:

  • What problem does it solve?
    This code estimates the equity growth over time based on the appreciation of the property and mortgage payments.

  • How can businesses or users benefit from customizing the code?
    Users can track how equity grows over time, helping them plan when to sell or refinance a property.

  • How can businesses or users adopt the solution further, if needed?
    The model can be expanded to simulate different scenarios (e.g., changes in rent income or mortgage rates).

Actual Python Code:


class EquityGrowth:
    def __init__(self, initial_property_value, loan_balance, annual_appreciation_rate, mortgage_payment, years):
        self.initial_property_value = initial_property_value
        self.loan_balance = loan_balance
        self.annual_appreciation_rate = annual_appreciation_rate
        self.mortgage_payment = mortgage_payment
        self.years = years
    
    def equity_growth(self):
        equity = []
        for year in range(1, self.years + 1):
            # Property value grows annually
            property_value = self.initial_property_value * (1 + self.annual_appreciation_rate) ** year
            # Mortgage balance decreases over time
            loan_balance = self.loan_balance - (self.mortgage_payment * year)
            # Calculate equity (property value - loan balance)
            equity.append(property_value - loan_balance)
        return equity

# Example usage
equity = EquityGrowth(initial_property_value=500000, loan_balance=300000, annual_appreciation_rate=0.05,
                      mortgage_payment=15000, years=10)

print(f"Equity Growth Over Time: {equity.equity_growth()}")

Real Estate Investment Analysis - Rental Yield Estimator

 

Notes:

  • What problem does it solve?
    This code calculates the rental yield (gross rental income divided by property price) for a given property.

  • How can businesses or users benefit from customizing the code?
    Users can use it to assess the rental income potential of properties and compare different investment options.

  • How can businesses or users adopt the solution further, if needed?
    This can be expanded to factor in local market conditions, multiple properties, or different rental income assumptions.

Actual Python Code:


class RentalYield:
    def __init__(self, annual_rent_income, property_value):
        self.annual_rent_income = annual_rent_income
        self.property_value = property_value
    
    def yield_percentage(self):
        return (self.annual_rent_income / self.property_value) * 100

# Example usage
yield_estimator = RentalYield(annual_rent_income=24000, property_value=300000)

print(f"Rental Yield: {yield_estimator.yield_percentage():.2f}%")

Real Estate Investment Analysis - Property Tax Estimator

 

Notes:

  • What problem does it solve?
    This code estimates the property tax based on the value of the property and the local property tax rate.

  • How can businesses or users benefit from customizing the code?
    Investors can use it to project taxes for new properties, factoring in local tax rates.

  • How can businesses or users adopt the solution further, if needed?
    Users can extend the model to consider tax deductions, exemptions, or varying tax rates across different regions.

Actual Python Code:


class PropertyTaxEstimator:
    def __init__(self, property_value, tax_rate):
        self.property_value = property_value
        self.tax_rate = tax_rate
    
    def annual_tax(self):
        return self.property_value * self.tax_rate

# Example usage
tax_estimator = PropertyTaxEstimator(property_value=350000, tax_rate=0.012)

print(f"Annual Property Tax: ${tax_estimator.annual_tax():.2f}")

Real Estate Investment Analysis - Discounted Cash Flow (DCF)

 

Notes:

  • What problem does it solve?
    This code calculates the net present value (NPV) of future cash flows to help investors assess the value of an investment.

  • How can businesses or users benefit from customizing the code?
    It helps users evaluate the long-term profitability of a property by discounting future cash flows back to their present value.

  • How can businesses or users adopt the solution further, if needed?
    Users can modify the discount rate or cash flow assumptions to tailor the analysis to different types of properties or investment strategies.

Actual Python Code:


class DCFAnalysis:
    def __init__(self, cash_flows, discount_rate):
        self.cash_flows = cash_flows
        self.discount_rate = discount_rate
    
    def npv(self):
        npv = 0
        for i, cash_flow in enumerate(self.cash_flows):
            npv += cash_flow / (1 + self.discount_rate) ** (i + 1)
        return npv

# Example usage
cash_flows = [50000, 55000, 60000, 65000, 70000]  # projected cash flows over 5 years
discount_rate = 0.1  # 10% discount rate
dcf = DCFAnalysis(cash_flows, discount_rate)

print(f"Net Present Value (NPV): ${dcf.npv():.2f}")

Real Estate Investment Analysis - Rent vs Buy Comparison

 

Notes:

  • What problem does it solve?
    This code compares the total cost of renting versus buying a property over a specified period.

  • How can businesses or users benefit from customizing the code?
    Homebuyers or investors can use it to assess whether it's more economical to rent or buy based on local market conditions.

  • How can businesses or users adopt the solution further, if needed?
    This model can be expanded to include tax benefits, appreciation rates, and interest rate changes.

Actual Python Code:


class RentVsBuy:
    def __init__(self, rent_per_month, property_value, mortgage_rate, loan_term, property_tax_rate, insurance_rate):
        self.rent_per_month = rent_per_month
        self.property_value = property_value
        self.mortgage_rate = mortgage_rate
        self.loan_term = loan_term
        self.property_tax_rate = property_tax_rate
        self.insurance_rate = insurance_rate
    
    def rent_total(self):
        return self.rent_per_month * 12 * self.loan_term
    
    def buy_total(self):
        monthly_mortgage = self.property_value * self.mortgage_rate / 12
        monthly_taxes = self.property_value * self.property_tax_rate / 12
        monthly_insurance = self.property_value * self.insurance_rate / 12
        monthly_costs = monthly_mortgage + monthly_taxes + monthly_insurance
        return monthly_costs * 12 * self.loan_term
    
    def compare(self):
        rent_cost = self.rent_total()
        buy_cost = self.buy_total()
        if rent_cost < buy_cost:
            return "Renting is cheaper."
        else:
            return "Buying is cheaper."

# Example usage
comparison = RentVsBuy(rent_per_month=1500, property_value=300000, mortgage_rate=0.04, loan_term=30, 
                      property_tax_rate=0.01, insurance_rate=0.005)

print(comparison.compare())

Real Estate Investment Analysis - Break-even Analysis

 

Notes:

  • What problem does it solve?
    This code calculates the break-even point for a real estate investment—how long it will take for rental income to cover acquisition and operating costs.

  • How can businesses or users benefit from customizing the code?
    Businesses can assess whether a property is likely to break even in a reasonable time frame, helping to make data-driven decisions.

  • How can businesses or users adopt the solution further, if needed?
    The tool can be integrated into cash flow forecasting systems, or used to evaluate multiple properties in one go.

Actual Python Code:


class BreakEvenAnalysis:
    def __init__(self, property_value, annual_income, annual_expenses):
        self.property_value = property_value
        self.annual_income = annual_income
        self.annual_expenses = annual_expenses
    
    def break_even_point(self):
        net_income_per_year = self.annual_income - self.annual_expenses
        return self.property_value / net_income_per_year

# Example usage
break_even = BreakEvenAnalysis(property_value=500000, annual_income=30000, annual_expenses=15000)

print(f"Break-even point: {break_even.break_even_point():.2f} years")

Real Estate Investment Analysis - Capitalization Rate (Cap Rate)

 

Notes:

  • What problem does it solve?
    This code calculates the capitalization rate for a real estate investment to help evaluate the property’s return relative to its price.

  • How can businesses or users benefit from customizing the code?
    Investors can use this tool to quickly compare the profitability of multiple properties.

  • How can businesses or users adopt the solution further, if needed?
    The tool can be extended to include various property types and include more complex revenue streams.

Actual Python Code:


class CapRateCalculator:
    def __init__(self, annual_net_income, property_value):
        self.annual_net_income = annual_net_income
        self.property_value = property_value
    
    def cap_rate(self):
        return (self.annual_net_income / self.property_value) * 100

# Example usage
cap_rate = CapRateCalculator(annual_net_income=24000, property_value=300000)

print(f"Cap Rate: {cap_rate.cap_rate():.2f}%")

Real Estate Investment Analysis - Property Appreciation Estimator

 

Notes:

  • What problem does it solve?
    This code estimates the property value appreciation over time based on an annual growth rate.

  • How can businesses or users benefit from customizing the code?
    This can help users predict future property values based on different growth assumptions, assisting in long-term investment planning.

  • How can businesses or users adopt the solution further, if needed?
    The model can be extended to include multiple properties, simulate market fluctuations, or integrate location-based data for more accuracy.

Actual Python Code:


class PropertyAppreciation:
    def __init__(self, initial_value, annual_growth_rate, years):
        self.initial_value = initial_value
        self.annual_growth_rate = annual_growth_rate
        self.years = years
    
    def estimated_value(self):
        return self.initial_value * ((1 + self.annual_growth_rate) ** self.years)

# Example usage
property_estimate = PropertyAppreciation(initial_value=400000, annual_growth_rate=0.05, years=10)

print(f"Estimated Property Value in 10 Years: ${property_estimate.estimated_value():.2f}")

Real Estate Investment Analysis - ROI Calculation

 

Notes:

  • What problem does it solve?
    This code calculates the return on investment (ROI) for a real estate property considering all acquisition costs, financing costs, and rental income.

  • How can businesses or users benefit from customizing the code?
    This can be used to assess the viability of an investment property. Users can customize it to evaluate different types of financing (loan vs. full payment).

  • How can businesses or users adopt the solution further, if needed?
    This can be integrated into a broader property evaluation system for investors to assess multiple properties simultaneously.

Actual Python Code:


class RealEstateROI:
    def __init__(self, purchase_price, loan_amount, annual_rent_income, annual_expenses, property_value_increase_rate):
        self.purchase_price = purchase_price
        self.loan_amount = loan_amount
        self.annual_rent_income = annual_rent_income
        self.annual_expenses = annual_expenses
        self.property_value_increase_rate = property_value_increase_rate
    
    def cash_on_cash_roi(self):
        # Cash invested is purchase price minus loan
        cash_invested = self.purchase_price - self.loan_amount
        # Cash flow (income minus expenses)
        cash_flow = self.annual_rent_income - self.annual_expenses
        return (cash_flow / cash_invested) * 100
    
    def total_roi(self):
        # Include appreciation in value (property value increase)
        annual_property_appreciation = self.purchase_price * self.property_value_increase_rate
        total_return = (self.annual_rent_income + annual_property_appreciation - self.annual_expenses) / self.purchase_price
        return total_return * 100

# Example usage
roi_analysis = RealEstateROI(purchase_price=500000, loan_amount=300000, annual_rent_income=36000, 
                             annual_expenses=8000, property_value_increase_rate=0.03)

print(f"Cash on Cash ROI: {roi_analysis.cash_on_cash_roi():.2f}%")
print(f"Total ROI: {roi_analysis.total_roi():.2f}%")

Real Estate Investment Analysis - Cash Flow Calculation

 

Notes:

  • What problem does it solve?
    This code calculates the monthly and yearly cash flow for a real estate investment based on income and expenses.

  • How can businesses or users benefit from customizing the code?
    Businesses can use it to quickly assess the potential profitability of different properties. Users can adapt the code for various property types or geographic regions by adjusting inputs.

  • How can businesses or users adopt the solution further, if needed?
    Users can integrate this into property management software or combine it with other financial models to provide more detailed investment analysis.

Actual Python Code:


class RealEstateInvestment:
    def __init__(self, rent_income, mortgage, property_tax, insurance, maintenance_costs, vacancy_rate):
        self.rent_income = rent_income
        self.mortgage = mortgage
        self.property_tax = property_tax
        self.insurance = insurance
        self.maintenance_costs = maintenance_costs
        self.vacancy_rate = vacancy_rate
        
    def monthly_cash_flow(self):
        # Calculate income after vacancy rate
        adjusted_rent_income = self.rent_income * (1 - self.vacancy_rate)
        # Monthly expenses (mortgage + taxes + insurance + maintenance)
        total_expenses = self.mortgage + self.property_tax / 12 + self.insurance / 12 + self.maintenance_costs / 12
        # Monthly cash flow
        return adjusted_rent_income - total_expenses
    
    def yearly_cash_flow(self):
        return self.monthly_cash_flow() * 12

# Example usage
investment = RealEstateInvestment(rent_income=3000, mortgage=1500, property_tax=2400, 
                                  insurance=1200, maintenance_costs=200, vacancy_rate=0.05)

print(f"Monthly Cash Flow: ${investment.monthly_cash_flow():.2f}")
print(f"Yearly Cash Flow: ${investment.yearly_cash_flow():.2f}")

Saturday, April 5, 2025

Healthcare Data Management - Health Insurance Claim Generator

 Notes:

  • What problem does it solve?: It simplifies the process of generating health insurance claims based on patient treatments.

  • How can businesses or users benefit from customizing the code?: Users can adapt the system to include different insurance providers or claim formats.

  • How can businesses or users adopt the solution further, if needed?: It can be integrated into broader healthcare management systems or customized for specific insurance policies.

Python Code:


class InsuranceClaim:

    def __init__(self, patient_name, diagnosis, total_amount):

        self.patient_name = patient_name

        self.diagnosis = diagnosis

        self.total_amount = total_amount


    def generate_claim(self):

        claim = f"Insurance Claim for {self.patient_name}:\n"

        claim += f"Diagnosis: {self.diagnosis}\n"

        claim += f"Total Amount: ${self.total_amount}"

        return claim


# Example Usage

claim = InsuranceClaim("John Doe", "Hypertension", 300)

print(claim.generate_claim())


Healthcare Data Management - Automated Patient Billing

 Notes:

  • What problem does it solve?: It automates the process of patient billing after a consultation, reducing human error and administrative costs.

  • How can businesses or users benefit from customizing the code?: Healthcare practices can adapt it to their specific billing rates and services.

  • How can businesses or users adopt the solution further, if needed?: The code can be extended to integrate with payment gateways or insurance claims processing systems.

Python Code:


class AutomatedBilling:

    def __init__(self):

        self.services = {'Consultation': 100, 'Blood Test': 30, 'X-ray': 50}

        self.total_amount = 0


    def add_service(self, service_name):

        if service_name in self.services:

            self.total_amount += self.services[service_name]


    def generate_invoice(self):

        return f"Total Bill: ${self.total_amount}"


# Example Usage

billing = AutomatedBilling()

billing.add_service('Consultation')

billing.add_service('Blood Test')

print(billing.generate_invoice())


Healthcare Data Management - Patient Feedback System

 Notes:

  • What problem does it solve?: It allows patients to provide feedback on their healthcare experience, helping organizations improve their services.

  • How can businesses or users benefit from customizing the code?: Healthcare organizations can modify the feedback questions and integrate the system into their website or patient management tools.

  • How can businesses or users adopt the solution further, if needed?: The feedback system can be linked to analytics tools for deeper insights.

Python Code:


class FeedbackSystem:

    def __init__(self):

        self.feedback = []


    def collect_feedback(self, patient_name, feedback_text):

        self.feedback.append({'patient': patient_name, 'feedback': feedback_text})


    def view_feedback(self):

        return self.feedback


# Example Usage

feedback_system = FeedbackSystem()

feedback_system.collect_feedback('John Doe', 'Great service, very professional.')

print("Patient Feedback:", feedback_system.view_feedback())


Healthcare Data Management - Prescription Refill Reminder

 Notes:

  • What problem does it solve?: It automatically sends prescription refill reminders to patients, improving medication adherence.

  • How can businesses or users benefit from customizing the code?: Healthcare providers can tailor this system for different medications and patient needs.

  • How can businesses or users adopt the solution further, if needed?: This can be extended to integrate with SMS or email systems for real-time reminders.

Python Code:


import datetime


class PrescriptionReminder:

    def __init__(self, medication_name, days_until_refill):

        self.medication_name = medication_name

        self.days_until_refill = days_until_refill

        self.last_refill_date = datetime.datetime.now()


    def next_refill_date(self):

        return self.last_refill_date + datetime.timedelta(days=self.days_until_refill)


    def remind_patient(self):

        next_refill = self.next_refill_date()

        print(f"Reminder: Refill {self.medication_name} by {next_refill.strftime('%Y-%m-%d')}")


# Example Usage

reminder = PrescriptionReminder("Amlodipine", 30)

reminder.remind_patient()


Healthcare Data Management - Patient Data Auditing

Notes:

  • What problem does it solve?: It audits patient data to ensure that it’s accurate, complete, and complies with regulations.

  • How can businesses or users benefit from customizing the code?: Healthcare organizations can customize the code to fit their auditing policies and compliance requirements.

  • How can businesses or users adopt the solution further, if needed?: The solution can be enhanced to automatically flag discrepancies and notify relevant personnel.

Python Code:


import pandas as pd


# Sample patient data

data = pd.DataFrame({

    'Patient_ID': [101, 102, 103, 104],

    'Name': ['John Doe', 'Jane Smith', 'Tom Brown', 'Lisa White'],

    'Age': [30, 45, 50, 39],

    'Condition': ['Diabetes', 'Hypertension', 'Asthma', 'Cancer']

})


# Check for missing values

def audit_data(df):

    missing_data = df.isnull().sum()

    print("Missing Data:\n", missing_data)


audit_data(data)

 

Healthcare Data Management - Healthcare Staff Scheduling

 Notes:

  • What problem does it solve?: It automates the scheduling of healthcare staff, optimizing staffing levels and preventing conflicts.

  • How can businesses or users benefit from customizing the code?: Hospitals or clinics can customize schedules based on shift patterns, staff availability, and other constraints.

  • How can businesses or users adopt the solution further, if needed?: This can be expanded to include features like automatic reminders or integration with HR systems.

Python Code:


import random


class StaffScheduler:

    def __init__(self):

        self.staff = ['Dr. Smith', 'Dr. Johnson', 'Nurse Williams', 'Nurse Brown']

        self.shifts = ['Morning', 'Afternoon', 'Night']


    def generate_schedule(self):

        schedule = {}

        for staff_member in self.staff:

            schedule[staff_member] = random.choice(self.shifts)

        return schedule


# Example Usage

scheduler = StaffScheduler()

schedule = scheduler.generate_schedule()

print("Staff Schedule:", schedule)


Healthcare Data Management - Medical Billing System

 Notes:

  • What problem does it solve?: It simplifies the generation of medical bills based on patient treatments and insurance information.

  • How can businesses or users benefit from customizing the code?: This system can be customized with different pricing models, insurance coverage, or payment methods.

  • How can businesses or users adopt the solution further, if needed?: Businesses can integrate the billing system with their existing healthcare management software.

Python Code:


class MedicalBilling:

    def __init__(self):

        self.services = {

            'Consultation': 100,

            'X-ray': 50,

            'Blood Test': 30

        }

        self.insurance_discount = 0.2  # 20% discount


    def generate_bill(self, services_used, has_insurance=False):

        total = sum(self.services[service] for service in services_used)

        if has_insurance:

            total -= total * self.insurance_discount

        return total


# Example Usage

billing = MedicalBilling()

services_used = ['Consultation', 'Blood Test']

bill_amount = billing.generate_bill(services_used, has_insurance=True)

print(f"Total Bill: ${bill_amount:.2f}")


Healthcare Data Management - Medication Adherence Tracker

 Notes:

  • What problem does it solve?: It tracks patient medication adherence, ensuring they follow prescribed treatments and improving health outcomes.

  • How can businesses or users benefit from customizing the code?: Healthcare providers can integrate this into their patient management systems to improve follow-up processes.

  • How can businesses or users adopt the solution further, if needed?: This system can be adapted to send reminders via SMS or email to patients.

Python Code:


from datetime import datetime, timedelta


class MedicationTracker:

    def __init__(self, medication_name, start_date, interval_days, doses_per_day):

        self.medication_name = medication_name

        self.start_date = start_date

        self.interval_days = interval_days

        self.doses_per_day = doses_per_day

        self.doses_taken = []


    def record_dose(self):

        today = datetime.now().date()

        dose_time = datetime.now().strftime("%H:%M:%S")

        self.doses_taken.append({'date': today, 'time': dose_time})


    def view_adherence(self):

        adherence = len(self.doses_taken) / (self.doses_per_day * (datetime.now().date() - self.start_date).days)

        return f"Adherence: {adherence * 100:.2f}%"


# Example Usage

med_tracker = MedicationTracker("Blood Pressure Med", datetime(2025, 1, 1), 30, 2)

med_tracker.record_dose()

print(med_tracker.view_adherence())


Healthcare Data Management - Data Visualization for Health Trends

 Notes:

  • What problem does it solve?: It visualizes health trends over time, helping medical professionals make data-driven decisions.

  • How can businesses or users benefit from customizing the code?: Clinics can create custom health dashboards that display trends such as patient conditions or treatment outcomes.

  • How can businesses or users adopt the solution further, if needed?: This can be expanded into more complex dashboards integrated into patient management systems.

Python Code:


import matplotlib.pyplot as plt


# Sample health data (e.g., patient blood sugar levels over 10 days)

days = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

blood_sugar_levels = [85, 90, 92, 91, 95, 96, 97, 98, 100, 103]


# Plot the trend

plt.plot(days, blood_sugar_levels, marker='o', color='b', label='Blood Sugar Level')

plt.xlabel('Days')

plt.ylabel('Blood Sugar Level (mg/dL)')

plt.title('Blood Sugar Trend Over 10 Days')

plt.legend()

plt.show()


Healthcare Data Management - Predictive Health Risk Model

 Notes:

  • What problem does it solve?: It uses patient data to predict health risks, allowing healthcare providers to take proactive steps.

  • How can businesses or users benefit from customizing the code?: Healthcare providers can customize the model for different diseases or conditions by training it on more specific datasets.

  • How can businesses or users adopt the solution further, if needed?: The solution can be further expanded to integrate with electronic health records (EHR) for real-time predictions.

Python Code:


import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.ensemble import RandomForestClassifier


# Example dataset

data = pd.DataFrame({

    'age': [25, 45, 65, 35, 55],

    'blood_pressure': [120, 140, 160, 130, 150],

    'cholesterol': [200, 250, 280, 220, 260],

    'risk': [0, 1, 1, 0, 1]  # 0: low, 1: high

})


X = data[['age', 'blood_pressure', 'cholesterol']]

y = data['risk']


# Train a predictive model

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = RandomForestClassifier()

model.fit(X_train, y_train)


# Predict health risk for a new patient

new_patient_data = pd.DataFrame({'age': [60], 'blood_pressure': [155], 'cholesterol': [270]})

prediction = model.predict(new_patient_data)

print("Predicted Health Risk:", "High Risk" if prediction[0] == 1 else "Low Risk")


Healthcare Data Management - Real-Time Appointment Scheduling

 Notes:

  • What problem does it solve?: It automates and streamlines appointment scheduling for healthcare professionals, improving efficiency and reducing scheduling errors.

  • How can businesses or users benefit from customizing the code?: Clinics can integrate this with existing calendar systems or patient management systems.

  • How can businesses or users adopt the solution further, if needed?: Businesses can adapt this solution to integrate with user interfaces or email notification systems for reminders.

Python Code:


from datetime import datetime, timedelta


class AppointmentScheduler:

    def __init__(self):

        self.appointments = []


    def schedule_appointment(self, patient_name, date_time):

        self.appointments.append({

            'patient_name': patient_name,

            'date_time': date_time

        })


    def view_appointments(self):

        return self.appointments


    def available_slots(self, start_date, end_date, slot_duration=30):

        current_time = start_date

        slots = []

        while current_time <= end_date:

            slots.append(current_time)

            current_time += timedelta(minutes=slot_duration)

        return slots


# Example Usage

scheduler = AppointmentScheduler()


# Get available slots for today

today = datetime.now().date()

start_time = datetime.combine(today, datetime.min.time())

end_time = start_time + timedelta(hours=8)

available_slots = scheduler.available_slots(start_time, end_time)


print("Available Slots:", available_slots)


# Schedule an appointment

scheduler.schedule_appointment('Jane Doe', available_slots[0])

print("Appointments:", scheduler.view_appointments())


Healthcare Data Management - Patient Record Encryption

 Notes:

  • What problem does it solve?: It secures patient records by encrypting sensitive data, ensuring privacy and compliance with healthcare regulations like HIPAA.

  • How can businesses or users benefit from customizing the code?: Healthcare providers can protect patient information, reducing the risk of data breaches and legal liabilities.

  • How can businesses or users adopt the solution further, if needed?: Businesses can expand this by integrating with databases or enhancing encryption algorithms as needed.

Python Code:


from cryptography.fernet import Fernet


# Generate a key for encryption and decryption

key = Fernet.generate_key()

cipher_suite = Fernet(key)


# Encrypt patient data

def encrypt_patient_data(patient_data):

    encrypted_data = cipher_suite.encrypt(patient_data.encode())

    return encrypted_data


# Decrypt patient data

def decrypt_patient_data(encrypted_data):

    decrypted_data = cipher_suite.decrypt(encrypted_data).decode()

    return decrypted_data


# Example

patient_data = "John Doe, DOB: 1990-01-01, Condition: Diabetes"

encrypted = encrypt_patient_data(patient_data)

decrypted = decrypt_patient_data(encrypted)


print("Encrypted:", encrypted)

print("Decrypted:", decrypted)


Thursday, April 3, 2025

Social Media Marketing Automation - Auto-Generate Analytics Report

 Notes:

  • Problem: Gathering analytics manually takes effort.

  • Benefit: Generates automated analytics reports from data.

  • Adoption: Extend to include AI-powered insights.

Python Code:


import pandas as pd

import matplotlib.pyplot as plt


def generate_report(data_file):

    df = pd.read_csv(data_file)

    

    engagement_rate = df["Likes"].sum() / df["Posts"].sum()

    avg_comments = df["Comments"].mean()


    print(f"Engagement Rate: {engagement_rate:.2f}")

    print(f"Average Comments per Post: {avg_comments:.2f}")


    df.plot(x="Date", y=["Likes", "Comments"], kind="line", title="Engagement Trend")

    plt.show()


if __name__ == "__main__":

    generate_report("social_media_data.csv")


Social Media Marketing Automation - Auto-Influencer Outreach

 Notes:

  • Problem: Manually reaching out to influencers is slow.

  • Benefit: Automates sending personalized outreach messages.

  • Adoption: Can integrate AI to tailor outreach messages.

Python Code:


import smtplib

from email.mime.text import MIMEText


def send_email(to_email, influencer_name):

    sender_email = "your_email@example.com"

    sender_password = "your_password"

    

    subject = "Collaboration Opportunity"

    body = f"Hi {influencer_name}, we love your content and would like to collaborate! Let's discuss further."


    msg = MIMEText(body)

    msg["Subject"] = subject

    msg["From"] = sender_email

    msg["To"] = to_email


    with smtplib.SMTP_SSL("smtp.gmail.com", 465) as server:

        server.login(sender_email, sender_password)

        server.sendmail(sender_email, to_email, msg.as_string())


if __name__ == "__main__":

    send_email("influencer@example.com", "Influencer Name")


Social Media Marketing Automation - Auto Story Uploader

 Notes:

  • Problem: Posting Instagram Stories manually takes time.

  • Benefit: Automates story uploads at scheduled times.

  • Adoption: Extend with AI-generated story content.

Python Code:


from selenium import webdriver

import time


def upload_story(username, password, image_path):

    driver = webdriver.Chrome()

    driver.get("https://www.instagram.com/accounts/login/")

    time.sleep(2)


    driver.find_element("name", "username").send_keys(username)

    driver.find_element("name", "password").send_keys(password + Keys.RETURN)

    time.sleep(5)


    driver.get("https://www.instagram.com/stories/create/")

    time.sleep(3)

    

    upload_input = driver.find_element("xpath", "//input[@type='file']")

    upload_input.send_keys(image_path)

    time.sleep(2)


    post_button = driver.find_element("xpath", "//button[contains(text(),'Share')]")

    post_button.click()

    time.sleep(2)


    driver.quit()


if __name__ == "__main__":

    upload_story("your_username", "your_password", "/path/to/image.jpg")


Social Media Marketing Automation - Competitor Monitoring

 Notes:

  • Problem: Manually tracking competitors' social media activity is inefficient.

  • Benefit: Automates competitor post tracking for analysis.

  • Adoption: Extend to track multiple competitors and compare metrics.

Python Code:


import requests

from bs4 import BeautifulSoup


def get_competitor_posts(competitor_username):

    url = f"https://www.instagram.com/{competitor_username}/"

    headers = {'User-Agent': 'Mozilla/5.0'}

    response = requests.get(url, headers=headers)

    

    soup = BeautifulSoup(response.text, 'html.parser')

    posts = soup.find_all('meta', property="og:description")[0]['content']

    

    return posts


if __name__ == "__main__":

    print(get_competitor_posts("competitor_handle"))


Social Media Marketing Automation - Video Caption Generator

 Notes:

  • Problem: Adding captions manually is tedious.

  • Benefit: Automates subtitle generation from video audio.

  • Adoption: Can support multiple languages.

Python Code:


import speech_recognition as sr


def generate_video_captions(audio_file):

    recognizer = sr.Recognizer()

    with sr.AudioFile(audio_file) as source:

        audio = recognizer.record(source)

        return recognizer.recognize_google(audio)


if __name__ == "__main__":

    print(generate_video_captions("video_audio.wav"))


Social Media Marketing Automation - Auto-Reply DM Bot

 Notes:

  • Problem: Manually responding to messages takes time.

  • Benefit: Automates DM replies based on predefined triggers.

  • Adoption: Can integrate with AI for smarter responses.

Python Code:


import time

from selenium import webdriver


def reply_to_dm(driver):

    driver.get("https://www.instagram.com/direct/inbox/")

    time.sleep(3)

    messages = driver.find_elements("xpath", "//div[@class='RK8tT']")

    for message in messages:

        message.click()

        time.sleep(2)

        try:

            reply_box = driver.find_element("xpath", "//textarea[@aria-label='Message...']")

            reply_box.send_keys("Thank you for reaching out! We'll get back to you soon." + Keys.RETURN)

        except:

            pass


if __name__ == "__main__":

    driver = webdriver.Chrome()

    driver.get("https://www.instagram.com/accounts/login/")

    time.sleep(2)

    reply_to_dm(driver)


Social Media Marketing Automation - AI Caption Generator

 Notes:

  • Problem: Writing captions is time-consuming.

  • Benefit: Uses AI to generate captions based on keywords.

  • Adoption: Can integrate with images for auto-captioning.

Python Code:


from transformers import pipeline


def generate_caption(description):

    generator = pipeline("text-generation", model="gpt2")

    return generator(f"Create a social media caption: {description}", max_length=30)[0]['generated_text']


if __name__ == "__main__":

    print(generate_caption("A beautiful sunset over the ocean"))


Social Media Marketing Automation - Sentiment Analysis

Notes:

  • Problem: Businesses struggle to gauge audience sentiment.

  • Benefit: Detects positive, neutral, or negative sentiment.

  • Adoption: Can trigger automated responses based on sentiment.

Python Code:


from textblob import TextBlob


def analyze_sentiment(text):

    blob = TextBlob(text)

    sentiment = blob.sentiment.polarity

    return "Positive" if sentiment > 0 else "Negative" if sentiment < 0 else "Neutral"


if __name__ == "__main__":

    print(analyze_sentiment("I love this product, it’s amazing!"))


Social Media Marketing Automation - Multi-Platform Posting

 Notes:

  • Problem: Posting manually on multiple platforms is repetitive.

  • Benefit: Automates posting across Facebook, Twitter, and Instagram.

  • Adoption: Add scheduling features for optimal timing.

Python Code:


import requests


def post_to_facebook(content):

    url = "https://graph.facebook.com/me/feed"

    data = {'message': content, 'access_token': 'your_access_token'}

    return requests.post(url, data=data).json()


def post_to_twitter(content):

    url = "https://api.twitter.com/1.1/statuses/update.json"

    headers = {'Authorization': 'Bearer your_bearer_token'}

    return requests.post(url, data={'status': content}, headers=headers).json()


if __name__ == "__main__":

    content = "Check out our latest blog post!"

    print(post_to_facebook(content))

    print(post_to_twitter(content))


Social Media Marketing Automation - Hashtag Analyzer

 Notes:

  • Problem: Finding effective hashtags can be challenging.

  • Benefit: Analyzes hashtags for better reach and engagement.

  • Adoption: Extend to track hashtag trends over time.

Python Code:


import requests

from bs4 import BeautifulSoup


def get_hashtag_data(hashtag):

    url = f"https://www.instagram.com/explore/tags/{hashtag}/"

    headers = {'User-Agent': 'Mozilla/5.0'}

    response = requests.get(url, headers=headers)

    

    soup = BeautifulSoup(response.text, 'html.parser')

    script_tag = soup.find('script', {'type': 'text/javascript'}).text

    data = script_tag.split('window._sharedData = ')[1].split(';</script>')[0]

    

    return data


if __name__ == "__main__":

    print(get_hashtag_data("digitalmarketing"))


Social Media Marketing Automation - Engagement Bot

Notes:

  • Problem: Engaging with posts (likes/comments) manually is inefficient.

  • Benefit: Automates engagement, increasing visibility.

  • Adoption: Extend with auto-follow or custom responses.

Python Code:


from selenium import webdriver

from selenium.webdriver.common.keys import Keys

import time


def login_to_instagram(username, password):

    driver = webdriver.Chrome()

    driver.get("https://www.instagram.com/accounts/login/")

    time.sleep(2)

    driver.find_element("name", "username").send_keys(username)

    driver.find_element("name", "password").send_keys(password + Keys.RETURN)

    time.sleep(5)

    return driver


def engage_with_posts(driver, hashtag):

    driver.get(f"https://www.instagram.com/explore/tags/{hashtag}/")

    time.sleep(2)


    posts = driver.find_elements("xpath", "//a[@href]")

    for post in posts[:5]:  

        post.click()

        time.sleep(3)

        try:

            like_button = driver.find_element("xpath", "//span[@aria-label='Like']")

            like_button.click()

            comment_box = driver.find_element("xpath", "//textarea[@aria-label='Add a comment…']")

            comment_box.send_keys("Great post!" + Keys.RETURN)

        except:

            pass

        driver.back()

        time.sleep(2)


if __name__ == "__main__":

    driver = login_to_instagram("your_username", "your_password")

    engage_with_posts(driver, "marketingtips")


Social Media Marketing Automation - Auto-Post Scheduling

 Notes:

  • Problem: Manually posting on social media is time-consuming.

  • Benefit: Automates post scheduling at specific times for consistency.

  • Adoption: Extend with content variation based on engagement insights.

Python Code:


import schedule

import time

from datetime import datetime


def post_to_social_media():

    print(f"Posting to social media at {datetime.now()}...")


def schedule_posts():

    schedule.every().day.at("09:00").do(post_to_social_media)

    schedule.every().day.at("12:00").do(post_to_social_media)

    schedule.every().day.at("18:00").do(post_to_social_media)


    while True:

        schedule.run_pending()

        time.sleep(1)


if __name__ == "__main__":

    schedule_posts()


IoT (Internet of Things) Automation - Smart Energy Usage Tracker

  Notes: Problem Solved: Logs and analyzes power usage from smart meters. Customization Benefits: Track per-device energy and set ale...