Aprenda Forecasting Future Earnings | Optimizing Freelance Business with Python

Forecasting is a method of using historical data and upcoming information to predict future outcomes. For freelancers, forecasting earnings is a powerful way to gain clarity about financial expectations and make informed decisions. By projecting your income for the next few months, you can prepare for fluctuations, plan investments, and avoid cash flow surprises. This proactive approach is especially valuable in freelancing, where income can be irregular and dependent on project schedules.


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# Hardcoded data: last 6 months of earnings and upcoming scheduled projects
past_earnings = [3200, 3400, 3100, 4000, 3800, 3700]  # dollars per month
scheduled_projects = [
    {"month": "July", "amount": 2000},
    {"month": "August", "amount": 2500},
    {"month": "September", "amount": 1800}
]

# Calculate average past earnings
average_past = sum(past_earnings) / len(past_earnings)

# Projected earnings for next 3 months
projected_earnings = []
for i, project in enumerate(scheduled_projects):
    # Combine average with scheduled project for a simple forecast
    projection = average_past + project["amount"]
    projected_earnings.append({"month": project["month"], "projected": projection})

# Display projections
for item in projected_earnings:
    print(f"{item['month']}: Projected earnings = ${item['projected']:.2f}")

Simple forecasting techniques, such as calculating the average of recent earnings and adding the value of scheduled projects, can provide a quick estimate of what you might expect in the coming months. This approach helps you spot trends and anticipate periods where earnings may rise or fall. When reviewing forecast results, look for months where income is lower than your average—these are potential gaps that may need attention, such as seeking new clients or additional projects.


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def summarize_projections(projected_earnings, average_past):
    print("Summary of Projected Earnings:")
    for item in projected_earnings:
        status = "OK"
        if item["projected"] < average_past:
            status = "Potential gap"
        print(f"{item['month']}: ${item['projected']:.2f} - {status}")

# Example usage:
# past_earnings and projected_earnings from previous code
summarize_projections(projected_earnings, average_past)

Tudo estava claro?

Obrigado pelo seu feedback!

Seção 3. Capítulo 6

Pergunte à IA

Pergunte o que quiser ou experimente uma das perguntas sugeridas para iniciar nosso bate-papo

Suggested prompts:

Can you explain why the projected earnings in the summary are higher than in the previous output?

How can I adjust the forecast if I expect fewer projects in the coming months?

What other methods can I use to make my earnings forecast more accurate?

Deslize para mostrar o menu


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# Hardcoded data: last 6 months of earnings and upcoming scheduled projects
past_earnings = [3200, 3400, 3100, 4000, 3800, 3700]  # dollars per month
scheduled_projects = [
    {"month": "July", "amount": 2000},
    {"month": "August", "amount": 2500},
    {"month": "September", "amount": 1800}
]

# Calculate average past earnings
average_past = sum(past_earnings) / len(past_earnings)

# Projected earnings for next 3 months
projected_earnings = []
for i, project in enumerate(scheduled_projects):
    # Combine average with scheduled project for a simple forecast
    projection = average_past + project["amount"]
    projected_earnings.append({"month": project["month"], "projected": projection})

# Display projections
for item in projected_earnings:
    print(f"{item['month']}: Projected earnings = ${item['projected']:.2f}")


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def summarize_projections(projected_earnings, average_past):
    print("Summary of Projected Earnings:")
    for item in projected_earnings:
        status = "OK"
        if item["projected"] < average_past:
            status = "Potential gap"
        print(f"{item['month']}: ${item['projected']:.2f} - {status}")

# Example usage:
# past_earnings and projected_earnings from previous code
summarize_projections(projected_earnings, average_past)

Tudo estava claro?

Obrigado pelo seu feedback!

Seção 3. Capítulo 6

Forecasting Future Earnings

1. What is the benefit of forecasting earnings for freelancers?

2. Which Python data structure is suitable for storing monthly earnings data?

3. How can forecasting help in planning for slow periods?

Forecasting Future Earnings

1. What is the benefit of forecasting earnings for freelancers?

2. Which Python data structure is suitable for storing monthly earnings data?

3. How can forecasting help in planning for slow periods?