AI for financial analysis and forecasting in AI for Everyone - Mini Project: Build & Apply

AI for Financial Analysis and Forecasting

📖 Scenario: You work in a finance team that wants to use AI to help analyze past sales data and predict future sales. This will help the company plan better and make smarter decisions.

🎯 Goal: Build a simple step-by-step understanding of how AI can be used for financial analysis and forecasting by organizing data, setting parameters, applying AI logic, and completing the forecasting setup.

📋 What You'll Learn

Create a data structure with monthly sales figures

Add a threshold value for minimum sales to consider

Use AI logic to filter and prepare data for forecasting

Complete the setup by defining the forecast period

💡 Why This Matters

🌍 Real World

Companies use AI to analyze past financial data and predict future trends to make better business decisions.

💼 Career

Financial analysts and data scientists use these skills to prepare reports and forecasts that guide company strategy.

1

DATA SETUP: Create monthly sales data

Create a dictionary called monthly_sales with these exact entries: 'January': 12000, 'February': 15000, 'March': 13000, 'April': 17000, 'May': 16000.

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# Create the monthly_sales dictionary with exact values
# Your code here

Need a hint?

Use curly braces to create a dictionary and separate each month and sales value with a colon.

2

CONFIGURATION: Set minimum sales threshold

Create a variable called min_sales_threshold and set it to 14000 to filter out months with low sales.

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monthly_sales = {
    'January': 12000,
    'February': 15000,
    'March': 13000,
    'April': 17000,
    'May': 16000
}
# Set the minimum sales threshold
# Your code here

Need a hint?

Assign the number 14000 to the variable named min_sales_threshold.

3

CORE LOGIC: Filter months meeting the sales threshold

Create a dictionary called filtered_sales that includes only months from monthly_sales where the sales are greater than or equal to min_sales_threshold. Use a dictionary comprehension with month and sales as variables.

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monthly_sales = {
    'January': 12000,
    'February': 15000,
    'March': 13000,
    'April': 17000,
    'May': 16000
}
min_sales_threshold = 14000
# Filter sales using dictionary comprehension
# Your code here

Need a hint?

Use a dictionary comprehension with for month, sales in monthly_sales.items() and an if condition.

4

COMPLETION: Define forecast period

Create a variable called forecast_months and set it to 3 to represent the number of months to forecast ahead.

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monthly_sales = {
    'January': 12000,
    'February': 15000,
    'March': 13000,
    'April': 17000,
    'May': 16000
}
min_sales_threshold = 14000
filtered_sales = {month: sales for month, sales in monthly_sales.items() if sales >= min_sales_threshold}
# Set the forecast period
# Your code here

Need a hint?

Assign the number 3 to the variable named forecast_months.