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RE: Payment for Coding done

thedragonslayer (64)in #codingfund • 10 months ago

Booking Optimization: For frequent travelers, having access to historical flight prices can help optimize booking decisions. You can identify patterns in pricing fluctuations and choose the best times to book flights.

10 months ago in #codingfund by thedragonslayer (64)

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thedragonslayer (64) 10 months ago

You can expand and customize this script to provide more detailed recommendations, consider factors like airline preferences or budget constraints, and integrate it into a user-friendly application for frequent travelers.

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thedragonslayer (64) 10 months ago

In this script:

flight_data represents the sample flight price data for different destinations. Replace this with your actual data or fetch it through web scraping or an API.
The recommend_booking_time function filters the flight data for the specified destination, calculates monthly average prices, and identifies the month with the lowest average price.
The script provides a recommendation for the best time to book a flight to the specified destination based on historical price trends.

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thedragonslayer (64) 10 months ago

Sample flight price data (You can replace this with your actual data)

flight_data = [
{"destination": "Paris", "date": "2023-09-01", "price": 500},
{"destination": "Paris", "date": "2023-09-02", "price": 480},
{"destination": "Paris", "date": "2023-09-03", "price": 520},
{"destination": "Paris", "date": "2023-09-04", "price": 490},
{"destination": "Paris", "date": "2023-09-05", "price": 510},
# Add more flight data here...
]

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1 vote

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thedragonslayer (64) 10 months ago

Function to recommend the best time to book a flight to a destination

def recommend_booking_time(flight_data, destination):
# Filter flight data for the specified destination
destination_data = [flight for flight in flight_data if flight["destination"] == destination]

if not destination_data:
    return "No data available for this destination."

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1 vote

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thedragonslayer (64) 10 months ago

Sort flight data by date

sorted_data = sorted(destination_data, key=lambda x: x["date"])

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thedragonslayer (64) 10 months ago

Calculate the average price for each month

monthly_average_prices = {}
for flight in sorted_data:
    year_month = flight["date"][:7]  # Extract year and month (e.g., "2023-09")
    if year_month not in monthly_average_prices:
        monthly_average_prices[year_month] = {"total_price": 0, "count": 0}
    monthly_average_prices[year_month]["total_price"] += flight["price"]
    monthly_average_prices[year_month]["count"] += 1

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1 vote

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thedragonslayer (64) 10 months ago

Calculate the average price for each month

for month, data in monthly_average_prices.items():
    data["average_price"] = data["total_price"] / data["count"]

$0.58

1 vote

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thedragonslayer (64) 10 months ago

Find the month with the lowest average price

best_month = min(monthly_average_prices, key=lambda x: monthly_average_prices[x]["average_price"])
best_month_avg_price = monthly_average_prices[best_month]["average_price"]

return f"The best time to book a flight to {destination} is in {best_month} with an average price of ${best_month_avg_price:.2f}."