The assessment for Section 2 involves correcting, enhancing, and developing Python scripts to calculate projected financial losses for buildings based on provided data about climate hazards. You are expected to complete Exercises 1, 2, and 3 at home. Exercise 4 is a live interview task.
- Slack Channel Invitation: You will receive an invitation via email to join our interview Slack channel, sent to the email address provided to your Climate X recruiter. Please follow the link in this email to join the channel. This channel will serve as the primary mode of communication throughout the assignment process and will be used to distribute materials for the live interview assessment.
- Submission Instructions: After completing Section 1 and Section 2 (Exercises 1-3), please submit your work in the Slack channel. You can provide either links to your work or directly upload the submissions in the requested format. Ensure that this is done prior to your interview date. Your interviewers will confirm receipt of your submissions.
- Interview Day Provision: On the day of your interview, a laptop and a private room will be made available to you. All necessary libraries and packages required for the task will be pre-installed on the laptop. You will have 30 minutes to complete Exercise 4 during the live interview. After completion, you will present your working code to the interviewers.
You are provided with a Python script (exercise1_losses_calculator.py) and a JSON file (data.json) containing data for five buildings. This script is designed to calculate total projected financial losses, taking into account factors such as inflation over time, the probability of hazard occurrence, and the process of discounting future losses to their present value.
- Future Cost Calculation: Calculate the future construction cost by adjusting the current cost using the inflation rate as an exponent. This method compounds the cost annually, reflecting the cumulative effect of inflation over the specified number of years.
- Risk-Adjusted Loss Calculation: Determine the risk-adjusted loss by modifying the future construction cost with the likelihood of experiencing a hazard. For each building, multiply its future construction cost by its hazard probability to assess the potential financial impact if the hazard were to occur.
- Discounting to Present Value: To calculate the present value of the estimated future losses, apply a discounting process that reflects the principle that future financial losses are less valuable in today's terms. For each building, determine the present value by dividing the risk-adjusted cost by the discount rate plus one. This method translates the future financial impact into an equivalent amount in today's dollars, considering the time value of money.
- Total Projected Loss Calculation: Sum the present values of the estimated losses from all buildings to arrive at the total projected financial impact. This total provides a comprehensive view of the potential losses across the portfolio, combining all individual risk assessments.
Each entry in the data.json file includes:
buildingId: Identifier for the buildingfloor_area: Floor area in square metersconstruction_cost: Construction cost per square meterhazard_probability: Likelihood of experiencing a climate-related hazardinflation_rate: Annual inflation rate applicable to the construction cost
- Review and correct any errors in the provided Python script.
- Ensure the script accurately implements the calculations as described.
- Output the total projected financial loss after running the script.
Implement a complex mathematical formula in Python to calculate the potential financial losses estimate ('LE') using detailed attributes of the same five buildings.
The script should output the individual and total estimated losses for all properties.
- Decompose and implement the specified complex loss estimation formula using data from
data.json. - Ensure the output is accurate and reflective of the calculated losses.
Provide a written explanation of how you would scale your Python script from Exercise 2 to efficiently handle a dataset of 1,000,000 buildings.
- Scalability Analysis: Evaluate the current script's performance with a larger dataset.
- Optimization Strategies: Suggest improvements for script efficiency and scalability.
- Resource Management: Discuss how to effectively manage memory and processing power for large datasets.
- Example Code: Optionally, include Python code snippets that showcase your scalability solutions.
- Written Explanation: Detail your strategy for scaling the script, including recommended technologies or methods and their effectiveness.
- Code Snippets: Optionally, provide code examples to support your explanations.
Develop a small React frontend application during the live interview that interfaces with the backend to upload data.json, calculate losses, and display the results.
- User Interface: Implement functionalities for file uploading, processing through a 'Calculate Losses' button, and result display.
- Functionality: Ensure the application handles data upload, interacts with the backend, and displays the losses accurately.
- Use the provided boilerplate React code to set up the basic functionalities.
- Focus on creating a user-friendly and effective interface.
- Be prepared to discuss your implementation and design choices during the interview.
- Complete Exercises 1, 2, and 3 at home and submit them before the interview.
- Exercise 4 will be conducted live. Familiarize yourself with the necessary technologies and prepare for live coding.
- Document any assumptions or important decisions in your approach.
# Run Flask API on port 5000
flask --app app run