I would like you to helo me with my assignment. "Real-World Applications of Data Science"
Assignment Description
In this assignment, you will be asked to identify a real-world problem that could be solved using data science techniques and the CRISP-DM framework we defined in Week 1. Please note that this assignment follows the CRISP-DM process for data mining, which is a widely used methodology for approaching data science problems. It does not require programming or technical skills, but it requires you to think critically about real-world problems and potential solutions using data science techniques. It also allows you to develop communication skills by summarizing technical concepts for a general audience. 

Assignment Structure
To complete the assignment, please follow the steps below.

Steps
Business Understanding: Identify a real-world problem that could be solved using data science techniques and define the objectives of the analysis. Examples could include predicting customer churn for a telecommunications company or identifying fraudulent activity in financial transactions.
Data Understanding: Research potential data sources for the problem and evaluate their suitability for analysis. Consider the size and complexity of the data as well as any limitations or biases that may exist.
Data Preparation: Identify potential problems that may emerge in collecting, cleaning, and preprocessing the data. You can get a sense of this by researching similar case studies to the one you have selected. Remember that data preparation includes removing missing or irrelevant data, converting categorical variables to numeric variables, and addressing any other issues identified during the data understanding phase.
Modeling: discuss potential approaches to model the problem and evaluate potential solutions. This may involve using predictive modeling, natural language processing, or machine learning, depending on the problem and data sources. Think about what analytical approach you will use in analyzing your data (high code versus low and no code) and justify your choice. 
Evaluation: In this stage, you only need parameters that might be used to assess the accuracy and precision of your model and whether you need to use alternative models to compare.
Deployment: Prepare a report summarizing the problem, data sources, and potential solutions, and communicate the results to stakeholders. Consider the implications of the analysis for the business or industry, and identify any further steps that may be necessary. Also, make sure you use the elements of effective communication you learned in Week 3. 
Process documentation: You will need to provide process documentation and upload this as part of your assignment.
Bibliography: Please provide a bibliography using an accepted reference style of your choice (e.g., APA, MLA, etc.) You may use this page as a guide. You must also cite work within your project.
As part of this project, you may utilize ChatGPT or other generative AI tools. Adhere to the following ethical guidelines:

Attribution: Clearly credit any AI-generated content or assistance in your project.
Originality: Use AI as a tool for ideation and problem-solving, not to replace your original thought process and contribution.
Integrity: Do not use AI to generate misleading or falsified content or data.
Fair Use: Use AI-generated outputs within the scope of fair use, avoiding copyright infringements.
Privacy: Ensure that the use of AI respects privacy, does not disclose sensitive information, and complies with applicable data protection laws.
Bias Awareness: Be critical of potential biases in AI outputs and address them in your methodology.
Transparency: When presenting your findings, be transparent about the extent of AI usage and its impact on your results.
Assignment Format
You must submit a 3-5-page Word or PDF document. To get full credit for this assignment, please be sure to address all of the points mentioned above in the "deployment" section. 

In my case, I want you to work on "Conservation efforts" as Real-World problem our environment is facing due to climate change. I want to use Data science to helps track and analyze wildlife populations, monitor deforestation rates, and assess the effectiveness of conservation efforts. This information is crucial for making informed decisions about resource management, protecting endangered species, and preserving biodiversity.