Junior Machine Learning Data Analyst - Agribusiness

Task Objective

Your task for Week 1 is to create a comprehensive strategy for data collection and preprocessing specifically tailored to the agribusiness domain. You will plan the approach required to gather relevant data from publicly available sources and outline the steps necessary to prepare the data for analysis, ensuring data quality and consistency.

Expected Deliverables

• A DOC file containing the detailed strategy document.
• A clear plan describing the types of data to be collected and the rationale behind your choices.
• Step-by-step guidelines explaining your data cleaning, transformation, and validation methods.

Key Steps to Complete the Task

1. Research and list potential public data sources relevant to agribusiness such as weather data, crop yield statistics, market prices, etc.
2. Develop a data collection plan that addresses how to retrieve and handle this data.
3. Design a detailed preprocessing plan including techniques for handling missing values, normalization, and data integration.
4. Explain potential challenges and propose contingency measures for data inconsistencies.
5. Compile the strategy in a well-organized DOC file, ensuring clarity and logical flow.

Evaluation Criteria

Your submission will be evaluated on the depth of research, clarity of your strategy, feasibility of your methodology, and the completeness of your steps. Ensure your document is detailed, well-structured, and contains more than 200 words, addressing all necessary aspects of data collection and preprocessing without reference to any internal resources.

This task requires an estimated commitment of 30 to 35 hours and is fully self-contained. No additional resources or human interaction are necessary.

Task Objective

In Week 2, your focus shifts to performing an Exploratory Data Analysis (EDA) on the agribusiness data you would hypothetically acquire. Your goal is to identify trends, correlations, and outlier patterns that can provide actionable insights.

Expected Deliverables

• A DOC file containing a detailed EDA report.
• Visual representations (screenshots or embedded images) of the graphs and plots you would generate.
• An explanation of the findings and potential implications in the context of agribusiness.

Key Steps to Complete the Task

1. Outline a plan for analyzing hypothetical or publicly sourced agribusiness data.
2. Identify key variables such as weather patterns, soil quality, crop performance, and market trends.
3. Propose methods and tools for data visualization such as histograms, scatter plots, and correlation matrices.
4. Describe how each visualization will help in detecting trends and anomalies with a view to supporting business decisions.
5. Write a detailed analysis of your findings, addressing any anomalies and suggesting potential areas for further investigation.

Evaluation Criteria

Your report will be evaluated based on the thoroughness of your EDA plan, the clarity of your visualizations, and the depth of your analytical insights. Ensure that your DOC file is well-organized, exceeds 200 words, and clearly articulates every step involved from data analysis to interpretation, thereby demonstrating a robust understanding of exploratory data methodologies.

Task Objective

During Week 3, your challenge is to design a baseline predictive model aimed at forecasting key agribusiness outcomes such as crop yield, market pricing, or resource allocation efficiency. Incorporate feature engineering techniques that could potentially enhance model performance using publicly available indicators relevant to agribusiness.

Expected Deliverables

• A DOC file that outlines your predictive modeling strategy and feature engineering plan.
• A clear explanation of chosen variables, model assumptions, and anticipated challenges in data integration.
• Proposed methods for model validation and performance assessment.

Key Steps to Complete the Task

1. Define the business problem and specify the prediction objective in an agribusiness context.
2. Identify potential features using public data indicators like meteorological data, historical crop data, and market trends.
3. Discuss the selection of a baseline modeling technique and justify your choice (e.g., linear regression, decision trees, etc.).
4. Outline data preprocessing steps required for the model, emphasizing feature scaling and transformation.
5. Detail a conceptual framework for model training, testing, and validation, including potential metrics for evaluating model performance.

Evaluation Criteria

Your submission will be evaluated on the logic behind feature selection, the clarity of your model design, and the robustness of your evaluation methodology. Ensure your document exceeds 200 words, is structured logically, and all explanations are clear enough for someone unfamiliar with your approach to understand the rationale behind each step.

Task Objective

For Week 4, you are to develop a comprehensive model evaluation and error analysis framework. This task requires you to analyze the performance of your previously proposed predictive model, identify potential sources of error, and suggest improvements. While this is a conceptual exercise, you must simulate the evaluation process using theoretical scenarios and public data references within the agribusiness context.

Expected Deliverables

• A DOC file detailing your evaluation framework including performance metrics and error analysis techniques.
• A discussion of potential biases and limitations in your model.
• Suggestions for iterative improvements and subsequent steps for model enhancement.

Key Steps to Complete the Task

1. Define a set of performance metrics suitable for assessing the baseline predictive model (e.g., RMSE, MAE, R-squared).
2. Outline a thorough error analysis plan, detailing how you would diagnose model performance issues.
3. Discuss common sources of error or bias in agribusiness data and elaborate on mitigation strategies.
4. Create a simulated scenario demonstrating how your evaluation framework would operate in practice.
5. Summarize your findings and propose detailed, step-by-step improvement strategies to refine model performance.

Evaluation Criteria

Your document will be assessed based on the depth of your evaluation approach, the clarity of your error analysis, and the realism of your simulated scenarios. Include comprehensive discussions that clearly outline the methods and best practices in model assessment while ensuring the document is more than 200 words and presented in a structured, methodical manner.

Task Objective

The final week's task focuses on synthesizing all your previous work into a comprehensive final report. This task will require you to craft a clear and actionable document that summarizes your data strategy, exploratory analysis, predictive modeling, and evaluation findings. Additionally, you should outline strategic recommendations for leveraging these insights within the agribusiness sector.

Expected Deliverables

• A DOC file containing a final comprehensive report integrating all aspects of your internship tasks.
• A summary section that synthesizes your overall approach, key findings, and business implications.
• Actionable recommendations for improving agribusiness operational strategies using data-driven insights.

Key Steps to Complete the Task

1. Consolidate the findings of your data collection, EDA, predictive modeling, and evaluation tasks.
2. Draft an executive summary that clearly explains your overall methodology, main insights, and how the project addresses key agribusiness challenges.
3. Provide a detailed discussion of how the insights derived can be applied to enhance decision-making and operational efficiencies.
4. Outline strategic recommendations, ensuring they are actionable and supported by the data analysis performed in previous weeks.
5. Include reflections on potential limitations and future directions for further data projects in agribusiness.

Evaluation Criteria

Your final report will be evaluated on its clarity, thoroughness, and practicality. Ensure the report is well-written, logically structured, and exceeds 200 words. The document should comprehensively cover every part of your internship tasks and provide credible, data-driven recommendations to improve agribusiness operations. This self-contained exercise should be completed independently and require approximately 30 to 35 hours of work.