Machine Learning Data Analyst - Agribusiness

Your first task as a Machine Learning Data Analyst intern focuses on data collection and cleaning.

You will be required to find a publicly available dataset related to agribusiness.

The dataset could be about crop yields, livestock production, weather patterns, or any other topic relevant to agribusiness.

After obtaining the dataset, your task is to clean and preprocess the data.

This involves handling missing values, removing duplicates, dealing with outliers, and normalizing the data if necessary.

You will write a comprehensive report detailing the process you followed, challenges you encountered, and how you overcame them.

The report should also include the statistical summary of the dataset.

The final deliverable is a DOC file including your cleaned dataset and the detailed report.

The task will be evaluated based on the complexity of the dataset chosen, the thoroughness of the cleaning process, and the clarity of the report.

Week 2 focuses on Exploratory Data Analysis (EDA).

Using the cleaned dataset from Week 1, perform a detailed EDA.

This involves visualizing data distributions, relationships, and patterns using appropriate graphs and charts.

You should also perform statistical analyses to understand the data better.

Your task includes identifying any trends, correlations, or anomalies in the dataset.

The final deliverable is a DOC file containing all visualizations and a detailed description of your findings, including any insights you were able to draw from the data.

You will be evaluated on the depth of your analysis, the clarity of your visualizations, and the insights you were able to draw from the data.

On Week 3, your task is to create a machine learning model using the dataset.

Depending on the nature of your dataset, you may choose to do a regression, classification, or clustering task.

You should split your dataset into training and testing sets to validate your model.

Discuss why you chose the specific machine learning algorithm and any assumptions made.

The final deliverable is a DOC file with code snippets of your model, a clear explanation of your process, and an evaluation of your model's performance.

You will be evaluated on the appropriateness of the chosen algorithm, the quality of your model, and the clarity of your explanations.

Your task for Week 4 is to synthesize your findings and make recommendations based on your model.

You should interpret the results in a way that would be understandable to someone without a background in data analysis.

Discuss the implications of your findings to the agribusiness sector.

Additionally, propose actionable recommendations for agribusiness stakeholders based on your analysis.

The final deliverable is a DOC file containing your comprehensive report.

You will be evaluated on the depth of your insights, the practicality of your recommendations, and the overall quality of your report.