Junior Data Scientist - Food Processing

For the first week of your internship, your task is to perform exploratory data analysis (EDA) on a food processing dataset. Your objective is to uncover patterns, insights, or relationships that could be useful for subsequent analysis. The dataset should be a publicly available food processing dataset of your choice. The deliverable for this task is a DOC file summarizing your findings. Include visualizations (screenshots), code snippets, and a detailed explanation of your findings. Key steps include data collection, data cleaning, data exploration (univariate, bivariate, multivariate analysis), and summarizing findings. Your work will be evaluated based on the thoroughness of the EDA, the clarity and coherence of your explanations, and the appropriateness of your chosen visualizations.

For the second week, your task is to perform data preprocessing on a food processing dataset. The objective is to prepare the dataset for further analysis. The dataset should be a publicly available food processing dataset of your choice. The deliverable for this task is a DOC file detailing your preprocessing steps, any challenges encountered and how they were resolved, and how the dataset changed before and after preprocessing. Key steps include data cleaning (handling missing data, outliers), data transformation (normalization, scaling), and data reduction. Your work will be evaluated based on the effectiveness of your preprocessing methods, your problem-solving skills in handling challenges, and the clarity of your explanations.

In the third week, you will focus on feature engineering. The objective is to create meaningful new features from the existing data in a food processing dataset. The dataset should be a publicly available food processing dataset of your choice. The deliverable for this task is a DOC file detailing the feature engineering steps, the rationale behind each new feature, and how these new features could be useful for a machine learning model. Key steps include brainstorming new features, creating new features, and evaluating the usefulness of these features. Your work will be evaluated based on the creativity and usefulness of the engineered features, and the clarity of your explanations.

For the fourth week, your task is to build a predictive model using the preprocessed and feature-engineered dataset from the previous weeks. The aim is to predict an outcome of your choice that is relevant to food processing. The deliverable for this task is a DOC file detailing the model building process, the performance of the model, and an interpretation of the results. Key activities include model selection, model training, model evaluation, and result interpretation. Your work will be evaluated based on the appropriateness of your model choice, the performance of your model, and the clarity of your explanations.

For the fifth week, your task is to optimize and validate the predictive model from the previous week. The objective is to improve the model's performance and ensure that it generalizes well to new data. The deliverable for this task is a DOC file detailing the optimization and validation process, the performance improvements achieved, and an interpretation of the results. Key steps include hyperparameter tuning, cross-validation, and performance evaluation on a hold-out set. Your work will be evaluated based on the improvement in model performance, the robustness of the validation process, and the clarity of your explanations.