Leveraging Supervised Learning to Uncover Patterns in Instacart Orders

• Introduction:

In today’s data-driven world, businesses strive to extract meaningful insights from their datasets to make informed decisions. In this Medium post, I will demonstrate how supervised learning techniques can be applied to the Instacart dataset to answer key questions and inform decision-making processes.

• Question and Stakeholder:

The question at hand is: How can we predict the department to which an item belongs based on the hour of the day it is ordered? The stakeholder interested in this question is the Instacart operations team, as this insight can optimize inventory management, enhance product recommendations, and improve overall customer experience

• Data collection and Description:

To collect the data for this project, I accessed the Instacart Market Basket Analysis dataset, which is available on Kaggle. This dataset, stored in a CSV file format, contains fields such as order_hour_of_day, department, num_orders_hour, and tot_orders_dept. These fields were generated based on customer orders, providing insights into the hour of the day each order was placed, the department of each item, and the total number of orders per hour and department.

Python libraries like Pandas were instrumental in reading and manipulating the dataset. Additionally, I imported necessary libraries such as train_test_split, RandomForestClassifier, classification_report, and confusion_matrix from scikit-learn to facilitate data analysis and model evaluation.

• Model Evaluation and justification

For this analysis, we’ve opted for a classification model. Our objective is to predict the department to which an item belongs based on the hour of the day it is ordered. Since the target variable (department) is categorical, classification is the appropriate approach. Regarding the features used for the supervised model, we’ve selected the order_hour_of_day as the sole predictor.

To train our model, we split the dataset into features (X) and the target variable (y). Then, utilizing the train_test_split function from scikit-learn, we divided the data into training and testing sets. We employed a Random Forest classifier, initialized with 100 estimators for training the model. The RandomForestClassifier from scikit-learn was utilized, with a random_state parameter set to ensure reproducibility.

After training the model, we evaluated its performance using various metrics such as accuracy, precision, recall, and F1-score. Additionally, we examined the confusion matrix to identify any misclassifications.

• Misclassification Analysis:

After applying our trained Random Forest classifier to the dataset, we investigated five samples where the model made incorrect predictions. These misclassifications may have arisen due to various factors such as outliers, noise in the data, or the inherent complexity of customer ordering patterns. It’s essential to delve deeper into these misclassifications to understand the nuances of the data and potentially refine our model’s performance.

• Data Analysis:

Our analysis of the Instacart dataset using supervised learning techniques revealed intriguing insights into customer ordering behavior, particularly in relation to the time of day. By employing a Random Forest classifier, we aimed to predict the department to which items belong based on the hour of the day they were ordered. However, the classification model exhibited low performance metrics, with an accuracy of 0%. Precision, recall, and F1-scores for most departments were consistently low, suggesting challenges in accurately predicting departmental recommendations. The confusion matrix highlighted numerous misclassified samples, indicating inaccuracies in the model’s predictions. These findings underscore limitations in the current approach, necessitating further investigation and refinement efforts to enhance model performance and mitigate potential biases in the dataset.

• Data Cleaning and Limitations:

During the preprocessing stage, I addressed missing values, handled outliers, and standardized the dataset to ensure the model’s robustness. However, it’s crucial to acknowledge the limitations of the analysis. Biases inherent in the data, such as seasonal variations or customer preferences, may impact the model’s performance and the generalizability of the findings. Additionally, common bugs encountered during data cleaning, such as handling categorical variables or encoding categorical data, were addressed to ensure the accuracy and reliability of our results.

Conclusion:

In conclusion, while the analysis provides valuable insights into customer ordering behavior and operational trends within the Instacart dataset, it’s essential to recognize the limitations and potential biases inherent in the data. By leveraging supervised learning techniques, we can gain a deeper understanding of customer preferences and optimize business strategies to enhance service quality and customer satisfaction.

• Data Sources:
  Instacart Market Basket Analysis. Kaggle. (n.d.). https://www.kaggle.com/c/instacart-market-basket-analysis/
• Github: https://github.com/larissakimberly4/INST-414-spr22.git