Set Up An Analytical Program Apply Data Structures Object ✓ Solved

Set Up An Analytical Programapply Data Structures Object

Taking the Rossman data set from Kaggle, you will use either the Python or R programming language to read in the associated data set. Next, you are to load the data into either an associative array or frame-based representation to make it suitable to analysis. Next, you are to apply the Python or R libraries which may include, but not be limited to, the R (CART) module or the associated Python (scikit learn). Perform the analysis and output the file containing only the limited feature set. Note: you will have only a single submission which will be your source code in a plain text file and output generated, and it will be implemented in your preference of either the Python or R programming language.

Paper For Above Instructions

In this assignment, the goal is to build an analytical model using the Rossman sales dataset obtained from Kaggle. The focus is on applying data structures, such as data frames or associative arrays, and utilizing decision tree analysis to identify attributes influencing high or low sales outcomes in clothing stores. This process involves multiple stages, including data reading, transformation, analysis, and output, all of which require proficient use of programming fundamentals and libraries.

Introduction

The primary purpose of this assignment is to showcase competency in setting up analytical programs, applying data structures, and implementing decision tree models with either R or Python. Leveraging the Rossman dataset, the analysis aims to uncover the key attributes that contribute to profitable sales scenarios. Using a rigorous approach to data handling and model building ensures reliable insights that can aid decision-making in retail business strategies.

Data Acquisition and Loading

The first step involves downloading the Rossman dataset from Kaggle, which provides detailed sales data for various stores. The dataset includes multiple attributes such as store type, assortment, competition distance, promotional activity, and other relevant variables.

Using Python or R, the dataset will be read into a suitable data structure. In Python, this involves reading CSV files directly into Pandas DataFrames, which offer efficient data manipulation and analysis capabilities. In R, the data is loaded into data frames, which are integral to R’s data analysis ecosystem.

Data Preparation and Transformation

Following data loading, initial data cleaning is necessary. This includes handling missing values, encoding categorical variables, and normalizing data if needed. The dataset will then be split based on sales performance into two groups: stores with sales over the median and those below it. This binarization facilitates the decision tree analysis to identify attributes linked with high or low sales.

Decision Tree Modeling

Next, the core analytical task involves applying a decision tree algorithm. In Python, the scikit-learn library provides the DecisionTreeClassifier, which can be used to model the relationship between store attributes and sales performance. In R, the rpart package offers similar functionality through the CART algorithm.

The model uses features such as store type, store size, competition distance, and promotional activities to classify whether a store's sales are above or below the median.

Model Output and Feature Selection

After training, the decision tree’s structure reveals which features most significantly influence sales performance. The output will include a set of attributes that are critical predictors. The final output file should contain only the limited feature set identified by the model, providing a concise and actionable summary of the key drivers of sales performance.

Implementation

The implementation involves writing a single, comprehensive source code script in either Python or R. This script reads in the dataset, preprocesses the data, performs the split at the median sales, trains the decision tree model, and outputs the selected features. The output should be a clean, readable file showing the relevant attributes deemed most influential in sales performance.

Conclusion

This project demonstrates proficiency in setting up analytical workflows, applying appropriate data structures, leveraging machine learning libraries, and interpreting model results to derive business insights. The exercise also emphasizes the importance of data preparation, feature selection, and accurate model deployment in data science applications.

References

• Scikit-learn: Machine Learning in Python. (2020). Pedregosa et al. Journal of Machine Learning Research, 12, 2825-2830.
• Kaggle: Rossmann Store Sales Dataset. Retrieved from https://www.kaggle.com/c/rossmann-store-sales
• R Core Team. (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
• Therneau, T. (2015). rpart: Recursive Partitioning and Regression Trees. R package version 4.1-15.
• Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining.
• Harrell Jr, F. E. (2015). Regression modeling strategies. Springer.
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer.
• Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer.
• Data Science Handbook: Essential Tools for Working with Data. (2018). Jake VanderPlas.
• Pedregosa, F., et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830.