Questions About Supermarket's New Organic Product Line

Questiona Supermarket Is Offering A New Line Of Organic Products The

Questiona Supermarket is offering a new line of organic products. The supermarket's management wants to determine which customers are likely to purchase these products. The supermarket has a customer loyalty program. As an initial buyer incentive plan, the supermarket provided coupons for the organic products to all of the loyalty program participants and collected data that includes whether these customers purchased any of the organic products. The ORGANICS dataset contains 13 variables and over 22,000 observations.

You are asked to perform multiple data processing and modeling tasks, including data quality check, imputation, variable encoding, model training, and comparison of model performance, with specific steps involving SAS code and procedures.

Paper For Above instruction

The goal of this analysis is to understand customer purchase behaviors related to organic products based on a comprehensive dataset collected from supermarket loyalty program participants. The dataset includes demographic, regional, and transactional variables. The overarching aim is to perform data cleaning, exploratory data analysis, feature engineering, and model building to identify key predictors of organic product purchase, and evaluate the performance of different modeling approaches.

Data Preparation and Quality Check

The initial step involves importing the dataset (`organics.csv`) into SAS. The variables present include numeric (interval) and categorical (nominal) types, with some variables such as `DemCluster` and `TargetAmt` specified to be removed from analysis. The `ID` variable, serving as a customer identifier, should be retained solely for referencing, not used as a predictor.

A thorough data quality check is essential. The distribution of continuous variables must be visually examined through histograms using SAS’s `proc univariate`; histograms reveal skewness and extreme values. Variables with skewed distributions can influence modeling, requiring transformations such as logarithmic adjustments. Simultaneously, missing values need to be identified via `proc univariate` and `proc freq`, assessing their extent and patterns within both continuous and categorical variables. Since the dataset does not provide false or unreasonable values, focus is on missing data.

Handling Missing Values

Continuous variables with missing values are imputed using their median, computed with `proc means`. To preserve information about missingness, binary indicators are added—`1` if a value was missing and `0` otherwise. Categorical variables with missing data are replaced with specific categories; for instance, variables other than `DemGender` have missing entries replaced with ‘Missing’, whereas `DemGender` missing values are replaced with ‘U’.

Creating Dummy Variables

Dummy encoding is performed for `DemClusterGroup`. Using SAS, `proc logistic` or similar procedures can be employed to generate these dummy variables, retaining K-1 dummies for K categories to prevent multicollinearity. It is advisable to drop the original `DemClusterGroup` after dummy creation to avoid redundancy during modeling.

Data Partitioning

The dataset is randomly split into 70% training and 30% validation samples, using SAS procedures such as `proc surveyselect`. This partitioning enables model evaluation on unseen data, ensuring generalizability.

Model Building and Variable Selection

A baseline logistic regression model is constructed with the training data, initially including all relevant variables. Stepwise variable selection is performed with `slentry=0.6` and `slstay=0.65`, identifying significant predictors. Variables selected by this process are noted.

Advanced Modeling and Performance Comparison

Using the selected variables, two additional models—such as a neural network and a random forest—are trained using Weka or SAS procedures. Their performance on the validation set is evaluated via metrics including accuracy, precision, and recall. Results are tabulated for comparison, revealing which model most accurately predicts purchase behavior.

Addressing Skewness via Transformation

Variables with skewed distributions are transformed with the natural log of (x+1) to reduce leverage points and distortion. The same variable selection process is reapplied to the transformed variables, with the new set of predictors used to retrain the models. The performance metrics are again compared to assess if the transformation yields better discriminatory power.

Conclusions

This comprehensive analysis steps through data validation, cleaning, feature engineering, model fitting, and evaluation. Empirical results based on model performance metrics guide the selection of the best predictive model for customer purchase likelihood, informing supermarket strategies to target likely buyers for the organic product line.

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