FRESCO Retail-Return-Prediction

The objective of the problem is to analyze Fresco Retail's customers' transaction data and predict their return decisions based on various data points. The data points include customer background information, payment modes, store types, product nature, and other relevant factors.

In the context of retail, product returns can have a significant impact on a company's bottom line and customer satisfaction. By understanding the factors that influence a customer's decision to return a product, Fresco Retail can take proactive measures to reduce returns, improve customer experience, and optimize their operations.

Approach & Steps:

1. Understanding the data variables: This step is important because it allows you to understand the meaning of each variable and how it might be related to the dependent variable. This understanding will help you to choose the right variables for your analysis and to interpret the results of your analysis.
2. Cleaning the data: This step is important because it ensures that the data is accurate and consistent. Missing values, outliers, and incorrect data types can all lead to inaccurate results.
3. Conducting EDA: This step allows you to summarize and explore the data. This includes looking at the distribution of the data, identifying outliers, and finding patterns in the data. EDA can help you to understand the data and to identify potential problems with the data.
4. Performing univariate and bivariate analysis: This step allows you to look at the distribution of each variable and to see how each variable is related to the dependent variable. This can help you to identify the most important variables that affect the dependent variable.
5. Creating new features: This step allows you to create new features that are based on the existing features. This can be helpful if you want to improve the accuracy of your model.
6. Identifying the most important variables: This step allows you to identify the variables that have the biggest impact on the dependent variable. This information can be used to build a more accurate model.
7. Dividing the data into two samples: This step allows you to use one sample to build the model and the other sample to test the model. This helps to ensure that the model is accurate.
8. Building the model: This step involves choosing a modeling technique and fitting the model to the data. The modeling technique that you choose will depend on the type of data that you have and the type of prediction that you want to make.
9. Improving the model accuracy: This step involves iterating through the model and making changes to the model in order to improve its accuracy. This can involve adding or removing variables, changing the modeling technique, or using different data transformations.
10. Comparing the chosen model with other similar models: This step allows you to see how the chosen model performs compared to other similar models. This can help you to choose the best model for your application.