rajiv-njit / walmart-sales-forecasting

To create a model that can forecast Walmart sales to stores in the future with accuracy. In addition to expanding on previous work on this dataset, we will investigate fresh and creative ideas.

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About Me

With over 16 years of experience, I am a seasoned professional specializing in leading and executing large-scale software development projects. My expertise extends to defining and implementing technical solutions, managing complex projects, and collaborating effectively with stakeholders at all levels.

Key Skills

• Programming Languages: Java, Python, SQL
• Frameworks: Spring Framework, Hibernate, Java EE
• Cloud Platforms: AWS, Azure
• Certifications: PMP (Project Management Professional), CSP (Certified Scrum Professional)

Specialties

Java Development

Experienced Java developer with in-depth knowledge of the Spring Framework, Hibernate, Java EE, SQL, JDBC, and MVC. Proven track record of designing, developing, and deploying high-quality, scalable, and reliable software.

Technical Architecture

Seasoned technical architect with expertise in AWS and Azure cloud computing. Proficient in translating business requirements into technical specifications and deliverables.

Cloud Migration

Expert in migrating on-premise applications to the cloud, with a portfolio of 30+ successful migrations.

Examples of Work

Let's Connect!

I'm passionate about technology, collaboration, and driving successful outcomes. Let's connect and explore how we can work together on exciting projects. Feel free to reach out for discussions, collaborations, or just to share ideas!

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walmart-sales-forecasting's Issues

User Story 7: Implementation of Time Series Models for Weekly Sales Forecasting

I want to explore the use of time series models for weekly sales forecasting. This involves researching time series modeling techniques, selecting an appropriate model (e.g., ARIMA), and evaluating its performance compared to the existing regression and complex models.

Tasks:

1. Time Series Modeling Research:

• Research and understand the principles of time series modeling techniques, focusing on weekly sales forecasting.
• Document the advantages and considerations of using time series models.

2. Model Selection:

• Choose a suitable time series model (e.g., ARIMA) for implementation.
• Understand the parameters and assumptions of the selected time series model.

3. Implementation:

• Integrate the chosen time series model into the existing prediction pipeline.
• Adjust the model to accommodate the time-dependent nature of the weekly sales data.

4. Performance Evaluation:

• Compare the performance of the time series model with existing regression and complex models.
• Assess the ability of the time series model to capture temporal patterns in weekly sales.

5. Documentation:

• Update the project's README with details on the exploration and implementation of time series models.
• Provide insights into the strengths and limitations of using time series models for weekly sales forecasting.

Acceptance Criteria:

• Principles of time series modeling techniques are documented.
• A suitable time series model is chosen and integrated into the prediction pipeline.
• Performance of the time series model is compared with existing regression and complex models.
• Documentation reflects the exploration and implementation of time series models.

User Story 2: PCA Feature Elimination Implementation and Enhancement

I want to explore and implement of feature elimination using Principal Component Analysis (PCA). This involves coding, understanding the PCA process for feature reduction, and suggesting enhancements or alternative approaches for better feature selection.

Tasks:

1. Code Review:

• Examine the code related to PCA feature elimination.
• Identify the components involved in creating and fitting the PCA object.

2. Exploration of Explained Variance:

• Investigate the plot of the total explained variance curve.
• Understand which key elements explain the majority of the data's instability.

3. Threshold Adjustment:

• Evaluate the threshold set for eliminating main elements.
• Suggest adjustments to the threshold based on the data characteristics.

4. Alternative Approaches:

• Research and propose alternative methods for feature elimination.
• Consider the impact on model performance and interpretability.

5. Documentation:

• Update the project's README with an explanation of the PCA feature elimination process.
• Include insights gained from exploring explained variance and any recommended adjustments.

Acceptance Criteria:

• The code implementation of PCA feature elimination is clearly documented.
• The impact of the threshold on feature elimination is understood and documented.
• Alternative approaches for feature elimination are proposed with associated considerations.

User Story 3: Missing Values Imputation for Data Preprocessing

I want to enhance the data preprocessing phase by incorporating missing values imputation techniques. This involves evaluating the presence of missing values, suggesting appropriate imputation methods, and implementing these methods for a more comprehensive data preparation.

Tasks:

1. Missing Values Assessment:

• Identify features with missing values in the dataset.
• Quantify the extent of missing data for each relevant feature.

2. Imputation Techniques:

• Research and propose suitable imputation techniques (mean, median, mode) for handling missing values.
• Consider the impact of each technique on the dataset.

3. Implementation:

• Integrate the selected imputation technique(s) into the data preprocessing pipeline.
• Ensure documentation is updated to reflect the imputation process.

4. Evaluation:

• Assess the impact of imputation on the dataset statistics and distribution.
• Compare the model performance before and after imputation.

5. Documentation:

• Include details on missing values imputation in the project's README.
• Provide a rationale for the chosen imputation technique(s) and any observed improvements.

6. Acceptance Criteria:

• The identification and quantification of missing values are documented.
• Selected imputation technique(s) are integrated into the data preprocessing pipeline.
• Model performance improvements, if any, are documented after imputation.

User Story 5: Hyperparameter Tuning for Model Optimization

I want to optimize the performance of regression models by implementing hyperparameter tuning. This involves assessing the current default hyperparameters, researching techniques for tuning, and implementing a hyperparameter tuning process for each regression model.

Tasks:

1. Current Hyperparameter Review:

• Identify and document the default hyperparameters used in the regression models.
• Understand the impact of these hyperparameters on model performance.

2. Hyperparameter Tuning Techniques:

• Research and choose appropriate hyperparameter tuning techniques (e.g., grid search or random search).
• Understand the advantages and limitations of the selected technique.

3. Implementation:

• Integrate hyperparameter tuning into the regression model training phase.
• Specify the hyperparameter search space and ranges.

4. Performance Evaluation:

• Compare the performance of models with tuned hyperparameters against the default models.
• Assess the impact of hyperparameter tuning on model accuracy.

5. Documentation:

• Update the project's README to include details on hyperparameter tuning.
• Provide insights into how hyperparameter tuning enhances the performance of regression models.

Acceptance Criteria:

• Default hyperparameters are documented for each regression model.
• An appropriate hyperparameter tuning technique is selected and implemented.
• The performance of models with tuned hyperparameters is compared with default models.
• Documentation reflects the integration of hyperparameter tuning and its impact on model accuracy.

Create README file

Create README file for the project.

User Story 8: Documentation Enhancement for Clear Summary and Recommendations

I want to enhance the project's documentation to include a clear summary of key findings and actionable recommendations. This involves reviewing the existing documentation, summarizing critical insights, and providing practical recommendations based on the analysis.

Tasks:

1. Analysis Summary:

• Review the code and analysis results to identify key findings and insights.
• Summarize the critical aspects of the analysis, focusing on model performance, data preprocessing, and feature selection.

2. Recommendations:

• Provide actionable recommendations based on the analysis, such as potential model improvements, data preprocessing enhancements, or alternative approaches.

3. Documentation Update:

• Update the project's README or documentation file with the summarized key findings and recommendations.
• Ensure that the documentation is clear and accessible to contributors and users.

4. Feedback Gathering:

• Encourage feedback from contributors and users regarding the clarity and usefulness of the updated documentation.

Acceptance Criteria:

• A clear summary of key findings is documented.
• Practical and actionable recommendations based on the analysis are provided.
• Project documentation, especially the README, is updated to reflect the summary and recommendations.
• Contributors and users provide positive feedback on the clarity and usefulness of the updated documentation.

User Story 6: Exploration of More Complex Models for Improved Predictions

I want to explore the use of more complex machine learning models, such as gradient boosting or random forest, to improve the accuracy of weekly sales predictions. This involves researching and understanding the benefits of these models, implementing them, and comparing their performance against the existing regression models.

Tasks:

1. Model Exploration:

• Research and understand the principles and advantages of more complex models like gradient boosting or random forest.
• Document how these models can capture non-linear associations in the data.

2. Model Implementation:

• Choose one or more complex models (e.g., gradient boosting or random forest) for implementation.
• Integrate these models into the existing training and prediction pipeline.

3. Performance Evaluation:

• Compare the performance of the complex models with the existing regression models.
• Assess the ability of these models to capture non-linear patterns in the data.

4. Documentation:

• Update the project's README to include details on the exploration and implementation of more complex models.
• Provide insights into the advantages and considerations of using these models.

5. Acceptance Criteria:

• Benefits and principles of more complex models are documented.
• One or more complex models are chosen and integrated into the prediction pipeline.
• Performance of complex models is compared with existing regression models.
• Documentation reflects the exploration and implementation of more complex models.

User Story 1: IQR Outlier Removal Understanding and Enhancement

I want to understand and implement the process of outlier removal using the Interquartile Range (IQR) method. This involves gaining clarity on how IQR is calculated for each feature, identifying outliers, and suggesting potential improvements or alternative techniques for outlier detection.

Tasks:

1. Understanding IQR:

• the code implementation related to IQR outlier removal.
• Summarize the IQR calculation process for each feature.

2. Outlier Identification:

• Identify instances where data points deviate by more than 1.5 times the IQR.
• Evaluate the effectiveness of the current approach in identifying outliers.

3. Alternative Techniques:

• Research and propose alternative methods for outlier detection.
• Provide reasoning for the suggested alternatives.

4. Documentation:

• Document the IQR outlier removal process in the project's README.
• Include explanations and any improvements suggested.

Acceptance Criteria:

• The code and documentation clearly explain the IQR outlier removal process.
• Identified outliers and their impact on the dataset are documented.
• Alternative methods for outlier detection are proposed with appropriate reasoning.

User Story 4: Implementation of Cross-Validation for Model Selection

I want to enhance the model selection process by implementing a more systematic approach, specifically cross-validation. This involves reviewing the current model evaluation methods, understanding the benefits of cross-validation, and incorporating it into the model selection process.

Tasks:

1. Current Model Evaluation Review:

• Understand the existing methods used for evaluating regression models (R2 and RMSE scores).
• Document the strengths and limitations of the current evaluation process.

2. Cross-Validation Implementation:

• Research and choose a suitable cross-validation technique (e.g., k-fold cross-validation).
• Implement cross-validation in the model selection phase.

3. Performance Metrics Comparison:

• Compare the results obtained through cross-validation with the previous evaluation metrics.
• Assess the impact of cross-validation on model performance.

4. Documentation:

• Update the project's README with details on the implemented cross-validation process.
• Provide insights into how cross-validation enhances the reliability of model evaluation.

5. Acceptance Criteria:

• The current model evaluation methods and their limitations are documented.
• A suitable cross-validation technique is chosen and implemented.
• Results obtained with cross-validation are compared with previous evaluation metrics.
• Documentation reflects the integration of cross-validation and its impact on model evaluation.