This project focuses on managing and utilizing data from data engineering pipelines to effectively support the data science team in developing accurate forecasting models. The goal is to optimize data preparation processes and facilitate the creation of robust predictive models that leverage the full potential of the datasets.

Objective

The objectives of this project are to:

Clean, sanitize, and manipulate raw data to extract useful features.
Derive meaningful insights from the processed data.
Support the data science team in developing accurate forecasting models.
Optimize data preparation processes.

Features

Data Cleaning: Handling missing values, outliers, and inconsistencies in the raw data.
Feature Extraction: Identifying and extracting relevant features for model building.
Data Insights: Providing visual and statistical insights into the data.
Predictive Modeling: Developing and evaluating forecasting models.

Dataset

The dataset includes various attributes collected from the logistics operations, including but not limited to:

Order Details: Information about orders, such as order ID, date, and customer details.
Shipment Details: Information about shipments, such as shipment ID, date, and destination.
Delivery Performance: Metrics related to delivery times, delays, and success rates.
Operational Data: Data related to logistics operations, including warehouse and transportation metrics.

Methodology

Data Cleaning: Handling missing values, outliers, and data inconsistencies.
Exploratory Data Analysis (EDA): Visualizing and understanding the relationships between different variables.
Feature Engineering: Creating and selecting the most relevant features for model building.
Model Building: Training various machine learning models for forecasting.
Model Evaluation: Evaluating the performance of the models using metrics like RMSE, MAE, and accuracy.

Results

The analysis identified key factors influencing logistics operations and delivery performance. The predictive models achieved high accuracy in forecasting delivery times and optimizing logistics operations. Usage

To use this project, follow these steps:

Clone the repository:

git clone https://github.com/lm934/Case-Study-Data-Management-and-Forecasting-Optimization.git

Conclusion

This project provides a comprehensive approach to managing and utilizing data for developing accurate forecasting models. It optimizes data preparation processes and enhances the ability to leverage datasets for robust predictive modeling.