Implement Logistic Regression Model for Amazon Order Status Prediction

[Salma-Mamdoh]

Have you read the Contributing Guidelines ?

Yes

Description

Description:

This pull request includes the implementation of a machine learning pipeline to predict the status of Amazon orders using logistic regression. The main changes and additions are as follows:

1. Data Loading and Initial Inspection:

Loaded the Amazon order dataset using pandas.
Displayed the first few rows to understand the dataset structure.
2. Data Preprocessing:

Identified categorical columns and converted them to the 'category' data type.
Implemented a pipeline to handle missing values and one-hot encode categorical features.
Standardized numerical features using a separate pipeline.
3. Feature Engineering:

Defined the feature set (X) by excluding the target variable (Status).
Split the dataset into training and testing sets (80% train, 20% test).
4. Model Building:

Created a preprocessing pipeline combining categorical and numerical transformers.
Implemented a logistic regression model with an increased iteration limit (max_iter=10000).
Combined the preprocessing pipeline and logistic regression model into a single pipeline.
5. Model Training and Evaluation:

Trained the logistic regression model on the training set.
Predicted the order statuses on the test set.
Evaluated the model using accuracy score, classification report, and confusion matrix.
6. Cross-Validation:

Implemented K-Fold cross-validation (5 folds) to assess the model's robustness.
Calculated and reported cross-validation scores and mean accuracy.
Results:

The model achieved an accuracy of 97.22% on the test set.
Detailed classification report and confusion matrix provided insights into the model's performance across different statuses.
Cross-validation confirmed the model's robustness with a mean accuracy of 97.21%.
Future Enhancements:

Explore other machine learning algorithms to further improve accuracy.
Incorporate additional features or data sources.
Implement advanced techniques to handle imbalanced classes.
Files Modified:

Added the script for loading, preprocessing, training, and evaluating the model.
Please review the changes and provide feedback or approval for merging into the main branch.
Fixes #1229

Checklist

• [ T] I've read the contribution guidelines.
• [ T] I've checked the issue list before deciding what to submit.
• [T] I've edited the README.md and link to my code.

Related Issues or Pull Requests

Fixes #1229

[github-actions]

@Salma-Mamdoh

It's great having you contribute to this project

Thank you for opening a Pull Request 🙌 , Welcome to Project Guidance 💖 We will review everything and get back to you :)

[Kushal997-das]

@Salma-Mamdoh Hi, please make the following changes:

1. Add the project to the basic folder.
2. Include your project under this link.
3. Add a few project output pictures to the readme.md file.