The "Vendor Boosting Regression - Metric Analysis (Random Forest)" project is a data-driven analytical approach designed to evaluate and score vendors based on multiple metrics. It uses machine learning techniques, specifically Random Forest regression, to understand and quantify the impact of various factors on vendor performance, focusing on key metrics such as Gross Sales and Services (GSS) and new subscriptions.
The primary goal of this project is to establish a systematic and objective way to score vendors. By analyzing historical data, the project aims to identify which factors most significantly influence a vendor's success in terms of GSS and new subscriptions. This scoring system is intended to assist in making informed decisions regarding vendor relationships, promotions, and strategic planning.
The project involves querying a database (presumably an Amazon Redshift instance) to retrieve vendor-related data. This data is likely to encompass various aspects of vendor performance and characteristics, such as web traffic, product types, and sales metrics over a specified period. The preprocessing steps include replacing categorical variables, standardizing numerical variables, and handling missing values to prepare the data for analysis.
A critical component of the project is the identification and selection of relevant features that impact GSS and new subscriptions. This is achieved through a combination of Recursive Feature Elimination (RFE) and the use of a RandomForestRegressor. The RandomForestRegressor not only aids in predicting outcomes but also provides insights into the relative importance of each feature, helping to focus on the most influential factors.
The core analytical tool in this project is the Random Forest regression model. This model is known for its robustness and ability to handle a large number of features and complex, non-linear relationships. In this context, it's used to understand how different variables contribute to the overall vendor score, which is crucial for making data-driven decisions.
The outcome of the model is a vendor scoring system that ranks vendors based on their predicted performance in key metrics. The project includes visualizations, particularly for feature importance, which help in interpreting the model results and providing actionable insights.
This system can be particularly useful for businesses that rely on a network of vendors for their operations. It allows for a data-driven approach to vendor management, ensuring that resources are allocated efficiently and relationships with vendors are optimized for mutual success.
Before running this notebook, ensure you have the following packages installed:
- numpy
- pandas
- pyodbc
- psycopg2
- seaborn
- matplotlib
- scikit-learn
- SQLAlchemy
- factor_analyzer
These packages can be installed via pip. For example:
pip install numpy pandas pyodbc psycopg2 seaborn matplotlib scikit-learn SQLAlchemy factor_analyzer
No additional installation is required apart from the prerequisites mentioned above.
To run the notebook, follow these steps:
- Ensure all prerequisites are installed.
- Open the notebook in your preferred Python notebook environment (e.g., Jupyter Notebook, Google Colab).
- Run the cells sequentially to connect to the database, preprocess the data, perform feature selection, and apply the Random Forest regression model.
- Database Connection: The notebook connects to a Redshift database using SQLAlchemy. Make sure to provide the correct credentials.
- Data Preprocessing: The data is fetched and preprocessed, including encoding categorical variables and normalizing numerical variables.
- Feature Selection: Features influencing GSS and new subscriptions are identified using Recursive Feature Elimination (RFE) and RandomForestRegressor.
- Model Training and Evaluation: A Random Forest Regressor model is trained and evaluated on selected features to understand their importance.
Given the nature of the data involved, the project takes care to handle sensitive information securely. Database credentials and other sensitive details are not hardcoded but securely fetched during runtime. It's crucial for users to ensure these credentials are protected and not exposed in public or shared environments.
Overall, this project stands out as a sophisticated approach to vendor analysis, combining advanced data analytics with machine learning to drive business decisions. Its adaptability to different datasets and scenarios makes it a valuable tool in the arsenal of data analysts and decision-makers in various industries.