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https://github.com/perpendicooler/smv


https://github.com/perpendicooler/smv

garments joblib prediction predictive-modeling random-forest smv smv-models streamlit textile xgboost

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README 

        
# SMV Prediction



![Company Logo](https://github.com/perpendicooler/SMV/blob/main/IND%20Logo%20PNG%20%2B.png)



## 🚀 The Journey of Predicting SMV: From Data to Results



The **SMV Prediction App** is a sophisticated tool developed to predict **Standard Minute Value (SMV)** in the textile industry. This project harnesses the power of machine learning to optimize operational efficiency and cost management based on critical features such as operation type, yarn type, machine speed, and knit construction.



### 📋 Problem Statement

The objective of this project is to accurately predict SMV using various factors affecting textile operations. By leveraging machine learning models, our aim is to enhance labor productivity and streamline cost estimation processes. This initiative is crucial for improving decision-making and operational workflows within our organization.



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## 🔍 Overview



### What is SMV?

Standard Minute Value (SMV) is a vital metric that quantifies the time required to complete specific operations in the textile industry. Accurate SMV predictions allow companies to estimate labor costs, optimize production efficiency, and improve resource allocation.



### Importance of the Project

In the competitive textile market, the ability to predict SMV accurately can lead to significant cost savings and efficiency gains. This project not only aids in the financial planning of production but also empowers our teams to make data-driven decisions that enhance overall productivity.



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## 📊 Data Collection & Preparation



1. **Data Collection:**  

   We collected a comprehensive dataset capturing various parameters influencing SMV, including:

   - Operation Type (e.g., sewing, knitting, dyeing)

   - Yarn Type (cotton, polyester, blends, etc.)

   - Machine Speed and Settings

   - Knit Construction Patterns



2. **Data Preprocessing:**  

   The raw data underwent rigorous preprocessing to ensure quality and usability. This phase involved:

   - **Cleaning:** Removing irrelevant or redundant data and addressing missing values.

   - **Encoding Categorical Variables:** Transforming categorical features into numerical formats using one-hot encoding.

   - **Scaling Numerical Features:** Normalizing numerical features to prevent biases during model training.



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## 🔧 Modeling Techniques



To achieve our predictive goals, we employed two robust machine learning algorithms:



- **Random Forest:** An ensemble method that constructs multiple decision trees and aggregates their predictions for improved accuracy and reduced overfitting.



- **XGBoost:** A highly optimized gradient boosting framework known for its speed and performance, particularly effective in handling structured data.



Both models were trained on the same dataset, allowing for direct performance comparison.



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## 📈 Results & Impact



After training and testing the models, we evaluated their performance using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-Squared. The insights gained from this analysis enabled us to choose the best-performing model for real-time predictions.



### Deployment and Usage

The deployed SMV Prediction App is now actively utilized by our staff for:

- Real-time SMV predictions that inform production planning.

- Data-driven decisions that enhance operational efficiency.

- Streamlining labor management and cost estimation processes.



This project represents a significant advancement in our ability to utilize data effectively within our operations, fostering a culture of innovation and continuous improvement.



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## 🎯 Conclusion

The **SMV Prediction App** not only showcases the capabilities of machine learning in transforming business processes but also highlights our commitment to leveraging data for enhanced decision-making. By accurately predicting SMV, we are poised to improve productivity, reduce costs, and achieve greater operational success in the textile industry.