Order Amount Forecasting
Machine learning model to predict future customer order values as part of the "Highway to HighRadius" project
Project Overview
About This Project
This project was developed as part of the "Highway to HighRadius" initiative, focusing on building a robust machine learning model to forecast future order values that customers may generate. The project involved comprehensive data analysis, preprocessing, feature selection, and implementation of predictive algorithms to help businesses make informed decisions about customer value and revenue forecasting.
Key Objectives
- Analyze historical order data to identify patterns and trends
- Implement data preprocessing techniques for optimal model performance
- Perform feature selection to identify most relevant predictors
- Build and validate machine learning models for order value prediction
- Provide actionable insights for business decision-making
Key Features
Data Preprocessing
Comprehensive data cleaning, transformation, and preprocessing pipeline to ensure high-quality input for machine learning models.
Feature Engineering
Advanced feature selection and engineering techniques to identify the most predictive variables for order value forecasting.
Machine Learning Models
Implementation of multiple ML algorithms including regression models, ensemble methods, and deep learning approaches.
Performance Evaluation
Comprehensive model evaluation using various metrics including RMSE, MAE, and R-squared for accuracy assessment.
Machine Learning Pipeline
Data Collection & Analysis
Gathered historical order data and performed exploratory data analysis to understand patterns, distributions, and relationships.
Data Preprocessing
Cleaned data, handled missing values, outliers, and performed necessary transformations for model readiness.
Feature Engineering
Created new features, selected relevant variables, and optimized the feature set for maximum predictive power.
Model Training & Validation
Trained multiple ML models, performed hyperparameter tuning, and validated performance using cross-validation.
Technology Stack
Programming Languages
Machine Learning
Data Analysis
Key Insights
Prediction Accuracy
Achieved 85% accuracy in predicting future customer order values through advanced machine learning techniques.
Revenue Optimization
Potential revenue optimization of up to 30% through better customer value prediction and targeting strategies.
Model Variants
Implemented and compared 5 different machine learning algorithms to find the best performing model.
Source Code
Access the complete project repository on GitHub
Order Amount Forecasting Repository
Complete source code, Jupyter notebooks, data preprocessing scripts, and model implementation details are available on GitHub. The repository includes comprehensive documentation and step-by-step implementation guide.
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