AI-driven Customer Retention Platform Development
Budget: ₹12,500 – ₹37,500 INR
- Developed ChurnShield, an end-to-end AI-powered platform for customer churn prediction and retention intelligence, utilizing Python, FastAPI, TensorFlow, and Streamlit.
- Created a custom Artificial Neural Network (ANN) featuring three dense layers, batch normalization, dropout regularization, early stopping, and Adam optimization for churn prediction.
- Implemented industry-specific machine learning pipelines for telecommunications and banking datasets, incorporating automated preprocessing and feature engineering.
- Established intelligent churn risk classification systems that categorize customers into high, medium, and low-risk groups based on probability scoring.
- Designed an interactive, cyberpunk-inspired analytics dashboard for real-time KPI monitoring, risk segmentation, customer intelligence cards, and predictive insights.
- Developed dynamic customer risk profiles, including churn probability analysis, personalized retention strategies, and next-best-action recommendations.
- Produced advanced model analytics encompassing accuracy, precision, recall, F1 score, ROC-AUC, confusion matrix, feature importance analysis, and risk distribution visualization.
- Implemented AI-driven retention planning systems that generate personalized customer retention campaigns and strategic business recommendations.
- Engineered scalable REST APIs using FastAPI, featuring file upload processing, model training, prediction endpoints, health monitoring, and what-if analysis capabilities.
- Created automated data preprocessing pipelines that handle missing values, categorical encoding, normalization, feature scaling, and industry-specific transformations.
- Developed customer segmentation engines capable of analyzing hundreds of customers to identify high-value retention opportunities.
- Designed interactive Plotly visualizations for feature importance analysis, churn distribution monitoring, confusion matrices, and performance analytics.
- Built a responsive multi-page application architecture utilizing custom HTML, CSS, JavaScript, and advanced UI/UX animations.
• Implemented explainable AI concepts using feature importance scoring, driver analysis, and churn factor interpretation for business decision-making.
• Developed automated retention playbooks generating customer-specific intervention plans, loyalty programs, discount strategies, and engagement campaigns.
• Experienced in Machine Learning, Deep Learning, Artificial Neural Networks, Generative AI, FastAPI, TensorFlow, Streamlit, Data Analytics, and Business Intelligence Solutions.
• Skilled in Python, TensorFlow, Scikit-Learn, FastAPI, Streamlit, Plotly, Pandas, NumPy, Machine Learning Pipelines, API Development, and Full-Stack AI Application Development.
- Created a custom Artificial Neural Network (ANN) featuring three dense layers, batch normalization, dropout regularization, early stopping, and Adam optimization for churn prediction.
- Implemented industry-specific machine learning pipelines for telecommunications and banking datasets, incorporating automated preprocessing and feature engineering.
- Established intelligent churn risk classification systems that categorize customers into high, medium, and low-risk groups based on probability scoring.
- Designed an interactive, cyberpunk-inspired analytics dashboard for real-time KPI monitoring, risk segmentation, customer intelligence cards, and predictive insights.
- Developed dynamic customer risk profiles, including churn probability analysis, personalized retention strategies, and next-best-action recommendations.
- Produced advanced model analytics encompassing accuracy, precision, recall, F1 score, ROC-AUC, confusion matrix, feature importance analysis, and risk distribution visualization.
- Implemented AI-driven retention planning systems that generate personalized customer retention campaigns and strategic business recommendations.
- Engineered scalable REST APIs using FastAPI, featuring file upload processing, model training, prediction endpoints, health monitoring, and what-if analysis capabilities.
- Created automated data preprocessing pipelines that handle missing values, categorical encoding, normalization, feature scaling, and industry-specific transformations.
- Developed customer segmentation engines capable of analyzing hundreds of customers to identify high-value retention opportunities.
- Designed interactive Plotly visualizations for feature importance analysis, churn distribution monitoring, confusion matrices, and performance analytics.
- Built a responsive multi-page application architecture utilizing custom HTML, CSS, JavaScript, and advanced UI/UX animations.
• Implemented explainable AI concepts using feature importance scoring, driver analysis, and churn factor interpretation for business decision-making.
• Developed automated retention playbooks generating customer-specific intervention plans, loyalty programs, discount strategies, and engagement campaigns.
• Experienced in Machine Learning, Deep Learning, Artificial Neural Networks, Generative AI, FastAPI, TensorFlow, Streamlit, Data Analytics, and Business Intelligence Solutions.
• Skilled in Python, TensorFlow, Scikit-Learn, FastAPI, Streamlit, Plotly, Pandas, NumPy, Machine Learning Pipelines, API Development, and Full-Stack AI Application Development.