Customer Churn Analysis & Prediction

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@tadinhkien

Greetings, Thank you for considering my application for this project. As an AI Engineer and Python Developer with over 8+ years of experience, I bring a wealth of knowledge and expertise in the field of Python, Deep Learning. I have carefully reviewed the project description and am eager to discuss your specific needs and requirements in more detail. My commitment is to provide dedicated support and consistent follow-up throughout the project's lifecycle. Please feel free to reach out to me to further discuss how I can contribute to the success of your project. Looking forward to the opportunity of working together. Best regards, KuroKien

@pkundu25

I am an experienced Data Science practitioner. Your job caught my eye and looks to be quite interesting to me as I have developed various algorithms pertaining to ML/DL/NLP/Computer Vision from exploratory data analysis (EDA) to model building till deployment.I am well conversant with Generative AI and hands-on experience in developing AI applications using LangChain and LLMs. I am confident that I will be able to help you by developing ML models for Customer Churn Analisis and Prediction as per your requirement. Similar work done in the past: - Ground water quality predictions - Unsupervised preventive maintenance models - Fetal brain abnormality detection - Telecom network Anomaly detection - Multiclass Intent classification Relevant Skills: - Python - Agentic AI - ML Algorithms - Numpy, pandas, scikit learn - GPT4o/Gemini/Llama3.2/Mistral - TensorFlow - Google Colab - OpenCV - Tableau Let's have a chat to understand the project objective and the dataset in details. I assure you the best quality results and ensure the customer satisfaction. Looking forward to hearing from you soon. Thanks for the opportunity.

@nrajuu07

Hi, I’m Naga Raju, a Data Scientist with strong experience in Python, Machine Learning, EDA, and predictive analytics. I can help you build an end-to-end Customer Churn Analysis & Prediction solution including data cleaning, feature engineering, visual dashboards, and high-performing ML models such as XGBoost, Gradient Boosting, Random Forest, and Logistic Regression. I have hands-on experience with SMOTE, imbalance handling, model tuning, ROC-AUC/F1 optimization, and actionable business insights. I will deliver a clean pipeline, model comparison report, and churn prediction system with clear recommendations to improve retention and profitability.

@chiragardeshna

Hi there, Strong alignment with this project comes from experience building end-to-end churn prediction systems using EDA, feature engineering, and advanced ML models for actionable insights. Clear understanding of the requirement to analyze customer behavior, handle class imbalance (SMOTE), and develop predictive models like Gradient Boosting and XGBoost for reliable churn detection. Hands-on expertise with Python, scikit-learn, and data visualization ensures accurate modeling, clear insights, and a production-ready prediction pipeline. Risk is minimized by validating models with proper metrics, ensuring balanced performance, and maintaining clean, reproducible workflows. Available to start immediately happy to discuss enhancements or deployment of this system. Recent work: https://www.freelancer.com/u/chiragardeshna Regards Chirag

@Srashtasoft

Hi there, I have read your project requirement. You need a structured and professional Customer Churn Analysis & Prediction solution that not only analyzes patterns but also delivers actionable insights and a deployable prediction pipeline. We can refine and enhance your existing work into a production-ready solution by improving data preprocessing, validating models, and optimizing performance (especially Gradient Boosting/XGBoost). We will also structure the outputs into a clean EDA report, model comparison dashboard, and a reusable prediction pipeline (API or script). Additionally, we can create business-focused insights and visual dashboards (Power BI/Tableau) to help stakeholders easily understand churn drivers and take action. Questions: ======== Do you want this converted into a deployable API/dashboard for real-time predictions? Should we improve model performance further (hyperparameter tuning, feature engineering)? Do you need a business presentation or investor-ready report? Will this be integrated into an existing system or used standalone? Best Regards, Srashtasoft Team

@Madnikpvtltd

Hello, I understand you need a complete churn analysis pipeline that not only predicts attrition but also delivers actionable business insights, and my approach would be: data preprocessing (cleaning, encoding, scaling) → EDA to identify key churn drivers → handling imbalance (SMOTE/class weighting) → model training (Logistic, Random Forest, XGBoost, Gradient Boosting) → evaluation using ROC-AUC, F1, recall → feature importance analysis → deployment-ready prediction pipeline → insight dashboard for business decisions; I focus on building models that are not just accurate but interpretable and useful for retention strategies, and I can also share similar ML/data analysis work for reference, so if you want a reliable churn prediction system with real business impact, let’s connect.

@Visharaa2024

Hi, I can help you deliver a complete and impactful Customer Churn Analysis and Prediction solution using a structured, end to end data science approach. I have strong experience working with customer datasets involving demographic, behavioral, and financial features, and I specialize in extracting actionable insights through deep exploratory data analysis and robust machine learning models. I will perform thorough EDA to uncover churn patterns across variables like age, geography, activity level, and product engagement, supported by clear visualizations such as heatmaps and distribution plots. For modeling, I will implement and compare multiple algorithms including Logistic Regression, Random Forest, SVM, KNN, Gradient Boosting, and XG Boost, while addressing class imbalance using techniques like SMOTE and class weighting to improve recall and F1 score. I will evaluate performance using metrics such as ROC-AUC, precision recall balance, and accuracy to ensure reliable predictions. The final deliverables will include a cleaned dataset, detailed EDA report, trained and optimized models, a comparison summary, and a prediction-ready pipeline. My goal is to help you identify high risk customers and enable data-driven retention strategies that improve customer lifetime value and business outcomes.

@souvikbasu

Your Gradient Boosting model hitting 0.859 ROC-AUC is solid, but I suspect we can push past 0.90 by engineering a few features the raw dataset misses — specifically a "dormancy score" combining tenure, IsActiveMember, and recency of product changes. Here's what I'd deliver: • Full EDA with interactive Plotly dashboards (not just static seaborn plots) — segmented by Geography × Age cohorts since those are your strongest churn drivers • Model pipeline with scikit-learn + XGBoost + LightGBM, with proper stratified k-fold CV and SMOTE applied only within each fold (critical mistake many make — applying SMOTE before splitting leaks information) • Hyperparameter tuning via Optuna for the top 2 models • SHAP-based feature importance analysis so the business team gets explainable predictions, not just a black box • Clean, modular prediction pipeline ready for deployment One question: are you planning to deploy this as a batch scoring job or do you need real-time predictions via an API? That'll shape how I package the final deliverable. I've built similar churn models for telecom and SaaS datasets — happy to share samples. Can start immediately and deliver the EDA + baseline models within 3 days.