You will get a machine learning customer segmentation and churn prediction solution
Rising Talent
Rising Talent
Project details
I develop end-to-end machine learning solutions that help businesses transform customer data into actionable insights. This service combines customer segmentation, churn prediction, and retention analysis using advanced machine learning techniques such as XGBoost and K-Means. Beyond model development, I deliver a complete production-ready solution with a FastAPI backend, interactive Streamlit dashboard, Dockerized deployment, and well-documented source code. My focus is on building practical, scalable, and business-oriented AI solutions that enable organizations to reduce customer churn, improve retention strategies, and make data-driven decisions.
Machine Learning Tools
ChatGPT, Deeplearning4j, Keras, MLflow, Open Neural Network Exchange, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$90
|
Standard
$140
|
Advanced
$200
|
|---|---|---|---|
| Delivery Time | 10 days | 14 days | 19 days |
Number of Revisions | 1 | 3 | 5 |
Number of Model Variations | 1 | 2 | 2 |
Number of Scenarios | 2 | 4 | 6 |
Number of Graphs/Charts | 1 | 4 | 6 |
Model Validation/Testing | - | - | |
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$10 - $20Frequently asked questions
About Md.tashin
AI & Machine Learning | Retrieval Augmented Generation, TensorFlow
Dhaka, Bangladesh - 2:40 pm local time
Steps for completing your project
After purchasing the project, send requirements so Md.tashin can start the project.
Delivery time starts when Md.tashin receives requirements from you.
Md.tashin works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Gathering & Data Review
Discuss your business objectives and project requirements. Review the provided dataset and understand the available customer information. Identify the target variable and define the expected deliverables.
Data Preparation & Feature Engineering
Clean and preprocess the dataset. Handle missing values, encode categorical features, and prepare the data for modeling. Perform feature engineering to improve model performance and capture meaningful customer behavior.