You will get Customer Churn Prediction & Analysis Using Python | ML Model + Insights
Project details
This project, titled "Customer Churn Prediction and Analysis for Business Insights," blends advanced machine learning techniques with business-driven analysis. I use Python (Pandas, NumPy, scikit-learn) and a Random Forest model to predict customer churn with up to 85% accuracy. Through in-depth exploratory data analysis (EDA), I uncover behavioral patterns across customer segments, tenure, and service usage.
I visualize critical churn indicators using Seaborn and Matplotlib, helping identify which customers are most at risk and why. The project also includes:
A clean, documented codebase with modular structure
Business-focused summary reports with actionable insights
Strategic recommendations for reducing churn
Visual dashboards and decision tree logic for clarity
A consulting-style delivery modeled after BCG case frameworks
What sets it apart is not only the technical accuracy but the clarity of communication—bridging the gap between data science and executive decision-making
I visualize critical churn indicators using Seaborn and Matplotlib, helping identify which customers are most at risk and why. The project also includes:
A clean, documented codebase with modular structure
Business-focused summary reports with actionable insights
Strategic recommendations for reducing churn
Visual dashboards and decision tree logic for clarity
A consulting-style delivery modeled after BCG case frameworks
What sets it apart is not only the technical accuracy but the clarity of communication—bridging the gap between data science and executive decision-making
Machine Learning Tools
Chainer, MATLAB, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, R, scikit-learn, SQL, TableauWhat's included
| Service Tiers |
Starter
$10
|
Standard
$15
|
Advanced
$20
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 6 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 3 | 5 |
Number of Graphs/Charts | 2 | 5 | 8 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | |||
Source Code | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$1
Additional Model Variation
(+ 1 Day)
+$1
Additional Graph/Chart
+$1
Model Validation/Testing
+$1About Abdelrhman
data scientist
Cairo, Egypt - 11:40 pm local time
🔹 Technical Skills:
• Programming: Python, SQL, C, Java
• Machine Learning & AI: Supervised & Unsupervised Learning, Deep Learning, Feature Engineering, Model Optimization
• Data Analysis & Visualization: Pandas, NumPy, Matplotlib, Seaborn, Power BI
• Big Data & Cloud: Apache Spark, Google Colab
• Databases: MySQL, PostgreSQL
• Tools & Frameworks: Scikit-Learn, TensorFlow, PyTorch
Key Strengths:
Strong problem-solving and algorithmic thinking (ICPC experience)
Ability to transform complex data into clear, impactful insights
Passion for ethical AI and responsible data usage
Quick learner, always exploring new AI & Data Science trends
I’m excited to collaborate on projects where data can create meaningful impact
Steps for completing your project
After purchasing the project, send requirements so Abdelrhman can start the project.
Delivery time starts when Abdelrhman receives requirements from you.
Abdelrhman works on your project following the steps below.
Revisions may occur after the delivery date.
Data Collection & Understanding
Review the provided dataset, assess data quality, and understand business goals to align the analysis with client expectations.
Data Cleaning & Preparation
Handle missing values, encode categorical features, normalize/scale numerical data, and prepare the dataset for modeling.