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Built a Customer Churn Prediction project using Python, Pandas, NumPy, and Scikit-learn. I cleaned and preprocessed the data, performed exploratory analysis, trained classification models, and evaluated performance using metrics such as Accuracy, Precision, Recall, F1-score, Confusion Matrix, and ROC analysis.
The goal was to identify customers at risk of leaving and uncover patterns that could help improve retention strategies.
The network for creativity
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Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Built a stock analysis and prediction project using Python, Pandas, NumPy, and Scikit-learn. I cleaned and prepared historical Mercedes-Benz stock data, explored price trends and returns, created visualizations, trained regression models, and compared model performance using evaluation metrics such as MAE, MSE, and R².
The goal was to understand historical stock behavior and build a machine learning workflow for predicting future price-related values.
Oetux – SaaS Revenue Analytics Dashboard
The client had a lot of data, but the real problem was making sense of it.
MRR, revenue, churn, active customers, growth, customer performance, goals. Every metric was important, but when everything gets the same attention, users don’t know where to look first.
The existing experience made users spend too much time switching between information and trying to connect the numbers.
So we focused on how SaaS teams actually read their business data. We structured Oetux around the questions that matter most.
How is revenue performing?
Are we growing?
Are customers staying?
Where are we losing revenue?
What needs attention right now?
Then we organized KPIs, revenue trends, customer performance, churn insights, goals, and AI powered insights into one clear experience.
The goal wasn’t to remove data.
It was to organize the data so users could understand it faster.
That’s what we solved with Oetux.
Turning a data heavy SaaS dashboard into an experience that helps teams see their business clearly and make better decisions.
Love the clean layout.
Built a customer churn prediction system using Python, Pandas, NumPy, and scikit-learn. The project covers data preprocessing, exploratory analysis, model training, evaluation, and prediction. The trained model was integrated with FastAPI and deployed in Docker, creating a complete ML-to-API workflow that was built and tested locally.
