The network for creativity
Join 1.25M professional creatives like you
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
Back to feedPost
Analyzed e-commerce data using Python and Pandas to uncover sales trends, customer behavior, product performance, and actionable business insights through data visualization.
Skills: Python · Pandas · Data Analysis · Data Visualization · Matplotlib · Seaborn
The network for creativity
Join 1.25M professional creatives like you
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
Related posts
Customer Churn Analysis & Prediction — An end-to-end data science project using Python, Pandas, SQL, and Scikit-learn to analyze customer behavior, identify churn patterns, compare machine learning models, and generate actionable insights for customer retention.
🚀 Have a dataset but don't know how to turn it into a useful Machine Learning model?
I can help you build a complete ML workflow — from raw data to a properly trained and evaluated model.
🔹 Data cleaning & preprocessing
🔹 Feature engineering
🔹 Exploratory data analysis
🔹 Model training
🔹 Model comparison
🔹 Hyperparameter tuning
🔹 Performance evaluation
🔹 Clean Python / Jupyter Notebook code
🔹 Saved model & documentation
I work with practical Machine Learning solutions using Python and Scikit-learn, including regression and classification problems.
Whether you're working on a personal project, startup idea, research project, or business dataset, I can help you choose an appropriate approach and build the model.
💬 Have a dataset or ML problem in mind?
Send me a message and let's discuss what you're trying to build.
My service starts at $30 and typical delivery starts from 3 days.
👉 Machine Learning Model Training & Evaluation:
https://contra.com/s/gr7XlWNF-machine-learning-model-training-and-evaluation
#MachineLearning #Python #DataScience #ScikitLearn #ModelTraining #DataAnalysis #AI #Freelance
I built an AI agent that handles customer operations — refunds, order lookups, and support tickets — with a human approval gate built into the workflow. The agent proposes the action, pauses, and waits for a human to approve before anything executes. The LLM never makes the final call. Role-based access and a full audit trail are enforced in code, not prompts. Deployed live on Azure Container Apps.
The pause for human sign off keeps refunds safe while still offloading the lookup work to the agent.