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2. Customer Churn Analysis
I analyzed customer data to identify patterns related to customer churn. I cleaned and explored the dataset, created visualizations to understand customer behavior, and used classification techniques to predict potential churn. I also worked with Decision Tree, Random Forest, and KNN models and reviewed feature importance to understand which factors were more relevant to churn.
Tools: R, RStudio, ggplot2, Machine Learning
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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Welcome! Are you looking to turn messy data into clear, actionable insights? You're in the right place.
As a professional data analyst, I specialize in transforming raw data into clean, structured, and visually compelling dashboards that help businesses make smarter decisions.
What I Offer:
• Data Cleaning & Preparation (Handling missing values, removing duplicates, formatting)
• Exploratory Data Analysis (EDA) to find key trends and patterns
• Interactive Dashboards & Reports (Power BI/ Excel)
• Custom DAX / SQL queries for deep metric tracking
• Professional summary report with key business insights
Tools I Use:
• Power BI
• Python & SQL
• Advanced Excel & Google Sheets
Why Choose Me?
• High-quality, clean design with user-friendly layouts
• Fast turnaround time and clear communication
• 100% satisfaction guarantee
Let's unlock the story behind your data! Please contact me before placing an order so we can discuss your specific project goals.
3. Customer / Business Data Analysis
I worked on data analysis projects involving data cleaning, exploratory data analysis, visualization, and finding useful patterns in datasets. I used Python and R to organize raw data, analyze trends, create visualizations, and turn the results into simple business insights. The projects helped me build practical experience with working with real-world datasets.
Tools: Python, R, Pandas, NumPy, Excel, Matplotlib, Seaborn, ggplot2
"Businesses lose time and money to invoice errors that go unnoticed until it's too late. I built an automated pipeline that extracts invoice data, cross-checks it against vendor history, and flags anomalies with clear AI-generated reasoning — all visualized in a real-time dashboard. Stack: LangChain, Groq, n8n, FastAPI, Next.js."
