You will get Complete Data Analysis and Visualisation
Project details
Turn your raw data into clear, actionable insights. You'll receive a complete analysis package: cleaned dataset, publication-quality visualizations, documented Jupyter notebook, and an executive summary with strategic recommendations.
I specialize in making complex data simple to understand. Whether you need to identify trends, compare performance, run classification or prediction tasks, or support a business decision — I deliver results that are visually clear and immediately useful.
What sets me apart:
→ Clean, professional visualizations ready for presentations or reports
→ Fully documented code you can reuse and adapt
→ Plain-language insights, not just numbers
→ Fast turnaround without sacrificing quality
I work with data in any format: CSV, Excel, Google Sheets, SQL exports, JSON, or APIs. From quick analyses to comprehensive deep-dives, I match the scope to your needs.
Tools: Python (pandas, matplotlib, seaborn, plotly), Jupyter, statistical analysis, and interactive dashboards when needed.
I specialize in making complex data simple to understand. Whether you need to identify trends, compare performance, run classification or prediction tasks, or support a business decision — I deliver results that are visually clear and immediately useful.
What sets me apart:
→ Clean, professional visualizations ready for presentations or reports
→ Fully documented code you can reuse and adapt
→ Plain-language insights, not just numbers
→ Fast turnaround without sacrificing quality
I work with data in any format: CSV, Excel, Google Sheets, SQL exports, JSON, or APIs. From quick analyses to comprehensive deep-dives, I match the scope to your needs.
Tools: Python (pandas, matplotlib, seaborn, plotly), Jupyter, statistical analysis, and interactive dashboards when needed.
Machine Learning Tools
Google Sheets, Keras, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, R, scikit-learn, SciPy, SQL, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$40
|
Standard
$100
|
Advanced
$220
|
|---|---|---|---|
| Delivery Time | 2 days | 5 days | 10 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 0 | 1 | 2 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 3 | 8 | 15 |
Model Validation/Testing | - | - | |
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$15 - $60
Additional Revision
+$30
Additional Graph/Chart
+$15
Model Documentation
+$15
Source Code
+$40About Alexy
Data Analyst
Saint-Etienne, France - 11:32 am local time
I turn messy data into clear insights and publication-quality visualizations. Whether you need simple data cleaning, an in-depth analysis, an interactive dashboard, or a comprehensive report — I deliver results that drive decisions.
What I do:
→ Data cleaning & preparation (any format: CSV, Excel, SQL, APIs)
→ Exploratory data analysis & statistical insights
→ Clear, professional visualizations ready for presentations
→ Interactive dashboards (Streamlit, Python)
→ Machine learning for prediction & classification
My background spans complex, multi-modal datasets — tabular data, time series, imaging (video and microscope fluorescence based) , and ML pipelines. I've worked in research as a data analyst (computational neurosciences)
Tools: Python (pandas, matplotlib, seaborn, plotly), Jupyter, scikit-learn, TensorFlow, Streamlit, SQL, LightGBM, CatBoost, XGBoost, U-net, Resnet, Efficientnet, Excel.
Steps for completing your project
After purchasing the project, send requirements so Alexy can start the project.
Delivery time starts when Alexy receives requirements from you.
Alexy works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Data Review
I review your data files, assess quality and structure, clarify any questions, and confirm the analysis approach. You'll receive a brief outline of what I'll deliver.
Data Cleaning & Preparation
Handle missing values, fix inconsistencies, standardize formats, and prepare the dataset for analysis. This ensures accurate, reliable results.