I will do data science, data analysis, and machine learning in python or r
Data Scientist
About this Gig
Looking for an experienced Data Scientist to turn raw, messy data into
actionable insights and accurate predictive models? You are in the right place.
I am a professional Data Scientist with deep expertise in Python, R, and IBM
SPSS. I deliver clean, production-ready code alongside high-impact insights.
What I Offer: Data Cleaning: Handling missing values, outliers, duplicates,
and feature engineering. EDA & Visualization: In-depth exploratory analysis,
trend identification, and clear charts (Matplotlib, Seaborn, ggplot2).
Statistical Modeling (SPSS & R): Hypothesis testing (t-tests, ANOVA,
Chi-Square), regression, and survey data analysis. Machine Learning
(Python/R): Classification, regression, clustering (Random Forest, XGBoost,
LightGBM, SVM) & model tuning.
Tools & Technologies: Python | R & RStudio | IBM SPSS | Jupyter Notebook |
Pandas | NumPy | Scikit-Learn
Why Choose Me?
- Clean, well-commented reproducible code
- Clear visual reports explaining the numbers
- Fast delivery and 100% satisfaction guaranteed
Please message me before placing an order to discuss your requirements and
project scope!
FAQ
Will you provide the source code?
Yes, absolutely. You will receive fully documented, readable, and reproducible source code (Jupyter Notebook .ipynb, R Markdown, or clean script).
My data is confidential. Can you sign an NDA?
Yes. I treat all client data with strict confidentiality. If required, I am happy to sign a Non-Disclosure Agreement (NDA).
Can you work with academic or survey data using SPSS?
Yes. I have extensive experience running statistical tests (t-tests, ANOVA, Chi-square, reliability analysis) and interpreting outputs in APA format using IBM SPSS and R.
What if I don't know which Machine Learning model is best for my problem?
No problem at all! Just share your dataset and your business goal. I will test multiple state-of-the-art algorithms and recommend the one that yields the highest accuracy and generalizability.

