I will do professional data cleaning and preprocessing using python and pandas
Python Data Cleaning and Preprocessing Specialist Pandas ML Ready Datasets
About this Gig
Is your dataset full of missing values, ERROR strings or UNKNOWN
placeholders? I will professionally clean it and deliver a 100%
ML-ready dataset.
WHAT I DELIVER:
Detection of all dirty values (UNKNOWN, ERROR, N/A, empty strings)
Standardisation of placeholders to proper NaN
Correct data type conversion (numeric, datetime, categorical)
Smart missing value imputation per column:
- Probabilistic sampling for categorical columns
- Business logic arithmetic for numerical columns
- Feature engineering for date columns
Jupyter Notebook - clean, commented, reproducible
PDF report with charts and logic explained
WHY MY APPROACH IS DIFFERENT:
Most freelancers fill every missing value with mean or median. I
analyse WHY values are missing and choose the statistically correct
strategy for each column separately.
PERFECT FOR:
Kaggle datasets before modelling
Business transaction data with POS errors
Survey data with incomplete responses
Any CSV or Excel with messy inconsistent entries
TOOLS: Python - Pandas - NumPy - Scikit-learn - Matplotlib - Seaborn
My Portfolio
FAQ
What file formats do you accept?
CSV, Excel (.xlsx, .xls) and most common tabular formats.
Will you just fill missing values with mean or median?
No. I analyse why each column has missing values and choose the correct strategy — probabilistic sampling, business logic derivation, or feature engineering depending on column type.
What exactly will I receive as deliverables?
Cleaned dataset (CSV), commented Jupyter Notebook with all cleaning code, and PDF report explaining every decision with visualisations.
What if my dataset is from a different industry?
No problem. My techniques apply to any dataset — finance, healthcare, e-commerce, surveys or any CSV/Excel file.
Is my data safe with you?
Your data is used only to complete this project and is never shared with anyone. I treat all client data as strictly confidential.

