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English
$
USD
I will do data cleaning, preprocessing, and exploratory data analysis in python
About this Gig
Are you struggling with messy, inconsistent, or missing data? I am a Computer Science student specialized in turning raw, "dirty" data into clean, analysis-ready datasets. Whether you need to handle outliers, encode variables, or prepare data for a Machine Learning model, Ive got you covered!
What I offer:
- Data Cleaning: Handling missing values, removing duplicates, and fixing structural errors.
- Preprocessing: Feature scaling, one-hot encoding, and handling outliers.
- Exploratory Data Analysis (EDA): Visualizing trends and correlations using Pandas, Matplotlib, and Seaborn.
- Model Readiness: Ensuring your data is perfectly formatted for Scikit-Learn or other frameworks.
Experience Highlights:
- Cleaned and preprocessed global COVID-19 datasets for country-based classification.
- Handled complex Housing datasets for accurate price prediction modeling.
My Portfolio
FAQ
Have you worked with time-series or geographical data before?
Yes! I have experience cleaning and preprocessing complex global COVID-19 datasets (geographical/time-series) and real estate data (numerical/categorical) for predictive modeling.
Will I get the Python code or just the cleaned dataset?
You will receive both! I deliver a clean, processed dataset (usually in CSV or Excel) and the Jupyter Notebook (.ipynb) or Python script (.py) containing the documented code so you can see exactly how the data was handled.
Can you help me if my data has a lot of missing values?
Absolutely. Depending on the context, I can perform imputation (filling in values using mean, median, mode, or more advanced KNN/Iterative methods) or advise on whether it's better to drop specific rows/columns to maintain the integrity of your analysis.
Can you prepare my data specifically for Machine Learning?
Yes! This is my specialty. I will handle Feature Scaling (Normalization/Standardization), Label Encoding, and One-Hot Encoding to ensure your dataset is 100% ready to be fed into models like Linear Regression or Random Forest.
