I will do data cleaning and eda with python for your data
Data Cleaning EDA Machine Learning
About this Gig
I will clean your data and perform exploratory data analysis (EDA)
Do you have messy or unstructured data? Do you need insightful visualizations to understand your dataset better? I am here to help!
I specialize in data cleaning and exploratory data analysis (EDA) to help you make sense of your data and prepare it for further analysis or machine learning models.
What I Offer:
- Handling missing values, duplicates, and incorrect data
- Formatting and standardizing data
- Removing outliers and fixing inconsistencies
- Data transformation and feature engineering
- Exploratory Data Analysis (EDA) with visualizations
- Summary statistics and key insights
Why Choose Me?
Experienced in data cleaning & EDA using Python (Pandas, NumPy, Matplotlib, Seaborn)
Efficient and detail-oriented approach
Easy communication and timely delivery
What You Need to Provide:
- Your dataset (CSV, Excel, or any structured format)
- Any specific requirements or focus areas
If you have any questions or custom requests, feel free to message me before placing an order!
for large dataset pls contact!
My Portfolio
FAQ
1. What kind of data can you work with?
I can work with structured data formats such as CSV, Excel, JSON. If you have data in another format, feel free to message me first!
2. What tools do you use for data cleaning and EDA?
I primarily use Python (Pandas, NumPy, Seaborn, Matplotlib) for data cleaning and visualization. If you need analysis in Excel, please let me know in advance.
3. How do you deliver the cleaned data and analysis?
I will provide the cleaned dataset in your preferred format (CSV, Excel, etc.) along with summary statistics, visualizations, and key insights in a report (PDF or Jupyter Notebook).
5. What if my dataset is too large?
If your dataset is larger than 1 million rows, please contact me before placing an order so we can discuss the best approach.
6. Can you work with missing values and outliers?
Yes! I handle missing values, outliers, duplicates, and incorrect data based on the best practices for data cleaning.

