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You will get a clean and well-analyzed Excel or CSV file using Python and Pandas

Anes M.Status: Offline
Anes M.
Preview image

Let a pro handle the details

Buy Data Entry & Cleaning services from Anes, priced and ready to go.
Anes M.Status: Offline
Anes M.
Preview image

Let a pro handle the details

Buy Data Entry & Cleaning services from Anes, priced and ready to go.

Project details

You will get a fully cleaned and well-structured Excel or CSV dataset, ready for analysis or reporting. I specialize in using Python and Pandas to handle messy data, fix formatting issues, remove duplicates, and provide clear summaries. What sets this project apart is my attention to detail and commitment to delivering clean, accurate, and professional results.
Data Tool
Python

What's included $20

These options are included with the project scope.

$20
  • Delivery Time 2 days
  • Number of Revisions 2
  • Number of Pages Mined/Scraped 1
  • Number of Sources Mined/Scraped 1
Optional add-ons You can add these on the next page.
Fast 1 Day Delivery
+$10
Additional Revision
+$5
Add another dataset or data source (+ 1 Day)
+$10

Frequently asked questions

Anes M.Status: Offline

About Anes

Anes M.Status: Offline
Data Analyst
Djebeniana, Tunisia - 12:12 am local time
I'm a passionate AI and Data Science enthusiast with hands-on experience in machine learning, deep learning, and Python programming. I've completed projects like a Face Recognition + Emotion Detection App, data visualization with Power BI, and exploratory data analysis on the Diamonds dataset. I’m actively learning through top-rated courses (Andrew Ng’s ML/DL Specializations, Cognitive Class by IBM) and contributing to open-source projects like Tensorzero.

Steps for completing your project

After purchasing the project, send requirements so Anes can start the project.

Delivery time starts when Anes receives requirements from you.

Anes works on your project following the steps below.

Revisions may occur after the delivery date.

Understanding the Dataset

-Review the dataset to understand its structure, features, and the goal of analysis. -Identify data types, missing values, and potential issues.

Data Cleaning

-Handle missing values by imputing or removing them. -Correct inconsistent or erroneous data entries. -Remove duplicates to ensure data integrity. -Format data properly (e.g., date formats, categorical variables).

Review the work, release payment, and leave feedback to Anes.