You will get a cleaned, structured and analysis ready dataset.
Project details
Project Title: Smartphone Dataset: Data Quality Assessment and Feature Engineering
This project was a hands-on exercise in transforming messy, real-world data into a clean, strategic asset. I took a raw, 1000+ row smartphone dataset that was fundamentally unusable for analysis due to severe quality and consistency issues, and prepared it for immediate use in machine learning models or business intelligence.
My process began with a thorough Data Quality Assessment, identifying over 28 distinct issues, from non-uniform naming conventions ("Jio" vs. "jio jio phone 2") and missing values, to complex, unstructured text in core feature columns.
The most valuable part of the transformation was advanced Feature Engineering. Utilizing Python and Regular Expressions, I broke down cluttered features like sim, processor, and camera into multiple atomic, analytical features. For instance, I successfully extracted the distinct number of cameras, confirmed the presence of NFC/IR Blaster, and standardized the processor frequency, which are vital inputs for any market segmentation or price prediction model.
This project was a hands-on exercise in transforming messy, real-world data into a clean, strategic asset. I took a raw, 1000+ row smartphone dataset that was fundamentally unusable for analysis due to severe quality and consistency issues, and prepared it for immediate use in machine learning models or business intelligence.
My process began with a thorough Data Quality Assessment, identifying over 28 distinct issues, from non-uniform naming conventions ("Jio" vs. "jio jio phone 2") and missing values, to complex, unstructured text in core feature columns.
The most valuable part of the transformation was advanced Feature Engineering. Utilizing Python and Regular Expressions, I broke down cluttered features like sim, processor, and camera into multiple atomic, analytical features. For instance, I successfully extracted the distinct number of cameras, confirmed the presence of NFC/IR Blaster, and standardized the processor frequency, which are vital inputs for any market segmentation or price prediction model.
Data Tool
pandasWhat's included
| Service Tiers |
Starter
$25
|
Standard
$60
|
Advanced
$120
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 5 days |
Number of Revisions | 1 | 2 | 3 |
Number of Pages Mined/Scraped | 2 | 5 | 8 |
Number of Sources Mined/Scraped | 1 | 2 | 5 |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$10 - $40
Additional Revision
+$5
Additional Page Mined/Scraped
(+ 2 Days)
+$10
Additional Source Mined/Scraped
(+ 3 Days)
+$20About Neev
Clean Data Specialist, Data Transformation, Aspiring Data Scientist
Jaipur, India - 1:33 pm local time
Expert Data Wrangling: Proficient in using Python (Pandas, NumPy) to identify, document, and systematically resolve data quality issues, including inconsistencies, missing values, and accuracy errors.
Feature Engineering & Transformation: Skilled in breaking down complex, multi-valued fields (e.g., product specifications, nested data) into atomic, usable features, creating the necessary structure for robust analysis and model training (as demonstrated in my Smartphone Data Cleaning Case Study).
Database Management: Solid command of MySQL for efficient data querying, extraction, and management, ensuring smooth data workflow from source to dashboard.
Data Visualization & Storytelling: Experienced in creating professional, interactive dashboards using Tableau and Streamlit to communicate actionable insights and validate the quality of transformed data.
Steps for completing your project
After purchasing the project, send requirements so Neev can start the project.
Delivery time starts when Neev receives requirements from you.
Neev works on your project following the steps below.
Revisions may occur after the delivery date.
Data sourcing and initial inspection
As the very first step the dataset will be loaded and have to perform an initial structural inspection on it to gather some understandings on the dataset.
Comprehensive data quality assessment
I will systematically document all the issues present in the data and identify them as tidiness issues or quality issues.