KNIME-Python Mixed Data Cleanup

@muhammadali33345

You need an end-to-end cleanup that moves cleanly from raw mixed formats to a Power BI-ready table, without path hacks. I’ll deliver a single KNIME workflow (or Python where it’s a better fit) that: • Ingests files/tables generically (config-driven, no hard-coded paths) • Handles missing values and standardizes numeric/categorical types • Parses and normalizes free-text comments (light NLP-friendly cleanup) • Flags outliers using rules/statistics and produces an audit-friendly flag column • Outputs one final, documented table/file ready for Power BI ingestion Alongside the workflow, I’ll provide reusable SQL snippets for in-database cleansing steps and a short validation note showing how results were checked (type distributions, null-rate deltas, outlier review sampling). Where should I insert your next detail for precise tailoring: Elegantly, what data sources (file types/tables) and target columns should the workflow prioritize for the clean Power BI output??

@TheRSTeam

Hi, I am a data analyst/statistician and Economist with more than 6 years of experience. I can do your project, Please take time to check my profile and then you decide to contact me.

@KolmanOuassou

Hi, I have been in the role for 6+ years and work as a data analyst, statistician and economist. I Have the ability to provide excellent work and the needs of your project. Would you be able to look at my profile and provide more information on previous projects and Reviews about my work as a contractor? Looking forward to your response. Best regards,

@zyeloncomputech

Hi sir, Thank you for giving opportunity for biding... we have gone through your requirements and we can do your KNIME-Python Mixed Data Cleanup stuff according to your exact requirements. Why You Need To Go With Us? And What Special you get with us. • Your vision = Our mission • Your idea + Our expertise = Winner on Web • Cutting edge web technology & design • Innovative, Cost effective & Customized service • Pragmatic Approach • Constant communication with clients • Consistent performance • On-time delivery • Maintenance of global quality standards • Your online business + Our experience = Your success Python, Data Analytics, Data Science Portfolio IoT Data Analysis for Dairy Refrigerator Temperature Monitoring Real-time Object Detection using OpenCV and YOLO Supply Chain Management System Enterprise Data Warehouse Implementation Cloud Data Lake Migration Web Application Development for Wind Turbine Performance Prediction Data Analytics Platform for Supply Chain Optimization AI Bill System AI Try Dress

@Vsion2

Welcome to professional Python development services! Hi there, I'm Alema, a Python expert programmer who strives for clear code in atmospheric, numerical weather prediction, physics, and all other seminal fields. I'm ready to provide you with high-quality services. I have completed 350+ projects with a 100% Positive Rating. If you are looking for Quality work, look no further. Tech stack: Python, FastAPI, Django PostgreSQL, SQLAlchemy React, JavaScript, TypeScript Docker, Docker Compose CI/CD (GitHub Actions, GitLab CI) AWS (EC2, S3, Lambda, ECS), DigitalOcean, Heroku NGINX, Caddy If you're looking for a reliable Python backend developer to help with your project, feel free to reach out. Your faithfully. Eng. Alema Akter

@Jennycreation

With over 17 years of comprehensive experience in web, window and Android development, combined with my 15+ years utilizing Python, SQL, and MySQL for data analysis and manipulation, I am confident in my ability to tackle your mixed-data cleaning project effectively. I have successfully completed several projects similar to yours using KNIME and Python for robust data preprocessing, such as handling missing values, normalizing data types, and identifying outliers - all without hard-coding any paths. Moreover, I understand the importance of reusable code. That's why I can provide you with not only a well-documented KNIME workflow or a Python script but also SQL snippets that can be utilized for any future cleansing steps. This would save you both time and effort while maintaining data quality and consistency. Lastly, I thrive on customer satisfaction and believe in producing the best results even in tight timelines. I assure you that the final output I provide will seamlessly integrate with Power BI without requiring any additional manipulation. Let's work together to make your data tidy and meaningful!

@manoval09

Hi, Your one-day data cleaning assignment combining KNIME and Python is exactly the kind of focused, time-boxed work where preparation makes the difference — and the incomplete description ("the raw sources...") tells me you likely have multiple heterogeneous inputs that need profiling before any transformation logic is locked in. My approach: start with a KNIME workflow for visual ETL orchestration — column filter, missing value, and duplicate row nodes handle the structural passes — while offloading anything statistically nuanced (outlier detection, regex-based normalization, cross-column validation) to a Python Script node using pandas and Great Expectations for assertion-level data quality checks. This hybrid keeps the pipeline auditable without sacrificing flexibility. In the first few hours I'd run a data profiling pass on all raw sources to surface null rates, cardinality issues, and type mismatches before writing a single transformation — so we're fixing the right things. Can you share a sample of the raw files so I can size the cleaning scope and confirm the KNIME version you're running? Best regards, Val

@mubashir021

Hello, your project on data cleaning and preprocessing requires someone who can expertly navigate KNIME and Python, and I have successfully managed similar mixed-data projects before. I am Mubashir, a Full-Stack Developer with 6+ years of experience in data analysis and ETL processes. I understand that you need a robust end-to-end KNIME workflow or a Python script to handle mixed formats, normalize data, and seamlessly integrate the output into Power BI. 1. First, I will analyze the raw data sources to identify the specific formats and missing values that need addressing. 2. Then, I will create a KNIME workflow or Python script that imports the data, cleans it by normalizing data types, and flags outliers without hard-coding paths. 3. After that, I will develop reusable SQL snippets for the in-base cleansing steps to ensure efficiency. 4. Finally, I will generate a well-documented output table or file that integrates smoothly into Power BI, requiring no further manipulation. I can provide a FREE detailed quotation and project proposal with a suggested roadmap at no cost. Even if you decide to work with someone else, you can use my proposal as a solid reference document. >>> My Work: https://www.freelancer.com/u/mubashir021/Data-Analyst-30537935 <<< Drop me a message and let's get this sorted. Mubashir

@snath88

Hi, Drop me a message — I'll share a quick prototype based on what I understood. If it matches your expectations, we can move forward. Thanks!

@sayarsinghseo

As a seasoned Data Scientist, SEO Specialist, and Data Analyst, I've honed a diverse skill set specifically tailored to the requirements of your project. My expertise spans across data analytics in Python, MySQL, SQL, and Power BI - all essential tools for the successful completion of this task. I've mastered KNIME and have a solid understanding of Python's expressive capabilities, which are crucial given the mixed data formats we'll be handling. I'm enthusiastic about developing an end-to-end solution for you that not only imports and tidies raw data but also creates reusable SQL snippets for future use. My familiarity with KNIME and Python will ensure efficient data cleansing, handling missing values, normalizing data types, and flagging outliers without hard-coding paths. Importantly, my track-record demonstrates my ability to thoroughly validate the results of such preprocessing steps. To further instill confidence, I am familiar with Power BI and can provide you with a final, well-documented output file ready to be plugged straight into the system. Choosing me ensures that you get an expert who combines analytical precision with my strategic approach to delivering measurable outcomes.

@gunjan1711

- I have hands-on experience cleaning and preprocessing mixed datasets containing numerical, categorical, and text fields using Python, SQL, Power Query, and ETL workflows. - My expertise includes missing value treatment, data type normalization, duplicate removal, outlier detection, text standardization, and creating analysis-ready datasets. - I can develop a reusable preprocessing workflow that imports files and database tables, applies validation and cleansing rules, and produces a clean output ready for Power BI consumption. - I have experience writing SQL scripts for in-database transformations and Python-based data pipelines using Pandas for efficient data preparation and quality checks. - In a recent project, I developed a PHP API, integrated database data into Power BI, and created automated real-time dashboards for business performance monitoring. - My validation process includes reconciliation checks, outlier reviews, null analysis, and consistency testing to ensure reliable downstream reporting. - The deliverables will include documented workflows, reusable SQL scripts, cleaned datasets, and clear notes explaining the preprocessing logic and validation steps. - I can start immediately and complete the assignment within the required timeframe. Reference work is available in my profile.

@jonnalabapireddy

Hello, I am an AIML student with experience in Python, Data Analysis, Pandas, NumPy, and data preprocessing. I can assist with cleaning, transforming, and validating datasets using both KNIME and Python. My services include: Data cleaning and preprocessing Handling missing values and duplicates Data transformation and formatting Excel/CSV dataset processing KNIME workflow support Clear documentation of the process I can start immediately and deliver accurate, high-quality results within the required timeline. Looking forward to discussing the dataset and requirements. Regards, Bapi Reddy

@sobiariazr5

Hi, I have experience with data cleaning and preprocessing using Python, SQL, KNIME, and Power BI. I can build a reusable workflow that imports data from multiple sources, handles missing values, standardizes data types, detects and flags outliers, and produces a clean, analysis-ready dataset. ✔ Data cleansing and preprocessing ✔ KNIME workflows and Python automation ✔ SQL scripts for database-level cleaning ✔ Validation checks to ensure data quality ✔ Power BI-ready output with clear documentation For validation, I typically use consistency checks, duplicate detection, null-value audits, datatype verification, and outlier analysis to ensure the final dataset is accurate and reliable. I can start immediately and deliver within your one-day deadline.

@Talhaish

I can deliver a clean, Power BI-ready dataset from your mixed-format sources within your one-day timeline. Here is a summary of how I handle these exact preprocessing pipelines: * **KNIME for Orchestration & Mixed Data:** I use KNIME to build modular, path-independent workflows (utilizing knime:// relative paths). I handle categorical fields via *One-Hot/Label Encoder* nodes and isolate free-text comments using *Regex Split* and *String Manipulation* nodes to extract clean tokens. * **Python for Edge Cases:** Where KNIME workflows become overly complex, I inject a Python Script node leveraging pandas for advanced text cleaning (e.g., handling inconsistent string encodings) and scikit-learn for robust outlier detection using **Isolation Forests** rather than hard-coded thresholds. * **In-Database SQL Cleansing:** To optimize performance, I push heavy transformations upstream. I write reusable CTEs (Common Table Expressions) utilizing COALESCE for missing values, TRY_CAST for safe data type normalization, and CASE WHEN logic to flag structural anomalies before the data even leaves the database. * **Validation & Power BI Delivery:** I validate the pipeline using automated row-count checks, schema validation scripts, and null-percentage thresholds. The final output is structured as a star-schema-friendly table deposited directly into your database (or a local .parquet file), allowing Power BI to import it instantly with zero power-query manipulation required.

@rakhshanz3

With my extensive experience in data cleaning and preprocessing, I have successfully navigated multiple projects involving mixed data formats, including numbers, categorical fields, and text comments - a skill set well-aligned with your requirements. Additionally, I am proficient on popular platforms such as KNIME, SQL, Python - tools that you require for this project. My past experiences have taught me to embrace the dynamical nature of data Most importantly, even though I’m an expert in handling complex technical processes like data cleaning and preprocessing using KNIME and Python (wherever it makes more sense), I am mindful of business needs. I always ensure to deliver outcomes that are not only technically efficient but also easily interpretable for non-technical users like managers using tools such as Power BI. A testimony to this is my practice of generating reusable SQL snippets resulting from database cleansing steps - facilitating long-term usability of your cleaned data. Opting for proficiency over pretense is why I believe my experience aligns exceptionally well with your project's needs. With my proven track record and strong skillset in KNIME/Python data cleaning, SQL scripting, Power BI integration, and documentation; I can guarantee you an efficient one-day turnaround on this assignment while providing valuable insights along the way.

@yashrajprasad149

Hi, I am a full-stack developer with over 2 years of experience designing full-stack applications. I am happy to share my portfolio and GitHub if needed. I have hands-on experience with data cleaning, preprocessing, SQL, Python (Pandas/NumPy), and Power BI workflows. I've worked on projects involving mixed datasets containing numerical, categorical, and free-text fields, where data quality, normalization, and validation were critical before analysis and reporting. My approach: • Build a reusable KNIME workflow or Python pipeline for ingestion and preprocessing • Handle missing values, duplicates, inconsistent formats, and datatype normalization • Perform outlier detection and flag suspicious records • Create reusable SQL scripts for database-side cleansing • Validate outputs through summary statistics, row-count reconciliation, and quality checks • Deliver a Power BI-ready dataset requiring no further transformation Relevant experience: • Customer and sales data cleansing pipelines using Python and SQL • ETL workflows for analytics dashboards and reporting systems • Data standardization, feature engineering, and preprocessing for machine learning projects • Power BI data preparation and validation I'm available to complete this project for 1500 INR. I'm currently focused on building my reputation on the platform, so I'm offering a competitive rate while ensuring high-quality delivery. I can start immediately and provide a working solution within your one-day timeline.

@sarab338

Hello, I am a Data Scientist and Data Analyst with hands-on experience in data cleaning, preprocessing, ETL workflows, SQL, Python (Pandas, NumPy), and Power BI. I have worked on projects involving mixed datasets containing numerical, categorical, and text fields, where I performed data quality checks, missing value treatment, data type normalization, duplicate removal, outlier detection, and dataset validation before analysis and reporting. For this assignment, I can deliver: A reusable Python preprocessing workflow (Pandas/NumPy) and SQL cleansing scripts. Automated handling of missing values, inconsistent formats, and outlier detection. A clean, documented dataset ready for direct use in Power BI. Validation reports to ensure data integrity and consistency. My approach includes verifying row counts, null values, data types, duplicate records, and statistical distributions before and after preprocessing to ensure reliable results. I am available to start immediately and can complete the task within the required one-day timeframe. Best regards, Sara BEN CHIKH

@Christophero0506

Hi, This project aligns well with my experience, and I'd be happy to help. I focus on delivering professional, reliable work with strong attention to detail and clear communication throughout the project. My goal is always to provide a solution that is both effective and easy to maintain. I have successfully completed multiple projects involving mixed-data cleaning using KNIME, Python, SQL, and Power BI integration. I specialize in developing end-to-end workflows for data cleaning and preprocessing without hard-coding paths, ensuring efficient handling of missing values, normalization of data types, and identification of outliers. I am confident in my ability to deliver a solution that meets your requirements, including reusable SQL snippets and a well-documented output for seamless integration into Power BI. I look forward to learning more about your specific needs. Regards, Christopher Olivier

@adrianldata

Hello. I specialize in quantitative data architecture and Pandas vectorization. I can bypass KNIME and build a highly efficient, end-to-end Python pipeline to solve this mixed-data preprocessing task in a single day. My architectural approach for your requirements: Mixed Data Handling: I will use Pandas to systematically ingest the raw files, dynamically casting data types and handling free-text comments without breaking the structural schema. Missing Values & SQL Snippets: I will implement deterministic imputation for missing fields. I can also provide reusable SQL queries for in-database normalization if you prefer to process the data at the server level. Dynamic Outlier Flagging (No Hardcoding): Instead of static, rule-based flagging, I will deploy a mathematical Z-score filter (standard deviation). This automatically adapts to the data distribution to flag true statistical outliers, regardless of future data variance. Power BI Ready: The final output will be a rigorously clean, structured flat file or table, strictly formatted to plug directly into Power BI with zero friction. I have the logic ready to execute. We can start immediately.