You will get Clean, structured data perfectly prepared for your ML model.
Project details
I offer professional data preprocessing and data cleaning services to help you transform raw, messy, or unstructured data into a high-quality dataset ready for advanced analysis or machine learning.
With expertise in ML pipelines and data engineering, I ensure your data is:
• Clean
• Structured
• Normalized
• Imputed
• Encoded
• Feature-engineered
• Free of inconsistencies
You will receive a dataset that is ready for model training or direct ingestion into your ML workflow.
🔹 Services Included
• Handling missing data (imputation, removal, strategies)
• Removing duplicates and errors
• Feature selection & feature engineering
• Encoding categorical variables
• Normalization/standardization (MinMax, z-score, etc.)
• Outlier detection and handling
• Data balancing (SMOTE, random sampling, etc.)
• Train/test split creation
• Dataset merging, transformation, reshaping
• Text cleaning (for NLP datasets)
• Image preprocessing
• Exploratory Data Analysis
🔹 Tech Stack
• Python
• Pandas, NumPy
• Scikit-learn
• NLTK / spaCy (text cleaning)
• OpenCV / PIL (image preprocessing)
• Jupyter / Colab
• SQL (if needed)
With expertise in ML pipelines and data engineering, I ensure your data is:
• Clean
• Structured
• Normalized
• Imputed
• Encoded
• Feature-engineered
• Free of inconsistencies
You will receive a dataset that is ready for model training or direct ingestion into your ML workflow.
🔹 Services Included
• Handling missing data (imputation, removal, strategies)
• Removing duplicates and errors
• Feature selection & feature engineering
• Encoding categorical variables
• Normalization/standardization (MinMax, z-score, etc.)
• Outlier detection and handling
• Data balancing (SMOTE, random sampling, etc.)
• Train/test split creation
• Dataset merging, transformation, reshaping
• Text cleaning (for NLP datasets)
• Image preprocessing
• Exploratory Data Analysis
🔹 Tech Stack
• Python
• Pandas, NumPy
• Scikit-learn
• NLTK / spaCy (text cleaning)
• OpenCV / PIL (image preprocessing)
• Jupyter / Colab
• SQL (if needed)
Machine Learning Tools
NumPy, pandas, Python, scikit-learnWhat's included
| Service Tiers |
Starter
$50
|
Standard
$150
|
Advanced
$300
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | - | ||
Source Code | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$5 - $20
Additional Revision
+$5
Additional Model Variation
(+ 1 Day)
+$15Frequently asked questions
About Izhar Ul
AI/ML Engineer | NLP, Computer Vision & Automation
Islamabad, Pakistan - 4:56 pm local time
🔹 What I Do Best
- Machine Learning model development
- Deep Learning for NLP and Computer Vision
- LLM integration (OpenAI, LangChain, RAG systems)
- AI-powered chatbots and automation tools
- Predictive analytics & forecasting models
- Recommendation systems
- Model optimization, fine-tuning, and deployment
- Data preprocessing, feature engineering, and pipelines
🔹 I’ve worked on AI projects ranging from chatbots and automation tools to predictive models and advanced NLP systems. I focus on clean solutions, clear communication, and measurable results. My goal is always to understand your needs deeply and deliver AI systems that truly solve your problem.
🔹I aim to help clients harness the power of AI/ML to innovate, automate, and scale their businesses with confidence.
Steps for completing your project
After purchasing the project, send requirements so Izhar Ul can start the project.
Delivery time starts when Izhar Ul receives requirements from you.
Izhar Ul works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Gathering
Client submits the dataset, project goals, and any specific preprocessing needs (e.g., missing values, feature engineering, formatting, etc.)
Initial Data Audit
- I review the dataset for quality issues, structure, inconsistencies, missing values, outliers, and formatting problems. - I share a quick summary of what needs to be done.