Poor model results often start with preprocessing: unencoded categories, unscaled features and data leakage between train and test sets. We clean, encode, scale and split your dataset without leakage and package it as a reusable preprocessing pipeline you can run again on new data.
Is this your problem?
Your model throws errors on categorical or text columns.
Training on raw data gives poor or unstable results.
You are not sure how to encode, scale or split the data correctly.
Your test results look too good to be true.
Why this happens
Most models need numeric, consistently scaled input, so categories must be encoded and features scaled. The most common mistake is data leakage: scaling or encoding the full dataset before splitting, or letting future information into training. That produces impressive test scores that collapse in real use.
What we do
Clean the data and fix types and missing values.
Encode categorical features using the right method for each column.
Scale numeric features where the model needs it.
Split into train, validation and test sets without leakage, including time-based or stratified splits.
Package everything as a pipeline that runs the same way on new data.
What you get
Processed, model-ready dataset
Reusable preprocessing pipeline (scikit-learn or equivalent)
Notes explaining every encoding, scaling and split decision
What we need from you
Your dataset
The target column you want to predict
The model goal (classification, regression, forecasting)
Common cases we handle
One-hot, ordinal and target encoding
Standard, min-max and robust scaling
Stratified, grouped and time-series splits
Imbalanced datasets and feature selection
Pricing and turnaround
Starting price | From $49 |
Delivery | Typically 24h |
Priority delivery | 12h for +50% |
Includes | Scope check, the work, deliverables and handover notes |
Every task gets a fixed price, confirmed after the free scope check. Your code and data are used only for this task, and we sign an NDA on request.
How it works
Submit your task. Tell us what you need and share the files or access listed above.
Free 30-minute scope check. An engineer confirms the scope and gives you a fixed price before any work starts.
We do the work. Your task is handled by Codersarts' own engineering team — not a freelancer marketplace.
Delivery and handover. You get the deliverables, a walkthrough of what changed, and time to ask questions.
Related tasks
Clean & Prepare Dataset — if the raw data needs full cleaning first.
Train a Model on Your Data — to train and evaluate a model on the prepared data.
PDF & Invoice to Structured Data — to extract training data from documents.
Need more than a single task?
For an end-to-end ML pipeline or product, see Codersarts Build Solutions.
FAQ
How much does ML data preprocessing cost?
From $49 for a Standard Task. The fixed price is confirmed after a free 30-minute scope check.
How fast will it be done?
Typically 24 hours. Priority 12-hour delivery is available for +50%.
What is data leakage and why does it matter?
Leakage is when information from the test set influences training. It makes results look better than they are, so the model fails in real use. Our pipeline prevents it.
Can I use the pipeline on new data?
Yes. The same pipeline transforms new data exactly as it transformed the training data.
Is my data confidential?
Yes. Your data is used only for this task, and we sign an NDA on request.
Prefer to learn it yourself? Explore hands-on courses at Codersarts Labs.