You will get Feature Engineering & Data Preparation for Machine Learning Models
Project details
I specialize in clean, leakage-free feature engineering and data preparation that directly improves model performance. You get well-documented, ML-ready datasets that will help models perform better and give reliable results.
Machine Learning Tools
NLTK, NumPy, pandas, Python, Python Scikit-Learn, RWhat's included
| Service Tiers |
Starter
$45
|
Standard
$85
|
Advanced
$140
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 2 | 4 | 6 |
Model Validation/Testing | - | ||
Model Documentation | - | - | |
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Additional Revision
+$10
Additional Graph/Chart
+$5
Model Documentation
+$25Frequently asked questions
About Nidhhi
Applied Machine Learning Engineer | Python | Feature Engineering
Nagpur, India - 1:02 pm local time
My work focuses on building end-to-end Machine Learning solutions that transform raw datasets into reliable prediction systems using Python and Scikit-learn.
I work best on projects where the data exists but needs careful transformation and engineering before models can perform well.
• Data Ingestion & Database Integration.
• Data Validation & Quality Checks.
• Feature Engineering & Preprocessing Pipelines.
• Model Training & Comparative Evaluation.
• Model Serialization & Inference Pipelines.
• Logging & Custom Exception Handling.
• Prediction Pipeline Development.
• Flask-based Local Deployment.
I’m currently building my freelance portfolio and take on well-scoped projects where I can deliver clean, production-ready work. If you need help improving model inputs or preparing data for production use, I’d be happy to help.
I can help with:
• Cleaning and preparing datasets for ML.
• Feature engineering for better model performance.
• Training and evaluating ML models.
• Building prediction pipelines.
• Debugging low-performing ML systems.
• Creating simple deployment workflows.
Technical Skills:
• Python (Pandas, NumPy, Scikit-learn).
• Feature Engineering & Data Preprocessing.
• Logistic Regression, Random Forest, XGBoost / LightGBM.
• Cross-validation & Model Evaluation.
• ML Debugging & Performance Improvement.
• Data Pipelines & ML Inference Scripts.
• Flask for ML Deployment.
• SQL / Cassandra Database Integration.
I also work as a content writer, creating clear, well-researched, and engaging content across multiple niches.
📊 Data Science / Tech Writing.
✈️ Travel Writing (Blogs, Destination guides).
🚗 Automobile Writing (Car reviews).
Steps for completing your project
After purchasing the project, send requirements so Nidhhi can start the project.
Delivery time starts when Nidhhi receives requirements from you.
Nidhhi works on your project following the steps below.
Revisions may occur after the delivery date.
Review
I review your dataset, problem statement, and goals.
Data Cleaning & Preparation
Handle missing values, outliers, duplicates, and data consistency issues.