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I will build machine learning models in python using scikit learn
Data Scientist, Python Automation Specialist, Machine Learning Engineer
Hi, I am a Computer Science graduate specializing in Data Science and Machine Learning pipelines. I help clients transform messy datasets into clean, structured, and actionable insights.
Core Experti...
About this Gig
Are you looking for reliable, high-performance Machine Learning models in Python? You are in the right place!
I will help you build, optimize, and evaluate complete machine learning pipelines tailored to your dataset using Scikit-Learn, Pandas, and XGBoost.
What I Offer:
- Data Preprocessing: Handling missing data, encoding, feature scaling, and outlier cleaning.
- Supervised Learning: Classification (Logistic Regression, Decision Trees, Random Forest, XGBoost) and Regression models.
- Unsupervised Learning: K-Means Clustering and PCA dimensionality reduction.
- Model Optimization: Hyperparameter tuning (GridSearch/RandomizedSearch), Cross-Validation, and metric evaluation (ROC-AUC, Confusion Matrix, F1-Score).
- Deliverables: Clean, well-commented Jupyter Notebook (.ipynb) with step-by-step documentation.
Tech Stack: Python, Scikit-learn, Pandas, NumPy, XGBoost, Matplotlib, Seaborn, Colab.
Why Choose Me?
- Clean, modular, and reproducible code
- Clear explanations and visual insights
- Fast communication & on-time delivery
- Please message me before placing an order so we can review your data and goals!
Programming language:
Python
•
SQL
•
Colab
Frameworks:
Scikit-learn
•
Keras
•
Panda
APIs:
Other
Tools:
Jupyter Notebook
•
TensorFlow
•
Excel
•
Colab
My Portfolio
FAQ
Question: What format will the final deliverable be in?
You will receive a clean, well-commented Jupyter Notebook (.ipynb) along with exported reports/plots or Python scripts (.py) as requested.
Question: Which machine learning algorithms do you work with?
I work with Linear/Logistic Regression, Decision Trees, Random Forest, SVM, XGBoost, and unsupervised algorithms like K-Means Clustering and PCA.
Question: Do you handle data cleaning and preprocessing?
Yes, all packages include data preprocessing such as missing value handling, categorical encoding, feature scaling, and train-test splitting.
Question: What do you need to get started on my project?
Please share your dataset (CSV, Excel, etc.) along with a brief description of your project goal, target variable, or specific requirements.
