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$
USD
I will do advanced machine learning models and python data science projects
Client satisfaction is my first priority
I am a Data Analyst & Machine Learning Specialist with expertise in Applied Statistics, Exploratory Data Analysis, and Predictive Modeling in Python.
I help clients solve complex data problems throug...
About this Gig
Are you looking for an expert in Advanced Machine Learning, Bayes Classifiers, and Model Performance Evaluation? You are in the right place!
I specialize in building, analyzing, and optimizing complex machine learning models using Python, Scikit-Learn, SciPy, and NumPy.
What I Offer:
- Bayes Classifier & Probability Analysis: Joint probability calculations, Multivariate Normal Distributions, Covariance, and Decision Region mapping.
- Model Accuracy & Evaluation: In-depth comparison of Resubstitution (Training) Accuracy, 10-Fold Cross-Validation, and True Test Accuracy.
- Algorithm Implementation: Linear Discriminant Analysis (LDC), 1-NN, Decision Trees, SVM, Bagging, and Random Forest Ensembles.
- Class Imbalance Solutions: Imbalanced Data Triangle analysis, Entropy evaluation, and resampling using SMOTE / imbalanced-learn.
- Python Data Science: Jupyter Notebooks, Clean Visualization (Matplotlib, Seaborn), and detailed theoretical reports.
Why Choose Me?
- Mathematically rigorous & accurate solutions
- Well-documented Python code
- 100% original work tailored to your guidelines
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Expertise:
Classification
•
Clustering
•
Decision trees
Programming language:
Python
Frameworks:
Scikit-learn
•
Panda
Tools:
Jupyter Notebook
My Portfolio
FAQ
What machine learning libraries and tools do you use?
I primarily use Python along with industry-standard libraries including Scikit-Learn, SciPy, NumPy, Pandas, Matplotlib, Seaborn, and imbalanced-learn for data modeling and visualization.
Do you provide complete source code and documentation?
Yes! You will receive clean, well-commented Python code (Jupyter Notebook or .py files) along with structured reports explaining the results and methodology.
Can you handle imbalanced datasets?
Absoluteley. I apply advanced techniques such as SMOTE oversampling, class weighting, and specialized evaluation metrics (F1-score, Precision, Recall) to effectively handle class imbalance.
How do you evaluate and validate the models?
I perform rigorous performance evaluations comparing Training/Resubstitution Accuracy, $k$-Fold Cross-Validation, and Test Accuracy across multiple algorithms like Bayes Classifiers, SVM, Decision Trees, and Ensembles.
