I will help you implement machine learning algorithm using Python and write report
- 4.1
- (5)
Project Details
Why Hire Me?
My expertise in Python and machine learning allows me to provide comprehensive guidance from conceptualization to implementation. I focus on enabling you to not only execute algorithms but also understand their workings and effectively communicate the results in detailed reports.
Key Strengths:
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Proficient in
scikit-learn
,TensorFlow
,Pandas
, and visualization libraries -
Experienced with Jupyter Notebook, Google Colab, and VS Code setups
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Emphasis on interpretability, reproducibility, and clarity in machine learning workflows
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Guidance tailored to beginners, students, or professionals in any domain
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Strong track record of academic and industry-level project delivery
What I Need to Start Your Work
To begin work effectively, please provide the following:
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Project Details
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A clear overview of your project and specific machine learning goals
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Targeted models or algorithms (e.g., regression, classification, clustering)
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Data & Description
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Dataset(s) in CSV, Excel, or JSON formats
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Source, variable types (numerical, categorical), and prior cleaning steps
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Exact Requirements
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Desired outcomes: prediction, pattern identification, feature importance, etc.
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Special constraints: model interpretability, imbalance handling, or runtime limits
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Reporting Preferences
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Detail level expected in reports and required visuals (e.g., confusion matrix, ROC curve)
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Formatting standards for academic or business contexts
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Communication Preferences
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Preferred contact method (email, WhatsApp, video call) and update frequency
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Key dates, if any, for project milestones or delivery
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Portfolio

Predicting Car Insurance Premiums Using Regression Models in Python
Developed a Python-based regression model for an insurance analyst to predict annual premiums using customer demographics and vehicle attributes. Delivered an interpretable report with visualizations and actionable model insights.

Loan Default Classification Using Machine Learning in Python for a Microfinance Startup
Developed a classification model in Python to predict loan default risk using historical borrower data. Delivered a deployable pipeline, model comparison report, and detailed business recommendations to enhance approval accuracy.

Customer Segmentation with K-Means Clustering in Python for a U.S.-Based Cosmetics Brand
Implemented K-Means clustering using Python to segment online cosmetics customers based on purchasing behavior. The project helped define marketing personas and drive personalized campaign strategies, improving campaign ROI by over 20%.
Process

Customer Reviews
5 reviews for this Gig ★★★★☆ 4.1
got my classification model running in colab with his help great visuals in the report though could use fewer abbreviations for non technical folks
i was totally stuck on how to clean my dataset for regression he did it all in pandas and showed me every step learned a lot
he used random forest on my project and explained how feature importance works very responsive on messages too
the kmeans clustering was fine but i wish the report had a few more charts still happy he revised it quickly when i asked
used jupyter notebook for my ML assignment clean code detailed comments and the accuracy score actually matched what i needed