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Programming & Development Math / Algorithms / Analytics

Python Data Analysis and ML

$12/hr Starting at $30

Have a dataset but not sure what to do with it? I'll run a full analysis, surface the patterns hiding in your data, and — if needed — build a machine learning model that predicts outcomes with measurable accuracy.


I don't just run code. I tell you what the results mean for your business.


What I deliver:

- Exploratory Data Analysis (EDA): distributions, correlations, outliers

- Data cleaning and feature engineering

- Visualization: charts, heatmaps, pairplots, trend lines

- ML models: regression, classification, clustering (Scikit-learn)

- Model evaluation: accuracy, R², confusion matrix, cross-validation

- Streamlit deployment for interactive web app (Premium)

- Clean Jupyter Notebook with comments throughout


Proven results: Built a Random Forest motor speed prediction model achieving R² = 0.9998. Deployed live as a Streamlit web application.


Tools: Python, Pandas, Scikit-learn, Matplotlib, Seaborn, Plotly, Streamlit


Who this is for: Researchers, startups, and businesses sitting on data they haven't analyzed — or analysts who need ML support they don't have in-house.


Deliverable: Jupyter Notebook (.ipynb) with all code, charts, and a written summary of findings. Optional: Streamlit app deployment.


Share your dataset or describe your problem — I'll tell you exactly what's possible before you pay anything.


About

$12/hr Ongoing

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Have a dataset but not sure what to do with it? I'll run a full analysis, surface the patterns hiding in your data, and — if needed — build a machine learning model that predicts outcomes with measurable accuracy.


I don't just run code. I tell you what the results mean for your business.


What I deliver:

- Exploratory Data Analysis (EDA): distributions, correlations, outliers

- Data cleaning and feature engineering

- Visualization: charts, heatmaps, pairplots, trend lines

- ML models: regression, classification, clustering (Scikit-learn)

- Model evaluation: accuracy, R², confusion matrix, cross-validation

- Streamlit deployment for interactive web app (Premium)

- Clean Jupyter Notebook with comments throughout


Proven results: Built a Random Forest motor speed prediction model achieving R² = 0.9998. Deployed live as a Streamlit web application.


Tools: Python, Pandas, Scikit-learn, Matplotlib, Seaborn, Plotly, Streamlit


Who this is for: Researchers, startups, and businesses sitting on data they haven't analyzed — or analysts who need ML support they don't have in-house.


Deliverable: Jupyter Notebook (.ipynb) with all code, charts, and a written summary of findings. Optional: Streamlit app deployment.


Share your dataset or describe your problem — I'll tell you exactly what's possible before you pay anything.


Skills & Expertise

Data AnalysisMachine LearningPandaPythonScikit LearnStatistical Analysis

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