Statistical Machine Learning — Practice Projects

This repository collects Python scripts and datasets used for statistical machine learning and data-analysis practice. The current files cover cancer-data analysis, diabetes-related analysis, housing data, and customer satisfaction/choice exercises.

This is a collection of learning exercises rather than one packaged production application. Each script should be reviewed and run independently; dataset names and filenames reflect the current repository contents.

Current contents

Area	Files currently present
Cancer data	cancer.py, Cancer_Data.csv
Diabetes	diabetic dataset insights.py, diabetes.csv
Housing	housing dataset.py, Housing.csv
Customer satisfaction / choice	kaggle datatset customer.py, manual customer satisfaction.py, raNDOM CHOICE CUSTOMER.py, Customer Satisfaction Scores and Behavior Data.xlsx
Other practice scripts	Untitled20.py, lab 3.py, ssh.py

Some filenames are informal or inconsistent; they are retained as-is so existing references do not break.

Typical workflow

flowchart TD
    D[CSV / XLSX dataset] --> L[Load data with pandas]
    L --> C[Inspect columns, types and missing values]
    C --> P[Clean and prepare data]
    P --> A[Statistical analysis / model]
    A --> E[Evaluate and interpret results]
    E --> R[Document findings and limitations]

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This is a recommended workflow for the exercises, not a claim that every existing script implements every step.

Environment setup

Requirements

• Python 3.10+ recommended
• pip

Create a virtual environment:

Windows PowerShell

python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install --upgrade pip
pip install pandas numpy matplotlib seaborn scikit-learn scipy openpyxl

macOS / Linux

python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
pip install pandas numpy matplotlib seaborn scikit-learn scipy openpyxl

Run a script from the repository root, for example:

python cancer.py
python "housing dataset.py"
python "diabetic dataset insights.py"

Check each script's data path and imports before running it. If a script expects a file in a different directory, update the path or run it from the repository root.

Recommended improvements

• Add a separate folder per project with its own README and clearly named entry point.
• Record each dataset's source and license.
• Add a reproducible requirements.txt after verifying actual imports.
• Include plots, metrics and written conclusions only when generated by the corresponding script.
• Add tests for data preparation and model evaluation.
• Rename informal filenames in a dedicated cleanup change after checking for references.

Data and responsible use

Do not treat analysis on public or classroom datasets as evidence of clinical validity or real-world predictive performance. Document data source, target definition, train/test split, evaluation metrics and known limitations for each model.

License

No explicit license is currently declared. Contact the repository owner before reusing the code or datasets.