Data_science_POC

This repository contains the notebook POC1_karthick.ipynb — a proof-of-concept that demonstrates a simple supervised learning workflow using student performance data.

What this notebook does

• Loads students_performance_tracker.csv (expects it to be in the same folder).
• Cleans missing values and drops unused name columns.
• Encodes categorical fields and prepares features and target (gender).
• Trains and compares three baseline classifiers: K-Nearest Neighbors, linear SVM, and Logistic Regression.
• Reports accuracy and confusion matrices for each model.

Quick instructions to run (choose one):

• Run in Google Colab (no local setup):

  1. Open POC1_karthick.ipynb in Colab (there is a Colab badge in the notebook).
  2. Use the upload cell to upload students_performance_tracker.csv when prompted.
  3. Run cells from top to bottom (Runtime ▶ Run all).

• Run locally (basic steps):

  1. Ensure Python is installed and on PATH.
  2. (Optional) Create a virtual environment and install dependencies:

     python -m venv .venv
     source .venv/Scripts/activate   # Git Bash on Windows
     pip install -r requirements.txt

  3. Start Jupyter Notebook and open POC1_karthick.ipynb:

     jupyter notebook

  4. Run cells from top to bottom.

Notes

• The notebook uses Latin-1 encoding when reading the CSV (see the first cells). Keep the CSV in the same folder as the notebook.
• If you prefer, run first in Colab to avoid local environment issues.

That's all this README contains — it documents POC1_karthick only.