Machine Learning Lab — Semester 6

Repository of laboratory assignments, experiments, and example implementations for the Machine Learning Lab course (Semester 6). This collection contains Jupyter notebooks, scripts, datasets pointers, and analysis reports intended to demonstrate concepts, algorithms, and practical workflows used in the course.

Table of contents

• Overview
• Learning goals
• Repository structure
• Prerequisites
• Quick start
• Running notebooks and scripts
• Datasets and data handling
• Reproducibility notes
• Contributing
• Maintainer and contact
• License

Overview

This repository contains practical implementations and lab exercises to help students learn and apply machine learning techniques. Typical content includes:

• Jupyter notebooks that walk through algorithms and experiments.
• Python scripts for data processing, training, and evaluation.
• Example visualizations and reports.
• Notes and README files describing experiment setup and results.

The materials are organized to be reproducible and easy to follow for students who want to run the experiments locally or adapt them for their assignments and projects.

Learning goals

By working through the materials in this repository you will:

• Understand and implement core supervised and unsupervised learning algorithms.
• Preprocess and explore real-world datasets using standard Python tools.
• Train, evaluate, and compare models using appropriate metrics.
• Visualize results and document experiments clearly.
• Develop reproducible workflows for machine learning experiments.

Repository structure

Note: actual folder names may vary. Adjust paths in commands below to match the repository layout.

• notebooks/ — Jupyter notebooks for experiments and demonstrations
• src/ — Reusable modules and helper functions (data loaders, models, utils)
• scripts/ — Standalone scripts for training, evaluation, or data processing
• data/ — Small sample datasets or pointers; large datasets are typically excluded
• reports/ — Plots, PDFs, or written lab reports
• requirements.txt — Python dependencies (if present)
• README.md — This file

If your repository uses different names, adapt the commands and paths accordingly.

Prerequisites

Recommended Python environment:

• Python 3.8 or later
• pip (or conda) for package management

Common Python libraries used in the notebooks and scripts:

• numpy
• pandas
• scikit-learn
• matplotlib
• seaborn
• jupyterlab or notebook

Optional (depending on experiments):

• tensorflow or torch (PyTorch)
• seaborn, plotly for advanced visualization

If this repository includes a requirements.txt, install from it. If not, create a virtual environment and install the libraries you need.

Quick start

1. Clone the repository

   git clone https://github.com/techieRahul17/Machine_LearningLabSem6.git
   cd Machine_LearningLabSem6
   

2. Create and activate a virtual environment (venv example)

   python -m venv .venv
   # macOS / Linux
   source .venv/bin/activate
   # Windows (PowerShell)
   .venv\Scripts\Activate.ps1
   

3. Install dependencies

   • If requirements.txt exists:

     pip install -r requirements.txt
     

   • Otherwise, install common packages:

     pip install numpy pandas scikit-learn matplotlib seaborn jupyterlab
     

4. Launch Jupyter

   jupyter lab
   # or
   jupyter notebook
   

5. Open the notebooks in notebooks/ to follow the labs step-by-step.

Running notebooks and scripts

• Notebooks: open via Jupyter Lab/Notebook and execute cells in order. Look for top cells that set paths and seed values.
• Scripts: run from repository root. Example:

  python scripts/train_model.py --config configs/experiment1.yaml
  

  (Adjust the script name and arguments according to actual files.)

Tip: If notebooks reference data/ or relative paths, run the notebook or script from the repository root so the relative paths resolve correctly.

Datasets and data handling

• Large datasets are typically not stored directly in the repository. Instead, notebooks include instructions or scripts to download and prepare datasets.
• Place local datasets under data/ following the structure expected by the notebooks, or update the path variables in the notebook to point at your dataset location.
• Keep raw data separate from processed data; use data/raw/ and data/processed/ if following a simple pipeline convention.

Reproducibility notes

• Set a fixed random seed for experiments where stochastic behavior matters (examples typically show how to do this).
• Record package versions (use pip freeze > pip-freeze.txt) if you need to reproduce results across machines.
• If heavy computation is required, consider sampling or using smaller subsets during development.

Maintainer and Author

• Authored and Maintained by: Rahul V S
• Github: (https://github.com/techieRahul17)

License

This repository is provided under the MIT License unless otherwise specified. See the LICENSE file for details. If no license file exists and you want to reuse code from this repository, please contact the maintainer to clarify licensing terms.