This repository serves as a structured documentation of the machine learning foundations and techniques I already work with.
Rather than a learning log, it is a curated space where I organize, implement, and showcase essential ML concepts —
from core fundamentals to practical algorithm implementations.
- Consolidate my understanding of key ML foundations
- Demonstrate clean workflows for data exploration and visualization
- Showcase implementations of widely used machine learning algorithms
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Data Preprocessing
Handling missing values, feature scaling, encoding, normalization, etc. -
Exploratory Data Analysis (EDA)
Visualizing datasets, understanding distributions, identifying correlations. -
Supervised Learning Algorithms
Linear Regression, Logistic Regression, SVM (Support Vector Machine), KNN (K Nearest Neighbours).
pip install -r requirements.txt to install the requirements for running the notebooks.