Welcome to my curated Machine Learning practice repository!
This portfolio is structured for recruiters and collaborators to quickly evaluate my skills, learning journey, and hands-on experience in both foundational and advanced machine learning concepts.
graph TD
A[Data Exploration] --> B[Visualization]
B --> C[Data Profiling]
C --> D[Core ML Algorithms]
D --> E[Model Comparison]
E --> F[Advanced Topics]
D --> G["Gradient Descent (from scratch)"]
F --> H[Clustering]
F --> I[Decision Trees]
D --> J[KNN]
D --> K[Logistic Regression]
H --> L[K-Means]
-
EDA.ipynb
Exploratory Data Analysis (EDA) notebook showing data cleaning, statistics, distributions, and key insights. Illustrates my approach to understanding and preparing data for modeling. -
PandasProfiling.ipynb
Automated data profiling with Pandas Profiling for comprehensive data summary reports, missing value detection, and variable distributions.
- Mathploty_Intro.ipynb
Hands-on introduction to Matplotlib for visualization, covering line plots, bar charts, histograms, scatter plots, and customizations. Demonstrates my ability to communicate data insights visually.
-
Gradient_Decent_Scratch.ipynb
Implements gradient descent from scratch for linear regression, reinforcing my understanding of optimization techniques and mathematical foundations. -
Logisticregression.ipynb
Performs binary classification using logistic regression, including model fitting, evaluation (confusion matrix, ROC), and visualizations. -
KNN.ipynb
Implementation of k-Nearest Neighbors algorithm with distance metrics, hyperparameter tuning, and practical examples. -
Comparing_Algo.ipynb
Compares multiple ML algorithms (e.g., KNN, Logistic Regression, Decision Trees, SVM) on the same dataset, demonstrating proficiency in model selection and metrics.
-
CLustering.ipynb
Explores unsupervised clustering techniques, including K-Means, Agglomerative Clustering, and DBSCAN, with practical applications and visualizations. -
K-Means.ipynb
Deep dive into K-Means clustering, showing step-by-step implementation, elbow method, and cluster visualization.
- Decision_Tree_small_example.ipynb
Walks through a small decision tree example, feature splits, entropy/Gini calculations, and visualization of tree structure.
- Breadth & Depth: Covers essential topics from EDA to advanced clustering and model comparison.
- Hands-On Approach: Every notebook is executable, with comments, visualizations, and real-world use cases.
- Mathematical Foundations: Includes “from scratch” implementations to prove deep understanding.
- Growth Mindset: Repository is regularly updated as my learning progresses.
Aditya
Aspiring Data Scientist & ML Engineer | GitHub Profile
Thank you for visiting my portfolio! If you have feedback, collaboration ideas, or opportunities, feel free to reach out.