A systems-first handbook and Python framework for ML engineers. It provides architectural patterns for CV, NLP, and tabular data. Includes production-ready modules for OOF pipelines, safe target encoding, and ensemble stacking.

This repository contains the full implementation of a machine learning pipeline developed for the Kaggle competition Loan Approval Prediction (Playground Series - Season 4, Episode 10). Our goal is to predict whether a loan will be approved based on applicant information and credit history, leveraging state-of-the-art ML models and feature engineer

Kaggle-Survey-2020-Commpetition The code in this Repo is submission for the annual Kaggle ML & DS survey Competition. Our goal In this competition we were given the data of the annual survey that Kaggle conducts every year and we were asked to explore this data and come up with some conclusions that might be unique and wouldn't be visible if we just glanced through the data. I performed EDA on the data on features like - Age of a person, the country they live in, The Language they use to code, the IDEs they preder, their job titles and much more. After exploration we were able to come up with some conclusions which i have mentioned at the end of my notebook. Few Results and Observations Most Kaggle user's are quite young with their age between 22-29. The Number of Men using Kaggle is huge as compared to the Woman. But we could see a significant growth in number of female Kagglers recently. Most Kaggler's are from India followed by USA and other countries. Most Kagglers have a Master's Degree. Majority of Kagglers are Students followed by Data Scientists and Machine Learning Engineers. Most Kagglers have Experience of 3-5 Years in the Programming and then there are Kagglers with an experience of 1-2 years. Most Kagglers use Python followed by SQL and R. The most Preffered IDEs are Jupyter, VScode and PyCharm. Most Recommended Languages for Data Science Beginners is Python followed by R. The most used data visualization Libraries are Matplotlib and Seaborn. The most used framework for Machine learning and Deep learning is Sci-Kit learn followed by Tensorflow along with Keras. The Most commonly used algorithms are Regression based followed by Decision trees, random forests and so on. Most users share their work on Github followed by Kaggle and Colab. There are also many who dont like to share their work. Most users preferred Coursera to learn Data science and Machine Learning followed by Kaggle Courses and Udemy. Most users make use of Kaggle notebooks and forums to stay updated about latest Data science and ML topics followed by Youtube and Blogs on various websites.

Engineered ML API for playground-series-s6e2 Kaggle Competition

Capstone Project for Udacity ML Engineer Nano degree. Approached Kaggle competition: "Predict Future Sales".

End-to-end ML project for Kaggle’s House Prices challenge. Cleans data, engineers features, applies log transformation, and trains a tuned Gradient Boosting model. Achieves ~19k RMSE and generates a Kaggle-ready submission.

Built an interactive dashboard using Python, Dash, and Plotly to analyze and compare job trends for Data Scientists and ML Engineers. Leveraged a Kaggle dataset to visualize key skills, library usage, and educational requirements turning raw data into actionable career insights through clean design and practical analysis.