Data Science, Machine Learning, Deep Learning, NLP, Python & Library's cheat Sheet - Interview Questions & Notes

线性回归；病态线性回归；聚类分析； 主成分分析 ； 多目标决策

This is all Data Science Assignments Files. I am currently working on it, so you may not find some files here.

An algorithm intended to predict the yield of any crop. Used Agricultural Data sets for building the Step-wise Regression Model. Technology Stack: R language, SQL, Linear Regression library, Plumber library, Swagger API

An intelligent machine learning application that predicts student exam scores and provides personalized recommendations for academic improvement using advanced AI and data analytics.

这是一个线性规划的算法，由python编写而成

Data visualisation using seaborn

Machine learning Basics. Just Started learning and uploading small chunk of codes and small projects .

Tutorials of Machine Learning in Jupyter Notebooks

I have applied the fundamental idea of Linear Regression with Single Variable input. I implemented the Gradian Descent algorithm simply from scratch with no libraries such as Scikit-Learn. I just used NumPy.

Codes and Project for Machine Learning

📚📝 Types of Ml algos

Pysprak

This repository contains many machine learning topics and projects

:chart_with_upwards_trend: :currency_exchange: Linear Regression Forecasting Exchange Rates

Blockchain for Waste Management in Smart Cities

Supervised and Unsupervised Algorithm implimentation on cleaned and preprocessed data

ML projects, which I worked on utilising different machine learning algorithms.

Demonstration of asymmetric cost functions in Linear Regression

This classifier web app basically developed using Streamlit (Python - framework) and it classifies the categories of "Diabetic" or "Not Diabetic" based on certain input parameters. In this app, we can choose different classifiers like; SVM, RandomForest, GridSearch, Logistics Regression, for the classification.

Pythonで単回帰分析と重回帰分析、ディープラーニングで回帰と分類

Tracking the sleeping schedule and calorie intake of a person based on hours spent on OTT(Over-the-top)

Regression is one of the foundational techniques in Machine Learning. Being one of the most well-understood algorithms, beginners always struggle to understand some fundamental terminology related to regression. In this series of projects, I will try to give you basic ideas of underlying concepts with the help of practical examples. If you are starting your career or want to brush up on your knowledge of regression, this repo is made up for you. These projects begins by introducing some simple real-life examples for regression. From a brief introduction to most of the concepts used in regression to hands-on experience, these projects will give you enough understanding to apply those in real-world problems. With the help of the background developed, you will code your regression model in python.

AI and ML

No description provided.

This project aims to predict medical insurance costs based on demographic and lifestyle factors using supervised machine learning models. The focus is on building an interpretable and deployable prediction system with a Streamlit dashboard.

Diabetes prediction

No description provided.

A comprehensive machine learning analysis comparing traditional ML algorithms with deep learning models for NASDAQ-100 15-minute price prediction.

Predicting Titanic survival using machine learning models with age, sex, ticket class, and fare. Tested linear regression, logistic regression, and KNN with cross-validation and metrics like accuracy and recall. The best-performing model is available on GitHub with code, data, and results.