There are 1 repository under scikitlearn topic.
This repository is a related to all about Machine Learning - an A-Z guide to the world of Data Science. This supplement contains the implementation of algorithms, statistical methods and techniques (in Python), Feature Selection technique in python etc. Follow Coursesteach for more content
📷 Crawl and Analyze Instagram Hashtag Data: KoNLPY to gensim word2Vec & scikit-learn TF-IDF
Drilling Activity Prediction: Oil and Gas operations are dramatically affected by supply, demand and several other factors that compromise the operational planning of resources. To overcome this challenge, predictive analytics could be applied to forecast rotary rig count inside United States using time-series data.
This repository is a collection of all the files, resources, notes, and code that I used to learn Machine Learning and Data Science. All the code and notes here have been gathered from various sources, and I have compiled them into this repository.
BTAlert-AI is an application based on Machine Learning to monitor and predict application outages.
This repository contains basic to advanced codes related to data science and machine learning concepts using python. This is a learning endeavour using several online resources.
2024 11조 AI를 활용한 퍼스널컬러 헤어스타일 추천 서비스 Only-You
In this data set we have perform classification or clustering and predict the intention of the Online Customers Purchasing Intention. The data set was formed so that each session would belong to a different user in a 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period.
This folder contains the basic algorithms of ML implemented with Python.
This project helps to model the data of house rent as function of different parameters of the property. Various models have been used to demonstrate the accuracy of different algorithms. All the instructions regarding the project are included in the Google Colab File.
Emotion detection from social media texts using machine learning algorithms.
🌸 Logistic Regression Classification on the Iris Dataset
This repository is a comprehensive guide for learning data science using Python. It covers various essential libraries and tools commonly used in the field of data science, including Jupyter Notebook, Matplotlib, NumPy, Pandas, Scikit-learn, and PyTorch.
This application is built using Streamlit to demonstrate Diabetes Prediction. It performs prediction on multiple parameters of a patient's health to predict whether they have diabetes or not.
Supervised Machine Learning algorithms based simple projects [ beginner level ]
analyse and build predictive model for Game of Thrones (War of The Five Kings) battles dataset
Prediction of water well failure in Uganda.
Recognizing-Hand-written-digits-by-scikit-learn
🤖💻This repository showcases a comprehensive Natural Language Processing (NLP) pipeline implemented in Python using Jupyter notebooks. The pipeline deploys various machine learning techniques to classify labeled dataset. The pipeline employs comparisons of the dataset using Recurrent Neural Network (RNN) and RandomForest Classifier algorithms.
A model server boilerplate for tensorflow & scikitlearn models
OpenClassrooms Data Analyst 2022-2023 - Projet 6
Classify digital ad data from different sources.
Used ML Techniques to predict pupilometry based on a case study
An openCV and Scikit-learn project to grade quizzes with OCR
An AI model built to understand the sentiments transmitted through a phrase.
10 weekly data science bootcamp projects covering data visualization, machine learning, neural networks, markov chains and more.
Regression Analysis to predict car selling price with given datasets. Used libraries: Pandas, Matplotlib, Seaborn, Scikitlearn
A rough notes and examples for machine learning library (scikit-learn)
A kyphosis is a spine curvature that is unusually convex. There are 81 rows and 4 columns in the kyphosis data frame. Data about children who have had corrective spinal surgery is represented. INPUTS: Age: in months Number: the number of vertebrae involved Start: the number of the first (topmost) vertebra operated on INPUTS: Age: in months Number: the number of vertebrae involved Number: the number of vertebrae involved Number: the number of vertebrae involved Number: the number of vertebrae involved Number: the number of vertebrae involved Number: the number of verte
Machine learning, data science, python libraries
This project predicts Apple stock prices using linear regression. It's based on historical stock price data and uses Python and popular data science libraries like Pandas, NumPy, Matplotlib, and scikit-learn.
Rendu projet machine learning - IIM année 5