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Predict Diabetes and its possibility of occurrence from the pathological lab reports on your own.
The following repository contains source code for a 100 Day personal machine learning coding challenge. It contains projects that I do as a part of my learning
This pipeline provides a way to perform pharmaceutical compounds virtual screening using similarity-based analysis, ligand-based and structure-based techniques. The pipeline contains a collections of modules to perform a variety of analysis.
Modello Random Forest per la creazione di una mappa di suscettibilità da frane superficiali // // Tesi di Laurea Magistrale in Scienze della Terra (Geologia Applicata) - Università degli Studi di Milano
Machine Learning Software that predicts planets based on their distance from the sun, number of satellites and various properties
Análise de dados sobre cotas de gênero e seu impacto nas eleições e proposições legislativas da Câmara dos Deputados Federais entre 1934 e 2021. Parte do TCC da pós-graduação em Inteligência Artificial e Aprendizado de Máquina na @pucminas
My Python learning experience 📚🖥📳📴💻🖱✏
A parser for scikit-learn exported text models to execute in the Java runtime.
Machine learning model Visualizer in web using streamlit
Identification of fake currency is a challenging problem for all. Fake banknotes are becoming more and more identical to the real ones. In this Fake Currency Detection model, I have used multiple machine learning algorithms to determine fake or real banknotes and was able to achieve more than 90% accuracy.
ML models for HR classification problem. For more information please visit the link: https://datahack.analyticsvidhya.com/contest/wns-analytics-hackathon-2018-1/
Natural Language Processing
Repository for the ENSF 612 final project.
In this project I intend to predict customer churn on bank data.
Final Project Of Computational Intelligence - Fall 2021 - LightGBM, RandomForest and StackingClassifier
Build a Machine Learning model that is able to classify whether or not a person believes in climate change, based on their novel tweet data
TitanicClassification.py file contains project based on binary classification. The dataset comprises of data related to passengers and binary value of whether they survived or not.
Classification Machine Learning project
Bank Notes Authenticity tester using RandomForestClassifier
There is war is going on between two countries submarine of the country is going under the water to another country and enemy country planted some mines in the oceans mine are nothing but explosive that explodes when some object comes in contact with it and there can be rocks in the ocean so submarine needs to predict whether it is crossing mine or rock our job is to make a system that can predict whether the object beneath the submarine is a mine or a rock so how this is done is submarine uses sonar signal that sends sound and receives switchbacks so this signal in the processed to detect whether the object is a mine or it's just a rock in the ocean to predict the rock and mine we use some types of algorithms like decision tree, KNN, Logistic Regression, Random Forest and SVM
Algerian forest fires Dataset (Classification Use Case)
Forecasting diabates using machine learning algorithms: End-to-end solution.
Predict tomorrow's S&P 500 index price using historical data. The data analysis has been preformed mainly using Jupyter Notebook and Python Machine Learning library of sklearn -RandomForestClassifier
This fraud detection system is powered by a Machine Learning model, which accurately identifies whether an initiated transaction is fraudulent.
Here you can explore different projects around Machine Learning Algorithms, Enjoy ☺️
This is a script that reads in Landsat-8 data, Esri Sentinel-2 10m land cover time series data and train a random forest classification algorithm to estimate fractional built cover at 30m scale. The trained model can be used to produce fractional land cover for other regions.
The Aim of this project is used to identify whether a new transaction is fraudulent or not.
Ai Doctor Assistant is Ai Programmed web site to Detect health related Problems using Ai , This is My final year Project
Credit risk is an inherently unbalanced classification problem, as good loans easily outnumber risky loans. Therefore, you’ll need to employ different techniques to train and evaluate models with unbalanced classes. Using the credit card credit dataset from LendingClub, a peer-to-peer lending services company,
Disaster Tweets Classifications by Machine Learning, which is a currently Kaggle Competition.
Application of Machine and Deep Learning to Sentiment Analysis
The objective of this project is to find the chances of winning for both the cricket teams in IPL( Indian Premier League).
This project aims to build a machine learning model using K-Nearest Neighbor, LogisticRegression, RandomForestClassifier to classify whether or not a person has heart disease based upon his medical attributes. (accuracy achieved : 88.52%)