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GraphPart, a data partitioning method for ML on biological sequences
This library allows reading and converting bounding box annotations in many popular formats
📁 Repo for python_splitter Python package. This package can split Images into Train, Test, Validation folders automatically by shuffling media/images for machine learning.
This is an algorithm for evenly partitioning.
To create a Decision Tree classifier and visualize it graphically, the purpose is if we feed any new data to this classifier, it would be able to predict the right class accordingly.
This repository contains introductory notebook for logistic regression
Supervised-ML-Decision-Tree-C5.0-Entropy-Iris-Flower-Using Entropy Criteria - Classification Model. Import Libraries and data set, EDA, Apply Label Encoding, Model Building - Building/Training Decision Tree Classifier (C5.0) using Entropy Criteria. Validation and Testing Decision Tree Classifier (C5.0) Model
Time series analysis on Yen Futures with ACF, PACF, ADF tests and seasonal decomposition to detect stationary trends. Screen for robust regression models on rolling train-test windows.
This creates an AWS Chatbot to give users their investment portfolio based on their risk tolerance level i.e. conservative, moderate, or aggressive. With the use of machine learning, the tool will be created to different portfolios based off that.
A time slicer for training and testing temporally correlated Machine Learning models.
Trained and evaluated two supervised machine learning models using original and resampled data to identify 'healthy loan' and 'high risk loan' applicants from financial disclosures.
Learning Project ML - Diabetes Prediction
Linear regression models are used to predict football player attacking stats based on attributes like finishing and passing, with the model trained, evaluated, and applied for predictions. Multiple features improve accuracy, and performance is assessed using metrics like MSE and R-squared.
classify the Size_Categorie using SVM
Customer Retention Deep Learning Model
Classification model to categorize clothing items into distinct classes
Model selection crucial in loan approval prediction project. Random Forest outperformed Logistic Regression, emphasizing importance of choosing appropriate models for accurate predictions.
This is a project where I use the Random Forest Regression and XGBoost Machine Learning Techniques to held predict the Sales Price of Houses..
This is a project where use the Random Forest Classifier and XGBoost Machine Learning Techniques to held predict what passengers survived the sinking of the Titanic.
To model the demand for shared bikes with the available independent variables
Understanding Train-Test-Split in R
It is a Turkish BERT-based model that will analyze people's bank complaints and classify them according to one of eight categories.
Machine Learning with Scikit Learn
Predicting The Energy Output Of Wind Turbine Based On Weather Condition DEMO LINK : https://youtu.be/ICfu49Ud2HU
Scientific programming through the SKLearn / Scikitlearn library
Repository of all exercises and assignments in 365 Data Science Machine Learning Course
In this project, you work as a Data Scientist for a professional football club. The owner of the team is very interested in seeing how the use of data can help improve the team's performance, and perhaps win them a championship! The draft is coming up soon (that's when you get to pick new players for your team), and the owner wants you to create a model to help score potential draftees. The model will look at attributes about the player and predict what their "rating" will be once they start playing professionally. The football club's data team has provided the data for 17,993 footballers from the league. Our job is to build a model or model, perform model selection, and make predictions on players you have not yet seen.
You can separate the images in your file as a train test txt file in the yolo format.
1. train_test_split 2.K_fold 3.LeaveoneOut 4.Cross Validation Score 5.Logistic Regression
A GitHub repository hosting an insurance prediction model employing Decision Tree, Random Forest, and KNN algorithms, with KNN achieving the highest accuracy score of 87.7% and tested for response on unseen data.
Module 13 - I am creating a binary classification model using a deep neural network by preprocessing data for a neural network model , using the model-fit-predict pattern to compile and evaluate a binary classification model , and optimize the model.
Different modeling techniques like multiple linear regression and random forest, etc. will be used for predicting the cement compressive strength. A comparative analysis will be performed to identify the best model for our prediction in terms of accuracy.
GroupSplit is a module to help split datasets into train and test sets for data science and machine learning projects.
Model-Validation-Methods