Machine Learning K-Nearest Neighbours classification algorithm practice in Jupyter Notebooks.
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Updated
Oct 28, 2020 - Jupyter Notebook
Machine Learning K-Nearest Neighbours classification algorithm practice in Jupyter Notebooks.
Car Evaluation Model using K Nearest Neighbors
Data Analysis and Machine Learning Projects
python implementation of mmachine learning of different classifiers and their comparison
Resume Screening Project with proper data visualization. Made using TFIDF Vectorizer, K Neighbors Classifier, and other basic python libraries. Used WordCloud,seaborn for visualization purposes
In this project, the price ranges of mobile phones are predicted based on the features the phone possesses.
In this repository I present some of the models i trained learning the MNIST DB.
This repository is for managing all my assignment for Artificial Intelligence Course
Implemented Artificial Neural Networks, and K nearest neighbors for classification problems. Used cross-validation for improving model accuracy. Plotted different types of learning curves like error rates vs train data size, error rates vs clock time. Compared performance using learning curves and confusion matrices across algorithms.
Supervised and unsupervised algorthimn analysis on APS Failure at Scania Trucks Dataset
Breast Cancer prediction app using KNN and SVC algorithm.
Machine learning / Deep learning / Data analysis notebooks, tools and scripts for learning purpose.
Used several Python libraries to make a K-Nearest Neighbor classifier that is trained to predict whether a patient has breast cancer
Mini project repository where we have implemented Credit card fraud detection using encoding, SMOTE-ing and KNN.
Doing algorithms on next sets of data
Identifying the conspirators in Enron fraud using Email Data
Identifying Image Orientation using Supervised Machine Learning Models of k-Nearest Neighbors, Adaboost and Multi-Layer Feed-Forward Neural Network trained using Back-Propagation Learning Algorithm
Iris Data : Classification / Pattern Recognition, Predict the Class of Flower based on Available Attributes.
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