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All codes, both created and optimized for best results from the SuperDataScience Course
Understand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and suggested a best model to predict the final sale price of a house. Seaborn is used to plot graphs and scikit learn package is used for statistical analysis.
Harvard Project - Accuracy improvement by adding seasonality premium pricing
A JavaScript module for generating random seeded distributions and its statistical analysis.
Deteksi penyakit pada (daun) jagung berbasis citra dengan menggunakan metode GLRLM dan FCH.
A minimal Deep learning library for the web.
Spark ML Dashboard built to plug-in and tweak the model params to real-time verify classification results on sample test data
Classic Machine Learning in R
In this project using New York dataset we will predict the fare price of next trip. The dataset can be downloaded from https://www.kaggle.com/kentonnlp/2014-new-york-city-taxi-trips The dataset contains 2 Crore records and 8 features along with GPS coordinates of pickup and dropoff
Predict the type of arrhythmia based on Electro-cardiogram (ECG) tool using machine learning models and algorithms.
This is the code for "Binary Classification using Keras Sequential, Functional and Model Subclassing" By M.Junaid Fiaz
Master's Thesis project at University of Agder, Spring 2020. Classification with Tsetlin Machine on board game 'GO'.
Predicting leaf using the K Nearest Neighbour algorithm using the Iris dataset.
Capstone Project for Udacity Machine Learning Nanodegree
Google colab notebook for the Kaggle Home prices submission
All assignments of Statistical Machine Learning Course
Solved tasks of "Machine Learning" course, contains implementations of main machine learning algorithms.
Creates a decision tree for training and predicts the targets!
Applied linear regression on Boston house prices data set to predict the sale price of a house.
Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.
A Matlab script that applies the basic sequential clustering to evaluate the number of user groups by using the hierarchical clustering and k-means algorithms. Using the k-means fold the classifiers that are a neural network and the other least squares to evaluate them.
NLP - Category prediction with logistic regression.
Prepare a model for glass classification using KNN and Implement a KNN model to classify the animals in to categorie.
Python and sklearn, KNN, logistic and linear regression, cross-validation
Face Mask Detection
Implementação em java do algoritmo KNN para classificação, combinado ao k-fold para validação cruzada.
Python cross-validation package with k-fold, leave-one-out and leave-one-subject-out
Códigos em Phyton utilizados na disciplina de engenharia médica, do curso de Engenharia Biomédica do Instituto de Ciência e Tecnologia - Universidade Federal de São Paulo
Trabalhos da disciplina Inteligência Artificial em 2021.2
Machine learning project made with MTG (Magic the Gathering) data
Python, K-fold, RMSE, Pearson