support-vector-classifier

🏆 A Comparative Study on Handwritten Digits Recognition using Classifiers like K-Nearest Neighbours (K-NN), Multiclass Perceptron/Artificial Neural Network (ANN) and Support Vector Machine (SVM) discussing the pros and cons of each algorithm and providing the comparison results in terms of accuracy and efficiecy of each algorithm.

NTHU EE6550 Machine Learning slides and my code solutions for spring semester 2017.

Interactive ML web application will allow users to choose classification algorithm, let them interactively set hyper-parameter values, and Input Image.

A web app for visualizing Binary Classification Results using Streamlit module in Python deployed on Heroku.

Build and evaluate various machine learning classification models using Python.

Predicting whether or not a customer will be approved for a credit card based on 15 predictor variables.

Wine-Classifier

A simple Flask application for data preprocessing, visualization and classification

Unsupervised and supervised learning for satellite image classification

Using a support vector machine to classify emails

Assignments from Applied Machine Learning Class (UTD BUAN-6341)

Fitting a support vector classifier to the Kepler Exoplanet Search data set.

NLP and Sentiment Analysis

Predicting financial well-being through survey data from the Consumer Financial Protection Bureau

Identification of the Employees who are most likely to switch the jobs for package negotiations & Job offerings. Also, Analyzing the particular departments where the attrition rate is high and to take the preventive measures by using Decision Tree Classifer, Random Forest Classifier, Support Vector Classifier, Logistic Regression, K Nearest Neighbor and Gaussian Naive Bayes.

Machine learning library for classification tasks

PCA, Clustering, and SVC/SVM for items on the McDonald's Menu.

Training and Evaluation of two multi-label models. Applied on stack overflow question title to predict a set of tags

Predictions for the English Premier League season

A machine learning model after detailed image processing applications that classifies a bee as either honey bee or bumble bee

Face Recognition with SVM, starting from basics

Using a credit-risk data-set to predict whether a person would repay a loan based on his anonymous account features.

Data Science Capstone Project: Healthcare

I contributed to a group project using the Life Expectancy (WHO) dataset from Kaggle where I performed regression analysis to predict life expectancy and classification to classify countries as developed or developing. The project was completed in Python using the pandas, Matplotlib, NumPy, seaborn, scikit-learn, and statsmodels libraries. The regression models were fitted on the entire dataset, along with subsets for developed and developing countries. I tested ordinary least squares, lasso, ridge, and random forest regression models. Random forest regression performed the best on all three datasets and did not overfit the training set. The testing set R2 was .96 for the entire dataset and developing country subset. The developed country subset achieved an R2 of .8. I tested seven different classification algorithms to classify a country as developing or developed. The models obtained testing set balanced accuracies ranging from 86% - 99%. From best to worst, the models included gradient boosting, random forest, Adaptive Boosting (AdaBoost), decision tree, k-nearest neighbors, support-vector machines, and naive Bayes. I tuned all the models' hyperparameters. None of the models overfitted the training set.

Classification to predict whether a bank currency note is authentic or not based on variance of the image wavelet transformed image, skewness, entropy, and curtosis of the image using Machine Learning classifiers.

Titanic Survivor Analysis and Prediction

Data Science - Case Study with Classification Application in Python Using scikit-learn

Sentiment and category classification of reviews using Machine Learning