Loan Approval Prediction
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Updated
Mar 13, 2021 - Jupyter Notebook
Loan Approval Prediction
Evaluate Machine Learning Models with Yellowbrick
A real world data analysis and sentiment analysis using NLP and supervised classification machine learning model #4
Kaggle Competition Home Credit Default Risk
Advanced feature selection techniques for selecting the most optimal features for any machine learning models.
Increasing your Reddit karma. Help Reddit Moderators improve karma by autosubmitting posts to the correct subreddit. Reddiquette: Cross-post if it belongs to either or both subs?
This repository contains codes for running naive bayes and k-NN classification algorithms on large dataset in python
Sample project of fraud detection using Machine-Learning algorithms and Mathematical tools (roc)
Design of classification model to predict customer churn rate.
Data analysis, visualization and prediction for the prevention of heart disease
This is a fake news classification project, using TFIDF and pre-trained w2v embedding as separate sets of features, along with text sentiment scores, to classify news text as fake or real.
Fully connected neural network analyzing sentiments in reviews for Amazon's Alexa.
На основании данных о поведении клиентов построить модель с максимально большим значением F1 для задачи классификации, которая будет определять клиентов, склонных к оттоку.
This is a project demonstrating Logistic Regression method using Python. An education company named X Education sells online courses to industry professionals. On any given day, many professionals who are interested in the courses land on their website and browse for courses.
This repo contains the assignments workload for the computer vision course in the MSc degree
This repository contains some of the machine learning projects I have done.
Предсказание сердечно-сосудистых заболеваний.
in this section will be numerical feature selection on diabetes dataset
Data Science Project: Comparing 3 Deep Learning Methods (CNN, LSTM, and Transfer Learning).
Training VAE and an Mixed-Loss VAE with normal network traffic, then implementing validation and test steps that fold in the attack traffic to ensure models' reconstruction error satisfactorily separates data at with accuracy. Leveraging Bayesian optimization to tune hyperparameters and testing one anomaly at a time hold out strategy with AUCs.
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