Build repository for brambox - https://gitlab.com/eavise/brambox
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Updated
Apr 2, 2024 - Shell
Build repository for brambox - https://gitlab.com/eavise/brambox
A Comprehensive Guide to Titanic Machine Learning from Disaster
Linear Regression, Logistic Regression, ML Pipeline
Предиктивный анализ оттока клиентов
Develop a model to predict which retail customers will respond to a marketing campaign. Logistic Regression shows the best performance.
This notebook describes how to compute and derive insights from various classification evaluation metrics.
Resampling exercise to predict accuracy, precision, and sensitivity in credit-loan risk
A Portuguese hotel group seeks to understand reasons for its excessive cancellation rates.
The project involves using machine learning techniques, like RandomForestClassifier and MLP, to predict whether a song will be popular or not based on its acoustic features. The input consists of various acoustic and metadata features, while the output is a binary classification.
This repository is for me to experiment with various Classification Models using Python.
Sampling unbalanced dataset using SMOTE and creating a classifier to classify if a HR will stay or leave.
Predict fraudulent credit card transactions using TensorFlow, Keras, K Neighbors, Decision Tree, SVM Regression and Logistic Regression classifiers .
Evaluation of different face detection algorithms existing in the literature using different benchmarks
Fully connected neural network analyzing sentiments in reviews for Amazon's Alexa.
A hotel chain is having issues with cancellations. This project analyzes customer booking data to identify which factors significantly influence cancellations, build models using logistic regression and decision trees to predict cancellations in advance, and help formulate profitable policies for cancellations and refunds for the hotel group
Script to compute Precision, Recall, AvP and MAP and to plot PR curves in the context of Information Retrieval evaluation.
Search engine that queries for information to find the best results based on custom analysers and indexing techniques.
Training binary classifier and multi-class classifier to classify the MNIST datase
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