Rank 4/125 MachineHack

BenchMetrics Prob: Benchmarking of probabilistic error performance evaluation instruments for binary-classification problems

Detect duplicate questions that have already been asked on Quora.

Basic machine learning neuron in pure ruby

January Hackathon of Machine Hack, involving Multi-class Classification Modeling, Advance Feature engineering, Optimizing Multi-Class log loss score as a metric to generalize well on unseen data.

Machine Learning with Python

Crop damage classification

My absolutely first Kaggle competition

Determining the class of cancer-causing mutations using text and genetic data

Create a machine learning model to help an insurance company understand which claims are worth rejecting and the claims which should be accepted for reimbursement.

Les bases du Deep Learning en Intelligence Artificielle.

An intro to tensorflow

load a dataset using Pandas and apply the following classification methods (KNN, Decision Tree, SVM, and Logistic Regression) to find the best one by accuracy evaluation methods (Jaccard, F1-score, LogLoss) for this specific dataset.

This repository has the implementation of Logistic Regression algorithm from scratch, using SGD (Stochastic Gradient Descent). Scikit Learn library is not used.

Identify American Express customers most likely to default in the next 3 months based on 190 anonymized transaction data features for over 500000 American Express customers.

Trained machine learning algorithms (Logistic Regression, KNN, SVM, Decision Tree) specifically, after performing visualization and pre-preocessing tasks on a loan dataset. Executed the evaluation metrics such as F1-score, Log loss and jaccard-similarity score to assess the algorithms performance.

We load a historical dataset from previous loan applications, clean the data, and apply different classification algorithms on the data.