Logistic-regression Libraries

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Logistic-regression Libraries

The "Python Machine Learning (1st edition)" book code repository and info resource

What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning ... from theory to the actual code that you can directly put into…

Plain python implementations of basic machine learning algorithms

If you have a favorite algorithm that should be included or spot a mistake in one of the notebooks, please let me know by creating a new issue. See the LICENSE file for license rights and limitations…

Python code for common Machine Learning Algorithms

Python codes for common Machine Learning Algorithms

Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera

If you've finished the amazing introductory Machine Learning on Coursera by Prof. Andrew Ng, you probably got familiar with Octave/Matlab programming. With this repo, you can re-implement them in Pyt…

This repository contains implementations of all Machine Learning Algorithms from scratch in Python. Mathematics required for ML and many projects have also been included.

Data Science & Machine Learning projects and tutorials in python from beginner to advanced level.

Predicts Daily NBA Games Using a Logistic Regression Model

Predicts NBA Games Using a Logistic Regression Model in Python The model uses eight factors scraped from stats.nba.com to determine the predicted result of an NBA game. Each stat is adjusted to per 1…

Basic Machine Learning implementation with python

I will update this repository to learn Machine learning with python with statistics content and materials

I will update this repository to learn Machine learning with python with statistics content and materials practise in Kaggle : Did necessary preprocessing and predicted the result using the Linear re…

Trajectory-Analysis-and-Classification-in-Python-Pandas-and-Scikit-Learn

Formed trajectories of sets of points.Experimented on finding similarities between trajectories based on DTW (Dynamic Time Warping) and LCSS (Longest Common SubSequence) algorithms.Modeled trajectories as strings based on a Grid representation.Benchmarked KNN, Random Forest, Logistic Regression classification algorithms to classify efficiently trajectories.

A university project for the postgraduate class of Data Mining. We were given a train_set with geographical points paired with the time interval. Firstly, we cleaned the dataset and then we formed t…

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