logistic-regression-algorithm
There are 152 repositories under logistic-regression-algorithm topic.
coding-ai/machine_learning_cpp
Machine Learning C++
kennethleungty/Logistic-Regression-Assumptions
Assumptions of Logistic Regression, Clearly Explained
Coldwave96/MaliciousURLs
人工智能检测恶意URL
Ruban2205/Iris_Classification
This repository contains the Iris Classification Machine Learning Project. Which is a comprehensive exploration of machine learning techniques applied to the classification of iris flowers into different species based on their physical characteristics.
Ankit152/IMDB-sentiment-analysis
Sentiment analysis of IMDB dataset.
Aakash1822/Fruit_prediction
Fruit Count prediction using its shape and size using Machine Learning
saichandrareddy1/Machine_Learning_basics
This is repository about the MachineLaering Basics including all the Machine learning Algorithms
Saket-Kr/ML-Prep
A repo holding the implementation as well as some theoretical explanation of the important relevant concepts. It is going to be in development for a long long time. I'll keep adding things everytime I have something to add to it, and I have the time for it. One can use it to learn the basics of Machine Learning from kind of scratch.
suubh/Machine-Learning
It includes my work on Machine learning during Coursera Assignment. It includes Linear regression and Logistic regression working model .It also include Neural Network implementation and Backpropagation Algorithm .It also include SVM implementation and also a Spam Classifier using SVM.
mehmoodulhaq570/Machine-Learning-Models
A repository consisting of machine learning models for predicting the future instance. More specifically this repository is a Machine Learning course for those who are interested in learning the basics of machine learning algorithms.
PatilNi3/PROJECT_MACHINE_LEARNING
Fake Currency Detection using Logistic Regression Algorithm
felipexw/guessb
Webapp para classificar comentários (positivos, negativos e neutros) advindos do Facebook usando Natural Language Toolkit (NLTK) + Django e Bootstrap na interface Web.
meuwebsite/Facebook--PredictCustumer-Click
Running a targetted marketing ads on facebook. The company wants to anaylze customer behaviour by predicting which customer clicks on the advertisement
NehaPant14/Loan-Prediction
Loan Prediction using Classification Techniques
ikanurfitriani/Diabetes-Prediction
This repository contains code archives for Diabetes Prediction with Machine Learning
Jspano95/Retail-Customer-Classification-Modelling
Classification ML models for predicting customer outcomes (namely, whether they're likely to opt into email / catalog marketing) depending on customer demographics (age, proximity to store, gender, customer loyalty duration) as well as sales and shopping frequencies by department
ligerfotis/CSE6363_Machine_Learning
Machine Learning algorithms from-scratch implementation. It covers most Supervised and Unsupervised algorithms. Homework assignments and Projects for graduate level Machine Learning Course taught by Dr Manfred Huber at UTA during Spring 21
Sarthak-Mohapatra/Classification-of-tumors-in-Human-Breast-as-Bening-or-Malignant-using-ML-Algorithms.
As part of this project, I have used Machine Learning (classification) algorithms for classification of tumors in Human Breasts as Non-Cancerous/ Benign or Cancerous/ Malignant tumors.
sauriii98/Deep-Learning-algorithms
Implementation of all basic algorithms needed in Deep Learning
Tanmayee2010/Heart-Attack-Prediction
Heart Attack Prediction Using Machine Learning Algorithm
thenomaniqbal/LogisticRegression-BreastCancerDS
logistic regression from scratch using python to solve binary classification problem using breast cancer dataset from scikit-learn. A complete breakdown of logistic regression algorithm.
tofti/python-logisticregression
python-logisticregression
Abhijit2505/Cat-Photo-Classification
This repository contains two models having Two - layers ANN and L - layers ANN respectively to classify Cat photo and Non-Cat photo. This ANN works on the mathematical principles of Logistic Regression and Cross Entropy.
Abhijit2505/Data-Science
This repository containts the projects that I have done along With my Data Science MOOCs from Coursera.
AhmedWageh97/Machine-Learning-Projects
This repository contains some machine learning projects as a practise on machine learning course on Coursera for Prof. Andrew Ng from Stanford University.
Ansu-John/Regression-Models
Build and evaluate various machine learning regression models using Python.
boosuro/predicting_numbers_in_image_with_logistic_regression
predicting numbers in image with logistic regression
dileepkorade/Machine-Learning_Projects
Projects based on Machine Leaning
MaklonFR/PredictiveAnalytics-StudentsPerformance
Submission Dicoding Indonesia - Machine Learning Terapan (Predictive Students Analytics)
Pushpendra9350/Sentiment-prediction-on-reviews
This is a single webpage application in which we need to enter a review and this will tell you whether the review is positive or not. To make this work NLP techniques and Logistic regression algorithm is used with 94% accuracy
Saadia-Hassan/ML-Classifiers
A simple classification problem where SVM, Logistic Regression, KNN and Decision Trees algorithms are used and the F1-score with Jaccard similarity scores are found out.
Sahil-Chavan/MicrosoftMalwareDetection
==>>Problem Statement : In the past few years, the malware industry has grown very rapidly that, the syndicates invest heavily in technologies to evade traditional protection, forcing the anti-malware groups/communities to build more robust softwares to detect and terminate these attacks. The major part of protecting a computer system from a malware attack is to identify whether a given piece of file/software is a malware. ==>>Source/Useful Link : Microsoft has been very active in building anti-malware products over the years and it runs it’s anti-malware utilities over <b>150 million computers</b> around the world. This generates tens of millions of daily data points to be analyzed as potential malware. In order to be effective in analyzing and classifying such large amounts of data, we need to be able to group them into groups and identify their respective families. -> Source: https://www.kaggle.com/c/malware-classification
Sarthak-Mohapatra/Building-Algorithm-from-scratch-for-prediction-of-Average-GPU-run-time-and-classifying-the-run-type.
As part of this project, I have developed algorithms from scratch using Gradient Descent method. The first algorithm developed will be used to predict the average GPU Run Time and the second algorithm will be used to classify a GPU run process as high or low time consuming process.
vaitybharati/Assignment-06-Logistic-Regression
Assignment-06-Logistic-Regression. Output variable -> y y -> Whether the client has subscribed a term deposit or not Binomial ("yes" or "no") Attribute information For bank dataset Input variables: # bank client data: 1 - age (numeric) 2 - job : type of job (categorical: "admin.","unknown","unemployed","management","housemaid","entrepreneur","student", "blue-collar","self-employed","retired","technician","services") 3 - marital : marital status (categorical: "married","divorced","single"; note: "divorced" means divorced or widowed) 4 - education (categorical: "unknown","secondary","primary","tertiary") 5 - default: has credit in default? (binary: "yes","no") 6 - balance: average yearly balance, in euros (numeric) 7 - housing: has housing loan? (binary: "yes","no") 8 - loan: has personal loan? (binary: "yes","no") # related with the last contact of the current campaign: 9 - contact: contact communication type (categorical: "unknown","telephone","cellular") 10 - day: last contact day of the month (numeric) 11 - month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec") 12 - duration: last contact duration, in seconds (numeric) # other attributes: 13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted) 15 - previous: number of contacts performed before this campaign and for this client (numeric) 16 - poutcome: outcome of the previous marketing campaign (categorical: "unknown","other","failure","success") Output variable (desired target): 17 - y - has the client subscribed a term deposit? (binary: "yes","no") 8. Missing Attribute Values: None
vipulvs91/LitModel
Fire Incident risk classification Data Mining project
zhuqiqi19941122/binary-classification-algorithm
Bank Precision Marketing Solutions-- using Logistic Regression and Tree Algorithms