Classifying customers into segments
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Updated
Dec 18, 2023 - Jupyter Notebook
Classifying customers into segments
Supervised Machine Learning and Credit Risk
Boston Crime Analisys test.
Classification problem using multiple ML Algorithms
ML project focused on predicting Titanic passenger survival using various algorithms and extensive data analysis techniques. This project includes detailed data visualization and interpretation to uncover key factors affecting survival. By leveraging various ML models the analysis aims to achieve high predictive accuracy.
Performed supervised machine learning using oversampling, undersampling and combination sampling techniques to determine credit risk for bank customers.
Predicting toxicity of molecules. Project on course "Data Mining 2"
Develop a prediction model capable of learning to detect whether a transaction is fraudulent or a genuine purchase.
This is about machine learning model where there are many algorithms is using to find out best accuracy.
In this repository, I will share the materials related to machine learning algorithms, as I enrich my knowledge in this field.
In this data analysis project, we embarked on a comprehensive exploration of Oracle's interview review data scraped from Glassdoor. Our objective was to gain valuable insights into the interview experiences of candidates applying for specific job postings at Oracle.
ML Based Firewall System
In this project, we will apply supervised learning techniques and an analytical mind on data collected for the U.S. census to help CharityML (a fictitious charity organization) identify people most likely to donate to their cause.
This project is part of the Capstone Project from the Data Science Nanodegree Program by Udacity in collaboration with Starbucks
This repository contains some Machine learning algorithms from scratch to better understand how they work, and are implemented under the hood.
Prediction-of-House-Grade-Classification using python ( Jupyter Notebook)
Classification of IMDB Reviews dataset and News Group dataset using Logistic Regression, Decision Trees, Support Vector Machines, Ada Boost and Random Forest. Methods and Accuracy of each model were compared and reported
Testing 6 different machine learning models to determine which is best at predicting credit risk.
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