Breast cancer detection using 4 different models i.e. Logistic Regression, KNN, SVM, and Decision Tree Machine Learning models and optimizing them for even a better accuracy.
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Updated
Sep 28, 2021 - Jupyter Notebook
Breast cancer detection using 4 different models i.e. Logistic Regression, KNN, SVM, and Decision Tree Machine Learning models and optimizing them for even a better accuracy.
Smart disease prediction system made using traditional machine learning algorithms and to create an user interface using streamlit. 🚀
Web editor for typed tree structures. (like decision / behaviour trees)
Insurance claim fraud detection using machine learning algorithms.
Cli tool for generating 'treescheme' files based on dotnet assemblies.
A powerful tree-based uplift modeling system.
Evaluation and Implementation of various Machine Learning models for creating a "Banking/Financial Transaction Fraud Prevention System"
machine-learning algorithms using Python.
All details in README.md
Credit card fraud detection model that is built using Machine Language and R programming
Prediction of customer will purchase iPhone or not using KNN classifier model and multiple supervised ML model.
This repository contains all resources for Homework 1 of TDT4173 fall 2021.
In this notebook, I'm using this dataset called 'flight-price-prediction', which contains the traveller information.In this notebook, I'm trying to run a dummy variable regression model at first, and after that, I'm trying to build a supervised ML Model with higher accuracy of predation.
Code for my Medium article: "How you can quickly deploy your ML models with FastAPI"
In this regression project, We will make use of different features like age, BMI, region, sex, smoker, etc to predict the medical insurance cost for an individual.
This project predicts iPhone purchases using demographic data (gender, age, salary). A Decision Tree Classifier was used, achieving 88.16% accuracy. Insights from the model can refine marketing strategies, optimize product offerings, and boost sales by targeting key customer segments.
This study demonstrates how numerous factors have an impact on bike rentals. Due to our understanding that many Koreans hire bikes throughout the week, we assumed that most of their use is for commuting to work or school. The number of rentals varies depending on a number of factors, including the day of the week, the hour of the day.
This project presents and discusses data-driven predictive models for predicting the defaulters among the credit card users.About Data Cleaning,Exploratory Data Analysis ,Handling Class Imbalance, Transforming Data , Fitting Different Model ,Cross Validation & Hyperparameter Tunning, Comparison of Model ,Combined ROC Curve, Feature Impotance.
Breast Cancer Detection: This project uses machine learning techniques to classify breast cancer as malignant or benign based on features extracted from breast mass biopsies. Models used include SVM, Decision Tree, Naive Bayes, and K-Nearest Neighbors.
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