Build classification models to predict whether the cancer type is Malignant or Benign.

Cette app t'aide à prendre n'importe quel decision

Done a comparative study between machine learning and deep learning algorithms to closely predict the close price of the stocks.

In the context of the project "SURGICAL-OPERATIONS-PREDICTION," we've been performing various data analysis and modeling tasks. This includes data preprocessing such as selecting specific columns ('T - 28' to 'T - 1'), computing statistics like mean, maximum, and standard deviation, and possibly visualizing data distributions.

Bank card fraud detection using machine learning. Web application using Streamlit framework

Predicting market stock prices using various Machine Learning models. Data trained and tested up until 2017. Data found of Kaggle.

This project evaluates logistic regression, random forest, decision tree, and gradient boosting classifier models for fake news detection. Using labeled data, it analyzes accuracy, confusion matrices, and ROC curves to understand each model's effectiveness in discerning between real and fake news.

Leveraging ColumnTransformer, pipelines, standardization, and encoding, we'll preprocess data. Using Logistic Regression, Decision Trees, Random Forest, and XGBoost, we'll analyze factors like job satisfaction, promotion, and salary to predict churn. This helps companies improve satisfaction, reduce turnover, and enhance stability.

Simple Project Using NASA dataset to classify objects near earth as hazardous or non-hazardous

This repository consists of ML projects including data modeling, clustering, classification, and dimensionality reduction models

Sklearn, logistic regression, Naive Bayes classifier, K-Nearest Neighbors, decision trees

Create a decision tree, plot it, convert the rules into IF-THEN format, and utilize cost-complexity pruning for minimal tree and interpretable rules.

Project for Kannada digits recognition using Kannada-MNIST dataset. Employ PCA for dimensionality reduction to 10 components, and apply Decision Trees, Random Forest, Naive Bayes, K-NN Classifier, and SVM for prediction. Evaluate models using Precision, Recall, F1-Score, Confusion Matrix, and RoC-AUC curve.

"Flight Price Prediction: GitHub repo for ML-based airline ticket price forecasting. Collect, preprocess data, train models, deploy, and evaluate. Open-source under MIT License."

Predictive Analytics Practice: Random Forest, K-NN, SVM, Regression, Logistic Regression

In this project, fault detection and diagnosis model with decision tree is developed based on ASHRAE RP 1312 data. Rules generated by decision tree can be verified with domain expertise.

Using the Rain in australia dataset for rainfall perdiction

Prevendo a satisfação dos clientes de um e-commerce.

A new method of supervised feature scaling using decision tree