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Repository For Codes And Concept Taught in Udemy Course
Golang Dynamic Decision Tree
Bank card fraud detection using machine learning. Web application using Streamlit framework
URL Based Spam Classification Using Machine Learning
Oasis Infobyte Internship Data Science task-1
Machine learning algorithms
Keywords—classification, supervised learning, Random Forest, K-Nearest Neighbor(K-NN), Naïve Bayes, Decision tree, Support Vector machine
Predicting churn customers using last 3 months data using PCA and multiple classification models like Logistic Regression, Decision Tree and Random Forest and finding the features influencing the churn.
Machine Learning Projects Portfolio
Machine Learning Algorithms uses LATEX for Documentation
Machine Learning Model using Decision Trees on US Voting Dataset
The primary objective of this study is to explore the feasibility of using machine learning algorithms to classify health insurance plans based on their coverage for routine dental services. To achieve this, I used six different classification algorithms: LR, DT, RF, GBT, SVM, FM(Tech: PySpark, SQL, Databricks, Zeppelin books, Hadoop, Spark-Submit)
Ensemble Learning | Flask
Udacity Data Scientist Nanodegree Project - Employ supervised algorithms to accurately model individuals income
Bachelor Gameplay demo with use of advanced interaction with AI
A Machine Learning Project to detect Fake News using Python, making use of Logistic Regression and Decision Trees.
Decision Tree implementation without any library on Iris dataset
My implementations of Linear Regression, Logistic Regression
Sklearn, logistic regression, Naive Bayes classifier, K-Nearest Neighbors, decision trees
In this project we solve the challenge posted on Kaggle to predict the price of house. In this project we make of models like linear regression, gradient boosting, random forest and decision tree.
Classification of mushrooms using decision tree in ID3 implementation
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Implemented Traditional ML models for Regression and Classification using sklearn.
Random Forest Classification
Done a comparative study between machine learning and deep learning algorithms to closely predict the close price of the stocks.
Simple Project Using NASA dataset to classify objects near earth as hazardous or non-hazardous
Cette app t'aide à prendre n'importe quel decision
Build classification models to predict whether the cancer type is Malignant or Benign.
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.
Predicting market stock prices using various Machine Learning models. Data trained and tested up until 2017. Data found of Kaggle.
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.
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.
Create a decision tree, plot it, convert the rules into IF-THEN format, and utilize cost-complexity pruning for minimal tree and interpretable rules.