Predicted the imbd rating of the movie using machine learning algorithm and Neural Network
-
Updated
Feb 14, 2020 - Jupyter Notebook
Predicted the imbd rating of the movie using machine learning algorithm and Neural Network
Python repo showcasing hands-on implementations of advanced ML algorithms. It includes practicals on neural networks, Gaussian mixture models, Naive Bayes, and generative vs discriminative models, along with sample datasets and visualizations.
All-in-1 notebook which applies different clustering (K-means, hierarchical, fuzzy, optics) and classification (AdaBoost, RandomForest, XGBoost, Custom) techniques for the best model.
Trying to predict survival rate of passengers using algorithms like Logistic Regression, Ada Boost, Gradient Boost , Decision Tree Classifiers , Extra Tree Classifiers , Random Forest Classifiers and XG Boost with appropriate data preprocessing techniques.
A machine learning project that uses an XGBoost model to predict optimal pricing strategies for Walmart products, incorporating confidence scores.
Using Exploratory Data Analysis(EDA) and building different Machine Learning Models(Logistic Regression, Decision Tree, Random Forest and XGBoost) We'll help Salifort Motors to predict employee turnover rate and retain their talents.
Comprehensive machine learning project for heart disease prediction using clinical data. Includes feature engineering, model tuning, ensemble learning, and performance analysis across multiple algorithms.
Machine Learning Projects
The task focuses on predicting and forecasting monthly and daily charges for a healthcare provider to submit to insurance companies. This ensures accurate billing and streamlines operations. Leveraging analytics and historical data enables precise charge projections aligned with insurance policies.
Capstone project for Udacity's Machine Learning Engineer Nanodegree and a submission to Kaggle's Allstate Severity Claims competition.
West Nile Virus prediction and cost benefit analysis
The task involves predicting and forecasting monthly and daily payments for a healthcare provider based on charges submitted to insurance. This ensures accurate financial planning, streamlines payment tracking, and minimizes discrepancies.
Prediction of survival using sklearn XGBoost
Predicting Salary for US Census Salary Data
Machine learning project done during Monsoon Semester 2023 in IIITD.
This repository contains PySpark code that implements three machine learning models for predicting diabetes readmission: Decision Forest, Random Forest, and Gradient Boosted models. These models are trained and evaluated using patient information.
Add a description, image, and links to the xg-boost topic page so that developers can more easily learn about it.
To associate your repository with the xg-boost topic, visit your repo's landing page and select "manage topics."