This is a Machine Learning model developed with "Decision Trees Algorithm" and "Random Forest Algorithm" to predict the turnover of HDFC bank with a given dataset of the previous turnovers and features.
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Updated
Dec 18, 2020 - Python
This is a Machine Learning model developed with "Decision Trees Algorithm" and "Random Forest Algorithm" to predict the turnover of HDFC bank with a given dataset of the previous turnovers and features.
A Random Forest Algorithm is a supervised machine learning algorithm that is extremely popular and is used for Classification and Regression problems in Machine Learning. We know that a forest comprises numerous trees, and the more trees more it will be robust.
This is my undergraduate capstone project
Instagram Fake account detection ML model using SciKit Learn
Laboratory with random forest, logistic regression and SVM. The dataset used for this test is a set of points generated randomly with the following specification: • Number of Samples: 1200 • Number of Classes: 3 • Number of Features: 2 (Length and Width).
Projects based on Machine Leaning
This system enhances safety with real-time health and position tracking using temperature and heart rate sensors, GPS, LoRa communication, and the Random Forest algorithm. Technologies include NodeMCU, Peltier modules, and LCD displays.
This is spark/Scala based Mobile Telecommunication Customer Churn Prediction model developed using Random Forest algorithm
supervised machine learning classifier model
Design and Implementation of Random Forest algorithm from scratch to execute Pacman strategies and actions in a deterministic, fully observable Pacman Environment.
Data analysis project on Digital Addiction for master thesis
Analysis of the Restaurant reviews by using the Naive Bayes & the Random Forests Algorithms
This is an example for how handwritten digits can be learnt with random forests
Regression and Classification task with sklearn.
Machine Learning competition on Kaggle.org: Random Forest algorithm and ensemble of algorithms to predict Titanic survivors. Top 8% rank
Developed Random-Forest-based machine learning model to precisely predict gold prices, achieving 85% accuracy in testing conditions. Integrated large datasets to generate forecasts for near-term price fluctuations.
This repository contains the Machine Learning lessons I took from the Clarusway Bootcamp between 10 Aug - 14 Sep 2022 and includes 17 sessions, 5 labs, 4 case studies, 5 weekly agendas, and 3 projects.
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