In this project, we are going to use a random forest algorithm (or any other preferred algorithm) from scikit-learn library to help predict the salary based on your years of experience. We will use Flask as it is a very light web framework to handle the POST requests.

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.

Understand and code some basic algorithms in machine learning from scratch

This project is about anomaly detection in social network, completely on network structure. We use random forest algorithm to train and test our classifier. Also, we are going to see how the effect of increase of trees in forest to the accuracy of prediction.

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.

Build and Tune Several Models

Projects based on Machine Leaning

Analyse prior taxi geolocation and pricing data to predict future pricing

This is spark/Scala based Mobile Telecommunication Customer Churn Prediction model developed using Random Forest algorithm

Design and Implementation of Random Forest algorithm from scratch to execute Pacman strategies and actions in a deterministic, fully observable Pacman Environment.

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.

Machine Learning algorithms. Generic code related to ML.

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

Machine Learning competition on Kaggle.org: Random Forest algorithm and ensemble of algorithms to predict Titanic survivors. Top 8% rank

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).

Regression and Classification task with sklearn.