Machine Learning Cheatsheet 2024

An algorithmic trading strategy incursion using Adaboost machine learning classifier, to create the first volatility security suitable for long term investors.

Evaluate several classification algorithms and pick the best & the worst ones based on accuracy. And, to explore techniques and best practices for fine-tuning and optimizing machine learning models

This is a practice Repository consisting of all the notebooks I have practiced for Machine Learning from basics to Advance

Implementations of AdaBoost and Bagging algorithms applied to multiple datasets, comparing performance across various scenarios with different tree depths.

⚡机器学习实战（Python3）：kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归

From scratch Implementation and analysis of the algorithm Adaboost.

Explore the application of AdaBoost utilizing the Wisconsin breast cancer dataset.

Final Data Analytics course project repository where we have implemented Breast Cancer tumor classification into malignant and benign thereby predicting the chance of breast cancer.

Performed Sunbase Customer Churn Prediction using Python

Adaboost, short for Adaptive Boosting, is a popular and powerful ensemble learning algorithm used in machine learning.

This project uses machine learning to predict the price of a used car. The model is trained on a dataset of historical car sales data, and it can then be used to predict the price of a car based on its features.

This repository contains projects related to the supervised Learning including various Classification Technique

Scratch code for a machine learning algorithm involves writing code from scratch to implement the algorithm rather than using pre-built libraries or frameworks.

Implements the Decision Tree (CART), AdaBoost and Random Forest algorithm from scratch using NumPy.

Classification of Gamma and Hadron events by training classifieres and machine learning algorithms on the MAGIC Gamma Telescope dataset.

Classification in TabularDataset

Solution to the (MBTI) Myers-Briggs Personality Type Dataset on Kaggle