This repository contains a comprehensive guide and implementation of ensemble modeling techniques, specifically focusing on Boosting, Bagging, and Voting. Ensemble methods are powerful techniques in machine learning that combine the predictions from multiple models to improve overall performance and robustness.

我也不知道我在寫什麼，大學的一個小專案，發想是買西瓜的時候長輩都會拍打西瓜，因為糖分影響密度，使得有些西瓜聲音比較低沉(咚咚咚)，有些西瓜聲音比較高(啪啪啪)。以此概念，藉由ftt再透過機器學習的決策樹，因為西瓜太貴了，我們樣本數嚴重不足(5顆)，即便拍打原西瓜，每次輸出結果還是不一樣，實驗以失敗告終。

These are all of my machine learning codes.You can find every code about machine learning.

Used Linear Regression, Decision Tree, Random Forest and XGBoost, AdaBoost, LightGBM and CatBoost ML Algorithm to Predict Bangalore housing price.

An innovative Python implementation of decision trees for machine learning, showcasing algorithmic learning from scratch with practical examples and a focus on AI principles.

Diabetes prediction using PIMA Indians dataset

Explore ML mini-projects with Jupyter notebooks. Discover predictive analysis for commercial sales, leveraging regression models such as linear regression, decision trees, random forests, lasso, ridge, and extra-trees regressor.

Artificial Inteligence projects

Decision Tree Classifier and Tree Algorithms

Flight price Prediction is made using decision tree model and Machine learning concepts

This project leverages machine learning techniques, including K-Nearest Neighbors (KNN) and Decision Trees, to identify exoplanets in astronomical data. By employing classification algorithms, the code sifts through vast datasets to detect potential exoplanets, aiding astronomers in their search for habitable worlds beyond our solar system.

Decision Support System Application with Machine Learning Approach in Diagnosis of Diabetes

Classification Algos were used for evaluating models for predictions.

This is a model which predicts air quality index of different cities using classification models.

Predicts which passengers are transported to an alternate dimension.

This model predicts whether the survivors of the Titanic survived or not. In this file, different classification models are compared and predictions are done from the model(s) having highest accuracy. Here, 'training_data.csv' is used for training and testing the models and 'testing data.csv' is used for predictions. These data sets are from Kaggle

Leveraging the power of Machine Learning as a tool, we delve into the realm of app permissions to discern the true nature of applications, whether they harbor malicious or benign intent. By analyzing and predicting based on these permissions, we unlock valuable insights to safeguard users in the digital landscape.

Predicting the risk of a person developing the coronary heart disease in 10 years using Machine Learning models.

Repository consists of my solutions from my Kaggle competitions