MachineLearning-concepts

Welcome to the Machine Learning and Data Analysis GitHub repository! This repository contains resources, theory, and example code related to various machine learning, and algorithm topics. Whether you're new to the field or an experienced practitioner, you'll find valuable information here.

Introduction

This repository is a collection of resources for those interested in machine learning and data analysis. It covers both fundamental concepts and practical implementations of various algorithms. Whether you're a beginner or an experienced data scientist, you'll find something useful here.

Exploratory Data Analysis

Exploratory Data Analysis (EDA) is the first step in any data analysis process. It involves summarizing and visualizing data to gain insights. Explore EDA techniques and example code in the EDA folder.

Machine Learning Algorithms

Regression

Regression is a supervised learning technique used for predicting continuous values. Explore different regression algorithms and example code in the Regression folder.

Principal Component Analysis (PCA)

PCA is a dimensionality reduction technique. It's used for feature selection and visualization. Learn about PCA and see example code in the PCA folder.

Dimensionality Reduction

Dimensionality reduction techniques, like PCA, help reduce the number of features in your data. Explore different methods and example code in the Dimensionality Reduction folder.

Decision Trees

Decision Trees are used for classification and regression tasks. Explore decision tree algorithms and example code in the Decision Trees folder.

Support Vector Machines

Support Vector Machines are powerful classifiers. Learn about SVM theory and see example code in the Support Vector Machines folder.

Clustering Algorithms

Clustering algorithms group similar data points. Explore various clustering algorithms and example code in the Clustering Algorithms folder.

Time Series Analysis

Time Series Analysis is used for forecasting. Learn about time series analysis theory and see example code in the Time Series Analysis folder.

K-Nearest Neighbors (KNN)

K-Nearest Neighbors is a classification and regression algorithm. Explore KNN theory and see example code in the KNN Algorithm folder.

Anomaly Detection

Anomaly Detection is used to identify unusual patterns in data. Learn about anomaly detection techniques and see example code in the Anomaly Detection folder.

Ensemble Techniques

Ensemble techniques combine multiple models for improved performance. Learn about ensemble techniques theory and see example code in the Ensemble Techniques folder.

Contribution

Contributions are welcome! If you'd like to add more content, fix issues, please create a pull request.

Thank you for visiting our repository and happy learning!

Name		Name	Last commit message	Last commit date
Latest commit History 62 Commits
CustomerChurn.csv		CustomerChurn.csv
EDA1.ipynb		EDA1.ipynb
EDA2.ipynb		EDA2.ipynb
KNN1.ipynb		KNN1.ipynb
KNN2.ipynb		KNN2.ipynb
KNN3.ipynb		KNN3.ipynb
MLintroduction1.ipynb		MLintroduction1.ipynb
MLintroduction2.ipynb		MLintroduction2.ipynb
README.md		README.md
anomalydetection1.ipynb		anomalydetection1.ipynb
anomalydetection2.ipynb		anomalydetection2.ipynb
breast_cancer_svc_classifier.pkl		breast_cancer_svc_classifier.pkl
clustering1.ipynb		clustering1.ipynb
clustering2.ipynb		clustering2.ipynb
clustering3.ipynb		clustering3.ipynb
clustering4.ipynb		clustering4.ipynb
clustering5.ipynb		clustering5.ipynb
decisiontree1.ipynb		decisiontree1.ipynb
decisiontree2.ipynb		decisiontree2.ipynb
dimensionalityreduction1.ipynb		dimensionalityreduction1.ipynb
dimensionalityreduction2.ipynb		dimensionalityreduction2.ipynb
dimensionalityreduction3.ipynb		dimensionalityreduction3.ipynb
dimensionalityreduction4.ipynb		dimensionalityreduction4.ipynb
ensembletechniques1.ipynb		ensembletechniques1.ipynb
ensembletechniques2.ipynb		ensembletechniques2.ipynb
ensembletechniques3.ipynb		ensembletechniques3.ipynb
ensembletechniques4.ipynb		ensembletechniques4.ipynb
ensembletechniques5.ipynb		ensembletechniques5.ipynb
ensembletechniques6.ipynb		ensembletechniques6.ipynb
ensembletechniques7.ipynb		ensembletechniques7.ipynb
featureEngineering1.ipynb		featureEngineering1.ipynb
featureEngineering2.ipynb		featureEngineering2.ipynb
featureEngineering3.ipynb		featureEngineering3.ipynb
featureEngineering4.ipynb		featureEngineering4.ipynb
featureEngineering5.ipynb		featureEngineering5.ipynb
featureEngineering6.ipynb		featureEngineering6.ipynb
flight_price.xlsx		flight_price.xlsx
heartdiseasedata.csv		heartdiseasedata.csv
logisticregression1.ipynb		logisticregression1.ipynb
logisticregression2.ipynb		logisticregression2.ipynb
logisticregression3.ipynb		logisticregression3.ipynb
naivebayes1.ipynb		naivebayes1.ipynb
naivebayes2.ipynb		naivebayes2.ipynb
regression1.ipynb		regression1.ipynb
regression2.ipynb		regression2.ipynb
regression3.ipynb		regression3.ipynb
regression4.ipynb		regression4.ipynb
regression5.ipynb		regression5.ipynb
regression6.ipynb		regression6.ipynb
supportvectormachine1.ipynb		supportvectormachine1.ipynb
supportvectormachine2.ipynb		supportvectormachine2.ipynb
supportvectormachine3.ipynb		supportvectormachine3.ipynb
timeseries1.ipynb		timeseries1.ipynb
timeseries2.ipynb		timeseries2.ipynb
wine.csv		wine.csv

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MachineLearning-concepts

Table of Contents

Introduction

Exploratory Data Analysis

Machine Learning Algorithms

Regression

Principal Component Analysis (PCA)

Dimensionality Reduction

Decision Trees

Support Vector Machines

Clustering Algorithms

Time Series Analysis

K-Nearest Neighbors (KNN)

Anomaly Detection

Ensemble Techniques

Contribution

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