Machine Learning Practices - README

This repository contains a collection of machine learning code examples and practices. Each topic is organized in its own folder and includes relevant code files, datasets, and additional resources.

Topics Covered The repository covers a wide range of machine learning practices, including but not limited to:

Introduction to Machine Learning.
Supervised Learning.
Unsupervised Learning.
Regression.

Regression

Regression is a fundamental topic in machine learning that deals with predicting continuous values based on input variables. It aims to model the relationship between independent variables (features) and a dependent variable (target) by fitting a function that best represents the data.

In this repository, you will find code examples and explanations for various regression techniques. Some of the commonly covered regression methods include:

Linear Regression
Polynomial Regression
Ridge Regression
Lasso Regression
Elastic Net Regression
Decision Tree Regression
Random Forest Regression
Support Vector Regression (SVR)
Gradient Boosting Regression
Time Series Analysis and Forecasting Along with the code, you will also find datasets or data files specific to each topic. These datasets can be used to practice and experiment with the code examples provided.

Name		Name	Last commit message	Last commit date
Latest commit History 54 Commits
Bagging-Classification.ipynb		Bagging-Classification.ipynb
Batch GD Regression.ipynb		Batch GD Regression.ipynb
DBSCAN.ipynb		DBSCAN.ipynb
Data imputation through Interpolation.ipynb		Data imputation through Interpolation.ipynb
Decision_Tree.ipynb		Decision_Tree.ipynb
EDA RED WINE.ipynb		EDA RED WINE.ipynb
EDA STUDENT (1).ipynb		EDA STUDENT (1).ipynb
Elasticnet Reg.ipynb		Elasticnet Reg.ipynb
Flight PRICE EDA.ipynb		Flight PRICE EDA.ipynb
Forest Fire EDA.ipynb		Forest Fire EDA.ipynb
General Ml prac.ipynb		General Ml prac.ipynb
Getting Started.ipynb		Getting Started.ipynb
Google Playstore EDA.ipynb		Google Playstore EDA.ipynb
Gradient Discent (by own class).ipynb		Gradient Discent (by own class).ipynb
Gradient Discent.ipynb		Gradient Discent.ipynb
Handling Missing values.ipynb		Handling Missing values.ipynb
Hierarical clustering , silhouctte score.ipynb		Hierarical clustering , silhouctte score.ipynb
MINI BATCH SGD.ipynb		MINI BATCH SGD.ipynb
Normalized (1).ipynb		Normalized (1).ipynb
Normalized.ipynb		Normalized.ipynb
One Hot Encoding.ipynb		One Hot Encoding.ipynb
PCA -2.ipynb		PCA -2.ipynb
PCA-1.ipynb		PCA-1.ipynb
README.md		README.md
Random Forest.ipynb		Random Forest.ipynb
Regression (SLM).ipynb		Regression (SLM).ipynb
Regression metrics.ipynb		Regression metrics.ipynb
Ridge regression (DG).ipynb		Ridge regression (DG).ipynb
SMOTE.ipynb		SMOTE.ipynb
SVC- Support vector machine.ipynb		SVC- Support vector machine.ipynb
Simple Linear Regression (OLS).ipynb		Simple Linear Regression (OLS).ipynb
Stacking Ensembles.ipynb		Stacking Ensembles.ipynb
Stochastic GD class code.ipynb		Stochastic GD class code.ipynb
Testing pipelines.ipynb		Testing pipelines.ipynb
Using pipelines.ipynb		Using pipelines.ipynb
feature scaling ml.ipynb		feature scaling ml.ipynb
k-mean clustering algo.ipynb		k-mean clustering algo.ipynb
multi-linear regression.ipynb		multi-linear regression.ipynb
multi.LRM.ipynb		multi.LRM.ipynb
pca on Wine dataset.ipynb		pca on Wine dataset.ipynb
voting.ipynb		voting.ipynb
without using piplines.ipynb		without using piplines.ipynb

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