This repository contains code samples in R and Python for performing basic data analysis, exploratory data analysis (EDA), classification, regression, and forecasting. The purpose of this project is for me to try/practise or demonstrate various data analysis techniques and their applications.

• Introduction
• Exploratory Data Analysis (EDA)
• Classification
• Regression
• Forecasting
• License

In this project, we have a collation of code samples written in R and Python to perform data analysis tasks using various techniques. The code samples are organized into different sections based on the analysis type.

Exploratory Data Analysis (EDA) is an essential step in data analysis, where we examine and understand the structure and characteristics of the dataset. It helps us identify patterns, relationships, and potential issues within the data. The EDA code samples in this repository demonstrate techniques such as data cleaning, data visualization, summary statistics, and feature engineering.

Classification is a machine learning technique used to categorize data into predefined classes or categories. It is commonly used for tasks such as spam detection, sentiment analysis, and image recognition. The classification code samples in this repository showcase algorithms like decision trees, random forests, logistic regression, and support vector machines.

Regression is a statistical modeling technique used to predict continuous numerical values based on input features. It is commonly used for tasks such as sales forecasting, stock price prediction, and demand estimation. The regression code samples in this repository demonstrate algorithms like linear regression, polynomial regression, and gradient boosting regression.

Forecasting is a specialized form of regression used to predict future values based on historical data patterns. It is commonly used in time series analysis to predict trends and make informed decisions. The forecasting code samples in this repository showcase techniques such as ARIMA (AutoRegressive Integrated Moving Average), exponential smoothing, and Prophet.

This project is licensed under the MIT License. Feel free to use the code samples and modify them according to your needs.