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# How to visualise regression analysis in R?

# How to visualise regression analysis in R?

This recipe helps you to visualise a regression analysis in R using ggplot()

This recipe uses the ggplot () package in R to visualize the output of a regression analysis. This visualization combines a regression line with confidence intervals and prediction intervals.

**What is Regression Analysis ?**

Regression analysis is a statistical technique used to find the relationship between 2 or more variables. It is used in business to understand what factors impact a specific outcome. Regression allows you to determine which factors matter most, which factors can be ignored, and how these factors influence each other. In order to conduct a regression analysis, you'll need to define a dependent variable that you hypothesize is being influenced by one or several independent variables.

**What is R ?**

R is a programming language used for statistics and data science computing. R has very powerful libraries (almost 12,000) for performing data analytics including regression, classification, visualisation etc.

In this deep learning project, you will find similar images (lookalikes) using deep learning and locality sensitive hashing to find customers who are most likely to click on an ad.

In this NLP Project, you will learn how to use the popular topic modelling library Gensim for implementing two state-of-the-art word embedding methods Word2Vec and FastText models.

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In this project, we will use time-series forecasting to predict the values of a sensor using multiple dependent variables. A variety of machine learning models are applied in this task of time series forecasting. We will see a comparison between the LSTM, ARIMA and Regression models. Classical forecasting methods like ARIMA are still popular and powerful but they lack the overall generalizability that memory-based models like LSTM offer. Every model has its own advantages and disadvantages and that will be discussed. The main objective of this article is to lead you through building a working LSTM model and it's different variants such as Vanilla, Stacked, Bidirectional, etc. There will be special focus on customized data preparation for LSTM.

Data Science Project in Python- Given his or her job role, predict employee access needs using amazon employee database.

CRNNs combine both convolutional and recurrent architectures and is widely used in text detection and optical character recognition (OCR). In this project, we are going to use a CRNN architecture to detect text in sample images. The data we are going to use is TRSynth100k from Kaggle. Given an image containing some text, the goal here is to correctly identify the text using the CRNN architecture. We are going to train the model end-to-end from scratch.

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In this ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.