# An Overview of Linear Regression Models

**regression**problem. The simplest model of such a relationship can be described by a linear function - referred as

*linear regression*.

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Skip to main content# An Overview of Linear Regression Models

# Interactive Data Visualization in Python

# A Practical guide to Autoencoders

# Descriptive Statistics

# EDA of Lending Club Data - II

# EDA of Lending Club Data

# Exploring Multiple Variables

# Pseudo Facebook Data - Exploring Two Variables

## SO WHAT DO YOU THINK ?

Modeling the relationship between a scalar response (or dependent variable)
and one or more explanatory variables (or independent variables) is commonly
referred as a **regression** problem. The simplest model of such a
relationship can be described by a linear function - referred
as *linear regression*.

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There are two types of data visualizations: *exploratory* and *explanatory*.
Explanatory analysis is what happens when you have something specific you want
to show an audience. The aim of **explanatory** visualizations is to tell
stories - they’re carefully constructed to surface key findings.

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Usually in a conventional neural network, one tries to predict a target vector
$y$ from input vectors $x$. In an auto-encoder network, one tries to predict
$x$ from $x$. It is trivial to learn a mapping from $x$ to $x$ if the network
has no constraints, but if the network is constrained the learning process
becomes more interesting. In this article, we are going to take a detailed
look at the mathematics of different types of autoencoders (with different
constraints) along with a sample implementation of it using Keras,
with a tensorflow back-end.

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One of the first tasks involved in any data science project is to get to understand the data. This can be extremely beneficial for several reasons:
Catch mistakes in data See patterns in data Find violations of statistical assumptions Generate hypotheses etc. We can think of this task as an exercise in summarization of the data. To summarize the main characteristics of the data, often two methods are used: numerical and graphical.

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In the last post we looked at some initial cleanup of the data. We will start from there by loading the pickled dataframe.

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We will first look at various aspects of the LendingClub data using techniques of Exploratory Data Analysis (EDA).
Please look at my past post for finding further details on EDA techniques.
Different data files for this analysis have already been downloaded in the current folder.

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In this section, we will continue re-using the data from the previous post based on Pseudo Facebook data from udacity.

The data from the project corresponds to a typical data set
at Facebook. You can load the data through the following command. Notice that this is a TAB delimited *tsv* file. This data set consists of 99000 rows of data. We will see the details of different columns using the command below.

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In this section, we will be re-using the data from the previous post based on Pseudo Facebook data from udacity.

The data from the project corresponds to a typical data set
at Facebook.
You can load the data through the following command. Notice that this is a TAB delimited *csv* file.
This data set consists of 99000 rows of data. We will see the details of different columns using the
command below.

Reading Time: 10 minutes Read more…

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