#### By N.W

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Linear regression for machine learning. The simple regression model and machine learning using python.

This is one of the most basic and well-known algorithms in the statistics and machine learning

community.

In this first post, we will learn;

● The mathematical principles underlying this algorithm.

● The assumptions made when using it.

● How the algorithm is applied.

According to Wikipedia [2], linear least squares regression (which will be discussed in this post), dates

from the 19th Century, published by Legendre and Gauss respectively in 1805 and 1809. Their goal was

to use astronomical observations to determine the orbit of comets around the sun.

Data :

Two datasets are considered here. The first one contains the volume in liters ( ) l of a liquid and the

corresponding mass of the container plus the solution in kilograms (kg). The second dataset consists of

distance measurements in kilometers (km) between a pickup address and the delivery address with the

corresponding time (in seconds) between arrival at the pickup address and the delivery at destination

specified by a client. The latter dataset was downloaded from ZINDI website [1], on the Sendy logistics

challenge, though adapted here to our convenience. The original dataset of more than 30 variables

provides data on order details, bike rider metrics and atmospheric in Nairobi based on orders made on

the Sendy platform. This dataset will be used a number of times in these tutorials and in a constructive

manner.

Now, we explore the datasets visually in the form of scatter plots to view the relationship between pairs of

variables using the following code:

As a discussion on the figures above (it is of good practice to discuss figures you include in a report or

work), one sees that though the linear relation between the distance between pick-up destination and

arrival destination is not a perfect linear one, there seems to be a linear trend (Figure 1). The linearity

between mass of container + solution and volume of liquid is rather straightforward on Figure 2. Also, we

check the distributions of both variables (response variables , this will be explained y − later) using

histograms. A histogram is a frequency plot that helps assess the frequency distribution of any set of

measurements. The following code using seaborn was used;

As a discussion on the figures above (it is of good practice to discuss figures you include in a report or

work), one sees that though the linear relation between the distance between pick-up destination and

arrival destination is not a perfect linear one, there seems to be a linear trend (Figure 1). The linearity

between mass of container + solution and volume of liquid is rather straightforward on Figure 2. Also, we

check the distributions of both variables (response variables , this will be explained y − later) using

histograms. A histogram is a frequency plot that helps assess the frequency distribution of any set of

measurements. The following code using seaborn was used;

- admin
- octobre 27, 2020
- 6:32

Even if we understand **LSTMs** theoretically, still many of us are confused about its **input** and **output** shapes while fitting the data to the network. This guide will help you understand the Input and Output shapes of the **LSTM**.

Let’s first understand the Input and its shape in LSTM Keras. The input data to **LSTM** looks like the following diagram.

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