If the data shows a curvy trend, then linear regression will not produce very accurate results when compared to a non-linear regression because, as the name implies, linear regression presumes that the data is linear. Let’s learn about non-linear regressions and apply an example in python. In this notebook, we fit a non-linear model to the data points corresponding to China’s GDP from 1960 to 2014.
# Basic Data Science libraries
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
Though Linear regression is very good to solve many problems, it cannot be used…
The objective of this blog is to design a Deep Learning model i.e. an Artificial Neural Network Model to predict if customers of the bank will remain with a bank or if they will leave the bank and its services.
The dataset which we will be using is ChurnData.csv to perform statistical tests and predictive modeling consists of details of customers of a bank situated in the countries like France, Spain, and Germany. It consists of 10,000 rows and 14 columns.
Our target variable is a column named Exit which represents whether a customer has left the bank or not…
Traffic Signs Recognition using Deep Learning i.e. Convolutional Neural Network (CNN)
One of the applications where deep learning is being used extensively is Autonomous Driving. The autonomous driving mechanism needs to detect innumerable objects (pedestrians, other cars, obstacles, etc.) and make decisions. One of the smaller problems is to detect traffic signs and make decisions accordingly. We are going to solve problems of detecting traffic signs on the road.
We are going to refer to the following blog. We are going to use The German Traffic Sign Recognition Benchmark(GTSRB) dataset.
The training dataset contains around 39,000 images while the test…
“Word clouds (also known as text clouds or tag clouds) work in a simple way: the more a specific word appears in a source of textual data (such as a speech, blog post, or database), the bigger and bolder it appears in the word cloud.”
First of all, let’s import all the primary libraries first.
import numpy as np # useful for many scientific computing in Python
import pandas as pd # primary data structure library
from PIL import Image # converting images into arrays
import matplotlib.pyplot as plt # for visualizing the data
Welcome to this blog about Autoencoders. In this blog, you will find an explanation of what is an autoencoder, how it works, and see an implementation of an autoencoder in TensorFlow.
An autoencoder, also known as Autoassociator or Diabolo networks, is an artificial neural network employed to recreate the given input. It takes a set of unlabeled inputs, encodes them, and then tries to extract the most valuable information from them. They are used for feature extraction, learning generative models of data, dimensionality reduction, and can be used for compression.
A Choropleth map is a thematic map in which areas are shaded or patterned in proportion to the measurement of the statistical variable being displayed on the map, such as population density or per-capita income. The choropleth map provides an easy way to visualize how a measurement varies across a geographic area or it shows the level of variability within a region. Below is a Choropleth map of the US depicting the population by square mile per state.
Now, let’s create our own Choropleth map of the world depicting immigration from various countries to Canada.
Let’s first download and import…
Let’s download and import the data on police department incidents using pandas `read_csv()` method.
Download the dataset and read it into a pandas dataframe:
df_incidents = pd.read_csv('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Police_Department_Incidents_-_Previous_Year__2016_.csv')print('Dataset downloaded and read into a pandas dataframe!')
Let’s take a look at the first five items in our dataset.
In this blog, you will learn how to create maps for different objectives. To do that, we will part ways with Matplotlib and work with another Python visualization library, namely Folium. What is nice about Folium is that it was developed for the sole purpose of visualizing geospatial data. While other libraries are available to visualize geospatial data, such as Plotly, they might have a cap on how many API calls you can make within a defined time frame. Folium, on the other hand, is completely free.
Part 1. Introduction to Folium
Part 2. Map with Markers
Part 3. Choropleth…
A Machine Learning enthusiast, a python developer, focusing on Deep Learning and NLP