Java Questions

Java Questions

1592317980

Project: Analyzing Suicide Clusters using Exploratory Data Analysis & Machine Learning

You have just been hired as a Data Scientist at the World Health Organization (WHO) with an alarming number of suicide clusters being reported across the world. A data collector hands you this data to perform Data Analysis and wants you to examine trends & correlations within our data. We would like to make a Machine Learning algorithm where we can train our AI to learn & improve from experience. Thus, we would want to predict the amount of suicides numbers in a certain demographic.
From growing up in the heart of Silicon Valley, I have always wondered what factors play a role in suicide. There have been a plethora of suicide clusters from my High School in Palo Alto. This project seeks to explore the underlying factors. We will use a sample of 44,000 data points gathered from 141 different countries, between the 80’s to 2016.

#data-analysis #data-exploration #machine-learning #data-science #suicide

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Project: Analyzing Suicide Clusters using Exploratory Data Analysis & Machine Learning
Java Questions

Java Questions

1592317980

Project: Analyzing Suicide Clusters using Exploratory Data Analysis & Machine Learning

You have just been hired as a Data Scientist at the World Health Organization (WHO) with an alarming number of suicide clusters being reported across the world. A data collector hands you this data to perform Data Analysis and wants you to examine trends & correlations within our data. We would like to make a Machine Learning algorithm where we can train our AI to learn & improve from experience. Thus, we would want to predict the amount of suicides numbers in a certain demographic.
From growing up in the heart of Silicon Valley, I have always wondered what factors play a role in suicide. There have been a plethora of suicide clusters from my High School in Palo Alto. This project seeks to explore the underlying factors. We will use a sample of 44,000 data points gathered from 141 different countries, between the 80’s to 2016.

#data-analysis #data-exploration #machine-learning #data-science #suicide

Aketch  Rachel

Aketch Rachel

1625001660

Exploratory Data Analysis in Few Seconds

EDA is a way to understand what the data is all about. It is very important as it helps us to understand the outliers, relationship of features within the data with the help of graphs and plots.

EDA is a time taking process as we need to make visualizations between different features using libraries like Matplot, seaborn, etc.

There is a way to automate this process by a single line of code using the library Pandas Visual Analysis.

About Pandas Visual Analysis

  1. It is an open-source python library used for Exploratory Data Analysis.
  2. It creates an interactive user interface to visualize datasets in Jupyter Notebook.
  3. Visualizations created can be downloaded as images from the interface itself.
  4. It has a selection type that will help to visualize patterns with and without outliers.

Implementation

  1. Installation
  2. 2. Importing Dataset
  3. 3. EDA using Pandas Visual Analysis

Understanding Output

Let’s understand the different sections in the user interface :

  1. Statistical Analysis: This section will show the statistical properties like Mean, Median, Mode, and Quantiles of all numerical features.
  2. Scatter Plot-It shows the Distribution between 2 different features with the help of a scatter plot. you can choose features to be plotted on the X and Y axis from the dropdown.
  3. Histogram-It shows the distribution between 2 Different features with the help of a Histogram.

#data-analysis #machine-learning #data-visualization #data-science #data analysis #exploratory data analysis

Exploratory Data Analysis

1. INTRODUCTION:

Suppose you are looking to book a flight ticket for a trip of yours. Now, you will not go directly to a specific site and book the first ticket that you see. You’ll first search for the tickets on multiple websites on multiple airline service providers. You will then compare the cost of the tickets with the services they are providing. Is there free WiFi available? Are breakfast and lunch complimentary? Is the overall rating of the airlines better than the others?

Whatever measures you will take from thinking about buying a ticket and finding the best ticket option for you and booking it is called “Data Analysis”. The formal definition of Exploratory Data Analysis can be given as:

Exploratory Data Analysis (EDA) refers to the critical process of performing initial investigations on data so as to discover patterns, to spot anomalies, to test hypotheses and to check assumptions with the help of summary statistics and graphical representations.


2. TYPES OF DATA:

Image for post

Types of Data (Image by author)

  • **Dichotomous Variable: **A dichotomous variable is a variable that takes only one out of two possible values when measured. For eg. Gender: male/female.
  • **Polynomic Variable: **A polynomic variable is a variable that has multiple values to choose from. For eg. Educational Qualifications: Uneducated/ Undergraduate/ Postgraduate/ Doctoral, etc.
  • **Discrete Variable: **Discrete variables are countable variables. For eg. your bank balance, no. of employees in an organization, etc.
  • **Continuous Variable: **A continuous variable is a variable that has an infinite no. of possible values. Any kind of measure is a continuous variable. For eg. Temperature is a continuous variable. The temperature of a particular area can be described as 30 °C, 30.2 °C, 30.22 °C, 30.221 °C, and so on.

#exploratory-data-analysis #data-science #data-analysis #analytics #machine-learning #data analysis

Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Sofia  Maggio

Sofia Maggio

1626077565

Sentiment Analysis in Python using Machine Learning

Sentiment analysis or opinion mining is a simple task of understanding the emotions of the writer of a particular text. What was the intent of the writer when writing a certain thing?

We use various natural language processing (NLP) and text analysis tools to figure out what could be subjective information. We need to identify, extract and quantify such details from the text for easier classification and working with the data.

But why do we need sentiment analysis?

Sentiment analysis serves as a fundamental aspect of dealing with customers on online portals and websites for the companies. They do this all the time to classify a comment as a query, complaint, suggestion, opinion, or just love for a product. This way they can easily sort through the comments or questions and prioritize what they need to handle first and even order them in a way that looks better. Companies sometimes even try to delete content that has a negative sentiment attached to it.

It is an easy way to understand and analyze public reception and perception of different ideas and concepts, or a newly launched product, maybe an event or a government policy.

Emotion understanding and sentiment analysis play a huge role in collaborative filtering based recommendation systems. Grouping together people who have similar reactions to a certain product and showing them related products. Like recommending movies to people by grouping them with others that have similar perceptions for a certain show or movie.

Lastly, they are also used for spam filtering and removing unwanted content.

How does sentiment analysis work?

NLP or natural language processing is the basic concept on which sentiment analysis is built upon. Natural language processing is a superclass of sentiment analysis that deals with understanding all kinds of things from a piece of text.

NLP is the branch of AI dealing with texts, giving machines the ability to understand and derive from the text. For tasks such as virtual assistant, query solving, creating and maintaining human-like conversations, summarizing texts, spam detection, sentiment analysis, etc. it includes everything from counting the number of words to a machine writing a story, indistinguishable from human texts.

Sentiment analysis can be classified into various categories based on various criteria. Depending upon the scope it can be classified into document-level sentiment analysis, sentence level sentiment analysis, and sub sentence level or phrase level sentiment analysis.

Also, a very common classification is based on what needs to be done with the data or the reason for sentiment analysis. Examples of which are

  • Simple classification of text into positive, negative or neutral. It may also advance into fine grained answers like very positive or moderately positive.
  • Aspect-based sentiment analysis- where we figure out the sentiment along with a specific aspect it is related to. Like identifying sentiments regarding various aspects or parts of a car in user reviews, identifying what feature or aspect was appreciated or disliked.
  • The sentiment along with an action associated with it. Like mails written to customer support. Understanding if it is a query or complaint or suggestion etc

Based on what needs to be done and what kind of data we need to work with there are two major methods of tackling this problem.

  • Matching rules based sentiment analysis: There is a predefined list of words for each type of sentiment needed and then the text or document is matched with the lists. The algorithm then determines which type of words or which sentiment is more prevalent in it.
  • This type of rule based sentiment analysis is easy to implement, but lacks flexibility and does not account for context.
  • Automatic sentiment analysis: They are mostly based on supervised machine learning algorithms and are actually very useful in understanding complicated texts. Algorithms in this category include support vector machine, linear regression, rnn, and its types. This is what we are gonna explore and learn more about.

In this machine learning project, we will use recurrent neural network for sentiment analysis in python.

#machine learning tutorials #machine learning project #machine learning sentiment analysis #python sentiment analysis #sentiment analysis