Analysis, Price Modeling and Prediction: AirBnB Data for Seattle.

Business Understanding

For all AirBnB users and hosts in Seattle, I will analyze and answer business-related questions in these aspects:

• Price Analysis
• Listings count Analysis
• Busiest time Analysis
• Occupancy rate and Reviews Analysis
• Modeling for Price Prediction

Questions and answers are covered below.

---

Data Understanding

Here I will perform Exploratory Data Analysis on the data provided by Inside Airbnb on Kaggle, you can download the data from here(zip file), Zip file contains 3 csv files: listing.csv, calendar.csv, and reviews.csv

Overview of listing.csv

Read the csv file using pandas as given below:

#read listing.csv, and its shape
listing_seattle = pd.read_csv(‘listings_seattle.csv’)
print(‘Shape of listing csv is’,listing_seattle.shape)
listing_seattle.sample(5)    #display 5 rows at random

Basic checks and high-level data analysis

Have a look at the data and have some sanity checks like the percentage of missing values per column, are the listing_ids unique throughout the dataset?, examine the summary of numerical columns, etc.

• Percentage of missing values in each column

Percentage of missing values per column

From the above bar chart, we get the important columns with the least missing values. Columns like license and square****feet have more than 95% of the data missing, hence we will drop these columns.

Are the ids unique for each row?
len(listing_seattle['id'].unique()) == len(listing_seattle)

Description of all numeric features
listing_seattle.describe()

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

What is GEEK

Buddha Community

Analysis, Price Modeling and Prediction: AirBnB Data for Seattle.

Business Understanding

For all AirBnB users and hosts in Seattle, I will analyze and answer business-related questions in these aspects:

• Price Analysis
• Listings count Analysis
• Busiest time Analysis
• Occupancy rate and Reviews Analysis
• Modeling for Price Prediction

Questions and answers are covered below.

---

Data Understanding

Here I will perform Exploratory Data Analysis on the data provided by Inside Airbnb on Kaggle, you can download the data from here(zip file), Zip file contains 3 csv files: listing.csv, calendar.csv, and reviews.csv

Overview of listing.csv

Read the csv file using pandas as given below:

#read listing.csv, and its shape
listing_seattle = pd.read_csv(‘listings_seattle.csv’)
print(‘Shape of listing csv is’,listing_seattle.shape)
listing_seattle.sample(5)    #display 5 rows at random

Basic checks and high-level data analysis

Have a look at the data and have some sanity checks like the percentage of missing values per column, are the listing_ids unique throughout the dataset?, examine the summary of numerical columns, etc.

• Percentage of missing values in each column

Percentage of missing values per column

From the above bar chart, we get the important columns with the least missing values. Columns like license and square****feet have more than 95% of the data missing, hence we will drop these columns.

Are the ids unique for each row?
len(listing_seattle['id'].unique()) == len(listing_seattle)

Description of all numeric features
listing_seattle.describe()

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

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

Predictive Modeling in Data Science

Predictive modeling is an integral tool used in the data science world — learn the five primary predictive models and how to use them properly.

Predictive modeling in data science is used to answer the question “What is going to happen in the future, based on known past behaviors?” Modeling is an essential part of data science, and it is mainly divided into predictive and preventive modeling. Predictive modeling, also known as predictive analytics, is the process of using data and statistical algorithms to predict outcomes with data models. Anything from sports outcomes, television ratings to technological advances, and corporate economies can be predicted using these models.

Top 5 Predictive Models

1. Classification Model: It is the simplest of all predictive analytics models. It puts data in categories based on its historical data. Classification models are best to answer “yes or no” types of questions.
2. Clustering Model: This model groups data points into separate groups, based on similar behavior.
3. **Forecast Model: **One of the most widely used predictive analytics models. It deals with metric value prediction, and this model can be applied wherever historical numerical data is available.
4. Outliers Model: This model, as the name suggests, is oriented around exceptional data entries within a dataset. It can identify exceptional figures either by themselves or in concurrence with other numbers and categories.
5. Time Series Model: This predictive model consists of a series of data points captured, using time as the input limit. It uses the data from previous years to develop a numerical metric and predicts the next three to six weeks of data using that metric.

#big data #data science #predictive analytics #predictive analysis #predictive modeling #predictive models

How Are Data analysis and Data science Different From Each Other

With possibly everything that one can think of which revolves around data, the need for people who can transform data into a manner that helps in making the best of the available data is at its peak. This brings our attention to two major aspects of data – data science and data analysis. Many tend to get confused between the two and often misuse one in place of the other. In reality, they are different from each other in a couple of aspects. Read on to find how data analysis and data science are different from each other.

Before jumping straight into the differences between the two, it is critical to understand the commonalities between data analysis and data science. First things first – both these areas revolve primarily around data. Next, the prime objective of both of them remains the same – to meet the business objective and aid in the decision-making ability. Also, both these fields demand the person be well acquainted with the business problems, market size, opportunities, risks and a rough idea of what could be the possible solutions.

Now, addressing the main topic of interest – how are data analysis and data science different from each other.

As far as data science is concerned, it is nothing but drawing actionable insights from raw data. Data science has most of the work done in these three areas –

• Building/collecting data
• Cleaning/filtering data
• Organizing data

#big data #latest news #how are data analysis and data science different from each other #data science #data analysis #data analysis and data science different

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

---

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management