Airbnb 2016 Project

This was my capstone project to receive the Google Data Analytics Professional Certificate.

I performed data analysis for Bellabeat, a high-tech manufacturer of health-focused products for women. I performed the analysis on R programming.

## Company Overview

Urška Sršen and Sando Mur founded Bellabeat, a high-tech company that manufactures health-focused smart products. Bellabeat would like to analyze smart device usage data in order to gain insight into how people are already using their smart devices. Then, using this information would like high-level recommendations for how these trends can inform Bellabeat's marketing strategy.

## Phase 1: Ask

### Business Task

Identify new growth opportunities based on analysis of smart device data.

### Stakeholders

*   Urška Sršen

*   Sando Mur

*   Marketing Analytics team

## Phase 2: Prepare

### Data Organisation

The dataset has 18 files, is in the long format and is stored in Kaggle. The data is generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016.The sample size is thirty eligible Fitbit users who consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring.

### ROCCC:

*   **Reliability:** Small sample size of 30 participants reduces reliability.

*   **Originality:** The dataset is from a third party, the survery was administered by Amazon Mechanical Turk Between 03.12.2016 - 05.12.2016.

*   \*\*Comprehensiveness: \*\*Limited data about participant characteristics such as age and gender.

*   **Current:** The data is from 2016, thus, it is outdated.

*   **Cited:** Credibility is difficult to determine due to the lack of information.

## Data Cleaning and Processing

*   Duplicates were removed by identifying the number of distinct users in each data frame.

*   Cells with "0" values were removed to prevent skewed results.

*   Date and time values in the daily sleep dataset were merged and needed to be separated.

*   Column names were standardised into all lowercase,

*   Date values have the wrong formatting and needed to be changed into the date format.

## Summary Statistics Key Findings

*   The average Fitbit user takes 8,329 steps.

*   The average Fitbit user spends 419.5 minutes asleep.

## Hypotheses

#### Hypothesis 1: The total steps taken are negatively correlated with sedentary minutes.

![](/images/H1.PNG)

**Finding:** The data visualization suggests a negative correlation between the total steps taken and sedentary minutes, meaning the greater the sedentary minutes, the lower the total steps taken.

#### Hypothesis 2: The total time in bed is positively correlated with time in bed.

![](/images/H2.PNG)

**Finding:** The data visualisation suggests a positive correlation between total minutes asleep and total minutes in bed, which means the greater minutes in bed, the greater the total minutes asleep.

#### Hypothesis 3: The total steps taken are positively correlated with calories burned.

**Finding:** The data visualisation suggests a positive correlation between calories and total steps, which means the greater the total steps taken, the greater calories burnt.

#### *Hypothesis 4:* The total minutes asleep is negatively correlated with calories.

![](/images/H4.PNG)


BellaBeat Project

## Executive Summary

The aim of this project was to explore consumer perceptions of green hotels through the factors that Azjen's Theory of Planned Behaviour suggests influence behaviour in addition to other variables suggested by the current literature concerning the model and green hotels to better understand how to persuade consumers to learn more about green hotels and visit these establishments.

A self-administered online questionnaire was distributed to a total of 303 consumers, 150 from Morocco and 153 from Qatar. The results were analysed through descriptive analysis and regression analysis.

The constructs, environmental concern, green hotel knowledge, attitude, subjective norms, perceived behavioural control and moral identity were all found to be statistically significant in predicting consumers’ intention to visit green hotels and their intentions to learn about green hotels.

### Hypotheses:

H1: Attitude has a positive impact on the intention to visit green hotels.

H2: Attitude has a positive impact on the intention to learn about green hotels.

H3: Subjective norm has a positive impact on the intention to visit green hotels.

H4: Subjective norm has a positive impact on the intention to learn about green hotels. 

H5: Perceived behavioural control has a positive impact on the intention to visit green hotels.

H6: Perceived behavioural control has a positive impact on the intention to learn about green hotels.

H7: Moral identity has a positive impact on the intention to visit green hotels.

H8: Moral identity has a positive impact on the intention to learn about green hotels.

H9: Environmental concern has a positive impact on the intention to visit green hotels.

H10: Environmental concern has a positive impact on the intention to learn about green hotels.

H11: Green hotel knowledge has a positive impact on the intention to visit green hotels

H12: Green hotel knowledge has a positive impact on the intention to learn about green hotels

Table 1 presents the sample composition

### Sample Composition:

![](/images/sample%20comp-fc5dcb01.PNG)

### Validity and reliability of the results:

To assess the internal consistency of the questions Cronbach’s Alpha was used. The alpha coefficient for the whole survey was 0.94 indicating an acceptable level. A breakdown of Cronbach’s Alpha for each variable measured is illustrated in Table 2

![](/images/corn%20alpha.PNG)

### Analysis

#### Multiple Regression Intention to Visit Green Hotels

![](/images/mult%20reg%201.PNG)

All variables were found to be statistically significant at p=0.00. The analysis revealed that 45% of the variance in intention to visit green hotels can be accounted for by green hotel knowledge, 34% can be accounted for by environmental concern, 34% can be accounted for by attitude, 85% can be accounted for by subjective norm, 34% can be accounted for by PBC and finally, 85% can be accounted for my moral identity. 

#### &#xA;&#xA;Multiple Regression of Intention to Learn About Green Hotels

![](/images/mult%20reg%202.PNG)

All variables were found to be statistically significant at p=0.00. The analysis revealed that 24% of the variance in intention to learn about green hotels can be accounted for by green hotel knowledge, 23% can be accounted for by environmental concern, 16% can be accounted for by attitude, 26% can be accounted for by subjective norm, 24% can be accounted for by PBC and finally, 32% can be accounted for by moral identity.



## Conclusion

The overall aim of this study was to explore consumer perception of green hotels in two contexts, Morocco and Qatar. This was explored through the lens of the Theory of Planned Behaviour to better understand the different factors influencing consumer behaviour in order to understand how to change consumer behaviour to persuade consumers to visit green hotels.

The results reveal that the twelve hypotheses were supported, all six variables positively impact the intention to visit green hotels and the intention to learn about green hotels. This study further confirms the applicability of the TPB in Morocco and Qatar and opens up avenues for future research to expand on the literature on green hotels in both national contexts.


Airbnb 2016 Project

Introduction

Step 1 Data Cleaning:

Step 2 Data Visualisation

Visualisation 1: Price by Zip code

Visualisation 2: Price per Zip code

Visualisation 3: Revenue for 2016

Visualisation 4: Average Price per Bedroom

Visualisation 5: Distinct Count of Bedroom Listings

Conclusion

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Airbnb 2016 Project

BellaBeat Project

Green Hotels Project