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The aim of this project is to analyze Hotel Booking Data and identify and Visualize Business Insights pertaining to revenue

## **Problem Statement**



### **Business Problem**

Data of Hotel Booking is given we need to identify business questions relating to

Identify hotel revenue growth

Segment Hotel Revenue

Comment about the Bookings

#### **Data**

The data consists of five tables

Revenue Data of year 2018,2019,2020

Market Segment Data

Discount Data

### **SQL DATABASE**

Exploratory Data Analysis

Uses MS SQL to run queries to identify required tables

Joined required tables to create a unified dataset

Isolated the dataset on identified KPIs

Get the data ready to load to Power BI

### **POWER BI DASHBOARD**

Identification of KPIs for Revenue Growth by creating 2 new measures

Segmenting the Revenue based on Hotel Type, Timeframe, Market Type

Identification of 3 KPIs for identifying trend in Bookings.

Filtering the data based on Country and Hotel Type

Dashboard building

![](/images/hotel_analysis.png)


HOTEL BOOKING ANALYSIS

The aim of this project is to develop a image search engine system that can search similar images and host them.

### **Problem Statement**

Designing an image search engine which takes input an image and can search and host similar images.

### **Data**

The Caltech101 dataset contains images from 101 object categories (e.g., “helicopter”, “elephant” and “chair” etc.) and a background category that contains the images not from the 101 object categories. For each object category, there are about 40 to 800 images, while
most classes have about 50 images. The resolution of the image is roughly about 300×200 pixels.
Provisions have been made to collect new labels and data through APIs.

### **Architecture**

![](/images/rev-img-se.drawio-3d4948a6.png)

### **Data Store Component**

Creation of AWS S3 folder to store image data for setting up hosting S3 link.

Creation of Mongo DB Collection to store Meta Data and Labels Information.

Creation of API links for image data collectors annotators to upload images and labels.

Creation of Docker File for the Pipeline.

Deploying the pipeline using AWS and GitHub Actions.

### **Model Trainer Component**

Ingestion of Data from S3.

Preprocessing the images for Model Training.

Employing Pre-trained Resnet34 architecture to generate embeddings.

Using Approximate Nearest Neighbors to build the embedding tree.

Upload Model and Artifacts to S3.

Creation of Docker File for the Pipeline.

Deploying the pipeline using AWS and GitHub Actions.

### **Model Prediction Component**

Download Model and Artifacts.

Taking input image from user through API.

Preprocessing the input.

Generating Embedding for the input using Previously built Model Architecture.

Employing Approximate Nearest Neighbors for similarity searching.

Get the publicly hosted S3 links of similar images.

Provide the links to the users.

Creation of Docker File for the Pipeline.

Deploying the pipeline using AWS and GitHub Actions.

***

#### **All code was written using Python and the mentioned technologies / frameworks following OOPs Principles and guidelines.**

******


Image Similarity Search System

The aim of this project is to predict human activities such as walking, running, standing, sitting from smartphone sensors data.

## **Problem Statement**

Designing a service that predicts user activities like running, walking, sitting etc. from smartphone sensors data.

Client is an electronics company who wants to develop a smartphone app to predict user’s activity.

### **Data**

The data consists of sensor’s data consisting of users performing activities of daily living, a 561-feature vector with time and frequency domain variables and
a ground truth label of performing activity.

### **DATA PIPELINE**

#### **Requirements**

Data stored in csv files stored in AWS S3 Bucket

Sensor’s data consists of streaming data so appropriate pipeline for it should be created.

#### **DATA PIPELINE ARCHITECTURE**

![](/images/data_pipeline_diagram.png)

Previous data stored in csv files are used to build the initial pipeline model.

Sensor's Data from Smartphone are connected to Apache Kafka Stream which takes the data and puts in in collection in Mongo DB

### **MODEL TRAINING PIPELINE**

#### **Requirements**

Create a model to predict activities

#### **MODEL TRAINING ARCHITECTURE**

![](/images/HLD_MODEL_PIPELINE.png)

Data Ingestion Component: - Responsible for ingesting the data from Mongo DB to our application and Split into Train, Validation, Test parts.

Data Validation Component: - Responsible for taking ingested data and checking validity (Data Missing, Numerical Columns Checking, Drift Calculation).

Data Transformation Component: - Responsible for  creating and saving a transformation pipeline ( Imputer, Scaling, Dimensionality Reduction) also Performs Target Encoding.

Model Trainer Component: - Uses a Linear SVM for Classification to train, evaluate train metric and save model, reports.

Model Evaluation Component: - Evaluates the model on Test Data and save reports.

Model Pusher Component: - Stores the best model to be used in production.

Model Prediction Component: - This part is used for prediction of Input Data.

For Model Deployment Docker File is created and deployed in an AWS EC2 machine using AWS ECR and GitHub Actions for CI/CD Pipeline.

#### **All code was written using Python and the mentioned technologies / frameworks following OOPs Principles and guidelines.**

##### **Check out code down here**

***https://github.com/bsb4018/activity_pred_main_proj***


Projects

HOTEL BOOKING ANALYSIS

Image Similarity Search System

Activity Prediction