🧳Travel Package Purchase Prediction

A End to End project using CICD pipeline which will predict that A new Customer will Purchase the Package or Not.

📝 Table of contents

• General info
• Data Source
• Problem Statement
• Demo Photos
• Library Used
• Structure Used
• Run Locally
• Deployment Techniques

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General info

"Visit with us". company wants to enable and establish a viable business model to expand the customer base. One of the ways to expand the customer base is to introduce a new offering of packages. Currently, there are 5 types of packages the company is offering - Basic, Standard, Deluxe, Super Deluxe, King. Looking at the data of the last year, we observed that 18% of the customers purchased the packages. However, the marketing cost was quite high because customers were contacted at random without looking at the available information.The company is now planning to launch a new product i.e. Wellness Tourism Package. Wellness Tourism is defined as Travel that allows the traveler to maintain, enhance or kick-start a healthy lifestyle, and support or increase one's sense of well-being.However, this time company wants to harness the available data of existing and potential customers to make the marketing expenditure more efficient.

We need to analyze the customers' data and information to provide recommendations to the Policy Maker and Marketing Team and also build a model to predict the potential customer who is going to purchase the newly introduced travel package.

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⏳Data Source

Travel Package Data

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Problem Statement

To predict which customer is more likely to purchase the newly introduced travel package Which variables are most significant. Which segment of customers should be targeted more.

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📷 Demo Photos

Our Website will be look like and when we hit the submit button prediction will happen. adn output will be that customer will take the package or customer will not the package.

🖥️Library Used

There will be file named requirement.txt which will contain all these libraries used in project.

pandas
numpy
seaborn
matplotlib
scikit-learn
xgboost
flask
dill

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⚙️Structure Used

There are structure used for different-different work:

• setup.pyThis contains all details about the Project.
• requirements.txt Contains the all the libraries used in the project.
• logger.py is responsible for the log all the information whatever is happening in the project at which perticular time or file.
• exception.py is responsible for the give the Customexception when an error in any file, So it give the file_name,Lineno and error also.
• .gitignore will add all the files which we don't want to push on the github.
• readme.md contain general informtion about the project steps and requiremnts for further explaination.
• datacontain the dataset.
• src contain many subfolder. we need to give a __init__.py file in each directory i.e. we can use each file as a module.
• src/data_ingestion.py responsible for the data ingestion from many different-different source like kaggle ,mongodb or MySQL etc. it split the data into train and test and store them in a perticular Artifacts folder.
• src/data_transformation.pyresponsible for the transform the categorical values into vectors. Also used in Scaling and Handle the Missing values and return a preprocessor which transform the data for the Machine Learning Models.
• src/Model_trainer.py is responsible for the model training and Hyperparameter tuning it return a Model Pickle file which is train on the data and used for the further Prediction.
• src/Prediction_Pipeline.py is responsible for the Creating the Pipeline using the app.py and utils.py for the Creating a Web Page for the prediction for the new data.
• utils.py is used for creating and storing the common function which are used whole through out the Project.
• app.py is web app file which interact with user and take the input for new datapoints from the user and show the output by using the pre-trained model.

👨🏻‍💻Run Locally

• Before the following steps make sure you have git, Anaconda or miniconda installed on your system
• Clone the complete project with git clonehttps://github.com/Gajender0707/Travel-Package-Project-using-CICD-Pipeline.git or you can just download the code and unzip it.
• Once the project is cloned, open VSCode prompt in the directory where the project was cloned and paste the following block python venv -m myenv python=3.11.3 after that
• myenv/Scripts/Activate.ps1
• pip install -r requirements.txt And finally run the project with python app.py.
• Open the localhost url provided after running app.py and now you can use the project locally in your web browser or put http://127.0.0.1:8080 which is your local host.

Deployment Techniques

• Deployment to AWS: The final step is to deploy the All files to an AWS server. This step is done by us using the ubuntu server where we deployed our model using the winSCP to connect the AWS server(EC2).

🎯Project Created BY

@Gajender