https://github.com/aakashsyadav1999/hotel-reservations-dataset-mlflow

The online hotel reservation channels have dramatically changed booking possibilities and customers’ behavior. A significant number of hotel reservations are called-off due to cancellations or no-shows. The typical reasons for cancellations include change of plans, scheduling conflicts, etc.
https://github.com/aakashsyadav1999/hotel-reservations-dataset-mlflow

dvc endtoendpipeline gridsearchcv machine-learning mlflow random-forest-classifier

Last synced: 1 day ago
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The online hotel reservation channels have dramatically changed booking possibilities and customers’ behavior. A significant number of hotel reservations are called-off due to cancellations or no-shows. The typical reasons for cancellations include change of plans, scheduling conflicts, etc.

• Host: GitHub
• URL: https://github.com/aakashsyadav1999/hotel-reservations-dataset-mlflow
• Owner: aakashsyadav1999
• License: mit
• Created: 2023-10-06T11:20:58.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-10-18T11:23:24.000Z (about 1 year ago)
• Last Synced: 2023-10-19T10:39:59.141Z (about 1 year ago)
• Topics: dvc, endtoendpipeline, gridsearchcv, machine-learning, mlflow, random-forest-classifier
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/datasets/ahsan81/hotel-reservations-classification-dataset/data
• Size: 44.6 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Hotel-Reservations-Dataset-mlflow

This repository consists of various machine learning projects in which each projects was done as end to end projects which means from Data Collection through feature engineering, feature selecion to Deployment and Maintainance. The whole app was built with Flask framework. You can launch my app by clicking here.

For building machine learning models, I have used scikit-learn alias sklearn.

Some extra-ordinary features that I have included in my app :

Visualizing Probability of classifcation in each classification type problems.

Added all the details about the projects such as data source, code source, libraries and frameworks used in each project's description.

# Installation

To run my app on your local machine, do the following steps.

## Step 1 :

I have written the Code with Python 3.9.17. If you don't have Python installed you can find it here.

If you are using a lower version of Python you can upgrade using the pip package, kindly ensure that you have the latest version of pip.

## Step 2 :

If you want the current version of my repository to be in your github, you can do forking my repository visiting https://github.com/aakashsyadav1999/Hotel-Reservations-Dataset-mlflow.git

Clone my repository to your local machine by running the following command. Before doing this, you have to install git on your machine and make sure you are having proper internet connection.

For Windows OS user, open git bash and run the following command.

git clone https://github.com/aakashsyadav1999/Hotel-Reservations-Dataset-mlflow.git

For Linus OS user, open Terminal and run the following command.

git clone https://github.com/aakashsyadav1999/Hotel-Reservations-Dataset-mlflow.git

If you don't want to mess up with all these things, you can just download the zip file of my GitHub repository by clicking here and extract it to any file location as your wish and then use it.

Now we have done with the downloading of my whole project.

## Step 3 :

After downloading the whole repo, get into the main folder by hit the following command in git bash for Windows OS users and Terminal for Linux OS users.

cd Hotel-Reservations-Dataset-mlflow

## Step 4 :

Now we are going to install all the dependency libraries for this project. Before that you must have Python 3.9.17 and latest version of pip.

To install all the dependency libraries in a single command, run the following command.

pip install -r requirements.txt

## Step 5 :

After installing all the dependency libraries, you are ready to run my app on your local machine.

To launch my app on your local machine, hit the following command.

python ml_projects.py

## Run

Now you have successfully launched my app on your local machine.

To view my app, hit the following URL in any of the browser such as Chrome, FireFox, etc..,

## http://127.0.0.1:5000 - For welcome page

## http://127.0.0.1:5000/predictdata - for prediction site

An end-to-end data science project typically involves several key steps, from defining the problem to deploying the solution. Here's an overview of the essential steps in an end-to-end data science project:

1. **Problem Definition:**

   - Begin by understanding the business problem or the goal of the project.

   - Define what you want to achieve with data science and analytics.

2. **Data Collection:**

   - Collect relevant data from various sources. This could be structured or unstructured data.

   - Ensure the data is clean, complete, and well-organized.

3. **Data Preprocessing:**

   - Clean the data by handling missing values, outliers, and noise.

   - Perform data transformations, such as feature scaling and encoding categorical variables.

4. **Exploratory Data Analysis (EDA):**

   - Explore the data to gain insights and a better understanding of its characteristics.

   - Visualize the data through graphs and charts to identify patterns and anomalies.

5. **Feature Engineering:**

   - Create new features or modify existing ones to improve the model's performance.

   - Feature selection can be a part of this step to reduce dimensionality.

6. **Model Selection:**

   - Choose appropriate machine learning algorithms or models based on the problem type (classification, regression, clustering, etc.).

   - Experiment with different models to find the best one.

7. **Model Training:**

   - Train the selected model using the training dataset.

   - Tune hyperparameters to optimize the model's performance.

8. **Model Evaluation:**

   - Assess the model's performance using appropriate metrics (e.g., accuracy, F1 score, RMSE).

   - Use cross-validation to ensure the model's generalization capability.

9. **Model Interpretation:**

   - Understand the model's decision-making process, especially for complex models like deep learning.

   - Interpret feature importance and model coefficients.

10. **Model Deployment:**

    - Prepare the model for deployment in a production environment.

    - Integrate the model into an application or workflow for real-time predictions.

11. **Monitoring and Maintenance:**

    - Continuously monitor the model's performance in a production setting.

    - Retrain the model periodically with new data to maintain its accuracy.

12. **Documentation:**

    - Document the entire project, including data sources, preprocessing steps, model details, and results.

    - Clear documentation is essential for collaboration and future reference.

13. **Communication:**

    - Present the results and insights to stakeholders in a clear and understandable manner.

    - Provide recommendations and insights that can guide business decisions.

14. **Feedback and Iteration:**

    - Gather feedback from stakeholders and end-users to identify areas for improvement.

    - Iterate on the model and the project as needed.

Each of these steps is crucial for the success of an end-to-end data science project. The process may vary based on the specific project and the problem you're addressing, but this outline provides a general roadmap for your project.