ML-Crate Repository (Proposing new issue)

🔴 Project Title: Hotel Booking Demand Prediction

🔴 Aim: Predict hotel booking cancellations and analyze factors affecting hotel bookings.

🔴 Dataset: https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand

🔴 Approach:

1. Exploratory Data Analysis (EDA):

   • Load, explore, and visualize the dataset.

2. Data Preprocessing:

   • Handle missing values.
   • Encode categorical variables.
   • Scale numerical features if necessary.

3. Model Building:

   • Implement and compare multiple models (at least 3-4 algorithms) such as Logistic Regression, Decision Trees, Random Forest, and Gradient Boosting.

4. Model Evaluation:

   • Use cross-validation and appropriate metrics (e.g., accuracy, precision, recall, ROC-AUC) to evaluate model performance.

5. Hyperparameter Tuning:

   • Optimize the models using techniques like Grid Search or Random Search.

Please add this project to the repository. Thank you!

📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

🔴🟡 Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

✅ To be Mentioned while taking the issue :

• Full name : Anish Awasthi
• GitHub Profile Link : https://github.com/anish3333
• Participant ID (If not, then put NA) : NA
• Approach for this Project : Conduct exploratory data analysis (EDA), preprocess the data (handle missing values, encode categorical variables, and scale numerical features), and implement multiple machine learning models. Compare the performance of these models using cross-validation and appropriate metrics, then optimize with hyperparameter tuning to identify the best-performing algorithm.
• What is your participant role? VSOC

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎