Open mariam7084 opened 4 months ago
Full name : Karan Kumar Bhagat GitHub Profile Link : https://github.com/karandomguy Participant ID (If not, then put NA) : NA Approach for the Project:
Load the dataset and examine its structure. Handle missing values and perform data cleaning. Conduct univariate, bivariate, and multivariate analysis. Visualize data trends using various plots (histograms, bar charts, scatter plots, etc.).
Create and encode new features. Scale numerical features.
Split the dataset into training and testing sets. Implement multiple algorithms (Decision Trees, Random Forest, Gradient Boosting, Logistic Regression, SVM, k-NN). Train and evaluate models using metrics (accuracy, precision, recall, F1-score, ROC-AUC).
Compare models based on evaluation metrics. Identify the best-performing model.
Document data cleaning, EDA, feature engineering, model building, and evaluation. Save visualizations in the "Images" folder. Summarize insights and conclusions.
What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.) : SSoC
Implement 5-6 models for this dataset. Assigned @karandomguy
Thank you for assigning this to me @abhisheks008 Will get this done asap
ML-Crate Repository (Proposing new issue)
:red_circle: Project Title : Hotel Booking Analysis :red_circle: Aim : Perform EDA and visualization :red_circle: Dataset : https://www.kaggle.com/datasets/thedevastator/hotel-bookings-analysis :red_circle: Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
📍 Follow the Guidelines to Contribute in the Project :
requirements.txt
- This file will contain the required packages/libraries to run the project in other machines.Model
folder, theREADME.md
file must be filled up properly, with proper visualizations and conclusions.:red_circle::yellow_circle: Points to Note :
:white_check_mark: To be Mentioned while taking the issue :
Happy Contributing 🚀
All the best. Enjoy your open source journey ahead. 😎