This repository contains a classification project utilizing a dataset sourced from Kaggle. The project encompasses various stages including Exploratory Data Analysis (EDA), Data Preprocessing, and Visualization.
- Data Exploration:
- Conducted comprehensive EDA, including data preprocessing and visualization.
- Modeling:
- Prepared the data for machine learning and trained multiple models, such as Decision Tree Classifier, K-Nearest Neighbors, Random Forest Classifier, Support Vector Machines (SVM), Naive Bayes, XGBoost, CatBoost, and Artificial Neural Network (ANN).
- Model Evaluation:
- Evaluated the performance of each model and compared their results.
- Deployment:
- Developed a web-based application using Streamlit for easy access to the analysis results.
- Decision Tree Classifier
- K-Nearest Neighbors
- Random Forest Classifier
- Support Vector Machines (SVM)
- Naive Bayes
- XGBoost
- CatBoost
- Artificial Neural Network (ANN)