Machine Learning Project on Resume Screening using Python

This project is to build a model that *predicts the human activities* such as __Walking, Walking_Upstairs, Walking_Downstairs, Sitting, Standing__ and __Laying__ as done in Smart-Watches.

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This project aims to predict online shoppers' purchase intentions using browsing history and user data from e-commerce sites. By analyzing clickstream and session information, the goal is to create a machine learning model that accurately forecasts customers' likelihood of making a purchase.

This project showcases skills in machine learning, data preprocessing, and model evaluation using Python libraries such as scikit-learn, XGBoost, and Optuna. It involves implementing various machine learning models, handling imbalanced data, and employing imputation techniques to enhance model performance for predicting cirrhosis outcomes.

This repository contains data analysis programs in the Python programming language.

Data Gathering -> Data Cleansing -> Data Preprocessing -> Data Visualization

Handling missing values and outliers improved data quality. Balancing the dataset using SMOTE helped improve model performance. Future improvements can include hyperparameter tuning and trying different models for better accuracy.