Phishing attacks have grown to be a big problem for people and businesses in the modern digital age.SMS messages are one of the most widely used channels for phishing attempts.This project seeks to investigate the application of cutting-edge machine learning and NLP methods for the identification of phishing SMS (smishing) messages.
Python保姆级教学附有对应的demo项目(成绩管理系统),适合小白打基础。此外还包含Numpy,Tensorflow,Pytorch示例代码提供学习使用上手
Use Machine Learning to predict house prices based on features such as location, area, number of bedrooms, amenities, etc. Include a web UI where users can input values and see predictions.
This repository contains an analysis of employee attrition trends at Green Destinations. It includes data preprocessing, exploratory data analysis (EDA), and a predictive model using Logistic Regression to determine attrition likelihood based on employee attributes.
This project is a Machine Learning-based House Price Predictor trained on a tabular house price dataset using the powerful XGBRegressor model.
The project demonstrate data cleaning, visualisation,eda from Bi data set
Introduction to data analysis using pandas
This work is based on a project from the Data Analyst Nanodegree of Udacity. It analyzes the characteristics of no-shows for medical appointments in Brazil and the effectiveness of SMS-reminders.
Scalable and resumable Python scraper for extracting large datasets from paginated REST APIs.
Machine Learning project that predicts Olympic medal counts using Linear Regression based on number of athletes and previous Olympic performance.
🐍🤯 Utilising Python w/ Pandas and Jupyter to create Dataframes to read in 1994 US Census data to compile an Analytical Base Table (ABT) which displays a Categorical and Continuous Data Quality Report. Dealing with Data Quality Issues (DQIs) such as Cardinality Issues, Outliers and Missing Values.
Final submission for Afretec Summer School in Smart Systems. We developed a deep learning module using PyTorch to predict COVID-19 cases. Utilizing an LSTM neural network, the model analyzes time-series data for forecasting. The project underscores the importance of data preprocessing and LSTM networks in sequential data analysis.
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