An R package pipelining omics-based cancer survival analysis

EDA Travel data by PW Skills Data Analytics Course.

The purpose of this project is to develop a machine learning model that predicts employee attrition (whether an employee will leave the company) and department assignment (which department an employee belongs to) based on various factors. These factors include age, travel frequency, education level, job satisfaction, marital status, and more.

This is the Data Mining Project for predicting the student's grade before the final and Mid-2 examination. I use Python and Jupyter Notebook for this Project.

Analyzing O3 Air Quality Index trends (2000-2023) in the U.S., this project identifies regions with rising pollution. Utilizing exploratory data analysis and time-series modeling, it offers actionable insights for informed policy decisions on urgent O3 pollution issues.

The purpose of this project is to predict student loan repayment success using a neural network. Neural networks are computational models inspired by the human brain's structure and function, consisting of layers of interconnected nodes or "neurons" that can learn to recognize patterns in data.

The purpose of this project is to develop and compare two machine learning models to detect spam emails. Spam detection is a crucial task in email filtering systems to protect users from unwanted and potentially harmful emails. The project involves using a dataset containing various features extracted from email content.

Exploring real estate data from the city of Boston in the 1970s to evaluate projects before initiating them.

Model selection crucial in loan approval prediction project. Random Forest outperformed Logistic Regression, emphasizing importance of choosing appropriate models for accurate predictions.

Machine Learning project on Classification before and after poisoning skin cancer images with Adversarial Attacks

Classification model to categorize clothing items into distinct classes

This is the project capstone of IBM Data Science Certified Professional certification. I analyze SpaceX data in it, get actionable insights from it, build a machine learning model on data, and check which model is best for the dataset. I've also used folium to draw points of launches on map and find their distance from nearest places like station.

Develop a tool in Google Colab using machine learning and neural networks to select applicants for funding with the best chance of success based on the source data provided by the organization.

Women's' E-commerce product review dataset downloaded from Kaggle will undergo in this code a sentiment analysis process

Repository of all exercises and assignments in 365 Data Science Machine Learning Course

This repository contains Machine Learning Classification algorithms implementation

A GitHub repository hosting an insurance prediction model employing Decision Tree, Random Forest, and KNN algorithms, with KNN achieving the highest accuracy score of 87.7% and tested for response on unseen data.

Lasso and Inductive Conformal Prediction Algorithm