A collection of 8 Applied Data Science projects.

FAST Change Point Detection in R

Nuclear Energy Generation Prediction Logistic Regression project is aimed at predicting the nuclear energy generation based on the production (generation) data from 1991 to 2023 by using Logistic Regression.

The completed phase-1 projects aimed at financial analysis and healthcare advancement include a stock market prediction model and a breast cancer prediction model.

This project detects spam messages in SMS, including those written in regional languages typed in English. It uses an extended SMS dataset and applies the Monte Carlo method with various supervised learning algorithms to improve spam detection.

Implementation of algorithms such as normal equations, gradient descent, stochastic gradient descent, lasso regularization and ridge regularization from scratch and done linear as well as polynomial regression analysis. Implementation of several classification algorithms from scratch i.e. not used any standard libraries like sklearn or tensorflow.

This repository is a related to all about Machine Learning - an A-Z guide to the world of Data Science. This supplement contains the implementation of algorithms, statistical methods and techniques (in Python), Feature Selection technique in python etc. Follow Coursesteach for more content

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.

Heart Disease Predictor using Linear regression, Logistics and Support Vector Algorithm in Python.

The project's objective is to harness a HR Analytics dataset. With predictive proccess I tried to equip HR management with actionable insights, enabling them to proactively address attrition issues and implement targeted retention strategies.

In this project, we aim to analyze hotel reviews to determine the underlying sentiment expressed by customers. Our goal is to differentiate between positive and negative reviews using Natural Language Processing (NLP) techniques and machine learning algorithms.

Logistic Regression model to Detect Breast Cancer

This repository contains a detailed analysis of the Spambase Dataset using different classification algorithms, including Logistic Regression, Logistic Regression with Backward Feature Elimination (BFE), Support Vector Machine (SVM), SVM with Normalized Data, Decision Trees, Random Forest, K-Nearest Neighbors (K-NN), and K-NN with Normalized Data.

This repository is about a trained Machine Learning model which predicts Whether the Heart Disease is present or not by considering few factors. This ML model is slected by considering different accuracies of various trained ML models.

Fast Best-Subset Selection Library

Course Material for Artificial Intelligence and Machine Learning - Unit 2 @ Computer Science Dept, Sapienza

XGBoost Predictive Model for TikTok's Claim Classification: EDA, Hypothesis Testing, Logistic Regression, Tree-Based Models

In this, i carried out EDA(exploratory data analysis) on adult_data.csv to extract meaningful insights and followed it by applying LogRegression on it.

Predict and prevent customer churn in the telecom industry with this project. Leverage advanced analytics and ML on a diverse dataset to build a robust classification model. Gain a deep understanding of customer behavior and identify key factors influencing churn. Clone the repository, explore insights, and enhance customer retention startegies.