Ayush Kumar

An exploratory data science and machine learning project focused on predicting an individual's personality type (introvert or extrovert) based on behavioral data.

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This is a machine learning project designed to classify text messages as either "ham" (legitimate) or "spam". The project uses the SMS Spam Collection Dataset and demonstrates a complete machine learning pipeline, from data preprocessing and feature engineering to model training and evaluation.

This project automates the analysis of large-scale customer feedback using Natural Language Processing (NLP) and Machine Learning. The core of this tool is a model that instantly translates raw review text into a corresponding star rating, providing rapid and actionable insights into product satisfaction.

This data science project analyzes historical transaction and customer behavior data to build a predictive model for forecasting revenue and optimizing marketing strategies. The core of this tool is a Random Forest regression model, which combines multiple decision trees to provide robust predictions of a customer's total purchase value.

This project involves scraping quotes from the web, performing exploratory data analysis (EDA), and using SQL for deep data insights. The goal is to uncover patterns in author popularity, sentiment distribution, common themes, and keyword trends. Ideal for practicing web scraping, data cleaning, SQL querying, and data visualization.

An analysis and machine learning model to predict survival on the Titanic, using the classic Kaggle dataset. This project explores data science and classification techniques.

Jivafit is a deep learning project that uses a Multi-Layer Perceptron (MLP) to analyze various health-related data points and predict disease risk. The model is designed to provide actionable insights into potential health risks, offering a scalable and predictive tool for public health analysis.