Exploratory Data Analysis

The wholesale distributor is considering changing its delivery service from currently 5 days a week to 3 days a week. However, the distributor will only make this change in delivery service for customers that react positively. How can the wholesale distributor use the customer segments to determine which customers, if any, would reach positively…

A collection of machine learning algorithms implemented for learning and practice, covering regression and classification models using Python.

End-to-end machine learning project predicting insurance charges, with deep analysis of key cost drivers like smoking behavior, BMI, and age.

Using SVM to predict whether a customer can retire or not based on his/her features.

In this i have performed complete feature engineering that is from handling null values, Categorical features upto performing feature scaling on our test_data and train_data.

This is about Treue Technologies Data science Internship tasks.

Developed an end-to-end Credit Card Risk Analysis system using machine learning, covering data preprocessing, feature engineering, and risk classification. Built an enterprise-grade dark UI with probability-based confidence visualization and an auto-demo mode to showcase real-world fintech decision support.

ML using NumPy and Pandas

Feature Scaling for Machine Learning: Understanding the Difference Btw Normalization&Standardization

The Global Superstore dataset is a comprehensive collection of sales data spanning multiple years, regions, and product categories. This rich dataset encapsulates critical business metrics including sales revenue, profit, order quantity, and shipping cost, making it ideal for various data analysis and machine learning projects.

Analyzing and predicting life expectancy of a country based on multiple factors using multiple regression techniques

A machine learning project for detecting fraudulent credit card transactions using classification algorithms. Focused on handling imbalanced data and optimizing for real-world fraud detection

A machine learning project to predict bike rental demand using historical data. This project uses an end-to-end workflow including data preprocessing, feature engineering, and a tuned XGBoost Regressor model to achieve high predictive accuracy.

This project contains dataset of house sale prices for USA. It includes homes sold between May 2019 and May 2020. Goal to determine the market price of a house given a set of features.Analyze and predict housing prices using attributes or features such as square footage, number of bedrooms, number of floors, and so on.

Production-ready Heart Disease Prediction system powered by a tuned Random Forest model (98.5% Accuracy). Features a high-performance Flask backend, a modern Glassmorphism UI, and real-time confidence scoring. Fully containerized and deployed on Render.

This repository contain all the file related to Feature Scaling,Label Encoding and corelation,Outliers Removal etc.in short it contain all files related to data preprocessing.