We going to build a basic model for predicting customer churn using Telco Customer Churn dataset. We're using some classification algorithm to model customers who have left, using Python tools such as pandas for data manipulation and matplotlib for visualizations.

This project focusing on statistical analysis to understand and prepare data for potential machine learning applications. The dataset house_price.csv includes property prices in Bangalore. The analysis aims to perform exploratory data analysis (EDA), detect and handle outliers, check data distribution and normality, and analyze correlations.

This repository contains a range of examples and techniques for feature engineering, aimed at improving dataset quality and boosting model performance. It covers essential methods such as Exploratory Data Analysis (EDA) and Interquartile Range (IQR) analysis for detecting and handling outliers.

Seismic Activity Detection Algorithm Based On LSTM Autoencoder (NASA International Space Apps Challenge Cleveland 3rd place Project)

Insurance charges calculation

Built to learn how to deploy and how one hot encoding works

An outlier is a data point that is noticeably different from the rest. They represent errors in measurement, bad data collection, or simply show variables not considered when collecting the data.An outlier is a data point that is noticeably different from the rest. They represent errors in measurement, bad data collection, or simply show variabl…

🚨Microsoft: Classifying Cybersecurity Incidents with Machine Learning🔐 This project leverages the power of Machine Learning to classify cybersecurity incidents 🚨, improving the efficiency of Security Operation Centers (SOCs) at Microsoft. We train a model to predict incident grades, helping analysts prioritize threats with precision🎯.