This Notebook explores the impact of different missing data handling techniques on a medical dataset by analyzing missingness patterns, variable distributions, and inter-feature relationships to inform appropriate imputation strategies.

🔸 Predicting House Prices with Linear Regression 🔸 This project predicts house prices using Linear Regression based on key features like square footage, bedrooms, bathrooms, lot size, and neighborhood quality. Built with Python, Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn for data analysis and visualization.

This project implements a machine learning model to predict breast cancer diagnosis. Utilizing techniques such as data preprocessing, feature selection, and various algorithms, the model aims to assist in early detection and improve healthcare outcomes. Explore the repository to understand the methodology and technologies used in this project.

This repository contains experiments on data wrangling techniques, focusing on methods for handling missing values, filtering, aggregation, and more.

In this repository, you'll find all the code solutions I've completed during my TECHNOHACKS EDUTECH internship as a data analyst intern.

📊 Build a predictive model to estimate house prices using Linear Regression, transforming raw data into actionable business insights.