Data analysis notebooks . Loan prediction analysis and notebooks for all classification algorithms

Implemented a flight prediction system using two algorithms, Linear Regression and Decision Trees, to forecast flight prices accurately. The project involved preprocessing and analyzing flight data to extract relevant features such as departure time, destination, and airline. Utilized the Linear Regression algorithm

A codebase for two data analysis projects, both developed using Python. The first project is a GUI application for managing student performance, using Tkinter and csv for input storage and analysis. The second project is a web scraping tool for analyzing movie data from IMDB, using bs4 and pandas for data processing and matplotlib for visualization

Tools for check poc vulnerabilities

clustering of the shipping data of a supply chain based company

The project provides a Regression on the Insurance Prediction Data which shows the features of individuals, tuned using Ridge & Lasso.

Get to know what personality you fall into by answering the given questions.

This repository is created to do the Social Economic and Political analysis of Bollywood songs .

This Project is about performing EDA on house prices dataset.

This project is a hands-on exploration of multiple regression modeling techniques applied to real-world challenges in the context of real estate. It aims to understand how different features influence house or property prices. By utilizing multiple regression models, the project provides insights, decision support, and enhances data science skills.

Portfolio of Deep Learning projects

This project showcases my work as a Data Analyst performing Exploratory Data Analysis (EDA) on datasets from three fictional companies.The primary objective of these projects was to prepare the data for subsequent modeling efforts by conducting comprehensive EDA.

This notebook explores and analyzes the Heart Disease UCI dataset using Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, and scikit-learn. It includes data visualization, feature engineering, model building using Random Forest Classifier, and evaluation of the model's performance in predicting the presence or absence of heart disease.