All of my past data science assignments put into one singular notebook. The notebook begins with Intro to Data Visualization and ends with Machine Learning.

Solved CodeBasics Resume Project Challenge #9 with the help of Python Notebook

Jupyter Notebooks and Data Sets for Pandas Library

This project involves analyzing a real-world dataset from Kaggle to explore the Accidents in US. Using Pandas and NumPy for data preparation and cleaning, followed by Matplotlib and Seaborn for visualization, key insights were regarding monthly yearly, and also area wise temporal patterns. The detailed analysis is documented in a Jupyter Notebook.

Pandas Mastry Notebook is a repository dedicated to exploring the capabilities of the pandas library for data manipulation, analysis, and visualization in Python. Dive into a variety of data operations, analytical techniques, and visualization methods to uncover insights from your datasets.

Pandas Notebook

In this repository, I have saved my Python_Amazon_sales_analysis Notebook. To do this Amazon_sales_analysis, I have done end to end process. cleaned the dataset, Did EDA, ploted graph and reached to the conclusion.

This repository contains code for analyzing and predicting outcomes in the Indian Premier League (IPL) cricket matches from 2008 to 2022. It includes data analysis notebooks, a prediction model, and a Flask-based web application for interactive predictions. Explore historical match data, gain insights, and make predictions on upcoming matches .

Notebook de iniciando en la libreria de Pandas para Analisis de Datos

A simple customizable pdf notebook generator

A trained ML model for prediction of house prices in IOWA using Random Forest Regression technique

Employed Jupyter Notebook and Python Pandas library to perform thorough dataset analysis, investigating racial representation, education-influenced income patterns, and various key statistics.

Using Python libraries in a Jupyter Notebook, this project explores Diwali sales data, revealing valuable insights: Buyer Dynamics: Females drive sales, showcasing higher purchasing power than males. Age Impact: The 26-35 age group, primarily females, contributes significantly.Marital Influence: Married women exhibit strong purchasing potential.

In this project i have worked on 'Movies' dataset to find out the Corelation nd data Cleaning & Manipulation using python in Jupiter Notebook.

This repo contains resources including Jupyter Notebook files for exploatory data analysis on a dataset for Programming for Engineers College Course.

This project is for analyze IMDB data using PANDAS Library on JUPITER NOTEBOOK

This repository contains the Jupyter notebooks and datasets for the Microsoft's Explore space with Python and Visual Studio Code; inspired by Netflix's Over the Moon.

I'm sharing my Kaggle Pandas Course - Exercise complete solution notebook which I have solved while undertaking this course.

This repository is containing exercise notebooks for pandas, Numpy and Matplot libraries. The exercises are all working on real life data.