A Perfect Repository For Data Anaysis with Jupyter Notebook.:chart_with_upwards_trend::chart_with_downwards_trend:

This repository contains the collection of Python and Javascript (Observable Notebook) projects made for the DTU Data Science course 02806: Social Data Analysis and Visualizations

jupyter notebooks that I used in drafting python codes in pandas, stocks analysis projects and many more.

The practice jupyter notebooks which were created during the course of a data science Bootcamp

A repo on data analysis through Jupyter Notebook!

Seaborn files using python3 in jupyter notebook

Jupyter Notebook with a Crypto-currency Historical Data generator

If you liked my analysis, pls upvote my notebook!

These are notebooks I made while learning pandas, numpy, matplotlib and seaborn with full explanation and documentation. They are great for starting and using as cheat sheet.

Colab notebook to test and play around with what seaborn has to offer.

Pump-up the glam in Jupyter Notebooks with innovative tools like Plotly, MatPlotLib and Seaborn.

Spotify data analysis for songs between 2010 and 2019 using Jupyter Notebooks including pandas and Seaborn plots.

In this notebook, I demonstrate visualizations using python's seaborn library to get insights and findings of a company's churning rate.

notebooks consist of various machine learning classification algorithm to predict climate based on the various factors. however this project does not implement time series algorithm.

I chose a dataset from kaggle and performed an EDA, over 30 insights (visualization) was produced. The key insights is presented in a Jupyter Notebook slide..

This is an Exploratory Data Analysis (EDA) utilizing Python, documented within Jupyter Notebook. It is part of the Google Data Analytics Course's capstone project.

This game is developed in Python in jupyter notebook environment. This game is similar to the game show: Who wants to be a millionaire?. This game is build using loops, conditional statements and dynamic strings.

This analysis reviews sales for the top 100 video games from the years 2000-2015 to gather insights. Within the notebook I use Python’s Pandas, Matplotlib, and Seaborn libraries to interact with the data and create graphs.

EDA on Spotify Top 50 Dataset. More than focusing on EDA in this I have focused on using visualizations better and adding more parameters to make them more refined and readable. More of a notebook to understand commonly used plots better and use available parameters to make the most of these plots.

This is a project that I did on Jupyter Notebook where I had to analyse missing data, correct misspellings, remove redundant to use reports from 2019. Moreover, I've analysed the following: a) What type of crimes are most prevalent? b) On which day is crime most reported? c) In which district is the reporting of the number of crimes the highest?