A repo on data analysis through Jupyter Notebook!

Seaborn files using python3 in jupyter notebook

If you liked my analysis, pls upvote my notebook!

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.

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.

This project is a Jupyter Notebook that analyzes how a regression model can be tuned to predict the stock market prices of Tesla (TSLA). The objective was to create a prediction algorithm to forecast the closing price of Tesla stock on a specific date.

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?

Titanic challenge part 1 In this notebook, we will be covering all of the steps required to wrangle the Titanic data set into a format that is suitable for machine learning. We will do each of the following: impute missing values create new features (feature engineering) Part 2 of this challenge involves fitting and tuning a random forest to mak…

Titanic challenge part 2 In this kernel, we will be covering all of the steps required to train, tune and assess a random forest model. Part 1 of this series dealt with the pre-processing and manipulation of the data. This notebook will make use of the data sets that were created in the first part. We will do each of the following: train and tes…

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

Jupyter Notebook with a Crypto-currency Historical Data generator

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..