Visualising and Analysing Data around us

Numbers decieve when they are written down. Numbers speak the truth when they are visualised accurately, and logically.

Passion to understand the world around me is the reason I entered into Programming. Logical thinking, and decision making is an art which is improved by the Data available to everyone of us. The tools and the ecosystem surrounding the data analysis, visualisation and prediction has helped me a lot.

Each notebook in the repository takes very basic approaches to the problem everyone faces with Data analysis, or atleast the ones I faced initially. Whether it is stock market ticker data, or information about the application usage on a learning platform everything can be visualised. Once visualised the graphs provide unique insights and knowledge that is very difficult to forget.

Libraries used in the notebooks are:

• Data Discovery:-

BeautifulSoup

Requests

Dataset

Selenium

• Data Wrangling:-

Pandas

Numpy

Textwrap

Glob

Os

• Data Visualising:-

Plotly

Seaborn

Matplotlib

Wordcloud

Dash

Jupyter_dash

• Data Prediction:-

Scikit Learn

XGBoost

CatBoost

Tensorflow

Statmodel for acf and pacf

Self-contained narrative of the data, the wrangling process followed by visualisation is the pattern followed in every notebook. The purpose and the findings are shown at the beginning. The contents following that, provides way points to specific locations in the notebook.

Many notebooks, websites and books have been read, referred and even the heuristics has been copied. My contribution is to use these heuristics and information in creative ways, and make the visualisation talk eloquently and truthfully.