Seaborn is a visualization library for Python that builds on matplotlib and pandas. It provides beautiful default styles and color palettes for different types of plots, such as histograms, distributions, regression, and matrix plots.

Different modeling techniques like multiple linear regression and random forest, etc. will be used for predicting the cement compressive strength. A comparative analysis will be performed to identify the best model for our prediction in terms of accuracy.

This is the complete seaborn playlist. In this almost all plots has been designed and code has been given.

First of all I have uploaded a dataset from Kaggle to the Colab. Then I have imported all the necessary libraries in google colaboratory. Then I started cleaning the data and manipulate the data. After that using matplotlib and seaborn I have created some visualised graphs.

Conducted in-depth time series analysis on stock market data for major tech companies like Amazon, Google, Apple, and Microsoft. Utilized multivariable analysis to explore the inter-relationship between stock closing prices and daily % return, visualized findings using Seaborn library, and performed value at risk calculations for each company.

Create a model for real estate valuations in 1970s Boston Massachusetts

Without clean data, ML model is useless. The repo is for the data preprocessing part for ML beginner.