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.

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.

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

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