The Efficient and Fair Line Construction project aims to construct a gas pipeline in a straight line that serves a set of houses with coordinates given by latitude and longitude. The project focuses on optimizing the pipeline placement to achieve efficiency and fairness while considering the distances from houses to the pipeline.
In this section, provide detailed instructions on how to set up the project on a local machine. This includes any necessary dependencies, software requirements, and installation steps. Make sure to include clear and concise instructions so that others can easily replicate your setup.
- Editor Used: Google collab. jupyter note book.
- to clone repo: https://github.com/princegoyal65/epoch
In this section, I include all the necessary dependencies needed to reproduce the project, so that the reader can install them before replicating the project. I categorize the long list of packages used as -
- General Purpose: General purpose packages like
urllib, os, request, and many more. - Data Manipulation: Packages used for handling and importing dataset such as
pandas, numpyand others. - Data Visualization: Include packages which were used to plot graphs in the analysis or for understanding the ML modelling such as
seaborn, matplotliband others. - Machine Learning: This includes packages that were used to generate the ML model such as
scikit, pandas, etc.
we have used california housing dataset https://inria.github.io/scikit-learn-mooc/python_scripts/datasets_california_housing.html
The project successfully addressed the challenges of efficient and fair line construction by designing algorithms that optimize pipeline placement while considering location constraints The implemented solutions demonstrated effective pipeline placement strategies and achieved significant improvements in efficiency and fairness.