Overview
This repository contains a Jupyter Notebook showcasing data visualisation techniques and linear regression analysis using a dataset of London borough profiles. It involves generating insightful plots and performing linear regression using gradient descent.
Descent graphs are saved as GIFs.
Contents
Data visualisation: Bar plots, pie charts, scatter plots, and seaborn plots.
Data partitioning: Splitting data into training, validation, and test sets.
Synthetic dataset generation.
Linear regression using gradient descent.
Evaluation of regression models using various metrics.
Installation
Ensure you have the following Python packages installed:
pandas, numpy, matplotlib, seaborn, sklearn
You can install these packages using pip or conda:
pip install pandas numpy matplotlib seaborn scikit-learn
Run the Jupyter Notebook cell by cell to see each step of the analysis. The notebook is divided into sections, each focusing on different aspects of data visualisation and linear regression.
Data Visualisation
Visualise various aspects of the London borough profiles dataset, including population density, political landscape, age distribution, etc.
Partitioning the Data
Prepare the data for regression analysis by splitting it into training, validation, and test sets.
Generate Synthetic Dataset
Create a synthetic dataset to demonstrate linear regression.
Linear Regression on Data (I): Gradient Descent
Implement gradient descent to perform linear regression and predict life expectancy.
Visualisation of Regression and Error Metrics
Plot the regression line and visualise error metrics over training epochs.
Contributing
Contributions, issues, and feature requests are welcome. Feel free to check issues page if you want to contribute.
License Distributed under the MIT License. See LICENSE for more information.
Contact Your Name - @oscarmoxon - oscar@oscarmoxon.com
Project Link: https://github.com/mrmoxon/Gradient_Descent_Implementations