Welcome to my repository featuring mini projects that I worked on during my time at the Institute of Data. This repository includes projects focusing on various aspects of data science and machine learning. Below is a brief overview of each project.
The findings for each project were presented to the class and trainers, and slides for each can be found in the presentation folder.
This project is divided into three parts, each focusing on different techniques and datasets:
Description Explore and analyse crime data from San Francisco by applying grid search techniques to tune hyperparameters and using multinomial models for classification.
- Data set: 'sf_crime_train.csv'
- Notebook: 'Part1_Gridsearch_SF-Crime.ipynb'
Description Cluster plutonium batches based on their isotopic composition using the K-Means algorithm. The dataset is sourced from a public repository to demonstrate clustering techniques.
- Data set: 'pluton.csv'
- Notebook: 'Part2_kmeans_Plutonium.ipynb'
Description DBSCAN clustering on synthetic data generated to form clusters.
- Data generation code:
from sklearn.datasets import make_circles
circles_X, circles_y = make_circles(n_samples=1000, random_state=123, noise=0.1, factor=0.2)- Notebook: 'Part3_DBSCAN_Circles.ipynb'