Description: Assignment task: Write a python script that adds two numbers together and prints their sum to the command line.
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Description: In this assignment, we as a class will collectively record a dataset with elevator arrival times on the ground floor in CDS. Using this dataset, we determined the best location to wait in order to minimize the expected walking distance to the next arriving elevator.
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Description: In this assignment, you will develop an interactive web application that demonstrates the KMeans clustering algorithm using various initialization methods. This project allows you to explore the impact of different initialization strategies on the clustering outcome.
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Description: In this assignment you will need to implement the Singular Value Decomposition (SVD) algorithm from scratch. You will also need to use the SVD to perform dimensionality reduction on a dataset, explore the effect of the number of dimensions on the performance of a classifier, and visualize your results for comparison and analysis.
GitHub Link: GitHub Link
Demo Link: GitHub Link
Description: Objective In this assignment, you will develop an interactive web application that implements a basic search engine using Latent Semantic Analysis (LSA). The search engine will take a user’s query, perform LSA on a pool of documents, and return the top documents based on cosine similarity.
GitHub Link: GitHub Link
Demo Link: GitHub Link
Description: In this assignment, you will implement a K-Nearest Neighbors (KNN) model from scratch to predict customer churn for a bank. Your goal is to identify customers who are likely to leave the bank based on historical data and submit your predictions in a mini Kaggle competition. You are provided with a dataset and a starter code to help you get started. Your task is to preprocess the data, implement KNN from scratch, train and evaluate the model, and tune its hyperparameters. Once your model is optimized, you will submit your predictions for ranking on Kaggle.
GitHub Link: GitHub Link