Principle Component Analysis in Python

Notebooks on PCA(Principal Component Analysis)

Jupyter notebook for the Kaggle task "Company Bankruptcy Prediction" - all relevant info and code are contained in the notebook

Repository with notebook and solutions for Kaggle competition

Contains some notebooks for understanding ML and DL concepts

R notebooks with code and explanations for various statistical analyses.

Machine learning Python notebooks based on the ML Course assignments

A jupyter notebook implementing PCA(dimensionality reduction technique) on the iris dataset

A Jupyter notebook that run PCA and KMeans on population demographic data.

Simple jupyter notebook which performs NN and PCA algorithms on sklearn datasets

A Jupyter Notebook with a Clustering and PCA Analysis of a Spotify songs dataset.

This repository contains pre-requisite notebooks of Feature Engineering Course from Kaggle for my internship as a Machine Learning Application Developer at Technocolabs.

This repository houses 3 different Jupyter Notebooks that each analyze the similarity in data points to most effectively inform customer recommendations in the retail space.

This repository contains one of the pre-requisite notebooks for my internship as a Data Analyst at Technocolabs. It includes some of the micro-courses from Kaggle.

Notebook and data used during the CdeCMX Challenge entitled "Scientific solutions to emerging problems: Life during the pandemic", as an introduction to machine learning and modeling of COVID-19 cases in Mexico.

Module 10 - Using python programming and unsupervised learning, I am creating a notebook that clusters cryptocurrencies by their performance in different time periods. Then I will plot the results for a better visual

A machine learning model that predicts runner finish times for 100 mile trail races. Trained on 12,000 race results collected from UltraSignup.com. Written in Python using Anaconda, Jupyter Notebooks, Pandas, NumPy, Matplotlib, and Scikit-Learn.

End to End implementation for a Flask App in Google Kubernetes Engine. The Notebook has EDA, model selection and training for a unclean structured text data. DNN and Combination of PCA and RandomForest is used for classification.

Explore cryptocurrency market trends with Python using unsupervised learning techniques. Using Jupyter Notebooks to implement K-means clustering and Principal Component Analysis (PCA) to analyze and predict price trends of cryptocurrencies over 24-hour and 7-day periods.

Machine_Learning_Techniques_Implementation notebooks. - Implement all ML techniques in python using SKLearn on different datasets. - Simple recommendation system - spam email classifier - Classify Yelp Reviews into 1 star or 5 star categories based off the text content in the reviews.