Implementation of statistics algorithms for Machine Learning & Data Mining. The algorithms were implemented with the Scikit-Learn Library
-
Updated
Feb 15, 2022 - Python
Implementation of statistics algorithms for Machine Learning & Data Mining. The algorithms were implemented with the Scikit-Learn Library
I aim to automate playlist creation for Moosic, a startup known for manual curation, using Machine Learning, while addressing skepticism about the ability of audio features to capture playlist "mood."
Exploratory Data Analysis & Feature Engineering - IBM
Using machine learning techniques to predict house prices. Also looking into what makes a house more expensive.
Fruit Dataset Classification
Normalizing | Preprocessing | scaling of data
This project explores and analyzes financial data of a number of securities, applies Hierarchical and K-means clustering to group securities and create cluster profiles to develop personalized portfolios and investment strategies for clients
Regression exercises and projects done at alx training
A machine learning model using Support Vector Machine classification to predict chances of an individual having a heart attack based on features like age, sex, cholestrol, blood pressure, chest pain, heart beat etc.
Creating a banking customer segmentation dataset using 3 initial datasets in the IBM SPSS environment
Embark on a journey of data-driven insights with our diabetes research project. Leveraging Python's pandas, matplotlib, and scikit-learn, we preprocess, visualize, and analyze 330 health features. Employing logistic regression, decision trees, KNN, and SVM, we predict diabetes with precision.
data rescaling, normalization and standardization techniques
Add a description, image, and links to the data-scaling topic page so that developers can more easily learn about it.
To associate your repository with the data-scaling topic, visit your repo's landing page and select "manage topics."