Creating a banking customer segmentation dataset using 3 initial datasets in the IBM SPSS environment

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."

The project involved using KMeans clustering to segment customers based on behavioral patterns and preferences providing customer segments for targeted marketing strategies.

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

Exploratory Data Analysis & Feature Engineering - IBM

Fruit Dataset Classification

Emotion and Facial Key-Point Detection Classify emotions and detect facial key-points using deep learning! This project combines CNNs and Residual Blocks to predict 15 facial key-points and categorize facial expressions into five emotions: Angry, Disgust, Sad, Happy, and Surprise.

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

Normalizing | Preprocessing | scaling of data

Using machine learning techniques to predict house prices. Also looking into what makes a house more expensive.

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, Heartbeat etc.

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.