Creating a banking customer segmentation dataset using 3 initial datasets in the IBM SPSS environment
Exploración y preparación de datos de una compañía de seguros para predecir la renovación de pólizas. Incluye análisis exploratorio, codificación de variables categóricas, normalización y visualización de correlaciones. Elaborado como parte del curso Machine Learning para la Toma de Decisiones de la Universidad del Pacífico.
Demonstrates the use of ML for Anomaly Detection for Credit Card Transactions: Identifying Fraudulent Activity using Imbalanced Data
Implementation of statistics algorithms for Machine Learning & Data Mining. The algorithms were implemented with the Scikit-Learn Library
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
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.
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."
Análisis de datos de empleados para identificar factores relacionados con promociones internas. Incluye exploración de variables, visualización y generación de insights. Elaborado como parte del curso Data Analytics de WE Educación Ejecutiva.
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.
Exploratory Data Analysis & Feature Engineering - IBM
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.
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.
The project involved using KMeans clustering to segment customers based on behavioral patterns and preferences providing customer segments for targeted marketing strategies.
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.
Проект по масштабированию признаков датасета Iris с использованием Python, Pandas, Scikit-learn, Seaborn и Plotly. Включает визуализацию данных, применение различных методов масштабирования и оценку производительности модели логистической регрессии.
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