Multiclass classification model of penguins species.

This project predicts credit scores ('Good', 'Standard', 'Poor') using a streamlined ML pipeline. It includes data extraction, cleaning, and preprocessing. Key techniques are Mutual Information for feature selection, PCA for dimensionality reduction, and XGBoost for accurate and efficient model training, ensuring reliable and robust predictions.

This code evaluates the performance of a logistic regression model on age prediction using various features to predict a binary target variable, calculating metrics to determine the performance. It evaluates the comparison, identifies favorable features, and visualizes the ROC-AUC curve to determine the best model performance.

At ferreyros, prioritizing sales opportunities for spare parts and services allows them to provide the best service to customers who need it most. This challenge is to use historical opportunity data to estimate the probability of closing new opportunities.

Recruiting and retaining drivers is seen by industry watchers as a tough battle for Ola. Churn among drivers is high and it’s very easy for drivers to stop working for the service on the fly or jump to Uber depending on the rates.

Given a set of attributes for an Individual, determine if a credit line should be extended to them. If so, what should the repayment terms be in business recommendations?

The goal is to eliminate manual work in identifying faulty wafers. Opening and handling suspected wafers disrupts the entire process. False negatives result in wasted time, manpower, and costs.

Identify which customer is willing to possess the insurance policy, so we campaign efficiently.

AUCC (Python Implementation)

(Python) ML models that predict diabetic status

Data Science - Neural Networks Work

Classification problem using multiple ML Algorithms

Develop and train image classification models using advanced deep learning techniques to identify diseases specific to apples.

Time Series Classification Part 2 Binary and Multiclass Classification. An interesting task in machine learning is classification of time series. In this problem, we will classify the activities of humans based on time series obtained by a Wireless Sensor Network.

This project involves predicting customer churn in a telecommunications company using machine learning techniques, exploring various features' impact, optimizing models, and identifying key factors influencing churn.

I aim in this project to analyze the sentiment of tweets provided from the Sentiment140 dataset by developing a machine learning sentiment analysis model involving the use of classifiers. The performance of these classifiers is then evaluated using accuracy and F1 scores.

Junky Union is creating a system to sort movie reviews. They aim to train a model to detect negative reviews using the IMBD dataset with polarity labeling. The model must classify reviews as positive or negative with an F1 score of at least 0.85.

Classifier that predicts bee species from images. We have used Support Vector Machine(SVM) model. It can predict different bee species with an accuracy of 68%.

Trained a classifier by using labeled data and oversampling and undersampling techniques to predict if a borrower will default on a loan. The model is intended to be used as a reference tool to help investors make informed decisions about lending to potential borrowers based on their ability to repay. The purpose is to lower risk & maximize profit.