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.

Spam SMS Detection Project implemented using NLP & Transformers. DistilBERT - a hugging face Transformer model for text classification is used to fine-tune to best suit data to achieve the best results. Multinomial Naive Bayes achieved an F1 score of 0.94, the model was deployed on the Flask server. Application deployed in Google Cloud Platform

Clustering validation with ROC Curves

Classification problem using multiple ML Algorithms

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

Build Customer Segments and predict customer responses to marketing campaign for Arvato Financial Services

Data analysis, visualization and prediction to predict whether a patient has benign or malignant breast cancer based on properties of the cancer

AUCC (Python Implementation)

Build a logistics regression learning model on the given dataset to determine whether the customer will churn or not.

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

Predict the Burned Area of Forest Fire with Neural Networks and Predicting Turbine Energy Yield (TEY) using Ambient Variables as Features.

Data Science - Neural Networks Work

R Shiny App to determine the factors that are most influential in patients’ survival of CHD. I created a Logistic Regression model in R using RStudio to predict the survival of CHD patients. Retrieved the data from the PHIS database using SQL & built tableau dashboards. The model predicted the survival of CHD with an AUC of over .90 and indicate…

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.

The aim is to develop an ML- based predictive classification model (logistic regression & decision trees) to predict which hotel booking is likely to be canceled. This is done by analysing different attributes of customer's booking details. Being able to predict accurately in advance if a booking is likely to be canceled will help formulate prof…

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.

Credit Score Prediction is a machine learning project that classifies credit scores ('Good', 'Standard', 'Poor') using a streamlined pipeline. It involves data extraction, cleaning, and preprocessing, with key techniques like Mutual Information for feature selection, PCA for dimensionality reduction, and XGBoost for efficient and accurate model tr

Predicting the presence of heart disease based on several health-related factors and Performing - i.) Data Cleaning ii.) Data Pre-Processing iii.) EDA iv.) Compare 5 different classification algorithms (Logistic Regression, Decision Tree, Random Forest, KNN and SVC)

In this project, we have worked with hundreds of anonymized features to predict if a customer is satisfied or dissatisfied with their banking experience.