For Recent Project, Kindly refer to my Github Repository Here

Topic Modelling LDA-Based Telemedicine - Github Repository

Project Date: May 2023

Implementing Topic Modeling using Telemedicine Questionnaire. The objective here is to create a model that is able to classify a topic to data we collect from scrapping. hoping that I am able to see a pattern of any health-issue related in that current year.

Technology/Tools: Python, Pandas, NumPy, NLP, Text Preprocessing, Gensim, Plotly

Recency Frequency Monetary Analysis - Video

Project Date: Apr 2022

In this case a machine learning model will be created, to predict whether a transaction that occurs is a fraudulent transaction or not.

Technology/Tools: Tableau, Python, Pandas, Numpy, Seaborn, Matplotlib, Scikit-Learn, Statsmodels, Streamlit

Telcom Company Customer Churn Prediction - GitHub Repository

Project Date: Apr 2022

Customer attrition or customer churn occurs when customers or subscribers stop doing business with a company or service. Customer churn is a critical metric because it is much more cost effective to retain existing customers than it is to acquire new customers as it saves cost of sales and marketing. Customer retention is more cost-effective as you’ve already earned the trust and loyalty of existing customers.

Technology/Tools: Python, Pandas, NumPy, Seaborn, Matplotlib, SciPy, Scikit-Learn, Feature-Engine, TensorFlow, Keras, Deep Learning

Fraudulent Transaction Prediction - GitHub Repository

Project Date: Apr 2022

In this case a machine learning model will be created, to predict whether a transaction that occurs is a fraudulent transaction or not.

Technology/Tools: Python, Pandas, Numpy, Seaborn, Matplotlib, Plotly, Machine Learning

Term Deposit Prediction - GitHub Repository

Project Date: Apr 2022

Given a dataset, which will be analyze its intricacies and employ data analysis techniques to derive meaningful insights. Then, construct a Machine Learning model to the dataset's specific requirements. In this case, the primary objective is to predict whether an individual is likely to accept an offer and subscribe to a term-deposit. This approach combines EDA with the development of a predictive model, ensuring a thorough understanding of the dataset and its potential impact on forecasting outcomes.

Technology/Tools: Tableau, Python, Pandas, Numpy, Seaborn, Matplotlib, Scikit-Learn, Statsmodels.