Bank Customer Churn Analysis

Analyzed banking customer data to identify patterns associated with customer churn. Performed data cleaning, Power Transformation for Age, outlier treatment, categorical encoding, feature engineering and scaling. Compared multiple machine learning algorithms including Logistic Regression, Decision Tree, Random Forest, KNN, SVM, Gradient Boosting and XGBoost. Built an Artificial Neural Network using TensorFlow/Keras and explored hyperparameter tuning using Keras Tuner. Gradient Boosting achieved 86.85% accuracy, while the tuned ANN achieved approximately 87.4% validation accuracy during tuning.

NLP Emotion Detection using Machine Learning

Developed an end-to-end NLP classification project to detect emotions from text. Performed text cleaning and preprocessing using Regex and NLTK, and created text representations using Bag of Words, TF-IDF and Word2Vec. Compared machine learning models including Logistic Regression, Decision Tree and Random Forest. Logistic Regression with Bag of Words achieved 85.60% accuracy. Also developed a Streamlit application for model inference.

Electric Vehicle Data Analysis Dashboard

An interactive Power BI dashboard project focused on analyzing electric vehicle data and presenting category-wise trends, comparisons and key performance indicators. The source dataset was prepared and transformed using Power Query, followed by the creation of DAX measures and KPI calculations. The dashboard provides interactive visualizations and filters to explore EV data and communicate analytical findings clearly.

Airline Price Prediction & Power BI Dashboard

An airline pricing analysis and prediction project focused on understanding patterns influencing ticket prices and presenting the findings through an interactive Power BI dashboard. The dataset was cleaned and explored to understand relationships between available variables and airline prices. A predictive model was developed in Python as part of the analytical workflow, while Power BI was used to visualize pricing trends, comparisons and key analytical findings.

Telecom Customer Churn Prediction & Power BI Dashboard

Worked on the project end-to-end, including data cleaning, validation, transformation and exploratory data analysis. Built and evaluated a customer churn prediction workflow using Python and Scikit-learn. Developed an interactive Power BI dashboard using Power Query and DAX to analyze churn trends, customer segments and KPIs. Also developed a Streamlit application for churn prediction and prepared reusable model, encoder, scaler and feature artifacts for deployment.

Bank_Marketing_Deposit_Subscription_Prediction

Developed a machine learning classification project to analyze bank customer and campaign data and identify patterns associated with customers subscribing to a term deposit. The project involved data cleaning, exploratory data analysis, preprocessing and feature preparation to create an analysis-ready dataset. Multiple classification approaches were evaluated, with XGBoost selected for prediction. Model performance was assessed using accuracy and ROC-AUC, achieving 86.48% accuracy and 92.81% ROC-AUC. The project demonstrates the use of customer and campaign data for predictive analysis and data-driven customer targeting.