Data Analytics Projects

Customer Churn Prediction for ABC Telecom Ltd (On going).

 

Project Summary:-

Customer churn is a critical business challenge where customers stop using a company’s services. This project aims to build a predictive machine learning model that identifies customers at risk of churning, enabling the business to take proactive retention measures.

The solution involves collecting customer data, preprocessing it, training various machine learning models (Logistic Regression, Random Forest, XGBoost), and deploying the best-performing model to predict churn. The model provides actionable insights for marketing and customer service teams to improve customer retention.

Healthcare Patient Risk Stratification Using Predictive Analytics (On going)

Project Summary:-

Patient risk stratification is a predictive analytics approach in healthcare that segments patients based on their risk of adverse outcomes (e.g., hospital readmissions, chronic illness complications, etc.). The goal is to prioritize care for high-risk patients, reduce hospital costs, and improve patient outcomes.

Key Objectives:

  • Identify high-risk patients early using predictive modeling.

  • Use historical and real-time data from Electronic Health Records (EHRs), lab results, claims data, and wearable devices.

  • Enable proactive interventions (telemedicine, personalized care, follow-ups).

  • Reduce emergency visits, hospitalizations, and care costs.

Key Technologies:

  • Data Sources: EHR, claims data, wearables, lab systems.

  • Data Engineering: ETL pipelines, data lakes/warehouses.

  • Machine Learning: Predictive models (XGBoost, Random Forest, Deep Learning).

  • Visualization: Dashboards for clinicians (e.g., Power BI, Tableau).

  • Cloud/DevOps: AWS/Azure/GCP, Docker, Kubernetes, CI/CD for ML ops.

Real-Time Social Media Sentiment Analysis Dashboard

 

Project Summary:-

The Real-Time Social Media Sentiment Analysis Dashboard is a data analytics solution that monitors and visualizes user sentiment on platforms like Twitter, Reddit, or Instagram. It captures live social media feeds, processes them using natural language processing (NLP) techniques to determine sentiment (positive, negative, neutral), and presents this data in an interactive dashboard. The system enables brands, political analysts, and marketers to make informed decisions by understanding public perception in real time.

Sales Forecasting Using Time Series Analysis

Project Summary:-

Sales forecasting using time series analysis involves using historical sales data to predict future sales trends. This approach helps businesses plan inventory, allocate resources, set sales targets, and optimize decision-making. By applying statistical and machine learning models to sequential data, organizations can anticipate changes in demand, seasonality, and market trends.

Key components include:

  • Data Collection: Gathering historical sales data, typically timestamped.

  • Data Preprocessing: Cleaning data, handling missing values, and transforming it for modeling.

  • Exploratory Data Analysis (EDA): Visualizing trends, seasonality, and anomalies.

  • Modeling: Applying time series models such as ARIMA, SARIMA, Prophet, LSTM, or XGBoost.

  • Evaluation: Measuring accuracy using metrics like MAE, RMSE, and MAPE.

  • Forecasting: Predicting future sales to aid in business strategy.

Common tools and libraries: Python, Pandas, Matplotlib, Statsmodels, Facebook Prophet