Learn how to analyze employee data and build a Machine Learning model to predict employee promotion using Python. This real-world project covers data preprocessing, exploratory analysis, encoding, model training, and deployment. You will also learn how to extract business insights using Grouped Bar Plots and Box Plots to help organizations make smarter, data-driven promotion decisions.

Learn how to perform Time Series Forecasting on the Air Passengers dataset using Python. This project covers importing data, understanding time series structures, visualizing trends and seasonality, transforming non-stationary data, and building forecasting models such as ARIMA and SARIMAX to generate accurate future predictions.

Learn how to apply Natural Language Processing (NLP) and Machine Learning to classify sentiments from real Twitter data. This hands-on case study walks through data cleaning, text preprocessing, TF-IDF vectorization, model building (Random Forest, Logistic Regression, Naive Bayes), evaluation, and real business applications such as brand monitoring and customer service analytics.