This project is a Sentiment Analysis System developed to analyze text data and determine whether the sentiment expressed is positive, negative, or neutral. The main objective of this project is to convert raw textual data such as customer reviews or feedback into meaningful insights that help understand user opinions and behaviour. The system is built using Python and data analysis libraries including Pandas, NumPy, Matplotlib, and Seaborn. First, the dataset is cleaned and preprocessed by removing unwanted symbols, converting text to lowercase, and handling missing values. After cleaning, Natural Language Processing techniques are applied to transform text into numerical format using methods like Count Vectorization or TF-IDF. A machine learning model such as Logistic Regression or Naive Bayes is trained on the processed data to classify sentiment. Once trained, the model can predict the sentiment of new user input. I also integrated a simple interface where users can enter text and instantly receive a sentiment result, making the project interactive and user-friendly. The project demonstrates an end-to-end workflow including data preprocessing, model training, prediction, and visualization. Charts are used to display sentiment distribution and basic performance metrics. The code is organized and reproducible so the analysis can be rerun with new data easily. Technologies Used: Python, Pandas, NumPy, Seaborn, Matplotlib, Scikit-learn Key Features: • Text cleaning and preprocessing • Sentiment classification (positive/negative/neutral) • Machine learning model training • Data visualization • Reproducible Python scripts This project highlights my skills in Python, data analysis, and machine learning. It shows my ability to build a complete data-driven solution from raw data to final insights, making it suitable for freelance data analysis and machine learning tasks.