📊 Customer Reviews Sentiment Analysis

🔗Link

https://www.twine.net/signin

📌 Overview

This project analyzes customer reviews using Natural Language Processing (NLP) and Machine Learning techniques to classify sentiments as Positive, Negative, or Neutral. The objective is to transform unstructured textual data into actionable insights that help businesses understand customer feedback and improve data-driven decision-making.

🎯 Business Objective

To automatically analyze customer reviews and identify sentiment trends, enabling businesses to monitor customer satisfaction, improve products and services, and enhance overall customer experience.

🧠 Approach

Data cleaning and text preprocessing Exploratory Data Analysis (EDA) Feature extraction using NLP techniques Model building and training Model evaluation and visualization

🔧 Techniques Used

Text Preprocessing Lowercasing Tokenization Stopword removal Lemmatization

Feature Extraction Bag of Words (BoW) TF-IDF Word Embeddings

Machine Learning Models Logistic Regression Naive Bayes Support Vector Machine (SVM) Decision Tree Random Forest AdaBoost Extreme Gradient Boosting (XGBoost)

Deep Learning Models Gated Recurrent Units (GRU) Long Short-Term Memory (LSTM) Bidirectional Encoder Representations from Transformers (BERT)

🛠️ Tech Stack

Programming Language Python

Libraries pandas numpy matplotlib seaborn scikit-learn nltk TensorFlow PyTorch

Development Environment Jupyter Notebook PyCharm

📈 Model Evaluation

Models are evaluated using: Accuracy Precision Recall F1-score

🚀 Results

The BERT model achieved the strongest performance across accuracy, precision, recall, and F1-score. Visual analysis highlights sentiment distribution and frequently occurring words in positive and negative customer reviews.

🔮 Future Improvements

Sentiment label simplification Improved evaluation for imbalanced datasets Model efficiency and optimization Scalable deployment architecture Domain-specific fine-tuning Continuous learning and model monitoring

👤 Author

Paul Stanley Data Scientist

📬 Contact

If you have feedback, questions, or suggestions, feel free to connect or open an issue in the repository.

📊 Telecom Customer Churn Prediction

🔗 Link
https://www.twine.net/signin

📌 Overview

This project focuses on predicting customer churn in the telecom industry using machine learning. The goal is to identify customers likely to leave and support data-driven retention strategies.

🎯 Business Objective

Customer churn directly impacts revenue. By predicting churn in advance, telecom companies can: Improve customer retention Reduce acquisition costs Target high-risk customers with proactive actions

🧠 Approach

Data cleaning and preprocessing Exploratory Data Analysis (EDA) Feature engineering Model training and evaluation Performance comparison of classification models

🛠️ Tech Stack

Programming: Python Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Models: Naive Bayes, K-Nearest Neighbors, SUpport Vector Machine, Decision Tree, Random Forest, AdaBoost, Gradient Boosting, Multilayer Perceptron

📈 Model Evaluation

Models are evaluated using: Accuracy Precision & Recall F1-score ROC-AUC

🚀 Results

The Multilayer Perceptron model successfully identifies high-risk churn customers and provides insights into key churn drivers such as contract type, tenure, and service usage.

🔮 Future Improvements

Deployment into CRM Integration with real-time data and monitoring (drift, performance decay)

👤 Author

Paul Stanley Data Scientist

📬 Contact

If you have feedback or suggestions, feel free to connect or open an issue.