Developed a robust predictive model to identify potential churn customers in the telecommunications industry, leveraging a comprehensive dataset containing customer demographics, service usage, contract terms, and billing preferences.

Addressed the binary classification challenge inherent in the dataset, employing advanced machine learning techniques to accurately classify customers at risk of churn.

Conducted in-depth exploratory data analysis (EDA) to gain insights into churn patterns, customer behavior, and key predictors influencing churn decisions.

Applied feature engineering techniques to extract relevant information and enhance the predictive power of the model, including handling categorical variables, scaling numerical features, and addressing class imbalance.

Utilized various modeling algorithms such as logistic regression, decision trees, random forests, and gradient boosting to build an ensemble model capable of effectively predicting customer churn.

Evaluated model performance using appropriate metrics such as accuracy, precision, recall, and F1-score, ensuring robustness and reliability in predicting churn outcomes.

Generated comprehensive reports and visualizations summarizing churn analysis results, providing actionable insights for strategic decision-making and customer retention strategies.

Implemented a cost-sensitive learning approach to minimize financial loss associated with customer churn, optimizing retention strategies based on the predicted value of retaining high-risk customers.

Built a sophisticated speech emotion recognition model capable of accurately classifying emotions conveyed in spoken language, including happiness, sadness, anger, fear, and more, enhancing natural language understanding.

Utilized signal processing techniques, such as Fourier Transform and Mel-frequency cepstral coefficients (MFCCs), to extract relevant acoustic features from speech signals, forming the basis for emotion classification and ensuring robust feature representation.

Implemented machine learning algorithms, particularly Convolutional Neural Networks (CNNs), to process acoustic features and build a robust model for emotion recognition, leveraging deep learning for complex pattern recognition.

Evaluated model performance using appropriate metrics such as accuracy, precision, recall, and F1-score, showcasing significant accuracy in emotion classification and validating the model's effectiveness.

Delivered comprehensive documentation detailing the extraction of acoustic features, model architecture, and evaluation methods, along with a complete codebase for reproducibility, facilitating transparency and replicability in research.

Developed a real-time emotion recognition module, allowing the model to analyze emotions from live audio input and demonstrating its effectiveness in real-time scenarios, enabling immediate feedback and applications in interactive systems.

Addressed the challenge of recognizing emotions from speech, offering a valuable tool for applications such as sentiment analysis, virtual assistants, and emotion-aware systems, enhancing user experience and enabling empathetic interactions.