Abhishek Shahani

Results-driven Data Science graduate with expertise in predictive modeling, statistical analysis, and machine learning algorithms. Experienced in internship role specializing in dataset collection, exploratory data analysis (EDA), and application of machine learning and deep learning algorithms. Received valuable learning experiences in projects, illustrating proficiency in data collection, preprocessing, and advanced analytics techniques. Strong business acumen, excellent communication skills, and proactive problem-solving approach. Proficient in Python, R, and statistical analysis techniques.

• Bhavanotsav Events Analysis & Prediction using ML —

The Bhavanotsav Events Analysis & Prediction project utilized machine learning algorithms such as logistic regression, decision trees, and random forest to forecast the success of cultural events. This project aimed to enhance event planning and marketing strategies by analyzing historical event data, predicting participant engagement, and optimizing resource allocation. The insights gained from this analysis helped in understanding trends and improving future editions of the Bhavanotsav festival.

• Customer Churn Prediction using ML —

The project focused on predicting customer churn in the telecommunications sector using machine learning. It involved data preprocessing, exploratory analysis, and applying models like Random Forest, Decision Tree, and Logistic Regression. Random Forest performed best with 96.8% accuracy. The analysis identified key factors influencing churn and demonstrated the effectiveness of predictive modeling for enhancing customer retention strategies.

• Garment Worker Performance Prediction using ML —

This project aimed to predict garment worker productivity using various machine learning models. The process involved data collection, preprocessing, and the application of algorithms like linear regression, random forest, and neural networks. By evaluating model performance through metrics like mean squared error and R-squared, the project provided insights for optimizing worker efficiency and improving factory management practices.

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