Experienced data scientist with over 3 years in the IT industry, specializing in Python and Machine Learning to drive actionable insights and solutions for complex business challenges. Passionate about leveraging data science expertise to deliver impactful results and seeking opportunities to further expand my skills while contributing to a dynamic team.
  	Skills
• Programming Languages: Python, SQL (MySQL)
• Machine Learning: Supervised Learning, Unsupervised Learning, Time Series Forecasting
• Natural Language Processing: Bag of Words, TF-IDF, Word2Vec, BERT, Fine-Tuning of BERT
• Deep Learning: Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN)
• Python Libraries and Frameworks: scikit-learn, Pandas, NumPy, Matplotlib, Seaborn,
 TensorFlow, NLTK, Transformers
• Databases: MySQL, MongoDB
  	Work History | Hexaware Technologies Limited

	Projects
Project Title: Customer Segmentation for Online Retail Marketing
Goal: Segment customers from an online retailer’s dataset to enhance marketing strategies and drive sales.
Responsibilities: Analysed and transformed transactional data into a customer-centric dataset, creating new features to support segmentation. Applied Principal Component Analysis (PCA) for dimensionality reduction, improving clustering efficiency while retaining key information. Implemented the K-means clustering algorithm to identify distinct customer segments based on purchasing behaviours, uncovering meaningful profiles and preferences. Collaborated with the business analyst team by handing over the segmented data for the creation of a Power BI dashboard, facilitating data-driven marketing insights.
Results: Achieved a silhouette score of 0.68, indicating well-defined clusters that provided valuable insights for targeted marketing efforts.


Project Title: Bank Loan Classification
Goal: Develop a predictive model to identify potential personal loan customers from Client depositor base, enabling targeted marketing with a high conversion rate while addressing the issue of class imbalance.
Responsibilities: Conducted EDA to understand key features impacting loan acquisition, such as income, credit card usage, and account activity. Used SMOTE to balance the dataset and mitigate the issue of imbalanced classes. Built and evaluated multiple models, including Logistic Regression, Decision Trees, etc and ensemble methods, applying cross-validation for optimal performance.
Results: Achieved an 86% accuracy resulting in improved identification of high-potential loan customers.
Project Title: Milk Supply Forecasting for Dairy Production Optimization
Goal: Forecast monthly milk supply from farmers and small dairies to optimize production planning and inventory management, addressing variability in milk inflow.
Responsibilities: Data Acquisition and Preparation: Gathered, cleaned, and prepared supply data from both farmers and small dairies, accounting for seasonal and monthly variations in milk production. EDA: Conducted in-depth analysis to uncover trends, seasonal patterns, and external factors affecting milk supply to understand supply fluctuations. Model Building: Developed and fine-tuned time series forecasting models, including ARIMA, SARIMA, Prophet, and LSTM, to predict milk supply accurately, supporting production and inventory planning. Model Evaluation: Evaluated models using MAPE and RMSE metrics, ensuring high accuracy in forecasting and refining models based on performance results.
Results: Achieved a specific 93% accuracy in predicting monthly milk supply, leading to a specific 36% reduction in overstocking or inventory holding costs


any umprovement or add if needed in my resume