Ganga Bhavani A Email & Phone Number

Bengaluru, Karnataka, India

Spearheaded data acquisition and aggregation, meticulously sourcing data from diverse repositories, including databases and external datasets. Executed rigorous data cleansing and preprocessing procedures to ensure precision, consistency, and suitability for subsequent analysis and modeling tasks. Expertly managed missing data and outliers to uphold data integrity.• Demonstrated mastery in deploying a comprehensive spectrum of machine learning algorithms, encompassing Linear Regression,… Show more Spearheaded data acquisition and aggregation, meticulously sourcing data from diverse repositories, including databases and external datasets. Executed rigorous data cleansing and preprocessing procedures to ensure precision, consistency, and suitability for subsequent analysis and modeling tasks. Expertly managed missing data and outliers to uphold data integrity.• Demonstrated mastery in deploying a comprehensive spectrum of machine learning algorithms, encompassing Linear Regression, Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Naive Bayes, k-Means Clustering, and Hierarchical Clustering. These algorithms were adeptly applied to tackle complex real-world regression and classification challenges.• Possessed a robust foundation in Natural Language Processing (NLP), proficiently handling tasks such as Tokenization, Stop Word Removal, Lemmatization, Text Classification, Language Modeling, Text Summarization, Machine Translation, Augmentation, and YOLO (You Only Look Once) for object detection.• Proficiently crafted queries utilizing Aggregate Methods and harnessed the power of Group BY Clauses in both MYSQL and SQL Server for effective data extraction and analysis.• Engineered and trained bespoke machine learning models tailored to address specific business objectives. Thoroughly evaluated and compared a diverse array of machine learning algorithms to discern the most effective models. Skillfully fine-tuned hyperparameters to optimize model performance and guard against overfitting.• Assessed model performance with precision, utilizing pertinent evaluation metrics such as accuracy, precision, recall, F1-score, and more. Employed cross-validation and other validation techniques to fortify model robustness and ensure broad applicability. Show less