T Pallavi

To secure a challenging position in an organization and dedicate myself for the growth of the company and also to improve my skills.

• Father's Name: T.Ananda Reddy
• Mother's Name: T.Lavanya
• Date of Birth: 01/22/2003
• Nationality: Indian
• Marital Status: Single

• Python
• Artificial intelligence
• Machine learning
• Cyber Security

Identification of Hate Speech using Machine learning:

Over the last decade, social media has acquired a lot of traction, both positively and negatively way. With the fast growth of social networking, People can communicate with one other via platforms and websites. Directly with no cultural or economic barriers While There have been several advantages to using social media, yet there are none. Less negative societal effects One such issue that has arisen is Hate speech has been more prevalent in recent years. Hateful speeches essentially the use of rude and abusive words.Hate speech generally refers to any type of text that targets a single person or group of people based on their caste,community,religion,ethnicity,race etc.In this we have developed a machine learning model to detect hate speech in social media text data .We utilized a dataset containing labeled examples of hate speech and non-hate speech posts and classified them accordingly.

Technology stack:

• Programming Languages: Python
• Domain: Machine Learning
• Algorithms: Logistic Regression,Naive bayes,NLP
• Machine Learning Libraries: TensorFlow, PyTorch, Scikit-learn,Numpy,Matplotlib
• Data Preprocessing: Tokenization, Word Embeddings,Text Cleaning Techniques
• Model Evaluation: Cross-validation, Precision, Recall, F1-score
• Deployment: Flask
• IDE: Jupyter Notebook, PyCharm

 Stress detection in IT professionals using ML and DL:

In today's fast-paced technology landscape, stress management is becoming increasingly important, especially among IT professionals. The work environment in the IT industry is often characterized by long hours, tight deadlines, and high expectations, which can lead to elevated stress levels. Unchecked stress not only impacts the health and well-being of professionals but also affects productivity and job satisfaction. This study aims to predict the stress levels of IT professionals using machine learning techniques, thereby aiding in proactive stress management. We utilize a range of features indicative of work stress, including Heart Rate, Skin Conductivity, Hours Worked, Number of Emails Sent, and Meetings Attended. These features provide a comprehensive view of both the physiological and work-related factors that contribute to stress.

Technology stack:

• Programming Languages: Python
• Domain: Machine Learning
• Algorithms: Logistic Regression,Naive bayes,NLP
• Machine Learning Libraries: TensorFlow, PyTorch, Scikit-learn,Numpy,Matplotlib
• Data Preprocessing: Tokenization, Word Embeddings,Text Cleaning Techniques
• Model Evaluation: Cross-validation, Precision, Recall, F1-score
• Deployment: Flask
• IDE: Jupyter Notebook, PyCharm