Data Scientist | Python | Machine Learning | NLP | Scikit-learn | Streamlit | Open to Opportunities

• Data Scientist with hands-on experience in Python, Machine Learning, and NLP. Built end-to-end projects including spam detection, sentiment analysis, and laptop price prediction systems. Skilled in Pandas, Scikit-learn, Streamlit, and data visualization. Experienced in building and deploying ML applications with performance up to 97% accuracy.

Career Goal: Seeking internship and full-time opportunities to build strong expertise in data science, machine learning, and AI while contributing to impactful projects, with the goal of growing into a successful Data Scientist.

Upcoming Skills:

• Advanced SQL & Database Management
• Deep Learning & Neural Networks
• Generative AI Applications & LLMs
• Agentic AI Systems & AI Automation

• Developed a Movie Success Prediction system using the TMDB dataset to analyze movie trends and estimate movie performance.
• Performed data cleaning, EDA, feature engineering, and applied Linear Regression, Random Forest, TF-IDF, and KMeans for revenue prediction, movie analysis, and clustering.
• Deployed a Streamlit web app for real-time blockbuster prediction.
• 🔗 movie-blockbuster-prediction

• Built a laptop price prediction pipeline to estimate market prices based on key hardware features, comparing multiple regression models where XGBoost achieved the best performance with an R² score of 0.87.
• Applied feature engineering and EDA using Pandas, NumPy, Matplotlib, and Seaborn.
• 🔗 laptop-price-predictor

• Developed an NLP-based Email/SMS Spam Detection system to identify and filter unwanted messages, achieving 97% accuracy and 94% precision using Multinomial Naive Bayes.
• Compared 5 ML models and evaluated performance using key classification metrics.
• Deployed a real-time spam classification web app with Streamlit Cloud.
• 🔗 email/sms-spam-classification

• Predicted positive and negative sentiment from customer reviews and feedback using TF-IDF vectorization on 50,000 IMDb reviews.
• Logistic Regression achieved 88% test accuracy, outperforming other models through evaluation using accuracy and confusion matrix metrics.
• Deployed an interactive Streamlit app for real-time sentiment prediction.
• 🔗 movie-review-sentiment-analysis

• Bachelors in Computer Science – Shah Abdul Latif University (2020–2023)

📫 Contact:

• 📞 +92-308-3484370
• ✉️ sankesh.lal12@gmail.com
• 🔗 LinkedIn
• 🔗 GitHub