Anshul Yadav

Computer Science Engineering graduate with strong expertise in data analysis, machine learning, and Python programming. Demonstrated ability to build predictive models and extract actionable business insights from complex datasets. Experience in fast-paced technical environments and international seller support. Aiming to apply analytical and technical skills in a Data Analyst or Machine Learning role.

• Data Scientist -AU IGNITE Future Skill (Completed: December 2024 – March 2025) Comprehensive training covering Python fundamentals, advanced data analysis, and practical machine learning implementation Curriculum emphasized proficiency in core libraries (NumPy, Pandas) and concluded with a major project on House Price Prediction model development.
• Machine learning - Simplilearn SkillUP (completed: 09th Oct 2024)
• Data Analytics using Excel Workshop - Newton School
• Entrepreneurship Development - Coursera

• National Service Scheme (NSS) Certificate: Recognized for active participation in social responsibility initiatives and community development projects, demonstrating strong leadership, teamwork, and commitment to civic engagement.

Customer Churn Prediction

• Developed a classification model (e.g., Random Forest) to predict customers who are likely to cancel their service (churn).
• Analyzed key customer attributes like contract type, tenure, and monthly charges to identify the most significant factors driving churn.
• The model enables the creation of proactive, targeted retention programs, demonstrating a focus on business impact.

Stock Price Prediction

• Created a machine learning model to forecast stock prices based on historical time-series data.
• Developed and compared various machine learning models, including Linear Regression, LSTM, and Random Forest, for forecasting, utilizing feature engineering methods to enhance prediction accuracy.

Sentiment Analysis on Customer Feedback

• Performed Natural Language Processing (NLP) on a large dataset of customer reviews/feedback.
• Classified text data into sentiment categories (Positive, Negative, Neutral) to quantify customer satisfaction levels.
• Generated data visualizations to present findings, helping identify key product/service pain points.

Zomato Data Analysis

• Conducted Exploratory Data Analysis (EDA) on the Zomato dataset to uncover valuable insights into restaurant ratings, cost, and location.
• Employed Python libraries like Pandas, NumPy, Matplotlib, and Seaborn for effective data manipulation and visualization.

Banking Management

• Developed a basic banking management system using Python for command-line interface operations.
• Incorporated features for creating new accounts, processing transactions, and securely managing balance inquiries.