🎯 Aspiring Data Scientist & Data Analyst | Python | Machine Learning | Data Visualization | Turning data into insights 📊

I am a passionate and motivated fresher with strong interest in Data Science, Data Analysis and Machine Learning.
I enjoy working with data to uncover insights, build predictive models, and create interactive applications.

I have hands-on experience in:

• Data Cleaning & Preprocessing
• Exploratory Data Analysis (EDA)
• Machine Learning Model Building
• Model Evaluation & Comparison
• Create end-to-end projects from data preprocessing to deployment
• Streamlit App Development

• Python
• SQL

• Pandas
• Numpy
• Matplotlib
• Seaborn
• Scikit-learn

• Linear Regression, Multiple Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• Support Vector Machines (SVM)
• K-Nearest Neighbors (KNN)
• Naive Bayes
• Gradient Boosting (XGBoost – basics)
• Clustering: K-Means
• Dimensionality Reduction:

• EDA
• Feature Engineering
• Data Cleaning

• Jupyter Notebook
• Spyder
• Streamlit
• Git
• Github

• MySQL

• Descriptive Statistics (mean, median, variance, standard deviation)
• Probability & Distributions (normal, binomial, poisson)
• Inferential Statistics (hypothesis testing, confidence intervals)
• Statistical Tests (t-test, z-test, chi-square, ANOVA)
• Correlation & Regression Analysis
• A/B Testing & Experimental Design (basic)

• Developed a Machine Learning regression model to predict student marks based on daily study hours.
• Trained and evaluated the model using Scikit-Learn, achieving reliable predictions for unseen inputs.
• Built a Flask-based web application to deploy the trained model and enable real-time user interaction.
• Implemented input validation to ensure realistic study hour values (1–24 hours).
• Stored prediction results dynamically in a CSV file for further analysis.
• Demonstrated an end-to-end ML workflow from data preprocessing to model deployment.

🔗 Project Link:
https://github.com/AnjaliPanduga/Student-Marks-Prediction-

• Built a machine learning classification model to predict customer churn for business retention analysis.
• Performed data preprocessing and feature engineering to handle missing values, encode categorical variables, and improve model performance.
• Applied and evaluated multiple algorithms including Logistic Regression, Random Forest, and XGBoost to identify the best-performing model.
• Used metrics such as accuracy, precision, recall, F1-score, and ROC-AUC for model evaluation and selection.
• Visualized data patterns and model results using Matplotlib and Seaborn for actionable insights.
• Deployed the model concept in a structured data science workflow, demonstrating skills in prediction and interpretability.

🔗 Project Link: https://github.com/AnjaliPanduga/Customer-Churn-Prediction-

• Developed a Student Registration System with both desktop (Tkinter) and web (Streamlit) interfaces to manage student data using Python and MySQL.
• Implemented CRUD operations (Create, Read, Update, Delete) for student records with real-time search and filtering functionality.
• Integrated MySQL database for structured data storage and executed SQL queries for efficient data manipulation.
• Designed intuitive GUI (Tkinter) for desktop users and an interactive web UI (Streamlit) for browser-based access.
• Added export and download features for data reporting, enabling CSV export for external analysis.
• Demonstrated effective use of SQL database design and Python application development in a real-world system.

🔗 Project Link: https://github.com/AnjaliPanduga/student-registration-dual-app

• NLP
• Deep Learning
• Arfificial Intelligent

• 📧 Email: pandugaanjali2003@gmail.com
• 💼 LinkedIn: https://www.linkedin.com/in/anjali-panduga-88935a266/
• 🐙 GitHub: https://github.com/AnjaliPanduga

⭐ I am actively looking for opportunities as a Data Scientist / Data Analyst (Fresher).