Ritesh Kumar Mishra

About

B.Tech Computer Science student focused on Machine Learning and Data Science, with hands-on experience in Python, scikit-learn, NLP, and data preprocessing. Built end-to-end ML projects covering preprocessing, model training, evaluation, APIs, and deployment.

Education

• B.Tech in Computer Science and Engineering
  BBS College of Engineering & Technology · AKTU
  Computer Science and Engineering · 2024 – 2028
• Class XII
  New RSJ Public School
  PCM · 2024
• Class X
  New RSJ Public School
  2022

Skills

• C <3 months
• FastAPI <3 months
• Python <3 months
• JavaScript <3 months
• Classification <3 months
• Feature Engineering <3 months
• Matplotlib <3 months
• Model Evaluation <3 months
• NumPy <3 months
• Pandas <3 months
• scikit-learn <3 months
• Seaborn <3 months
• JSON <3 months
• API Integration Understanding <3 months
• Data Structures and Algorithms <3 months
• Pydantic <3 months
• Regression <3 months
• Oop <3 months
• Naive Bayes <3 months
• Pipelines <3 months
• Tf-Idf <3 months
• Cross Validation <3 months

Tools / apps / platforms

• Git <3 months
• GitHub <3 months
• Jupyter Notebook <3 months
• MongoDB <3 months
• VS Code <3 months

Projects

• Patient Management API
  FastAPI, Pydantic, Python, JSON

  REST endpoints for patient-data management with Pydantic validation, HTTP exception handling, and JSON-based persistence.

• Movie Recommendation System
  Python, Pandas, Scikit-learn, Streamlit, TMDB API

  Content-based recommendation system using movie metadata and similarity techniques, with an interactive Streamlit interface and TMDB API integration.

• California Housing Price Prediction
  Python, Pandas, Scikit-learn, Linear Regression, Random Forest

  Regression pipeline using feature engineering, stratified sampling, preprocessing, and feature scaling, evaluated with RMSE and cross-validation.

• SMS Spam Classifier
  Python, NLP, Scikit-learn, TF-IDF, Multinomial Naive Bayes

  NLP text-classification model using TF-IDF vectorization and Multinomial Naive Bayes, with the trained model and vectorizer persisted for reuse.

• Telco Customer Churn Prediction
  Python, Pandas, Scikit-learn, Logistic Regression, Random Forest

  End-to-end churn prediction pipeline with missing-value imputation, categorical encoding, feature scaling, and cross-validation.

• Student Mental Health Score Predictor
  Python, Pandas, NumPy, Scikit-learn, Random Forest, Streamlit, FastAPI, Joblib

  End-to-end regression application predicting a student's Mental Health Score from demographic, academic, lifestyle, stress, and social-media usage features. Includes preprocessing pipelines, model comparison, and deployment through Streamlit and FastAPI.

Courses & certifications

• 3-Day Generative AI Bootcamp · WikiClub Tech, United University
• Open Machine Learning Workshop · Cognizance, IIT Roorkee