Anoop George

Aspiring Data scientist with a robust foundation in Python, SQL, machine learning, data analysis, statistics, and data visualization. Adept at leveraging analytical skills and programming expertise to drive data-driven decision-making. The proven ability to tackle complex problems through innovative solutions is demonstrated through hands-on projects and continuous learning. I am excited to contribute to impactful projects and thrive within a dynamic, collaborative team environment.

House Price Prediction Project

• Developed a predictive model to estimate house prices using features such as area, bedrooms, bathrooms, stories, parking, and furnishing status.
• Conducted data preprocessing, including handling missing values, encoding categorical variables, and normalizing numerical features.
• Analyzed a dataset with house characteristics and prices; conducted exploratory data analysis (EDA).
• Implemented and compared Linear Regression model; evaluated model performance using cross-validation, RMSE, MAE, and R-squared metrics.
• Utilized Python, Pandas, NumPy, Scikit-learn, and Matplotlib for analysis and model building.

Fake News Prediction Project

• Developed a machine learning model to classify news articles as real or fake using natural language processing (NLP) techniques.
• Preprocessed text data by removing stop words, tokenizing, and vectorizing using TF-IDF.
• Implemented and compared classification models, including Logistic Regression, and identified the best model based on accuracy and F1 score.
• Evaluated model performance using cross-validation, precision, recall, and F1 score metrics.
• Utilized Python, Pandas, NumPy, Scikit-learn, and NLTK for data processing and model building.

Movie Recommendation System Project

• Developed a recommendation system to suggest movies to users based on their preferences and viewing history.
• Implemented collaborative filtering techniques, including user-based and item-based filtering, to predict user ratings for movies.
• Utilized matrix factorization methods such as Singular Value Decomposition (SVD) to improve recommendation accuracy.
• Evaluated model performance using metrics like RMSE and precision at K.
• Utilized Python, Pandas, NumPy, Scikit-learn, and Surprise library for data processing and model building.