- **Developed a predictive model for estimating crop yield** based on environmental and agricultural factors using machine learning algorithms.
- **Implemented data preprocessing and feature engineering techniques**, including handling missing values, scaling, and feature selection, to improve model performance.
- **Built regression models** (Linear Regression, Random Forest, XGBoost, etc.) to accurately forecast crop yield, optimizing for RMSE and R² metrics.
- **Utilized Python and key libraries** (Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn) to analyze, visualize, and interpret agricultural data trends.
- **Build the app for this model using Flask** for an interactive web application, enabling stakeholders to make data-driven decisions for agricultural planning.

- 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.

- 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.