Machine Learning Projects

Create a book recommendation system with machine learning using TF-IDF, KMeans clustering, and cosine similarity for accurate, data-driven suggestions

Multi-class text classification using RNN and LSTM for analyzing customer complaints, and providing real-world business insights and solutions.

Build and assess time series forecasting models such as ARIMA, ARIMAX, and SARIMAX using real-world data from sectors like Healthcare and Banking for precise predictions.

Clean and analyze IoT sensor data by building and evaluating MA and AR models, using RMSE and visualizations to determine the best forecasting model.

Understand time series forecasting with Linear Regression, ARIMA, and anomaly detection. Learn feature engineering, trend analysis, and model evaluation effectively.

Predict Healthcare trends using Gaussian Process Regression. Understand preprocessing, modeling, and evaluation techniques for precise time-series forecasting results.

Preprocess data, engineer features, and apply models like Random Forest or XGBoost. Evaluate performance and use LIME/SHAP for interpretability.

Build a model to predict house prices using Linear Regression. Understand data cleaning, feature selection, and model evaluation for accurate price forecasts.

Predict customer churn with Decision Trees! Learn data cleaning, SMOTE, and model evaluation using Python. Compare Decision Tree and Logistic Regression models to find the best approach in this hands-on, beginner-friendly project.

Build and evaluate regression models (Linear, Lasso, Ridge) to predict laptop prices with effective data preprocessing and performance metrics.

Understand how CBOW, Skip-Gram, and FastText models capture word meanings, visualize embeddings, and evaluate model performance for various NLP tasks.

Learn retail sales prediction with machine learning. This project builds regression models to analyze sales trends and drive data-driven decisions for retail success.

Accurate loan eligibility prediction with machine learning, applying SMOTE, data processing, and Random Forest for fair and efficient credit decisions.

Learn how to implement polynomial regression to capture complex patterns in data. Discover applications in finance, healthcare, and forecasting with detailed insights and methods.

This tutorial on Vegetable Classification using Machine Learing, guides you through automating the sorting process, improving quality control, and enhancing efficiency in agriculture and food industries.

This project builds an XGBoost Regressor to predict healthcare costs, ensuring insurance profitability. We'll compare it with a linear regression baseline and learn to communicate results effectively to non-technical stakeholders.