Project 1: Diabetes Prediction Description: Developed a machine learning model to predict diabetes risk using patient data. Implemented various preprocessing techniques and used different algorithms to achieve optimal performance. Deployed the model to make real-time predictions.
Project 2: Loan Status Prediction Description: Built a predictive model to determine loan approval status based on applicant information. Utilized data cleaning, feature engineering, and model selection techniques to enhance accuracy. The model was deployed for practical use in loan approval processes.
Project 3: Employee Attrition Prediction Description: Designed a machine learning model to forecast employee turnover. Analyzed employee data to identify key factors influencing attrition. The model helps organizations implement retention strategies by predicting potential employee exits.
Project 4: Gold Price Prediction Description: Constructed a time series forecasting model to predict gold prices using historical data. Employed various statistical and machine learning methods to improve prediction accuracy. The model provides insights for investors and traders.
Project 5: Hyperparameter Tuning Description: Explored and applied hyperparameter tuning techniques to optimize machine learning models. Used grid search, random search, and Bayesian optimization to enhance model performance. Documented the impact of different hyperparameters on model outcomes.
Project 6: Cat-Dog Classifier CNN Description: Implemented a Convolutional Neural Network (CNN) to classify images of cats and dogs. Preprocessed image data and fine-tuned the CNN architecture to achieve high classification accuracy. Deployed the model to classify new images in real-time.
Project 7: CIFAR-10 CNN Model Description: Developed a CNN model to classify images from the CIFAR-10 dataset, which includes ten different classes. Applied data augmentation and regularization techniques to improve model generalization. Achieved significant accuracy in image classification tasks.
Project 8: Sentiment Analysis Description: Created an NLP pipeline to perform sentiment analysis on text data. Utilized techniques such as tokenization, stemming, and lemmatization. The model can classify text as positive, negative, or neutral, aiding in sentiment analysis tasks for various applications.
Project 9: ANN (Heart Disease Prediction) Description: Developed an Artificial Neural Network (ANN) to predict heart disease risk based on patient health metrics. Focused on data preprocessing, feature selection, and model training to enhance prediction accuracy. The model is designed for early detection of heart disease.
Project 10: Parts of Speech Tagging Description: Implemented a Natural Language Processing (NLP) model for parts of speech tagging. Used various NLP techniques and algorithms to accurately tag parts of speech in sentences. The model is useful for linguistic analysis and language processing tasks.