Machine Learning Projects.

• Breast Cancer Classification:

  • The primary focus of this machine learning model is to be able to differentiate between malignant and benign tumours based on the tumour shape and its geometry.
  • The data was extracted from Scikit learn library. Here Seaborn library is been used for visualizations.
  • In here Support Vector Machine algorithm is used to train the model.
  • In the end, to improve the model GridSearchCV from Scikit learn library is been used to select the best parameters from the listed hyperparameters.

• Flights Fare Prediction Using Machine Learning:

  • The primary focus of this project is to use Machine Learning to Predict the Fare of Airlines tickets.
  • Here Data Preprocessing, Data Wrangling, Feature selection is being practiced.
  • Here Multiple Machine Learning algorithms have been used.
  • Linear Regression, Decision Tree Regressor, Random Forest Regressor and KNN Regressor have been used.
  • Also Hyperparameter Tuning is done here to get best parameters and have best score.

• Password Strength Classifier:

  • The primary focus of this project is to provide knowledge to the user about how Strong is the password which the user is likely to use for their security.
  • Here the concept of Natural Language Processing and Machine is being used.
  • Here TF - IDF (Term Frequency - Inverse Document Frequency), an NLP technique is being used to Preprocess the text data into vectors for ML models.
  • XGBoost (eXtreme Gradient Boosting) is being used for more accurate performance.

• Anticipating Menstrual Migraine Using Deep Learning:

  • Empowering women with a tool to predict menstrual migraines, aiding early intervention and personalized treatment (traditional) migraine medication may not be suitable).
  • Deep learning models trained on anonymized data to classify migraines as menstrual or non-menstrual.
  • Exploratory data analysis (EDA) identified 24 relevant features. Experimentation with various deep learning architectures is ongoing.
  • Improved quality of life for women suffering from menstrual migraines.

• Gaze Detection:

  • Anticipating User's eye focus on screens for UI design, marketing (A/B testing), and accessibility.
  • Uses Python & numpy, dlib, CV2, etc.
  • Refining accuracy & exploring integration with applications.
  • Further to be used for marketing ads along with A/B testing to et better engagements.