varmatilak22

Hate_Speech_Detection

Hate speech detection using a Decision Tree algorithm classifies text as hateful or non-hateful by evaluating features such as words and phrases. The algorithm builds a tree of decision rules, splitting nodes based on feature values, to predict the category of text, aiding in identifying and moderating harmful content.

Credit_Score_Prediction

This project predicts credit scores ('Good', 'Standard', 'Poor') using a streamlined ML pipeline. It includes data extraction, cleaning, and preprocessing. Key techniques are Mutual Information for feature selection, PCA for dimensionality reduction, and XGBoost for accurate and efficient model training, ensuring reliable and robust predictions.

learning_tensorflow

MVP

MVP approach involves creating a simple model with basic features and minimal preprocessing. This allows for rapid testing, gathering feedback, and iterative improvements based on performance and user insights. Applied on Linear Regression.

Spam_SMS_Classification

This project aims to develop a machine learning model to classify SMS messages as spam or not spam. The project encompasses the entire pipeline from data collection and preprocessing to model training, evaluation, and deployment using Streamlit for an interactive user interface.

access_net

Amazon_ML_Hackthon

data

Food_Vision_App

investment_chatbot

Kaggle

PPT_generation

varmatilak22.github.io