ML-powered Loan-Marketer Customer Filtering Engine
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Updated
Sep 13, 2021 - Python
ML-powered Loan-Marketer Customer Filtering Engine
Text Classification Problem : Wrote a module to classify Amazon-Product Reviews as favourable/unfavourable. Achieved accuracy of 78% and an F1 score of .81 using Logistic Regression on a test-train split of 20%, where total records were around 50000.
The model is trained on the dataset from kaggle. used CNNs for training model. Has a accuracy of 97%.
TitanicClassification.py file contains project based on binary classification. The dataset comprises of data related to passengers and binary value of whether they survived or not.
The code of the random forest project I shared on my Medium account
The "Cherry Leaf Health Detector" is a comprehensive repository housing a sophisticated Streamlit web application designed to assess the health of cherry leaves by distinguishing between healthy leaves and those affected by powdery mildew.
Developed system for the "Climate Activism Stance and Hate Event Detection" Shared Task at CASE 2024
Benchmark of sci-kit learn models and a tensorflow model for Binary Image Classification
Project for my semester evaluation, course Artificial Intelligence.
Models Breast Cancer data to make predictions
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