CNN-based method for movie genre classification from posters, with data pre-processing (one-hot encoding, missing values, imbalance, resizing). Performance compared to LeNet, AlexNet, VGG, ResNet-50, Logistic Regression, and Random Forest.

Heart disease detection using Machine Learning

Digit recognizer with our own dataset

Datamining concepts

Machine and Deep Learning Projects and basics

Predict survival on the Titanic

Forecasting stock prices of S&P500 using RandomForest and LSTM (Pytorch)

Some of my Machine Learning (SVM, Decision Trees, Random Forest, K Nearest Neighbors, Deep learning aka Neural Network, XGBoost) projects

An Oil Detection App built with RandomForestClassifier, Heroku, plotly, and Flask

A Machine learning binary classification project using sklearn based on Covid19 symptoms dataset.

Advanced Machine Learning

Predict the activity category of a human.

A Python-based classification framework comparing Random Forest, Decision Trees, SVM, KNN, Logistic Regression, and Naive Bayes to predict individual income brackets

Heart disease classification project with different models (LogisticRegression, KNeighboursClassifier, RandomForestClassifier) and detailed reports.

Note : This Repository consists files of the Project -Detecting fraud for transactions in a payment gateway - Ind Avenue which was held as a Mid Term Hackathon Competition a part of my PGP Data Science Program @ Insofe.

This is a ML prediction model which predict personalized items for a particular customer

We built a music genre classification model using Random Forest and SVM. Accuracy: Random Forest ~54%, SVM ~56%.