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Classifying remote sensing data with random forest
Jupyter notebook: GRASS GIS 8 and processing of multitemporal EO data
A Machine learning binary classification project using sklearn based on Covid19 symptoms dataset.
Location information about commuter activities is vital for planning for travel disruptions and infrastructural development. The Mobility Sensing Project aims to find innovative and novel ways to identify travel patterns from GPS data and other multi-sensory data collected in smartphones. This will be transformative to provide personalised travel information.
Digit recognizer with our own dataset
Explore and predict the used car price by building the machine learning model based on the existing data. Then examining the model between the actual price and the predicted price.
An Oil Detection App built with RandomForestClassifier, Heroku, plotly, and Flask
Code templates for data prep and different ML algorithms in Python.
Heart disease classification project with different models (LogisticRegression, KNeighboursClassifier, RandomForestClassifier) and detailed reports.
Forecasting stock prices of S&P500 using RandomForest and LSTM (Pytorch)
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.
HANDWRITTEN DIGIT RECOGNITION USING ML IN PYTHON WITH THE HELP OF RANDOM FOREST CLASSIFIER
Heart disease detection using Machine Learning
Machine and Deep Learning Projects and basics
Create a machine learning model to predict if the policyholder will file a claim in the next 6 months or not based on the set of car and policy features.
Examined Chicago Car Crash data and created multiple models including logistic regression, KNN, Decision Tree and Random Forest. This project was to help identify what are the leading indicators to what may cause a car accident in Chicago.
Some of my Machine Learning (SVM, Decision Trees, Random Forest, K Nearest Neighbors, Deep learning aka Neural Network, XGBoost) projects
Predict the activity category of a human.
Advanced Machine Learning
This is a ML prediction model which predict personalized items for a particular customer
Datamining concepts
We built a music genre classification model using Random Forest and SVM. Accuracy: Random Forest ~54%, SVM ~56%.
Competición Kaggle: analizar clientes fugados y activos y entrenar un sistema que sea capaz de predecir si un cliente se ha fugado.
Predict survival on the Titanic