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I developed Machine Learning Software with multiple models that predict and classify AID362 biology lab data. Accuracy values are 99% and above, and F1, Recall and Precision scores are average (average of 3) 78.33%. The purpose of this study is to prove that we can establish an artificial intelligence (machine learning) system in health. With my regression model, you can predict whether it is Inactive or Inactive (Neural Network or Extra Trees). In classification (Neural Network or Extra Trees), you can easily classify the provided data whether it is Inactive or Active.
Analytics Vidhya Hackathon
Fast-API base StockSeer-API uses different machine learning alogs to forecast closing stock prices.
A machine learning project that explores and predicts the prices of houses in Washington, USA
Explore ML mini-projects with Jupyter notebooks. Discover predictive analysis for commercial sales, leveraging regression models such as linear regression, decision trees, random forests, lasso, ridge, and extra-trees regressor.
Regression Using NeuralNet and CNN and a detailed analysis over the results obtained from regression techniques in ML and DL.
This repository contains a machine learning project that predicts restaurant ratings. It leverages the Flask framework for model deployment, providing an interactive interface for real-time predictions.
Experiment testing the feasibility of individual bird recognition from audio recordings
Loan Prediction Problem Analysis
This is a end to end data science project where the flight fare has been predicted using Random Forest model.
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.
In this project we will predict the time taken by NYC taxis to complete their trips using regression.
Perform extensive Exploratory Data Analysis(EDA) on the Zomato Dataset. Building an appropriate Machine Learning Model that will help various Zomato Restaurants to predict their respective Ratings based on certain features deploy the Machine learning model via Flask
Predicting the bike count required at each hour for the stable supply of rental bikes.
Estimation of permeability reduction due to asphaltene deposition and precipitation with the help of machine learning algorithms
The main goal of this machine learning project is to build a machine learning model to predict the car price.
This repository contains implementations of regression models on the Starbucks stock market. The goal is to provide a comprehensive understanding of the performance of these models. Also, implement metrics without relying on external machine learning libraries. ☕️📈
This project utilizes housing features to develop a regression model for predicting housing prices.
2020 DACON CUP - 최종 2위
Temperature prediction for Machine Learning course.
Development of a site where flight prices could be predicted.
Development of a ratings predictor for Zomato food places.
Kaggle competition : prediction of the energy uses with machine learning
Video transition time estimation with different regression techniques
Explore machine learning for automotive testing optimization. Predictive analytics to reduce testing time and environmental impact.
Naive, XGBoost, AdaBoost and other Regressors
Predicted the energy consumed by various appliances using moisture content, temperature & other external conditions given in the data set. We used feature engineering techniques and the LSTM Network for training and got the best results. We were ranked 2nd out of 50+ participants after the final evaluation