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svm-model

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In this project, the performance of speech emotion recognition is compared between two methods (SVM vs Bi-LSTM RNN).Conventional classifiers that uses machine learning algorithms has been used for decades in recognizing emotions from speech. However, in recent years, deep learning methods have taken the center stage and have gained popularity fo…

  • Updated Jan 2, 2024
  • Jupyter Notebook

Stock Market Price Prediction: Used machine learning algorithms such as Linear Regression, Logistics Regression, Naive Bayes, K Nearest Neighbor, Support Vector Machine, Decision Tree, and Random Forest to identify which algorithm gives better results. Used Neural Networks such as Auto ARIMA, Prophet(Time-Series), and LSTM(Long Term-Short Memory…

  • Updated Jul 18, 2020
  • Jupyter Notebook

The project aims at building a machine learning model that will be able to classify the various hand gestures used for fingerspelling in sign language. In this user independent model, classification machine learning algorithms are trained using a set of image data and testing is done. Various machine learning algorithms are applied on the datase…

  • Updated Mar 18, 2018
  • Python

Credit card fraud is a significant problem, with billions of dollars lost each year. Machine learning can be used to detect credit card fraud by identifying patterns that are indicative of fraudulent transactions. Credit card fraud refers to the physical loss of a credit card or the loss of sensitive credit card information.

  • Updated Feb 7, 2024
  • Jupyter Notebook
Classification-thanks-to-the-SVM-model-with-7-years-of-ozone-data-with-Machine-Learning

I developed 2 machine learning software that predict and classify ozone day and non-ozone day. The working principle of the two is similar but there are differences. I got the dataset from ics.icu. Each software has a different mathematical model, Gaussian RBF and Linear Kernel, and classifications are visualized in different ways. I would be ha…

  • Updated Sep 6, 2021
  • Python

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