/Data-Science

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Data-Science

Hello, welcome!

Anomaly Detection:

  1. Network Intrusion Detector using AutoEncoder

Classification models:

  1. Bank Note Authentication - Classification to predict if bank notes are genuine or forged specimens.
  2. Credit Card Fraud Detection - Analysis and machine learning model to predict Fraud or Non Fraud transactions - The datasets contains transactions made by credit cards in September 2013 by european cardholders.
  3. Wine quality prediction using pyspark - Analysis and machine learning model to predict wine quality.

Cluster models:

  1. Credit Card - Clustering
  2. KMeans Cluster: leaves images

Data analysis (EDA):

  1. Netflix Deployed application - It can take some time to load & render.
  2. House prices EDA - Simple exploratory data analysis.
  3. Netflix EDA - Exploratory data analysis: Netflix movies & TV shows.

Network-graphs

  1. Exploring Graphs utilizing NetworkX + PyTrends

Neural networks

  1. CNN (Keras) for digit recognition.
  2. MLP for digit recognition
  3. CNN - TransferLearning - Mask & Without Mask
  4. CNN - Keras - Paper Clip Couting - Regression
  5. Multiclass Classification - Multi-output Model - Model detect apple diseases using TransferLearning (Xception).
  6. Neural Machine Translation (NMT) - Model to translate human readable dates ("25th of June, 2009") into machine readable dates ("2009-06-25").
  7. 7 NLP - Sentiment Analysis - Predicting how travelers in February 2015 expressed their feelings on Twitter.

Regression models

  1. House prices EDA - Regression model to predict the final price of each home.

Image2Text

Audio2Text

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