The ML_Projects

->The Repo contains projects done during the course Machine Learning A-Z™ Hands-On Python & R In Data Science on Udemy.

The FIFA 19 project file takes 2 teams input and then predicts the team with better overall.
The DMT file uses the MNIST dataset for implementing PCA and then classify the data using 5 classifiers and also uses ICA components. The methods used are Fast ICA, truncated SVD, PCA and t-SNE.
The intrusion detection model uses the data set provided to classify the data using Logistic regression and Support vector machines. Various hyper parameters and kernels are used for the models.

All Datasets used can be found on the link below:

Name		Name	Last commit message	Last commit date
Latest commit History 66 Commits
ANN1.ipynb		ANN1.ipynb
CSE3501_IntrusionDetectionModel.ipynb		CSE3501_IntrusionDetectionModel.ipynb
DMT.ipynb		DMT.ipynb
DtreeClass.ipynb		DtreeClass.ipynb
DtreeRegression.ipynb		DtreeRegression.ipynb
FIFA19Model.ipynb		FIFA19Model.ipynb
Final reviw 3 Code.ipynb		Final reviw 3 Code.ipynb
GraphNodeClassification.ipynb		GraphNodeClassification.ipynb
HClustering.ipynb		HClustering.ipynb
Intrusion Detection using Machine Learning Techniques.ipynb		Intrusion Detection using Machine Learning Techniques.ipynb
KNN.ipynb		KNN.ipynb
KmeansClustering.ipynb		KmeansClustering.ipynb
LDA.ipynb		LDA.ipynb
LogisticRegression.ipynb		LogisticRegression.ipynb
NLP.ipynb		NLP.ipynb
NaiveBayes.ipynb		NaiveBayes.ipynb
PCA.ipynb		PCA.ipynb
Pregression.ipynb		Pregression.ipynb
README.md		README.md
RandomFRegresion.ipynb		RandomFRegresion.ipynb
RandomForestClass.ipynb		RandomForestClass.ipynb
Regression.ipynb		Regression.ipynb
Restaurant_Reviews.tsv		Restaurant_Reviews.tsv
SVMrbf.ipynb		SVMrbf.ipynb
SVMtypes.ipynb		SVMtypes.ipynb
UpperConfidenceBound.ipynb		UpperConfidenceBound.ipynb
apriori.ipynb		apriori.ipynb

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