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breast-cancer-wisconsin-dataset

Visualize the data in 2-D scatter plot and write the inferences, Make a boxplot for each feature and highlight the outlier, if any, then remove the outlier, make again box plot to show the outlier effect and write the inferences.

dietanics-dataset

Perform 1. Data input 2. Basic head view of the data 3. Data description 4. Data summary 5. Univariate analysis

hbos-technique

Hbos (Histogram based Outlier Score) Technique

ibm-bigdata

linear-regression-decision-tree-gaussian-naive-bayes

Perform Linear Regression on the given dataset. Also perform K-Fold cross-validation

preprocessing-hidden-markov-model

Preprocessing-Hidden-Markov-Model

stroke-prediction-dataset

You need to download ‘Stroke Prediction Dataset’ data using the library Scikit learn; ref is given below. [5] 2. Divide the data randomly in training and testing with a 7:3 ratio 100 times, perform the following tasks with training data and test the performance on testing data. Testing data should remain unseen for all steps.

wine-data-clusterring

1. You need to download “Wine” data from the kaggle Perform at least 5 Clustering methods with varying cluster sizes. Find correct cluster numbers for each method and show with line plot, how you finalized this cluster number.