Pinned Repositories
Association-Rules
Prepare rules for the all the data sets 1) Try different values of support and confidence. Observe the change in number of rules for different support,confidence values 2) Change the minimum length in apriori algorithm 3) Visulize the obtained rules using different plots
Basic-Statistics_1
Basic-Statistics_2
Clustering
Perform clustering (hierarchical,K means clustering and DBSCAN) for the airlines data to obtain optimum number of clusters. Draw the inferences from the clusters obtained. 2 .Perform Clustering(Hierarchical, Kmeans & DBSCAN) for the crime data and identify the number of clusters formed and draw inferences.
Decision-Tree_2
Use decision trees to prepare a model on fraud data treating those who have taxable_income <= 30000 as "Risky" and others are "Good"
Decision_Trees
Problem Statement: A cloth manufacturing company is interested to know about the segment or attributes causes high sale. Approach - A decision tree can be built with target variable Sale (we will first convert it in categorical variable) & all other variable will be independent in the analysis.
Download-YouTube-videos-in-Python
Forecasting-_1
Forecast the CocaCola prices data set. Prepare a document for each model explaining
Forecasting_2
Forecast the Airlines Passengers data set. Prepare a document for each model explaining how many dummy variables you have created and RMSE value for each model. Finally which model you will use for Forecasting.
House-Prices