The four mini data analysis projects can be helpful for any new data analysis enthusiast learn some basic concepts. The peojects are prepared by Pranik Koirala.

Analysis 1: The dataset contains data concerning pavement durability. We have measurement of the change in rut (y) of 31 experimental asphalt pavements that were prepared under different conditions specfied by the values of five explanatory variables.

Analysis2: The Anscombe's quartet example problem. Anscombe's quartet was constructed in 1973 by statistician Francis Anscombe to illustrate the importance of plotting data before you analyze it and build your model.

Analysis3: Stepwise linear regression to investigate important variable contributing to target variable prediction. The dataset comprised of records from 109 different models of vehicles.

Analysis4: Exploratory analysis and logistic regression model training utilizing concepts like normalization, cross validation. The dataset is comprised of records from 768 different people to determine whether a given patient shows sign of diabetes. The dataset consists of information of eight attributes and a label to indiate a patient or a healthy individual

Analysis5: KNN classification using the five nearest neighbors with california housing dataset

Analysis6: using naive bayes classifier and support vector machine (SVM) to understand contributing factors for diabetes in Pima population.The Pima are Native Americans based in Arizona. As a result of changes in diet and physical activity, they have developed a very high incidence of Type 2 diabetes. The anonymous medical data used in this notebook was obtained from 768 Pima women. 

Analysis7: Demonstration of Hierarchical clustering and K-Means clustering method using sample dataset.

Analysis8: K-Mean clustering with a higher dimensional data.