There is an unsupervised machine learning technique called clustering.There are different types of clustering.But here i am using more robust results giving clustering technique called K-Means clustering. Using this i have divided a few types of wines into groups/segments. Each wine has different characteristics.So, we can't segment a wine just by a descriptive statistics methods. Here we should use a robust machine learning technique called K-means clustering. This technique has a wider application in customer segmentation.

Here there is a 15 lakh rows of information on various brewery types and beers.Here one dealer wants to make some sense out of the data.Let us help him! I have applied basic techniques on this large data set to pull out the information that is very useful.

A Finance company deals in all home loans. They have presence across all urban, semi urban and rural areas. Customer first apply for home loan after that company validates the customer eligibility for loan.Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specifically target these customers.

As the countries are getting developed the pollution is also increasing. So, i want to find out how the GDP and the co2 emission are increased since 1960 for various countries.I am also curios to find out the relation between the GDP and the co2 emission. I minned data that is available with the world bank and discovered few interesting facts.

Do unemployed people die soon ? Is there any relation between unemployment and life expectancy ? Just to explore these facts i have pulled some data sets of different countries about the unemployment rate and the life expectacy. I conducted Exploratory Data Analysis and discovered few interesting facts.

A magazine seller want’s to sell an email to the customer about his new kid’s magazine.Before making his marketing efforts he wants to know that which customer will buy the kid’s magazine. We have the past record of the details of around 700 customers and their purchase history.Based on this record, i have used a machine learning technique called Logistic regression and predicted the chance of a customer purchasing the kid’s magazine.

Here we have taken a data set that consists of around 2380 purchase records of yogurt products.We have id of each purchase , the time of purchase , the number of different yogurts purchased by the purchase id and the total price of purchase of the purchase id. Here, i have used histograms , line plots and scatter plots to understand the purchase behavior.

The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. Here i have taken the passenger’s data of titanic ,applied the tools of machine learning to predict which passengers survived the tragedy. Logistic regression allows us to predict a categorical outcome using categorical and numeric data. Logistic regression tells us “How likely is it?” Here i predicted how likely a passenger survive the tragedy,