srutirp's repositories
CountryClassification
HELP International is an international humanitarian NGO that is committed to fighting poverty and providing the people of backward countries with basic amenities and relief during the time of disasters and natural calamities. It runs a lot of operational projects from time to time along with advocacy drives to raise awareness as well as for funding purposes. The objective is to categorise the countries using some socio-economic and health factors that determine the overall development of the country. Then we need to suggest the countries which the CEO needs to focus on the most.
LoanPrediction
Dream Housing Finance company deals in all kinds of home loans. They have presence across all urban, semi urban and rural areas. Customer first applies for home loan and 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 provided a dataset to identify the customers segments that are eligible for loan amount so that they can specifically target these customers.
BigMartSalesPrediction
The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and predict the sales of each product at a particular outlet. Using this model, BigMart will try to understand the properties of products and outlets which play a key role in increasing sales. Data Dictionary: We have train (8523) and test (5681) data set, train data set has both input and output variable(s). We need to predict the sales for test data set. Variable Description: Item_Identifier -Unique product ID Item_Weight- Weight of product Item_Fat_Content- Whether the product is low fat or not Item_Visibility- The % of total display area of all products in a store allocated to the particular product Item_Type- The category to which the product belongs Item_MRP- Maximum Retail Price (list price) of the product Outlet_Identifier- Unique store ID Outlet_Establishment_Year- The year in which store was established Outlet_Size- The size of the store in terms of ground area covered Outlet_Location_Type- The type of city in which the store is located Outlet_Type- Whether the outlet is just a grocery store or some sort of supermarket Item_Outlet_Sales- Sales of the product in the particular store. This is the outcome variable to be predicted. About No description, website, or topics provided. Topics Resources Readme Releases No releases published Create a new release Packages No packages published Publish your first package Languages Jupyter Notebook 100.0%
OnlineNewsPopularityPrediction
Objective: To predict number of shares of online news articles based on various factors and provide insight to publishers /writers to know which factors influences the number of shares of the articles. Outcome: This analysis gives insights and key recommendations to make online articles more popular among the readers and this help publishers to make their articles popular.
Data-science-cheet-sheet
contains cheetsheet for data science cource