surendranravimathi Goto Github PK

cnn-in-welding

Detecting Faults and Measuring Severity in Welding using Radiographic Images

flagging-a-property-ml-project

Flagging a property: The real estate client wanted to predict the probabilities of property which is going to be unfit using its property history and demography details.

insurance-cross-selling-ml-project

k-means-clustering-iris-data

linear-regression-with-python-scikit-learn

Prediction using Supervised ML (Level - Beginner)

manufaturing_project_using_r

Part backorders is a common supply chain problem. Working to identify parts at risk of backorder before the event occurs so the business has time to react. Data file contains the historical data for the 8 weeks prior to the week we are trying to predict. The data was taken as weekly snapshots at the start of each week. went_on_backorder - Product actually went on backorder or not. This is the target value.

multiclass-multilabel-prediction-for-stack-overflow-questions___surendran_r.

music-genre-identification___surendran_r

prediction-using-decision-tree-algorithm

product007

real_estate_project_using_r

Price of a property is one of the most important decision criterion when people buy homes. Real state firms need to be consistent in their pricing in order to attract buyers . Having a predictive model for the same will be great tool to have , which in turn can also be used to tweak development of properties , putting more emphasis on qualities which increase the value of the property. We have given you two datasets , housing_train.csv and housing_test.csv . You need to use data housing_train to build predictive model for response variable "Price". Housing_test data contains all other factors except "Price", you need to predict that using the model that you developed and submit your predicted values in a csv files. Evaluation Criterion : Score will be calculated as: Score =212467/RMSE

retail_project_using_r

This data set is related with retail domain and challenge is to predict whether a store should get opened or not based on certain factors such as sales, population,area etc. We have given you two datasets , store_train.csv and store_test.csv . You need to use data store_train to build predictive model for response variable ‘store’. store_test data contains all other factors except ‘store

ssopva

SSOPVA

stock-market-prediction-using-numerical-and-textual-analysis

titanic_kaggle

Basic Kaggle stater project

zerodha_live_automate_trading-_using_ai_ml_on_indian_stock_market-using-basic-python

Online trading using Artificial Intelligence Machine leaning with basic python on Indian Stock Market, trading using live bots indicator screener and back tester using rest API and websocket 😊