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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
Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. Build a prediction model for Salary_hike
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.
Perform Clustering(Hierarchical, Kmeans & DBSCAN) for the crime data and identify the number of clusters formed and draw inferences.
Telecommunication customer churn prediction
Problem Statement: A cloth manufacturing company is interested to know about the segment or attributes causes high sale.
Use decision trees to prepare a model on fraud data treating those who have taxable_income <= 30000 as "Risky" and others are "Good"
Forecast the CocaCola prices 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.
Forecast the Airlines Passengers data set. Prepare a document for each model explaining.
They audit a certain % of the customer order forms. Any error in order form renders it defective and has to be reworked before processing. The manager wants to check whether the defective % varies by centre. Please analyze the data at 5% significance level and help the manager draw appropriate inferences
Analyze the data and determine whether there is any difference in average TAT among the different laboratories at 5% significance level.
A randomly selected sample of cutlets was collected from both units and measured? Analyze the data and draw inferences at 5% significance level. Please state the assumptions and tests that you carried out to check validity of the assumptions.
Prepare a model for glass classification using KNN
Implement a KNN model to classify the animals into categories
The data is about client information of a Bank and the task is given as to predict whether the client has subscribed a term deposit or not
Consider only the below columns and prepare a prediction model for predicting Price. Corolla<-Corolla[c("Price","Age_08_04","KM","HP","cc","Doors","Gears","Quarterly_Tax","Weight")]
Prepare a prediction model for profit of 50_startups data. Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model.
1) Prepare a classification model using Naive Bayes for salary data
predict the burned area of forest fires with Neural Networks
The dataset contains 36733 instances of 11 sensor measures aggregated over one hour (by means of average or sum) from a gas turbine.
The Repository contains Task 2 from Oasis Infobyte
This Repository contains Task 3 from Oasis Infbyte
About the data: Letβs consider a Company dataset with around 10 variables and 400 records.
Use Random Forest to prepare a model on fraud data treating those who have taxable_income <= 30000 as "Risky" and others are "Good"
Problem statement. Build a recommender system by using cosine similarties score.
Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. Predict delivery time using sorting time
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TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
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Bring data to life with SVG, Canvas and HTML. πππ
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google β€οΈ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.