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association-rules

Prepare rules for the all the data sets

association-rules-

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

clustering

Perform Clustering(Hierarchical, Kmeans & DBSCAN) for the crime data and identify the number of clusters formed and draw inferences. Data Description: Murder -- Muder rates in different places of United States Assualt- Assualt rate in different places of United States UrbanPop - urban population in different places of United States Rape - Rape rate in different places of United States

clustering-

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. Data Description: The file EastWestAirlinescontains information on passengers who belong to an airline’s frequent flier program. For each passenger the data include information on their mileage history and on different ways they accrued or spent miles in the last year. The goal is to try to identify clusters of passengers that have similar characteristics for the purpose of targeting different segments for different types of mileage offers ID --Unique ID Balance--Number of miles eligible for award travel Qual_mile--Number of miles counted as qualifying for Topflight status cc1_miles -- Number of miles earned with freq. flyer credit card in the past 12 months: cc2_miles -- Number of miles earned with Rewards credit card in the past 12 months: cc3_miles -- Number of miles earned with Small Business credit card in the past 12 months: 1 = under 5,000 2 = 5,000 - 10,000 3 = 10,001 - 25,000 4 = 25,001 - 50,000 5 = over 50,000 Bonus_miles--Number of miles earned from non-flight bonus transactions in the past 12 months Bonus_trans--Number of non-flight bonus transactions in the past 12 months Flight_miles_12mo--Number of flight miles in the past 12 months Flight_trans_12--Number of flight transactions in the past 12 months Days_since_enrolled--Number of days since enrolled in flier program Award--whether that person had award flight (free flight) or not

decision-tree

A cloth manufacturing company is interested to know about the segment or attributes causes high sale. Approach - A decision tree can be built with target variable Sale

decision-tree-

Use decision trees to prepare a model on fraud data treating those who have taxable_income <= 30000 as "Risky" and others are "Good"

hypothesis-testing

Sales of products in four different regions is tabulated for males and females. Find if male-female buyer rations are similar across regions.

hypothesis-testing-

A F&B manager wants to determine whether there is any significant difference in the diameter of the cutlet between two units. 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.

hypothesis-testing_

TeleCall uses 4 centers around the globe to process customer order forms. 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

hypothesis_testing-

A hospital wants to determine whether there is any difference in the average Turn Around Time (TAT) of reports of the laboratories on their preferred list. They collected a random sample and recorded TAT for reports of 4 laboratories. TAT is defined as sample collected to report dispatch. Analyze the data and determine whether there is any difference in average TAT among the different laboratories at 5% significance level.

infliation

infliation-

knn

Prepare a model for glass classification using KNN

knn-

Implement a KNN model to classify the animals in to categorie

logistic-regression

Output variable -> y y -> Whether the client has subscribed a term deposit or not Binomial ("yes" or "no")

multi-linear-regression

preparing a prediction model for predicting Price

multipal--linear-regression

Preparing a prediction model for profit of 50_startups data

multipal-linear-regression

preparing a prediction model for predicting Price

naive-bayes

Prepare a classification model using Naive Bayes for salary data

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 Dataset includes gas turbine parameters (such as Turbine Inlet Temperature and Compressor Discharge pressure) in addition to the ambient variables.

nikhilrajurkar

pca-principal-component-analysis-

Perform Principal component analysis and perform clustering using first 3 principal component scores (both heirarchial and k mean clustering(scree plot or elbow curve) and obtain optimum number of clusters and check whether we have obtained same number of clusters with the original data (class column we have ignored at the begining who shows it has 3 clusters)df

project-on-energy-production-prediction-of-ccpp

Create a Data Model to Predict the Net Hourly Electrical Energy Output ( EP ) of the Plant using the Following Hourly Average Ambient Variables: Temperature ( AT ) Exhaust Vacuum ( EV ) Ambient Pressure ( AP ) Relative Humidity ( RH )

random-forest

A cloth manufacturing company is interested to know about the segment or attributes causes high sale

random-forest-

Use Random Forest to prepare a model on fraud data treating those who have taxable_income <= 30000 as "Risky" and others are "Good"

recommendation-system

Problem statement. Build a recommender system by using cosine simillarties score.

simple-linear-regression

Building a simple linear regression model by performing EDA and necessary transformations and selecting the best model using Python

simple-linear-regression_

Building a simple linear regression model by performing EDA and necessary transformations and selecting the best model using Python

svm

task-3-performing-eda-on-samplesuperstore-dataset

Task #3:Performing EDA on SampleSuperstore Dataset