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association-rules-2-my-movies

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

association-rules-q1-books-

build-a-prediction-model-for-salary-hike-simple-linear-regression

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

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.

clustering---crime-data

Perform Clustering(Hierarchical, Kmeans & DBSCAN) for the crime data and identify the number of clusters formed and draw inferences.

data-science-project---customer-churn-prediction

Telecommunication customer churn prediction

decision-tree-q1-company_data

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

decision-tree-q2-fraud_data

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

forecasting-q1-cocacola-

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.

forecasting-q2-airlines

Forecast the Airlines Passengers data set. Prepare a document for each model explaining.

hypothesis-testing-2-customerorderform

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-3-labtat

Analyze the data and determine whether there is any difference in average TAT among the different laboratories at 5% significance level.

hypothesis-testing-4-cutlets

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-q1--buyer-ratio

k-nearest-neighbourhood-classifier-knn---data

Prepare a model for glass classification using KNN

k-nearest-neighbourhood-classifier-knn---zoo-data

Implement a KNN model to classify the animals into categories

logistic-regression

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

multi-linear-regression

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")]

multilinearlinear-regression-startups

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.

naive-bayes-salary-data

1) Prepare a classification model using Naive Bayes for salary data

neural-networks-q1-forestfires

predict the burned area of forest fires with Neural Networks

neural-networks-q2-gas_turbines

The dataset contains 36733 instances of 11 sensor measures aggregated over one hour (by means of average or sum) from a gas turbine.

oasis-infobyte-task-1

oasis-infobyte-task2

The Repository contains Task 2 from Oasis Infobyte

oasis-infobyte-task3

This Repository contains Task 3 from Oasis Infbyte

principal-component-analysis

random-forests-q1-company_data-

About the data: Let’s consider a Company dataset with around 10 variables and 400 records.

random-forests-q2-fraud_check

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 similarties score.

simple-linear-regression-delivery-data

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