hrithik-sharma-317 Goto Github PK

apple-stock-forecasting-

apple_stock_forecasting

Forecasting model for apple stock

association-rules---assignment-9

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

bank_assignment-06-logistic-regression

book-recommender-system

clustering_assignment_airlines

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

clustering_assignments_crime_data

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

decision_tree_assignment_companydata

Decision_tree_Assignment_CompanyData

decision_tree_assignment_fraudcheck

Decision_tree_Assignment_FraudCheck

forecasting_assignment_airlines

Forecast the CocaCola prices and Airlines Passengers 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_assignment_cococola

Forecast the CocaCola prices and Airlines Passengers 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.

knn_assignment_glass

Prepare a model for glass classification using KNN

knn_assignment_zoo

Implement a KNN model to classify the animals in to categorie

multiple-linear-regression_50-startups

multiple-linear-regression_50 startups

naive_bayes_assignment

Prepare a classification model using Naive Bayes for salary data

neural_network_assignment_forest_fires

PREDICT THE BURNED AREA OF FOREST FIRES WITH NEURAL NETWORKS

neural_network_assignment_gasturbines

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.

pca

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

random_forest_assignment_companydata

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

random_forest_assignment_fraudcheck

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

recomendation-system-books

recommendation-system

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

svm_assignment_forestfire

Service Vector machines Assignment ForestFire

svm_assignment_salary

Service Vector Machies assignment salary

text_mining_assignment

For Text Mining assignment ONE: 1) Perform sentimental analysis on the Elon-musk tweets (Exlon-musk.csv) TWO: 1) Extract reviews of any product from ecommerce website like amazon 2) Perform emotion mining

toyata-multi-linear-regression

Toyata Multi linear regression

user-based

recomendation system

user_based_recommendation-system_project

Books User based recommendation system