abstract-dex/neural_nets

Projects based on Artificial Neural Networks

Neural_Nets

Projects based on Artificial Neural Networks

Project 1: Classification of Breast Cancer Data using Neural Networks

Description

UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29

	Features	Description
0	ID	ID number
1	Diagnosis	Diagnosis (M = malignant, B = benign)
2	radius	Mean of distances from the center to points on the perimeter
3	texture	Standard deviation of gray-scale values
4	perimeter	Perimeter
5	area	Area
6	smoothness	Local variation in radius lengths
7	compactness	(Perimeter^2 / Area) - 1.0
8	concavity	Severity of concave portions of the contour
9	concave_points	Number of concave portions of the contour
10	symmetry	Symmetry
11	fractal_dimension	"Coastline approximation" - 1

Class distribution: 357 benign, 212 malignant

Project 2: House Price Prediction using Neural Networks

Description

Data from Kaggle:

https://www.kaggle.com/harlfoxem/housesalesprediction

	Features	Description
0	id	Unique ID for each home sold
1	date	Date of the home sale
2	price	Price of each home sold
3	bedrooms	Number of bedrooms
4	bathrooms	Number of bathrooms, where .5 accounts for a room with a toilet but no shower
5	sqft_living	Square footage of the apartments interior living space
6	sqft_lot	Square footage of the land space
7	floors	Number of floors
8	waterfront	A dummy variable for whether the apartment was overlooking the waterfront or not
9	view	An index from 0 to 4 of how good the view of the property was
10	condition	An index from 1 to 5 on the condition of the apartment
11	grade	An index from 1 to 13, where 1-3 falls short of building construction and design, 7 has an average level of construction and design, and 11-13 have a high-quality level of construction and design
12	sqft_above	The square footage of the interior housing space that is above ground level
13	sqft_basement	The square footage of the interior housing space that is below ground level
14	yr_built	The year the house was initially built
15	yr_renovated	The year of the house’s last renovation
16	zipcode	What zipcode area the house is in
17	lat	Latitude
18	long	Longitude
19	sqft_living15	The square footage of interior housing living space for the nearest 15 neighbors
20	sqft_lot15	The square footage of the land lots of the nearest 15 neighbors

Project 3: Loan Repaid Status Prediction using Neural Networks

Description

Data from Kaggle:

https://www.kaggle.com/datasets/wordsforthewise/lending-club

	Features	Description
0	loan_amnt	The listed amount of the loan applied for by the borrower. If at some point in time, the credit department reduces the loan amount, then it will be reflected in this value.
1	term	The number of payments on the loan. Values are in months and can be either 36 or 60.
2	int_rate	Interest Rate on the loan
3	installment	The monthly payment owed by the borrower if the loan originates.
4	grade	LC assigned loan grade
5	sub_grade	LC assigned loan subgrade
6	emp_title	The job title supplied by the Borrower when applying for the loan.*
7	emp_length	Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years.
8	home_ownership	The home ownership status provided by the borrower during registration or obtained from the credit report. Our values are: RENT, OWN, MORTGAGE, OTHER
9	annual_inc	The self-reported annual income provided by the borrower during registration.
10	verification_status	Indicates if income was verified by LC, not verified, or if the income source was verified
11	issue_d	The month which the loan was funded
12	loan_status	Current status of the loan
13	purpose	A category provided by the borrower for the loan request.
14	title	The loan title provided by the borrower
15	zip_code	The first 3 numbers of the zip code provided by the borrower in the loan application.
16	addr_state	The state provided by the borrower in the loan application
17	dti	A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income.
18	earliest_cr_line	The month the borrower's earliest reported credit line was opened
19	open_acc	The number of open credit lines in the borrower's credit file.
20	pub_rec	Number of derogatory public records
21	revol_bal	Total credit revolving balance
22	revol_util	Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit.
23	total_acc	The total number of credit lines currently in the borrower's credit file
24	initial_list_status	The initial listing status of the loan. Possible values are – W, F
25	application_type	Indicates whether the loan is an individual application or a joint application with two co-borrowers
26	mort_acc	Number of mortgage accounts.
27	pub_rec_bankruptcies	Number of public record bankruptcies