Please find below the Predictive Modelling project instructions:

You have to submit 2 files : Answer Report: In this, you need to submit all the answers to all the questions in a sequential manner. Your answer should include detailed explanations & inferences to all the questions. Your report should not be filled with codes. You will be evaluated based on the business report.

Note: In the business report, there should be a proper interpretation of all the tasks performed along with actionable insights. Only the presence of interpretation of the models is not sufficient to be eligible for full marks in each of the criteria mentioned in the rubric. Marks will be deducted wherever inferences are not clearly mentioned. THE REPORT HAS TO BE STRICTLY SUBMITTED IN A PDF/DOC FORMAT. ANY OTHER FORMAT WILL NOT BE CONSIDERED FOR GRADING. 6 Marks are allotted for the "Quality of Business Report".

Problem 2: Logistic Regression and LDA

You are hired by a tour and travel agency which deals in selling holiday packages. You are provided details of 872 employees of a company. Among these employees, some opted for the package and some didn't. You have to help the company in predicting whether an employee will opt for the package or not on the basis of the information given in the data set. Also, find out the important factors on the basis of which the company will focus on particular employees to sell their packages.

Dataset for Problem 2: Holiday_Package.csv

Data Dictionary:

Variable Name	Description
Holiday_Package	Opted for Holiday Package yes/no?
Salary	Employee salary
age	Age in years
edu	Years of formal education
no_young_children	The number of young children (younger than 7 years)
no_older_children	Number of older children
foreign	foreigner Yes/No

Important Note: Please reflect on all that you have learned while working on this project. This step is critical in cementing all your concepts and closing the loop. Please write down your thoughts here.

2.1 Data Ingestion: Read the dataset. Do the descriptive statistics and do null value condition check, write an inference on it. Perform Univariate and Bivariate Analysis. Do exploratory data analysis.

2.2 Do not scale the data. Encode the data (having string values) for Modelling. Data Split: Split the data into train and test (70:30). Apply Logistic Regression and LDA (linear discriminant analysis).

2.3 Performance Metrics: Check the performance of Predictions on Train and Test sets using Accuracy, Confusion Matrix, Plot ROC curve and get ROC_AUC score for each model Final Model: Compare Both the models and write inference which model is best/optimized.

2.4 Inference: Basis on these predictions, what are the insights and recommendations.

Please explain and summarise the various steps performed in this project. There should be proper business interpretation and actionable insights present.

Quality of Business Report(Please refer to the Evaluation Guidelines for Business report checklist. Marks in this criteria are at the moderator's discretion)

Holiday_Package-1.csv

	Holliday_Package	Salary	age	educ	no_young_children	no_older_children	foreign
1	no	48412	30	8	1	1	no
2	yes	37207	45	8	0	1	no
3	no	58022	46	9	0	0	no
4	no	66503	31	11	2	0	no
5	no	66734	44	12	0	2	no
6	yes	61590	42	12	0	1	no
7	no	94344	51	8	0	0	no
8	yes	35987	32	8	0	2	no
9	no	41140	39	12	0	0	no
10	no	35826	43	11	0	2	no
11	no	42643	45	11	0	2	no
12	no	35157	60	12	0	0	no
13	no	75327	33	11	2	0	no
14	no	148221	56	14	0	0	no