Please find below the Predictive Modelling Extended project instructions:
• You have to submit 2 files :
Business 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.
Jupyter Notebook file: This is a must and will be used for reference while evaluating.
Any assignment found copied/ plagiarized with another person will not be graded and marked as
zero. Please ensure timely submission as a post-deadline assignment will not be accepted.
Problem 1: Linear Regression
You are a part of an investment firm and your work is to do research about these 759 firms. You
are provided with the dataset containing the sales and other attributes of these 759 firms.
Predict the sales of these firms on the bases of the details given in the dataset so as to help your
company in investing consciously. Also, provide them with 5 attributes that are most important.
Questions for Problem 1:
1.1) Read the data and do exploratory data analysis. Describe the data briefly. (Check the null
values, data types, shape, EDA). Perform Univariate and Bivariate Analysis. (8 marks)
1.2) Impute null values if present? Do you think scaling is necessary in this case? (8 marks)
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1.3) Encode the data (having string values) for Modelling. Data Split: Split the data into test and
train (70:30). Apply Linear regression. Performance Metrics: Check the performance of
Predictions on Train and Test sets using R-square, RMSE. (8 marks)
1.4) Inference: Based on these predictions, what are the business insights and
recommendations. (6 marks)
Data Dictionary for Firm_level_data:
1. sales: Sales (in millions of dollars).
2. capital: Net stock of property, plant, and equipment.
3. patents: Granted patents.
4. randd: R&D stock (in millions of dollars).
5. employment: Employment (in 1000s).
6. sp500: Membership of firms in the S&P 500 index. S&P is a stock market index that measures
the stock performance of 500 large companies listed on stock exchanges in the United States
7. tobinq: Tobin's q (also known as q ratio and Kaldor's v) is the ratio between a physical asset's
market value and its replacement value.
8. value: Stock market value.
9. institutions: Proportion of stock owned by institutions.
Dataset for Problem 1: Firm_level_data.csvView in a new window
Problem 2: Logistic Regression and Linear Discriminant Analysis
You are hired by the Government to do an analysis of car crashes. You are provided details of car
crashes, among which some people survived and some didn't. You have to help the government
in predicting whether a person will survive or not on the basis of the information given in the
data set so as to provide insights that will help the government to make stronger laws for car
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manufacturers to ensure safety measures. Also, find out the important factors on the basis of
which you made your predictions.
Questions for Problem 2:
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. (8 marks)
2.2) 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). (8 marks)
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. Compare
both the models and write inferences, which model is best/optimized. (8 marks)
2.4) Inference: Based on these predictions, what are the insights and recommendations. (6
marks)
Data Dictionary for Car_Crash
1. dvcat: factor with levels (estimated impact speeds) 1-9km/h, 10-24, 25-39, 40-54, 55+
2. weight: Observation weights, albeit of uncertain accuracy, designed to account for varying
sampling probabilities. (The inverse probability weighting estimator can be used to demonstrate
causality when the researcher cannot conduct a controlled experiment but has observed data to
model)
3. Survived: factor with levels Survived or not_survived
4. airbag: a factor with levels none or airbag
5. seatbelt: a factor with levels none or belted
6. frontal: a numeric vector; 0 = non-frontal, 1=frontal impact
7. sex: a factor with levels f: Female or m: Male
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8. ageOFocc: age of occupant in years
9. yearacc: year of accident
10. yearVeh: Year of model of vehicle; a numeric vector
11. abcat: Did one or more (driver or passenger) airbag(s) deploy? This factor has levels deploy,
nodeploy and unavail
12. occRole: a factor with levels driver or pass: passenger
13. deploy: a numeric vector: 0 if an airbag was unavailable or did not deploy; 1 if one or more
bags deployed.
14. injSeverity: a numeric vector; 0: none, 1: possible injury, 2: no incapacity, 3: incapacity, 4:
killed; 5: unknown, 6: prior death
15. caseid: character, created by pasting together the populations sampling unit, the case
number, and the vehicle number. Within each year, use this to uniquely identify the vehicle.
Dataset for Problem 2: Car_Crash.csv