Master repository to store the case studies and the coursework I did during my post graduate diploma in Data Science from IIIT-Bangalore.
The case studies folder has the following content:
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Digit Recognizer using SVM: Develop a model using Support Vector Machine which should correctly classify the handwritten
digits from 0-9 based on the pixel values given as features. -
Eleckart Ecommerce Case Study: Developed a market mix model to observe the actual impact of different marketing variables over the last year and recommend the optimal budget allocation for different marketing levers for the next year.
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Housing Price Case Study: Build a regression model using regularization, so as to predict the actual value of the prospective properties and decide whether to invest in them or not.
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Hpyothesis Testing UseCase: Hypothesis testing concepts are used to answer some questions on pharmaceutical company Sun Pharma dataset which manufactuers painkiller drugs which are due for testing.
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Investment Case Study: Using exploratory data analysis identify the best sectors, countries, and a suitable investment type for making investments.
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Lead Score Case Study: Build a logistic regression model to assign a lead score between 0 and 100 to each of the leads which can be used by the company to target potential leads. A higher score would mean that the lead is hot, i.e. is most likely to convert whereas a lower score would mean that the lead is cold and will mostly not get converted.
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Letter Image Recognition: Develop a model using Support Vector Machine which should correctly classify the letter images from A-Z based on the pixel values given as features.
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Market Industry case study: Using Exploratory Data analysis to understand how consumer attributes and loan attributes influence the tendency of default.
10.NYC Parking Case Study: Exploratory data analysis using PySpark for NYC parking tickets data to have better parking and fewer tickets.
11.PCS Country Case Study: Categorise the countries using some socio-economic and health factors that determine the overall development of the country i.e; cluster the countries into groups.
12.Recommendation System: Build a recommendation system (collaborative) for your store, where customers will be recommended the
beer that they are most likely to buy.
13.Telechom Churn Case Study: The objective is to predict the churn in the last (i.e. the ninth) month using the data (features) from the first three months. To reduce customer churn, telecom companies need to predict which customers are at high risk of churn.
14.Uber Supply Demand Case Study: Exploratory Data Analysis is done to identify the root cause of the problem i.e. cancellation and non-availability of cars and recommend ways to improve the situation.