This repo contains all of mine assignments and learnings during my Internship period at Radix Educational Trust.
The rise in vehicles on the road will also lead to multiple challenges and the road will be
more vulnerable to accidents. Increased accident rates also lead to more insurance
claims and payouts rise for insurance companies.
In order to preemptively plan for the losses, the insurance firms leverage accident data
to understand the risk across the geographical units e.g. Postal code/district, etc.
You have to predict the “Accident_Risk_Index” against the postcodes.
Accident_Risk_Index (mean casualties at a postcode) =
sum(Number_of_causalities)/count(Accident_ID).
Solution: Code
This dataset was collected from UC Irvine Machine Learning Repository and comprises
a range of biomedical voice measurements from 31 people, 23 with Parkinson's disease
(PD). Each column in the table is a particular voice measure, and each row corresponds
to one of 195 voice recordings from these individuals ("name" column). The main aim of
the data is to discriminate healthy people from those with PD, according to the "status"
column which is set to 0 for fit and 1 for PD, i.e. this is a binary classification problem.
The test should have at maximum a False Negative Rate of 0.1%, that is the test
sensitivity should be high, at least 0.9% as it is more important to identify patients with
the disease correctly.
Solution: Code
Using the following 4 Methods:
- Support Vector Regression
- Decision Trees
- Random Forest
- 3-layer dense Neural Network
I tried to show the relative performance of each method to predict forest fires from the dataset available at the UCI Machine Learning Repository.