Collison course

Inspiration

We are a team of 4 members and we love data and data related technologies. Our motivation in life is to use this technology as our superpower and solve the problems in this world to create a positive impact on society. Each of us holds a specific skillset and hence we are the avengers of the data science community. For this particular project, we all have our friends and someone from our family get into an accident and we would go to any extent to not let that happen to anyone. The moment we saw the data set we knew we had to this.

What it does

Our product is built on two core values:- 1) Business values We are providing some hidden insights which were not easy to find at all looking at the huge amount of data set given. We tried to build to analyze all the features given to us in the data set and we performed a rigorous engineering data analysis that would give us in and out view of the data set like a 3-D cube which Statefarm can look in any perspective they want and build their insurance strategy. 2) User values For all the Statefarm users we decided to build a model that would give them insights into an accident happening in a particular region given certain circumstances. This would help state farms customers avoid any accidents and hence benefit state farm too.

How I built it

We used python libraries like tableau along with advance visualization techniques like a calculative field, data blending, interactive dashboard to create a story that will provide hidden insights to the client. Post that we use pands libraries like numpy, scikit learn, matplotlib, seaborn to perform feature engineering and building a machine learning model for generating user values.

Challenges I ran into

We can look at our challenges into 2 broad categories

1)Design We knew the challenge given to us was something close to all 4 of us and hence we wanted to bring out the best insights.. over the course of the initial 3-4 hours we were just thinking to brainstorm what problems do we have and how can we provide a solution. The problem with this was we couldn't find a definite flow in our progress and lost some time to work on the technical end which we covered up by sleeping less :) Next, once we moved on to the implementation phase we had some better ideas and rephrasing our implementation strategy took some extra efforts.

2)Technical The only issue in the technical end was building working with flask API and integrating our machine learning model to it. This was because of our team members having more expertise on data end,

Accomplishments that I'm proud of

1) Solving an unresolved problem in our society 2) Learning some new technologies 3) Collaborating and working with experienced individuals 4) Making an impact

What I learned

1) Our main learning from the entire project was to keep our focus on the end goal and believing in it. Once we all had that and were on the same page everything else just fell in place for us.

What's next for Collison course

The grind doesn't stop. We will work more on this and build an end to end model. We look forward to participating in more such events and growing our technical skillset as well meeting individuals in a similar forte