LogicalRhythm2k20

This repository contains the final solution notebooks of the problem statements solved by me and my team in Logical Rhythm conducted by MNNIT CC club during Avishkar (Annual Technical fest of MNNIT)

During this event which is held over a period of 1 month the participants are required to solve 3 different problem statements in machine learning.

The problem statements are:

1. Tow-Mater Labs: In this problem statement, we are required to predict wether or not pople will buy insurance for their cars given the data of an insurance company with various features such as vehicle age, policy sales channel, annual premium etc. You can find the link to the final notebook here.

2. Game Price Prediction: In this problem statement, we are required to predict the price of a given game over the play store given various features such as developers, positive reviews, number of users etc. You can find the link to the final notebook here.

3. Sports Image Classificaton: In this problem statement given is an image and we have to predict the sport which is being played on the image.You can find the link to the final notebook here.

    Since the train and test files were too large for this problem, they have not been uploaded to the repository.
    Please visit the kaggle link of the problem statement if you wish to download the datasets and run notebook.
   

PS:There are interactive and dynamic plotly charts in these files which are not rendered on the github viewer and therefore I have uploaded the whole notebook in HTML format separately for reference which comes with all the graphs.

If you only want to see the notebook you can download and view the HTML version or visit the kaggle link to view it.

If you however wish to run the code on your own, you can either:

1. Download the ipynb format of the notebook along with the train and test sets and set the path of train test inside 
the notebook and run it on your computer. Before running please make sure to install all the relevant libraries.

2. Visit kaggle links provided and fork the pinned version of the notebook and then run the notebook.

Team Members:

1. Sidhant Agarwal (20188028)  username: "sidagar"
2. Vivek Rai (20185024)  username: "blazer007"
3. Deepanshu Raj (20185058)  username: "davalpha"

Final Results: Secured 1st position