Monsoon 2020: Completed #classroom Weight: 20.0

# DA Project RECID

Predict whether an individual will be back to prison

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## 🕵️ Introduction

Recidivism is measured by criminal acts that resulted in rearrest, reconviction or return to prison with or without a new sentence during a three-year period following the prisoner's release.

In this task you will be provided with data and asked to predict weather the indiviual will be back to prison in the three year period or not.

Understand with code! Here is `getting started code` for you.`😄`

## 💾 Dataset

This database contains information of criminal history, jail and prison time, demographics and COMPAS risk scores for defendants from Broward County. For each individual there are 14 variables including the target variable. The initial databse that was collecetd has some un wanted information,The data was subsequently preprocessed and reduced to relevant features for classification. The `target variable is two_year_recid which indicates recidivism`.

## 📁 Files

Following files are available in the `resources` section:

• `train.csv` - (`4222` samples) File that should be used for training. It contains the information about the individual along with the target value.
• `test.csv` - (`1056` samples) File that will be used for testing. Unlike the training file it contains only the individual information and not the target value.

## 🚀 Submission

• Prepare a python file which should produce a csv file with name "submission.csv" containing header as label and predicted value as either 0(No) or 1(Yes).
• Your submission should read the train and test data (available as environment variables) and should write 'submission.csv' containing the predictions for the test set.
• Sample submission format is available in the resources section of the challenge page as sample_submission.csv.                          Make your first submission here 🚀 !!

## 🖊 Evaluation Criteria

During evaluation F1 score will be used to test the efficiency of the model where,

$F1 = 2 * \frac{precision*recall}{precision+recall}$