ExplorerGumel/UmojaHack-Africa-2022-Begineer-Challenge

# UmojaHack-Africa-2022-Begineer-Challenge This notebook contains a code + some modifications to the solution i submitted while competing on Umoja Hack Africa(A Biggest inter-universities Data science competition in Africa) 2022 Beginner challenge organised by zindi Africa. # OVERVIEW AirQo’s air quality sensing network has more than 120 low-cost devices deployed across Uganda; in most cases, these devices are deployed in unmonitored or perilous environments. These low-cost electronic devices are susceptible to breakdown caused by communication malfunction, aging, wear and tear, manufacturing deficiencies, incorrect calibration, mishandling and other external environmental factors. Faults lead to data inaccuracies and data loss, which impacts decisions and policies that could significantly impact people’s lives. Device failure detection and monitoring is critical to AirQo’s business; faulty devices need to be identified, isolated and fixed or replaced with urgency. Data received from a device can be used to identify whether the device is working correctly or not. In this challenge, the task is to develop a classification model to identify a device has an off set fault or not, regardless of the device. The model can be used by AirQo to automatically flag a device that is returning faulty data. # DATASET The Dataset is split into training and testing datasets.The train file contains approximately 300,000 readings and the test contains approximately 100,000 readings.Each row in the training set consist of the following informations; 1. ID 2. Datetime 3. Sensor1 PM2.5 4. Sensor2 PM2.5 5. Temperature 6. Relative Humidity 7. Offset fault (Target) The testing set has the same features as the training set, except that it does not include the offset fault feature, as this is the target variable that we are to predict. # APPROACH To solve this problem, I used a simple Gradient Boosting model. I first cleaned the data by removing missing values and outliers. Then,ext …