parth-17512/Water-Table-Problem---Tanzania

# Water Table Problem - Tanzania This project focuses on the classic Tanzania water pump prediction challenge from DrivenData. The goal is to predict the operational status of waterpoints across Tanzania using a dataset of collected field data. ## Problem description The objective is to predict the operating condition of a waterpoint for each record in the dataset. Each row contains information about a waterpoint and the target label tells whether it is: - `functional` - `functional needs repair` - `non functional` Successful models help identify which wells are likely to fail or need maintenance, which is valuable for managing water infrastructure and improving service reliability. ## Dataset overview The dataset contains a mix of geographic, construction, management, and water source attributes. These features describe each waterpoint and help model the condition of the infrastructure. ### Features in the dataset The following fields are included in the training and test data: - `amount_tsh` - Total static head (amount of water available to the waterpoint) - `date_recorded` - The date the row was entered - `funder` - Who funded the well - `gps_height` - Altitude of the well - `installer` - Organization that installed the well - `longitude` - GPS coordinate - `latitude` - GPS coordinate - `wpt_name` - Name of the waterpoint if there is one - `num_private` - Number of private connections - `basin` - Geographic water basin - `subvillage` - Geographic location - `region` - Geographic location - `region_code` - Geographic location (coded) - `district_code` - Geographic location (coded) - `lga` - Geographic location - `ward` - Geographic location - `population` - Population around the well - `public_meeting` - True/False - `recorded_by` - Group entering this row of data - `scheme_management` - Who operates the waterpoint - `scheme_name` - Who operates the waterpoint - `permit` - If the waterpoint is permitted - `construction_year` - Year the waterpoint was con …