Cdasilver29/Tanzania-Water-wells-Project

# Tanzania-Water-wells-Project ## **Business Understanding** Tanzania faces a water crisis due to limited infrastructure, uneven distribution, poor water quality, climate change, and population growth. We are working on a project that utilizes machine learning to predict well success, optimize resource allocation, and ultimately break the cycle of water insecurity and poverty. ## **Project Goal** Our goal in this project is to utilize machine learning algarithms to build a model predicting water point functionality in Tanzania. This empowers Tanzanian authorities to identify wells needing maintenance, guide future well placement for better success rates, and ultimately optimize water resource management across the country. By predicting well functionality, the model can help target limited resources for repairs, inform data-driven decisions on new well construction, and promote efficient water utilization, tackling Tanzania's water crisis head-on. ## **Data Understanding** This project utilizes data from the Tanzanian Ministry of Water (drivendata.org) provided in three CSV files: training set values (well features), training set labels (actual well functionality), and test set values (features for wells where functionality needs prediction). Our goal is to build a machine learning model using the training data to predict the functionality (functional, non-functional, needs repair) of wells in the test set. #### Data Exploration Revealed Key Insights Our initial analysis revealed valuable insights about the Tanzanian water point data. The dataset contains 59,400 entries for water points, described by 41 features (10 numerical, 31 categorical). Importantly, after examining the data structure and content, we identified 21 redundant features providing the same information. These features were removed to streamline the data and optimize our model development process. #### Steps to fo …