TaibatNofiu/africa_crisis

we are going to work on the 'Systemic Crisis, Banking Crisis, inflation Crisis In Africa' dataset that was provided by Kaggle # africa_crisis # What You're Aiming For In this checkpoint, we are going to work on the 'Systemic Crisis, Banking Crisis, inflation Crisis In Africa' dataset that was provided by Kaggle. Dataset description : This dataset focuses on the Banking, Debt, Financial, Inflation and Systemic Crises that occurred, from 1860 to 2014, in 13 African countries, including: Algeria, Angola, Central African Republic, Ivory Coast, Egypt, Kenya, Mauritius, Morocco, Nigeria, South Africa, Tunisia, Zambia and Zimbabwe. The ML model objective is to predict the likelihood of a Systemic crisis emergence given a set of indicators like the annual inflation rates. # Instructions 1. Import you data and perform basic data exploration phase * Display general information about the dataset * Create a pandas profiling reports to gain insights into the dataset * Handle Missing and corrupted values * Remove duplicates, if they exist * Handle outliers, if they exist * Encode categorical features 2. Select your target variable and the features 3. Split your dataset to training and test sets 4. Based on your data exploration phase select a ML classification algorithm and train it on the training set 5. Assess your model performance on the test set using relevant evaluation metrics 6. Discuss with your cohort alternative ways to improve your model performance