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Understanding the problem statement and importing the file
Initializing the libraries and understand it's use
Using info and describe the function and extracting information from the results
Checking for the null values and performing necessary imputations
Plotting histogram and bar plot for numerical versus target variable for advanced EDA
How to analyze categorical variables using graphs
How to plot heatmap and FacetGrid in seaborn
Creating new features from existing features (Feature Engineering)
Understanding One Hot and Label encoding and it's implementation
Applying ensembling method Random Forest and extracting important features using feature_importance function
Difference between Deep learning model and the ML model
Creating a function for extensive Feature Engineering and Pre-processing of the Dataset
Preparing dataset for LightGBM
Initializing parameters for LightGBM
Selecting the right metrics according to the Dataset
Training the model and making predictions
Plotting graphs different metrics and models to select the best one out
Home Credit makes use of a variety of alternative data--including telco and transactional information--to predict their clients' repayment abilities. Many people struggle to get loans due to insufficient or non-existent credit histories. And, unfortunately, this population is often taken advantage of by untrustworthy lenders. Home Credit strives to broaden financial inclusion for the unbanked population by providing a positive and safe borrowing experience.