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Understanding the problem statement
Importing the Train and Test dataset directly from the source
Performing basic EDA and checking for null values
Filling the null values using most suitable methods
Visualization using Density plot
Using correlation plot for understanding correlation between different features
Visualizing combined effect of different variables on the target
Using box and whiskers plot for visualizing and handling outliers
Autocorrelation, Normal distribution, Multicollinearity, and heteroscedasticity
Applying Linear model and plotting graphs for the residuals
Preparing the dataset for fitting in XGBOost model
Defining parameters for XGBoost model
Training the XGBoost models and calculating the accuracy
Making final predictions for the test dataset
Plotting Graphs for Train_loss versus Train_preds
When you’ve been devastated by a serious car accident, your focus is on the things that matter the most: family, friends, and other loved ones. Pushing paper with your insurance agent is the last place you want your time or mental energy spent. This is why Allstate, a personal insurer in the United States, is continually seeking fresh ideas to improve their claims service for the over 16 million households they protect.
In this data science project, you will develop automated methods for predicting the cost, and severity, of claims.
