. Customers of age between 30 to 60 are more likely to buy insurance. .Customerswith Driving License have higher chance of buying Insurance. . Customers with Vehicle_Damage are likely to buy insurance. The variable such as Age,Previously_insured,Annual_premium are more afecting the target variable. .Random Forest model preform better.

Technology:- numpy,pandas,RandomForestClassifier

This project performed sentimental analysis based on opinion words (like positive , negative) As we are doing sentiment analysis, it is important to tell our model what is positive sentiment and what is a negative sentiment. result with accuracy of 94.28% and F1 Score of 0.9696.The Random Classifier Algo with 100 trees works efficiently to train the machine in predicting positive and negative reviews. Technology:- NLTK, Spacy, scikit learn

We will choose decision tree based model. We choose random forest. Because some of the features have null value, I will use either drop the records with null values or filla value instead.Ieventually choose to fill the missing feature value which appears the most often in each feature becauseto few training data.Predict if a loan will get approved or not.

Technology: Random Forest | python