. Customers of age between 30 to 60 are more likely to buy insurance. . Customers with 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 fill a value instead.I eventually choose to fill the missing feature value which appears the most often in each feature because to few training data.Predict if a loan will get approved or not.

Technology: Random Forest | python