Dream Housing Finance company deals in all home loans. The company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. Certain details were provided. To automate this process, they have given a problem to identify the customers segments, those that are eligible for loan amount so that they can specifically target these customers. From the Exploratory Data Analysis that was performed, I was able to generate insight about the data. The model shows how each features relates to the target. Furthermore, from the evaluation I conducted, it can be seen that of all the models employed for the analysis, Logistic Regression Algorithm(LRA) performed far better(83%) than others. Random Forest Classifier(RFC) did better than Decision Tree Classifier(DTC). Support Vector Machine(SVM) also did bettter than K-Nearest Neighbors(KNN).

The dataset for this project contains booking information for a City hotel and a Resort hotel. By utilizing the predictive model for this project, hotels will be able to identify the reasons and proffer answers to these questions. Furthermore, this analysis will help hotels get prepared with adequate and timely arrangements on heavy and low tourist visits period. Using exploratory data analysis, I was able to visualize the different prospective, which gave me different insights into: Which are the busiest months? How many bookings were cancelled? etc. From the summary table presented on this project, I observe through using various classification model that Random Forest algorithm was the best algorithm for this project analysis. I also presented that bookings got cancelled 37% of the time. While booking guest checked-in (did not cancel the bookings) almost 63% of the time.