Closed aryantiwari10 closed 3 years ago
Sure, issue is assigned to you. @aryantiwari10
Mam now what I have to do next can I upload the ipynb directly which I have made?
On Thu, 8 Jul 2021, 00:46 Prathima Kadari, @.***> wrote:
Assigned #395 https://github.com/prathimacode-hub/ML-ProjectKart/issues/395 to @aryantiwari10 https://github.com/aryantiwari10.
— You are receiving this because you were assigned. Reply to this email directly, view it on GitHub https://github.com/prathimacode-hub/ML-ProjectKart/issues/395#event-4990998763, or unsubscribe https://github.com/notifications/unsubscribe-auth/AT72GU7EXUK6AYTU3O5KTYDTWSRYJANCNFSM477HAEBQ .
Now, you can create a PR if your project is ready. Follow the project structure given in README file of this repo. @aryantiwari10
Define You: My name is Aryan Tiwari . I am a data science and machine learning enthusiast and currently pursuing B.Tech from Srm University , I am in 3rd year and my branch is computer science.
Plz assign this project to me I want to contribute it PROJECT TITLE : Hotel Booking Prediction
GOAL : To predict the possibility of the booking ,whether the booking is successfull or not .
DATASET : https://www.kaggle.com/jessemostipak/hotel-booking-demand
WHAT I HAD DONE : In this project first I performed a exploratory data analysis on the hotel booking dataset which includes of data cleaning , data manipulation, data preprocessing , data visualization and after that I did the model building using different machine learning classification and regression algorithms and then predicted the accuracy of every model . In the model prediction part I used total 9 machine learning algos . In each algo I had included the accuracy score , training score , classification report , confusion matrix . While in the EDA part I have included different plots for the different visualizations of our dataset .
MODELS USED : I used total 9 machine leaning algos in the dataset
LIBRARIES NEEDED : Pandas Numpy Matplotlib Seaborn Scipy Sklearn
CONCLUSION : I got a accuracy score of 97 % using the Random Forest Classifier and get to know that the booking is succesful.