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Unzipping folders and loading the dataset

Visualizing different images available in the dataset

Using the summary function for basic EDA

Understanding left-skew and right-skew of the dataset

Preprocessing the train dataset for initial predictions

Apply ensemble model Random Forest for predictions

Use the Importance function in R for extracting the necessary features

Plotting graphs for feature versus MeanDecresedGini

Hyper-parameter tuning Random Forest and selecting the best parameters for this model

Plotting graphs for against parameters and OOB errors

Importing FNN library and using K-nearest neighbors as the training model

Importing XGBoost and converting Dataset into DMatrix for performing predictions

Defining parameters and performing Cross Folds validation using XGBoost model

Predicting using XGBoost and saving the predictions in form of CSV

Installing h2o package for using complete RAM and CPU cores available

Initializing an h2o cluster

Initializing a DeepLearning Neural Networks model

Defining , Understanding parameters and Training Neural Networks for predictions

Plotting Confusion matrix and interpreting the result

Predicting the result and saving it in the form of CSV

Shutting down the h2o created cluster

Build a predictive model to correctly classify products between 9 product categories (fashion, electronics, etc.) using the Otto Group dataset.

In this data science project, you will contextualize customer data and predict the likelihood a customer will stay at 100 different hotel groups.

In this project, we will automate the loan eligibility process (real-time) based on customer details while filling the online application form.

27-Feb-2016

04h 29m