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Understanding the problem statement
Importing the dataset and initializing the libraries
Applying basic EDA and checking or null values
Visualizing relationship between different variables using barplot
Plotting scatter plot for visualization
Plotting and visualizing a time series plot
Merging different dataset for evaluation
Understanding seasonality and trend
Spearman Correlation and calculation
Visualizing correlation using histogram
Basics of Neural Networks
Installing and initializing Keras framework
Creating a model from scratch , embedding and adding bias
Training the Artificial Neural Networks on Dataset
Plotting graphs as for loss versus the number of epochs
Another year, another chance to predict the upsets, call the probabilities and put your bracketology skills to the leaderboard test. In this keras deep learning project, we will once again attempt to predict the outcomes of this year's US men's college basketball tournament. But unlike most deep learning projects, we will pick the winners and losers using a combination of rich historical data and computing power, while the ground truth unfolds on national television.
If you are unfamiliar with the format and intricacies of the NCAA tournament, we encourage reading the wikipedia page before diving into the data. The data description and schema may seem daunting at first but is not as complicated as it appears.
As a reminder, you are encouraged to incorporate your own sources of data. We have provided team-level historical data to jump-start the modeling process, but there is also player-level and game-level data that may be useful.
We extend our gratitude to Kenneth Massey for providing much of the historical data.