report-template.md

Report: Predict Bike Sharing Demand with AutoGluon Solution

GANESH CHOWDHARY PINNAMANENI

Initial Training

What did you realize when you tried to submit your predictions? What changes were needed to the output of the predictor to submit your results?

I had to assign the values of the predictior to the "count" column of the submission DataFrame / replaced the existing values in the "count" column with the predicted values.

What was the top ranked model that performed?

WeightedEnsemble_L3

Exploratory data analysis and feature creation

What did the exploratory analysis find and how did you add additional features?

Through EDA I found that "Season" and "Weather" column were presented as int64 data type while they were supposed to be categorical Extracted year, month, and day as separate features from "datetime" column adding new features to my dataset Extract new time-based features "hour_of_day" from the "datetime" Through EDA I found high correlation between "atemp" and "temp" columns and I decided to drop "atemp" to avoid multicollinearity

How much better did your model preform after adding additional features and why do you think that is?

There was a substantial increase in model perfomance.

This is because the new feature provide additional information or insights about the target variable, leading to better predictions. Also the new feature may be correlated with the target variable, meaning it has a strong relationship or influence on the target.

Hyper parameter tuning

How much better did your model preform after trying different hyper parameters?

Little better than the previous model

If you were given more time with this dataset, where do you think you would spend more time?

Exploratory data analysis and feature creation making notes on how different feature's interact with the model. and also in Tunning of Hyperparameters

Create a table with the models you ran, the hyperparameters modified, and the kaggle score.

Model Name	Hyperparameters Modified	Kaggle Score
initial	DEFAULT	1.81151
add_features	DEFAULT	0.51204
hpo	NN ,GBM,CAT,RF	0.54099

Create a line plot showing the top model score for the three (or more) training runs during the project.

Create a line plot showing the top kaggle score for the three (or more) prediction submissions during the project.

Summary

This project equipped me with practical skills in using AutoGluon for tabular prediction tasks, enhanced my understanding of data preparation and analysis, and provided valuable experience in participating in Kaggle competitions and sharing my work with the data science community.