TABLE OF CONTENTS

1. IMPORTING DATA

2. LOADING DATA

3. DATA VISUALIZATION AND TECHNIQUES

4. DATA PREPROCESSING

5. MODEL BUILDING

6. CONCLUSION

7. End

IMPORTING LIBRARIES

LOADING DATA

1. ABOUT THE DATA

Context

Predict next-day rain by training classification models on the target variable Rain Tomorrow.

Content

This dataset contains about 10 years of daily weather observations from many locations across Australia.

Rain Tomorrow is the target variable to predict. It means — did it rain the next day, Yes or No? This column is Yes if the rain for that day was 1mm or more.

LINK- https://www.kaggle.com/jsphyg/weather-dataset-rattle-package

DATA VISUALIZATION AND CLEANING

Points to notice:

• There are missing values in the dataset
• Dataset includes numeric and categorical values

Steps involves in this section:

• Count plot of target column
• Correlation amongst numeric attributes
• Parse Dates into datetime
• Encoding days and months as continuous cyclic features

DATA PREPROCESSING

Steps involved in Data Preprocessing:

• Label encoding columns with categorical data
• Perform the scaling of the features
• Detecting outliers
• Dropping the outliers based on data analysis

MODEL BUILDING

Following steps are involved in the model building

• Assigning X and y the status of attributes and tags
• Splitting test and training sets
• Initializing the neural network
• Defining by adding layers
• Compiling the neural network
• Train the neural network

Plotting training and validation loss over epochs

CONCLUSIONS

Concluding the model with:

• Testing on the test set
• Evaluating the confusion matrix
• Evaluating the classification report

DEEP CC

Notebook Link: Here

Credit: Hrithikgupta