GEEKFESTIA-2021

DAY-1

Brushing up the basics, going through some python libraries.

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DAY-2

1. Importing the necessary libraries :

• Pandas, Numpy and Scikit-Learn

2. Reading the dataset :

• There are 31527 samples in the dataset.
• All the Columns have a few NaN values.
• Wind Direction is a categorical data with 16 categories representing various directions.

3. Cleaning and Processing the Dataset :

• Convert all the numeric data from object data-type to float/int data-type.
• Filled all the NaN values cells with suitable values.
• Converted the given datetime data to Standard format.
• Exported the cleaned data as Clean_Data.csv.

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DAY-3 and DAY-4

• Visualization of features using various graphs using matplotlib and seaborn.
• Explained the observations from the graphs briefly.
• Plotted the co-relation using heatmap.

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DAY-5 and DAY-6

• Prediction of PM2.5 using various models.
• Tried ARIMA and VAR models, but both models failed to give satisfactory results.
• So, atlast we had to use Random Forest Regressor.

DAY-7, DAY-8 and DAY-9

• Successfully deployed our model.

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You can access our model using this link -https://air-quality-prediction-gfg.herokuapp.com/

Contributors-

• https://github.com/aryan7781
• https://github.com/SouravBiswal