This project was built while interning at Gustovalley Technovations LLP as a Machine Learning intern.
This project involved numerous steps before obtaining a final prediction system. Following steps were implemented-
Step 1: Data Collection: Collected the real-time data using Air Quality API.
Step 2: Understanding the Data: The obtained dataset had 435742 rows with 13 coloumns.
[Link to the dataset is provided in the description section of the repository (on the right side of the your screen)]
Step 3: Data Visualization: Implemented various visualization techniques
- Pairplot
- Histogram
- Bar Plot(grouped and coloured)
Step 4: Checking the Null values and treated by cleaning them.
Step 5: Creating individual calculation functions.
Step 6: Creating a function to calculate the AQI of every data.
Step 7: Splitting the data into Dependent and Independent columns for model training.
Step 8: Implementing various algorithms such as: Linear Regression, Decision Tree Regressor, Random Forest Regressor, Logistic Regression, Decision Tree Classifier, Random Forest Classifier, K-Nearest Neighbours.
Step 9: Coming to the final conclusion and working for the trained model.