Predicting Co2 Emission in Cars from it's Engine Size with a Simple Regression Model

Let's begin!

The feature is Engine Size and the label is Co2 Emission.

On a linear equation, we can state that:

y = mx + c

Co2 Emission = (Slope * Engine Size) + Intercept

As mentioned, Coefficient and Intercept in the simple linear regression, are the parameters of the fit line. Given that it is a simple linear regression, with only 2 parameters, and knowing that the parameters are the intercept and slope of the line, sklearn can estimate them directly from our data. Notice that all of the data must be available to traverse and calculate the parameter. You can also try and run it by yourself!

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Face Detection and Recognition Model

Let's begin! The base model for Face Detection and Recognition. It uses face recognition library, which has face encodings to detect and recognize different faces. I've created a class where we can initialize it with the image path and label which save it's encoding then from predict function we check whether the encodings is same or not! If the encodings is same we return the image with label name. Else we return the image with default label Unknown .

Face Detection and Recognition API

Let's begin! Face Detection and Recognition API. I've used the same model as discussed in previous blog and used FastAPI framework to create the API and Uvicorn for the server to run it on Google Colab. The API has four request: Index (GET) : Homepage of API Upload (POST) : User can upload the image and label to be used as dataset. Target (POST) : Target image on which prediction has to be done. Prediction (GET) : Get the prediction. The purpose of this project was to understand the creation of a ML API and how to use it and it's use is restricted for educational purpose only.

Predicting whether the room is clean or messy from a Flutter based app.

It's an application made using Flutter, Dart and TensorflowLite which can be used to predict the cleanliness of the room. The app consist of an A.I. model which has been trained to classify the room as messy or clean. The model has been compressed and added to the app with TensorFlowLite and it works offline. The app allows user to pick a image (Room Image) by clicking on the gallery button and thus provide the image to model to classify as clean or messy. It can be downloaded by the following link for android devices. CleanVsMessy

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