Day 3 - 30 Days 30 Machine Learning Projects

Good Morning! It is Day 3 of the 30 Day 30 Machine Learning Projects Challenge, and it is going great. I woke up at 5:17. All credit goes to my cat, Green, for scratching my head with his paws for his morning hunt. :)

If you want to go straight to the code, I’ve uploaded it to this repository GIT REPO

The flow is going to be the same as I had briefly explained in the Day 1 and Day 2 progress posts. I will be using ChatGPT and moving forward with follow-up questions.

Talk about the Problem Please!

The problem of the day was “Recognizing handwritten digits with k-Nearest Neighbors on MNIST”. It is another classic machine learning problem. Here, we have to predict handwritten digits using the K-Nearest Neighbors Algorithm. It also requires using MNIST data.

Wikipedia: The MNIST database (Modified National Institute of Standards and Technology database) is a large database of handwritten digits that is commonly used for training various image processing systems.

Undestanding the Data

I used the data from scikit-learn’s datasets fetch_openml with arguments:

  • mnist_784: This indicates that we want the MNIST data in which 28x28 size images are flattened into 784-feature vectors.
  • version=1: I specified that I wanted version 1 of the MNIST data.
  • as_frame=True: I specified that I wanted it in Panda DataFrame format, as it is easier to debug, visualize, and manipulate Panda DataFrames.
  • parser='auto': On my local machine, I was getting a warning about the parser version, so I set it to auto to pick the one that works best for the environment.

Code Workflow

The workflow is divided into seven steps:

  1. Load the MNIST data
  2. Preprocess the Data
  3. Normalize the Data
  4. Split data in training and validation sets
  5. Create and Train Model
  6. Make Predictions and Evaluate
  7. Visualization

Let’s understand each step:

Step 1: Load the MNIST data

I have already mentioned in brief that I am using fetch_openml. See the Understand the Data section.

Step 2: Preprocess the Data

I had mentioned in Step 1 to load the data as Panda DataFrame, I use mnist_data.keys() to know about the list of keys the data contains. It has the following fields:

dict_keys(['data', 'target', 'frame', 'categories', 'feature_names', 'target_names', 'DESCR', 'details', 'url'])

I used the data and target to build my features (X) and target (y) sets.

Step 3: Normalize the Data

When dealing with image data, pixel values can range from 0 to 255 for 8-bit grayscale images. Normalizing these pixel values to the range between 0 and 1 is a common preprocessing step in machine learning tasks, particularly for algorithms that are sensitive to the scale of the input data, like k-Nearest Neighbors (k-NN).

I did X /= 255.0

Step 4: Split data.

I divided the data into an 80-20 ratio, that is, traning (80%) and validation (20%) sets using train_test_split(X, y, test_size=0.2, random_state=42)

Here random_state=42 is used to set the seed for randomness. It will ensure that the same split occurs on every run. The number 42 is a commonly used arbitrary number.

No logic behind it.

Step 5: Create and Train Model

I am using KNeighborsClassifier from scikit-learn neighbors package.

k-NN is a simple, instance-based learning algorithm that classifies new cases based on the majority votes of the k nearest neighbor samples from the training dataset. The ’nearest neighbors’ are determined by a distance metric, typically Euclidean distance. Here, K is user-defined.

Initially, I chose K=3.

Step 6: Make Predictions and Evaluate

I used a variable named predictions to store the predicted values against the 20% validation data X_val.

Since it is a classification type of model, relying on accuracy alone is not sufficient. I used a Confusion Matrix to learn more about the efficiency of the model.

A Confusion Matrix is helpful because it shows True Positives, False Positives, True Negatives, and False Negatives. I know it can be a little difficult to understand; please use the resources mentioned below to grasp it better.

  1. https://youtu.be/jr_BcU4QlNE?si=8vZi-XUbVx8s4AHa
  2. https://www.ibm.com/topics/confusion-matrix

Visualization

I used matplotlib.pyp and seaborn to create a heatmap of the confusion matrix. See how it looks.

Day 3 Problem Confusion Matrix

Outcome of Experimenting with Different K.

  • At K=3, Accuracy: 0.9712857142857143
  • At K=2, Accuracy: 0.9642142857142857
  • At K=1, Accuracy: 0.972
  • At K=5, Accuracy: 0.9700714285714286
  • At K=10, Accuracy: 0.9657857142857142

I decided to stick with K=3.

Gratitude

Today, I was feeling confident writing the code and using libraries. It is the second problem on classification; maybe that has helped. I solved it in under 40 minutes, but then I started experimenting with different K values. It was fun. I am now enjoying the process and looking forward to solving more problems.

Stay Tuned!!

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