Blogs

1. Handwritten Digits Classification (Python)

Github

• The Handwritten Digits Classification project aims to classify images of handwritten digits from 0 to 9 using neural networks.
• The code uses TensorFlow and Keras to create a simple neural network with an input layer of 784 elements and an output layer of 10 elements.
• The input images are flattened into a single column containing 784 elements to be fed as input to the neural network.
• The model is trained on the MNIST dataset with 60,000 images of 28x28 pixels for five epochs and achieves an accuracy of 92.55%.
• Finally, the model is evaluated on the test dataset with 10,000 images and achieves an accuracy of 97.73%.

2. Home Price Deduction

Github

• This Python code utilizes pandas, numpy, and scikit-learn to perform linear regression for predicting home prices based on their areas. It trains a model on the data from homeprices.csv, visualizes it, and predicts prices for new areas from areas.csv.

3. Canada per-capita-income

Github

• Conducted linear regression analysis on Canada’s per capita income data from 1970 to 2017 using pandas, numpy, and sklearn libraries.
• Predicted per capita income for the year 2020 using the linear regression model and manually calculated the same using the formula y = mx + b.Visualized the data and model predictions using matplotlib.