Predicting House Prices with Linear Regression

This Python code embarks on a journey to predict the prices of houses based on their characteristics. It employs the trusty tool of linear regression, weaving a web of connections between features and their impact on market value.

First, we dive into the depths of the data, uncovering its secrets through vivid visualizations and detailed statistics. From heatmaps highlighting hidden relationships to descriptive numbers painting a picture of distribution, we gain a deep understanding of our landscape.

Next, we handpick key features - square footage, bedrooms, and bathrooms - like trusty companions on this pricing quest. These chosen allies hold the potential to unveil the mysteries of house values.

Then, we train our linear regression model, meticulously feeding it the training data. It diligently learns the intricate dances between features and prices, crafting a formula to predict market worth.

With newfound knowledge, the model confidently steps into the unknown, predicting prices for houses it has never seen before. We anxiously compare these predictions with the actual values, measuring the accuracy of our model's gaze.

Finally, we peer into the heart of the predictions, dissecting errors and analyzing the influence of each feature. This introspection reveals the strengths and weaknesses of our model, paving the way for future refinements and a deeper understanding of the housing market's intricate tapestry.

This exploration through data and code is just the beginning. By refining features, exploring alternative models, and ensuring our assumptions hold true, we can create an even more accurate and insightful picture of the factors that govern the prices of houses.