{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/34429661f929403193c7b6bf29f93444\" frameborder=\"0\" width=\"1728\" height=\"1296\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1296,"width":1728,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1296,"thumbnail_width":1728,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/34429661f929403193c7b6bf29f93444-badba095ff4a89ee.gif","duration":707.418,"title":"Analyzing California Housing Data to Predict House Prices","description":"In this video, I analyzed California housing data to predict house prices and classify properties as expensive or cheap. I walked through the process of loading the dataset, checking for missing values, and visualizing the price distribution, noting that many houses were capped at a price of five, which we need to address. I trained a baseline linear regression model and found that the number of bedrooms and the median income of neighbors significantly impacted house prices. I also performed feature engineering to improve our model's predictions and tested various regression models, concluding that the random forest model achieved the highest accuracy at 77.40%. I encourage you to review the findings and consider how we can apply these insights moving forward."}